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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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Redefining Mucosal Inflammation with Spatial Genomics
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Fuller, B. T., Buczynksi, E. M., Beshay, P. E., & Song, J. W. (2022) · Advances in cancer research · doi:10.1016/bs.acr.2022.01.001 · Cited by 5
Highly-Multiplexed Tissue Imaging with Raman Dyes
Shi, L., Wei, M., & Min, W. (2022) · Journal of Visualized Experiments · doi:10.3791/63547 · Cited by 5
Chen, H. Y., Palendira, U., & Feng, C. G. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.09.005 · Cited by 6
“This technology incorporates DNA-conjugated antibodies with dye-labeled nucleotides for multiplex imaging [25].”
BackgroundMethodInfluentialSingle-cell technologies uncover intra-tumor heterogeneity in childhood cancers
Lo, Y., Liu, Y., Kammersgaard, M. B., Koladiya, A., Keyes, T., & Davis, K. L. (2023) · Seminars in Immunopathology · doi:10.1007/s00281-022-00981-1 · Cited by 6
Towards multiomic analysis of oral mucosal pathologies
Einhaus, J. F., Han, X., Feyaerts, D., Sunwoo, J. B., Gaudilliere, B. L., Ahmad, S. H., Aghaeepour, N., Bruckman, K. C., Ojcius, D. M., Schürch, C. M., et al. (2023) · Seminars in Immunopathology · doi:10.1007/s00281-022-00982-0 · Cited by 8
“Imaging mass cytometry (IMC) [87], multiplexed ion beam imaging by time of flight (MIBI-TOF) [88], or co-detection by indexing (CODEX/PhenoCycler) [89, 96] allow the simultaneous detection of up to 60 protein markers for phenotype and function in tissues.”
BackgroundMethodSMAC Mimetics Synergistically Cooperate with HDAC Inhibitors Enhancing TNF-α Autocrine Signaling
Shibuya, Y., Kudo, K., Zeligs, K., Anderson, D., Hernandez, L. F., Ning, F., Cole, C. B., Fergusson, M. M., Kedei, N., Lyons, J. F., et al. (2023) · Cancers · doi:10.3390/cancers15041315 · Cited by 6
“Quantitative analysis of immune cell infiltrates was performed using the CODEX platform [18].”
MethodPrecancerous Lesions of the Head and Neck Region and Their Stromal Aberrations: Piecemeal Data
Harris, A., & Andl, T. (2023) · Cancers · doi:10.3390/cancers15082192 · Cited by 6
Lee, E., Chern, K. T., Nissen, M. D., Wang, X., Huang, C. C., Gandhi, A. K., Bouchard‐Côté, A., Weng, A. P., & Roth, A. J. L. (2023) · Bioinformatics · doi:10.1093/bioinformatics/btad242 · Cited by 6
“Examples of such technologies include imaging mass cytometry (IMC) 1 , multiplexed ion beam imaging (MIBI) 2 , and co-detection by indexing imaging (CODEX) 3 .”
BackgroundHematoxylin and Eosin Architecture Uncovers Clinically Divergent Niches in Pancreatic Cancer
Guo, J. L., Lopez, D. M., Mascharak, S., Foster, D. S., Khan, A., Davitt, M. F., Nguyen, A. T., Burcham, A. R., Chinta, M. S., Guardino, N. J., et al. (2024) · Tissue Engineering Part A · doi:10.1089/ten.tea.2024.0039 · Cited by 6
New Technologies to Image Tumors
McNamara, G. T., Lucas, J., Beeler, J. F., Basavanhally, A. N., Lee, G., Hedvat, C. V., Baxi, V. A., Locke, D., Borowsky, A. D., & Levenson, R. M. (2020) · Cancer treatment and research · doi:10.1007/978-3-030-38862-1_2 · Cited by 6
“Their CODEX2 platform is a major reengineering of the academic CODEX1 [67].”
BackgroundPourmir, I., Gazeau, B., Basile, H. D. S., & Fabre, E. (2020) · Cancer Drug Resistance · doi:10.20517/cdr.2020.14 · Cited by 5
The prospects of single‐cell analysis in autoimmunity
Sulen, A., Islam, S., Wolff, A. S. B., & Oftedal, B. E. (2020) · Scandinavian Journal of Immunology · doi:10.1111/sji.12964 · Cited by 6
Application of Single-Cell Approaches to Study Myeloproliferative Neoplasm Biology
Royston, D. J., Mead, A. J., & Psaila, B. (2021) · Hematology/Oncology Clinics of North America · doi:10.1016/j.hoc.2021.01.002 · Cited by 7
Translational considerations for adipose-derived biological scaffolds for soft tissue repair
Peña, A. N., Garcia, J. A., & Elisseeff, J. H. (2021) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2021.100321 · Cited by 6
Self-supervised learning of cell type specificity from immunohistochemical images
Murphy, M. A., Jegelka, S., & Fraenkel, E. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac263 · Cited by 6
“predictions of regional specificity over transcriptomics We evaluate our model’s performance in classifying images of proteomic markers for the four anatomical regions specified in Habuka et al. (2014), against the common practice of one-versus-rest T-tests and Wilcoxon tests (Luecken and Theis, 2019) as well as the s…”
MethodWang, B. G., Yao, Y., Huang, H., Zhou, J., & Ni, C. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1131259 · Cited by 6
“A representative spatial proteomics technology based on fluorescence imaging is the codetection by indexing (CODEX) single-cell proteomics analysis platform developed by Akoya (50).”
MethodSubcellular localization of drug distribution by super-resolution ion beam imagingPreprint
Rovira‐Clavé, X., Jiang, S., Bai, Y., Barlow, G. L., Bhate, S. S., Coskun, A. F., Han, G., Zhu, B., Ho, C. K., Hitzman, C., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/557603 · Cited by 6
Phenotype Reports: A new Manuscript Type
Zotto, G. D., Preijers, F. W. M. B., & Tárnok, A. (2019) · Cytometry Part A · doi:10.1002/cyto.a.23796 · Cited by 6
“This includes all quantitative assays to analyze single cells including imaging and image cytometry (3), mass (4) and imaging mass cytometry (5), single-cell expressomics (RNA expression) (6), proteomics and genomics, and combinations of these technologies.”
BackgroundCatena, R., Özcan, A., Kuett, L., Plüss, A., Schraml, P., Moch, H., & Bodenmiller, B. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.24.113571 · Cited by 6
Immune cell topography predicts response to PD-1 blockade in cutaneous T cell lymphomaPreprint
Nolan, G. P., Phillips, D. J., Matusiak, M., Rivero‐Gutiérrez, B., Bhate, S. S., Barlow, G. L., Jiang, S., Demeter, J., Smythe, K. S., Pierce, R. H., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-122285/v1 · Cited by 6
emObject: domain specific data abstraction for spatial omicsPreprint
Baker, E. A. G., Huang, M., Lam, A., Rahim, M. K., Bieniosek, M. F., Wang, B., Zhang, N. R., Mayer, A. T., & Trevino, A. E. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.06.07.543950 · Cited by 5
Shimonov, S., Cunningham, J. M., Talmon, R., Aizenbud, L., Desai, S. J., Rimm, D. L., Schalper, K. A., Kluger, H. M., & Kluger, Y. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.12.30.573739 · Cited by 5
Maurer, K., Park, C. Y., Mani, S., Borji, M., Penter, L., Jin, Y., Zhang, J. Y., Shin, C. S., Brenner, J. R., Southard, J., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.09.579677 · Cited by 6
Yosofvand, M., Edmiston, S. N., Smithy, J. W., Peng, X., Kostrzewa, C. E., Lin, B., Ehrich, F. D., Reiner, A., Miedema, J., Moy, A. P., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.16.608247 · Cited by 7
Dutta, S., Pang, M., Coughlin, G. M., Gudavalli, S., Roukes, M. L., Chou, T., & Gradinaru, V. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.10.637505 · Cited by 6
Sherman, M. S., McMahon-Skates, T., Gaston, L. S., Katzen, S. W., Majzoub, J. A., & Goessling, W. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101047 · Cited by 8
“These include iterative indirect IF imaging (4i), 6 cyclic IF (CycIF), 7,8 multiple iterative labeling by antibody neodeposition (MILAN), 9,10 the co-detection by indexing (CODEX) platform, 11,12 and imaging mass spectrometry.”
Peng, X., Smithy, J. W., Yosofvand, M., Kostrzewa, C. E., Bleile, M., Ehrich, F. D., Lee, J., Postow, M. A., Callahan, M. K., Panageas, K. S., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61821-y · Cited by 5
“elements such as cell densities and inter-cellular distances 1,2,6 , or con-ductingbasicspatialdomainanalysesthatprimarilyfocusonbinarized tissue compartments, such as tumor versus stroma 12 – 14 .”
InfluentialMolecular tools for decoding multicellular systems: from mechanisms to medicine
Li, J., Guo, W., Zou, P., Chu, C., Wu, J., Guo, Z., Huang, Y., Yang, J., & Chen, P. R. (2025) · National Science Review · doi:10.1093/nsr/nwaf473 · Cited by 6
“2 c) [28 ].”
Spatial transcriptomics in bone research: navigating hype and hurdles
Sokolowskei, D., Shah, A., Trostle, A. J., Juan, C., Sammarco, M. C., & Tower, R. J. (2025) · Pathology · doi:10.1016/j.pathol.2025.10.003 · Cited by 6
Mapping multipathology via spatial omic integration
Palaganas, R. G., Sidiropoulos, D. N., Morris, M., & Stein-O’Brien, G. (2025) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2025.103398 · Cited by 6
Alamukii, N. A., Kovács, A., Raghavan, S., Ilio, J., Karlsson, P., Helou, K., & Parris, T. (2025) · npj Breast Cancer · doi:10.1038/s41523-025-00881-1 · Cited by 4
Spatial profiling of the metabolism-immune axis in ovarian cancer
Wang, Z.-B., Long, M., Yu, P., Wang, Y., Yang, Z., & Huang, M.-S. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1672020 · Cited by 5
“CODEX generates spatially resolved single-cell protein maps (Goltsev et al., 2018), delineating the immune tumor microenvironment in lung cancer and revealing cellular/ molecular progression from precancerous lesions to malignancy (Rozenblatt-Rosen et al., 2020).”
Biotechnology advances and the parasitology paradigm: From genomes to multi-omics and translation
Gasser, R. B. (2026) · Biotechnology Advances · doi:10.1016/j.biotechadv.2026.108813 · Cited by 5
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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