
==== Front
Immunooncol Technol
Immunooncol Technol
Immuno-Oncology and Technology
2590-0188
Elsevier

S2590-0188(24)00024-8
10.1016/j.iotech.2024.100727
100727
Technology Explained
Region of interest localization, tissue storage time, and antibody binding density—a technical note on the GeoMx® Digital Spatial Profiler
Böning S. 1
Schneider F. 1
Huber A.-K. 1
Langhoff D. 1
Lin H. 1
Kaczorowski A. 1
Stenzinger A. 2
Hohenfellner M. 3
Duensing S. 1
Duensing A. anette.duensing@med.uni-heidelberg.de
4⁎
1 Molecular Urooncology, Department of Urology, University Hospital Heidelberg, Heidelberg
2 Institute of Pathology, University Hospital Heidelberg, Heidelberg
3 Department of Urology, University Hospital Heidelberg, and National Center for Tumor Diseases (NCT), Heidelberg
4 Precision Oncology of Urological Malignancies, Department of Urology, University Hospital Heidelberg, Heidelberg, Germany
⁎ Correspondence to: PD Dr Anette Duensing, Precision Oncology of Urological Malignancies, Department of Urology, University Hospital Heidelberg, Im Neuenheimer Feld 517, D-69120 Heidelberg, Germany. Tel: +49-(0)6221-56-6258; Fax: +49-(0)6221-56-7659 anette.duensing@med.uni-heidelberg.de
20 8 2024
9 2024
20 8 2024
23 100727© 2024 The Author(s)
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background

Spatial biology is an emerging concept to interrogate tumor heterogeneity. The NanoString GeoMx® Digital Spatial Profiling (DSP) platform has become increasingly available. It combines high-plex analysis of protein or messenger RNA expression using barcoded antibodies or oligonucleotide probes with investigator-driven selection of regions of interest. Cell populations, e.g. immune cells, can be selectively analyzed via segmentation. A key advantage is the use of archived formalin-fixed, paraffin-embedded tissue, however, begging the question whether and to what extent tissue fixation and storage time affect the results.

Materials and methods

Antibody binding density (ABD), i.e. the number of barcodes/μm2, is a key quality control measure for DSP spatial proteomics. To assess whether regional differences in tissue fixation have an influence on ABD, we compared 652 regions of interest selected from tumor center and periphery of 49 prostate cancer and 25 renal cell carcinoma (RCC) specimens. Moreover, the effect of tissue storage time on ABD was examined. Finally, we tested whether regional differences have an influence on ABD of segmented CD45+ or CD8+ cells.

Results

No significant differences in ABD between tumor center and periphery were found in prostate cancer or RCC. However, ABD was significantly higher in recent specimens (≤5 years) when compared with those that were older (>5 years; P = 0.027). There was a trend towards higher ABD in the tumor periphery of RCC specimens after segmentation for immune cells, albeit without reaching statistical significance.

Conclusions

The NanoString GeoMx® DSP platform delivers robust data to interrogate tumor heterogeneity, but tissue storage time should be considered when interpreting the results.

Highlights

• Spatial biology is an emerging concept to interrogate tumor heterogeneity.

• A key advantage is the use of FFPE tissue, but it is unclear whether tissue fixation and storage time affect the results.

• Antibody binding density showed no regional differences in 80 prostate and renal carcinomas, but declined with tissue age.

• The GeoMx DSP platform yields robust data to study tumor heterogeneity, but tissue storage time needs to be considered.

Key words

digital spatial profiling
spatial biology
tumor heterogeneity
prostate cancer
renal cell carcinoma
immuno-oncology
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pmcMalignant tumors can be described as an ecosystem, i.e. a heterogeneous population of tumor cells that not only undergoes evolutionary changes, but also interacts with an equally heterogeneous microenvironment consisting of non-malignant cells, such as immune cells.1,2 It is increasingly being recognized that these tumor ecosystems contain spatial niches,3 whereby a niche not only describes the localization of a particular cell type, but also encompasses functional aspects with potential relevance for malignant progression. The most obvious manifestation of intratumoral spatial niches are the tumor center and the tumor periphery. However, various other intratumoral compartments very likely also represent spatial niches and harbor cells with certain functional properties.3, 4, 5

Prostate cancer is the most common non-cutaneous malignancy in men and is generally not viewed as an immunologically vulnerable tumor. The opposite is true for renal cell carcinoma (RCC), the most lethal urological cancer once metastatic.6 For the latter, immune checkpoint blockade has become an oncological standard-of-care for patients with advanced stage disease.7

Spatial niches have been reported for both tumor entities, albeit with remarkable differences. While the tumor periphery is a hotspot for tumor cell proliferation in RCC,3 prostate cancer frequently harbors senescent cells in the tumor periphery.8 With respect to the immune landscape, prostate cancer is generally viewed as a ‘cold’ tumor, unless a BRCA1/2 mutation is present.9 In contrast, RCCs can be immunologically ‘cold’, ‘inflamed’, or ‘immune excluded’. Of note, a high T-cell infiltration is associated with an unfavorable patient prognosis in RCC since cytotoxic T cells are frequently dysfunctional or terminally exhausted.10

Spatial biology is an emerging field in tumor biology and a number of recent technical advances have led to a surge in interest.11 Several studies underscore the power of spatial biology to interrogate tumor heterogeneity in both prostate cancer and RCC.5,12, 13, 14, 15

There are several techniques available for measuring protein and messenger RNA (mRNA) expression in a spatially defined manner.16 The GeoMx® Digital Spatial Profiling (DSP) platform (NanoString Technologies, Seattle, WA, recently acquired by Bruker, Billerica, MA) combines a versatile, investigator-driven selection of regions of interest (ROIs) with high-plex analysis of protein or mRNA using formalin-fixed, paraffin-embedded (FFPE) tissue.17,18

The DSP workflow to assess protein expression involves deparaffinization and antigen retrieval followed by incubation with a probe mix consisting of antibodies linked to UV-cleavable oligonucleotide tags. Tissue samples are then loaded onto the GeoMx® instrument, followed by scanning of the slides and ROI selection. In a next step, oligonucleotide tags in the annotated regions are cleaved by UV light and collected. After overnight hybridization of the oligonucleotide tags with fluorescent probes, the hybridization products are extracted (nCounter® Prep Station, NanoString Technologies) and the fluorescent barcodes are counted (nCounter® Digital Analyzer; NanoString Technologies).17

ROIs can be individually selected in size and shape, with a minimum size of 6 μm × 6 μm (total surface area of 36 μm2) and a maximum size of 660 μm × 785 μm (total surface area of 518 100 μm2). This allows for a highly versatile approach for the exploration of tumor heterogeneity (Figure 1).Figure 1 Representative GeoMx® Digital Spatial Profiling (DSP) scan of an archival FFPE clear-cell RCC specimen following staining for the morphology markers CD45 (magenta) and pan-cytokeratin (green). Nuclei are stained with SYTO13 (blue). Rectangular and polygonal ROIs (white) in the tumor periphery, tumor center, and areas adjacent to necrosis are shown (scale bar: 500 μm). FFPE, formalin-fixed, paraffin-embedded; RCC, renal cell carcinoma; ROI, region of interest.

Segmentation can be used to analyze protein or mRNA expression in separate cellular compartments. Using standard (e.g. CD45) or customized (e.g. CD8) morphology markers, it is possible to define areas of illumination (AOIs) within a pre-defined ROI. Through UV illumination of the AOI, it is then possible to measure protein or mRNA expression specifically in this cellular compartment (e.g. CD45+ cells; Figures 1 and 2).Figure 2 AOI segmentation based on CD45+ immunofluorescence staining. (A) Scan of an archival FFPE RCC specimen stained by immunofluorescence for CD45 (magenta) and pan-cytokeratin (green). Nuclei are stained with SYTO13 (blue). ROI 001 has been defined manually (scale bar: 250 μm). (B) A segmentation mask (grey) is generated based on the fluorescence signal of the CD45+ channel. Shown is the overlay of the CD45 segmentation mask onto the scan. (C) Only UV-cleaved barcodes from CD45+ cells within the pre-defined ROI are extracted. Shown is the extracted CD45 segmentation mask of ROI 001. AOI, area of illumination; FFPE, formalin-fixed, paraffin-embedded; RCC, renal cell carcinoma; ROI, region of interest.

The use of FFPE tissue raises the question whether and to what extent tissue processing, mainly fixation and storage time, affects DSP results when comparing distinct regions, e.g. the tumor center and periphery (Figure 1).

To address this question, we analyzed regional differences in antibody binding density (ABD) in order to assess potential effects of uneven tissue fixation. We compared a total of 652 ROIs, including 464 ROIs from either the tumor center (n = 197) or the tumor periphery (n = 267), obtained from 49 prostate cancer specimens and 188 ROIs from the tumor center (n = 82), tumor periphery (n = 85), and regions adjacent to necrosis (n = 21) from 25 RCCs. In addition, we analyzed 44 ROIs in which segmentation for CD45+ immune cells was carried out (tumor center, n = 22; tumor periphery, n = 22), and 43 ROIs, in which CD8+ cells were segmented (tumor center, n = 22; tumor periphery, n = 21), from a total of six RCCs (Table 1).Table 1 Regions of interest (ROIs) and antibody binding densities (ABDs)

		ROI surface area, μm2, median (range)	Antibody binding density, barcodes/μm2, median (range)	
Prostate cancer	Center	195 234 (9792-290 881)	0.49 (0.23-0.59)	
Periphery	177 379 (21 558-281 178)	0.47 (0.26-0.57)	
Renal cell carcinoma	Center	114 195 (22 583-194 893)	0.57 (0.34-0.69)	
Periphery	60 571 (26 001-164 968)	0.58 (0.32-0.69)	
Adjacent to necrosis	64 413 (21 117-191 865)	0.54 (0.39-0.62)	
Prostate cancer	≤5 years	185 596 (138 248-206 177)	0.53 (0.37-0.56)	
>5 years	183 741 (17 937-256 554)	0.48 (0.27-0.53)	
Prostate cancer (≤5 years)	Center	195 234 (143 418-255 113)	0.51 (0.37-0.59)	
Periphery	178 161 (81 717-248 761)	0.53 (0.37-0.57)	
Prostate cancer (>5 years)	Center	195 234 (9792-290 881)	0.48 (0.23-0.59)	
Periphery	175 129 (21 558-281 178)	0.47 (0.26-0.55)	
Renal cell carcinoma (CD45 segmented)	Center	9062 (4836-34 565)	0.41 (0.30-0.47)	
Periphery	5657 (1194-61 095)	0.48 (0.30-0.50)	
Renal cell carcinoma (CD8 segmented)	Center	12 989 (4528-37 842)	0.42 (0.31-0.49)	
Periphery	6255 (2713-17 019)	0.43 (0.31-0.43)	

ABD is defined as the total number of counted barcodes per μm2 and is determined before count normalization as part of the internal quality control of the Nanostring GeoMx® workflow. ABD is therefore a key quality control measure for spatial proteomics using FFPE tissue. It can be influenced by various factors, such as the overall number of targets included in the antibody panels as well as the expression levels of the respective proteins. Only ROIs in which the same antibody panels were used were compared with each other in the current study.

The majority of experiments was carried out using a total of 53 antibodies encompassing the GeoMx® PI3K/AKT and MAPK signaling assays, the GeoMx® Cell Death assay, and the GeoMx® Immune Cell Profiling Core assay (Supplementary Table S1, available at https://doi.org/10.1016/j.iotech.2024.100727). For the DSP analysis that included tissue segmentation (CD45+ or CD8+) in RCC, we used the GeoMx® Immune Cell Profiling Core assay combined with the GeoMx® Human IO Drug Target and the GeoMx® Human Immune Activation Status assays, amounting to a total of 42 antibodies (Supplementary Table S2, available at https://doi.org/10.1016/j.iotech.2024.100727). Each experiment included three barcode-labeled secondary antibodies as negative controls [rabbit immunoglobulin G (IgG), mouse IgG1, IgG2a] and antibodies against three housekeeping proteins [histone H3, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), ribosomal protein S6] for normalization.

There were no statistically significant differences in ABDs between the tumor center and tumor periphery in the prostate cancer specimens (median 0.49, range 0.23-0.59 versus median 0.47, range 0.26-0.57, P = 0.601; Table 1 and Figure 3A). Likewise, no statistically significant differences were detected in the ABDs of the tumor center, tumor periphery or areas adjacent to necrosis in RCC (median 0.57, range 0.34-0.69 versus median 0.58, range 0.32-0.69 versus median 0.54, range 0.39-0.62, P = 0.960; Table 1 and Figure 3B).Figure 3 Comparison of ABDs in different tumor regions and after different FFPE tissue storage times. (A) Comparison of ABDs in the tumor center (n = 197 ROIs) and tumor periphery (n = 267 ROIs) of 49 prostate cancer specimens (P = 0.601, Mann–Whitney U test). (B) Comparison of ABDs in the tumor center (n = 82 ROIs), tumor periphery (n = 85 ROIs), and areas adjacent to necrosis (n = 21 ROIs) of 25 RCC specimens (P = 0.960, ANOVA after log10 transformation). (C) Comparison of ABDs between FFPE tissue samples from prostate cancers with a storage time of ≤5 years (n = 110 ROIs) or >5 years (n = 354 ROIs; P = 0.027, Mann–Whitney U test). (D) Comparison of ABDs between the tumor center und tumor periphery of prostate cancer samples stored for ≤5 years (n = 50 and n = 60 ROIs, respectively; P = 0.699; left panel) or >5 years (n = 147 and n = 207 ROIs, respectively; P = 0.436, right panel; Mann–Whitney U test). (E) Comparison of ABDs in the tumor periphery (n = 22 ROIs) compared with the tumor center (n = 22 ROIs) in RCC samples segmented for CD45+ immune cells (P = 0.172, Mann–Whitney U test). (F) Comparison of ABDs between the tumor center (n = 22 ROIs) and tumor periphery (n = 21 ROIs) in RCC samples segmented for CD8+ immune cells (P = 0.688, Mann–Whitney U test). Each box and whisker plot represents median, interquartile range, the minimum and the maximum value based on the mean values of individual specimens. ABD, antibody binding density; ANOVA, analysis of variance; FFPE, formalin-fixed, paraffin-embedded; RCC, renal cell carcinoma; ROI, region of interest.

We next analyzed whether tissue storage time (i.e. time from tissue fixation to DSP analysis) had an influence on ABD. Of note, prostate cancer specimens stored for ≤5 years (n = 11) had a significantly higher ABD (median 0.53, range 0.37-0.56; n = 110 ROIs) when compared with specimens stored for >5 years (n = 38; median 0.48, range 0.27-0.53, P = 0.027; n = 354 ROIs; Table 1 and Figure 3C). Of note, there were no statistical differences in ABD when comparing tumor center and periphery from prostate cancer specimens with the same storage time (P = 0.699, ≤5 years, and P = 0.436, >5 years, respectively; Table 1 and Figure 3D). When we plotted all ABDs over the years of tissue storage time (ranging from 1 to 22 years) and applied a linear regression model, we found a gradual decline of ABD with increasing tissue age (Supplementary Figure S1, available at https://doi.org/10.1016/j.iotech.2024.100727). Similarly, we also found a decline of GAPDH counts with increasing storage time. Hence, using housekeeping proteins as a means to equalize differences in tissue storage time may not be feasible. All RCC specimens in our study had been stored for ≤5 years and were hence not included in this analysis.

Lastly, we sought to determine whether regional differences in ABD affect the analysis of immune cells. While there was a trend towards higher ABDs in the tumor periphery compared with the tumor center for CD45+ segmented cells (tumor periphery, median 0.48, range 0.30-0.50; tumor center, median 0.41, range 0.30-0.47; Table 1 and Figure 3E) and CD8+ segmented cells (tumor periphery, median 0.43, range 0.31-0.43; tumor center, median 0.42, range 0.31-0.49; Table 1 and Figure 3F), neither reached statistical significance (P = 0.172 and P = 0.688, respectively).

In summary, the NanoString GeoMx® DSP platform delivers robust and comparable results with respect to ABD to interrogate intratumoral heterogeneity on the protein expression level. However, special attention should be given, when using tissue samples with considerable differences in storage time and we strongly recommend using age-matched FFPE samples for comparative analyses.

Supplementary data

Supplementary Figure S1

Effect of tissue storage time on antibody binding density (ABD). Box and whisker plots showing ABD for each year of tissue storage time. Darker shades of grey indicate overlapping data points. A linear regression model was applied to the data, showing a gradual decline in ABD with increasing tissue age.

Supplementary Table S1

Supplementary Table S2

Acknowledgements

We are grateful to the tissue bank of the National Center for Tumor Diseases Heidelberg for tissue procurement. We acknowledge financial support for the publication costs by the University of Heidelberg.

Funding

This work was supported by the German 10.13039/100021130 Federal Ministry for Economic Affairs and Climate Action (01MT21004A ).

Disclosure

The authors have declared no conflicts of interest.
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