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Brain Struct Funct
Brain Structure & Function
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10.1007/s00429-024-02819-y
Correspondence
Proteomic features of gray matter layers and superficial white matter of the rhesus monkey neocortex: comparison of prefrontal area 46 and occipital area 17
Castro-Mendoza Paola B. 12
Weaver Christina M. 3
Chang Wayne 4
Medalla Maria 12
Rockland Kathleen S. 1
Lowery Lisa 5
McDonough Elizabeth 5
Varghese Merina 6
Hof Patrick R. 6
Meyer Dan E. 5
Luebke Jennifer I. jluebke@bu.edu

12
1 https://ror.org/05qwgg493 grid.189504.1 0000 0004 1936 7558 Department of Anatomy and Neurobiology, Boston University Chobanian and Avedisian School of Medicine, Boston, MA 02118 USA
2 https://ror.org/05qwgg493 grid.189504.1 0000 0004 1936 7558 Center for Systems Neuroscience, Boston University, Boston, MA 02215 USA
3 https://ror.org/04fp4ps48 grid.256069.e 0000 0001 2162 8305 Department of Mathematics, Franklin and Marshall College, Lancaster, PA 17604 USA
4 grid.47100.32 0000000419368710 Yale School of Medicine, 333 Cedar St, New Haven, CT 06510 USA
5 grid.418143.b 0000 0001 0943 0267 GE HealthCare Technology and Innovation Center, Niskayuna, NY 12309 USA
6 https://ror.org/04a9tmd77 grid.59734.3c 0000 0001 0670 2351 Nash Family Department of Neuroscience, Friedman Brain Institute, and Center for Discovery and Innovation, Icahn School of Medicine at Mount Sinai, New York, NY 10019 USA
28 6 2024
28 6 2024
2024
229 7 14951525
1 3 2024
8 6 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
In this novel large-scale multiplexed immunofluorescence study we comprehensively characterized and compared layer-specific proteomic features within regions of interest of the widely divergent dorsolateral prefrontal cortex (A46) and primary visual cortex (A17) of adult rhesus monkeys. Twenty-eight markers were imaged in rounds of sequential staining, and their spatial distribution precisely quantified within gray matter layers and superficial white matter. Cells were classified as neurons, astrocytes, oligodendrocytes, microglia, or endothelial cells. The distribution of fibers and blood vessels were assessed by quantification of staining intensity across regions of interest. This method revealed multivariate similarities and differences between layers and areas. Protein expression in neurons was the strongest determinant of both laminar and regional differences, whereas protein expression in glia was more important for intra-areal laminar distinctions. Among specific results, we observed a lower glia-to-neuron ratio in A17 than in A46 and the pan-neuronal markers HuD and NeuN were differentially distributed in both brain areas with a lower intensity of NeuN in layers 4 and 5 of A17 compared to A46 and other A17 layers. Astrocytes and oligodendrocytes exhibited distinct marker-specific laminar distributions that differed between regions; notably, there was a high proportion of ALDH1L1-expressing astrocytes and of oligodendrocyte markers in layer 4 of A17. The many nuanced differences in protein expression between layers and regions observed here highlight the need for direct assessment of proteins, in addition to RNA expression, and set the stage for future protein-focused studies of these and other brain regions in normal and pathological conditions.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00429-024-02819-y.

Keywords

Cortical layers
Rhesus monkey
Cellular
Vascular
Fibrillar
Immunofluorescence
NIH/NIAR01 AG059028 R01 AG059028 R01 AG059028 R01 AG059028 R01 AG059028 R01 AG059028 R01 AG059028 Castro-Mendoza Paola B. Weaver Christina M. Medalla Maria Lowery Lisa Hof Patrick R. Meyer Dan E. Luebke Jennifer I. issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2024
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pmcIntroduction

A major goal of systems neuroscience is to gain a detailed and comprehensive understanding of how neocortical areas in the primate brain mediate diverse sensory, motor, and cognitive functions. The neocortex can be described as a relatively uniform-appearing gray matter sheet about 2 mm-thick in the rhesus monkey, by convention considered to have six layers of differentially distributed neurons, glial cells, fibers, and vasculature. The quantitative parameters and spatial distribution of these features, however, have so far been investigated at a rather macroscopic scale, due to technical limitations. This is nonetheless sufficient to indicate that functionally distinct cortical areas differ in several striking and many nuanced ways with regard to their intrinsic cellular and fibrillar architecture, connectivity, and topographic organizational features. Recent improvements in technical resolution and throughput promise significant advances in our understanding of the underpinnings of functional specificity in the neocortex.

The identification and differentiation of cortical areas by histological criteria has a long history. The Brodmann parcellation scheme (Brodmann 1905, 1909), which defined 52 separate cortical areas based on cytoarchitecture—the size, density, and distribution of neurons across the 6 layers—was influential and Brodmann’s area numbers remain in widespread use. Later studies using a combination of cytoarchitecture, myeloarchitecture (distinctive patterns of myelinated axons) as well as cholinesterase histochemistry have proposed a range of different parcellation schemes for the monkey cortex ranging from 91 (Markov et al. 2014) to 161 (Paxinos et al. 2000) distinct areas (for review, see Van Essen and Glasser 2018).

Neocortical areas have more recently been differentiated based on additional features such as laminar organization and density of neurotransmitter receptors. These demonstrate area-specific layering patterns across the macaque cerebral cortex that do not necessarily coincide with cyto- or myeloarchitectural laminar boundaries (Caspers et al. 2015; Palomero-Gallagher and Zilles 2019; Rapan et al. 2022, 2023; Froudist-Walsh et al. 2023). Indeed, the laminar profiles of receptor densities are distributed as gradients in an approximately caudal to rostral manner from sensory to high-order association cortices.

Significant new cellular subtype identification criteria at the molecular and cellular levels derive from major technological advances in genomics and in microscopy (Zhang et al. 2022; Choe et al. 2023; Jung and Kim 2023; Piwecka et al. 2023; Xing et al. 2023; Tian et al. 2023; Balaram et al. 2023). Single-nucleus RNA sequencing (sNucSeq) and spatial transcriptomics, by enabling high-resolution characterization of gene expression in individual cells, have revealed dozens of neuron types that can be clustered based on their transcriptomic properties in diverse brain layers and areas in mice, monkeys and humans (Tasic et al. 2018; Bakken et al. 2021; Berg et al. 2021; Khrameeva et al. 2020; Krienen et al. 2020; Lei et al. 2022; Lein et al. 2017; Yao et al. 2021; Zhu et al. 2018; Jorstad et al. 2023; Maynard et al. 2021; Chiou et al. 2023). Jorstad et al. (2023) employed deep sNucSeq in eight human cortical regions spanning from primary sensory to association cortices, which comprised consistently 24 broad cell classes based on distinctive RNA expression. A systematic comparison of sensory and association cortices revealed a marked area-specific difference in relative proportions of excitatory neuron subclasses and further reported distinct laminar distribution patterns of both astrocytes and oligodendrocytes in the different cortical areas. Notably, the primary visual cortex was found to differ markedly from all other brain areas with regard to transcriptomic features, ratios of excitatory to inhibitory neurons, diversity of layer 4 excitatory neuron types and specialized inhibitory neurons (Jorstad et al. 2023). These approaches provide important information on the genetic diversity of cells in the neocortex but are only predictive of potential expressed protein phenotypes of these cells.

Here we used an iterative multiplexed immunofluorescence (MxIF) approach to compare the laminar distributions, intensities and colocalization of a comprehensive panel of 28 different cellular, fibrillar and vascular proteins in the same individual sections prepared from the primary visual cortex (V1, area 17) and dorsolateral prefrontal cortex (DLPFC, area 46) of middle-aged rhesus monkeys. These areas lie at two poles of the telencephalon and differ extensively in their laminar cyto- and myeloarchitectural patterns. V1 in the macaque monkey has a prominent and complex layer 4 with at least 4 sublayers, and functionally V1 is a prototypical primary sensory cortex. In contrast, DLPFC has a more homogeneous layer 4 and is representative of association cortex involved in cognitive function. These two areas are also differentially vulnerable to the effects of normal aging and Alzheimer’s disease in humans, with the DLPFC exhibiting earlier and more severe vulnerability compared to V1 (Braak and Braak 1991; Hof and Morrison 1990; Hof et al. 1990; Grothe et al. 2018; Mrdjen et al. 2019).

We sought to determine whether the expression of a broad range of proteins would differentiate layers within and between these two highly distinctive neocortical areas. These proteins included 11 cellular, 4 fibrillar and 3 vascular markers, as well as 10 markers of oxidative stress, misfolded proteins, and other intracellular proteins. The numerous identified area-specific laminar proteomic profiles reveal relationships across multiple cell types and neuropil markers, setting the stage for future investigations of cellular-molecular interactions in diverse regions in the normal young, aged and pathological primate brain.

Materials and methods

Subjects and tissue preparation

Brain tissue was obtained from seven middle-aged rhesus monkeys (Macaca mulatta, Table 1) with complete health records that were acquired from national primate research facilities and from private vendors and were part of our larger studies of normal brain aging. All procedures were approved by the Boston University Institutional Animal Care and Use Committee and were conducted in strict accordance with the Guide for the Care and Use of Laboratory Animals (National Research Council 2011). Monkeys were sedated with ketamine hydrochloride (10 mg/kg), subsequently anesthetized with sodium pentobarbital (15 mg/kg, to effect) and then perfused through the ascending aorta with ice-cold Krebs–Henseleit buffer, which contained, in mM: 6.4 Na2HPO4, 1.4 Na2PO4, 137 NaCl, 2.7 KCl, 5 glucose, 0.3 CaCl2, and 1 MgCl2, pH 7.4 (Sigma-Aldrich, St. Louis MO USA). Roughly cubic brain tissue blocks with edges about 10 mm in size were taken from the frontal and occipital cortices. The frontal region of interest was in Brodmann area 46 (A46) located in the caudal half of the ventral bank of the principal sulcus within the DLPFC (Barbas and Pandya 1989). The occipital primary visual cortex region of interest was within the lateral region of V1, Brodmann area 17 (A17; Rockland and Pandya 1979). Blocks were immersion-fixed immediately in 4% paraformaldehyde for 48 h at 4 °C. Fixed tissue blocks were then further blocked into 3-mm-thick blocks, placed in cassettes, and embedded in paraffin according to the following protocol: 6 steps of graded (70%, 80%, 95%, 95%, 100%, 100%) ethanol at 37 °C for 10 min each; 2 steps of xylene at 37 °C for 10 min each; 3 steps of paraffin wax at 57 °C for 10 min each. These blocks were then used as donors in the construction of tissue microarrays (TMAs, Fig. 1), which were made using 4.5-mm punches of the donor blocks that were arrayed into a 3-by-5 spot format with pial surface oriented upwards (Pantomics Inc., Richmond, CA). The resulting TMAs were sectioned at 5 µm thickness onto charged, 75 × 25-mm glass microscope slides (SuperFrost™ Plus, Thermo Fisher Scientific, Waltham, MA). One tissue section per area/subject was used for all subsequent analyses. The Cell DIVE™ MxIF procedure including staining of sections, image acquisition and pre-processing pipeline, image analysis and data analyses is shown graphically in Fig. 1 and described in detail below.Table 1 Subjects

Subject	Age	Sex	A46	A17	DRST-S	DRST-O	
AM303	15.3	Male	√	√	2.44	3.93	
AM311c	20.3	Male	√	–	3.19	3.15	
AM340	17.6	Female	√	√	2.12	2.76	
AM342c	19.2	Female	√	√	2.94	2.73	
AM347c	14.6	Female	√	–	2.45	3.3	
AM350c	17.3	Female	√	√	3.92	2.52	
AM353c	14.5	Male	–	√	2.68	4.01	
DRST delayed recognition span task, S spatial, O object

Fig. 1 Overall experimental design for this study. Formalin fixed paraffin embedded (FFPE) tissues as TMAs on microscope slides undergo sequential rounds of MxIF, each staining up to three markers (1). Resulting epifluorescence images are pre-processed (2), and then analyzed (3). Resulting data are intensity normalized and thresholded, then features and intensity data are extracted and analyzed with a variety of statistical approaches (4)

Slide preparation

Formalin-fixed, paraffin-embedded (FFPE) tissue sample slides were deparaffinized and subjected to antigen retrieval as previously described in Gerdes et al. (2013), with minor modifications. In brief, sample slides were baked at 60 °C overnight, deparaffinized with xylene, and rehydrated by 2 × 5-min aqueous washes with decreasing ethanol concentrations (xylene, 100%, 95%, 70%, 50%, ethanol, and 1 × PBS), and then permeabilized with a 10-min incubation in 0.3% TritonX-100 in phosphate buffered saline (PBS). Next, the slides underwent a two-step antigen retrieval treatment using 10 mM citrate buffer (Vector Labs, Newark, CA) at pH 6.0 and TRIS buffer (10 mM TRIS, 1 mM EDTA, 0.5 mM Tween20) at pH 8.5. This was accomplished in a sealed pressure vessel at nominal 4psi and ramped to 110 °C. Next, to inhibit nonspecific binding, slides were incubated overnight at 4 °C with 10% (wt/vol) donkey serum, 3% (wt/vol) bovine serum albumin (BSA) in PBS. To reduce the background fluorescence of neuronal tissue, slides were exposed to a multi-array LED illumination (mixed LEDs comprising 660 nm and 610 nm red, 460 nm blue, and 6500K white; AgroMax OTD84 LED Grow Light, HTG Supply, Callery, PA) for 72 h at 4 °C while in 0.02% sodium azide in PBS. Slides were then incubated with 1 µg/ml (wt/vol) 4′,6-diamidino-2-phenylindole (DAPI) and coverslipped with mounting medium consisting of 80% (vol/vol) glycerol in 1 M TRIS.

Antibody labeling and validation

A panel of markers for cellular, fibrillar and vascular components of the neuropil were selected as optimal for comprehensive assessment of protein constituents of neocortical gray and white matter (Table 2). A set of target antibodies were evaluated in a series of tests to select an optimal antibody for MxIF imaging using positive control FFPE tissue as previously published (McDonough et al. 2020). Antibodies were selected based on staining specificity and sensitivity in indirect immunofluorescence and their retention of specificity after the sample tissue was exposed to dye inactivation chemistry. Specificity was assessed qualitatively by visual inspection of expected localization patterns. Positive control tissue slides were prepared as described above and incubated with the target antibody in 3% (wt/vol) BSA in PBS for 1 h at room temperature. For secondary detection, slides were washed with PBS and a species-specific, dye-labelled IgG, at a working concentration of 5 µg/ml (wt/vol) or 1:250 in 3% BSA in PBS for 1 h at room temperature. After another PBS wash, the test slides were coverslipped with mounting medium as above. To identify the best antibodies for each marker, up to three different antibodies were tested; optimal antibodies selected for the study are specified in Supplementary Table 1. Once selected, the primary antibody was either (i) directly conjugated to a cyanine (Cy) dye as previously published (Sood et al. 2021), (ii) dye labelled with a Zenon™ kit (Thermo Fisher Scientific, Waltham, MA) following manufacturer’s protocol and used within 30 min of labelling, or iii) purchased as dye-conjugated (Suppl. Table 1). The dye-labelled antibodies were then stained as above and evaluated for direct immunofluorescence. As an additional element of validation, the potential loss of antigens targeted following dye inactivation were evaluated using control slides that were repeatedly dye inactivated to mimic 1 ×, 5 ×, and 10 × rounds of multiplexed immunofluorescence, then stained for comparison to a control slide that had not received dye inactivation treatment. The specificity and sensitivity of each conjugate was evaluated, using at least two antibody dilutions, in comparison to the unconjugated primary antibody. If direct immunofluorescence failed, the antibody was included in the study as an indirect stain.Table 2 Markers assessed in this study in consecutive staining rounds (markers indicated in each row/round) on the same tissue sections. Color coding: cellular, , ,

Abbreviations indicating cellular location of protein ce = cellular (nuclear + cytoplasmic); cyt = cytoplasmic; n = nuclear; Cy = cyanine; +  = AF488 dye; ^ = AF555 dye; * = AF647 dye (See Suppl. Table 1)

Multiplexed immunofluorescence imaging

Procedures for dye-deactivation-based MxIF have been previously published (Gerdes et al. 2013; McDonough et al. 2020). Prior to staining, each slide was imaged to acquire unstained tissue background and generate virtual H&E images (vH&E, generated using DAPI stain and Cy3 tissue autofluorescence images) for pathology review and selection of regions of interest. Following image acquisitions, coverslips were removed by inverting the slide and incubating in PBS at room temperature, allowing the coverslip to gently detach from the slide over time. Once ready for staining, antibodies were diluted in a 3% (vol/vol) BSA solution in PBS to their respective optimized concentrations (typical range 0.1–10 μg/ml, see “working concentration” in Suppl. Table 1) and applied for 1 h at room temperature using autostainer (Leica BOND-MAX™, Leica Biosystems, Deer Park, IL). Samples were then washed with 1 × Bond wash (Leica Biosystems) in three exchanges, 5 min each. In the case of secondary antibody detection, samples were incubated with a primary antibody species-specific secondary donkey IgG conjugated to either Cy3 or Cy5, for 1 h at room temperature using an autostainer. Slides were then washed with 1 × Bond wash as described above. When slides were ready for imaging, they were rinsed with PBS, then mounted with coverslips using mounting medium described above. Slides were then imaged via an IN Cell Analyzer 2200 (Leica Microsystems, Wetzlar, Germany) using a 10 × objective to set field focus and alignment, then 20 × for image capture. Exposure times were set manually after evaluating multiple fields of view prior to acquisition. After image acquisition from a round of staining, coverslips were removed as above and slides underwent dye inactivation by incubating them in an alkaline solution containing H2O2 for 15 min at room temperature, then washing with PBS. The slides were then coverslipped and imaged to acquire a new background image post dye inactivation. After re-imaging, coverslips were removed again as described above and restained with another round of antibodies via Leica Bond stainer as described above. Sample slides were imaged again for target staining. The cycle of staining, imaging, dye inactivation, imaging was repeated through all targets. Sample slides underwent iterative staining and imaging of 28 markers across 15 staining rounds (Table 2).

Image preparation

Background-subtracted, epifluorescence images were rotated to orient the pial surface horizontally and cropped to regions of interest (ROIs) 400-µm wide and extending from the pial surface through the white matter. ROIs were chosen to avoid tears or artifacts present in the tissue. Cropped images were then deconvolved using AutoQuant X3 software (Meyer Instruments). Multi-channel OME-TIF files were created for each individual tissue section using the deconvolved images. Laminar boundaries were delineated based on the literature (Barbas and Pandya 1989; Rockland and Pandya 1979); these laminar boundaries are indicated by white lines in Fig. 2b (Suppl. Table 2). In this study we did not discriminate between layers 2 and 3 and present these laminar data as layer 2/3. For A17, layer 4 was further subdivided into 4 sublayers (Lund et al. 1994), with 4A, 4B, 4Cα, and 4Cβ limits based on an approximation of the percentage of the total depth of the layer belonging to each sublayer—10%, 20%, 20% and 50%, respectively. Areas for all cortical layers were calculated using the individual layer limits except for layer 1. Due to the variability of the pial surface curvature, layer 1 area was calculated in QuPath (v0.3.2; Bankhead et al. 2017) by creating an annotation that outlined this layer only and extracting its area value. For all images, the total sampled depth is from the pial surface to 100 µm into the white matter, except for one section that did not have white matter to be sampled.Fig. 2 Markers and tissues analyzed in this study. a Photomicrographs of the 28 markers in the identical field of view (marker colors are randomly assigned). b Photomicrographs showing representative staining for each marker (different fields of view) c) 400-µm wide ROIs from A46 and A17. All images are from a 17.3-year-old female monkey. Blue = DAPI; red = HuD; green = MBP; White lines delineate laminar boundaries. Scale bars: a, b = 25 µm; c = 100 µm

Cellular analyses

Using the image analysis open-source software QuPath, images were added to a single project to allow for batch processing. A custom script that implements the software’s Cell Detection function with uniform parameters and segments objects based on DAPI staining was run for the whole project via batch processing. The cellular detection parameters included a cell expansion of 2 µm from the nucleus, allowing us to gather data from three compartments defined by QuPath as “Cell” (inclusive of “Nucleus” and “Cytoplasm”), “Cytoplasm” (excludes “Nucleus”) and “Nucleus” (excludes “Cytoplasm”). Having a conservative expansion of 2 µm ensures that the data collected is indeed from the cell corresponding to a given DAPI object (Fig. 1). Once all DAPI objects were segmented, these were manually classified into different categories: “Neuron”, “Astrocyte”, “Microglia”, “Oligodendrocyte”, “Endothelial Cell”, “Other”, or “Multiple”. Objects were classified based on the presence of cell type-specific markers: HuD and NeuN for neurons; ALDH1L1 and GFAP for astrocytes; Iba1 for microglia; Olig2 for oligodendrocytes (and BCAS1 and CNPase); and collagen IV and vimentin for endothelial cells. Objects that contained more than one cell type specific marker were classified as “Multiple” while those that did not appear to fall within any of the cell type classifications were classified as “Other”. Since these objects represented a small proportion of classified objects (~ 4% in both brain areas) they were excluded from subsequent analyses. The resulting dataset included location, shape, and all marker intensity data for all detections. Coordinates for each cell were then used to assign each cell to a cortical layer based on previously defined layer limits. Counts, densities, and relative proportions of the different cell types were calculated both within layers as well as in the sampled depth. Relative intensities of different pathological markers within the different cell types and neuron subgroups were also assessed.

Analysis and thresholding of marker intensities

Since data on cell marker intensities are known to be log-normally distributed (Bagwell 2005; Lin et al. 2015, 2018, 2023), we took the natural logarithm of raw intensity values, after shifting all values by 1 to avoid the discontinuity of ln(0). We then applied a z-score transformation to all intensity values in each tissue section. These data, hereafter called mean grayscale intensity (MGI), were then combined for comparison across all tissue sections as a proxy for protein expression level. Some of the cellular markers are known to express primarily in certain cell types, which allowed us to assign threshold values of MGI for each marker to decide whether a given cell was considered as expressing that marker. We manually determined appropriate thresholds in the reference cell type (Suppl. Table 3). These thresholds in the reference cell type then identified corresponding threshold values of MGI: any cell with an MGI value above that threshold was considered positive for that marker; otherwise, the cell was negative for that marker (Suppl. Fig. 1)

Marker colocalization analysis and MGI dimension reduction

We selected 21 markers for cell types and intracellular proteins (all markers except those for fibers, namely neurofilament markers and myelin, or vessels, namely vimentin, SMA, and collagen IV) and removed the lowest 0.2% of MGI values for each. Separately for each cell type (neurons, oligodendrocytes, astrocytes, and microglia) we performed a principal components analysis of the MGI for all 21 markers. In each case, we identified the principal components (PCs) with largest eigenvalues that collectively accounted for 80% of the variation in the data and examined the scores of each cell projected onto those PCs. For neurons, this was the first 8 PCs. A two-way ANOVA of these scores was performed, determining whether the scores projected onto each PC differed by Brain Area and Layer. Finally, as a top-down cluster analysis, we performed a multivariate analysis of variance (MANOVA) of MGI values to determine which brain area/layer combination differed significantly from the others.

Blood vessel analyses

To analyze blood vessel properties, a new QuPath project was created that included the same multi-channel images used for the cellular analyses. Individual pixel classifiers were trained to recognize positive staining for vimentin, collagen IV, and smooth muscle actin (SMA). Using the collagen IV pixel classifier, we created objects based on positive pixel classification and manually edited them to ensure they captured the whole vessel to accurately obtain measurements such as area, perimeter, circularity, and area of positive pixels for each marker within the vessel objects.

Fiber analyses

For analysis of fibrillar markers, images were added to a new QuPath project and a script was created to overlay a grid of 20 × 20 µm tiles on the image, starting at the pial surface and ending at the bottom of the image and generate intensity measurements for selected markers. Intensity measurements within each tile were used to sample the expression of the markers across the tissue section. The resulting data were exported and, for each individual image and marker, the mean intensity measures of the tiles were normalized by using a log + 1 transformation and then z-scoring the resulting values. For each image and marker, the transformed data for all tiles at each depth were averaged to obtain an average-at-depth measurement for all depths within a tissue section. All depths were then assigned to a layer based on individual image coordinates and normalized to a depth scale with the pia at 0 and the white matter line at 100 for all tissue sections.

Statistical analyses

All analyses were performed in RStudio 2022.12.0+353 (Posit teams 2022), except for clustering analyses performed in MATLAB. For each marker, we removed the top and bottom 0.2% of MGI values within each cell type; for microglia, which were present in lower numbers, we omitted the top and bottom 1% of MGI values. We used generalized linear mixed effects models (GLMMs) to explore interactions of our different variables of interest, using subject as a random effect blocking factor (Chang et al. 2022; Darian-Smith et al. 2013; Grafen and Hails 2002). We used one-way, two-way, and three-way ANOVAs as appropriate to determine whether there were differences in our variables of interest. Post-hoc analyses were conducted using Šidák-adjusted comparisons of estimated marginal means (emmeans). For all analyses, the significance level was α = 0.05, applying a Bonferroni correction for multiple comparisons. Two-way ANOVAs with independent factors of brain area and cortical layer were used for the dependent variables of density and average number of cell types, as well as MGI of individual neuronal markers. For the average number of cells of each marker-expressing subtype within a cell class, a three-way ANOVA was conducted to compare between-area, between-layer and between-subtype. For data analyses within layer 4, we performed separate one-way or two-way ANOVAs of dependent variables versus A46 and A17 sublayers. Statistical outcomes are presented in Supplementary Tables (4–12) associated with each of Figs. 3, 4, 5, 6, 7, 8, 9, 10, 11.Fig. 3 Density, number, and proportions of the five cell classes in A46 vs. A17. a Density of neurons across cortical layers and white matter and in A17 L4 sublayers (top left and middle). Each dot represents the mean value for one monkey. The density of microglia (top right) astrocytes (bottom left), oligodendrocytes (bottom middle) and endothelial cells (bottom right) are shown for each layer and WM. There were no differences in the density of these cells within A17 L4 sublayers so these data are not shown. b Ring graphs showing the relative overall proportions of the five cell types. c Graph showing the numbers of neurons, endothelial cells, astrocytes, and oligodendrocytes in each layer of A46 and A17. d Bar graphs showing the proportions of each of the five main cell types across the six cortical layers

Fig. 4 Number, proportions, and marker intensity of HuD+ and NeuN+ neurons in A46 vs. A17. a Photomicrographs showing HuD + (green), NeuN+ (magenta) and double-labeled (white) neurons in A46 and A17 (long panels) from a 17.3-year-old female monkey. Small panels are zoomed-in micrographs from A17 (white boxed area, rightmost panel). Scale bars: left, 100 µm; right, 25 µm b Ring graphs showing the relative overall proportions of neurons that were both HuD + /NeuN+ vs those that contained only HuD + or only NeuN+. c Graph showing the numbers of HuD + only, NeuN+ only and HuD + /NeuN+ neurons in each layer of A46 and A17. d Bar graphs showing the relative proportions of HuD+/NeuN+, HuD+ only and NeuN+ only neurons across laminae and in WM. e Mean grayscale intensity (MGI) of HuD (left) and of NeuN (right) in all neurons across cortical layers

Fig. 5 Number, proportions, and marker intensity of inhibitory neurons in A46 and A17. a Photomicrographs showing GAD65+ (yellow), GAD67+ (cyan), PV+ (green) and CB+ (magenta) in A17 of a 14.5-year-old male monkey. Scale bar: 25 µm. b Ring graphs showing the relative overall proportions of neurons that were positive for each of the 4 inhibitory neuron protein markers or double-labeled with two of these markers. c Graph showing the mean numbers of GAD65+  or GAD67+ neurons that were positive for PV, CB or both PV and CB in A46 and A17. d Graph showing the numbers of GAD67 + (top), GAD65+ (middle) or GAD65+/GAD67+ neurons that were positive for PV, CB or both PV and CB in each layer of A46 and A17. e Mean grayscale intensity (MGI) of GAD67 (left column) or GAD65 (right column) in all GAD+ neurons (top), PV+ (middle) and CB+ (bottom) neurons across cortical layers

Fig. 6 Number, proportions, and marker intensity of GFAP+ and ALDH1L1+ astrocytes in A46 vs. A17. a Photomicrographs showing ALDH1L1+ (green), GFAP+ (magenta) and double-labeled (white) neurons in A46 and A17 (long panels) from a 15.3-year-old male monkey. Small panels are zoomed micrographs from A46 (white boxed area left panels). Scale bars: left, 100 µm; right, 25 µm. b Ring graphs showing the relative overall proportions of neurons that were both GFAP+/ALDH1L1+ and those that stained only ALDH1L1 or only GFAP. c Graph showing the mean number of ALDH1L1+ only, GFAP+ only and GFAP+/ALDH1L1+ astrocytes in each layer of A46 and A17. d Bar graphs showing the relative proportions of GFAP+/ALDH1L1+, ALDH1L1+ only and GFAP+ only astrocytes across laminae and in the white matter. e Mean grayscale intensity (MGI) of GFAP in GFAP+/ALDH1L1+ (left) and of ALDH1L1 in ALDH1L1+ only (right) astrocytes

Fig. 7 Number, proportions, and marker intensity of Olig2+, CNPase+ and BCAS1+ oligodendrocytes in A46 vs. A17. a Photomicrographs showing Olig2+ (green), CNPase+ (magenta) and BCAS1+ oligodendrocytes in A46 and A17 (long panels) from a 15.3-year-old male monkey. Small panels are zoomed micrographs from A46 (white boxed area top panels). Scale bars: left, 100 µm; bottom, 25 µm. b Ring graphs showing the relative overall proportions of oligodendrocytes that were Olig2+ only vs Olig2+/CNPase+, Olig2+/BCAS1+ or contained all 3 oligodendrocyte markers. c Graph showing the mean number of Olig2+ only, Olig2+/CNPase+, Olig2+/BCAS1+ and Olig2+/CNPASE+/BCAS1+ oligodendrocytes in each layer of A46 and A17. d Bar graphs showing the relative proportions of oligodendrocytes staining for the 3 markers across laminae in A46 and A17. e Mean grayscale intensity (MGI) of Olig2 (left), CNPase (middle) and of BCAS1 (right) in all Olig2+, Olig2+/CNPase+, Olig2+/BCAS1+ oligodendrocytes respectively

Fig. 8 Intensity of 6 markers for fibrillar components of the neuropil in A46 vs. A17. a left—Photomicrographs showing the 6 fibrillar markers—BCAS1, CNPase, MBP, MAP2, pan-pNF and npNF in A46 and A17 (long panels) from a 17.3-year-old female monkey. Scale bar: 100 µm. Zoomed-in images of fibers from the white boxed area in A) A17. Right—Line graphs showing (from top to bottom) the mean grayscale intensities (MGIs) of BCAS1, CNPase, MBP, MAP2, pan-pNF and npNF as a function of normalized depth in the two brain areas. b Mean grayscale intensity (MGI) of each of the 6 markers across cortical layers in A46 and A17

Fig. 9 Characteristics of vascular markers in A46 vs. A17. a Photomicrographs showing the 3 vascular markers -collagen IV, vimentin and SMA- in A46 and A17 (long panels) and at a higher zoom from the A46 from a 17.6-year-old female monkey. Scale bars: 100 µm. b Left: Distribution of all (collagen IV+) vessels circularity in A46 vs A17. Middle: Mean circularity of vessels across layers. Right: mean circularity of vessels in A46 L4 and in the sublayers of A17 L4. c Left: histogram of vessel perimeters. Middle: line graph showing the mean perimeter of vessels across layers. Right: mean perimeter of vessels in A46 L4 and in the sublayers of A17 L4. d Area occupied by collagen IV+ vessels across layers (left two panels) and fraction of each layer occupied by vessels (right two panels). e Vertical dot plot graphs demonstrating the fraction of each layer and WM occupied by pixels positive for collagen IV (left), vimentin (middle) and SMA (right)

Fig. 10 Oxidative stress and misfolded protein markers in A46 vs. A17 cell types. a Photomicrographs showing the presence of 7 different markers of oxidative stress or pathology in HuD+ neurons. Scale bar: 25 µm. b Vertical dot plots showing the mean grayscale intensities (MGIs) of the 7 markers—Aβ, pTau, Cleaved Caspase3, S6, TSPO, 3NT and TDP43- in top: all (NeuN+ and/or HuD +) neurons, excitatory (GAD67-) neurons and inhibitory (GAD67+) neurons; bottom: astrocytes (ALDH1L1+ and/or GFAP+), oligodendrocytes (Olig2+) and microglia (Iba1+)

Fig. 11 Cluster Analyses and MANOVA. a t-SNE plot of individual fields per cell type x layer x area x case; each field is an average of marker expression by cell type. t-SNE plot shows relative distances of fields (Euclidean distance, perplexity = 15) based on average expression Z intensity of 21 markers for each cell type, layer and area. b t-SNE plots (left and middle) and MANOVA (right) plot of first two canonical variables of individual fields per layer × area x case of averaged total marker expression (regardless of cell type). Plots are based on distance matrices from pair-wise correlations of 37 outcome measures, namely mean grayscale intensities (MGIs) of 21 markers and cell densities of 16 markers. c MANOVA (right) plot of first two canonical variables and dendrogram (left) of Euclidean distances between group means, of fields based on neuronal expression profiles and d glial expression profiles

Dimensional reduction, clustering and MANOVA analyses

To determine how distinct layers of each region are (dis)similar to each other based on neuronal and glial expression profiles of multiplex protein markers, we used dimensional reduction, clustering and MANOVA, as described (Gilman et al. 2017; Medalla et al. 2017; Fig. 11). First, we took the average normalized Z intensity of 21 markers across each cell segmented object classified either as Neuron, Microglia, Astrocytes, Oligodendrocytes, Endothelial, other, or multiple per field (layer × area × case). We then ran unsupervised clustering and dimensional reduction of individual fields per cell type to see if fields clustered by cell type. A distance matrix (based on Euclidean distances) was first computed based on the multidimensional dataset. Using this distance matrix, the multidimensional data was reduced to two dimensions via t-distributed stochastic neighbor embedding (t-SNE), which assigns a coordinate value to plot each data point in a two-dimensional space. In this dimensional reduction plot, the distances between each data point represent the similarities in the multidimensional properties—the closer the distance, the more similar the properties of the fields.

We then ran clustering, dimensional reduction and multivariate analysis of variance (MANOVA) analyses of fields per area × layer × case based on total combined features (Fig. 11b), and neuronal (Fig. 11c) vs glial (Fig. 11d) expression profiles of all the variables. For the total expression profile, we included 37 input variables based on normalized Z intensities (21 markers) and cell densities (16 markers) of markers of interest to cluster the distinct fields. Then, two separate sets of MANOVA were ran to classify fields: (1) based on a subset of neuron specific and pathological markers (15 intensity and 9 cell density measures); and (2) based on a subset of glial specific and pathological markers (13 intensity and 6 cell densities of glial specific and pathological markers; Suppl. Table 12). Each variable was normalized to z-score values, and a distance matrix (based on squared Euclidean distances) was computed.

We first ran t-SNE analyses on the full dataset and annotated the individual fields to visualize and see clustering based on categorical grouping variables as follows: by layer (1, 2–3, 4, 4A, 4B, 4Cα, 4Cβ, 5, 6), by areas (A46 or A17) by layer x area (A46: 1, 2–3, 4, 5, 6; A17: 1, 2–3, 4, 4A, 4B, 4Cα, 4Cβ, 5, 6). To validate the bottom-up t-SNE clustering scheme, a one-way MANOVA was used for supervised clustering, to compare the relative degree of influence each outcome measure contributes to the separation of the fields by area and layer, as described (Medalla et al. 2017). The properties were normalized to z-scores, and data points were plotted based on the first two canonical variables —weighted linear sums of all included variables— that best separate out the groups. For each canonical variable, the absolute value of the coefficient represents the degree of influence that each outcome measure contributes to the separation of the groups, with higher absolute values indicating a greater influence.

Results

Multiplexed immunofluorescence (MxIF) was performed on 5-µm-thick paraffin embedded brain sections from 7 middle-aged monkeys (4 female, 3 male; Table 1). Subjects ranged from 14.5–20.3 years of age, and 4 of the monkeys contributed both A46 and A17 while 2 contributed only A46 and 1 contributed only A17 to this study (Table 1). A total of 28 different markers were assessed in these tissue sections (Table 2; Fig. 2). Photomicrographs of the 28 markers acquired on the same tissue section in 3 channels- Cy2/AF488, Cy3/AF555, and Cy5/AF647 at 20 × using the Cell DIVE™ MxIF platform are shown in Fig. 2a. For each tissue section, 400-µm wide regions of interest (ROIs) extending from the pial surface to 100 µm deep into the white matter (WM) from A46 and/or A17 were cropped from variably larger images (e.g., Fig. 2b). Laminar boundaries were delineated based on the layers established in the literature (Barbas and Pandya 1989; Rockland and Pandya 1979). For A17, layer (hereafter L) 4 was subdivided into 4 sublayers which were defined by (Lund et al. 1994; Suppl. Table 2). Laminar boundaries are demarcated by white lines in Fig. 2b.

Cellular composition of the neuropil in A46 and A17

The density, overall proportions, number, and laminar proportions, of five major cell classes–neurons, endothelial cells, astrocytes, oligodendrocytes, and microglia—in A46 and in A17 are shown in Fig. 3 and associated statistical comparisons presented in Supplementary Table 3. As described in Methods, following segmentation of DAPI objects in QuPath, all objects were manually classified into one of these cell classes based on the presence of cell type-specific markers: HuD and/or NeuN for neurons; ALDH1L1 and/or GFAP for astrocytes; Iba1 for microglia; Olig2 for oligodendrocytes; collagen IV and vimentin for endothelial cells (which also were identified based on their association with vascular elements). A small percentage of DAPI objects in each region could not be unequivocally classified (4.72% in A46 and 4.42% in A17) and these objects were excluded from subsequent statistical analyses. A total of 30,139 cells were manually classified in six A46 ROIs and 36,858 cells were manually classified in five A17 sections.

The density of neurons in each cortical layer, the superficial WM and in each of the A17 L4 sublayers were calculated and compared (Fig. 3a, top left and middle). The mean density of neurons was significantly lower in A46 than in A17 for each of L2/3–6, including A46 L4 versus sublayers 4A, 4B, and 4Cβ of A17 L4 but did not differ between areas in L1 or in the WM. The mean density of neurons in L2/3–6 of A46 was typically approximately half that of A17. The mean density of neurons as a function of layer were similar within A46 while laminar differences were observed in A17. The mean density of neurons in L1 and WM of A17 were both significantly lower than in each of layers L2/3–6 and the mean density of neurons in L2/3 was significantly higher than that of L6.

The mean density of microglia was low in both A46 and A17 compared to other cell types (Fig. 3a, top right) and did not differ across areas or across layers in A46. However, in A17 the mean density of microglia was higher in both L1 and WM than in L2/3 and L5. The mean density of astrocytes (Fig. 3a, bottom left) did not differ between the two brain areas. The mean density of astrocytes in L1 was significantly higher than in L2/3 and L4 in A46 and significantly higher than L2/3, 4, 5 and 6 in A17. There were no differences in the mean density of astrocytes across the four sublayers of A17 L4. Oligodendrocyte (Fig. 3a, bottom middle) mean densities did not differ between areas. In both A46 and in A17, there were laminar differences in the mean density of oligodendrocytes, with a lower density in each cortical layer than in the WM. In the A46, L1 had lower mean densities of oligodendrocytes than did L6 while in A17 the mean density of these cells was lower in L2/3 than in L4. There were no differences in the mean density of oligodendrocytes across the four sublayers of A17 L4. Similarly, there were no differences across areas in endothelial cell mean density (Fig. 3a, bottom right). In the A46, endothelial cell mean density was similar across all layers but in A17 the mean density of endothelial cells was higher in L1 than in WM. There were no differences in the mean density of endothelial cells across the four sublayers of A17 L4.

The relative overall proportions based on the mean number of the five cell types in A46 and in A17 layers 1–6 are shown in Fig. 3b. The majority of cells were neurons in both areas, but the ratio of non-neuronal cells to neurons differed at 43:57 (0.75) in A46 and 27:73 (0.37) in A17. Specifically, non-neuronal cells comprised a higher proportion of all cells in A46 than they did in A17. The glia (astrocytes, oligodendrocytes, and microglia) to neuron ratio (GNR) was 32:57 (0.56) in A46 and 18:73 (0.25) in A17.

We then quantified the mean number of each of the five cell classes within each lamina and in the superficial WM to ascertain the relative distribution of different cell types as a function of layer (Fig. 3c). In both areas, the highest total number of cells were found in L2/3, specifically due to greater numbers of neurons compared to other layers. Within each area, the majority of the neurons were concentrated in L2–3, while L1 contained the lowest proportion of neurons in both areas, significantly lower than other layers.

We next assessed the relative laminar distribution of each cell type across the six cortical layers within each brain area (Fig. 3d). WM was excluded from these calculations because only 100 µm of WM could be sampled, rather than its entire vertical extent as with the cortical laminae. Between-area comparisons show that the mean proportion of neurons in L4 of A17 was much higher than that of A46 L4, while the proportion of neurons in L5 were higher in A46 than A17. When comparing A46 L4 and the sublayers of L4 A17, we observe a higher mean proportion of neurons in L4Cβ than in all other sublayers of A17 L4, as well as than in A46 L4. However, the mean proportion of neurons within A46 L4 was higher than that of A17 L4A and L4Cα.

Within both A46 and A17 each non-neuronal cell type had a distinctive laminar distribution. Oligodendrocytes were enriched in the deep layers of both regions and in L4 of A17 compared to other cell types, while astrocytes, microglia and endothelial cells exhibited a similar laminar distribution to neurons, except for a relatively greater proportion in L1 (Fig. 3d). For each cell type, we next examined the mean grayscale intensity (MGI) as a proxy for expression level and co-expression patterns of canonical cell type-specific markers across areas and layers (below).

Distribution and marker intensity of HuD+ and NeuN+ neurons across layers in A46 and A17

We first examined coexpression patterns of HuD and NeuN, two canonical pan-neuronal markers. The mean proportions and marker intensity of HuD+ and NeuN+ neurons in A46 vs. A17 are shown in Fig. 4 and statistical comparisons provided in Suppl. Table 4. Representative photomicrographs across the pia-WM extent of A46 and A17 of a 17.3-year-old female monkey (Fig. 4a) show the varying intensities of HuD+ (green), NeuN+ (magenta) and double-labeled (white) neurons across layers. The relative overall proportions of neurons that were both HuD+/NeuN+ vs those that contained only HuD+ or only NeuN+ are shown in ring graphs (Fig. 4b). In both brain areas most neurons contained both HuD and NeuN (96% in A46; 91% in A17). The percentage of neurons that were NeuN+ only were slightly lower in A46 compared to A17 (3% vs 5%), while HuD + only neurons represented 4% in A17 compared to just 1% in A46. There was a distinctive pattern of HuD + /NeuN+, HuD+ only and NeuN+ only neurons across laminae and in WM in both brain areas as shown in laminae and WM relative proportions histograms (Fig. 4c, d). L2/3 contained the highest proportion of NeuN+ neurons in both brain areas followed by L1, with just a few NeuN+ only neurons being present in the deeper layers. By contrast HuD + only neurons were distributed more evenly across layers.

The MGIs of HuD and of NeuN in the entire population of HuD and/or NeuN labeled neurons are plotted in Fig. 4e. While both areas showed a near linear increase in HuD MGI from superficial to deep layers, the intensity of label in L2/3, L4 and L5 was significantly less in A17 compared to A46 (Fig. 4e, left). The NeuN MGI showed marked laminar specificity with levels dropping significantly in layers 4 and 5, most notably in A17 (Fig. 4e, right). This pattern can be readily seen in Fig. 4a (A17 middle panel). The relationships between HuD MGI and NeuN MGI in the total population of neurons from all subjects by cortical layer are shown in 2-D scatter plots (Suppl. Fig. 2). Except for L1 in A17 and WM in A46 there was a significant positive linear relationship between HuD and NeuN MGI in each layer. Within the layers that showed significant correlations for both areas (L2/3–6), A17 showed a stronger correlation of the two markers in all but L4 for which the opposite was true.

Additionally, we assessed the expression and intensity of MAP2 within HuD + and NeuN+ neurons (Suppl. Fig. 3; Suppl. Table 5). Zoomed-in representative photomicrographs of A17 of a 19.2-year-old male monkey (Suppl. Fig. 3a) show the presence of MAP2 (red) in the cytoplasm of HuD+ (green), NeuN+ (magenta) neurons. Most neurons across both brain areas were positive for MAP2 (Suppl. Fig. 3b–c). The MGI of MAP2 in HuD+ and in NeuN+ neurons was significantly lower in L4 than in the other layers in both brain areas (Suppl. Fig. 3d).

Distribution and marker intensity of HuD+ GABAergic neurons containing GAD67, GAD65, PV and/or CB proteins across layers in A46 and A17

Within HuD+ neurons, we quantified the proportions and MGIs of pan-GABAergic neuronal markers, GAD65+ and GAD67+, as well as markers for specific inhibitory neuron subpopulations, parvalbumin (PV) and calbindin (CB) (Fig. 5; Suppl. Table 6). Representative photomicrographs in Fig. 5a show GAD65+ (yellow), GAD67+ (cyan), PV+ (green), and CB+ (magenta) neurons in a section of A17 from a 14.5-year-old male monkey. An important caveat related to these markers is that GAD67 and GAD65 do not label all inhibitory neurons and that GAD65 is primarily a synaptic not a somatic marker. Thus, the reported numbers are likely an underestimate of the total population of inhibitory neurons. The relative overall proportions of HuD+ neurons that were positive for either one or both GABA markers are shown in ring graphs (Fig. 5b). In both areas, the majority of HuD+ neurons were not GABAergic (i.e., they were negative for both GAD67 and GAD65). There were three distinct subpopulations of GABAergic neurons: GAD67+ only, GAD65+ only, and GAD65+/GAD67+ coexpressing neurons (8, 7, and 5% of the total neurons, respectively, in A46; and 5, 3, and 4% respectively in A17). Thus, GABAergic neurons comprised 20% of all neurons in A46 and 12% of all neurons in A17, consistent with previous estimates (Fig. 5b). The average excitatory (GAD-) to inhibitory (GAD+) neuron ratio (E:I) in the sampled area (L2/3-L6) was 4:1 in A46 and 7.5:1 in A17.

We then assessed GABAergic HuD+ neuron subpopulations based on GAD67/65 coexpression with PV and CB, two markers of functionally distinct inhibitory neuron subclasses (DeFelipe 1997; Hof et al. 1999; Ascoli et al. 2008). The majority of GAD67+ only, GAD65+ only, or GAD65+/GAD67+ expressing neurons did not express either PV or CB (Fig. 5c). Among those GABAergic neurons that did express CB or PV, most were GAD67+ only neurons and the remaining were GAD67+/GAD65+ (Fig. 5c). Additionally, we assessed the expression of calcium binding proteins CB and PV in HUD+/GAD− principal neurons within A46 and A17. Within both brain areas, the number of GAD− neurons negative for both PV and CB was significantly higher than those of GAD− neurons positive for either PV or CB only, or for both markers.

The relative number of GABAergic HuD+ neurons expressing PV and CB across laminae (L1 and WM were not included since there were no calcium-binding protein positive neurons observed) are shown in Fig. 5d. The three subpopulations of GABAergic neurons based on GAD65 and GAD67 expression showed distinct laminar distributions depending on area. Between-area differences were most pronounced for GAD65/GAD67 coexpressing neurons, with layer distributions showing opposite patterns. Interestingly, the number of GAD65+/GAD67+ neurons was highest in A17 L4Cβ compared to all other layers and sublayers. The laminar proportions of GAD67+ and GAD65+ neurons expressing CB and PV also differed by subtype. Specifically, for GAD67+ only neurons, the proportion expressing CB was greatest in the upper layers L2/3 for both areas. In contrast, the GAD67+/PV+ were more evenly distributed across the layers, with a greater proportion making up the total number of GAD67+ only cells in the middle to deep layers in both areas. The small population of GAD67/65 and CB coexpressing neurons were also preferentially located in L2/3. In contrast, the laminar distribution of GAD67/65 and PV coexpressing neurons was dependent on region, with enrichment in the upper layers of A46 but in the middle to deep layers of A17.

We then examined the MGIs of GAD67 (left) and GAD65 (right) within each cell for the total GAD+ (top), PV+ (middle) and CB+ (bottom) subpopulations of neurons across cortical layers (Fig. 5e). Within the total population of GABAergic neurons in both areas, the MGI of GAD67 was lower in L2/3 than in all other layers (Fig. 5e, top left). No differences in GAD67 intensity were found within the PV+ or CB+ subpopulations. The intensity of GAD65 differed between the two brain areas in L2/3 and L6, with A46 showing higher GAD65 MGI than A17 (Fig. 5e, top right). Within each brain area, GABAergic neurons showed a general pattern of higher GAD65 MGI in the upper to middle layers (L2-4) relative to the deep layers. Specifically, GABAergic neurons in A46 L2/3 and A46 L4 showed a higher MGI of GAD65 than those in A46 L5 and L6 (Fig. 5e, top right). In A17, GABAergic neurons in L6 showed lower GAD65 MGI than all other A17 layers. No significant differences in the intensity of GAD65 were observed in the PV+ subpopulation but in the CB+ subpopulation, a higher intensity of GAD65 was seen in A17 L4 than in A17 L6. When looking at the subpopulation of CB+ and PV+ GAD67+/GAD65+ neurons, we found that this positive correlation between GAD67 and GAD65 MGI was stronger in PV+ compared to CB+ neurons in both areas. We compared the MGI of PV+ versus CB+ within GABAergic HuD+ neurons in each area and layer and found that, similar to cell distribution, intensity per cell of CB+ vs PV+ had distinct laminar patterns. CB MGI showed greatest intensity per cell in L2/3 and decreased in L4. In contrast, PV MGI was relatively uniform across layers in A17, with a peak in L4 in A46 (Suppl. Fig. 4; Suppl. Table 5).

We assessed the relationship between the MGI of GAD67 and GAD65 in GAD+ neurons that were PV+ and CB+ using 2-D scatterplots (Suppl. Fig. 4, left). Both inhibitory neuron populations showed positive correlations in both brain areas. PV+ inhibitory neurons showed a stronger relationship between GAD65 and GAD67 in A46 (Suppl. Fig. 4, top left) while CB+ inhibitory neurons showed a stronger relationship in A17 (Suppl. Fig. 4, bottom left). Additionally, we assessed the MGI of PV and CB in GAD+ neurons positive for the respective marker in the different layers (Suppl. Fig. 4, right) and no laminar or areal differences were observed.

Finally, we examined the expression of the potassium channel Kv3.1 in HuD+ inhibitory neurons. Within all GAD67+ neurons, there were, on average, 43% of cells that were Kv3.1+ in A46 and 56% that were Kv3.1+ in A17. Kv3.1 channel expression was interneuron subtype specific; in A46 all GAD67+/PV+ cells were Kv3.1+ and in A17 95% of GAD67+/PV+ cells were Kv3.1+. In contrast, a lower proportion of GAD67+/CB+ cells were Kv3.1+, with 76% and 84% of these neurons being Kv3.1+ in A46 and A17, respectively (not shown).

Distribution and marker intensity of astrocytes across layers in A46 and A17

Astrocytes were identified based on the expression of two canonical markers, GFAP and ALDH1L1 (Jurga et al. 2021; Khakh and Deneen 2019). Here we quantify the extent to which these markers colocalize and reveal the distribution of marker-specific astrocytes within A46 and A17 (Fig. 6; Suppl. Table 7). Representative photomicrographs showing ALDH1L1+ (green), GFAP+ (magenta) and double-labeled ALDH1L1+/GFAP+ (white) astrocytes in A46 and A17 from a 15.3-year-old male monkey are presented in Fig. 6a. The relative overall proportions of astrocytes that were GFAP+/ALDH1L1+ and those that stained only for ALDH1L1 or only for GFAP are provided in Fig. 6b. Most astrocytes contained both markers in both A46 and A17 (76% and 78% respectively). There was a slightly higher proportion of ALDH1L1+ only astrocytes in A46 compared to A17 (23% vs 18%). Very few astrocytes were GFAP+ only, with a higher proportion of these in A17 compared to A46 (4% vs 1% respectively). Bar graphs showing the relative numbers and proportions of GFAP+/ALDH1L1+, ALDH1L1+ only and GFAP+ only astrocytes across layers and in the white matter reveal distinct laminar patterns for the different populations of astrocytes (Fig. 6c, d). Astrocytes containing GFAP+ only were mostly limited to L1 and to the white matter. Of all astrocytes in L4 of A46, 52% were ALDH1L1+ only astrocytes; this type of astrocyte also comprised a significant proportion of L4B and L4Cα of A17 (48% and 40% respectively). Within A17 the highest proportion of ALDH1L1+ only astrocytes was found in L4Cβ, and the lowest proportion in L6 (45% vs 3%).

The MGI of GFAP and ALDH1L1 in GFAP+/ALDH1L1+ and of ALDH1L1 in ALDH1L1+ only astrocytes are shown in Fig. 6e. The patterns of intensity of these markers were similar in A46 and A17 with the only significant differences being a higher intensity of GFAP in GFAP+/ALDH1L1+ astrocytes in L2/3 of A17 compared to A46 and of ALDH1L1 in GFAP+/ALDH1L1+ astrocytes in L4 of A17 compared to A46. The MGI of ALDH1L1 in ALDH1L1+ only astrocytes in L5 of A17 was significantly higher compared to A46. In both brain areas the MGI of GFAP demonstrated a U-shaped laminar pattern with relatively high intensity in L1, low intensity in L2/3 and L4, then linearly increasing levels from L5 to L6, and highest intensity in the white matter. The pattern of ALDH1L1 MGI was the inverse of GFAP with, in both brain areas, a low intensity in L1 and WM and highest levels of intensity in L2/3 and 4. Within L4, ALDH1L1 intensity in GFAP+/ALDH1L1+ astrocytes in A46 differed from both L4Cα and L4Cβ of A17 while within A17, L4Cβ differed from both L4A and L4B. Additionally, ALDH1L1 intensity in ALDH1L1+ only astrocytes in A46 L4 differed from those in L4Cβ of A17 and within A17 the intensity of this marker differed from L4A vs both L4Cα and L4Cβ.

Linear regression analyses of ALDH1L1 and GFAP MGIs within astrocytes in each layer revealed a regional difference (Suppl. Fig. 5). Specifically, in L1 and L2/3, there was a positive correlation between ALDH1L1 and GFAP MGI in A17 but not in A46. Interestingly, in the middle deep layers (L4-L6), the positive correlation between these markers was weaker (A46 L6) or absent, or in the case of L5 A17, reversed. In L5 A17, astrocytes with greater ALDH1L1+ intensity exhibited lower GFAP+ intensity. In the white matter strong positive correlation between the markers was present in both areas.

Of note, the oligodendrocyte marker Olig2 was expressed in some astrocytes (Suppl. Fig. 6a; Suppl. Table 7). A large proportion of astrocytes in A17 (34%) but not A46 (4%) exhibited above-threshold values of Olig2 expression (Suppl. Fig. 6b). The Olig2+ astrocytes were mostly ALDH1L1+/GFAP+ or ALDH1L1+ only astrocytes with only a small number of GFAP+ only astrocytes in L1 and WM being Olig2+ (Suppl. Fig. 6b–d). The MGI of Olig2 in the different groups of Olig2+ astrocytes was also assessed and no significant differences between layers or areas were found.

Distribution and marker intensity of oligodendrocytes across layers in A46 and A17

We then assessed coexpression of canonical markers for oligodendrocytes, and their subpopulations and state of differentiation (Fard et al. 2017; Kuhn et al. 2019). The proportions and marker intensity of Olig2+, CNPase+ and BCAS1+ oligodendrocytes in A46 compared to A17 are shown in Fig. 7 and associated statistical comparisons presented in Suppl. Table 8. Photomicrographs showing Olig2+ (green), CNPase+ (magenta) and BCAS1+ oligodendrocytes in A46 and A17 (long panels) from a 15.3-year-old male monkey (Fig. 7a). Olig2 was present in all oligodendrocytes as a nuclear marker. CNPase and BCAS1 by contrast are proteins associated with myelinating oligodendrocytes, which are localized within oligodendrocyte processes and cytoplasm (Fig. 7a). BCAS1 in particular is thought to mark newly myelinating subpopulation of oligodendrocytes (Fard et al. 2017; Kaji et al. 2020). The labeling thus appears fibrillar in the neuropil, due to the presence of these proteins where oligodendrocyte processes associate with myelin and can also be clearly seen in the perinuclear cytoplasm and processes of Olig2+ oligodendrocytes (Fig. 7a).

The relative overall proportions of oligodendrocytes that were Olig2+ only vs Olig2+/CNPase+ only, Olig2+/BCAS1+ only or contained all 3 oligodendrocyte markers are shown in Fig. 7b. The proportion of oligodendrocytes that contained just the Olig2 marker was similar across A46 and A17 (78% vs 69% respectively). Some Olig2+ oligodendrocytes also stained for CNPase in both brain areas, comprising 19% of the oligodendrocytes in A46 and 25% in A17. BCAS1+ oligodendrocytes were relatively rare in both areas but were observed at a higher proportion of the total population of oligodendrocytes in A17 (4%) compared to A46 (2%). Oligodendrocytes containing all 3 markers were observed in very small quantities in both A46 and A17, comprising 1% and 2% of the total population, respectively. Histograms showing the relative proportions and average counts of oligodendrocytes staining for the 3 markers across layers are shown in Fig. 7c, d. WM was not included in estimates since the CNPase+ and BCAS+ myelinated fibers were too dense to allow unequivocal assessment of these markers’ colocalization with Olig2. Layer 1 oligodendrocytes stained exclusively with Olig2, and this group also represented most of the oligodendrocytes in L2/3 (Fig. 7c). Olig2+ only oligodendrocytes also were predominant in L4 of A46 and L4A and L4B of A17. Layer 4C in A17 marked the appearance of a large proportion of Olig2+/CNPase+ only cells with 20% of all Olig2+/CNPase+ only cells in L4Cα and 43% in L4Cβ. The proportion of Olig2+/CNPase+ oligodendrocytes did not differ in L5 or L6 in either brain area.

The MGIs of Olig2 in all Olig2+ oligodendrocytes, CNPase in all Olig2+/CNPase+ oligodendrocytes and of BCAS1 in all Olig2+/BCAS1+ oligodendrocytes are shown in Fig. 7e. The intensity of Olig2 was markedly greater in A17 compared to A46 oligodendrocytes in every layer except L1. The intensity of Olig2 did not differ across layers in either brain area. CNPase intensity in CNPase+ oligodendrocytes was lower in A46 than in A17 L4 and L5, but there was no significant difference in the intensity of this marker in any other layers across regions. Finally, BCAS1 staining intensity in BCAS+ oligodendrocytes did not differ between or within areas.

Linear regression reveals distinct relationships of Olig2 expression per cell with BCAS1 and CNPase intensity (Suppl. Fig. 7). The strongest correlations between MGIs of oligodendrocyte markers were found in L1, wherein BCAS1 intensity exhibited a positive correlation with Olig2 intensity in A17 (Suppl. Fig. 7a); but CNPase was significantly negatively correlated with Olig2 intensity only in L1 of A46 (Suppl. Fig. 7b). In addition, there was a significant positive correlation between BCAS1 and Olig2 MGI in L2/3–6 of A46, and in L4 and L6 of A17 (Suppl. Fig. 7a).

Fibrillar components of the neuropil in A46 and A17

Photomicrographs showing 6 fibrillar markers—BCAS1, CNPase, MAP2, MBP, pan-pNF and npNF- in A46 and A17 (long panels) from a 17.3-year-old female monkey are shown in Fig. 8a. Line graphs showing (from top to bottom) the normalized and z-scored intensity of BCAS1, CNPase, MBP, MAP2, pan-pNF, and npNF as a function of normalized depth in the two brain areas reveal patterns of myelinated axons, axons, and dendrites in the neuropil (Fig. 8a). The MGIs of BCAS1, CNPase and MBP across cortical layers, demonstrate that the laminar expression pattern of oligodendrocyte markers, BCAS1 and CNPase, mirrored that of MBP; the three markers showed concomitant increasing intensities with increasing depth/distance from pia in both areas (Fig. 8b, top 3 panels; Suppl. Table 9). In addition, consistent with classic myeloarchitectural observations, A17, but not A46, displayed a prominent a non-linear peak of these myelin-oligodendrocyte markers in L4.

Within-area comparisons showed that BCAS1 MGI in A46 L1 differed significantly from all other layers, while in A17 L1 differed from L4, L6 and WM and L2/3 differed from WM. CNPase intensity of A46 L1 was lower than all other layers, that of L2/3 differed significantly from all other layers, having a higher MGI than that of L1 but lower than L4-WM, while A46 WM was significantly higher than that of every layer except L6. Within A17, L1 and L2/3 intensity of CNPase and MBP was significantly lower than that from L4, L5, L6 and WM while that of the WM was higher than all other laminae for CNPase but not in L6 for MBP. MGI of MBP in L1 of A46 was lower than all other A46 layers while L2/3 was lower than that of L5, L6 and WM and intensity in the WM was also higher than that of L4. No differences between areas were observed for BCAS1, CNPase, MBP. We then examined the laminar distribution of neurofilament proteins that primarily label axons (pan-pNF) and dendrites (npNF and MAP2) in the neuropil. The axonal marker pan-pNF showed a laminar expression pattern comparable to myelin/oligodendrocyte markers (Fig. 8b). In both areas, the MGI of pan-pNF was generally lower in upper layers compared deep layers and the WM. The MGI of the non-phosphorylated neurofilament protein, npNF, which is enriched in proximal dendrites of a subset of L3 and L5 neurons, only showed significant laminar differences within A46, with L1 having lower intensity than L4, L5, L6 and WM. The microtubule associated protein marker MAP2 showed similar MGIs in L1 and L2/3 of the two brain areas and followed the same trend of decline in L4 of both A46 and A17 before increasing in L5 and L6 (Fig. 8b, bottom left panel). Between-area analyses showed no significant differences in pan-pNF, npNF and MAP2 MGIs.

Characteristics of vascular profiles in A46 and A17

Three different vascular markers—collagen IV, vimentin and SMA—were used to identify blood vessels in A46 and A17 (Fig. 9a, left, long panels) and at a higher zoom from the A46 (Fig. 9a, right, square panels) from a 17.6-year-old female monkey. Vessels with a perimeter larger than 400 µm were considered outliers and excluded from analysis. Statistical comparisons are presented in Suppl. Table 10. Circularity was assessed as a control to ensure that plane of section did not differ significantly in the two brain areas prior to further characterization. Figure 9b (left) is a histogram showing that the distribution of all vessels (as defined by collagen IV staining) in A46 vs A17 was very similar; indeed, the mean circularity of vessels did not differ across layers in either brain region and did not differ between regions (Fig. 9b, middle panel). The mean circularity of vessels also did not differ between A46 L4 and between any of the sublayers of A17 L4 (Fig. 9b, right panel). Having established that vessels were not differentially sectioned with regard to their long axes in the two regions, we next assessed the perimeter of the vessels as a proxy for their size to compare the two brain areas and to assess potential differences in the size of vessels between layers. The distribution of vessel perimeters did not differ significantly between brain areas and most vessels (53%) had a perimeter of less than 50 µm with a range of 11.05–386.61 µm (Fig. 9c, left panel). Furthermore, the mean perimeter of vessels increased linearly from L1 to the WM and this pattern was seen in both A46 and in A17 (Fig. 9c, middle panel). The mean perimeter of vessels did not differ between A46 L4 and between any of the sublayers of A17 L4 (Fig. 9c, right panel).

To gain insight into the area of neuropil occupied by vessels in the two brain areas we assessed the area occupied by vessels across layers (Fig. 9d, left two panels) as well as the fraction of each layer occupied by vessels (Fig. 9d, right two panels). Based on area occupied, A46 L1 and WM were significantly less vascularized than A46 L2/3 and L6 while the same was true for A17 L1 and WM when compared to A17 L2/3 and L4. Vascular occupancy of the neuropil did not differ significantly between brain areas except in L4 which was significantly more vascularized in A17 than in A46. Within area comparisons showed that vascular occupancy of the neuropil in L4 was greater than in L1, L5, and WM in A17, while in A46 L4 was less occupied with vessels than L2/3 and 6. Interestingly, there was also a marked difference in the area occupied by vessels within the sublayers of L4 in A17 with L4Cβ showing a higher area occupied but not fraction occupied, reflecting the larger extent of this sublayer. The fraction of each layer and WM occupied by pixels positive for collagen IV (left), vimentin (middle) and SMA (right) shown in Fig. 9e demonstrate that the only difference in the laminar distribution of these marker-specific vessels was found within A17, where L1 has a greater average positive fraction of pixels than WM.

Presence of pathology markers within neurons, astrocytes, oligodendrocytes, and microglia

We then assessed the co-expression of cell type-specific proteins with seven different markers of oxidative stress or misfolded proteins—Aβ, pTau, Cleaved Caspase3, S6, TSPO, 3NT and TDP43 (Fig. 10). These markers were prevalent in all neurons, and more specifically in excitatory (negative for GAD67) neurons (Fig. 10b, top left and middle; Suppl. Table 11). In excitatory neurons, 5 of the 7 markers were present at significantly higher intensity in of A46 than of A17, with only Cleaved Caspase3 being present at the same intensity and TSPO being present at significantly higher intensity in A17 neurons. For the inhibitory neuron group (positive for GAD67), intensities were higher in A46 than in A17 for 4 of the markers (pTau, Cleaved Caspase3, 3NT, and TDP43) while no differences between areas were observed for the other marker intensities (Fig. 10b, top right; Suppl. Table 11). Differences in mean intensity of pathological markers in astrocytes only differed between areas for Aβ, TDP43, and TSPO with the former two being higher in A46 and TSPO being higher in A17 (Fig. 10b, bottom left; Suppl. Table 11). In oligodendrocytes the mean intensity of Aβ, S6 and TDP43 was lower in A17 than in A46 while that of pTau and 3NT was higher in A17 than in A46, with the remaining markers showing no differences between areas A17 (Fig. 10b, bottom middle; Suppl. Table 11). The mean intensity of pathological markers in microglia did not differ between brain areas (Fig. 10b, bottom right; Suppl. Table 11).

Dimensional reduction and clustering reveal cortical regional and laminar organization based on multivariate neuronal and glial proteomic profiles.

Using dimensional reduction, clustering and MANOVA, we assessed how distinct layers of each region are (dis)similar to each other based on multiple neuronal and glial expression profiles. First, we took the MGI of 21 markers for each cell type layer and area. We then ran unsupervised clustering and t-SNE dimensional reduction and found that the fields clustered most robustly according to cell type (Fig. 11a). There is also some subclustering of fields by area, within cell type profiles.

We then ran separate clustering and MANOVA analyses of fields per area x layer x case based on total (Fig. 11b), and neuronal (Fig. 11c) vs glial (Fig. 11d) expression profiles of all the variables. We included 35 input variables based on normalized Z intensities (21 markers) and cell densities (14 markers) of markers of interest (Suppl. Table 12). MANOVA based on total expression of both neuronal and glial profiles significantly segregated, fields per area x layer x case into six groups, based on five significant canonical variables (c1, c2, c3, c4, c5; p < 0.05, Suppl. Table 12). Specifically, when a combination of total neuronal and glial features was considered together, the first canonical variable (c1) separated A17 WM from the rest of the groups. The second canonical variable (c2) separated L1 fields from the rest of groups; followed by a between-area separation of A17 L1 from A46 L1 based on the 3rd canonical variable. The 4th and 5th canonical variables then segregated A17 L2/3 and A46 WM, respectively, from the rest of the fields. These indicates that based on the combined expression of neuronal and glial markers, A17 WM and fields in L1, followed by A17 L2/3 and A46 WM, were the most dissimilar than the rest of the cortical fields examined. Interestingly, the clustering of fields × area × layer seems to be more driven by neuronal than glial features. Among all variables, the density of neurons, specifically HUD+, pyramidal/stellate neurons and MAP2+ neuron densities, had the highest value of coefficients that most influenced the significant canonical variables (c1–c5) that determined the separation of clusters. However, among glial markers, the density and intensities of CNPase oligodendrocyte marker and GFAP astrocyte markers are significant drivers of clustering, especially in the distinction of L1 from the rest of the fields (c2, see Suppl. Table 12).

We then assessed the independent effects of neuronal expression and glial expression profiles on the similarity of layers and areas by running two distinct MANOVAs with two sets of neuronal versus glial features. To assess clustering of fields based on neuronal expression, the set of outcome measures for MANOVA included 15 intensity and 8 cell densities of neuron specific and pathological markers (Suppl. Table 12). Based on this neuronal expression profile, we found a strong clustering of the fields by area and layer, with significant separation of 4 clusters based on 3 significant canonical variables (Fig. 11c, MANOVA p < 0.05; Suppl. Table 12). The first segregation based on neuron features was between L1 from both areas (c1) and the rest of the fields (Fig. 11c, Suppl. Table 12). Then inter-areal subclusters emerge, with the 2nd canonical variable (c2) segregating A46 L1 from A17 L1, and the 3rd canonical variable (c3) segregating the rest of the A17 layers from A46 layers. The clustering also showed that between-layer dissimilarities were more pronounced in A17 than A46, with distances between A17 layers greater than A46 layers. Among all neuronal features, cell densities of HuD+, pyramidal/stellate and MAP2+ neurons had the strongest influence on separations based on the first 3 significant canonical variables (Suppl. Table 12). Canonical variables were also driven by GAD65+ intensities and density of npNF+ neurons.

To assess clustering of fields based on glial expression, MANOVA included 13 intensity and 6 cell densities of glial specific and pathological markers. Similar to neuronal features, glial expression profiles only had three significant canonical variables that segregated four distinct clusters (Fig. 11d, MANOVA p < 0.05; Suppl. Table 12). However, glial expression profiles did not systematically discriminate between areas and between all layers compared to neuronal expression profiles. The two main clusters were between gray and white matter (separated by c1). Among all glial outcome features, the intensities of ALDH1L1 and S6 as well as the densities of oligo markers Olig2 and BCAS1, were the strongest drivers of this WM/GM segregation. Then within the grey matter cluster, L1 from both areas separated into a distinct cluster from other fields (c2), which was driven mainly by differences in glial cell densities of astrocytes and oligodendrocytes. Then a significant segregation based the third canonical variable with A17 L2/3 and the rest of the fields (c3). Interestingly, this last separation was mainly driven by intensity of the pathological markers pTau and Cleaved Caspase3 within glial cells (Suppl. Table 12).

In summary, the neuronal and glial features segregated the groups distinctly, with between-area clustering more pronounced based on neuronal profiles than on glial profiles. Not only did neuronal features significantly cluster between distinct groups based on area × layer, but the differences (Euclidean distances) between the clusters were greater (Fig. 11c, d). These data show that neuronal features are more divergent across areas and layers compared to glial features.

Discussion

We used iterative MxIF to compare the distribution of 28 cellular and subcellular protein markers across laminae in the gray matter neuropil and superficial white matter of the primary visual cortex (A17) and the dorsolateral prefrontal cortex (A46) of the adult rhesus monkey. Recent single cell transcriptomic studies (including sNucSeq) have yielded data that are predictive of phenotypic variation and point to significantly greater cellular diversity in the mammalian neocortex than previously appreciated (Tasic et al. 2018; Bakken et al. 2021; Berg et al. 2021; Khrameeva et al. 2020; Krienen et al. 2020; Lei et al. 2022; Lein et al. 2017; Yao et al. 2021; Zhu et al. 2018; Jorstad et al. 2023). As many as 24 transcriptomically distinct subclasses of neuronal and non-neuronal cells in diverse neocortical regions were recently described with differences between areas primarily due to differences in the distribution of diverse subclasses of excitatory neurons (Jorstad et al. 2023). In the present study we similarly found that differences between layers within areas and between areas were primarily accounted for by differences in neuronal number and distribution.

sNucSeq methods are based on the presence of RNA molecules in the nucleus, and thus only indirectly point to the potential presence or levels of translated proteins in various cellular compartments. Since translation, protein turnover and posttranslational modifications often differ between cells, transcript levels and cognate protein levels do not necessarily correlate (Nie et al. 2007; Liu et al. 2016). Highly polarized cells such as neurons add additional complexity to understanding the relationship between nuclear RNA expression and cellular phenotype (Moritz et al. 2019) since many proteins are specifically localized to cytoplasmic regions in the cell body, dendrites, and axonal processes. Further, whether transcriptomic features are indicative of functionally distinct cell types or perhaps of different states of the same cell is an important unanswered question.

While gene expression studies are highly informative, the compilation of a comprehensive brain cell census requires -in addition to morphological and functional characterization of individual cells across the lifespan- the direct assessment of the expression of diverse proteins per se. In the present study, we assessed 28 cellular and subcellular protein markers. Fourteen of these markers were specific for neurons and non-neuronal cells, and seven identified markers of oxidative stress or misfolded proteins. A further four markers labeled myelinated fibers and neurofilament proteins in axons or dendrites while three markers labeled vascular components of the neuropil and white matter.

A rich dataset of 66,997 precisely localized cells were segmented as DAPI objects and manually classified—30,139 cells in A46 from 6 subjects and 36,858 cells in A17 from 5 subjects. MxIF on the same tissue section(s) has the benefit of revealing protein expression intensities in diverse morphological compartments beyond the nucleus and they are capable of much greater spatial resolution and capture efficiency since transcripts that are present at low levels cannot be detected with sNucSeq (Thrupp et al. 2020; Cardona-Alberich et al. 2021).

Density, distribution and proportions of neurons and non-neuronal cells

The density of neurons identified with HuD and/or NeuN in L2-6 was approximately twice as high in A17 compared to in A46, while the density of neurons in L1 and in WM (very low in both areas) did not differ. This interareal difference in neuronal density is consistent with previous stereological estimates (e.g. Hilgetag et al. 2016). In particular, the density of neurons in the expanded L4 was about three times greater in A17 than in A46 with a lower density of neurons in L5 of A17 compared to A46. In both brain regions neurons were the principal cell type present in the gray matter neuropil, but the ratio of non-neuronal cells (glial cells and endothelial cells) to neurons differed significantly at 0.75 (43:57) in A46, compared to 0.37 (27:73) in A17. In both brain areas endothelial cells comprised ~ 30% of the non-neuronal cell population and did not exhibit significant areal or laminar differences, consistent with previous estimates in other brain areas and species (review, von Bartheld et al. 2016). The GNR was 32:57 (0.56) in A46 and 18:73 (0.25) in A17. The GNR for A46 is somewhat lower than the value of ~ 0.9 reported previously for prefrontal area 46 in the rhesus monkey (Dombrowski et al. 2001). Similarly, the GNR in A17 was lower than the previous report of ~ 0.49 in the primary visual cortex in the rhesus monkey (O’Kusky and Colonnier 1982).

The higher glia to neuron ratios observed in previous studies compared to this study is likely due to methodological differences. Earlier studies primarily employed Nissl staining in which it is notoriously difficult to discriminate small neurons from glia; thus, it is possible that the number of neurons were underestimated, and the number of glia overestimated with these methods. By using immunofluorescence for both NeuN and HuD to identify neurons and multiple markers specific for glial cells we circumvent these issues by visually identifying cells with highly specific cell markers.

This is the first study to systematically compare the proportions of different glial cells across A17 and A46 using immunofluorescence for specific glial markers. Oligodendrocytes comprised 46.9% and 50% of all glial cells in A46 and in A17 respectively, with astrocytes comprising 43.8% and 44.5% of all glial cells in A46 and in A17 respectively. Microglial cells identified using Iba1 comprised 9.3% of glial cells in PFC and 5.5% in A17. Our findings are only partially consistent with earlier studies in that we observed a higher proportion of oligodendrocytes and a lower proportion of astrocytes. For example an early study by O’Kusky and Colonnier (1982) quantified the following proportions of glial cells in macaque V1: 65% astrocytes, 29% oligodendrocytes, and 6% microglia and a subsequent electron microscopy study reported similar proportions of 57% astrocytes, 35% oligodendrocytes, and 8% microglia within A17 of the rhesus monkey (Peters et al. 1991). What accounts for the 10–15% difference in glia proportions between studies? Again, this could be due to the technical difficulty in discriminating glial subtypes with Nissl (O’Kusky and Collonier 1982) and the small sample area available for electron microscopy analyses (Peters et al. 1991).

Distribution and intensities of HuD- and NeuN-positive neurons

Neurons were identified as DAPI objects containing the RNA binding protein HuD, the nuclear protein NeuN or most commonly, containing both markers. In both A46 and A17 most neurons (96% and 91% respectively) contained both HuD and NeuN but at markedly different intensities. HuD-only neurons were scarce in both A46 (1%) and A17 (4%) and NeuN-only cells were similarly sparse, comprising just 3% and 5% of the total neuronal population in A46 and A17 respectively. While HuD-only neurons were relatively uniformly distributed across laminae in both areas, NeuN only neurons were confined primarily to supragranular layers 1–3 in both regions.

The signal intensity of HuD and NeuN differed significantly in the two regions. HuD signal increased linearly from L1 to L6 in both brain regions and for L2–5 was significantly higher in A46 compared to A17. HuD plays an important role in neurogenesis, neuronal morphology and neuronal signaling by regulating the metabolism of target mRNAs through modifications of stability, translation, splicing, and adenylation (Jung and Lee 2021). NeuN (also known as Fox 3), a nuclear protein that is specific to post-mitotic neurons, acts as a regulator of neuronal differentiation (Mullen et al. 1992; Kim et al. 2009; for review, see Gusel’nikova and Korzhevskiy 2015). Thus, the different expression levels of these neuronal markers in different layers and across regions is noteworthy and has significant implications for potential downstream protein expression and functional differences.

NeuN has been broadly used both to discriminate neurons from non-neuronal cells histologically (Gittins and Harrison 2004; Halene et al. 2016; García-Cabezas et al. 2016) and for cell-sorting in transcriptomic studies (Martin et al. 2017; e.g., Jorstad et al. 2023). Data presented here and in previous studies clearly indicate that NeuN labeling can be sporadic, e.g., only 18–57% of neurons were NeuN-positive in histological sections from human cortex (Lyck et al. 2009), and thus sound a cautionary note about relying solely on NeuN for identification or sorting of neurons. The functional relevance of the differences in intensity of staining for the two neuronal markers is currently unclear and awaits further investigation of the relevance of these modifiers of RNA translation in specific cell types. Given that the distribution patterns of these two markers can evidently be quite different, further evaluation may uncover differentiated roles in protein expression. In A17, this might reveal a highly layer-specific pattern.

Ratio of inhibitory to excitatory neurons and other features of interneurons

Interneurons were identified based on the presence within HuD+ neurons of the ubiquitous marker for GABAergic neurons GAD67 (responsible for GABA synthesis unrelated to neurotransmission) and GAD65, (responsible for GABA synthesis for neurotransmission, so primarily present at nerve terminals). Importantly GAD67 and GAD65 do not label all inhibitory neurons thus the reported numbers are likely an underestimate of the total population of inhibitory neurons. HuD+ neurons that were positive for GAD67, GAD65 or both were relatively similar in distribution between the two areas: 8, 7, and 5% of the interneurons in A46 and 5, 3, and 4% of the interneurons in A17 (both areas: L1–6).

A recent report showed highly distinctive excitatory to inhibitory neuron ratios in V1 compared to PFC in human with ratios of 4.5:1 vs 2:1 respectively (Jorstad et al. 2023). We similarly observed a higher E:I ratio in A17 compared to A46 at 7.3:1 and 4:1 respectively. Hornung and De Tribolet (1994) also reported a ratio of 4:1 in human frontal cortex. The interneuron specific markers PV and CB overlapped with GAD67 and GAD67+ GAD65 markers but not with cells that were GAD65+ only and which were localized primarily to L1. In this study we found that only about a third of GAD67+ neurons labeled with PV or with CB, indicating that a substantial number of interneurons are likely of the calretinin (CR, one of the markers for which staining failed in this study), nitric oxide, or undetermined subtypes.

Distribution and intensities of glial cell markers

We assessed the density, distribution, and marker intensity of each major glial class. Both brain areas showed a similar distribution and intensity of Iba1 staining of sparse microglia, with the highest density in L1 and in WM, and consistently low density in L2–6 (peaking in L5).

Astrocytes. Many astrocytes were positive for both ALDH1L1 and GFAP, with this group comprising 76% and 78% of the astrocyte population in A46 and A17 respectively. Nearly all other astrocytes were ALDH1L1+ only. ALDH1L1+/GFAP+ astrocytes were relatively uniformly distributed across cortical layers in both brain regions except for a notable decrease in density of this marker-specific class in A17 L4, particularly in L4A and B, consistent with a recent observation in human V1 (Jorstad et al. 2023). While ALDH1L1+ only positive astrocytes were quite uniformly distributed across layers in A46, this population was heavily localized to L4 (and specifically L4Cβ) in A17.

GFAP is routinely employed as a marker for astrocytes, but a relatively small proportion of astrocytes (1% in A46, 4% in A17) expressed only GFAP. These cells were localized almost exclusively in L1 and the superficial white matter in both brain areas. This study and others highlight the importance of employing multiple markers for a more thorough assessment of the diverse astrocyte populations, which are typically divided into protoplasmic astrocytes seen throughout the gray matter, interlaminar restricted to L1 and fibrillar, observed primarily in the white matter (Falcone et al. 2019, 2021, 2022; review, Escartin et al. 2021; Jurga et al. 2021). The intensity of the markers within the total population of astrocytes (containing either marker) exhibited inverse intensity distributions, being U-shaped for GFAP and bell-shaped for ALDH1L1, suggesting a more prominent role for GFAP in L1 (interlaminar astrocytes) and deep layers (fibrous astrocytes) and more important role for ALDH1L1 in middle layers (protoplasmic astrocytes).

We observed that a significant proportion of astrocytes in A17 (34%) but not A46 (4%) were also weakly positive for the oligodendrocyte marker Olig2. The presence of Olig2 in astrocytes has been previously reported in the adult mouse CNS (Wang et al. 2021). The presence of robustly Olig2+ astrocytes as a substantial subset of astrocytes is consistent with the idea of further functional specialization and potential subclassification of this type of glial cell and highlights the utility of multiple marker assessment in cell census studies. As has been previously suggested (Wang et al. 2021), astrocytes and oligodendrocytes may share common properties and the interplay of Olig2 in regulating both glial cell types offer intriguing avenues for further investigation.

Oligodendrocytes. Approximately half of the glial cell population in both areas was composed of Olig2+ oligodendrocytes (that did not express astrocytic markers or Iba1). These were present in approximately equivalent distributions across layers in A46, and were most dense in L4 of A17, as expected for this heavily myelinated layer. Olig2 staining intensity was significantly more intense in A17 than in A46 in all layers except L1.

The subclass of CNPase-positive oligodendrocytes was heavily prevalent in L4 of A17 (comprising half of the population of oligodendrocytes in L4C) but was sparse in L4 of A46. CNPase is associated with 4% of the total myelin protein in the brain and has been established to be primarily expressed in actively pre-myelinating and myelinating oligodendrocytes (Verrier et al. 2013). CNPase acts to metabolize the mitochondrial toxin 2′,3′-cAMP to 2-AMP in glial cells (Myllykoski et al. 2012). By reducing this toxin and increasing levels of the neuroprotectant adenosine, CNPase+ oligodendrocytes may protect white matter—particularly in highly metabolically active V1—from neurodegenerative processes.

A relatively low proportion of Olig2+ oligodendrocytes were also positive for the marker BCAS1 (2% and 4% in A46 and A17, respectively) and similarly for all 3 oligodendrocyte markers (1% and 2% in A46 and A17, respectively). The small population of BCAS1+ cells was localized throughout the layers except L1. BCAS1 (breast-carcinoma amplified sequence 1) is a specific marker for oligodendrocytes that are typically only transiently present during oligodendrogliogenesis and active myelination/remyelination (Fard et al. 2017; Pol et al. 2017). Since the brain tissue studied was from healthy adult macaques, it is to be expected that the expression of immature BCAS1+ oligodendrocytes was low.

Markers of oxidative stress and misfolded proteins

As noted, tissue was obtained from healthy adult monkeys with no known pathology or neurodegenerative disease. However, it is known that a broad variety of markers that are upregulated in disease are present, albeit at significantly lower levels, in a baseline state in a variety of cells. Here we assessed levels of 5 markers of oxidative stress or free radicals (Cleaved Caspase3, S6, TSPO, 3NT and TDP43) as well as two markers of misfolded proteins (Aβ and pTau) across neurons, astrocytes, oligodendrocytes, and microglia. The mean normalized intensity of the markers in glial cells did not differ between regions. However, the intensities of 5 of the 7 markers (all markers except Cleaved Caspase3 and TSPO) were higher in A46 than in A17 neurons. The higher levels of markers often associated with pathological states in A46 compared to A17 may be associated with the selective vulnerability of association cortical areas compared to primary sensory regions during normal aging and in neurodegenerative diseases (Braak and Braak 1991; Grothe et al. 2018; Hof and Morrison 1990; Hof et al. 1990; Mrdjen et al. 2019). Higher baseline levels of oxidative stress and misfolded proteins could in part be responsible for selective vulnerability of frontal compared to occipital regions.

Conclusions and future directions

Taken together, data from this study demonstrate the power of the MxIF approach to reveal nuanced and granular characteristics of proteins expressed in the brain and their potential interactions. Our multivariate clustering and dimensional reduction analyses based on large scale multiplexed protein expression data revealed that neuronal proteomic expression profiles are strong determinants of regional and laminar differences. In contrast, glial protein expression profiles influenced primarily laminar differences within each area. Neuronal proteomic features significantly clustered groups by area and layer, and the differences between the clusters were greater compared to clustering based on glial protein expression. These data show that neuronal features are more divergent across areas and layers compared to glial features, consistent with recent transcriptomic studies (Jorstad et al. 2023; Chiou et al. 2023).

Notably, protein-focused studies such as this, together with genetic and transcriptomic approaches, offer promising approaches for linking cellular and other (fibrillar, vascular, intracellular protein) features with function. It should be kept in mind that single cell transcriptomic and proteomic data, while important for cell classification schemes, do not capture the full complexity of cellular diversity nor spatial relationships in the microenvironment. Unique morphological and physiological properties, and developmental history are also key phenotypic characteristics to consider. Proteomic or transcriptomic data may be associated with, but do not reveal, major morphological differences, such as soma size of neurons among cortical regions, marked differences in spine number/density, axonal arborization patterns and connections, or different excitability properties (Elston 2000; Elston et al. 2001; Elston and DeFelipe 2002; Hsu et al. 2017; Medalla and Luebke 2015; Gilman et al. 2017; Luebke 2017; Medalla et al. 2017). We nevertheless are optimistic that further data along these lines will advance our understanding of the complex underpinnings of the diverse functional capacities of cortical regions.

Supplementary Information

Below is the link to the electronic supplementary material.Supplementary file1 (PDF 9701 KB)

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Acknowledgements

The authors are grateful to Dr. Douglas Rosene for his assistance with harvesting brain tissue and Dr. Jean-Pierre Roussarie for helpful conversations. This work was funded by NIH National Institute on Aging grants R01 AG059028 and R21 AG072154 (Luebke), R01 AG043478 and R01 AG043640 (Rosene). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Author contributions

J.I.L., D.E.M., P.R.H. contributed to the study conception and design. Material preparation, data collection and analysis were performed by P.B.C., C.M.W., W.C., M.M, K.S.R., L.L., E.M., D.E.M. and J.I.L. The first draft of the manuscript was written by P.B.C. and J.I.L. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Funding

Funding was supported by NIH/NIA (R01 AG059028).

Data availability

Enquiries about data availability should be directed to the authors.

Declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Paola B. Castro-Mendoza, Christina M. Weaver and Wayne Chang co-first authors.
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References

Ascoli GA Alonso-Nanclares L Anderson SA Barrionuevo G Benavides-Piccione R Burkhalter A Buzsáki G Cauli B Defelipe J Fairén A Feldmeyer D Fishell G Fregnac Y Freund TF Gardner D Gardner EP Goldberg JH Helmstaedter M Hestrin S Karube F Kisvárday ZF Lambolez B Lewis DA Marin O Markram H Muñoz A Packer A Petersen CC Rockland KS Rossier J Rudy B Somogyi P Staiger JF Tamas G Thomson AM Toledo-Rodriguez M Wang Y West DC Yuste R Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex Nat Rev Neurosci 2008 9 7 557 568 10.1038/nrn2402 18568015
Ascoli GA, Alonso-Nanclares L, Anderson SA, Barrionuevo G, Benavides-Piccione R, Burkhalter A, Buzsáki G, Cauli B, Defelipe J, Fairén A, Feldmeyer D, Fishell G, Fregnac Y, Freund TF, Gardner D, Gardner EP, Goldberg JH, Helmstaedter M, Hestrin S, Karube F, Kisvárday ZF, Lambolez B, Lewis DA, Marin O, Markram H, Muñoz A, Packer A, Petersen CC, Rockland KS, Rossier J, Rudy B, Somogyi P, Staiger JF, Tamas G, Thomson AM, Toledo-Rodriguez M, Wang Y, West DC, Yuste R (2008) Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat Rev Neurosci 9(7):557–56818568015 10.1038/nrn2402
Bagwell CB Hyperlog—a flexible log-like transform for negative, zero, and positive valued data Cytom Part A J Int Soc Anal Cytol 2005 64 1 34 42 10.1002/cyto.a.20114
Bagwell CB (2005) Hyperlog—a flexible log-like transform for negative, zero, and positive valued data. Cytom Part A J Int Soc Anal Cytol 64(1):34–42. 10.1002/cyto.a.2011410.1002/cyto.a.20114
Bakken TE Jorstad NL Hu Q Lake BB Tian W Kalmbach BE Crow M Hodge RD Krienen FM Sorensen SA Eggermont J Yao Z Aevermann BD Aldridge AI Bartlett A Bertagnolli D Casper T Castanon RG Crichton K Daigle TL Dalley R Dee N Dembrow N Diep D Ding SL Dong W Fang R Fischer S Goldman M Goldy J Graybuck LT Herb BR Hou X Kancherla J Kroll M Lathia K van Lew B Li YE Liu CS Liu H Lucero JD Mahurkar A McMillen D Miller JA Moussa M Nery JR Nicovich PR Niu SY Orvis J Osteen JK Owen S Palmer CR Pham T Plongthongkum N Poirion O Reed NM Rimorin C Rivkin A Romanow WJ Sedeño-Cortés AE Siletti K Somasundaram S Sulc J Tieu M Torkelson A Tung H Wang X Xie F Yanny AM Zhang R Ament SA Behrens MM Bravo HC Chun J Dobin A Gillis J Hertzano R Hof PR Höllt T Horwitz GD Keene CD Kharchenko PV Ko AL Lelieveldt BP Luo C Mukamel EA Pinto-Duarte A Preissl S Regev A Ren B Scheuermann RH Smith K Spain WJ White OR Koch C Hawrylycz M Tasic B Macosko EZ McCarroll SA Ting JT Zeng H Zhang K Feng G Ecker JR Linnarsson S Lein ES Comparative cellular analysis of motor cortex in human, marmoset and mouse Nature 2021 598 7879 111 119 10.1038/s41586-021-03465-8 34616062
Bakken TE, Jorstad NL, Hu Q, Lake BB, Tian W, Kalmbach BE, Crow M, Hodge RD, Krienen FM, Sorensen SA, Eggermont J, Yao Z, Aevermann BD, Aldridge AI, Bartlett A, Bertagnolli D, Casper T, Castanon RG, Crichton K, Daigle TL, Dalley R, Dee N, Dembrow N, Diep D, Ding SL, Dong W, Fang R, Fischer S, Goldman M, Goldy J, Graybuck LT, Herb BR, Hou X, Kancherla J, Kroll M, Lathia K, van Lew B, Li YE, Liu CS, Liu H, Lucero JD, Mahurkar A, McMillen D, Miller JA, Moussa M, Nery JR, Nicovich PR, Niu SY, Orvis J, Osteen JK, Owen S, Palmer CR, Pham T, Plongthongkum N, Poirion O, Reed NM, Rimorin C, Rivkin A, Romanow WJ, Sedeño-Cortés AE, Siletti K, Somasundaram S, Sulc J, Tieu M, Torkelson A, Tung H, Wang X, Xie F, Yanny AM, Zhang R, Ament SA, Behrens MM, Bravo HC, Chun J, Dobin A, Gillis J, Hertzano R, Hof PR, Höllt T, Horwitz GD, Keene CD, Kharchenko PV, Ko AL, Lelieveldt BP, Luo C, Mukamel EA, Pinto-Duarte A, Preissl S, Regev A, Ren B, Scheuermann RH, Smith K, Spain WJ, White OR, Koch C, Hawrylycz M, Tasic B, Macosko EZ, McCarroll SA, Ting JT, Zeng H, Zhang K, Feng G, Ecker JR, Linnarsson S, Lein ES (2021) Comparative cellular analysis of motor cortex in human, marmoset and mouse. Nature 598(7879):111–119. 10.1038/s41586-021-03465-834616062 10.1038/s41586-021-03465-8
Balaram P, Takasaki K, Hellevik A, Tandukar J, Turschak E, MacLennan B, Ouellette N, Torres R, Laughland C, Gliko O, Seshamani S, Perlman E, Taormina M, Peterson E, Juneau Z, Potekhina L, Glaser A, Chandrashekar J, Logsdon M, Cao K, Dylla C, Hatanaka G, Chatterjee S, Ting J, Vumbaco D, Waters J, Bair W, Tsao D, Gao R, Reid C (2023) Microscale visualization of cellular features in adult macaque visual cortex. bioRxiv: the preprint server for biology, 2023.11.02.565381. 10.1101/2023.11.02.565381
Bankhead P Loughrey MB Fernández JA Dombrowski Y McArt DG Dunne PD McQuaid S Gray RT Murray LJ Coleman HG James JA Salto-Tellez M Hamilton PW QuPath: open source software for digital pathology image analysis Sci Rep 2017 7 1 16878 10.1038/s41598-017-17204-5 29203879
Bankhead P, Loughrey MB, Fernández JA, Dombrowski Y, McArt DG, Dunne PD, McQuaid S, Gray RT, Murray LJ, Coleman HG, James JA, Salto-Tellez M, Hamilton PW (2017) QuPath: open source software for digital pathology image analysis. Sci Rep 7(1):16878. 10.1038/s41598-017-17204-529203879 10.1038/s41598-017-17204-5
Barbas H Pandya DN Architecture and intrinsic connections of the prefrontal cortex in the rhesus monkey J Comp Neurol 1989 286 3 353 375 10.1002/cne.902860306 2768563
Barbas H, Pandya DN (1989) Architecture and intrinsic connections of the prefrontal cortex in the rhesus monkey. J Comp Neurol 286(3):353–375. 10.1002/cne.9028603062768563 10.1002/cne.902860306
Berg J Sorensen SA Ting JT Miller JA Chartrand T Buchin A Bakken TE Budzillo A Dee N Ding SL Gouwens NW Hodge RD Kalmbach B Lee C Lee BR Alfiler L Baker K Barkan E Beller A Berry K Bertagnolli D Bickley K Bomben J Braun T Brouner K Casper T Chong P Crichton K Dalley R de Frates R Desta T Lee SD D'Orazi F Dotson N Egdorf T Enstrom R Farrell C Feng D Fong O Furdan S Galakhova AA Gamlin C Gary A Glandon A Goldy J Gorham M Goriounova NA Gratiy S Graybuck L Gu H Hadley K Hansen N Heistek TS Henry AM Heyer DB Hill D Hill C Hupp M Jarsky T Kebede S Keene L Kim L Kim MH Kroll M Latimer C Levi BP Link KE Mallory M Mann R Marshall D Maxwell M McGraw M McMillen D Melief E Mertens EJ Mezei L Mihut N Mok S Molnar G Mukora A Ng L Ngo K Nicovich PR Nyhus J Olah G Oldre A Omstead V Ozsvar A Park D Peng H Pham T Pom CA Potekhina L Rajanbabu R Ransford S Reid D Rimorin C Ruiz A Sandman D Sulc J Sunkin SM Szafer A Szemenyei V Thomsen ER Tieu M Torkelson A Trinh J Tung H Wakeman W Waleboer F Ward K Wilbers R Williams G Yao Z Yoon JG Anastassiou C Arkhipov A Barzo P Bernard A Cobbs C de Witt Hamer PC Ellenbogen RG Esposito L Ferreira M Gwinn RP Hawrylycz MJ Hof PR Idema S Jones AR Keene CD Ko AL Murphy GJ Ng L Ojemann JG Patel AP Phillips JW Silbergeld DL Smith K Tasic B Yuste R Segev I de Kock CPJ Mansvelder HD Tamas G Zeng H Koch C Lein ES Human neocortical expansion involves glutamatergic neuron diversification Nature 2021 598 7879 151 158 10.1038/s41586-021-03813-8 34616067
Berg J, Sorensen SA, Ting JT, Miller JA, Chartrand T, Buchin A, Bakken TE, Budzillo A, Dee N, Ding SL, Gouwens NW, Hodge RD, Kalmbach B, Lee C, Lee BR, Alfiler L, Baker K, Barkan E, Beller A, Berry K, Bertagnolli D, Bickley K, Bomben J, Braun T, Brouner K, Casper T, Chong P, Crichton K, Dalley R, de Frates R, Desta T, Lee SD, D’Orazi F, Dotson N, Egdorf T, Enstrom R, Farrell C, Feng D, Fong O, Furdan S, Galakhova AA, Gamlin C, Gary A, Glandon A, Goldy J, Gorham M, Goriounova NA, Gratiy S, Graybuck L, Gu H, Hadley K, Hansen N, Heistek TS, Henry AM, Heyer DB, Hill D, Hill C, Hupp M, Jarsky T, Kebede S, Keene L, Kim L, Kim MH, Kroll M, Latimer C, Levi BP, Link KE, Mallory M, Mann R, Marshall D, Maxwell M, McGraw M, McMillen D, Melief E, Mertens EJ, Mezei L, Mihut N, Mok S, Molnar G, Mukora A, Ng L, Ngo K, Nicovich PR, Nyhus J, Olah G, Oldre A, Omstead V, Ozsvar A, Park D, Peng H, Pham T, Pom CA, Potekhina L, Rajanbabu R, Ransford S, Reid D, Rimorin C, Ruiz A, Sandman D, Sulc J, Sunkin SM, Szafer A, Szemenyei V, Thomsen ER, Tieu M, Torkelson A, Trinh J, Tung H, Wakeman W, Waleboer F, Ward K, Wilbers R, Williams G, Yao Z, Yoon JG, Anastassiou C, Arkhipov A, Barzo P, Bernard A, Cobbs C, de Witt Hamer PC, Ellenbogen RG, Esposito L, Ferreira M, Gwinn RP, Hawrylycz MJ, Hof PR, Idema S, Jones AR, Keene CD, Ko AL, Murphy GJ, Ng L, Ojemann JG, Patel AP, Phillips JW, Silbergeld DL, Smith K, Tasic B, Yuste R, Segev I, de Kock CPJ, Mansvelder HD, Tamas G, Zeng H, Koch C, Lein ES (2021) Human neocortical expansion involves glutamatergic neuron diversification. Nature 598(7879):151–158. 10.1038/s41586-021-03813-834616067 10.1038/s41586-021-03813-8
Braak H Braak E Neuropathological stageing of Alzheimer-related changes Acta Neuropathol 1991 82 4 239 259 10.1007/BF00308809 1759558
Braak H, Braak E (1991) Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol 82(4):239–259. 10.1007/BF003088091759558 10.1007/BF00308809
Brodmann K Vergleichende Lokalisationslehre der Grosshirnrinde (in German) 1909 Leipzig Johann Ambrosius Barth
Brodmann K (1909) Vergleichende Lokalisationslehre der Grosshirnrinde (in German). Johann Ambrosius Barth, Leipzig
Brodmann K (1905) Die Rindenfelder der niederen Affen. J Psychol Neurolog, Bd 4
Cardona-Alberich A Tourbez M Pearce SF Sibley CR Elucidating the cellular dynamics of the brain with single-cell RNA sequencing RNA Biol 2021 18 7 1063 1084 10.1080/15476286.2020.1870362 33499699
Cardona-Alberich A, Tourbez M, Pearce SF, Sibley CR (2021) Elucidating the cellular dynamics of the brain with single-cell RNA sequencing. RNA Biol 18(7):1063–1084. 10.1080/15476286.2020.187036233499699 10.1080/15476286.2020.1870362
Caspers J Palomero-Gallagher N Caspers S Schleicher A Amunts K Zilles K Receptor architecture of visual areas in the face and word-form recognition region of the posterior fusiform gyrus Brain Struct Funct 2015 220 1 205 219 10.1007/s00429-013-0646-z 24126835
Caspers J, Palomero-Gallagher N, Caspers S, Schleicher A, Amunts K, Zilles K (2015) Receptor architecture of visual areas in the face and word-form recognition region of the posterior fusiform gyrus. Brain Struct Funct 220(1):205–219. 10.1007/s00429-013-0646-z24126835 10.1007/s00429-013-0646-z
Chang W Weaver CM Medalla M Moore TL Luebke JI Age-related alterations to working memory and to pyramidal neurons in the prefrontal cortex of rhesus monkeys begin in early middle-age and are partially ameliorated by dietary curcumin. Neurobiol Aging 2022 109 113 124 10.1016/j.neurobiolaging.2021.09.012 34715442
Chang W, Weaver CM, Medalla M, Moore TL, Luebke JI (2022) Age-related alterations to working memory and to pyramidal neurons in the prefrontal cortex of rhesus monkeys begin in early middle-age and are partially ameliorated by dietary curcumin. Neurobiol Aging 109:113–124. 10.1016/j.neurobiolaging.2021.09.01234715442 10.1016/j.neurobiolaging.2021.09.012
Chiou KL, Huang X, Bohlen MO, Tremblay S, DeCasien AR, O’Day DR, Spurrell CH, Gogate AA, Zintel TM, Cayo Biobank Research Unit, Andrews MG, Martínez MI, Starita LM, Montague MJ, Platt ML, Shendure J, Snyder-Mackler N (2023) A single-cell multi-omic atlas spanning the adult rhesus macaque brain. Sci Adv 9(41):eadh1914. 10.1126/sciadv.adh1914
Choe K Pak U Pang Y Hao W Yang X Advances and challenges in spatial transcriptomics for developmental biology Biomolecules 2023 13 1 156 10.3390/biom13010156 36671541
Choe K, Pak U, Pang Y, Hao W, Yang X (2023) Advances and challenges in spatial transcriptomics for developmental biology. Biomolecules 13(1):156. 10.3390/biom1301015636671541 10.3390/biom13010156
Darian-Smith C Lilak A Alarcón C Corticospinal sprouting occurs selectively following dorsal rhizotomy in the macaque monkey J Comp Neurol 2013 521 2359 2372 10.1002/cne.23289 23239125
Darian-Smith C, Lilak A, Alarcón C (2013) Corticospinal sprouting occurs selectively following dorsal rhizotomy in the macaque monkey. J Comp Neurol 521:2359–2372. 10.1002/cne.2328923239125 10.1002/cne.23289
DeFelipe J Types of neurons, synaptic connections and chemical characteristics of cells immunoreactive for calbindin-D28K, parvalbumin and calretinin in the neocortex J Chem Neuroanat 1997 14 1 1 19 10.1016/s0891-0618(97)10013-8 9498163
DeFelipe J (1997) Types of neurons, synaptic connections and chemical characteristics of cells immunoreactive for calbindin-D28K, parvalbumin and calretinin in the neocortex. J Chem Neuroanat 14(1):1–19. 10.1016/s0891-0618(97)10013-89498163 10.1016/s0891-0618(97)10013-8
Dombrowski SM Hilgetag CC Barbas H Quantitative architecture distinguishes prefrontal cortical systems in the rhesus monkey Cereb Cortex 2001 11 10 975 988 10.1093/cercor/11.10.975 11549620
Dombrowski SM, Hilgetag CC, Barbas H (2001) Quantitative architecture distinguishes prefrontal cortical systems in the rhesus monkey. Cereb Cortex 11(10):975–988. 10.1093/cercor/11.10.97511549620 10.1093/cercor/11.10.975
Elston GN Pyramidal cells of the frontal lobe: all the more spinous to think with J Neurosci 2000 20 RC95 10.1523/JNEUROSCI.20-18-j0002.2000 10974092
Elston GN (2000) Pyramidal cells of the frontal lobe: all the more spinous to think with. J Neurosci 20:RC95. 10.1523/JNEUROSCI.20-18-j0002.200010974092 10.1523/JNEUROSCI.20-18-j0002.2000
Elston GN DeFelipe J Spine distribution in cortical pyramidal cells: a common organizational principle across species Prog Brain Res 2002 136 109 133 10.1016/s0079-6123(02)36012-6 12143375
Elston GN, DeFelipe J (2002) Spine distribution in cortical pyramidal cells: a common organizational principle across species. Prog Brain Res 136:109–133. 10.1016/s0079-6123(02)36012-612143375 10.1016/s0079-6123(02)36012-6
Elston GN Benavides-Piccione R DeFelipe J The pyramidal cell in cognition: a comparative study in human and monkey J Neurosci 2001 21 RC163 10.1523/JNEUROSCI.21-17-j0002.2001 11511694
Elston GN, Benavides-Piccione R, DeFelipe J (2001) The pyramidal cell in cognition: a comparative study in human and monkey. J Neurosci 21:RC163. 10.1523/JNEUROSCI.21-17-j0002.200111511694 10.1523/JNEUROSCI.21-17-j0002.2001
Escartin C Galea E Lakatos A O'Callaghan JP Petzold GC Serrano-Pozo A Steinhäuser C Volterra A Carmignoto G Agarwal A Allen NJ Araque A Barbeito L Barzilai A Bergles DE Bonvento G Butt AM Chen WT Cohen-Salmon M Cunningham C Deneen B De Strooper B Díaz-Castro B Farina C Freeman M Gallo V Goldman JE Goldman SA Götz M Gutiérrez A Haydon PG Heiland DH Hol EM Holt MG Iino M Kastanenka KV Kettenmann H Khakh BS Koizumi S Lee CJ Liddelow SA MacVicar BA Magistretti P Messing A Mishra A Molofsky AV Murai KK Norris CM Okada S Oliet SHR Oliveira JF Panatier A Parpura V Pekna M Pekny M Pellerin L Perea G Pérez-Nievas BG Pfrieger FW Poskanzer KE Quintana FJ Ransohoff RM Riquelme-Perez M Robel S Rose CR Rothstein JD Rouach N Rowitch DH Semyanov A Sirko S Sontheimer H Swanson RA Vitorica J Wanner IB Wood LB Wu J Zheng B Zimmer ER Zorec R Sofroniew MV Verkhratsky A Reactive astrocyte nomenclature, definitions, and future directions Nat Neurosci 2021 24 3 312 325 10.1038/s41593-020-00783-4 33589835
Escartin C, Galea E, Lakatos A, O’Callaghan JP, Petzold GC, Serrano-Pozo A, Steinhäuser C, Volterra A, Carmignoto G, Agarwal A, Allen NJ, Araque A, Barbeito L, Barzilai A, Bergles DE, Bonvento G, Butt AM, Chen WT, Cohen-Salmon M, Cunningham C, Deneen B, De Strooper B, Díaz-Castro B, Farina C, Freeman M, Gallo V, Goldman JE, Goldman SA, Götz M, Gutiérrez A, Haydon PG, Heiland DH, Hol EM, Holt MG, Iino M, Kastanenka KV, Kettenmann H, Khakh BS, Koizumi S, Lee CJ, Liddelow SA, MacVicar BA, Magistretti P, Messing A, Mishra A, Molofsky AV, Murai KK, Norris CM, Okada S, Oliet SHR, Oliveira JF, Panatier A, Parpura V, Pekna M, Pekny M, Pellerin L, Perea G, Pérez-Nievas BG, Pfrieger FW, Poskanzer KE, Quintana FJ, Ransohoff RM, Riquelme-Perez M, Robel S, Rose CR, Rothstein JD, Rouach N, Rowitch DH, Semyanov A, Sirko S, Sontheimer H, Swanson RA, Vitorica J, Wanner IB, Wood LB, Wu J, Zheng B, Zimmer ER, Zorec R, Sofroniew MV, Verkhratsky A (2021) Reactive astrocyte nomenclature, definitions, and future directions. Nat Neurosci 24(3):312–325. 10.1038/s41593-020-00783-433589835 10.1038/s41593-020-00783-4
Falcone C Wolf-Ochoa M Amina S Hong T Vakilzadeh G Hopkins WD Hof PR Sherwood CC Manger PR Noctor SC Martínez-Cerdeño V Cortical interlaminar astrocytes across the therian mammal radiation J Comp Neurol 2019 527 10 1654 1674 10.1002/cne.24605 30552685
Falcone C, Wolf-Ochoa M, Amina S, Hong T, Vakilzadeh G, Hopkins WD, Hof PR, Sherwood CC, Manger PR, Noctor SC, Martínez-Cerdeño V (2019) Cortical interlaminar astrocytes across the therian mammal radiation. J Comp Neurol 527(10):1654–1674. 10.1002/cne.2460530552685 10.1002/cne.24605
Falcone C Penna E Hong T Tarantal AF Hof PR Hopkins WD Sherwood CC Noctor SC Martínez-Cerdeño V Cortical interlaminar astrocytes are generated prenatally, mature postnatally, and express unique markers in human and nonhuman primates Cereb Cortex 2021 31 1 379 395 10.1093/cercor/bhaa231 32930323
Falcone C, Penna E, Hong T, Tarantal AF, Hof PR, Hopkins WD, Sherwood CC, Noctor SC, Martínez-Cerdeño V (2021) Cortical interlaminar astrocytes are generated prenatally, mature postnatally, and express unique markers in human and nonhuman primates. Cereb Cortex 31(1):379–395. 10.1093/cercor/bhaa23132930323 10.1093/cercor/bhaa231
Falcone C McBride EL Hopkins WD Hof PR Manger PR Sherwood CC Noctor SC Martínez-Cerdeño V Redefining varicose projection astrocytes in primates Glia 2022 70 1 145 154 10.1002/glia.24093 34533866
Falcone C, McBride EL, Hopkins WD, Hof PR, Manger PR, Sherwood CC, Noctor SC, Martínez-Cerdeño V (2022) Redefining varicose projection astrocytes in primates. Glia 70(1):145–154. 10.1002/glia.2409334533866 10.1002/glia.24093
Fard MK van der Meer F Sánchez P Cantuti-Castelvetri L Mandad S Jäkel S Fornasiero EF Schmitt S Ehrlich M Starost L Kuhlmann T Sergiou C Schultz V Wrzos C Brück W Urlaub H Dimou L Stadelmann C Simons M BCAS1 expression defines a population of early myelinating oligodendrocytes in multiple sclerosis lesions Sci Transl Med 2017 9 419 eaam7816 10.1126/scitranslmed.aam7816 29212715
Fard MK, van der Meer F, Sánchez P, Cantuti-Castelvetri L, Mandad S, Jäkel S, Fornasiero EF, Schmitt S, Ehrlich M, Starost L, Kuhlmann T, Sergiou C, Schultz V, Wrzos C, Brück W, Urlaub H, Dimou L, Stadelmann C, Simons M (2017) BCAS1 expression defines a population of early myelinating oligodendrocytes in multiple sclerosis lesions. Sci Transl Med 9(419):eaam7816. 10.1126/scitranslmed.aam781629212715 10.1126/scitranslmed.aam7816
Froudist-Walsh S Xu T Niu M Rapan L Zhao L Margulies DS Zilles K Wang XJ Palomero-Gallagher N Gradients of neurotransmitter receptor expression in the macaque cortex Nat Neurosci 2023 26 7 1281 1294 10.1038/s41593-023-01351-2 37336976
Froudist-Walsh S, Xu T, Niu M, Rapan L, Zhao L, Margulies DS, Zilles K, Wang XJ, Palomero-Gallagher N (2023) Gradients of neurotransmitter receptor expression in the macaque cortex. Nat Neurosci 26(7):1281–1294. 10.1038/s41593-023-01351-237336976 10.1038/s41593-023-01351-2
García-Cabezas MÁ John YJ Barbas H Zikopoulos B Distinction of neurons, glia and endothelial cells in the cerebral cortex: an algorithm based on cytological features Front Neuroanat 2016 10 107 10.3389/fnana.2016.00107 27847469
García-Cabezas MÁ, John YJ, Barbas H, Zikopoulos B (2016) Distinction of neurons, glia and endothelial cells in the cerebral cortex: an algorithm based on cytological features. Front Neuroanat 10:107. 10.3389/fnana.2016.0010727847469 10.3389/fnana.2016.00107
Gerdes MJ Sevinsky CJ Sood A Adak S Bello MO Bordwell A Can A Corwin A Dinn S Filkins RJ Hollman D Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue Proc Natl Acad Sci 2013 110 29 11982 11987 10.1073/pnas.1300136110 23818604
Gerdes MJ, Sevinsky CJ, Sood A, Adak S, Bello MO, Bordwell A, Can A, Corwin A, Dinn S, Filkins RJ, Hollman D (2013) Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc Natl Acad Sci 110(29):11982–11987. 10.1073/pnas.130013611023818604 10.1073/pnas.1300136110
Gilman JP Medalla M Luebke JI Area-specific features of pyramidal neurons—a comparative study in mouse and rhesus monkey Cereb Cortex 2017 27 3 2078 2094 10.1093/cercor/bhw062 26965903
Gilman JP, Medalla M, Luebke JI (2017) Area-specific features of pyramidal neurons—a comparative study in mouse and rhesus monkey. Cereb Cortex 27(3):2078–2094. 10.1093/cercor/bhw06226965903 10.1093/cercor/bhw062
Gittins R Harrison PJ Neuronal density, size and shape in the human anterior cingulate cortex: a comparison of Nissl and NeuN staining Brain Res Bull 2004 63 2 155 160 10.1016/j.brainresbull.2004.02.005 15130705
Gittins R, Harrison PJ (2004) Neuronal density, size and shape in the human anterior cingulate cortex: a comparison of Nissl and NeuN staining. Brain Res Bull 63(2):155–160. 10.1016/j.brainresbull.2004.02.00515130705 10.1016/j.brainresbull.2004.02.005
Grafen A Hails R Modern statistics for the life sciences 2002 New York Oxford University Press
Grafen A, Hails R (2002) Modern statistics for the life sciences. Oxford University Press, New York
Grothe MJ Sepulcre J Gonzalez-Escamilla G Jelistratova I Schöll M Hansson O Teipel SJ Alzheimer’s Disease Neuroimaging Initiative Molecular properties underlying regional vulnerability to Alzheimer’s disease pathology Brain 2018 141 9 2755 2771 10.1093/brain/awy189 30016411
Grothe MJ, Sepulcre J, Gonzalez-Escamilla G, Jelistratova I, Schöll M, Hansson O, Teipel SJ, Alzheimer’s Disease Neuroimaging Initiative (2018) Molecular properties underlying regional vulnerability to Alzheimer’s disease pathology. Brain 141(9):2755–2771. 10.1093/brain/awy18930016411 10.1093/brain/awy189
Gusel'nikova VV Korzhevskiy DE NeuN as a neuronal nuclear antigen and neuron differentiation marker Acta Nat 2015 7 2 42 47 10.32607/20758251-2015-7-2-42-47
Gusel’nikova VV, Korzhevskiy DE (2015) NeuN as a neuronal nuclear antigen and neuron differentiation marker. Acta Nat 7(2):42–4710.32607/20758251-2015-7-2-42-47
Halene TB Kozlenkov A Jiang Y Mitchell AC Javidfar B Dincer A Park R Wiseman J Croxson PL Giannaris EL Hof PR Roussos P Dracheva S Hemby SE Akbarian S NeuN+ neuronal nuclei in non-human primate prefrontal cortex and subcortical white matter after clozapine exposure Schizophr Res 2016 170 2–3 235 244 10.1016/j.schres.2015.12.016 26776227
Halene TB, Kozlenkov A, Jiang Y, Mitchell AC, Javidfar B, Dincer A, Park R, Wiseman J, Croxson PL, Giannaris EL, Hof PR, Roussos P, Dracheva S, Hemby SE, Akbarian S (2016) NeuN+ neuronal nuclei in non-human primate prefrontal cortex and subcortical white matter after clozapine exposure. Schizophr Res 170(2–3):235–244. 10.1016/j.schres.2015.12.01626776227 10.1016/j.schres.2015.12.016
Hilgetag CC Medalla M Beul SF Barbas H The primate connectome in context: principles of connections of the cortical visual system Neuroimage 2016 134 685 702 10.1016/j.neuroimage.2016.04.017 27083526
Hilgetag CC, Medalla M, Beul SF, Barbas H (2016) The primate connectome in context: principles of connections of the cortical visual system. Neuroimage 134:685–702. 10.1016/j.neuroimage.2016.04.01727083526 10.1016/j.neuroimage.2016.04.017
Hof PR Morrison JH Quantitative analysis of a vulnerable subset of pyramidal neurons in Alzheimer's disease: II. Primary and secondary visual cortex J Comp Neurol 1990 301 1 55 64 10.1002/cne.903010106 1706358
Hof PR, Morrison JH (1990) Quantitative analysis of a vulnerable subset of pyramidal neurons in Alzheimer’s disease: II. Primary and secondary visual cortex. J Comp Neurol 301(1):55–64. 10.1002/cne.9030101061706358 10.1002/cne.903010106
Hof PR Cox K Morrison JH Quantitative analysis of a vulnerable subset of pyramidal neurons in Alzheimer’s disease: I. Superior frontal and inferior temporal cortex J Comp Neurol 1990 301 1 44 54 10.1002/cne.903010105 2127598
Hof PR, Cox K, Morrison JH (1990) Quantitative analysis of a vulnerable subset of pyramidal neurons in Alzheimer’s disease: I. Superior frontal and inferior temporal cortex. J Comp Neurol 301(1):44–54. 10.1002/cne.9030101052127598 10.1002/cne.903010105
Hof PR Glezer II Condé F Flagg RA Rubin MB Nimchinsky EA Vogt Weisenhorn DM Cellular distribution of the calcium-binding proteins parvalbumin, calbindin, and calretinin in the neocortex of mammals: phylogenetic and developmental patterns J Chem Neuroanat 1999 16 2 77 116 10.1016/s0891-0618(98)00065-9 10223310
Hof PR, Glezer II, Condé F, Flagg RA, Rubin MB, Nimchinsky EA, Vogt Weisenhorn DM (1999) Cellular distribution of the calcium-binding proteins parvalbumin, calbindin, and calretinin in the neocortex of mammals: phylogenetic and developmental patterns. J Chem Neuroanat 16(2):77–116. 10.1016/s0891-0618(98)00065-910223310 10.1016/s0891-0618(98)00065-9
Hornung JP De Tribolet N Distribution of GABA-containing neurons in human frontal cortex: a quantitative immunocytochemical study Anat Embryol (berl) 1994 189 2 139 145 10.1007/BF00185772 8010412
Hornung JP, De Tribolet N (1994) Distribution of GABA-containing neurons in human frontal cortex: a quantitative immunocytochemical study. Anat Embryol (berl) 189(2):139–145. 10.1007/BF001857728010412 10.1007/BF00185772
Hsu A Luebke JI Medalla M Comparative ultrastructural features of excitatory synapses in the visual and frontal cortices of the adult mouse and monkey J Comp Neurol 2017 525 9 2175 2191 10.1002/cne.24196 28256708
Hsu A, Luebke JI, Medalla M (2017) Comparative ultrastructural features of excitatory synapses in the visual and frontal cortices of the adult mouse and monkey. J Comp Neurol 525(9):2175–2191. 10.1002/cne.2419628256708 10.1002/cne.24196
Jorstad NL Close J Johansen N Yanny AM Barkan ER Travaglini KJ Bertagnolli D Campos J Casper T Crichton K Dee N Ding SL Gelfand E Goldy J Hirschstein D Kiick K Kroll M Kunst M Lathia K Long B Martin N McMillen D Pham T Rimorin C Ruiz A Shapovalova N Shehata S Siletti K Somasundaram S Sulc J Tieu M Torkelson A Tung H Callaway EM Hof PR Keene CD Levi BP Linnarsson S Mitra PP Smith K Hodge RD Bakken TE Lein ES Transcriptomic cytoarchitecture reveals principles of human neocortex organization Science 2023 382 6667 eadf6812 10.1126/science.adf6812 37824655
Jorstad NL, Close J, Johansen N, Yanny AM, Barkan ER, Travaglini KJ, Bertagnolli D, Campos J, Casper T, Crichton K, Dee N, Ding SL, Gelfand E, Goldy J, Hirschstein D, Kiick K, Kroll M, Kunst M, Lathia K, Long B, Martin N, McMillen D, Pham T, Rimorin C, Ruiz A, Shapovalova N, Shehata S, Siletti K, Somasundaram S, Sulc J, Tieu M, Torkelson A, Tung H, Callaway EM, Hof PR, Keene CD, Levi BP, Linnarsson S, Mitra PP, Smith K, Hodge RD, Bakken TE, Lein ES (2023) Transcriptomic cytoarchitecture reveals principles of human neocortex organization. Science 382(6667):eadf6812. 10.1126/science.adf681237824655 10.1126/science.adf6812
Jung N Kim TK Spatial transcriptomics in neuroscience Exp Mol Med 2023 55 10 2105 2115 10.1038/s12276-023-01093-y 37779145
Jung N, Kim TK (2023) Spatial transcriptomics in neuroscience. Exp Mol Med 55(10):2105–2115. 10.1038/s12276-023-01093-y37779145 10.1038/s12276-023-01093-y
Jung M Lee EK RNA-binding protein HuD as a versatile factor in neuronal and non-neuronal systems Biology (basel) 2021 10 5 361 10.3390/biology10050361 33922479
Jung M, Lee EK (2021) RNA-binding protein HuD as a versatile factor in neuronal and non-neuronal systems. Biology (basel) 10(5):361. 10.3390/biology1005036133922479 10.3390/biology10050361
Jurga AM Paleczna M Kadluczka J Kuter KZ Beyond the GFAP-astrocyte protein markers in the brain Biomolecules 2021 11 9 1361 1388 10.3390/biom11091361 34572572
Jurga AM, Paleczna M, Kadluczka J, Kuter KZ (2021) Beyond the GFAP-astrocyte protein markers in the brain. Biomolecules 11(9):1361–1388. 10.3390/biom1109136134572572 10.3390/biom11091361
Kaji S Mak T Ueda J Ishimoto T Inoue Y Yasuda K Sawamura M Hikawa R Ayaki T Yamakado H Takahashi R BCAS1-positive immature oligodendrocytes are affected by the α-synuclein-induced pathology of multiple system atrophy Acta Neuropathol Commun 2020 8 1 120 10.1186/s40478-020-00997-4 32727582
Kaji S, Mak T, Ueda J, Ishimoto T, Inoue Y, Yasuda K, Sawamura M, Hikawa R, Ayaki T, Yamakado H, Takahashi R (2020) BCAS1-positive immature oligodendrocytes are affected by the α-synuclein-induced pathology of multiple system atrophy. Acta Neuropathol Commun 8(1):120. 10.1186/s40478-020-00997-432727582 10.1186/s40478-020-00997-4
Khakh BS Deneen B The emerging nature of astrocyte diversity Annu Rev Neurosci 2019 42 187 207 10.1146/annurev-neuro-070918-050443 31283899
Khakh BS, Deneen B (2019) The emerging nature of astrocyte diversity. Annu Rev Neurosci 42:187–207. 10.1146/annurev-neuro-070918-05044331283899 10.1146/annurev-neuro-070918-050443
Khrameeva E Kurochkin I Han D Guijarro P Kanton S Santel M Qian Z Rong S Mazin P Sabirov M Bulat M Efimova O Tkachev A Guo S Sherwood CC Camp JG Pääbo S Treutlein B Khaitovich P Single-cell-resolution transcriptome map of human, chimpanzee, bonobo, and macaque brains Genome Res 2020 30 776 789 10.1101/gr.256958.119 32424074
Khrameeva E, Kurochkin I, Han D, Guijarro P, Kanton S, Santel M, Qian Z, Rong S, Mazin P, Sabirov M, Bulat M, Efimova O, Tkachev A, Guo S, Sherwood CC, Camp JG, Pääbo S, Treutlein B, Khaitovich P (2020) Single-cell-resolution transcriptome map of human, chimpanzee, bonobo, and macaque brains. Genome Res 30:776–789. 10.1101/gr.256958.11932424074 10.1101/gr.256958.119
Kim KK Adelstein RS Kawamoto S Identification of neuronal nuclei (NeuN) as Fox-3, a new member of the Fox-1 gene family of splicing factors J Biol Chem 2009 284 45 31052 31061 10.1074/jbc.M109.052969 19713214
Kim KK, Adelstein RS, Kawamoto S (2009) Identification of neuronal nuclei (NeuN) as Fox-3, a new member of the Fox-1 gene family of splicing factors. J Biol Chem 284(45):31052–31061. 10.1074/jbc.M109.05296919713214 10.1074/jbc.M109.052969
Krienen FM Goldman M Zhang Q Del Rosario CHR Florio M Machold R Saunders A Levandowski K Zaniewski H Schuman B Wu C Lutservitz A Mullally CD Reed N Bien E Bortolin L Fernandez-Otero M Lin JD Wysoker A Nemesh J Kulp D Burns M Tkachev V Smith R Walsh CA Dimidschstein J Rudy B Kean SL Berretta S Fishell G Feng G McCarroll SA Innovations present in the primate interneuron repertoire Nature 2020 586 7828 262 269 10.1038/s41586-020-2781-z 32999462
Krienen FM, Goldman M, Zhang Q, C H Del Rosario R, Florio M, Machold R, Saunders A, Levandowski K, Zaniewski H, Schuman B, Wu C, Lutservitz A, Mullally CD, Reed N, Bien E, Bortolin L, Fernandez-Otero M, Lin JD, Wysoker A, Nemesh J, Kulp D, Burns M, Tkachev V, Smith R, Walsh CA, Dimidschstein J, Rudy B, S Kean L, Berretta S, Fishell G, Feng G, McCarroll SA (2020) Innovations present in the primate interneuron repertoire. Nature 586(7828):262–269. 10.1038/s41586-020-2781-z32999462 10.1038/s41586-020-2781-z
Kuhn S Gritti L Crooks D Dombrowski Y Oligodendrocytes in development, myelin generation and beyond Cells 2019 8 11 1424 10.3390/cells8111424 31726662
Kuhn S, Gritti L, Crooks D, Dombrowski Y (2019) Oligodendrocytes in development, myelin generation and beyond. Cells 8(11):1424. 10.3390/cells811142431726662 10.3390/cells8111424
Lei Y Cheng M Li Z Zhuang Z Wu L Sun Y Han L Huang Z Wang Y Wang Z Xu L Yuan Y Liu S Pan T Xie J Liu C Volpe G Ward C Lai Y Xu J Wang M Yu H Sun H Yu Q Wu L Wang C Wong CW Liu W Xu L Wei J Chen D Shang Z Li G Ma K Cheng L Ling F Tan T Chen K Tasic B Dean M Ji W Yang H Gu Y Esteban MA Li Y Chen A Niu Y Zeng H Hou Y Liu L Liu S Xu X Spatially resolved gene regulatory and disease-related vulnerability map of the adult Macaque cortex Nat Commun 2022 13 1 6747 10.1038/s41467-022-34413-3 36347848
Lei Y, Cheng M, Li Z, Zhuang Z, Wu L, Sun Y, Han L, Huang Z, Wang Y, Wang Z, Xu L, Yuan Y, Liu S, Pan T, Xie J, Liu C, Volpe G, Ward C, Lai Y, Xu J, Wang M, Yu H, Sun H, Yu Q, Wu L, Wang C, Wong CW, Liu W, Xu L, Wei J, Chen D, Shang Z, Li G, Ma K, Cheng L, Ling F, Tan T, Chen K, Tasic B, Dean M, Ji W, Yang H, Gu Y, Esteban MA, Li Y, Chen A, Niu Y, Zeng H, Hou Y, Liu L, Liu S, Xu X (2022) Spatially resolved gene regulatory and disease-related vulnerability map of the adult Macaque cortex. Nat Commun 13(1):6747. 10.1038/s41467-022-34413-336347848 10.1038/s41467-022-34413-3
Lein E Borm LE Linnarsson S The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing Science 2017 358 6359 64 69 10.1126/science.aan6827 28983044
Lein E, Borm LE, Linnarsson S (2017) The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing. Science 358(6359):64–69. 10.1126/science.aan682728983044 10.1126/science.aan6827
Lin JR Fallahi-Sichani M Sorger PK Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method Nat Commun 2015 24 6 8390 10.1038/ncomms9390
Lin JR, Fallahi-Sichani M, Sorger PK (2015) Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method. Nat Commun 24(6):8390. 10.1038/ncomms939010.1038/ncomms9390
Lin JR Izar B Wang S Yapp C Mei S Shah PM Santagata S Sorger PK Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes Elife 2018 7 e31657 10.7554/eLife.31657 29993362
Lin JR, Izar B, Wang S, Yapp C, Mei S, Shah PM, Santagata S, Sorger PK (2018) Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. Elife 7:e31657. 10.7554/eLife.3165729993362 10.7554/eLife.31657
Lin JR Chen YA Campton D Cooper J Coy S Yapp C Tefft JB McCarty E Ligon KL Rodig SJ Reese S George T Santagata S Sorger PK High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers Nat Cancer 2023 4 7 1036 1052 10.1038/s43018-023-00576-1 37349501
Lin JR, Chen YA, Campton D, Cooper J, Coy S, Yapp C, Tefft JB, McCarty E, Ligon KL, Rodig SJ, Reese S, George T, Santagata S, Sorger PK (2023) High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers. Nat Cancer 4(7):1036–1052. 10.1038/s43018-023-00576-137349501 10.1038/s43018-023-00576-1
Liu Y Beyer A Aebersold R On the dependency of cellular protein levels on mRNA abundance Cell 2016 165 535 550 10.1016/j.cell.2016.03.014 27104977
Liu Y, Beyer A, Aebersold R (2016) On the dependency of cellular protein levels on mRNA abundance. Cell 165:535–550. 10.1016/j.cell.2016.03.01427104977 10.1016/j.cell.2016.03.014
Luebke JI Pyramidal neurons are not generalizable building blocks of cortical networks Front Neuroanat 2017 11 11 10.3389/fnana.2017.00011 28326020
Luebke JI (2017) Pyramidal neurons are not generalizable building blocks of cortical networks. Front Neuroanat 11:11. 10.3389/fnana.2017.0001128326020 10.3389/fnana.2017.00011
Lund JS Yoshioka T Levitt JB Peters A Rockland KS Substrates for interlaminar connections in area V1 of macaque monkey cerebral cortex Primary visual cortex in primates. Cerebral cortex 1994 Boston Springer
Lund JS, Yoshioka T, Levitt JB (1994) Substrates for interlaminar connections in area V1 of macaque monkey cerebral cortex. In: Peters A, Rockland KS (eds) Primary visual cortex in primates. Cerebral cortex, vol 10. Springer, Boston. 10.1007/978-1-4757-9628-5_2
Lyck L Santamaria ID Pakkenberg B Chemnitz J Schrøder HD Finsen B Gundersen HJ An empirical analysis of the precision of estimating the numbers of neurons and glia in human neocortex using a fractionator-design with sub-sampling J Neurosci Methods 2009 182 2 143 156 10.1016/j.jneumeth.2009.06.003 19520115
Lyck L, Santamaria ID, Pakkenberg B, Chemnitz J, Schrøder HD, Finsen B, Gundersen HJ (2009) An empirical analysis of the precision of estimating the numbers of neurons and glia in human neocortex using a fractionator-design with sub-sampling. J Neurosci Methods 182(2):143–156. 10.1016/j.jneumeth.2009.06.00319520115 10.1016/j.jneumeth.2009.06.003
Markov NT Vezoli J Chameau P Falchier A Quilodran R Huissoud C Lamy C Misery P Giroud P Ullman S Barone P Dehay C Knoblauch K Kennedy H Anatomy of hierarchy: feedforward and feedback pathways in macaque visual cortex J Comp Neurol. 2014 522 1 225 259 10.1002/cne.23458 23983048
Markov NT, Vezoli J, Chameau P, Falchier A, Quilodran R, Huissoud C, Lamy C, Misery P, Giroud P, Ullman S, Barone P, Dehay C, Knoblauch K, Kennedy H (2014) Anatomy of hierarchy: feedforward and feedback pathways in macaque visual cortex. J Comp Neurol 522(1):225–259. 10.1002/cne.2345823983048 10.1002/cne.23458
Martin D Xu J Porretta C Nichols CD Neurocytometry: flow cytometric sorting of specific neuronal populations from human and rodent brain ACS Chem Neurosci 2017 8 2 356 367 10.1021/acschemneuro.6b00374 28135061
Martin D, Xu J, Porretta C, Nichols CD (2017) Neurocytometry: flow cytometric sorting of specific neuronal populations from human and rodent brain. ACS Chem Neurosci 8(2):356–367. 10.1021/acschemneuro.6b0037428135061 10.1021/acschemneuro.6b00374
Maynard KR Collado-Torres L Weber LM Uytingco C Barry BK Williams SR Catallini JL 2nd Tran MN Besich Z Tippani M Chew J Yin Y Kleinman JE Hyde TM Rao N Hicks SC Martinowich K Jaffe AE Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex Nat Neurosci 2021 24 425 436 10.1038/s41593-020-00787-0 33558695
Maynard KR, Collado-Torres L, Weber LM, Uytingco C, Barry BK, Williams SR, Catallini JL 2nd, Tran MN, Besich Z, Tippani M, Chew J, Yin Y, Kleinman JE, Hyde TM, Rao N, Hicks SC, Martinowich K, Jaffe AE (2021) Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat Neurosci 24:425–436. 10.1038/s41593-020-00787-033558695 10.1038/s41593-020-00787-0
McDonough L, Chadwick C, Ginty F, Surrette C, Sood A (2020) Cell DIVE™ Platform|antibody characterization for multiplexing. protocols.io. 10.17504/protocols.io.bpyxmpxn
Medalla M Luebke JI Diversity of glutamatergic synaptic strength in lateral prefrontal versus primary visual cortices in the rhesus monkey J Neurosci 2015 35 1 112 127 10.1523/JNEUROSCI.3426-14.2015 25568107
Medalla M, Luebke JI (2015) Diversity of glutamatergic synaptic strength in lateral prefrontal versus primary visual cortices in the rhesus monkey. J Neurosci 35(1):112–127. 10.1523/JNEUROSCI.3426-14.201525568107 10.1523/JNEUROSCI.3426-14.2015
Medalla M Gilman JP Wang JY Luebke JI Strength and diversity of inhibitory signaling differentiates primate anterior cingulate from lateral prefrontal cortex J Neurosci 2017 37 18 4717 4734 10.1523/JNEUROSCI.3757-16.2017 28381592
Medalla M, Gilman JP, Wang JY, Luebke JI (2017) Strength and diversity of inhibitory signaling differentiates primate anterior cingulate from lateral prefrontal cortex. J Neurosci 37(18):4717–4734. 10.1523/JNEUROSCI.3757-16.201728381592 10.1523/JNEUROSCI.3757-16.2017
Moritz CP Mühlhaus T Tenzer S Schulenborg T Friauf E Poor transcript-protein correlation in the brain: negatively correlating gene products reveal neuronal polarity as a potential cause J Neurochem 2019 149 5 582 604 10.1111/jnc.14664 30664243
Moritz CP, Mühlhaus T, Tenzer S, Schulenborg T, Friauf E (2019) Poor transcript-protein correlation in the brain: negatively correlating gene products reveal neuronal polarity as a potential cause. J Neurochem 149(5):582–604. 10.1111/jnc.1466430664243 10.1111/jnc.14664
Mrdjen D Fox EJ Bukhari SA Montine KS Bendall SC Montine TJ The basis of cellular and regional vulnerability in Alzheimer’s disease Acta Neuropathol 2019 138 5 729 749 10.1007/s00401-019-02054-4 31392412
Mrdjen D, Fox EJ, Bukhari SA, Montine KS, Bendall SC, Montine TJ (2019) The basis of cellular and regional vulnerability in Alzheimer’s disease. Acta Neuropathol 138(5):729–749. 10.1007/s00401-019-02054-431392412 10.1007/s00401-019-02054-4
Mullen RJ Buck CR Smith AM NeuN, a neuronal specific nuclear protein in vertebrates Development 1992 116 1 201 211 10.1242/dev.116.1.201 1483388
Mullen RJ, Buck CR, Smith AM (1992) NeuN, a neuronal specific nuclear protein in vertebrates. Development 116(1):201–211. 10.1242/dev.116.1.2011483388 10.1242/dev.116.1.201
Myllykoski M Raasakka A Han H Kursula P Myelin 2′,3′-cyclic nucleotide 3′-phosphodiesterase: active-site ligand binding and molecular conformation PLoS ONE 2012 7 2 e32336 10.1371/journal.pone.0032336 22393399
Myllykoski M, Raasakka A, Han H, Kursula P (2012) Myelin 2′,3′-cyclic nucleotide 3′-phosphodiesterase: active-site ligand binding and molecular conformation. PLoS One 7(2):e32336. 10.1371/journal.pone.003233622393399 10.1371/journal.pone.0032336
National Research Council (2011) https://nap.nationalacademies.org/catalog/12910/guide-for-the-care-and-use-of-laboratory-animals-eighth. Accessed Jan 2023
Nie L Wu G Culley DE Scholten JC Zhang W Integrative analysis of transcriptomic and proteomic data: challenges, solutions and applications Crit Rev Biotechnol 2007 27 63 75 10.1080/07388550701334212 17578703
Nie L, Wu G, Culley DE, Scholten JC, Zhang W (2007) Integrative analysis of transcriptomic and proteomic data: challenges, solutions and applications. Crit Rev Biotechnol 27:63–75. 10.1080/0738855070133421217578703 10.1080/07388550701334212
O’Kusky J Colonnier M A laminar analysis of the number of neurons, glia, and synapses in the adult cortex (area 17) of adult macaque monkeys J Comp Neurol 1982 210 3 278 290 10.1002/cne.902100307 7142443
O’Kusky J, Colonnier M (1982) A laminar analysis of the number of neurons, glia, and synapses in the adult cortex (area 17) of adult macaque monkeys. J Comp Neurol 210(3):278–290. 10.1002/cne.9021003077142443 10.1002/cne.902100307
Palomero-Gallagher N Zilles K Cortical layers: cyto-, myelo-, receptor- and synaptic architecture in human cortical areas Neuroimage 2019 197 716 741 10.1016/j.neuroimage.2017.08.035 28811255
Palomero-Gallagher N, Zilles K (2019) Cortical layers: cyto-, myelo-, receptor- and synaptic architecture in human cortical areas. Neuroimage 197:716–741. 10.1016/j.neuroimage.2017.08.03528811255 10.1016/j.neuroimage.2017.08.035
Paxinos G, Huang X, Toga AW (2000) The rhesus monkey brain in stereotaxic coordinates. Academic Press
Peters A Josephson K Vincent SL Effects of aging on the neuroglial cells and pericytes within area 17 of the rhesus monkey cerebral cortex Anat Rec 1991 229 3 384 398 10.1002/ar.1092290311 2024779
Peters A, Josephson K, Vincent SL (1991) Effects of aging on the neuroglial cells and pericytes within area 17 of the rhesus monkey cerebral cortex. Anat Rec 229(3):384–398. 10.1002/ar.10922903112024779 10.1002/ar.1092290311
Piwecka M Rajewsky N Rybak-Wolf A Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease Nat Rev Neurol 2023 19 6 346 362 10.1038/s41582-023-00809-y 37198436
Piwecka M, Rajewsky N, Rybak-Wolf A (2023) Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease. Nat Rev Neurol 19(6):346–362. 10.1038/s41582-023-00809-y37198436 10.1038/s41582-023-00809-y
Pol SU Polanco JJ Seidman RA O'Bara MA Shayya HJ Dietz KC Sim FJ Network-based genomic analysis of human oligodendrocyte progenitor differentiation Stem Cell Rep 2017 9 2 710 723 10.1016/j.stemcr.2017.07.007
Pol SU, Polanco JJ, Seidman RA, O’Bara MA, Shayya HJ, Dietz KC, Sim FJ (2017) Network-based genomic analysis of human oligodendrocyte progenitor differentiation. Stem Cell Rep 9(2):710–723. 10.1016/j.stemcr.2017.07.00710.1016/j.stemcr.2017.07.007
Posit team (2022) RStudio: integrated development environment for R. Posit Software, PBC, Boston, MA. http://www.posit.co/. Accessed June 2023
Rapan L Niu M Zhao L Funck T Amunts K Zilles K Palomero-Gallagher N Receptor architecture of macaque and human early visual areas: not equal, but comparable Brain Struct Funct 2022 227 4 1247 1263 10.1007/s00429-021-02437-y 34931262
Rapan L, Niu M, Zhao L, Funck T, Amunts K, Zilles K, Palomero-Gallagher N (2022) Receptor architecture of macaque and human early visual areas: not equal, but comparable. Brain Struct Funct 227(4):1247–1263. 10.1007/s00429-021-02437-y34931262 10.1007/s00429-021-02437-y
Rapan L Froudist-Walsh S Niu M Xu T Zhao L Funck T Wang XJ Amunts K Palomero-Gallagher N Cytoarchitectonic, receptor distribution and functional connectivity analyses of the macaque frontal lobe Elife 2023 12 e82850 10.7554/eLife.82850 37578332
Rapan L, Froudist-Walsh S, Niu M, Xu T, Zhao L, Funck T, Wang XJ, Amunts K, Palomero-Gallagher N (2023) Cytoarchitectonic, receptor distribution and functional connectivity analyses of the macaque frontal lobe. Elife 12:e82850. 10.7554/eLife.8285037578332 10.7554/eLife.82850
Rockland KS Pandya DN Laminar origins and terminations of cortical connections of the occipital lobe in the rhesus monkey Brain Res 1979 179 1 3 20 10.1016/0006-8993(79)90485-2 116716
Rockland KS, Pandya DN (1979) Laminar origins and terminations of cortical connections of the occipital lobe in the rhesus monkey. Brain Res 179(1):3–20. 10.1016/0006-8993(79)90485-2116716 10.1016/0006-8993(79)90485-2
Sood A, Williams E, McDonough L (2021) Cell DIVE™ Platform|ab conjugation: initial conjugation and scale up conjugation. protocols.io. 10.17504/protocols.io.bp55mq86
Tasic B Yao Z Graybuck LT Smith KA Nguyen TN Bertagnolli D Goldy J Garren E Economo MN Viswanathan S Penn O Bakken T Menon V Miller J Fong O Hirokawa KE Lathia K Rimorin C Tieu M Larsen R Casper T Barkan E Kroll M Parry S Shapovalova NV Hirschstein D Pendergraft J Sullivan HA Kim TK Szafer A Dee N Groblewski P Wickersham I Cetin A Harris JA Levi BP Sunkin SM Madisen L Daigle TL Looger L Bernard A Phillips J Lein E Hawrylycz M Svoboda K Jones AR Koch C Zeng H Shared and distinct transcriptomic cell types across neocortical areas Nature 2018 563 72 78 10.1038/s41586-018-0654-5 30382198
Tasic B, Yao Z, Graybuck LT, Smith KA, Nguyen TN, Bertagnolli D, Goldy J, Garren E, Economo MN, Viswanathan S, Penn O, Bakken T, Menon V, Miller J, Fong O, Hirokawa KE, Lathia K, Rimorin C, Tieu M, Larsen R, Casper T, Barkan E, Kroll M, Parry S, Shapovalova NV, Hirschstein D, Pendergraft J, Sullivan HA, Kim TK, Szafer A, Dee N, Groblewski P, Wickersham I, Cetin A, Harris JA, Levi BP, Sunkin SM, Madisen L, Daigle TL, Looger L, Bernard A, Phillips J, Lein E, Hawrylycz M, Svoboda K, Jones AR, Koch C, Zeng H (2018) Shared and distinct transcriptomic cell types across neocortical areas. Nature 563:72–78. 10.1038/s41586-018-0654-530382198 10.1038/s41586-018-0654-5
Thrupp N Sala Frigerio C Wolfs L Skene NG Fattorelli N Poovathingal S Fourne Y Matthews PM Theys T Mancuso R de Strooper B Fiers M Single-nucleus RNA-Seq is not suitable for detection of microglial activation genes in humans Cell Rep 2020 32 13 108189 10.1016/j.celrep.2020.108189 32997994
Thrupp N, Sala Frigerio C, Wolfs L, Skene NG, Fattorelli N, Poovathingal S, Fourne Y, Matthews PM, Theys T, Mancuso R, de Strooper B, Fiers M (2020) Single-nucleus RNA-Seq is not suitable for detection of microglial activation genes in humans. Cell Rep 32(13):108189. 10.1016/j.celrep.2020.10818932997994 10.1016/j.celrep.2020.108189
Tian L Chen F Macosko EZ The expanding vistas of spatial transcriptomics Nat Biotechnol 2023 41 6 773 782 10.1038/s41587-022-01448-2 36192637
Tian L, Chen F, Macosko EZ (2023) The expanding vistas of spatial transcriptomics. Nat Biotechnol 41(6):773–782. 10.1038/s41587-022-01448-236192637 10.1038/s41587-022-01448-2
Van Essen DC Glasser MF Parcellating cerebral cortex: how invasive animal studies inform noninvasive mapmaking in humans Neuron 2018 99 4 640 663 10.1016/j.neuron.2018.07.002 30138588
Van Essen DC, Glasser MF (2018) Parcellating cerebral cortex: how invasive animal studies inform noninvasive mapmaking in humans. Neuron 99(4):640–663. 10.1016/j.neuron.2018.07.00230138588 10.1016/j.neuron.2018.07.002
Verrier JD Jackson TC Gillespie DG Janesko-Feldman K Bansal R Goebbels S Nave KA Kochanek PM Jackson EK Role of CNPase in the oligodendrocytic extracellular 2′,3′-cAMP-adenosine pathway Glia 2013 61 10 1595 1606 10.1002/glia.22523 23922219
Verrier JD, Jackson TC, Gillespie DG, Janesko-Feldman K, Bansal R, Goebbels S, Nave KA, Kochanek PM, Jackson EK (2013) Role of CNPase in the oligodendrocytic extracellular 2′,3′-cAMP-adenosine pathway. Glia 61(10):1595–1606. 10.1002/glia.2252323922219 10.1002/glia.22523
von Bartheld CS Bahney J Herculano-Houzel S The search for true numbers of neurons and glial cells in the human brain: a review of 150 years of cell counting J Comp Neurol 2016 524 18 3865 3895 10.1002/cne.24040 27187682
von Bartheld CS, Bahney J, Herculano-Houzel S (2016) The search for true numbers of neurons and glial cells in the human brain: a review of 150 years of cell counting. J Comp Neurol 524(18):3865–3895. 10.1002/cne.2404027187682 10.1002/cne.24040
Wang H Xu L Lai C Hou K Chen J Guo Y Sambangi A Swaminathan S Xie C Wu Z Chen G Region-specific distribution of Olig2-expressing astrocytes in adult mouse brain and spinal cord Mol Brain 2021 14 1 36 10.1186/s13041-021-00747-0 33618751
Wang H, Xu L, Lai C, Hou K, Chen J, Guo Y, Sambangi A, Swaminathan S, Xie C, Wu Z, Chen G (2021) Region-specific distribution of Olig2-expressing astrocytes in adult mouse brain and spinal cord. Mol Brain 14(1):36. 10.1186/s13041-021-00747-033618751 10.1186/s13041-021-00747-0
Xing Y Zan C Liu L Recent advances in understanding neuronal diversity and neural circuit complexity across different brain regions using single-cell sequencing Front Neural Circuits 2023 17 1007755 10.3389/fncir.2023.1007755 37063385
Xing Y, Zan C, Liu L (2023) Recent advances in understanding neuronal diversity and neural circuit complexity across different brain regions using single-cell sequencing. Front Neural Circuits 17:1007755. 10.3389/fncir.2023.100775537063385 10.3389/fncir.2023.1007755
Yao Z van Velthoven CTJ Nguyen TN Goldy J Sedeno-Cortes AE Baftizadeh F Bertagnolli D Casper T Chiang M Crichton K Ding SL Fong O Garren E Glandon A Gouwens NW Gray J Graybuck LT Hawrylycz MJ Hirschstein D Kroll M Lathia K Lee C Levi B McMillen D Mok S Pham T Ren Q Rimorin C Shapovalova N Sulc J Sunkin SM Tieu M Torkelson A Tung H Ward K Dee N Smith KA Tasic B Zeng H A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation Cell 2021 184 12 3222 3241.e26 10.1016/j.cell.2021.04.021 34004146
Yao Z, van Velthoven CTJ, Nguyen TN, Goldy J, Sedeno-Cortes AE, Baftizadeh F, Bertagnolli D, Casper T, Chiang M, Crichton K, Ding SL, Fong O, Garren E, Glandon A, Gouwens NW, Gray J, Graybuck LT, Hawrylycz MJ, Hirschstein D, Kroll M, Lathia K, Lee C, Levi B, McMillen D, Mok S, Pham T, Ren Q, Rimorin C, Shapovalova N, Sulc J, Sunkin SM, Tieu M, Torkelson A, Tung H, Ward K, Dee N, Smith KA, Tasic B, Zeng H (2021) A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell 184(12):3222–3241.e26. 10.1016/j.cell.2021.04.02134004146 10.1016/j.cell.2021.04.021
Zhang L Chen D Song D Liu X Zhang Y Xu X Wang X Clinical and translational values of spatial transcriptomics Sig Transduct Target Ther 2022 7 111 10.1038/s41392-022-00960-w
Zhang L, Chen D, Song D, Liu X, Zhang Y, Xu X, Wang X (2022) Clinical and translational values of spatial transcriptomics. Sig Transduct Target Ther 7:111. 10.1038/s41392-022-00960-w10.1038/s41392-022-00960-w
Zhu Y, Sousa AMM, Gao T, Skarica M, Li M, Santpere G, Esteller-Cucala P, Juan D, Ferrández-Peral L, Gulden FO, Yang M, Miller DJ, Marques-Bonet T, Imamura Kawasawa Y, Zhao H, Sestan N (2018) Spatiotemporal transcriptomic divergence across human and macaque brain development. Science 362(6420):eaat8077. 10.1126/science.aat8077
