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Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging
Open accessPublished version of a preprint
Abstract
A highly multiplexed cytometric imaging approach, termed co-detection by indexing (CODEX), is used here to create multiplexed datasets of normal and lupus (MRL/lpr) murine spleens. CODEX iteratively visualizes antibody binding events using DNA barcodes, fluorescent dNTP analogs, and an in situ polymerization-based indexing procedure. An algorithmic pipeline for single-cell antigen quantification in tightly packed tissues was developed and used to overlay well-known morphological features with de novo characterization of lymphoid tissue architecture at a single-cell and cellular neighborhood levels. We observed an unexpected, profound impact of the cellular neighborhood on the expression of protein receptors on immune cells. By comparing normal murine spleen to spleens from animals with systemic autoimmune disease (MRL/lpr), extensive and previously uncharacterized splenic cell-interaction dynamics in the healthy versus diseased state was observed. The fidelity of multiplexed spatial cytometry demonstrated here allows for quantitative systemic characterization of tissue architecture in normal and clinically aberrant samples.
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“For example, multiplexed antibody-based imaging (mABI) technologies such as imaging mass cytometry (IMC) [31] and codetection by indexing (CODEX) [32] can detect 20–50 selected genes simultaneously in each cell at the protein level, which is a useful complement to transcript-based detection – especially given the potential for poor correlation of transcriptomic and proteomic data sets implicated”
From The heterogeneity of cellular senescence: insights at the single-cell level (2023) · cites this paper as [32]
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“In the future, advanced techniques such as Slide-seq and CODEX (CO-Detection by indEXing) should be incorporated to identify the most relevant biomarker for T-cell imaging in a given scenario (39,40).”
MethodSpatiotemporal multi-omics: exploring molecular landscapes in aging and regenerative medicine
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“DNA barcoded techniques like DNA exchange imaging [106], CODEX [107, 108], and immuno-SABER [109], address the spectral limitations of fluorescence and bolster multiplexing capabilities.”
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“…scRNA-seq and scATAC-seq datasets facilitating spatial structure identi fi cation of B cell follicles in tonsil As a recently developed technology, co-detection by indexing (CODEX) enables highlymultiplexed and spatiallyresolved pro fi ling of proteinswithintissuesectionsatsingle-cellresolution 6 .”
Enhancing the Value of Histopathological Assessment of Allograft Biopsy Monitoring
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Next-Generation Pathology by Multiplexed Immunohistochemistry
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Toward reproducible, scalable, and robust data analysis across multiplex tissue imaging platforms
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“MTI methods like cyclic immunofluorescence (CyCIF) (Lin et al., 2018; Eng et al., 2020), CO-Detection by indEXing (CODEX) (Goltsev et al., 2018), multiplex immunohistochemistry (mIHC) (Tsujikawa et al., 2017), imaging mass cytometry (IMC) (Giesen et al., 2014), and multiplex ion beam imaging (Angelo et al., 2014) enab…”
In Vivo Click Chemistry Enables Multiplexed Intravital Microscopy
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“Another imaging platform, Co-Detection by IndeXing (CODEX)[79], is designed for up to 40 proteins using cyclic detection of DNA-indexed antibody panels.”
MethodWang, Y. X., Holbrook, C. A., Hamilton, J. N., Garoussian, J., Afshar, M., Su, S., Schürch, C. M., Lee, M. Y., Goltsev, Y., Kundaje, A. B., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.06.10.494732 · Cited by 31
“This finding fits 241 well with findings by others that cells are known to organize into cellular neighborhoods 242 through chemoattractant or chemorepellent signals (Goltsev et al., 2018; Schürch et al., 2020) 243 and reside in niches that depend on the presence or absence of other cells (Fuchs and Blau, 2020).”
BackgroundMethodResultInfluentialZhang, D., Ni, Y., Wang, Y., Feng, J., Zhuang, N., Li, J., Liu, L., Shen, W., Zheng, J., Zheng, W., et al. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04336-8 · Cited by 28
“These studies indicated that although a complex system, like an enormous genome encoding just a limited number of markers, the TME also has countable cell-tocell interactions or CN patterns [20, 21].”
BackgroundZhang, R.-J., Yang, Y., Hu, C., Huang, M., Cen, W., Ling, D., Long, Y., Yang, X., Xu, B.-H., Peng, J., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-43249-4 · Cited by 29
“…of highly multiplexed tissue imaging technologies, such as imaging mass cytometry (IMC) 34 , multiplexed ion beam imaging (MIBI) 35 , co-detection by indexing (CODEX) 36 , and t-CyCIF 37,38 , could be valuable in measuring the distance between different cell types within the tumor microenvironment.”
Omics Approaches Unveiling the Biology of Human Atherosclerotic Plaques
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“Alternative techniques using single-stranded DNA (ssDNA) for barcoding signals, like immuno-SABER 16 or CODEX 17 , focus on amplifying signals on multi-target, resulting in large-area histological imaging (for review, see ref.”
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“Tissue preparation for CODEX (Goltsev et al., 2018, Schürch et al., 2020) multiplexed immuno- fluorescence analysis. a. Remove fresh frozen tissue from 80 C and place inside a desiccator for at least 20 min.”
MethodDiversity in the bone marrow niche: Classic and novel strategies to uncover niche composition
Sánchez‐Lanzas, R., Kalampalika, F., & Ganuza, M. (2022) · British Journal of Haematology · doi:10.1111/bjh.18355 · Cited by 28
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Expanded vacuum-stable gels for multiplexed high-resolution spatial histopathology
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The multifaceted role of macrophages during acute liver injury
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Spatial Dissection of the Immune Landscape of Solid Tumors to Advance Precision Medicine
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“2B ); after each cycle, the reporter oligonucleotides are removed chemically (30, 31).”
BackgroundQuality control for single-cell analysis of high-plex tissue profiles using CyLinter
Baker, G. J., Novikov, E., Zhao, Z., Vallius, T., Davis, J. A., Lin, J., Muhlich, J. L., Mittendorf, E. A., Santagata, S., Guerriero, J. L., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02328-0 · Cited by 31
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“This enables simultaneous imaging of 66 markers in formalin-fixed and paraffin-embedded (FFPE) tissue [51].”
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“The CODEX technology is based on differently barcoded antibodies, which are detected via fluorescently labeled complementary oligonucleotides in a cyclic staining approach (Goltsev et al., 2018) (Kennedy-Darling et al.”
MethodGoldsborough, T., O’Callaghan, A., Inglis, F., Leplat, L., Filby, A. J., Bilen, H., & Bankhead, P. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.09.04.611150 · Cited by 27
“Recently developed high-plex imaging methods such as CODEX [1], CyCIF [2] and MIBI [3] enable the capture of dozens of biomarkers in separate imaging channels.”
MethodCai, Y., Kanyo, J. E., Wilson, R. S., Bathla, S., Cardozo, P. L., Тонг, Л., Qin, S., Fuentes, L. A., Pinheiro‐de‐Sousa, I., Huynh, T., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00823-3 · Cited by 27
“Future studies could leverage multiplexed high-resolution quantitative imaging 87 for comparisons at different disease stages.”
Inhibition of 15-hydroxy prostaglandin dehydrogenase promotes cartilage regeneration
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Single cell analyses to understand the immune continuum in atherosclerosis
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Super-multiplexed vibrational probes: Being colorful makes a difference
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Interactive single-cell data analysis using Cellar
Hasanaj, E., Wang, J., Sarathi, A., Ding, J., & Bar‐Joseph, Z. (2022) · Nature Communications · doi:10.1038/s41467-022-29744-0 · Cited by 24
“We did not normalize any of the CODEX data.”
MethodInfluentialMultispectral confocal 3D imaging of intact healthy and tumor tissue using mLSR-3D
van Ineveld, R. L., Collot, R. V. U., Román, M. B., Pagliaro, A., Bessler, N., Ariese, H. C., Kleinnijenhuis, M., Kool, M., Alieva, M., Lopes, S. M. C. D. S., et al. (2022) · Nature Protocols · doi:10.1038/s41596-022-00739-x · Cited by 29
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Single‐cell proteomic analysis
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“In 2018, Goltsev et al. (2018) developed an alternative approach to eliminate fluorescence signals on oligonucleotide conjugated antibodies.”
BackgroundThe future of cerebral organoids in drug discovery
Salick, M. R., Lubeck, E., Riesselman, A., & Kaykas, A. (2021) · Seminars in Cell & Developmental Biology · doi:10.1016/j.semcdb.2020.05.024 · Cited by 29
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Spatial single-cell omics: new insights into liver diseases
Suo, Y., Thimme, R., & Bengsch, B. (2025) · Gut · doi:10.1136/gutjnl-2024-332105 · Cited by 29
“Techniques such as DNA exchange imaging 40 and CODEX (codetection by indexing) 41 use primary antibodies conjugated with unique DNA oligonucleotides, allowing a one-time antibody binding step followed by shorter iterative cycles of fluorescent labelling, imaging and signal removal.”
Advances in quantitative immunohistochemistry and their contribution to breast cancer
Yaghoobi, V., Martínez-Morilla, S., Liu, Y., Charette, L. A., Rimm, D. L., & Harigopal, M. (2020) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2020.1743178 · Cited by 26
“frequency and duration of blinks), essentially providing a distinct kinetic barcode for hundreds of unique molecular species (no spectral imaging is involved at all) [103] DNA conjugated labeling with hybridization-based exchange of fluorophores‘Codex’ [86] Iteratively visualization of antibody binding using DNA barco…”
MethodIn Situ Classification of Cell Types in Human Kidney Tissue Using 3D Nuclear Staining
Woloshuk, A., Khochare, S., Almulhim, A., McNutt, A. T., Dean, D. F., Barwinska, D., Ferkowicz, M. J., Eadon, M. T., Kelly, K. J., Dunn, K. W., et al. (2020) · Cytometry Part A · doi:10.1002/cyto.a.24274 · Cited by 25
“Although such multiplexed staining is possible using immunohistochemistry, this approach is typically done using immunofluorescence microscopy because of the availability of established methods of quantitating fluorescence and the ability to multiplex spectrally distinguishable fluorophores for various cell markers in…”
MethodScope2Screen: Focus+Context Techniques for Pathology Tumor Assessment in Multivariate Image Data
Jessup, J., Krueger, R., Warchol, S., Hoffer, J., Muhlich, J. L., Ritch, C. C., Gaglia, G., Coy, S., Chen, Y.-A., Lin, J., et al. (2021) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2021.3114786 · Cited by 25
3D imaging for driving cancer discovery
van Ineveld, R. L., van Vliet, E. J., Wehrens, E. J., Alieva, M., & Rios, A. C. (2022) · The EMBO Journal · doi:10.15252/embj.2021109675 · Cited by 27
“In recent years, novel multiplexing technologies based on DNA-barcoding antibodies, including CODEX (Goltsev et al , 2018) (Table 2), implement a single step primary antibody staining procedure, followed by sequential imaging cycles for deep-phenotyping, comparable to flow or mass cytometry.”
SPF: A spatial and functional data analytic approach to cell imaging data
Vu, T., Wrobel, J., Bitler, B. G., Schenk, E. L., Jordan, K. R., & Ghosh, D. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009486 · Cited by 25
“More specifically, multiplex tissue imaging (MTI) [12] methods such as cyclic immunoflourescence (CyCIF) [13], CO-Dectection by indEXing (CODEX) [14], multiplex immunohistochemistry (mIHC) [15], imaging mass cytometry (IMC) [16], and multiplex ion beam imaging (MIBI) [17] are capable of measuring…”
Morphodynamical cell state description via live-cell imaging trajectory embedding
Copperman, J., Gross, S. M., Chang, Y. H., Heiser, L. M., & Zuckerman, D. M. (2023) · Communications Biology · doi:10.1038/s42003-023-04837-8 · Cited by 25
7-UP: Generating in silico CODEX from a small set of immunofluorescence markers
Wu, E., Trevino, A. E., Wu, Z., Swanson, K., Kim, H., D’Angio, H. B., Preska, R., Chiou, A. E., Charville, G. W., Dalerba, P. D., et al. (2023) · PNAS Nexus · doi:10.1093/pnasnexus/pgad171 · Cited by 23
Kim, E. N., Chen, P. Z., Bressan, D., Tripathi, M., Miremadi, A., di Pietro, M., Coussens, L. M., Hannon, G. J., Fitzgerald, R. C., Zhuang, L., et al. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100595 · Cited by 25
Applications of single-cell technologies in drug discovery for tumor treatment
Liu, B., Hu, S., & Wang, X. (2024) · iScience · doi:10.1016/j.isci.2024.110486 · Cited by 24
Highly multiplexed imaging reveals prognostic immune and stromal spatial biomarkers in breast cancer
Eng, J. R., Bucher, E., Hu, Z., Walker, C. R., Risom, T., Angelo, M., Ericsson, P. I. G., Sanders, M. E., Chakravarthy, A. B., Pietenpol, J. A., et al. (2025) · JCI Insight · doi:10.1172/jci.insight.176749 · Cited by 24
Quantitative characterization of tissue states using multiomics and ecological spatial analysis
Ding, D. Y., Tang, Z., Zhu, B., Ren, H., Shalek, A. K., Tibshirani, R. J., & Nolan, G. P. (2025) · Nature Genetics · doi:10.1038/s41588-025-02119-z · Cited by 30
“We applied MESA’s eco-spatial framework to a CODEX spleen dataset of healthy and MRL/lpr (a murine model of systemic autoimmune disease) mice 2 .”
Gao, D., Kan, P., He, Y., Sun, S., Tang, L., & Yang, F. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1656733 · Cited by 25
“Advanced spatially resolved tools — including tissue-based cyclic immuno fl uorescence, imaging mass cytometry, and CODEX — enable detailed analysis of the spatial distribution and heterogeneity of senescent immune cells across different tumor types and patient populations (232 – 234).”
Stachtea, X., Loughrey, M. B., Salvucci, M., Lindner, A. U., Cho, S., McDonough, E., Sood, A., Graf, J. F., Santamaría-Pang, A., Corwin, A. D., et al. (2021) · Modern Pathology · doi:10.1038/s41379-021-00953-0 · Cited by 24
Advancements in technology for characterizing the tumor immune microenvironment
Yan, H., Ju, X., Huang, A., & Yuan, J. (2024) · International Journal of Biological Sciences · doi:10.7150/ijbs.92525 · Cited by 24
Unified molecular approach for spatial epigenome, transcriptome, and cell lineages
Huang, Y., Belk, J. A., Zhang, R., Weiser, N. E., Chiang, Z., Jones, M. G., Mischel, P. S., Buenrostro, J. D., & Chang, H. Y. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2424070122 · Cited by 25
Meylan, M., Tian, Y., Wu, L., Ling, A. L., Kovarsky, D., Barlow, G. L., Nguyen, L. D., Pyrdol, J. W., Marx, S., Westphal, L., et al. (2026) · Cell · doi:10.1016/j.cell.2025.12.055 · Cited by 23
Islam, M., Chen, B., Spraggins, J. M., Kelly, R., & Lau, K. S. (2020) · Gastroenterology · doi:10.1053/j.gastro.2020.04.073 · Cited by 23
Evaluating microglial phenotypes using single-cell technologies
Sankowski, R., Monaco, G., & Prinz, M. (2021) · Trends in Neurosciences · doi:10.1016/j.tins.2021.11.001 · Cited by 25
Immunobiology of the biliary tract system
Björkström, N. K. (2022) · Journal of Hepatology · doi:10.1016/j.jhep.2022.08.018 · Cited by 26
Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data
Warchol, S., Krueger, R., Nirmal, A. J., Gaglia, G., Jessup, J., Ritch, C. C., Hoffer, J., Muhlich, J. L., Burger, M. L., Jacks, T., et al. (2022) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2022.3209378 · Cited by 23
“Our collaborators commonly use cyclic immunofluorescence (CyCIF) [49] to generate these data, though other imaging technologies [20, 22] are capable of producing similar image data.”
MethodDeciphering Cell–Cell Interactions with Integrative Single‐Cell Secretion Profiling
Li, L., Su, H., Ji, Y., Zhu, F., Deng, J., Bai, X., Li, H., Liu, X., Luo, Y., Lin, B., et al. (2023) · Advanced Science · doi:10.1002/advs.202301018 · Cited by 22
Dissecting mammalian reproduction with spatial transcriptomics
Zhang, X., Cao, Q., Rajachandran, S., Grow, E. J., Evans, M., & Chen, H. (2023) · Human Reproduction Update · doi:10.1093/humupd/dmad017 · Cited by 25
Multielement Z-tag imaging by X-ray fluorescence microscopy for next-generation multiplex imaging
Strotton, M. C., Hosogane, T., Michiel, M. D., Moch, H., Varga, Z., & Bodenmiller, B. (2023) · Nature Methods · doi:10.1038/s41592-023-01977-x · Cited by 23
Matusiak, M., Hickey, J. W., Luca, B. A., Lu, G., Kidziński, Ł., Zhu, S., Colburg, D. R. C., Phillips, D. J., Brubaker, S. W., Charville, G. W., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-2393443/v1 · Cited by 23
Prognostic Impact and Spatial Interplay of Immune Cells in Urothelial Cancer
Debatin, N. F., Bady, E., Mandelkow, T., Huang, Z., Lurati, M. C. J., Raedler, J. B., Müller, J. H., Vettorazzi, E., Plage, H., Samtleben, H., et al. (2024) · European Urology · doi:10.1016/j.eururo.2024.01.023 · Cited by 22
Spatial proteomics of human diabetic kidney disease, from health to class III
Kondo, A., McGrady, M., Nallapothula, D., Ali, H., Trevino, A. E., Lam, A. M., Preska, R., D’Angio, H. B., Wu, Z., Lopez, L. N., et al. (2024) · Diabetologia · doi:10.1007/s00125-024-06210-8 · Cited by 23
“We developed Spatial TissuE Proteomics (STEP), a pipeline that combines curated human kidney tissues, a multiplexed immunofluorescence platform [8] and powerful analysis tools to compare expression of 21 proteins in human kidneys from people with diabetes and histologically normal kidneys (10…”
Lacinski, R. A., Dziadowicz, S. A., Melemai, V. K., Fitzpatrick, B., Pisquiy, J. J., Heim, T. E., Lohse, I., Schoedel, K. E., Llosa, N. J., Weiss, K. R., et al. (2024) · Bone Research · doi:10.1038/s41413-024-00359-z · Cited by 24
“After fi rst demonstration by Goltsev et al., 32 this technology, known as co-detection by indexing (CODEX), has been used to characterize the TME of numerous cancer types including, but not limited to, cutaneous T cell lymphoma, 33,34 colorectal cancer, 35 glioblastoma, 36 hepatocellular carcinoma,…”
MethodThe evolving landscape of spatial proteomics technologies in the AI age
Hu, B., Zhu, J., & Zhao, F. (2024) · Fundamental Research · doi:10.1016/j.fmre.2024.11.023 · Cited by 25
“Notable examples of such techniques include multiplexed points accumulation for imaging in nanoscale topography (Exchange-PAINT) [ 43 ], DNA exchange imaging (DEI) [ 44 ], co-detection by indexing (CODEX) [ 45 ], and FFPE-CODEX [ 46 ].”
Tang, J., Fan, W., Ruan, Y., Liu, X., Qiu, F., Feng, J., Huang, G., Yan, M., Wang, H., Mu, Q., et al. (2025) · Cancer Cell · doi:10.1016/j.ccell.2025.08.006 · Cited by 25
Graph neural networks learn emergent tissue properties from spatial molecular profiles
Ali, M., Richter, S., Ertürk, A., Fischer, D. S., & Theis, F. J. (2025) · Nature Communications · doi:10.1038/s41467-025-63758-8 · Cited by 30
“CODEX - colorectal cancer .”
InfluentialNew technologies and applications in infant B cell immunology
Nielsen, S. C. A., & Boyd, S. D. (2019) · Current Opinion in Immunology · doi:10.1016/j.coi.2018.12.005 · Cited by 22
“In parallel, improved methods using DNA oligonucleotide-labeled monoclonal antibody reagents and cycles of fluorophore-labeled nucleotide extension or probe hybridization, have provided additional routes for highly multiplexed histological immunostaining [14-16].”
MethodDissecting intercellular signaling with mass spectrometry–based proteomics
Swietlik, J. J., Sinha, A., & Meissner, F. (2020) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2019.12.002 · Cited by 23
Gatenbee, C., Minor, E. S., Slebos, R. J. C., Chung, C. H., & Anderson, A. R. A. (2020) · Cancer Control · doi:10.1177/1073274820946804 · Cited by 22
Avraham‐Davidi, I., Mages, S., Klughammer, J., Moriel, N., Imada, S., Hofree, M., Murray, E. M., Chen, J. H., Pelka, K., Mehta, A., et al. (2025) · eLife · doi:10.1101/2022.10.02.508492 · Cited by 24
Multiomic Spatial Mapping of Myocardial Infarction and Implications for Personalized Therapy
Schumacher, D., & Kramann, R. (2022) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.122.318333 · Cited by 24
Charting the tumor microenvironment with spatial profiling technologies
Toninelli, M., Rossetti, G., & Pagani, M. (2023) · Trends in cancer · doi:10.1016/j.trecan.2023.08.004 · Cited by 22
Spatial omics techniques and data analysis for cancer immunotherapy applications
Zhang, Y., Lee, R. Y., Tan, C. W., Guo, X., Yim, W. W., Lim, J., Wee, F. Y. T., Wu, Y., Kharbanda, M., Lee, J.-Y. J., et al. (2024) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2024.103111 · Cited by 23
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GLUER: integrative analysis of single-cell omics and imaging data by deep neural networkPreprint
Peng, T., Chen, G. M., & Tan, K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.25.427845 · Cited by 21
“and H" randomly and uniformly distributed on [0,1], and updating them with the following rules until convergence.”
BackgroundUntangling Alzheimer’s disease with spatial multi-omics: a brief review
Marshall, C. R., Farrow, M. A., Djambazova, K. V., & Spraggins, J. M. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1150512 · Cited by 23
“Revealing spatial distributions of proteins in biological tissue is traditionally driven by antibody-based technologies, including immunohistochemistry (IHC) and immunofluorescence (IF), providing the ability to map targeted proteins at high spatial resolution (Weissleder, 2002; Goltsev et al., 2018; Saka et al., 2019…”
BackgroundLi, Y., Zhang, J., Gao, X., & Zhang, Q. C. (2024) · Cell Systems · doi:10.1016/j.cels.2024.05.001 · Cited by 21
López‐Janeiro, Á., Wong, E. M., Jiménez‐Sánchez, D., Ortíz-de-Solórzano, C., Lozano, M. D., Teijeira, Á., Schalper, K. A., Melero, I., & de Andrea, C. E. (2024) · Journal for ImmunoTherapy of Cancer · doi:10.1136/jitc-2023-008589 · Cited by 23
Spatial biology – unravelling complexity within the glioblastoma microenvironment
Robinson, S. D., Filippopoulou, C., Besta, S., Samuels, M. E., Betrán, A. L., Ajamieh, M. A., Vella, V., Jones, W., & Giamas, G. (2025) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2025.01.014 · Cited by 23
Application of Spatial Omics in the Cardiovascular System
Hu, Y., Jia, H., Cui, H., & Song, J. (2025) · Research · doi:10.34133/research.0628 · Cited by 24
“Akoya Biosciences has introduced the PhenoCycler-Fusion platform (PCF; formerly CODEX [ 50 ]), a next-generation spatial proteomics technology based on multiplexed antibody fluorescence imaging [ 51 ].”
Metadata
- Authors
- Goltsev, Y., Samusik, N., Kennedy-Darling, J., Bhate, S., Hale, M., Vazquez, G., Black, S., & Nolan, G. P.
- Affiliations
- Stanford Medicine (US)
- Corresponding author
- Nolan, Garry P.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2018-08
- Volume
- 174
- Issue
- 4
- Pages
- 968-981.e15
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC6086938
- OpenAlex
- W2949238013
- Cited by
- 1,501
- References
- 20
- Citations by year
- 2026: 231 · 2025: 244 · 2024: 232 · 2023: 198 · 2022: 194 · 2021: 194 · 2020: 125 · 2019: 72 · 2018: 9
- FWCI
- 35.39
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Cell (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- US NIH (U19AI057229, 1U19AI100627, R01CA184968, 1R33CA183654-01, R33CA183692, 1R01GM10983601, 1R21CA183660, 1R01NS08953301, 5UH2AR067676, 1R01CA19665701, R01HL120724, R01HL128173)
- Bill and Melinda Gates foundation (OPP1113682)
- Department of Defense (W81XWH-14-1-0180)
- Northrop-Grumman Corporation (7500108142)
- NIH (U54-CA209971)
- Rachford & Carlotta A. Harris Endowed Chair
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
- Keywords
- codices, splenic architecture, MRL/lpr mice, systemic lupus erythematosus, cell-cell interactions, autoimmune disease
- MeSH terms
- Animals, Antibodies, Disease Models, Animal, Female, Image Processing, Computer-Assisted, Lupus Erythematosus, Systemic, Male, Mass Spectrometry, Spleen, Oligonucleotide Probes, Mice, Inbred MRL lpr, Mice
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