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CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity

Nature Genetics volume 56, pages 74–84 (2024)Cite this article

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Abstract

Tissues are organized in cellular niches, the composition and interactions of which can be investigated using spatial omics technologies. However, systematic analyses of tissue composition are challenged by the scale and diversity of the data. Here we present CellCharter, an algorithmic framework to identify, characterize, and compare cellular niches in spatially resolved datasets. CellCharter outperformed existing approaches and effectively identified cellular niches across datasets generated using different technologies, and comprising hundreds of samples and millions of cells. In multiple human lung cancer cohorts, CellCharter uncovered a cellular niche composed of tumor-associated neutrophil and cancer cells expressing markers of hypoxia and cell migration. This cancer cell state was spatially segregated from more proliferative tumor cell clusters and was associated with tumor-associated neutrophil infiltration and poor prognosis in independent patient cohorts. Overall, CellCharter enables systematic analyses across data types and technologies to decode the link between spatial tissue architectures and cell plasticity.

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Fig. 1: CellCharter identifies, characterizes and compares spatial clusters.
Fig. 2: CellCharter is accurate, scalable and flexible.
Fig. 3: Spatial cellular niches in the spleen of healthy mice and mice affected by systemic lupus.
Fig. 4: Spatial transcriptomics in NSCLC.
Fig. 5: Spatial interactions among cancer cell states and the TME in LUAD.
Fig. 6: TANs and response to hypoxia are associated with worse patient prognosis.

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Data availability

The DLPFC Visium dataset was downloaded from the project’s GitHub page (https://github.com/LieberInstitute/HumanPilot). The mouse spleen CODEX dataset was obtained from the original publication5 (https://data.mendeley.com/datasets/zjnpwh8m5b/1). The NSCLC CosMx dataset was obtained from the original publication41 (https://nanostring.com/products/cosmx-spatial-molecular-imager/nsclc-ffpe-dataset/). The lung cancer MERFISH data was downloaded from Vizgen’s website (https://info.vizgen.com/ffpe-showcase). The lung cancer IMC dataset was obtained upon request to the corresponding author. The extended single-cell lung cancer atlas (LuCA)63 was downloaded from cellxgene (https://cellxgene.cziscience.com/collections/edb893ee-4066-4128-9aec-5eb2b03f8287). The mouse brain RNA + ATAC multiomics dataset was downloaded from the UCSC Cell and Genome Browser (https://brain-spatial-omics.cells.ucsc.edu/). The lung cancer bulk RNA-seq datasets were retrieved from the original publications11,64,65,66,67,68,69,70,71 through the following links and Gene Expression Omnibus accession numbers: https://doi.org/10.5281/zenodo.3941896, https://gdac.broadinstitute.org/ (LUAD, rnaseqv2-RSEM_genes), GSE68465, GSE72094, GSE31210, GSE50081, https://src.gisapps.org/OncoSG_public/ (GIS031), GSE37745 and https://xenabrowser.net/datapages/.

Code availability

CellCharter is released as an open-source Python library on GitHub at https://github.com/CSOgroup/cellcharter. Code to reproduce all the analyses presented in this manuscript is available at https://github.com/CSOgroup/cellcharter_analyses.

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Acknowledgements

We thank R. Gottardo (Centre hositalier universitaire vaudois, Lausanne), D. Gfeller (University of Lausanne), G. La Manno and E. Oricchio (École Polytechnique Fédérale de Lausanne) for their precious feedback during the development of this project. This project was in part supported by the Swiss Institute for Experimental Cancer Research TANDEM Research Project (G.C.). D.T. is supported by the Personalized Health and Related Technologies (PHRT) iPostdoc Project (grant no. 2022-476). A.S.-M. is supported by the PHRT Technology transfer project (grant no. 2021-553).

Author information

Authors and Affiliations

  1. Department of Computational Biology, University of Lausanne, Lausanne, Switzerland

    Marco Varrone, Daniele Tavernari & Giovanni Ciriello

  2. Swiss Cancer Center Léman, Lausanne, Switzerland

    Marco Varrone, Daniele Tavernari, Albert Santamaria-Martínez & Giovanni Ciriello

  3. Swiss Institute of Bioinformatics, Lausanne, Switzerland

    Marco Varrone, Daniele Tavernari & Giovanni Ciriello

  4. Swiss Institute for Experimental Cancer Research, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Daniele Tavernari & Albert Santamaria-Martínez

  5. Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada

    Logan A. Walsh

  6. Department of Human Genetics, McGill University, Montreal, Quebec, Canada

    Logan A. Walsh

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  1. Marco Varrone
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Contributions

M.V. designed the algorithm and performed the computational analyses. D.T. analyzed the RNA-seq datasets. A.S.-M. contributed to the interpretation of the CODEX mouse spleen results. L.A.W. provided the data for the IMC lung cancer dataset and contributed to the interpretation of the results. M.V. and G.C. designed the project and wrote the manuscript with contributions from all authors.

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Correspondence to Giovanni Ciriello.

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Nature Genetics thanks Naveed Ishaque, Hamid Ali, Shashwat Sahay and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 CellCharter’s features and benchmarking of spatial clustering methods.

a, Example of 3 spatial clusters (color coded) in a tissue sample of mouse spleen analyzed by CODEX (left) and symmetric (top right) vs. asymmetric (bottom right) neighborhood enrichment analysis. b, Schematic representation of the four metrics (curl, elongation, linearity, and purity) implemented in CellCharter to describe the shape of spatial clusters. c, Example of spatial cluster components (color coded) in a tissue sample of mouse spleen analyzed by CODEX (left). Heatmap representation of the shape metric values for each cluster component (right). Cluster components are grouped in representative shape classes: linear, round, circular, and irregular. d, Manual annotations of the Visium DLPFC samples. e, Previously adopted strategy based on hyperparameter tuning and clustering testing on the same dataset. f, strategy proposed in this study based on independent tuning and testing datasets. g, Mean Adjusted Rand Index (ARI) for each DLPFC sample (over 10 repetitions) obtained by the listed methods upon performing individual spatial clustering of the samples (n = 9 samples). The boxes show the quartiles of the dataset while the whiskers extend to points that lie within 1.5 inter-quartile ranges (IQRs) of the lower and upper quartiles. h, ARI for each DLPFC sample obtained by the listed methods upon performing joint clustering of all samples (n = 9 samples). The boxes show the quartiles of the dataset while the whiskers extend to points that lie within 1.5 IQRs of the lower and upper quartiles.

Extended Data Fig. 2 Comparison between CellCharter STAGATE and evaluation of cluster stability analysis.

a, Runtime divided by processing step of the two best-performing methods (STAGATE and CellCharter) in clustering all 12 samples of the DLPFC dataset on GPU and CPU. b, From left to right: Bayesian Information Criterion (BIC), Akaike Information Criterion (AIC), and Negative Log-Likelihood (NLL) values (y-axis) for a number of clusters ranging between 2 and 100 (x-axis) for the pilot (12 samples) and full (42 samples) version of the DLPFC dataset. c, Purity of the n = 756 cluster components in the CODEX mouse spleen dataset for the clusters of the two best-performing methods. The boxes show the quartiles of the dataset while the whiskers extend to points that lie within 1.5 inter-quartile ranges (IQRs) of the lower and upper quartiles. d, CellCharter cluster stability for range of numbers of cluster (x-axis) using a concatenation of the embeddings from chromatin accessibility and gene expression. The most stable cluster solution is highlighted. Data presented as mean values (solid line) with a 95% confidence interval (shaded area).

Extended Data Fig. 3 Characterization of CellCharter’s spatial clusters of the CODEX mouse spleen dataset.

a, CellCharter’s spatial cluster at n = 11 clusters for all cells of the 3 healthy (BALBc) and 6 systemic lupus erythematosus samples (MRL). b, (left) Spatial distribution of cells assigned to spatial cluster C1 in three selected samples. (right) Representative images of immunofluorescence staining artifacts corresponding to C1: autofluorescence (1), missing tile (2), fluorophore accumulation (3).

Extended Data Fig. 4 Differences in the spatial organization between healthy and systemic lupus spleen.

a, Spatial distribution of CD4+ T cells, B cells, and B220+ T cells in the clusters associated to the germinal center, marginal zone, and GC-PALS boundary for representative examples of healthy (BALBc-1), early lupus (MRL-4) and intermediate lupus (MRL-8) spleen. b, Normalized intensity of ERTR7, CD31, and Ly6G markers in the intermediate lupus sample MRL-5. The MRL samples show the presence of two distinct clusters associated with trabecular structures. Insets (1 to 6) highlight the different expression of the markers in correspondence trabecular clusters C10 (insets 2, 4, 6) and C11 (insets 1, 3, 5).

Extended Data Fig. 5 Characterization of CellCharter’s spatial clusters of the CosMx non-small cell lung cancer (NSCLC) dataset.

a, CellCharter’s spatial cluster at n = 20 clusters for all cells of the CosMx NSCLC samples. b, Entropy of patient proportion between n = 10 tumor microenvironment-enriched (TME-enriched) clusters and n = 10 tumor-enriched clusters. The thick central line of each box plot represents the median entropy, the bounding box corresponds to the 25th–75th percentiles, and the whiskers extend up to 1.5 times the interquartile range. p-values computed by two-tailed Wilcoxon test. c, Immune and stromal cell type fractions in tumor samples from 5 non-small cell lung cancer patients (LUAD: lung adenocarcinoma, LUSC: lung squamous cell carcinoma, NK-cell: natural killer cell).

Extended Data Fig. 6 Cell signature scores in two non-small cell lung cancer (NSCLC) patients.

a, Gene expression signature score of n = 41,088 tumor cells in spatial clusters C0 and C12. The boxes show the quartiles of the dataset while the whiskers extend to points that lie within 1.5 inter-quartile ranges (IQRs) of the lower and upper quartile. b-e, Gene expression signature score of tumor cells of patients LUAD-9 and LUAD-12 for four gene signatures: neutrophil chemotaxis, hypoxia, epithelial-to-mesenchymal transition (EMT-II) and cell proliferation. The boxes show the quartiles of the dataset while the whiskers extend to points that lie within 1.5 IQRs of the lower and upper quartiles.

Extended Data Fig. 7 Spatial organization of tumor associated neutrophils and hypoxic tumor cells in CellCharter and STAGATE.

a, Average hypoxia gene signature score for tumor cells at increasing hop-distance from all neutrophils (left), neutrophils assigned to spatial cluster C11 (center), and neutrophils not assigned in cluster C11 (right). Error bands correspond to the 95% confidence interval. b, cluster concordance between CellCharter (rows) and STAGATE (columns). c, Example of CellCharter and STAGATE clusters on LUAD-9 R1. d, Hypoxia and cell proliferation signatures in n = 128,454 cells of tumor-enriched spatial clusters determined with STAGATE (NK: natural killer cell, Treg: regulatory T cell, mDC: myeloid dendritic cell, pDC: plasmacytoid dendritic cell). The boxes show the quartiles of the dataset while the whiskers extend to points that lie within 1.5 inter-quartile ranges (IQRs) of the lower and upper quartiles. e, Cell type enrichment of tumor-enriched clusters and neutrophil-enriched clusters obtained with STAGATE. f, Coarse cell type labels in LUAD-9 R1. g, Comparison of neutrophil-enriched clusters detected by STAGATE (left, C13) and CellCharter (right, C11).

Extended Data Fig. 8 Association between tumor hypoxia and neutrophil infiltration in the MERFISH and Image Mass Cytometry (IMC) datasets.

a, Cluster stability (y-axis) for range of numbers of cluster (x-axis) on the full MERFISH dataset. Data presented as mean values (solid line) with a 95% confidence interval (shaded area). b, Runtime divided by processing step of CellCharter in clustering the MERFISH dataset (left) and IMC dataset (right) on GPU and CPU. c, Cluster stability (y-axis) for range of numbers of cluster (x-axis) on the 2 lung cancer samples of the MERFISH dataset. Data presented as mean values (solid line) with a 95% confidence interval (shaded area). d, Cell type enrichment of tumor-enriched and tumor microenvironment-enriched (TME-enriched) clusters in HumanLungCancerPatient1 obtained with CellCharter. e, Neighborhood enrichment between the TME-enriched clusters (source) and the tumor-enriched clusters (target) in HumanLungCancerPatient1. p-values computed by unpaired two-sided t-test. f, Cell type enrichment analysis (left) and hazard ratio and Cox-regression p-values (right) for the clusters C15 and C17 identified by CellCharter in n = 413 samples of the IMC dataset. Data are presented as mean values with 95% confidence interval. g, Cell type enrichment of tumor-enriched and TME-enriched clusters in the IMC dataset obtained with CellCharter. Only TME clusters for which there is a positive neighborhood enrichment with at least one tumor-enriched cluster are shown.

Extended Data Fig. 9 Association of tumor-associated neutrophils (TANs), normal-associated neutrophils (NANs), and hypoxia with overall survival.

a, Pearson correlation between hypoxia gene signature score and TAN score in 9 bulk RNA-seq datasets of lung adenocarcinoma11,64,65,66,67,68,69,70,71. Data are presented as mean values (solid line) with a 95% confidence interval (shaded area). b, Pearson correlation between hypoxia gene signature score and NAN score in 9 bulk RNA-seq datasets of lung adenocarcinoma. Data are presented as mean values (solid line) with a 95% confidence interval (shaded area). c, Association of TAN, NAN, and hypoxia score with overall survival, corrected for sex, age, and stage, in the 7 bulk RNA-seq datasets that contained survival information64,65,66,67,68,69,70. Data are presented as mean values with 95% confidence interval.

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Varrone, M., Tavernari, D., Santamaria-Martínez, A. et al. CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity. Nat Genet 56, 74–84 (2024). https://doi.org/10.1038/s41588-023-01588-4

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