CoChat
Sign Up
Cited by 108Single-cell and spatial transcriptomicsCell Image Analysis TechniquesAI in cancer detection

Identification of cell types in multiplexed in situ images by combining protein expression and spatial information using CELESTA

Open access
Zhang, Weiruo, Li, Irene, Reticker-Flynn, Nathan E., Good, Zinaida, Chang, Serena, Samusik, Nikolay, Saumyaa, Saumyaa, Li, Yuanyuan +9 more·2022·Nature Methods
DOI 10.1038/s41592-022-01498-zPMC PMC9728133
Open this paper in CoChat — free account

Abstract

Advances in multiplexed in situ imaging are revealing important insights in spatial biology. However, cell type identification remains a major challenge in imaging analysis, with most existing methods involving substantial manual assessment and subjective decisions for thousands of cells. We developed an unsupervised machine learning algorithm, CELESTA, which identifies the cell type of each cell, individually, using the cell's marker expression profile and, when needed, its spatial information. We demonstrate the performance of CELESTA on multiplexed immunofluorescence images of colorectal cancer and head and neck squamous cell carcinoma (HNSCC). Using the cell types identified by CELESTA, we identify tissue architecture associated with lymph node metastasis in HNSCC, and validate our findings in an independent cohort. By coupling our spatial analysis with single-cell RNA-sequencing data on proximal sections of the same specimens, we identify cell-cell crosstalk associated with lymph node metastasis, demonstrating the power of CELESTA to facilitate identification of clinically relevant interactions.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41592-022-01498-z

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • CELESTA classifies cells based on marker protein expression.

    “Cell types were automatically annotated based on marker gene expression using the CELESTA algorithm [34].”

    From APOE Christchurch enhances a disease-associated microglial response to plaque but suppresses response to tau pathology (2025) · cites this paper as [34]

    The Results (Overview of CELESTA) describe automatic cell-type assignment using marker expression and a cell-type signature matrix. The Extended Data passage identifies the measured markers through image staining, including cytokeratin and CD31. This supports classification based on marker protein expression, but not the citing sentence’s specification of marker gene expression. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 39

  1. [1]Stack EC, Wang C, Roman KA & Hoyt CC Multiplexed immunohistochemistry, imaging, and quantitation: a review, with an assessment of tyramide signal amplification, multispectral imaging and multiplex analysis. Methods 70, 46– 58 (2014). 25242720
  2. [2]Angelo M Multiplexed ion beam imaging (MIBI) of human breast tumors. Nat. Med 20, 436– 442 (2014). 24584119 PMC4110905
  3. [4]Ptacek J Multiplexed ion beam imaging (MIBI) for characterization of the tumor microenvironment across tumor types. Lab. Invest 100, 1111– 1123 (2020). 32203152
  4. [5]Parra ER, Francisco-Cruz A & Wistuba II State-of-the-art of profiling immune contexture in the era of multiplexed staining and digital analysis to study paraffin tumor tissues. Cancers (Basel) 11, 247 (2019). 30791580 PMC6406364
  5. [6]Schürch CM Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182, 1341– 1359 (2020). 32763154 PMC7479520
  6. [7]Gillies RJ, Verduzco D & Gatenby RA Evolutionary dynamics of carcinogenesis and why targeted therapy does not work. Nat. Rev. Cancer 12, 487– 493 (2012). 22695393 PMC4122506
  7. [8]Heindl A, Nawaz S & Yuan Y Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology. Lab. Invest 95, 377– 384 (2015). 25599534
  8. [9]Alfarouk KO, Ibrahim ME, Gatenby RA & Brown JS Riparian ecosystems in human cancers. Evol. Appl 6, 46– 53 (2013). 23396634 PMC3567470
  9. [10]Little SE Receptor tyrosine kinase genes amplified in glioblastoma exhibit a mutual exclusivity in variable proportions reflective of individual tumor heterogeneity. Cancer Res. 72, 1614– 1620 (2012). 22311673
  10. [11]Herzenberg LA, Tung J, Moore WA, Herzenberg LA & Parks DR Interpreting flow cytometry data: a guide for the perplexed. Nat. Immunol 7, 681– 685 (2006). 16785881
  11. [12]Aghaeepour N Critical assessment of automated flow cytometry data analysis techniques. Nat. Methods 10, 228– 238 (2013). 23396282 PMC3906045
  12. [13]Shekhar K, Brodin P, Davis MM & Chakraborty AK Automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE). Proc. Natl Acad. Sci. USA 111, 202– 207 (2014). 24344260 PMC3890841
  13. [15]Black S CODEX multiplexed tissue imaging with DNA-conjugated antibodies. Nat. Protoc 16, 3802– 3835 (2021). 34215862 PMC8647621
  14. [16]Ren X Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand-receptor mediated self-assembly. Cell Res. 30, 763– 778 (2020). 32541867 PMC7608415
  15. [17]Lee HC, Kosoy R, Becker CE, Dudley JT & Kidd BA Automated cell type discovery and classification through knowledge transfer. Bioinformatics 33, 1689– 1695 (2017). 28158442 PMC5447237
  16. [19]Storath M, Weinmann A, Frikel J & Unser M Joint image reconstruction and segmentation using the Potts model. Inverse Probl. 31, 025003 (2015).
  17. [21]Pettit JB Identifying cell types from spatially referenced single-cell expression datasets. PLoS Comput. Biol 10, e1003824 (2014). 25254363 PMC4177667
  18. [22]Li Q, Yi F, Wang T, Xiao G & Liang F Lung cancer pathological image analysis using a hidden Potts model. Cancer Inform. 16, 1176935117711910 (2017). PMC5462552 28615918
  19. [24]Samusik N, Good Z, Spitzer MH, Davis KL & Nolan GP Automated mapping of phenotype space with single-cell data. Nat. Methods 13, 493– 496 (2016). 27183440 PMC4896314
  20. [25]Van Gassen S FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87, 636– 645 (2015). 25573116
  21. [26]Aghaeepour N, Nikolic R, Hoos HH & Brinkman RR Rapid cell population identification in flow cytometry data. Cytometry A 79, 6– 13 (2011). 21182178 PMC3137288
  22. [27]Liu X A comparison framework and guideline of clustering methods for mass cytometry data. Genome Biol. 20, 297 (2019). 31870419 PMC6929440
  23. [30]Arnol D, Schapiro D, Bodenmiller B, Saez-Rodriguez J & Stegle O Modeling cell–cell interactions from spatial molecular data with spatial variance component analysis. Cell Rep. 29, 202– 211 (2019). 31577949 PMC6899515
  24. [32]Butler A, Hoffman P, Smibert P, Papalexi E & Satija R Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol 36, 411– 420 (2018). 29608179 PMC6700744
  25. [33]Stuart T Comprehensive integration of single-cell data. Cell 177, 1888– 1902 (2019). 31178118 PMC6687398
  26. [34]Puram SV Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171, 1611– 1624 (2017). 29198524 PMC5878932
  27. [35]Reticker-Flynn N Lymph node colonization induces tumor-immune tolerance to promote distant metastasis. Cell (2022). 10.1016/j.cell.2022.04.019 PMC9149144 35525247 · DOI
  28. [36]Zhu G CXCR3 as a molecular target in breast cancer metastasis: inhibition of tumor cell migration and promotion of host anti-tumor immunity. Oncotarget 6, 43408– 43419 (2015). 26485767 PMC4791240
  29. [37]Cambien B Organ-specific inhibition of metastatic colon carcinoma by CXCR3 antagonism. Br. J. Cancer 100, 1755– 1764 (2009). 19436305 PMC2695685
  30. [38]Walser TC Antagonism of CXCR3 inhibits lung metastasis in a murine model of metastatic breast cancer. Cancer Res. 66, 7701– 7707 (2006). 16885372
  31. [39]Kim D, Curthoys NM, Parent MT & Hess ST Bleed-through correction for rendering and correlation analysis in multi-colour localization microscopy. J. Opt 15, 094011 (2013). 26185614 PMC4501387
  32. [40]Rich RM Elimination of autofluorescence background from fluorescence tissue images by use of time-gated detection and the AzaDiOxaTriAngulenium (ADOTA) fluorophore. Anal. Bioanal. Chem 405, 2065– 2075 (2013). 23254457 PMC3566262
  33. [41]Groom JR & Luster AD CXCR3 in T cell function. Exp. Cell Res 317, 620– 631 (2011). 21376175 PMC3065205
  34. [42]Wightman SC Oncogenic CXCL10 signalling drives metastasis development and poor clinical outcome. Br. J. Cancer 113, 327– 335 (2015). 26042934 PMC4506383
  35. [43]Ranasinghe R & Eri R Modulation of the CCR6-CCl20 axis: a potential therapeutic target in inflammation and cancer. Medicina 54, 88 (2018). 30453514 PMC6262638
  36. [44]Rubie C CCL20/CCR6 expression profile in pancreatic cancer. J. Transl. Med 8, 45 (2010). 20459729 PMC2877001
  37. [45]Osuala KO & Sloane BF Many roles of CCL20: emphasis on breast cancer. Postdoc J. 2, 7– 16 (2014). 27631019 PMC5019369
  38. [47]Tusher VG, Tibshirani R & Chu G Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl Acad. Sci. USA 98, 5116– 5121 (2001). 11309499 PMC33173
  39. [48]Haribhai D Regulatory T cells dynamically control the primary immune response to foreign antigen. J. Immunol 178, 2961– 2972 (2007). 17312141

Cited by

Sorted by citations

Authors
Zhang, W., Li, I., Reticker-Flynn, N. E., Good, Z., Chang, S., Samusik, N., Saumyaa, S., Li, Y., Zhou, X., Liang, R., Kong, C. S., Le, Q.-T., Gentles, A. J., Sunwoo, J. B., Nolan, G. P., Engleman, E. G., & Plevritis, S. K.
Affiliations
  • Stanford Medicine (US)
  • Stanford University (US)
Corresponding author
Plevritis, Sylvia K.
ORCID
Venue
Nature Methods
Publisher
Springer Science and Business Media LLC
Published
2022-06
Volume
19
Issue
6
Pages
759-769
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
DOI
10.1038/s41592-022-01498-z
PMC
PMC9728133
OpenAlex
W4281733104
Cited by
108
References
4
Citations by year
2026: 15 · 2025: 31 · 2024: 31 · 2023: 25 · 2022: 6
FWCI
7.59
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Methods (publishedVersion, public-domain)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • U.S. Department of Health & Human Services | NIH | National Cancer Institute (U54 CA209971)
  • Parker Institute for Cancer Immunotherapy at San Francisco, CA, USA, and Stanford Cancer Institute, CA, USA
  • Tobacco-Related Disease Research Program (T31IP1598)
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
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
cell type annotation, celesta, unsupervised learning, protein expression patterns, geospatial data, multiplex immunofluorescence, colorectal cancer, head and neck squamous cell carcinoma, lymph node metastasis, tissue structure, cell-cell crosstalk, single-cell RNA sequencing
MeSH terms
Squamous Cell Carcinoma of Head and Neck, Head and Neck Neoplasms, Humans, Lymphatic Metastasis, Cohort Studies

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review