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Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging
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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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“CODEX uses antibodies conjugated with DNA barcodes that are iteratively rendered visible by cycles of hybridization and chemical denaturation with fluorophore-conjugated complementary DNA probes (18, 19).”
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“During the revision of the manuscript, two additional multiplexed image systems were reported (Goltsev et al., 2018; Gut et al., 2018).”
BackgroundImmune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphoma
Phillips, D. J., Matusiak, M., Rivero‐Gutiérrez, B., Bhate, S. S., Barlow, G. L., Jiang, S., Demeter, J., Smythe, K. S., Pierce, R. H., Fling, S. P., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26974-6 · Cited by 230
“20 Goltsev, Y. et al. Deep Profiling of Mouse Splenic Architecture with CODEX Multiplexed Imaging. Cell 174, 968-981 e915, doi:10.1016/j.cell.2018.07.010 (2018).”
BackgroundInfluentialKuett, L., Catena, R., Özcan, A., Plüss, A., Ali, H. R., Sa’d, M. A., Alon, S., Aparicio, S. A. J. R., Battistoni, G., Balasubramanian, S., et al. (2021) · Nature Cancer · doi:10.1038/s43018-021-00301-w · Cited by 219
“(6) Assess whether the unsupervised clustering method of the resulting segmented data yielded the expected phenotypes based on the antibody panel, such as CD20+ B cells, CD8+ T cells, CD68+ macrophages, vWF+CD31+ endothelial cells, CK5+ basal cells and different clusters of cytokeratin-positive cells in accordance wit…”
BackgroundModeling Cell-Cell Interactions from Spatial Molecular Data with Spatial Variance Component Analysis
Arnol, D., Schapiro, D., Bodenmiller, B., Sáez-Rodríguez, J., & Stegle, O. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.08.077 · Cited by 214
“Alternatively, there exist regression-based models to assess interactions on gene expression profiles of genes based on predefined features that capture specific aspects of the cell neighborhood (Goltsev et al., 2018; Battich et al., 2015).”
BackgroundResultDesigning Cancer Immunotherapies That Engage T Cells and NK Cells
Kyrysyuk, O., & Wucherpfennig, K. W. (2022) · Annual Review of Immunology · doi:10.1146/annurev-immunol-101921-044122 · Cited by 223
CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity
Varrone, M., Tavernari, D., Santamaria‐Martínez, A., Walsh, L. A., & Ciriello, G. (2023) · Nature Genetics · doi:10.1038/s41588-023-01588-4 · Cited by 220
“A simple approach to determine cellular niches is clustering cells based on the proportion of cell types within their neighborhood 5,23,24 .”
Radtke, A. J., Kandov, E., Lowekamp, B., Speranza, E. E., Chu, C. J., Gola, A., Thakur, N., Shih, R. M., Li, Y., Yaniv, Z., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2018488117 · Cited by 213
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Spatial biology of cancer evolution
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Shiao, S. L., Gouin, K. H., Ing, N., Ho, A. Y., Basho, R. K., Shah, A. S., Mebane, R. H., Zitser, D., Martinez, A., Mevises, N.-Y., et al. (2024) · Cancer Cell · doi:10.1016/j.ccell.2023.12.012 · Cited by 212
Multi-immersion open-top light-sheet microscope for high-throughput imaging of cleared tissues
Glaser, A. K., Reder, N. P., Chen, Y., Yin, C., Wei, L., Kang, S., Barner, L. A. E., Xie, W., McCarty, E. F., Mao, C., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10534-0 · Cited by 204
The diversification of methods for studying cell–cell interactions and communication
Armingol, E., Baghdassarian, H., & Lewis, N. E. (2024) · Nature Reviews Genetics · doi:10.1038/s41576-023-00685-8 · Cited by 229
Immune monitoring using mass cytometry and related high-dimensional imaging approaches
Hartmann, F. J., & Bendall, S. C. (2019) · Nature Reviews Rheumatology · doi:10.1038/s41584-019-0338-z · Cited by 208
Tumor-associated macrophages trigger MAIT cell dysfunction at the HCC invasive margin
Ruf, B., Bruhns, M., Babaei, S., Kedei, N., Ma, L., Revsine, M., Benmebarek, M.-R., Ma, C., Heinrich, B., Subramanyam, V., et al. (2023) · Cell · doi:10.1016/j.cell.2023.07.026 · Cited by 207
SM-Omics is an automated platform for high-throughput spatial multi-omics
Vicković, S., Lötstedt, B., Klughammer, J., Mages, S., Segerstolpe, Å., Rozenblatt–Rosen, O., & Regev, A. (2022) · Nature Communications · doi:10.1038/s41467-022-28445-y · Cited by 197
Integration of whole transcriptome spatial profiling with protein markers
Ben‐Chetrit, N., Niu, X., Swett, A. D., Sotelo, J., Jiao, M. S., Stewart, C. M., Potenski, C. J., Mielinis, P., Roelli, P., Stoeckius, M., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01536-3 · Cited by 214
Ferreira, R. M., Sabo, A. R., Winfree, S., Collins, K. S., Janosevic, D., Gulbronson, C. J., Cheng, Y., Casbon, L., Barwinska, D., Ferkowicz, M. J., et al. (2021) · JCI Insight · doi:10.1172/jci.insight.147703 · Cited by 187
“To validate the localization of immune cells in each model, we performed CODEX multiplexed immunofluorescence imaging on sections from the same animals (26).”
MethodHigh-dimensional cell-level analysis of tissues with Ce3D multiplex volume imaging
Li, W., Germain, R. N., & Gerner, M. Y. (2019) · Nature Protocols · doi:10.1038/s41596-019-0156-4 · Cited by 181
The tumor microenvironment’s role in the response to immune checkpoint blockade
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Mass Cytometry Imaging for the Study of Human Diseases—Applications and Data Analysis Strategies
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“**There is no hard upper limit for serial staining protocols, but published data has shown approximately 60 markers per section (4, 7, 8).”
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“…and PD-L1 (PD-L1 IHC 22C3 pharmDx, Dako) antibodies with oligonucleotides without any reason, we performed IHC for HER2 and MET in cases with overexpression, and PD-L1 in all cases with the same tissue sections after CODEX-multiplexed tissue imaging as described previously (71, 72). editing.”
Multiplex Immunohistochemistry and Immunofluorescence: A Practical Update for Pathologists
Harms, P. W., Frankel, T. L., Moutafi, M. K., Rao, A. U. K., Rimm, D. L., Taube, J. M., Thomas, D., Chan, M. P., & Pantanowitz, L. (2023) · Modern Pathology · doi:10.1016/j.modpat.2023.100197 · Cited by 187
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“With the help of CODEX, Goltsev et al. 151 observed many previously uncharacterized splenic cell-interaction dynamics in fresh-frozen spleen tissues from animals with systemic autoimmune disease, which is promising for enabling the systemic characterization of tissue architecture.”
MethodThe Immunology of Hormone Receptor Positive Breast Cancer
Goldberg, J., Pastorello, R., Vallius, T., Davis, J. A., Cui, Y. X., Agudo, J., Waks, A. G., Keenan, T. E., McAllister, S. S., Tolaney, S. M., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.674192 · Cited by 182
Spatially Segregated Macrophage Populations Predict Distinct Outcomes in Colon Cancer
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“We used CO-Detection by indEXing (CODEX) multiplexed tissue imaging to simultaneously visualize 36 protein markers on a single tissue microarray section of breast and colon benign and tumor tissue (34–36).”
A reference tissue atlas for the human kidney
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“The CODEX technology comprises of immunofluorescence staining with antibodies conjugated to unique oligonucleotide sequence targeting markers of interest probed with complementary oligo-nucleotide reporters tagged to fluorophores ( 67 ).”
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Next-generation computational tools for interrogating cancer immunity
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Highly Multiplexed Immunohistochemical MALDI-MS Imaging of Biomarkers in Tissues
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Spatial proteomics in neurons at single-protein resolution
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“Previous works typically assigned cells to cellular neighbour-hoods according to the cell-type compositions of their immediate neighbours 8,11–13 .”
BackgroundZipSeq: barcoding for real-time mapping of single cell transcriptomes
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“(7) Label all particles with area greater than MIN = 4000 and less than MAX = 12,000 pixels, and with circularity greater than 0.”
BackgroundDeciphering the state of immune silence in fatal COVID-19 patients
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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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