Characterization of immune cell populations in the tumor microenvironment of colorectal cancer using high definition spatial profiling
Open accessAbstract
Colorectal cancer (CRC) is the second-deadliest cancer in the world, yet a deeper understanding of spatial patterns of gene expression in the tumor microenvironment (TME) remains elusive. Here, we introduce the Visium HD platform (10x Genomics) and use it to investigate human CRC and normal adjacent mucosal tissues from formalin fixed paraffin embedded (FFPE) samples. The first assay available on Visium HD is a probe-based spatial transcriptomics workflow that was developed to enable whole transcriptome single cell scale analysis. We demonstrate highly refined unsupervised spatial clustering in Visium HD data that aligns with the hallmarks of colon tissue morphology and is notably improved over earlier Visium assays. Using serial sections from the same FFPE blocks we generate a single cell atlas of our samples, then we integrate the data to comprehensively characterize the immune cell types present in the TME, specifically at the tumor periphery. We observed enrichment of two pro-tumor macrophage subpopulations with differential gene expression profiles that were localized within distinct tumor regions. Further characterization of the T cells present in one of the samples revealed a clonal expansion that we were able to localize in the tissue using in situ gene expression analysis. In situ analysis also allowed us to perform in-depth characterization of the microenvironment of the clonally expanded T cell population and we identified a third macrophage subpopulation with gene expression profiles consistent with an anti-tumor response. Our study provides a comprehensive map of the cellular composition of the CRC TME and identifies phenotypically and spatially distinct immune cell populations within it. We show that the single cell-scale resolution afforded by Visium HD and the whole transcriptome nature of the assay allows investigations into cellular function and interaction at the tumor periphery in FFPE tissues, which has not been previously possible.
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“We applied CellPie to a Visium HD human colorectal cancer (CRC) dataset from patient P1 [ 9 ].”
InfluentialLessons learned from spatial transcriptomic analyses in clear-cell renal cell carcinoma
Jespersen, J., Lindgaard, C., Iisager, L., Ahrenfeldt, J., & Lyskjær, I. (2025) · Nature Reviews Urology · doi:10.1038/s41585-024-00980-x · Cited by 12 · see citing works
“10X Genomics has also released the Visium HD platform, which, like Visium, enables whole-transcriptomic sequencing of tissue samples, but at an improved resolution of 2 µm 33 .”
Wang, T., Chen, Z., Wang, W., Wang, H., & Li, S. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1556386 · Cited by 12 · see citing works
Yeo, Y. Y., Chang, Y., Qiu, H., Yiu, S. P. T., Michel, H. A., Wu, W., Jin, X., Kure, S., Parmelee, L. K., Luo, S., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.20.629650 · Cited by 12 · see citing works
Shulman, E. D., Campagnolo, E. M., Lodha, R., Stemmer, A., Cantore, T., Ru, B., Chang, T., Biswas, S., Dhruba, S. R., Wang, A., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.16.618609 · Cited by 11 · see citing works
“As the amount of spatial transcriptomics data continues to grow, with higher read counts and finer spatial resolution—even at a single-cell level 58 —we expect improved predictions of spatial gene expression and more precise inferences of cellular composition.”
Systematic Benchmarking of High-Throughput Subcellular Spatial Transcriptomics PlatformsPreprint
Ren, P., Zhang, R., Wang, Y., Zhang, P., Luo, C., Wang, S., Li, X., Zhang, Z., Zhao, Y., He, Y., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.12.23.630033 · Cited by 10 · see citing works
Next‐generation spatial transcriptomics: unleashing the power to gear up translational oncology
Wang, N., Hong, W., Wu, Y., Chen, Z., Bai, M., Wang, W., & Zhu, J. Y. (2024) · MedComm · doi:10.1002/mco2.765 · Cited by 9 · see citing works
“27,59,111 In real practice, it is also worth noting that for spatial-barcoded ST with near single-cell resolution, typical analytical points often require data binning to incorporate enough reads for profiling.”
BackgroundInfluentialChrysalis: decoding tissue compartments in spatial transcriptomics with archetypal analysis
Túrós, D., Vasiljević, J., Hahn, K., Rottenberg, S., & Valdeolivas, A. (2024) · Communications Biology · doi:10.1038/s42003-024-07165-7 · Cited by 8 · see citing works
Integrating Dynamical Systems Modeling with Spatiotemporal scRNA-Seq Data Analysis
Zhang, Z., Sun, Y., Peng, Q., Li, T., & Zhou, P. (2025) · Entropy · doi:10.3390/e27050453 · Cited by 12 · see citing works
“Image-based techniques [19–21] detect hundreds to thousands of genes with cellular or sub-cellular resolution, while sequencing-based methods [15–18] allow whole-transcriptome analysis but are usually limited to spot-level resolution.”
Seq-Scope-eXpanded: spatial omics beyond optical resolution
Anacleto, A., Cheng, W., Feng, Q., Park, A., Cho, C., Hwang, Y., Kim, Y., Si, Y., Hsu, J.-E., Zhao, Q., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-69346-8 · Cited by 11 · see citing works
Periyakoil, P. K., Smith, M. H., Kshirsagar, M., Ramirez, D. C., DiCarlo, E. F., Goodman, S. M., Rudensky, A. Y., Donlin, L. T., & Leslie, C. S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.08.631928 · Cited by 8 · see citing works
Xia, C., Cao, Z., & Gao, G. (2025) · Nature Communications · doi:10.1038/s41467-025-63140-8 · Cited by 10 · see citing works
“Given the rapidly increasing throughput of new spatial technologies 38,39 , scalability becomes a critical challenge for existing method.”
Hahn, K., Amberg, B., Rodríguez, J. M. M., Verslegers, M., Kang, B., Wils, H., Saravanan, C., Bangari, D. S., Long, S. Y., Youssef, S. A., et al. (2025) · Toxicologic Pathology · doi:10.1177/01926233241311258 · Cited by 7 · see citing works
“In line with this, Oliveira et al 72 recently published a preprint using Visium HD, single-cell sequencing from FFPE material, Xenium and Visium CytAssist from consecutive colorectal cancer sections.”
Liu, J., Chen, Z., Teng, Z., Tan, Y., Qin, Y., Chen, H., Liu, M., Chen, J., Wu, H., Chen, G., et al. (2025) · Journal of Affective Disorders · doi:10.1016/j.jad.2025.02.072 · Cited by 7 · see citing works
Wang, R., Yan, Q., Guo, X., Song, F., Xiong, Z., Cai, S., Bian, X., Wong, M. H., Cao, Q., Cheng, L., et al. (2025) · Genome Medicine · doi:10.1186/s13073-025-01441-9 · Cited by 6 · see citing works
“The resolution is further enhanced in subsequent technologies, e.g., improved to 10 µm in diameter in Slide-seq and Slide-seqV2 [14, 15], 2 µm in HDST [16] and Visium HD [17], and below 1 µm in Stereo-seq [18] and Seq-Scope [19].”
Hao, Q., Zhou, T. Y., Yan, H., Ren, Z., Mao, W., Huang, L., Chen, Y.-Y., Wang, Y., Xiao, M., He, Y., et al. (2026) · Molecular Cancer · doi:10.1186/s12943-025-02548-8 · Cited by 8 · see citing works
“High-resolution Visium HD deconvolution of FFPE CORE tumor samples [81] demonstrated Grm1 enrichment in normal intestinal epithelium, whereas Grm2 was diffusely distributed within tumor regions (Fig.”
Targeting immune hubs to advance cancer immunotherapy
Pelka, K. (2025) · Science Translational Medicine · doi:10.1126/scitranslmed.adx7698 · Cited by 6 · see citing works
Towards deciphering the bone marrow microenvironment with spatial multi-omics
Yip, R. K. H., Hawkins, E. D., Bowden, R. J., & Rogers, K. L. (2025) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2025.01.001 · Cited by 5 · see citing works
Zhan, X., Liu, Y., Guo, Y., Zhou, W., Yan, Y., Zeng, H., Dong, X., Chen, X., Ma, R., Liu, Z., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.01.16.630222 · Cited by 5 · see citing works
Spatial Proteomics towards Cellular Resolution
Kwon, Y., Fulcher, J., Paša-Tolić, L., & Qian, W.-J. (2024) · Espert Review of Proteomics · doi:10.1080/14789450.2024.2445809 · Cited by 6 · see citing works
“For example, spatial transcriptomics ( i.e ., spatially resolved RNA measurements) can be conducted with fluorescence in situ hybridization ( e.g ., MERFISH [10]) or barcoding of sequencing reads (e.g. Slide-seq [11], Visium [12], and more recently Visium HD [13] and Stereo-Seq [14]).”
Nucera, M. R., Charitakis, N., Leung, R. F., Leichter, A. L., Tuano, N., Walkiewicz, M., Sawant, V., Rowley, L., Scurr, M., Er, P. X., et al. (2026) · iScience · doi:10.1016/j.isci.2026.115827 · Cited by 6 · see citing works
Cheng, X., Tang, C., Dong, K., You, Y., Zhao, X., Duan, B., Chen, S., Chuai, G., Zhang, Z., & Liu, Q. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100893 · Cited by 4 · see citing works
Dong, Y., Saglietti, C., Bayard, Q., Espín-Pérez, A., Carpentier, S., Buszta, D., Tissot-Renaud, S., Dubois, R., Kamburov, A., Kang, S., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.01.621259 · Cited by 4 · see citing works
“The current best full-transcriptome resolution would be the Visium HD method with a 2x2μm grid 19 .”
MethodWhat spatial omics is teaching us about field cancerisation in prostate and bladder cancer
Byrne, M. H. V., Anbarasan, T., Browning, L., & Woodcock, D. J. (2025) · British Journal of Urology · doi:10.1111/bju.16830 · Cited by 4 · see citing works
Roadmap for spatial transcriptomics of HIV in tissues
Guan, Q., Eshetu, A., & Ho, Y. (2025) · Current Opinion in HIV and AIDS · doi:10.1097/coh.0000000000000961 · Cited by 3 · see citing works
Sun, C., & Zhang, Y. (2025) · Genome biology · doi:10.1186/s13059-025-03608-4 · Cited by 3 · see citing works
“Recently, VisiumHD from 10X Genomics is further available for formalin-fixed paraffin-embedded (FFPE) samples to profile whole transcriptome in spots of size 2 × 2 µm, a sub-cellular size for most cell types and tissue contexts [10].”
InfluentialZhang, X., Zhang, M., Xu, Y., Song, Y., Yang, X., Wu, Y., Zhao, X., Ran, D., Liu, X., Huang, H., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03900-3 · Cited by 4 · see citing works
Deciphering seed development and germination in the single-cell era
Schouveiler, P., Liew, L. C., Corso, M., Rajjou, L., Zinsmeister, J., & Lewsey, M. G. (2025) · Seed Science Research · doi:10.1017/s0960258524000199 · Cited by 3 · see citing works
Advances in spatial multi-omics technologies
Zhang, W., Huang, X., He, L., & Zhao, X. (2025) · Chinese Science Bulletin (Chinese Version) · doi:10.1360/tb-2024-1403 · Cited by 3 · see citing works
PRTS: Predicting Single-Cell Spatial Transcriptomic Maps from Histological Images
Wen, J., Zou, L., Liu, J., Guo, X., Liu, C., Wang, B., Deng, T., Liu, C., Tang, R., Yang, Y., et al. (2025) · Research · doi:10.34133/research.0961 · Cited by 2 · see citing works
“Emerging methods like Stereo-seq [ 5 ] and Visium HD [ 6 ] offer genome-wide coverage with a subcellular resolution, overcoming the limitations of traditional technologies.”
Integrating cytological images and spatial transcriptomics for cell segmentation with DISSECT
He, Y., ZHAO, Y., Zhang, R., Yang, H., Zhang, Z., Ren, P., Luo, C., Zhang, P., Zhang, Z. K., Hou, S., et al. (2026) · Nature Computational Science · doi:10.1038/s43588-026-01020-x · Cited by 2 · see citing works
Rapid and memory-efficient analysis and quality control of large spatial transcriptomics datasetsPreprint
Kövér, B., & Vigilante, A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.23.604776 · Cited by 1 · see citing works
Geospatially informed representation of spatial genomics data with SpatialFeatureExperimentPreprint
Moses, L., Huseynov, A., Rich, J. M., & Pachter, L. S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.24.640007 · Cited by 2 · see citing works
Single-cell, Spatially-Resolved TCR Profiling Links T Cell Phenotype and Clonality in Human TumorsPreprint
McCord, K. A., Kan, E., Hyslop, S., Xia, A. Y., Hofferek, C. J., Lewis, J. S., Wieland, A., Hernandez, D. J., Sandulache, V. C., & Hudson, W. H. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.06.668925 · Cited by 2 · see citing works
Stool shed cell transcriptomics mirrors tumor biology and enables colorectal cancer diagnosis
Halpern, K. B., Kent, I., Yakubovsky, O., Ben‐Moshe, S., Barkai, T., Fine, M., Novoselsky, R., Israeli, A., Nachmany, I., & Itzkovitz, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-17411-5 · Cited by 1 · see citing works
Xu, K., Zhou, X., & Zhang, L. (2026) · Bioinformatics · doi:10.1093/bioinformatics/btag071 · Cited by 1 · see citing works
Zhang, F., Kong, Y., Fu, X., Zuo, J., Guo, Q., Wang, J., Xu, M., Zeng, Q., Zhang, Y., ling, J., et al. (2026) · International Endodontic Journal · doi:10.1111/iej.70200 · Cited by 1 · see citing works
Spatial Transcriptomics in Breast Cancer: Advances and Applications
Cao, Y., Xu, K., Li, X. Y., Zhang, J., Jin, W., & Liu, Y.-X. (2026) · Biology · doi:10.3390/biology15131061 · Cited by 1 · see citing works
Liang, C., Yue, M., Zhang, K., Zhou, S., Xu, X., Wang, S., & Liu, S. (2026) · Frontiers in Immunology · doi:10.3389/fimmu.2026.1734327 · Cited by 2 · see citing works
Chadoutaud, L., Lerousseau, M., Herrero-Saboya, D., Ostermaier, J., Fontugne, J., Barillot, E., & Walter, T. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.11.07.622225 · Cited by 1 · see citing works
Joint imputation and deconvolution of gene expression across spatial transcriptomics platformsPreprint
Zheng, H., Sarkar, H., & Raphael, B. J. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.17.638195 · Cited by 1 · see citing works
“In contrast, in situ sequencing [45, 21] and in situ hybridization based SRT such as 10x Genomics Xenium [19, 32], merFISH [9], CosMx (NanoString), MERSCOPE (Vizgen) measure the expression for a subset of pre-selected genes at cellular or sub-cellular resolution.”
Karlsen, V. T., Majid, U., Lindholm, H. T., Umu, S. U., Vander-Elst, K. R., Beitnes, A.-C. R., Merok, M. A., Hagen, K. T., Nilsen, H. R., Yaqub, S., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.03.31.646286 · Cited by 1 · see citing works
Easydecon: Efficient Cell Type Mapping for High-Definition Spatial Transcriptomic DataPreprint
Umu, S. U., Karlsen, V. T., Bækkevold, E. S., Jahnsen, F. L., & Domańska, D. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.08.647764 · Cited by 1 · see citing works
“It is also possible to use 2 µm (offering millions of squared 2x2 µm spots) or 16 µm sized bins, which is a tremendous improvement compared to the previous version [11].”
VIB Hackathon on spatial omics tools and methodsPreprint
Rombaut, B., Pollaris, L., Sang-aram, C., Cruysse, M. V., Cannoodt, R., Vernaillen, F., Defauw, A., Mortier, J., Ali, M., Bestak, K., et al. (2024) · doi:10.37044/osf.io/uf7d3
Visual analysis of spatial transcriptomics data with RedeVizPreprint
Wang, D., & Ren, X. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.09.602652
“The nuclei segmentation of raw Visium HD data was performed with official workflow 10 .”
MethodLearning Phenotype Associated Signature in Spatial Transcriptomics with PASSAGE
Guo, C., Xia, C., Peng, G., Cao, Z., & Gao, G. (2025) · Small Methods · doi:10.1002/smtd.202401451
Zhang, Y., Yang, Y., Kong, Y., Zhong, B., Nakai, K., & Lu, H. M. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.22.639602
Xu, Y., Sargeant, C. J., You, Y., You, Y., Su, S., Wang, C., Tian, L., Chen, Y., & Ritchie, M. E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.16.649254
Joint Imputation and Deconvolution of Gene Expression Across Spatial Transcriptomics Platforms
Zheng, H., Sarkar, H., & Raphael, B. J. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-90252-9_39
Advances and breakthroughs in single-cell nucleic acid omics
Sun, Z., Li, Z., Tang, Y., & Dong, F. (2025) · Scientia Sinica Vitae · doi:10.1360/ssv-2025-0107
Liu, J., Eckstein, M., Wang, Z., Feuerhake, F., & Merhof, D. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.10.657985
STRESS: Spatial Transcriptome Resolution Enhancing Method based on the State Space ModelPreprint
Wang, X. H., Ye, F., & Liu, X. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.25.661624
Chen, K., Zhao, S., Guo, M., Reza, H. A., Wagner, A., Cakar, A. C., Jiang, C., Zhang, E. Y., Green, J., Martin, E. T., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.07.663390
Abdelkareem, A. O., Gill, G. S., Manoharan, V. T., Verhey, T. B., & Morrissy, A. S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.02.668292
Zheng, J., Zhang, F., Kong, Y., Fu, X., Zuo, J., Guo, Q., Wang, J., Xu, M., Zeng, Q., Zhang, Y., et al. (2025) · Research Square · doi:10.21203/rs.3.rs-7096435/v1
Acellular Tissue Engineered Vessels as Coronary Artery Bypass Grafts
Williams, A. R., Nash, K. M., Kirkton, R. D., Levitan, G. S., Daubert, M. A., Whitney, S. A., Naegeli, K. M., Benkert, A. R., McCartney, S. L., Prichard, H. L., et al. (2025) · JACC Basic to Translational Science · doi:10.1016/j.jacbts.2025.101379
Spatial and Multiomic profiling of muscle regeneration dynamics in Duchenne Muscular DystrophyPreprint
Virtanen, L., D’Ercole, C., Saillard, L., Grandi, F. C., Peccate, C., Hoareau‐Coudert, B., Karunanuthy, V., Bertholle, C., Izac, B., Saintpierre, B., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.02.691824
“In brief, frozen muscle tissues were sectioned onto Visium HD slides, stained with hematoxylin and eosin (H&E), imaged, and processed to capture barcoded mRNA, which was then mapped back to the tissue section 18 .”
Pattison, A., Engel, R. M., Chan, W. H., Greatorex, S., Nickless, D. K., Skinner, L., Cohen, C. A., Hickey, J., Georges, C., Fletcher, A. L., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.09.693138
Wayfarer: A multiscale framework for spatial analysis of tumor progressionPreprint
Moses, L., Hérault, A., Cabon, L., & Dumitrascu, B. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.16.706245
“Spatial resolutions of sequencing-based technologies range from 100 μm between spot centers in Visium to 2 μm in Visium HD 7 and 0.5 μm in Stereo-seq 8 , while in practice, Visium HD and Stereo-seq data are binned into coarser resolutions for downstream analyses 9–11 .”
InfluentialXiao, W., Chen, H., Osakwe, A., Zhang, Q., & Li, Y. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.16.712067
Kim, H., Park, B. J., Jung, J., Lee, S. W., Panahandeh, S., Kwon, S., Li, J. J., Madan, E., Kim, D., Kim, J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.08.723913
“For scRNA-seq, cell type annotations were provided as meta data in the Github repository of original publication 12 .”
InfluentialA modular generalist-specialist AI framework for ROI selection across spatial profiling workflowPreprint
Castillo, S. P., Gautam, T., Gonzales, K., Salvatierra, M. E., Serrano, A., Ercan, C., Rodriguez, B. L., Acosta, P., Chen, P., Shokrollahi, Y., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.26.734862
Jin, Y., Myers, J. D., Rajbhandari, P., Zhang, J. Y., Fang, K. W., Moazami, J. S., Hosny, N., Stockwell, B. R., & Azizi, E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.07.09.737574
dbverse scales spatial omics analysis with embedded analytical databasesPreprint
Ruiz, E. C., Jarzabek, V., Chen, J. G., Rizvanov, T., Amin, I., & Dries, R. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.08.09.743742
Kang, Y., Wang, Q., Xu, S., Guo, J., Ming, Y., Chen, W., Wang, M., Huang, Z., Huang, Y., & Jia, B. (2026) · Clinical and Translational Medicine · doi:10.1002/ctm2.70858
“37 Platform selection thus entails careful trade-offs among spatial resolution, transcript detection sensitivity, gene coverage, tissue area, sample compatibility, throughput and cost.”
On technical biases in imaging spatial transcriptomicsPreprint
Chen, C., Borm, L. E., Reguant, A. P., Ruano, I., Risso, D., & Crowell, H. L. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.16.759529
“[16] and [3], Xenium data from [51].”
- Authors
- Oliveira, M. F., Romero, J. P., Chung, M., Williams, S., Gottscho, A. D., Gupta, A., Pilipauskas, S. E., Mohabbat, S., Raman, N., Sukovich, D., Patterson, D., Visium HD Development Team, & Taylor, S. E. B.
- Affiliations
- 10X Genomics (United States) (US)
- Corresponding author
- Taylor, Sarah E. B.
- ORCID
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2024-06-05
- OpenAlex
- W4399365283
- Cited by
- 95
- References
- 48
- Citations by year
- 2026: 19 · 2025: 52 · 2024: 24
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nd
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Life Sciences › Immunology and Microbiology › Immunology › Immune cells in cancer
- Keywords
- colorectal cancer, tumor microenvironment, spatial transcriptomics, Visium HD, immune cell subsets, tumor-associated macrophages, macrophage subpopulations, T cell clonal expansion, tumor periphery, antitumor immunity, formalin-fixed paraffin-embedded tissue
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