==== Front IBRO RepIBRO RepIBRO Reports2451-8301Elsevier S2451-8301(17)30039-010.1016/j.ibror.2018.01.002ArticleExpression of aggrecan components in perineuronal nets in the mouse cerebral cortex Ueno Hiroshi dhe422007@s.okayama-u.ac.jpabc⁎Fujii Kazuki kfujii@cts.u-toyama.ac.jpcdSuemitsu Shunsuke ssue@med.kawasaki-m.ac.jpeMurakami Shinji muraka@med.kawasaki-m.ac.jpeKitamura Naoya n-kitamura@med.kawasaki-m.ac.jpeWani Kenta kenta99101@yahoo.co.jpeAoki Shozo shoaoki@med.kawasaki-m.ac.jpeOkamoto Motoi mokamoto@md.okayama-u.ac.jpbIshihara Takeshi t-ishihara@med.kawasaki-m.ac.jpeTakao Keizo takao@cts.u-toyama.ac.jpcda Department of Medical Technology, Kawasaki University of Medical Welfare, Okayama, 701-0193, Japanb Department of Medical Technology, Graduate School of Health Sciences, Okayama University, Okayama, 700-8558, Japanc Life Science Research Center, University of Toyama, Toyama, 930-0194, Japand Department of Behavioral Physiology, Graduate School of Innovative Life Science, University of Toyama, Toyama, 930-0194, Japane Department of Psychiatry, Kawasaki Medical School, Okayama, 701-0192, Japan⁎ Corresponding author at: Department of Medical Technology, Kawasaki University of Medical Welfare, 288, Matsushima, Kurashiki, Okayama, 701-0193, Japan. dhe422007@s.okayama-u.ac.jp08 2 2018 6 2018 08 2 2018 4 22 37 2 9 2017 27 1 2018 © 2018 The Authors2018This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Highlights • In mice, WFA-positive PNNs express aggrecan in selective brain regions. • Appearance and glycosylation of aggrecan-positive PNNs is brain-region specific. • Density of AB1031-, Cat-315-, and Cat-316-positive PNNs is brain-region specific. • Localization of WFA-, AB1031-, Cat-315-, and Cat-316-positive molecules differ in PNNs. Specific regions of the cerebral cortex are highly plastic in an organism’s lifetime. It is thought that perineuronal nets (PNNs) regulate plasticity, but labeling for Wisteria floribunda agglutinin (WFA), which is widely used to detect PNNs, is observed throughout the cortex. The aggrecan molecule—a PNN component—may regulate plasticity, and may also be involved in determining region-specific vulnerability to stress. To clarify cortical region-specific plasticity and vulnerability, we qualitatively analyzed aggrecan-positive and glycosylated aggrecan-positive PNNs in the mature mouse cerebral cortex. Our findings revealed the selective expression of both aggrecan-positive and glycosylated aggrecan-positive PNNs in the cortex. WFA-positive PNNs expressed aggrecan in a region-specific manner in the cortex. Furthermore, we observed variable distributions of PNNs containing WFA- and aggrecan-positive molecules. Together, our findings suggest that PNN components and their function differ depending on the cortical region, and that aggrecan molecules may be involved in determining region-specific plasticity and vulnerability in the cortex. Abbreviations FrA, frontal association cortexDLO, dorsolateral orbital cortexLO, lateral orbital cortexVO, ventral orbital cortexCg, cingulate cortexPL, prelimbic cortexIL, infralimbic cortexDP, dorsal peduncular cortexM1, primary motor cortexM2, secondary motor cortexMPtA, medial parietal association cortexLPtA, lateral parietal association cortexS1Tr, primary somatosensory cortex–trunk regionS1BF, primary somatosensory cortex–barrel fieldS2, secondary somatosensory cortexV2MM, secondary visual cortex–mediomedial areaV2ML, secondary visual cortex mediolateral areaV1M, primary visual cortex monocular areaV1B, primary visual cortex binocular areaV2L, secondary visual cortex lateral areaAu1, primary auditory cortexAuD, secondary auditory cortex dorsal areaAuV, secondary auditory cortex ventral areaTeA, temporal association cortexEct, ectorhinal cortexPRh, perirhinal cortexDIEnt, dorsintermed entorhinal cortexDLEnt, dorsolateral entorhinal cortexRSD, retrosplenial dysgranular cortexRSGc, retrosplenial granular cortex c regionRSGb, retrosplenial granular cortex b regionRSGa, retrosplenial granular cortex a regionKeywords AggrecanBrain region-specificChondroitin sulfate proteoglycanExtracellular matrixPerineuronal netsPlasticity ==== Body 1 Introduction To preserve region-specific functions, certain areas of the cortex are associated with high and low plasticity over the course of development (Craig and Commins, 2006; Kolb, 2009). One molecule that is crucial in maintaining cortical plasticity is the perineuronal net (PNN), a highly condensed extracellular matrix (ECM) molecule in the central nervous system (Sorg et al., 2016). The PNN is a mesh-like structure that surrounds the cell body, proximal dendrites, and axonal initial segment of specific neurons. Approximately 15% of neurons in the mature brain are surrounded by PNNs (Guimarães et al., 1990; McRae et al., 2007), most of which are parvalbumin (PV)-positive GABAergic interneurons, and a small portion of which are pyramidal cells (Härtig et al., 1994; Wegner et al., 2003). Maturation of GABAergic circuity in the sensory cortex implies the onset of a critical period that is associated with cortical plasticity (Hensch and Fagiolini, 2005; Maffei and Turrigiano, 2008). The formation of PNNs around PV-positive interneurons in the sensory cortex indicates the end of this so-called critical period (Pizzorusso et al., 2002; McRae et al., 2007). In the visual cortex of the mature brain, it has been shown that treatment of PNNs with the enzyme, chondroitinase ABC, can restore plasticity (Pizzorusso et al., 2002). Similar mechanisms have been described for sensory input-dependent plasticity in other brain regions (Balmer et al., 2009; Gogolla et al., 2009). Although the detailed function of PNNs is not clear, it is thought that their main roles involve neural plasticity, synaptic stability, and neuroprotective function (Sorg et al., 2016). The main constituents of PNNs include hyaluronic acid, tenascin-R, and the lectican family of chondroitin sulfate proteoglycans (CSPGs) (i.e., aggrecan, versican, brevican, and neurocan) (Bandtlow and Zimmermann, 2000; Yamaguchi, 2000). The plant-derived lectin, Wisteria floribunda agglutinin (WFA), has been widely used to detect PNNs through binding of N-acetylgalactosamine (Brückner et al., 1993; Schweizer et al., 1993; Seeger et al., 1994; Giamanco et al., 2010). Another method used to detect PNNs is through antibodies against aggrecan, the main PNN component (Matthews et al., 2002). These antibodies include AB1031 and Cat-315, with the former recognizing the central protein domain of the chondroitin sulfate glycosaminoglycan binding region of aggrecan (Giamanco et al., 2010; Lendvai et al., 2013), and the latter recognizing the HNK-1 carbohydrate epitope of aggrecan (Matthews et al., 2002; Dino et al., 2006; McRae et al., 2007). While there is general agreement that PNNs regulate plasticity, WFA-labeled PNNs have been found throughout the cortex of the mature mouse (Brückner et al., 2000; Horii-Hayashi et al., 2015). Moreover, WFA-positive PNN labeling does not appear to vary over development (Horii-Hayashi et al., 2015). Considering that the plasticity of specific brain regions is highly variable over the span of an organism’s lifetime, it is unlikely that WFA-positive PNNs control plasticity. Some studies have suggested the possibility that aggrecan molecules regulate plasticity as, during postnatal development, aggrecan expression is delayed when sensory input is deprived (McRae et al., 2007; Ye and Miao, 2013; Ueno et al., 2017b). Furthermore, when mice are housed in enriched environments after experimental cerebral ischemia, Cat-315-positive PNNs decrease (Madinier et al., 2014). In fact, it has been suggested that aggrecan molecules are not ubiquitously expressed throughout the cortex (Morawski et al., 2012a, Morawski et al., 2012b; Ueno et al., 2017a). However, a quantitative analysis of aggrecan-positive PNNs in the cortex has not been conducted. Along with their possible role in developmental plasticity, it has been suggested that aggrecan molecules are necessary for mediating the neuroprotective function of PNNs (Suttkus et al., 2014). It is well-established that certain brain regions are more susceptible to damage in neuropsychiatric disorders and neurodegenerative diseases. Interestingly, postmortem studies of patients with schizophrenia and autism show selective PNN abnormalities in the prefrontal and entorhinal cortices (Pantazopoulos et al., 2010; Mauney et al., 2013; Berretta et al., 2015). One theory explaining the cause of neuropsychiatric disorders is oxidative stress, which aggrecan-positive PNNs show resistance to (Gawryluk et al., 2011; Cabungcal et al., 2013; Suttkus et al., 2014). Indeed, in Alzheimer's disease, aggrecan-expressing PNNs are less susceptible to tau protein-induced damage (Morawski et al., 2010). It is therefore possible that aggrecan molecules are involved in neurological disorders, which target specific brain regions. In this study, we focused on the quantitative measurement of aggrecan-positive PNNs and glycosylated aggrecan-positive PNNs in the mature mouse cortex. We examined the region-specific presence of aggrecan using three antibodies (i.e., AB1031, Cat-315, and Cat-316) that recognize different components of the aggrecan molecule (McRae et al., 2007, McRae et al., 2010; Foster et al., 2014; Madinier et al., 2014; Suttkus et al., 2014; Carstens et al., 2016; Morikawa et al., 2017). Note that Cat-316 recognizes the o-linked chondroitin sulfate epitope of aggrecan (Lander et al., 1997; Matthews et al., 2002). We believe that our findings will contribute to clarifying the state of region-selective vulnerability and plasticity in the cortex of individuals with neuropsychiatric disorders. 2 Materials and methods 2.1 Animals Five adult male mice (C57BL/6J) were used for these experiments. Animals were purchased from Charles River Laboratories (Kanagawa, Japan), and housed in cages (3–5 animals per cage) with food and water available ad libitum under a 12 h light/dark cycle at 23–26 °C. All efforts were made to minimize the number of animals used and their suffering. All experimental protocols were performed in accordance with the U.S. National Institute of Health (NIH) Guide for the Care and Use of Laboratory Animals (NIH Publication No. 80-23, revised in 1996), and were approved by the Committee for Animal Experiments at Kawasaki Medical School Advanced Research Center and the Institutional Animal Care and Use Committee of University of Toyama. 2.2 Tissue preparation For tissue preparation, animals were deeply anesthetized with a lethal dose of sodium pentobarbital (120 mg/kg, i.p.), and transcardially perfused, first with ice-cold phosphate buffered saline (PBS) for 2 min and then with 4% paraformaldehyde in PBS (pH 7.4) for 10 min (10 ml/min). Brains were dissected and postfixed overnight with 4% paraformaldehyde in PBS at 4 °C, and cryoprotected by immersion in 15% sucrose for 12 h followed by 30% sucrose for 20 h at 4 °C. Brains were frozen in O.C.T. Compound (Tissue-Tek; Sakuma Finetek, Tokyo, Japan) using dry ice-cold normal hexane, and serial coronal sections of 40-μm thickness were prepared using a cryostat (CM3050S; Leica Wetzlar, Germany) at −20 °C. Sections were collected in ice-cold PBS containing 0.05% sodium azide. 2.3 Immunohistochemistry Cryostat sections were treated with 0.1% Triton X-100 in PBS at 20 °C for 15 min. After three washes in PBS, sections were incubated with 10% normal goat serum (ImmunoBioScience Corp, WA, USA) in PBS at room temperature for 1 h, washed three time in PBS, and incubated overnight at 4 °C in PBS containing biotinylated WFA (B-1355, Vector Laboratories, Funakoshi Co., Tokyo, Japan; 1:200) and primary antibodies (described below). After washing in PBS, sections were incubated with corresponding secondary antibodies (indicated below) and streptavidin-conjugated Texas Red (SA-5006, Vector Laboratories) at room temperature for 2 h. Labeled sections were rinsed again with PBS and mounted on glass slides with Vectashield medium (H-1400, Vector Laboratories). Prepared slides were either immediately imaged or stored at 4 °C. 2.4 Antibodies The following primary antibodies were used for staining: rabbit anti-aggrecan (AB1031, Millipore, Tokyo, Japan; 1:200), mouse anti-aggrecan (Cat-315; MAB1581, Millipore, 1:1000), or mouse anti-aggrecan (Cat-316; MAB1582, Millipore, 1:10 000). The following secondary antibodies were used for visualization: Alexa Fluor 488-conjugated goat anti-rabbit IgG (ab150077, Abcam; Cambride, MA; 1:1000) or FITC-conjugated anti-mouse IgM (sc-2082, Santa Cruz, Texas, USA, 1:1000). 2.5 Microscopy imaging For the quantification of WFA-, aggrecan-, Cat-315-, and Cat-316-positive PNNs, sections were stained as described previously and imaged using confocal microscopy (LSM700; Carl Zeiss, Oberkochen, Germany). Images (1024 × 1024 pixel) were saved as TIFF files with ZEN software (Carl Zeiss). Briefly, low magnification analysis was performed using a 10 × objective lens, and a pinhole setting corresponding to a focal plane thickness of less than 1 μm. For observing PNN morphology, samples were randomly selected, and high-magnification images using a 100× objective lens were acquired. Images from whole sections were obtained using a 10× objective lens on a fluorescence microscope (BZ-X, KEYENCE, Tokyo, Japan), and merged using the KEYENCE BZ-X Analyzer software (KEYENCE). 2.6 Quantification of labeled PNNs Brain regions were determined in accordance with the mouse brain atlas of Paxinos and Franklin (2012). From each mouse, 12 coronal sections were selected from the intermediate frontal, intermediate parietal, and rostral occipital cortices, and processed for staining. All confocal images were acquired as TIFF files, and analyzed with the NIH ImageJ software (NIH, Bethesda, MD, USA; http://rsb.info.nih.gov/nih-image/). The number of PNNs was quantified from at least three sections per region. Stained PNNs (soma size above 60 μm2) were manually tagged and counted within the area of interest, and PNN density was calculated as cells/mm2. Slides were coded and quantified by a blinded independent observer. 2.7 Data analysis Data are expressed as the mean ± SEM of five animals. Statistical analyses were carried out using SPSS Statistics (IBM Corp., Armonk, NY, USA). Statistical significance was determined using a one-way analysis of variance (ANOVA) with post hoc tests, and statistical significance was set at *p < .05. 3 Results 3.1 WFA-, AB1031-, Cat-315-, and Cat-316-positive PNNs in the mouse cerebral cortex To examine PNN composition in the mouse cerebral cortex, we labeled PNNs with WFA lectin, as well as the following anti-aggrecan antibodies: AB1031, Cat-315, and Cat-316 (Fig. 1, Fig. 2, Fig. 3). Overall, we observed more WFA-positive (WFA+) PNNs in the mouse cerebral cortex than PNNs positive for AB1031, Cat-315, and Cat-316. While WFA+ PNNs appeared in many cortical areas, the expression of AB1031-, Cat-315-, and Cat-316-positive PNNs was area-specific. In general, we observed a greater number of labeled PNNs in the primary sensory cortices than in the association cortices and, as noted, the distribution of labeled PNNs was not homogenous across different areas or layers (Fig. 1, Fig. 2, Fig. 3, Fig. 4). More specifically, a laminar pattern of labeling was observed in many cortical areas, with the most prominent and conspicuous labeling occurring in the mid-cortical layers and in layer I, respectively. Taken together, these histochemical analyses revealed that the spatial expression of PNN components was region-specific.Fig. 1 Distribution of WFA-, AB1031-, Cat-315-, and Cat-316-positive PNNs in the mouse frontal cortex. Representative whole brain sections labeled for WFA, AB1031, Cat-315, and Cat-316. Scale bar = 1 mm. Fig. 1Fig. 2 Distribution of WFA-, AB1031-, Cat-315-, and Cat-316-positive PNNs in the mouse parietal cortex. Representative whole brain sections labeled for WFA, AB1031, Cat-315, and Cat-316. Scale bar = 1 mm. Fig. 2Fig. 3 Distribution of WFA-, AB1031-, Cat-315-, and Cat-316-positive PNNs in the mouse temporal cortex. Representative whole brain sections labeled for WFA, AB1031, Cat-315, and Cat-316. Scale bar = 1 mm. Fig. 3Fig. 4 Distribution patterns of WFA-, AB1031-, Cat-315-, and Cat-316-positive PNNs in the mouse cerebral cortex. Confocal images of WFA (A-G) and AB1031 (A’-G’) labeling, and the merged image (A”-G”). Confocal images of Cat-315 (H-N) and AB1031 (H’-N’) labeling, and the merged image (H”-N”). Confocal images of Cat-316 (O-U) and AB1031 (O’-U’) labeling, and the merged image (O”-U”). Images of M2 (A-A”, H-H”, O-O”), LO (B-B”, I-I”, P-P”), S1BF (C-C”, J-J”, Q-Q”), S2 (D-D”, K-K”, R-R”), V2MM (E-E”, L-L”, S-S”), RSGc (F-F”, M-M”, T-T”), and Ect (G-G”, N-N”, U-U”). Scale bar = 100 μm in U” (applies to A–U”). Fig. 4 3.2 Comparative distribution of WFA- and AB1031-positive PNNs The density of WFA+ PNNs within the mouse cerebral cortex is shown in Fig. 5A. In the frontal cortex, low numbers of WFA+ PNNs were observed in the cingulate (Cg), prelimbic (PL), infralimbic (IL), and dorsal peduncular (DP) subregions. WFA+ PNNs were significantly more abundant in the motor cortex (M1/M2), as well as in the remaining subregions of the frontal cortex (i.e., dorsolateral orbital [DLO], lateral orbital [LO], and ventral orbital [VO] cortices). A high number of WFA+ PNNs was a common feature in the parietal cortex. In the primary somatosensory barrel field (S1BF), the number of WFA+ PNNs was higher than in the remaining subregions of the parietal cortex. The number of WFA+ PNNs was higher in the primary (V1 monocular [V1 M] and V1 binocular [V1B]), relative to the secondary (V2 mediomedial [V2 M], V2 medial lateral [V2ML], and V2 lateral [V2L]) visual cortices of the occipital cortex. WFA+ PNNs were highly abundant in all subregions of the auditory cortex (i.e., primary auditory [Au1], as well as secondary auditory, dorsal [AuD], and ventral [AuV] areas). The number of WFA+ PNNs in subregions of the entorhinal cortex (perirhinal [PRh], dorsolateral entorhinal [DLEnt], and dorsolateral entorhinal [DLEnt]) was significantly lower when compared to that in the auditory cortices. The number of WFA+ PNNs in the retrosplenial cortex was significantly higher than in the other cortices .Fig. 5 Quantitative analyses of WFA- and AB1031-positive PNNs in the mouse cerebral cortex. Quantified number of WFA+ PNNs in the mouse cerebral cortex (A). Quantified number of AB1031+ PNNs in the mouse cerebral cortex (B). The proportion of WFA+ PNNs co-localized with AB1031 (C), and the proportion of AB1031+ PNNs co-localized with WFA (D) in the mouse cerebral cortex. Data are expressed as the mean ± SEM. The respective P values are listed in Table 1. Fig. 5Table 1 P values for Fig. 5, Fig. 6. Table 1A: Fig. 5A WFA+ PNNs / mm2 Frontal Cortex Parietal Cortex Temporal Cortex FrA vs DLO 0.0025 VO vs Cg <0.0001 MPtA vs LPtA 0.1807 AuV vs Au1 0.0198 FrA vs LO <0.0001 VO vs PL <0.0001 MPtA vs S1Tr 0.0348 AuV vs AuD 0.9101 FrA vs VO <0.0001 VO vs IL <0.0001 MPtA vs S1BF <0.0001 AuV vs TeA 0.0024 FrA vs Cg 0.2428 VO vs DP <0.0001 MPtA vs S2 0.0305 AuV vs Ect 0.0002 FrA vs PL 0.0443 VO vs M1 <0.0001 LPtA vs S1Tr 0.3082 AuV vs PRh <0.0001 FrA vs IL 0.1999 VO vs M2 <0.0001 LPtA vs S1BF <0.0001 AuV vs DLEnt <0.0001 FrA vs DP 0.0289 Cg vs PL 0.2048 LPtA vs S2 0.4426 AuV vs DIEnt <0.0001 FrA vs M1 0.0229 Cg vs IL 0.8325 S1Tr vs S1BF 0.0041 Au1 vs AuD 0.0148 FrA vs M2 0.7034 Cg vs DP 0.1261 S1Tr vs S2 0.6562 Au1 vs TeA <0.0001 DLO vs LO 0.0002 Cg vs M1 <0.0001 S1BF vs S2 <0.0001 Au1 vs Ect <0.0001 DLO vs VO 0.0003 Cg vs M2 0.0246 Occipital Cortex Au1 vs PRh <0.0001 DLO vs Cg 0.0053 PL vs IL 0.3234 V2MM vs V2ML 0.041 Au1 vs DLEnt <0.0001 DLO vs PL <0.0001 PL vs DP 0.7341 V2MM vs V1M <0.0001 Au1 vs DIEnt <0.0001 DLO vs IL <0.0001 PL vs M1 <0.0001 V2MM vs V1B <0.0001 AuD vs TeA 0.0033 DLO vs DP <0.0001 PL vs M2 0.0006 V2MM vs V2L 0.0035 AuD vs Ect 0.0003 DLO vs M1 0.1901 IL vs DP 0.2093 V2ML vs V1M < 0.0001 AuD vs PRh <0.0001 DLO vs M2 0.0003 IL vs M1 <0.0001 V2ML vs V1B <0.0001 AuD vs DLEnt <0.0001 LO vs VO 0.7967 IL vs M2 0.0201 V2ML vs V2L 0.3587 AuD vs DIEnt <0.0001 LO vs Cg <0.0001 DP vs M1 <0.0001 V1M vs V1B 0.6917 TeA vs Ect 0.4414 LO vs PL <0.0001 DP vs M2 0.0004 V1M vs V2L 0.0001 TeA vs PRh <0.0001 LO vs IL <0.0001 M1 vs M2 0.0055 V1B vs V2L <0.0001 TeA vs DLEnt <0.0001 LO vs DP <0.0001 Retrosplenial Cortex TeA vs DIEnt <0.0001 LO vs M1 <0.0001 RSD vs RSGa 0.0464 Ect vs PRh 0.0001 LO vs M2 <0.0001 RSD vs RSGb 0.544 Ect vs DLEnt <0.0001 RSD vs RSGc 0.5042 Ect vs DIEnt <0.0001 RSGa vs RSGb 0.1759 PRh vs DLEnt 0.7486 RSGa vs RSGc 0.1609 PRh vs DIEnt 0.1735 RSGb vs RSGc 0.9814 DLEnt vs DIEnt 0.2959 B: Fig. 5B AB1031+ PNNs / mm2 Frontal Cortex Parietal Cortex Temporal Cortex FrA vs DLO <0.0001 VO vs Cg <0.0001 MPtA vs LPtA 0.012 AuV vs Au1 <0.0001 FrA vs LO <0.0001 VO vs PL <0.0001 MPtA vs S1Tr <0.0001 AuV vs AuD 0.3619 FrA vs VO <0.0001 VO vs IL <0.0001 MPtA vs S1BF <0.0001 AuV vs TeA <0.0001 FrA vs Cg 0.6815 VO vs DP <0.0001 MPtA vs S2 <0.0001 AuV vs Ect 0.0003 FrA vs PL 0,4445 VO vs M1 <0.0001 LPtA vs S1Tr 0.0017 AuV vs PRh <0.0001 FrA vs IL 0.3161 VO vs M2 <0.0001 LPtA vs S1BF <0.0001 AuV vs DLEnt <0.0001 FrA vs DP 0.3697 Cg vs PL 0.664 LPtA vs S2 0.016 AuV vs DIEnt <0.0001 FrA vs M1 <0.0001 Cg vs IL 0.4662 S1Tr vs S1BF <0.0001 Au1 vs AuD <0.0001 FrA vs M2 0.0399 Cg vs DP 0,5484 S1Tr vs S2 0.2349 Au1 vs TeA <0.0001 DLO vs LO 0.0084 Cg vs M1 <0.0001 S1BF vs S2 <0.0001 Au1 vs Ect <0.0001 DLO vs VO 0.0002 Cg vs M2 0.0026 Occipital Cortex Au1 vs PRh <0.0001 DLO vs Cg <0.0001 PL vs IL 0.7602 V2MM vs V2ML 0.1345 Au1 vs DLEnt <0.0001 DLO vs PL <0.0001 PL vs DP 0.8599 V2MM vs V1M <0.0001 Au1 vs DIEnt <0.0001 DLO vs IL <0.0001 PL vs M1 <0.0001 V2MM vs V1B <0.0001 AuD vs TeA <0.0001 DLO vs DP <0.0001 PL vs M2 0.0006 V2MM vs V2L 0.0599 AuD vs Ect 0.0069 DLO vs M1 0.2827 IL vs DP 0.8999 V2ML vs V1M <0.0001 AuD vs PRh <0.0001 DLO vs M2 0.0003 IL vs M1 <0.0001 V2ML vs V1B <0.0001 AuD vs DLEnt <0.0001 LO vs VO 0.1678 IL vs M2 0.0002 V2ML vs V2L 0.7072 AuD vs DIEnt <0.0001 LO vs Cg <0.0001 DP vs M1 <0.0001 V1M vs V1B 0.4069 TeA vs Ect 0.0007 LO vs PL <0.0001 DP vs M2 0.0004 V1M vs V2L <0.0001 TeA vs PRh 0.8235 LO vs IL <0.0001 M1 vs M2 0.0011 V1B vs V2L <0.0001 TeA vs DLEnt 0.843 LO vs DP <0.0001 Retrosplenial Cortex TeA vs DIEnt 0.5046 LO vs M1 <0.0001 RSD vs RSGa <0.0001 Ect vs PRh 0.0003 LO vs M2 <0.0001 RSD vs RSGb 0.0001 Ect vs DLEnt 0.0003 RSD vs RSGc 0.2784 Ect vs DIEnt 0.0005 RSGa vs RSGb 0.0004 PRh vs DLEnt 0.9797 RSGa vs RSGc <0.0001 PRh vs DIEnt 0.6527 RSGb vs RSGc 0.0698 DLEnt vs DIEnt 0.6345 C: Fig. 5C AB1031+ PNNs / WFA+ PNNs (%) Frontal Cortex Parietal Cortex Temporal Cortex FrA vs DLO 0.0017 VO vs Cg 0.0003 MPtA vs LPtA 0.3152 AuV vs Au1 0.2105 FrA vs LO 0.003 VO vs PL <0.0001 MPtA vs S1Tr 0.0888 AuV vs AuD 0.3723 FrA vs VO 0.0038 VO vs IL <0.0001 MPtA vs S1BF 0.0003 AuV vs TeA 0.0016 FrA vs Cg 0.7328 VO vs DP 0.0036 MPtA vs S2 0.0039 AuV vs Ect 0.7153 FrA vs PL 0.8156 VO vs M1 0.7913 LPtA vs S1Tr 0.3834 AuV vs PRh 0.0053 FrA vs IL 0.7461 VO vs M2 0.3727 LPtA vs S1BF 0.0062 AuV vs DLEnt 0.0017 FrA vs DP 0.4426 Cg vs PL 0.3987 LPtA vs S2 0.054 AuV vs DIEnt 0.0017 FrA vs M1 0.0016 Cg vs IL 0.3506 S1Tr vs S1BF 0.143 Au1 vs AuD 0.0341 FrA vs M2 0.0176 Cg vs DP 0.5331 S1Tr vs S2 0.4715 Au1 vs TeA 0.0002 DLO vs LO 0.6843 Cg vs M1 <0.0001 S1BF vs S2 0.3152 Au1 vs Ect 0.1076 DLO vs VO 0.6171 Cg vs M2 0.0029 Occipital Cortex Au1 vs PRh <0.0001 DLO vs Cg 0.0001 PL vs IL 0.8896 V2MM vs V2ML 0.1993 Au1 vs DLEnt 0.0003 DLO vs PL <0.0001 PL vs DP 0.1581 V2MM vs V1M 0.0025 Au1 vs DIEnt 0.0003 DLO vs IL <0.0001 PL vs M1 <0.0001 V2MM vs V1B 0.0008 AuD vs TeA 0.0197 DLO vs DP 0.0015 PL vs M2 0.0002 V2MM vs V2L 0.1182 AuD vs Ect 0.5966 DLO vs M1 0.7794 IL vs DP 0.1418 V2ML vs V1M 0.0695 AuD vs PRh 0.0518 DLO vs M2 0.1815 IL vs M1 <0.0001 V2ML vs V1B 0.0307 AuD vs DLEnt 0.0182 LO vs VO 0.9194 IL vs M2 0.0003 V2ML vs V2L 0.7985 AuD vs DIEnt 0.0182 LO vs Cg 0.0002 DP vs M1 0.0009 V1M vs V1B 0.7043 TeA vs Ect 0.0047 LO vs PL 0.0001 DP vs M2 0.0261 V1M vs V2L 0.1064 TeA vs PRh 0.6847 LO vs IL <0.0001 M1 vs M2 0.2216 V1B vs V2L 0.0484 TeA vs DLEnt 0.8661 LO vs DP 0.0027 Retrosplenial Cortex TeA vs DIEnt 0.8661 LO vs M1 0.8744 RSD vs RSGa 0.4697 Ect vs PRh 0.0144 LO vs M2 0.3188 RSD vs RSGb 0.427 Ect vs DLEnt 0.0047 RSD vs RSGc 0.2265 Ect vs DIEnt 0.0047 RSGa vs RSGb 0.0104 PRh vs DLEnt 0.5817 RSGa vs RSGc 0.0028 PRh vs DIEnt 0.5817 RSGb vs RSGc 0.7226 DLEnt vs DIEnt 1 D: Fig. 5D WFA+ PNNs / AB1031+ PNNs (%) Frontal Cortex Parietal Cortex Temporal Cortex FrA vs DLO 0.7176 VO vs Cg 0.7809 MPtA vs LPtA 0.7934 AuV vs Au1 0.743 FrA vs LO 0.4442 VO vs PL 0.8357 MPtA vs S1Tr 0.1987 AuV vs AuD 0.8496 FrA vs VO 0.8357 VO vs IL <0.0001 MPtA vs S1BF 0.6527 AuV vs TeA 0.8577 FrA vs Cg 1 VO vs DP 0.8357 MPtA vs S2 0.6667 AuV vs Ect 0.2151 FrA vs PL 1 VO vs M1 0.4318 LPtA vs S1Tr 0.1339 AuV vs PRh 0.3513 FrA vs IL <0.0001 VO vs M2 0.1929 LPtA vs S1BF 0.4643 AuV vs DLEnt 0.0198 FrA vs DP 1 Cg vs PL 1 LPtA vs S2 0.4762 AuV vs DIEnt 0.5572 FrA vs M1 0.5717 Cg vs IL <0.0001 S1Tr vs S1BF 0.3192 Au1 vs AuD 0.8968 FrA vs M2 0.4226 Cg vs DP 1 S1Tr vs S2 0.3116 Au1 vs TeA 0.9452 DLO vs LO 0.4344 Cg vs M1 0.4447 S1BF vs S2 0.9827 Au1 vs Ect 0.3427 DLO vs VO 0.7516 Cg vs M2 0.2793 Occipital Cortex Au1 vs PRh 0.4764 DLO vs Cg 0.6325 PL vs IL <0.0001 V2MM vs V2ML 0.0055 Au1 vs DLEnt 0.0107 DLO vs PL 0.7176 PL vs DP 1 V2MM vs V1M 0.0084 Au1 vs DIEnt 0.6543 DLO vs IL <0.0001 PL vs M1 0.5717 V2MM vs V1B 0.0002 AuD vs TeA 0.9748 DLO vs DP 0.7176 PL vs M2 0.4226 V2MM vs V2L 0.0018 AuD vs Ect 0.2965 DLO vs M1 0.6941 IL vs DP <0.0001 V2ML vs V1M 0.6119 AuD vs PRh 0.4286 DLO vs M2 0.3822 IL vs M1 <0.0001 V2ML vs V1B 0.3963 AuD vs DLEnt 0.0152 LO vs VO 0.2385 IL vs M2 <0.0001 V2ML vs V2L 0.7534 AuD vs DIEnt 0.6158 LO vs Cg 0.3061 DP vs M1 0.5717 V1M vs V1B 0.1226 TeA vs Ect 0.417 LO vs PL 0.4441 DP vs M2 0.4226 V1M vs V2L 0.3776 TeA vs PRh 0.5048 LO vs IL <0.0001 M1 vs M2 0.5765 V1B vs V2L 0.5909 TeA vs DLEnt 0.0351 LO vs DP 0.4442 Retrosplenial Cortex TeA vs DIEnt 0.6481 LO vs M1 0.6425 RSD vs RSGa 0.0005 Ect vs PRh 1 LO vs M2 0.95 RSD vs RSGb 0.0164 Ect vs DLEnt 0.0021 RSD vs RSGc 0.6531 Ect vs DIEnt 1 RSGa vs RSGb 0.1813 PRh vs DLEnt 0.0103 RSGa vs RSGc 0.0015 PRh vs DIEnt 1 RSGb vs RSGc 0.0432 DLEnt vs DIEnt 0.0733 E: Fig. 6A Cat-315+ PNNs / mm2 Frontal Cortex Parietal Cortex Temporal Cortex FrA vs DLO 0.1879 VO vs Cg <0.0001 MPtA vs LPtA 0.4051 AuV vs Au1 <0.0001 FrA vs LO 0.0257 VO vs PL <0.0001 MPtA vs S1Tr 0.0234 AuV vs AuD 0.4884 FrA vs VO <0.0001 VO vs IL <0.0001 MPtA vs S1BF <0.0001 AuV vs TeA 0.0637 FrA vs Cg 0.6584 VO vs DP <0.0001 MPtA vs S2 0.0161 AuV vs Ect 0.0018 FrA vs PL 0.2165 VO vs M1 0.0363 LPtA vs S1Tr 0.1331 AuV vs PRh 0.0069 FrA vs IL 0.1719 VO vs M2 <0.0001 LPtA vs S1BF <0.0001 AuV vs DLEnt 0.3664 FrA vs DP 0.8083 Cg vs PL 0.0962 LPtA vs S2 0.1106 AuV vs DIEnt 0.0018 FrA vs M1 <0.0001 Cg vs IL 0.0733 S1Tr vs S1BF 0.0001 Au1 vs AuD <0.0001 FrA vs M2 0.0395 Cg vs DP 0.4943 S1Tr vs S2 0.9821 Au1 vs TeA <0.0001 DLO vs LO 0.7698 Cg vs M1 0.0001 S1BF vs S2 <0.0001 Au1 vs Ect <0.0001 DLO vs VO 0.0013 Cg vs M2 0.1009 Occipital Cortex Au1 vs PRh <0.0001 DLO vs Cg 0.3122 PL vs IL 0.0733 V2MM vs V2ML 0.5425 Au1 vs DLEnt <0.0001 DLO vs PL 0.0312 PL vs DP 0.3182 V2MM vs V1M 0.0005 Au1 vs DIEnt <0.0001 DLO vs IL 0.0249 PL vs M1 <0.0001 V2MM vs V1B <0.0001 AuD vs TeA 0.0141 DLO vs DP 0.1381 PL vs M2 0.0015 V2MM vs V2L 0.0987 AuD vs Ect 0.0002 DLO vs M1 0.0659 IL vs DP 0.2586 V2ML vs V1M 0.0028 AuD vs PRh 0.0011 DLO vs M2 0.872 IL vs M1 <0.0001 V2ML vs V1B < 0.0001 AuD vs DLEnt 0.0011 LO vs VO <0.0001 IL vs M2 0.001 V2ML vs V2L 0.2829 AuD vs DIEnt 0.0002 LO vs Cg 0.0691 DP vs M1 <0.0001 V1M vs V1B 0.1062 TeA vs Ect 0.173 LO vs PL 0.0008 DP vs M2 0.0224 V1M vs V2L 0.0362 TeA vs PRh 0.3664 LO vs IL 0.0006 M1 vs M2 0.0188 V1B vs V2L 0.0006 TeA vs DLEnt 0.3711 LO vs DP 0.0142 Retrosplenial Cortex TeA vs DIEnt 0.173 LO vs M1 0.0294 RSD vs RSGa 0.0004 Ect vs PRh 0.6371 LO vs M2 0.8523 RSD vs RSGb 0.5417 Ect vs DLEnt 0.6307 RSD vs RSGc 0.0064 Ect vs DIEnt 1 RSGa vs RSGb 0.0029 PRh vs DLEnt 0.9928 RSGa vs RSGc <0.0001 PRh vs DIEnt 0.6371 RSGb vs RSGc 0.0035 DLEnt vs DIEnt 0.6307 F: Fig. 6B Cat-316+ PNNs / mm2 Frontal Cortex Parietal Cortex Temporal Cortex FrA vs DLO 0.0052 VO vs Cg <0.0001 MPtA vs LPtA 0.1062 AuV vs Au1 0.0326 FrA vs LO <0.0001 VO vs PL <0.0001 MPtA vs S1Tr 0.0002 AuV vs AuD 0.1075 FrA vs VO <0.0001 VO vs IL <0.0001 MPtA vs S1BF <0.0001 AuV vs TeA <0.0001 FrA vs Cg 0.7054 VO vs DP <0.0001 MPtA vs S2 0.0018 AuV vs Ect 0.0016 FrA vs PL 0.6572 VO vs M1 <0.0001 LPtA vs S1Tr 0.0067 AuV vs PRh <0.0001 FrA vs IL 1 VO vs M2 <0.0001 LPtA vs S1BF <0.0001 AuV vs DLEnt <0.0001 FrA vs DP 0.8637 Cg vs PL 0.9256 LPtA vs S2 0.0776 AuV vs DIEnt <0.0001 FrA vs M1 0.0016 Cg vs IL 0.5933 S1Tr vs S1BF 0.0066 Au1 vs AuD 0.0004 FrA vs M2 0.133 Cg vs DP 0.7702 S1Tr vs S2 0.1991 Au1 vs TeA <0.0001 DLO vs LO 0.0004 Cg vs M1 0.0001 S1BF vs S2 <0.0001 Au1 vs Ect <0.0001 DLO vs VO 0.0014 Cg vs M2 0.1108 Occipital Cortex Au1 vs PRh <0.0001 DLO vs Cg 0.0024 PL vs IL 0.5308 V2MM vs V2ML 0.7465 Au1 vs DLEnt <0.0001 DLO vs PL 0.0028 PL vs DP 0.7001 V2MM vs V1M <0.0001 Au1 vs DIEnt <0.0001 DLO vs IL 0.0008 PL vs M1 0.0002 V2MM vs V1B <0.0001 AuD vs TeA 0.0029 DLO vs DP 0.0013 PL vs M2 0.1323 V2MM vs V2L 0.1201 AuD vs Ect 0.078 DLO vs M1 0.8093 IL vs DP 0.8083 V2ML vs V1M <0.0001 AuD vs PRh 0.0029 DLO vs M2 0.0432 IL vs M1 <0.0001 V2ML vs V1B <0.0001 AuD vs DLEnt 0.0016 LO vs VO 0.6008 IL vs M2 0.0361 V2ML vs V2L 0.2114 AuD vs DIEnt 0.0026 LO vs Cg <0.0001 DP vs M1 <0.0001 V1M vs V1B 0.8902 TeA vs Ect 0.1997 LO vs PL <0.0001 DP vs M2 0.0614 V1M vs V2L <0.0001 TeA vs PRh 0.8873 LO vs IL <0.0001 M1 vs M2 0.0127 V1B vs V2L <0.0001 TeA vs DLEnt 0.9586 LO vs DP <0.0001 Retrosplenial Cortex TeA vs DIEnt 0.9131 LO vs M1 <0.0001 RSD vs RSGa 0.0668 Ect vs PRh 0.2298 LO vs M2 <0.0001 RSD vs RSGb 0.3277 Ect vs DLEnt 0.1651 RSD vs RSGc 0.6879 Ect vs DIEnt 0.2178 RSGa vs RSGb 0.0081 PRh vs DLEnt 0.8392 RSGa vs RSGc 0.0294 PRh vs DIEnt 0.9727 RSGb vs RSGc 0.558 DLEnt vs DIEnt 0.8659 G: Fig. 6C Cat-315+ PNNs / AB1031+ PNNs (%) Frontal Cortex Parietal Cortex Temporal Cortex FrA vs DLO 0.9812 VO vs Cg 0.9751 MPtA vs LPtA 0.9042 AuV vs Au1 0.0007 FrA vs LO 0.8194 VO vs PL 0.2426 MPtA vs S1Tr 0.6842 AuV vs AuD 0.0568 FrA vs VO 0.2297 VO vs IL 0.0509 MPtA vs S1BF 0.0871 AuV vs TeA 0.2413 FrA vs Cg 0.2951 VO vs DP 0.2426 MPtA vs S2 0.2953 AuV vs Ect 0.0729 FrA vs PL 0.0749 VO vs M1 0.168 LPtA vs S1Tr 0.5466 AuV vs PRh 0.2413 FrA vs IL 0.1776 VO vs M2 0.4038 LPtA vs S1BF 0.0379 AuV vs DLEnt 0.2413 FrA vs DP 0.0749 Cg vs PL 0.2513 LPtA vs S2 0.1785 AuV vs DIEnt 0.2413 FrA vs M1 0.0132 Cg vs IL 0.061 S1Tr vs S1BF 0.0993 Au1 vs AuD 0.0414 FrA vs M2 0.0532 Cg vs DP 0.2513 S1Tr vs S2 0.4283 Au1 vs TeA 0.0017 DLO vs LO 0.89 Cg vs M1 0.2051 S1BF vs S2 0.31 Au1 vs Ect <0.0001 DLO vs VO 0.4055 Cg vs M2 0.4331 Occipital Cortex Au1 vs PRh 0.0017 DLO vs Cg 0.4461 PL vs IL 0.0199 V2MM vs V2ML 0.4141 Au1 vs DLEnt 0.0017 DLO vs PL 0.1173 PL vs DP 1 V2MM vs V1M 0.2244 Au1 vs DIEnt 0.0017 DLO vs IL 0.2269 PL vs M1 0.6654 V2MM vs V1B 0.1046 AuD vs TeA 0.024 DLO vs DP 0.1173 PL vs M2 0.4845 V2MM vs V2L 0.1157 AuD vs Ect 0.0009 DLO vs M1 0.0755 IL vs DP 0.0199 V2ML vs V1M 0.666 AuD vs PRh 0.0241 DLO vs M2 0.1616 IL vs M1 0.0092 V2ML vs V1B 0.3777 AuD vs DLEnt 0.0241 LO vs VO 0.327 IL vs M2 0.0194 V2ML vs V2L 0.39 AuD vs DIEnt 0.024 LO vs Cg 0.3966 DP vs M1 0.6654 V1M vs V1B 0.6482 TeA vs Ect 1 LO vs PL 0.0952 DP vs M2 0.4845 V1M vs V2L 0.6501 TeA vs PRh 1 LO vs IL 0.1432 M1 vs M2 0.6196 V1B vs V2L 0.9859 TeA vs DLEnt 1 LO vs DP 0.0952 Retrosplenial Cortex TeA vs DIEnt 1 LO vs M1 0.0226 RSD vs RSGa 0.1749 Ect vs PRh 1 LO vs M2 0.0828 RSD vs RSGb 0.9902 Ect vs DLEnt 1 RSD vs RSGc 0.2489 Ect vs DIEnt 1 RSGa vs RSGb 0.217 PRh vs DLEnt 1 RSGa vs RSGc 0.0268 PRh vs DIEnt 1 RSGb vs RSGc 0.2908 DLEnt vs DIEnt 1 H: Fig. 6D Cat-316+ PNNs / AB1031+ PNNs (%) Frontal Cortex Parietal Cortex Temporal Cortex FrA vs DLO 0.0895 VO vs Cg 0.8069 MPtA vs LPtA 0.0605 AuV vs Au1 0.4166 FrA vs LO 0.0049 VO vs PL 0.7294 MPtA vs S1Tr 0.0202 AuV vs AuD 0.9726 FrA vs VO 0.006 VO vs IL 0.006 MPtA vs S1BF 0.0047 AuV vs TeA 0.4279 FrA vs Cg 0.0123 VO vs DP 0.5185 MPtA vs S2 0.0224 AuV vs Ect 0.5732 FrA vs PL 0.0213 VO vs M1 0.5917 LPtA vs S1Tr 0.4731 AuV vs PRh 0.0919 FrA vs IL 1 VO vs M2 0.0406 LPtA vs S1BF 0.2617 AuV vs DLEnt 0.1222 FrA vs DP 0.009 Cg vs PL 0.9031 LPtA vs S2 0.6397 AuV vs DIEnt 0.9931 FrA vs M1 0.011 Cg vs IL 0.0123 S1Tr vs S1BF 0.7671 Au1 vs AuD 0.3976 FrA vs M2 0.0759 Cg vs DP 0.4443 S1Tr vs S2 0.7631 Au1 vs TeA 0.1511 DLO vs LO 0.1512 Cg vs M1 0.8351 S1BF vs S2 0.5057 Au1 vs Ect 0.1863 DLO vs VO 0.182 Cg vs M2 0.1223 Occipital Cortex Au1 vs PRh 0.0195 DLO vs Cg 0.2983 PL vs IL 0.0213 V2MM vs V2ML 0.0309 Au1 vs DLEnt 0.0316 DLO vs PL 0.399 PL vs DP 0.4176 V2MM vs V1M 0.5469 Au1 vs DIEnt 0.6565 DLO vs IL 0.0895 PL vs M1 0.9647 V2MM vs V1B 0.879 AuD vs TeA 0.4441 DLO vs DP 0.1406 PL vs M2 0.223 V2MM vs V2L 0.3267 AuD vs Ect 0.5955 DLO vs M1 0.3241 IL vs DP 0.009 V2ML vs V1M 0.0703 AuD vs PRh 0.0974 DLO vs M2 0.8428 IL vs M1 0.011 V2ML vs V1B 0.0258 AuD vs DLEnt 0.1286 LO vs VO 0.887 IL vs M2 0.0759 V2ML vs V2L 0.1518 AuD vs DIEnt 0.9921 LO vs Cg 0.7135 DP vs M1 0.3423 V1M vs V1B 0.6139 TeA vs Ect 0.7614 LO vs PL 0.6307 DP vs M2 0.0727 V1M vs V2L 0.6659 TeA vs PRh 0.4698 LO vs IL 0.0049 M1 vs M2 0.1013 V1B vs V2L 0.3527 TeA vs DLEnt 0.4987 LO vs DP 0.5724 Retrosplenial Cortex TeA vs DIEnt 0.6316 LO vs M1 0.4947 RSD vs RSGa 0.894 Ect vs PRh 0.2529 LO vs M2 0.0297 RSD vs RSGb 0.5058 Ect vs DLEnt 0.2927 RSD vs RSGc 0.3893 Ect vs DIEnt 0.7614 RSGa vs RSGb 0.5933 PRh vs DLEnt 1 RSGa vs RSGc 0.4648 PRh vs DIEnt 0.3255 RSGb vs RSGc 0.8415 DLEnt vs DIEnt 0.3408 Further, we quantified AB1031-positive (AB1031+) PNNs in the mouse cerebral cortex (Fig. 5B). The density of AB1031+ PNNs showed significantly different variations throughout the mouse cortical regions examined. In many regions (i.e., frontal association [FrA], Cg, PL, IL, DP, temporal association [TeA], ectorhinal [Ect], PRh, DLEnt, and DIEnt), AB1031+ PNNs were very low compared with other cortices. In the frontal cortex, a high number of AB1031+ PNNs was observed in the DLO, LO, and VO subregions. In the remaining subregions of the frontal cortex (primary and secondary motor areas [M1 and M2, respectively]), the density of AB1031+ PNNs was high compared to that in the frontal cortex subregions, FrA, Cg, PL, IL, and DP. In the S1BF subregion, the number of AB1031+ PNNs was higher than in the remaining subregions of the parietal cortex (i.e., medial parietal association [MPtA], lateral parietal association [LPtA], somatosensory 1, trunk region [S1Tr], and somatosensory 2 [S2] areas). In the temporal cortex, subregions of the auditory cortex (i.e., AuV, Au1, and AuD) exhibited high AB1031+ PNN densities, followed by lower densities in the entorhinal cortex subregions (i.e., TeA, Ect, PRh, DIEnt, and DLEnt). Finally, the number of AB1031+ PNNs in subregions of the retrosplenial cortex was significantly higher than in other cortices. 3.3 Co-localization of WFA+ and AB1031+ PNNs Further, we determined the percentage of overlapping WFA+ and AB1031+ PNNs in the mouse cerebral cortex (Fig. 5C), and determined this value to be below 50% in many regions. More specifically, the percentage of co-localized WFA+ and AB1031+ PNNs in the FrA, Cg, PL, IL, and DP subregions of the frontal cortex was lower than in the remaining subregions (i.e., DLO, LO, VO, M1, and M2). Approximately 10% of WFA+ PNNs in the FrA, Cg, PL, IL, and DP subregions of the frontal cortex were co-localized with AB1031. In the parietal cortex, the percentage of WFA+ PNNs co-localized with AB1031 was high, while WFA-AB1031 co-localization in MPtA/LPtA subregions of the parietal association cortex was low compared to the S1Tr/S1BF/S2 somatosensory cortex subregions. In the V1M/V1B subregions of the occipital cortex, the percentage of WFA-AB1031-positive PNNs was high compared to that in the V2MM/V2ML/V2L subregions of the secondary visual cortex. In the temporal cortex, the Au1/AuD/AuV subregions of the auditory cortex exhibited high percentages of WFA-AB1031 co-localization, followed by lower percentages in the TeA/PRh/DLEnt/DIEnt subregions of the entorhinal cortex. In all subregions of the retrosplenial cortex, about 40% of WFA+ PNNs co-localized with AB1031. Taken together, our findings revealed that AB1031-WFA co-localization was high (∼80%) in the PNNs of most regions in the cerebral cortex (Fig. 5D). Note that IL/DP subregions of the frontal cortex were excluded from analysis since the number of AB1031+ PNNs was significantly low in these areas. 3.4 Comparative distribution of Cat-315- and Cat-316-positive PNNs Further, we quantified the amount of PNNs co-labeled for Cat-315 and Cat-316 in the mouse cerebral cortex (Fig. 6A,B). The density of both Cat-315- and Cat-316-positive PNNs showed significantly different variations throughout the various cortical regions examined. In many areas (i.e., FrA, Cg, PL, IL, DP, TeA, Ect, PRh, DLEnt, and DIEnt), the number of both Cat-315- and Cat-316-positive PNNs was relatively low.Fig. 6 Quantitative analyses of Cat-315- and Cat-316-positive PNNs in the mouse cerebral cortex. Quantified number of Cat-315-positive PNNs in the mouse cerebral cortex (A). Quantified number of Cat-316-positive PNNs in the mouse cerebral cortex (B). The proportion of AB1031+ PNNs co-localized with Cat-315 (C), and the proportion of AB1031+ PNNs co-localized with Cat-316 (D) in the mouse cerebral cortex. Data are expressed as the mean ± SEM. The respective P values are listed in Table 1. Fig. 6 Cat-315 labeled many PNNs in the VO (Fig. 6A) and M1/M2 subregions of the motor cortex. In contrast, the density of Cat-315-positive PNNs was relatively low in the remaining subregions of the frontal cortex (i.e., FrA, Cg, PL, IL, and DP). In the parietal cortex, the Cat-315-positive PNN density was high, while in the MPtA/LPtA subregions of the parietal association cortex, Cat-315-positive PNN expression was low compared to the S1Tr/S1BF/S2 subregions of the somatosensory cortex. In the occipital cortex, low Cat-315-positive PNN density was observed in secondary (i.e., V2MM, V2ML, and V2L) compared to primary (i.e., V1M and V1B) subregions of the visual cortex. In the temporal cortex, Au1 exhibited high Cat-315-positive PNN density, followed by lower densities in AuV and AuD. The remaining subregions of the temporal cortex exhibited much lower Cat-315-positive PNNs than the other cortices. High numbers of Cat-315-positive PNNs were seen in the retrosplenial dysgranular cortex (RSD), as well as the b and c regions of the retrosplenial granular cortex (RSGb and RSGc, respectively). Cat-315-positive PNN density was lower in the remaining subregion of the retrosplenial cortex (i.e., RSGa). In the LO/VO subregions of the frontal cortex, the density of Cat-316-positive PNNs was high compared to that in the remaining subregions (Fig. 6B). The FrA, Cg, PL, IL, and DP subregions exhibited lower Cat-316-positive PNN density relative to the DLO, LO, VO, M1, and M2 subregions of the frontal cortex. In the parietal cortex, low Cat-316-positive PNN density was observed in the MPtA/LPtA subregions of the association cortex. In the S1Tr, S1BF, and S2 subregions of the somatosensory cortex, the density of Cat-316-positive PNNs was high relative to that observed in the MPtA/LPtA subregions. In the occipital cortex, the density of Cat-316-positive PNNs in the primary visual cortex (i.e., V1M and V1B) was high compared to that observed in the secondary visual cortex (i.e., V2MM, V2ML, and V2L). In the temporal cortex, the Au1/AuD/AuV subregions of the auditory cortex exhibited high Cat-316-positive PNN densities, followed by lower densities in the TeA/Ect/PRh/DLEnt/DIEnt subregions of the entorhinal cortex. High numbers of Cat-316-positive PNNs were also seen in the RSD, RSGb, and RSGc subregions, while the remaining subregion of the retrosplenial cortex (i.e., RSGa) exhibited fewer Cat-316-positive PNNs. 3.5 Co-localization of Cat-315, Cat-316, and AB1031 In order to examine the relationship of aggrecan components in PNNs of the cerebral cortex, we carried out a quantitative analysis of AB1031 and Cat-315 co-localization, as well as of AB1031 and Cat-316 co-localization (Fig. 6C and D). Regions that exhibited low levels of AB1031 and Cat-315 labeling were excluded from the analysis (i.e., IL, DP, TeA, Ect, PRh, DLEnt, and DIEnt; Figs. 5B and 6A,C). In most other regions, AB1031+ PNNs also labeled with Cat-315 (Fig. 6C). This double-labeling was highest in all subregions of the parietal and retrosplenial cortices, and was also high in the PL subregion of the frontal cortex, as well as in the M1/M2 subregions of the motor cortex (∼70%). In the remaining subregions of the frontal cortex (i.e., FrA, DLO, VO, and Cg), about half of the AB1031+ PNNs were positive for Cat-315. In the V2MM/V2ML subregions of the occipital cortex, a low percentage of AB1031+ PNNs co-localized with Cat-315. In the temporal cortex, Au1 exhibited a higher percentage of AB1031-Cat-315 co-localization relative to the remaining auditory cortex subregions (i.e., AuV and AuD). Regarding the evaluation of PNNs double-labeled for AB1031 and Cat-316, regions which exhibited little individual labeling of AB1031 and Cat-316 (i.e., FrA and IL) were excluded from the analysis (Figs. 5B and 6B,D). In the frontal cortex, the percentage of AB1031+ PNNs that also labeled for Cat-316 was about 70%. In the MPtA subregion of the parietal cortex, about 50% of AB1031+ PNNs co-localized with Cat-316, while double labeling was higher (∼70%) in the remaining subregions (i.e., LPtA, S1Tr, S1BF, and S2). The percentage of AB1031+ PNNs co-localized with Cat-316 in the V2ML subregion of the occipital cortex was lower than in the remaining subregions (i.e., V2MM, V1M, V1B, and V2L). The PRh/DLEnt subregions of the temporal cortex exhibited a lower number of AB1031+ PNNs co-localized with Cat-316 than the remaining subregions of the temporal cortex. Finally, the percentage of AB1031+ PNNs that also labeled with Cat-316 was similar throughout all subregions of the retrosplenial cortex. 3.6 Perisomatic distribution patterns of WFA-, AB1031-, Cat-315-, and Cat-316-positive PNN components To address the perisomatic distribution of WFA-, AB1031-, Cat-315-, and Cat-316-positive PNN components in the mouse cerebral cortex, we conducted high-magnification image analyses of stained sections. Using this approach, we found labeled components displayed a punctate distribution pattern around the soma (Fig. 7). Further, we found that AB1031+ puncta appeared close to, but did not overlap with, WFA+ molecules (Fig. 7A–G). Puncta co-labeled for Cat-315 and Cat-316 were also observed in PNNs (Fig. 7H–N, O–U) and, interestingly, these overlapped with PNN components positive for AB1031 (Fig. 7H”–N”, O”–U”).Fig. 7 Distribution patterns of WFA-, AB1031-, Cat-315-, and Cat-316-positive molecules in PNNs of the mouse cerebral cortex. High-magnification confocal images of WFA (A-G) and AB1031 (A’-G’) labeling, and the merged image (A”-G”). High-magnification confocal images of Cat-315 (H-N) and AB1031 (H’-N’) labeling, and the merged image (H”-N”). High-magnification confocal images of Cat-316 (O-U) and AB1031 (O’-U’) labeling, and the merged image (O”-U”). Images of M2 (A-A”, H-H”, O-O”), LO (B-B”, I-I”, P-P”), S1BF (C-C”, J-J”, Q-Q”), S2 (D-D”, K-K”, R-R”), V2MM (E-E”, L-L”, S-S”), RSGc (F-F”, M-M”, T-T”), and Ect (G-G”, N-N”, U-U”) are shown. Scale bar = 5 μm in U” (applies to A–U”). Fig. 7 3.7 WFA+ molecules in the upper level of layer 1 of the PRh, DLEnt, and DIEnt Since we observed intense WFA and Cat-316 reactivity in layer 1 of PRh/DLEnt/DIEnt subregions in the temporal cortex (Fig. 3), we decided to further analyze the composition of the labeled PNN components in this area. Low magnification views of WFA, AB1031, Cat-315, and Cat-316 reactivity within layer 1 showed similar associations between positively labeled components (Fig. 8A–C). High magnification images revealed AB1031-, Cat-315-, and Cat-316-positive molecules scattered throughout the upper level of layer 1 (Fig. 8D–F and D’–F’), and that WFA+ molecules did not co-localize with AB1031+ molecules in this area (Fig. 8D–D”). Moreover, AB1031+ molecules did not co-localize with either Cat-315+ or Cat-316+ molecules (Fig. 8E–E” and F–F”).Fig. 8 Distribution of WFA-, AB1031-, Cat-315-, and Cat-316-positive molecules in the mouse entorhinal cortex. Representative confocal images showing double labeling of WFA and AB1031 (A), Cat-315 and AB1031 (B), and Cat-316 and AB1031 (C) in the mouse entorhinal cortex. High magnification images of WFA (D’) and AB1031 (D’) labeling, and the merged image (D”), in the upper region of L1 in the DLEnt. High magnification images of Cat-315 (E) and AB1031 (E’) labeling, and the merged image (E”), in the upper region of L1 in the DLEnt. High magnification images of Cat-316 (F) and AB1031 (F’) labeling, and the merged image (F”), in the upper region of L1 in the DLEnt. Scale bar = 100 μm in C (applies to A−C) and 5 μm in F” (applies to D–F”). Fig. 8 4 Discussion This study was the first to quantitatively show region-specific distribution of PNNs in the mouse cortex through antibodies that recognize different aggrecan epitopes. Although WFA+ PNNs were present throughout the cortex, the existence of AB1031-, Cat-315-, and Cat-316-positive PNNs was disparate and region-specific. Together, our findings revealed 1) varied, brain region-specific aggrecan expression, 2) varied brain region-specific expression of aggrecan-positive PNN components, and 3) the existence of WFA+ PNNs without aggrecan in the mature mouse cortex. Previous studies have reported the existence of region-selective WFA+ PNN expression throughout the mature rodent cortex (Brückner et al., 2000, Brückner et al., 2003; Alpár et al., 2006; Horii-Hayashi et al., 2015). However, until now, WFA+ PNN densities in the various cortical regions had not been clarified. Our study revealed heterogeneous WFA+ PNN densities across the cortex, suggesting diverging, region-specific PNN functions. A widely held theory is that WFA+ PNNs control plasticity. However, the expression of WFA+ PNNs has been observed throughout the cortex. As only certain cortical regions (e.g., the frontal cortex) are highly plastic over the course of an organism’s lifetime (Sadato et al., 2004; Canto et al., 2008; Chapman et al., 2008; Jung et al., 2008; Kolb, 2009), it is unlikely that WFA+ PNN regulate plasticity in all cortices. Thus, WFA+ PNNs must have brain region-specific functions. Detection of PNNs using WFA is widely used but, as demonstrated in recent studies, PNN components are brain region- and cell type-specific (Berretta et al., 2015; Dauth et al., 2016; Ueno et al., 2017a). It is therefore plausible that the functions of WFA+ PNNs in each cortical region may differ depending on its components. In the current study, we observed that almost all AB1031+ PNNs were also positive for WFA. However, in some regions of the mouse cortex, WFA+ PNNs could be detected in the absence of AB1031-, Cat-315-, and Cat-316-positive components. This is concurrent with previous findings, which have shown that aggrecan protein and aggrecan mRNA are not ubiquitously expressed in all cortical regions (Morawski et al., 2012a, Morawski et al., 2012b). Several reports have suggested the possibility that aggrecan is involved in plasticity (McRae et al., 2007; Nakamura et al., 2009; Ye and Miao, 2013; Ueno et al., 2017b). Aggrecan expression in PNNs signals the end of the critical period, and is dependent on sensory input (Sur et al., 1988; Hockfield et al., 1990). In the absence of such input, the formation of both Cat-315- and Cat-316-positive, but not WFA+, PNNs decrease (McRae et al., 2007; Lander et al., 1997; Ueno et al., 2017a, Ueno et al., 2017b, Ueno et al., 2017c). This is because PNNs in the sensory cortex are formed during postnatal development, and AB1031- and Cat-315-positive PNNs are formed after WFA+ PNNs (Ueno et al., 2017a, Ueno et al., 2017b, Ueno et al., 2017c; Ye and Miao, 2013). In addition, WFA+ PNNs that do not have the Cat-315 epitope increase with age (Karetko-Sysa et al., 2014). Together, this suggests that neural plasticity is lost following the expression of aggrecan in WFA+ PNNs. Previous reports have suggested that to access the backbone of the aggrecan molecule for staining with the AB1031 antibody, the sections must be treated with Chondroitinase ABC to remove the GAG chains (Härtig et al., 2016; Giamanco et al., 2010). Chondroitinase ABC treatment removes the GAG chain recognized by WFA. When this enzyme treatment is performed, double staining of WFA and AB1031 cannot be performed. However, it is also known that AB1031+ aggrecan can be sufficiently stained without this enzyme treatment (Carstens et al., 2016; Dauth et al., 2016; Miyata and Kitagawa, 2016; Yamada and Jinno, 2017). For these reasons, we performed double immunostaining without enzyme treatment in the present study. In order to compare the distributions of AB1031+ and WFA+ molecules in more detail, it is necessary to develop new experimental methods. In recent years, it has been shown that the expression of a chondroitin sulfate group (recognized by Cat-316) in PNNs is involved in cortical plasticity (Matthews et al., 2002; Dino et al., 2006; Miyata et al., 2012). Moreover, the HNK-1 epitope (Dino et al., 2006) (recognized by Cat-315) has also been reported to relate to synaptic plasticity (Senn et al., 2002). Both chondroitin sulfate and HNK-1 are widely present in the central nervous system as ECM molecules (Kleene and Schachner, 2004). CSPGs and other ECM proteins inhibit plasticity and suppress the reformation of both axons and synapses (Gilbert et al., 2005; Silver and Silver, 2014). During CNS trauma, CSPG expression (including aggrecan) increases around the site of injury and suppresses axonal reorganization (Oohira et al., 1994; McKeon et al., 1999; Moon et al., 2002, Jones et al., 2003). In vitro, these functions are dependent on CSPG (versican, neurocan, brevican, and aggrecan) core proteins, which, after chondroitinase ABC treatment, have no inhibitory function (Snow et al., 1990; Maeda and Noda, 1996; Margolis et al., 1996; Yamada et al., 1997; Schmalfeldt et al., 2000). These reports indicate that the inhibitory functions of CSPGs and other ECM proteins are dependent on chondroitin sulfate chains rather than on CSPG core proteins. It is therefore possible that WFA+ PNNs that contain aggrecan expressing a chondroitin sulfate group control synaptic plasticity. Our findings suggest that plasticity may be high in areas where aggrecan expression is low. Indeed, we found that areas with low aggrecan corresponded to the prefrontal, entorhinal, and secondary sensory cortices, which have been shown to be highly plastic over the course of an organism’s lifetime. Compared to other lecticans, aggrecan is expressed on many PNNs (Matsui et al., 1998; Dauth et al., 2016), albeit in smaller amounts (Dauth et al., 2016). Generally, PNNs are thought to always contain aggrecan (Galtrey et al., 2008; Giamanco et al., 2010). Indeed, a previous study reported no WFA reactivity in primary cultured neurons of aggrecan-deficient mice, suggesting that aggrecan is essential for the formation of PNNs (Giamanco and Matthews, 2012). Conversely, some studies, including the current investigation, have reported the existence of WFA+ PNNs that do not contain aggrecan (Morawski et al., 2012a, Morawski et al., 2012b; Karetko-Sysa et al., 2014; Ueno et al., 2017a), suggesting that aggrecan may not be an essential component in the PNNs of specific brain regions. Interestingly, it is indicated that absence of aggrecan has no effect on the expression patterns of other PNN markers, hyaluronan proteoglycan link protein 1, tenascin-R, hyaluronan, and brevican (Giamanco et al., 2010). It is reasonable to suggest that the constituent elements of PNN are brain-region specific. It is difficult to clarify the distribution of the various PNN components using low magnification analysis. Therefore, until recent years, each component was considered to be consistent in its localization. Our previous study indicated divergent distributions of WFA- and Cat-315-positive molecules (Ueno et al., 2017b). High-magnification analysis in the current study corroborated these findings, revealing varied localization of WFA-, AB1031-, Cat-315-, and Cat-316-positive molecules. Even in the rat cortex, the distribution of aggrecan-positive PNNs is different depending on the antibody used to detect them (i.e., Cat-315 or AB1031) (Madinier et al., 2014). Lectin WFA and the monoclonal antibodies, AB1031, Cat-315, and Cat-316, recognize different aggrecan epitopes; thus, divergent distributions are reasonable. Moreover, it is possible that varied PNN component distributions are due to each CSPG being secreted from different cell types. For example, aggrecan seems to be secreted from PV-positive interneurons (Lander et al., 1998), while brevican has been shown to be secreted from neurons and glia (Carulli et al., 2006). Regardless of the etiology for the distribution heterogeneity, these findings suggest that the various PNN components may have different functions, and that there is no single lectin or antibody that is capable of detecting all of the PNNs in the cortex. While the detailed functions of PNNs remain unclear, ECM proteins are widely expressed in the central nervous system, particularly in the brainstem and cerebellum. Hence, it is assumed that these proteins must possess a protective function. Among the ECM family of proteins, aggrecan, tenascin-R, and the link proteins of PNNs are known to play neuroprotective roles (Morawski et al., 2012a, Morawski et al., 2012b; Suttkus et al., 2014). Consistent with this, overexpression of aggrecan in the cortex is associated with less susceptibility to tau protein-induced cytotoxicity in Alzheimer’s disease (Morawski et al., 2010). In addition, PNNs protect neurons from oxidative stress (Cabungcal et al., 2013; Ueno et al., 2017c), and PNN-rich brain regions are less susceptible to trauma (Harris et al., 2009). It has also been shown that PNNs and CSPGs are decreased in the entorhinal cortex of patients with schizophrenia (Pantazopoulos et al., 2010). Similarly, a decrease in prefrontal cortex PNNs has also been observed in this patient population (Mauney et al., 2013). These reports indicate that CSPGs, such as PNNs, may be one of the underlying etiologies of schizophrenia, although it remains unclear why a decrease in PNNs does not appear in the sensory cortex of patients with schizophrenia. Interestingly, our current study revealed that aggrecan was either not present or expressed in very low levels in areas of the association cortex, where human studies have revealed decreased PNNs in patients with schizophrenia (i.e., the entorhinal and prefrontal subregions). Thus, PNNs in these brain areas may have little to no neuroprotective function. It should be noted that there is some debate as to what WFA recognizes, as recent studies have questioned the assumption that the plant-derived lectin binds to the N-acetylgalactosamine of aggrecan (Karetko-Sysa et al., 2011; Miyata and Kitagawa, 2017; Ueno et al., 2017b). In our previous study, we found an accumulation of WFA+ molecules in the upper region of the temporal cortex. In previous studies, WFA-recognition molecules have been clearly shown, but no report has focused on them yet (Brückner et al., 2003; Horii-Hayashi et al., 2015; Yang et al., 2015). In the current study, AB1031-, Cat-315-, and Cat-316-positive molecules were observed to exist in this region, but they appeared uniform in each layer of the cortex. Layer 1 is special, as it contains no pyramidal cells. Instead, this layer comprises the axons of pyramidal cells in layers 2–6, as well as those of inhibitory interneurons and neurons from other cortical regions (Felleman and Van Essen, 1991; Vogt, 1991). Condensed WFA+ molecules are likely present because of some unknown role; thus, future studies should evaluate layer 1 of the temporal cortex, as it likely holds the key to clarifying which molecule is recognized by WFA, as well as its function. 5 Conclusions The present clarified the region-specific expression of aggrecan components in the mature mouse cortex. Specifically showing divergent localizations of WFA-, AB1031-, Cat-315-, and Cat-316-positive PNN molecules. These results indicate that plasticity and neuroprotective function may differ depending on the cortical region, and provide useful insight for the development of methods to treat neuropsychiatric disorders and neurodegenerative diseases. Author contributions Study concept and design: H.U., M.O., and T.K. Data acquisition: H.U., K.F., K.T., and S.S. Data analysis and interpretation: H.U., K.F., K.T., and S.S. Drafting of the manuscript: H.U., M.O., and T.K. Critical revision of the manuscript for important intellectual content: S.M., N.K., K.W., S.A., and T.I. Statistical analyses: H.U and S.S. Study supervision: M.O. and T.I. Funding sources This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflict of interest The authors declare they have no competing financial interests. Acknowledgements We thank Kawasaki Medical School Central Research Institute for making instruments available to support this study. We thank Y. Koshidaka and M. Adachi for the technical assistance. The authors would also like to thank Editage (www.editage.jp) for English language editing. ==== Refs References Alpár A. Gärtner U. Härtig W. Brückner G. Distribution of pyramidal cells associated with perineuronal nets in the neocortex of rat Brain Res. 1120 2006 13 22 16996045 Balmer T.S. Carels V.M. Frisch J.L. Nick T.A. Modulation of perineuronal nets and parvalbumin with developmental song learning J. Neurosci. 29 2009 12878 12885 19828802 Bandtlow C.E. Zimmermann D.R. Proteoglycans in the developing brain: new conceptual insights for old proteins Physiol. Rev. 80 2000 1267 1290 11015614 Berretta S. Pantazopoulos H. Markota M. Brown C. Batzianouli E.T. Losing the sugar coating: potential impact of perineuronal net abnormalities on interneurons in schizophrenia Schizophr. 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