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Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Nature Reviews Molecular Cell Biology volume 26, pages 11–31 (2025)Cite this article

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Abstract

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

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Fig. 1: Roadmap of single-cell and spatial transcriptomics.
Fig. 2: Key technical considerations in scRNA-seq.
Fig. 3: Trajectory mapping and cellular plasticity.
Fig. 4: Spatial ecotypes are recurrent multicellular neighbourhoods composed of co-associated cell states.
Fig. 5: Pathways from single-cell and spatial transcriptomics to clinical application.
Fig. 6: Future directions in single-cell and spatial transcriptomics.

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References

  1. Quake, S. R. A decade of molecular cell atlases. Trends Genet. 38, 805–810 (2022).

    Article  CAS  PubMed  Google Scholar 

  2. Baysoy, A., Bai, Z., Satija, R. & Fan, R. The technological landscape and applications of single-cell multi-omics. Nat. Rev. Mol. Cell Biol. 24, 695–713 (2023).

    Article  CAS  PubMed  Google Scholar 

  3. Longo, S. K., Guo, M. G., Ji, A. L. & Khavari, P. A. Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics. Nat. Rev. Genet. 22, 627–644 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Rozenblatt-Rosen, O., Stubbington, M. J. T., Regev, A. & Teichmann, S. A. The Human Cell Atlas: from vision to reality. Nature 550, 451–453 (2017).

    Article  CAS  PubMed  Google Scholar 

  5. Rood, J. E., Maartens, A., Hupalowska, A., Teichmann, S. A. & Regev, A. Impact of the Human Cell Atlas on medicine. Nat. Med. 28, 2486–2496 (2022).

    Article  CAS  PubMed  Google Scholar 

  6. Ferreira, P. G. et al. The effects of death and post-mortem cold ischemia on human tissue transcriptomes. Nat. Commun. 9, 490 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Baechler, E. C. et al. Expression levels for many genes in human peripheral blood cells are highly sensitive to ex vivo incubation. Genes Immun. 5, 347–353 (2004).

    Article  CAS  PubMed  Google Scholar 

  8. Massoni-Badosa, R. et al. Sampling time-dependent artifacts in single-cell genomics studies. Genome Biol. 21, 112 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zhu, Y., Wang, L., Yin, Y. & Yang, E. Systematic analysis of gene expression patterns associated with postmortem interval in human tissues. Sci. Rep. 7, 5435 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  10. O’Flanagan, C. H. et al. Dissociation of solid tumor tissues with cold active protease for single-cell RNA-seq minimizes conserved collagenase-associated stress responses. Genome Biol. 20, 210 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Liu, Y. et al. Digestion of nucleic acids starts in the stomach. Sci. Rep. 5, 11936 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Martinez-Diez, M. C., Serrano, M. A., Monte, M. J. & Marin, J. J. Comparison of the effects of bile acids on cell viability and DNA synthesis by rat hepatocytes in primary culture. Biochim. Biophys. Acta 1500, 153–160 (2000).

    Article  CAS  PubMed  Google Scholar 

  13. Sorrentino, S. & Libonati, M. Human pancreatic-type and nonpancreatic-type ribonucleases: a direct side-by-side comparison of their catalytic properties. Arch. Biochem. Biophys. 312, 340–348 (1994).

    Article  CAS  PubMed  Google Scholar 

  14. Denisenko, E. et al. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol. 21, 130 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Cossarizza, A. et al. Guidelines for the use of flow cytometry and cell sorting in immunological studies. Eur. J. Immunol. 47, 1584–1797 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Lahoz-Beneytez, J. et al. Human neutrophil kinetics: modeling of stable isotope labeling data supports short blood neutrophil half-lives. Blood 127, 3431–3438 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Quan, Y. et al. Impact of cell dissociation on identification of breast cancer stem cells. Cancer Biomark. 12, 125–133 (2012).

    Article  CAS  PubMed  Google Scholar 

  19. Autengruber, A., Gereke, M., Hansen, G., Hennig, C. & Bruder, D. Impact of enzymatic tissue disintegration on the level of surface molecule expression and immune cell function. Eur. J. Microbiol. Immunol. 2, 112–120 (2012).

    Article  CAS  Google Scholar 

  20. Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).

    Article  CAS  PubMed  Google Scholar 

  21. Butto, T. et al. Nuclei on the rise: when nuclei-based methods meet next-generation sequencing. Cells 12, 1051 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Caglayan, E., Liu, Y. & Konopka, G. Neuronal ambient RNA contamination causes misinterpreted and masked cell types in brain single-nuclei datasets. Neuron 110, 4043–4056.e5 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Thrupp, N. et al. Single-nucleus RNA-Seq is not suitable for detection of microglial activation genes in humans. Cell Rep. 32, 108189 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Pitchiaya, S. et al. Dynamic recruitment of single RNAs to processing bodies depends on RNA functionality. Mol. Cell 74, 521–533.e6 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Hicks, S. C., Townes, F. W., Teng, M. & Irizarry, R. A. Missing data and technical variability in single-cell RNA-sequencing experiments. Biostatistics 19, 562–578 (2018).

    Article  PubMed  Google Scholar 

  26. Koenig, A. L. et al. Single-cell transcriptomics reveals cell-type-specific diversification in human heart failure. Nat. Cardiovasc. Res. 1, 263–280 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Chervov, A. & Zinovyev, A. Computational challenges of cell cycle analysis using single cell transcriptomics. Preprint at https://doi.org/10.48550/arXiv.2208.05229 (2022).

  28. Osorio, D. & Cai, J. J. Systematic determination of the mitochondrial proportion in human and mice tissues for single-cell RNA-sequencing data quality control. Bioinformatics 37, 963–967 (2021).

    Article  CAS  PubMed  Google Scholar 

  29. Tran, H. T. N. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Zhang, Y., Parmigiani, G. & Johnson, W. E. ComBat-seq: batch effect adjustment for RNA-seq count data. NAR Genom. Bioinform. 2, lqaa078 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  32. Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Polański, K. et al. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics 36, 964–965 (2020).

    Article  PubMed  Google Scholar 

  34. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hie, B., Bryson, B. & Berger, B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37, 685–691 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887.e17 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Nguyen, H. C. T., Baik, B., Yoon, S., Park, T. & Nam, D. Benchmarking integration of single-cell differential expression. Nat. Commun. 14, 1570 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Junttila, S., Smolander, J. & Elo, L. L. Benchmarking methods for detecting differential states between conditions from multi-subject single-cell RNA-seq data. Brief. Bioinform. 23, bbac286 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Soneson, C. & Robinson, M. D. Bias, robustness and scalability in single-cell differential expression analysis. Nat. Methods 15, 255–261 (2018).

    Article  CAS  PubMed  Google Scholar 

  41. Dann, E., Henderson, N. C., Teichmann, S. A., Morgan, M. D. & Marioni, J. C. Differential abundance testing on single-cell data using k-nearest neighbor graphs. Nat. Biotechnol. 40, 245–253 (2022).

    Article  CAS  PubMed  Google Scholar 

  42. Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Lakkis, J. et al. A joint deep learning model enables simultaneous batch effect correction, denoising, and clustering in single-cell transcriptomics. Genome Res. 31, 1753–1766 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Li, H., McCarthy, D. J., Shim, H. & Wei, S. Trade-off between conservation of biological variation and batch effect removal in deep generative modeling for single-cell transcriptomics. BMC Bioinform. 23, 460 (2022).

    Article  CAS  Google Scholar 

  45. Lotfollahi, M., Wolf, F. A. & Theis, F. J. scGen predicts single-cell perturbation responses. Nat. Methods 16, 715–721 (2019).

    Article  CAS  PubMed  Google Scholar 

  46. Bahrami, M. et al. Deep feature extraction of single-cell transcriptomes by generative adversarial network. Bioinformatics 37, 1345–1351 (2021).

    Article  CAS  PubMed  Google Scholar 

  47. Tyler, S. R., Guccione, E. & Schadt, E. E. Erasure of biologically meaningful signal by unsupervised scRNAseq batch-correction methods. Preprint at bioRxiv https://doi.org/10.1101/2021.11.15.468733 (2023).

  48. Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41–50 (2022).

    Article  CAS  PubMed  Google Scholar 

  49. Zhang, Z. et al. Signal recovery in single cell batch integration. Preprint at bioRxiv https://doi.org/10.1101/2023.05.05.539614 (2023).

  50. Dann, E. et al. Precise identification of cell states altered in disease using healthy single-cell references. Nat. Genet. 55, 1998–2008 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Svensson, V., da Veiga Beltrame, E. & Pachter, L. A curated database reveals trends in single-cell transcriptomics. Database 2020, baaa073 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Regev, A. et al. The Human Cell Atlas. eLife 6, e27041 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Eraslan, G. et al. Single-nucleus cross-tissue molecular reference maps toward understanding disease gene function. Science 376, eabl4290 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Slyper, M. et al. A single-cell and single-nucleus RNA-seq toolbox for fresh and frozen human tumors. Nat. Med. 26, 792–802 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Zhang, A. W. et al. Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling. Nat. Methods 16, 1007–1015 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Pliner, H. A., Shendure, J. & Trapnell, C. Supervised classification enables rapid annotation of cell atlases. Nat. Methods 16, 983–986 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Ianevski, A., Giri, A. K. & Aittokallio, T. Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data. Nat. Commun. 13, 1246 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Franchini, M., Pellecchia, S., Viscido, G. & Gambardella, G. Single-cell gene set enrichment analysis and transfer learning for functional annotation of scRNA-seq data. NAR Genom. Bioinform. 5, lqad024 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Kiselev, V. Y., Yiu, A. & Hemberg, M. scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15, 359–362 (2018).

    Article  CAS  PubMed  Google Scholar 

  61. de Kanter, J. K., Lijnzaad, P., Candelli, T., Margaritis, T. & Holstege, F. C. P. CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing. Nucleic Acids Res. 47, e95 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  62. Lyu, P., Zhai, Y., Li, T. & Qian, J. CellAnn: a comprehensive, super-fast, and user-friendly single-cell annotation web server. Bioinformatics 39, btad521 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Boufea, K., Seth, S. & Batada, N. N. scID uses discriminant analysis to identify transcriptionally equivalent cell types across single-cell RNA-seq data with batch effect. iScience 23, 100914 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Lin, Y. et al. scClassify: sample size estimation and multiscale classification of cells using single and multiple reference. Mol. Syst. Biol. 16, e9389 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Yin, Q. et al. scGraph: a graph neural network-based approach to automatically identify cell types. Bioinformatics 38, 2996–3003 (2022).

    Article  CAS  PubMed  Google Scholar 

  66. Alquicira-Hernandez, J., Sathe, A., Ji, H. P., Nguyen, Q. & Powell, J. E. scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data. Genome Biol. 20, 264 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Li, C. et al. SciBet as a portable and fast single cell type identifier. Nat. Commun. 11, 1818 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Hou, W. & Ji, Z. Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis. Nat. Methods https://doi.org/10.1038/s41592-024-02235-4 (2024).

  69. Lotfollahi, M., Yuhan, H., Theis, F. J. & Satija, R. The future of rapid and automated single-cell data analysis using reference mapping. Cell 187, 2343–2358 (2024).

    Article  CAS  PubMed  Google Scholar 

  70. Michielsen, L. et al. Single-cell reference mapping to construct and extend cell-type hierarchies. NAR Genom. Bioinform. 5, lqad070 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  71. Lotfollahi, M. et al. Mapping single-cell data to reference atlases by transfer learning. Nat. Biotechnol. 40, 121–130 (2022).

    Article  CAS  PubMed  Google Scholar 

  72. Osumi-Sutherland, D. et al. Cell type ontologies of the Human Cell Atlas. Nat. Cell Biol. 23, 1129–1135 (2021).

    Article  CAS  PubMed  Google Scholar 

  73. Flanagin, A., Frey, T. & Christiansen, S. L. Updated guidance on the reporting of race and ethnicity in medical and science journals. JAMA 326, 621–627 (2021).

    Article  PubMed  Google Scholar 

  74. Brbić, M. et al. Annotation of spatially resolved single-cell data with STELLAR. Nat. Methods 19, 1411–1418 (2022).

    Article  PubMed  Google Scholar 

  75. Wen, L. & Tang, F. Single-cell sequencing in stem cell biology. Genome Biol. 17, 71 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Chen, H., Ye, F. & Guo, G. Revolutionizing immunology with single-cell RNA sequencing. Cell Mol. Immunol. 16, 242–249 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Gavish, A. et al. Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours. Nature 618, 598–606 (2023).

    Article  CAS  PubMed  Google Scholar 

  78. Zhang, Y. et al. Single-cell RNA sequencing in cancer research. J. Exp. Clin. Cancer Res. 40, 81 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Machado, L. et al. Tissue damage induces a conserved stress response that initiates quiescent muscle stem cell activation. Cell Stem Cell 28, 1125–1135.e7 (2021).

    Article  CAS  PubMed  Google Scholar 

  80. Uniken Venema, W. T. C. et al. Gut mucosa dissociation protocols influence cell type proportions and single-cell gene expression levels. Sci. Rep. 12, 9897 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).

    Article  PubMed  Google Scholar 

  82. Jones, R. C. et al. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science 376, eabl4896 (2022).

  83. Tabula Muris Consortium. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

    Article  Google Scholar 

  84. Kumar, T. et al. A spatially resolved single-cell genomic atlas of the adult human breast. Nature 620, 181–191 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Chen, J. Y. et al. Hoxb5 marks long-term haematopoietic stem cells and reveals a homogenous perivascular niche. Nature 530, 223–227 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Rossi, L., Challen, G. A., Sirin, O., Lin, K. K. & Goodell, M. A. Hematopoietic stem cell characterization and isolation. Methods Mol. Biol. 750, 47–59 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Ikuta, K. & Weissman, I. L. Evidence that hematopoietic stem cells express mouse c-kit but do not depend on steel factor for their generation. Proc. Natl Acad. Sci. USA 89, 1502–1506 (1992).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Liu, D. D. et al. Purification and characterization of human neural stem and progenitor cells. Cell 186, 1179–1194.e15 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Chan, C. K. F. et al. Identification of the human skeletal stem cell. Cell 175, 43–56.e21 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Ziegenhain, C. et al. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65, 631–643.e4 (2017).

    Article  CAS  PubMed  Google Scholar 

  91. Al’Khafaji, A. M. et al. High-throughput RNA isoform sequencing using programmed cDNA concatenation. Nat. Biotechnol. 42, 582–586 (2023).

    Article  PubMed  Google Scholar 

  92. Salmen, F. et al. High-throughput total RNA sequencing in single cells using VASA-seq. Nat. Biotechnol. 40, 1780–1793 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Herman, J. S., Sagar & Grün, D. FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data. Nat. Methods 15, 379–386 (2018).

    Article  CAS  PubMed  Google Scholar 

  94. Jindal, A., Gupta, P., Jayadeva & Sengupta, D. Discovery of rare cells from voluminous single cell expression data. Nat. Commun. 9, 4719 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  95. Fa, B. et al. GapClust is a light-weight approach distinguishing rare cells from voluminous single cell expression profiles. Nat. Commun. 12, 4197 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Dong, R. & Yuan, G. C. GiniClust3: a fast and memory-efficient tool for rare cell type identification. BMC Bioinform. 21, 158 (2020).

    Article  CAS  Google Scholar 

  97. Wegmann, R. et al. CellSIUS provides sensitive and specific detection of rare cell populations from complex single-cell RNA-seq data. Genome Biol. 20, 142 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  98. Song, D., Li, K., Hemminger, Z., Wollman, R. & Li, J. J. scPNMF: sparse gene encoding of single cells to facilitate gene selection for targeted gene profiling. Bioinformatics 37, i358–i366 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Neufeld, A., Gao, L. L., Popp, J., Battle, A. & Witten, D. Inference after latent variable estimation for single-cell RNA sequencing data. Biostatistics 25, 270–287 (2023).

    Article  PubMed  Google Scholar 

  100. Song, D., Li, K., Ge, X. & Li, J. J. ClusterDE: a post-clustering differential expression (DE) method robust to false-positive inflation caused by double bioRxiv (2023).

  101. Persad, S. et al. SEACells infers transcriptional and epigenomic cellular states from single-cell genomics data. Nat. Biotechnol. 41, 1746–1757 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Singhal, V. et al. BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis. Nat. Genet. 56, 431–441 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Cannoodt, R., Saelens, W. & Saeys, Y. Computational methods for trajectory inference from single-cell transcriptomics. Eur. J. Immunol. 46, 2496–2506 (2016).

    Article  CAS  PubMed  Google Scholar 

  104. Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547–554 (2019).

    Article  CAS  PubMed  Google Scholar 

  105. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  106. Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).

    Article  CAS  PubMed  Google Scholar 

  107. Gorin, G., Svensson, V. & Pachter, L. Protein velocity and acceleration from single-cell multiomics experiments. Genome Biol. 21, 39 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Hendriks, G. J. et al. NASC-seq monitors RNA synthesis in single cells. Nat. Commun. 10, 3138 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  109. Erhard, F. et al. scSLAM-seq reveals core features of transcription dynamics in single cells. Nature 571, 419–423 (2019).

    Article  CAS  PubMed  Google Scholar 

  110. Buenrostro, J. D. et al. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation. Cell 173, 1535–1548.e16 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Pei, W. et al. Polylox barcoding reveals haematopoietic stem cell fates realized in vivo. Nature 548, 456–460 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Sharma, R. et al. The TRACE-seq method tracks recombination alleles and identifies clonal reconstitution dynamics of gene targeted human hematopoietic stem cells. Nat. Commun. 12, 472 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Yang, D. et al. Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution. Cell 185, 1905–1923.e25 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Spencer Chapman, M. et al. Lineage tracing of human development through somatic mutations. Nature 595, 85–90 (2021).

    Article  CAS  PubMed  Google Scholar 

  115. Muyas, F. et al. De novo detection of somatic mutations in high-throughput single-cell profiling data sets. Nat. Biotechnol. 42, 758–767 (2024).

    Article  CAS  PubMed  Google Scholar 

  116. Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176, 1325–1339.e22 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Gabbutt, C. et al. Fluctuating methylation clocks for cell lineage tracing at high temporal resolution in human tissues. Nat. Biotechnol. 40, 720–730 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. DuPage, M. & Bluestone, J. A. Harnessing the plasticity of CD4+ T cells to treat immune-mediated disease. Nat. Rev. Immunol. 16, 149–163 (2016).

    Article  CAS  PubMed  Google Scholar 

  119. Huyghe, A., Trajkova, A. & Lavial, F. Cellular plasticity in reprogramming, rejuvenation and tumorigenesis: a pioneer TF perspective. Trends Cell Biol. 34, 255–267 (2024).

    Article  CAS  PubMed  Google Scholar 

  120. Setty, M. et al. Characterization of cell fate probabilities in single-cell data with Palantir. Nat. Biotechnol. 37, 451–460 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  121. Stassen, S. V., Yip, G. G. K., Wong, K. K. Y., Ho, J. W. K. & Tsia, K. K. Generalized and scalable trajectory inference in single-cell omics data with VIA. Nat. Commun. 12, 5528 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Pandey, K. & Zafar, H. Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET. Nucleic Acids Res. 50, e86 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Lönnberg, T. et al. Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves Th1/Tfh fate bifurcation in malaria. Sci. Immunol. 2, eaal2192 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Velten, L. et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat. Cell Biol. 19, 271–281 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Lange, M. et al. CellRank for directed single-cell fate mapping. Nat. Methods 19, 159–170 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  126. Weiler, P., Lange, M., Klein, M., Pe’er, D. & Theis, F. CellRank 2: unified fate mapping in multiview single-cell data. Nat. Methods 21, 1196–1205 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  127. Schiebinger, G. et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176, 928–943.e22 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Tong, A., Huang, J., Wolf, G., van Dijk, D. & Krishnaswamy, S. TrajectoryNet: a dynamic optimal transport network for modeling cellular dynamics. Proc. Mach. Learn. Res. 119, 9526–9536 (2020).

    PubMed  PubMed Central  Google Scholar 

  129. Kamimoto, K. et al. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614, 742–751 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Weissman, I. L. Stem cells: units of development, units of regeneration, and units in evolution. Cell 100, 157–168 (2000).

    Article  CAS  PubMed  Google Scholar 

  131. Senra, D., Guisoni, N. & Diambra, L. ORIGINS: a protein network-based approach to quantify cell pluripotency from scRNA-seq data. MethodsX 9, 101778 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Malta, T. M. et al. Machine learning identifies stemness features associated with oncogenic dedifferentiation. Cell 173, 338–354.e15 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Müller, F. J. et al. Regulatory networks define phenotypic classes of human stem cell lines. Nature 455, 401–405 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  134. Zhang, F. et al. FitDevo: accurate inference of single-cell developmental potential using sample-specific gene weight. Brief. Bioinform. 23, bbac293 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  135. Gulati, G. S. et al. Single-cell transcriptional diversity is a hallmark of developmental potential. Science 367, 405–411 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Grün, D. et al. De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell 19, 266–277 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  137. Kannan, S., Farid, M., Lin, B. L., Miyamoto, M. & Kwon, C. Transcriptomic entropy benchmarks stem cell-derived cardiomyocyte maturation against endogenous tissue at single cell level. PLoS Comput. Biol. 17, e1009305 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  138. Jin, S., MacLean, A. L., Peng, T. & Nie, Q. scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data. Bioinformatics 34, 2077–2086 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Guo, M., Bao, E. L., Wagner, M., Whitsett, J. A. & Xu, Y. SLICE: determining cell differentiation and lineage based on single cell entropy. Nucleic Acids Res. 45, e54 (2017).

    PubMed  Google Scholar 

  140. Teschendorff, A. E. & Enver, T. Single-cell entropy for accurate estimation of differentiation potency from a cell’s transcriptome. Nat. Commun. 8, 15599 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Teschendorff, A. E., Maity, A. K., Hu, X., Weiyan, C. & Lechner, M. Ultra-fast scalable estimation of single-cell differentiation potency from scRNA-seq data. Bioinformatics 37, 1528–1534 (2021).

    Article  CAS  PubMed  Google Scholar 

  142. Ni, X. et al. Accurate estimation of single-cell differentiation potency based on network topology and gene ontology information. IEEE/ACM Trans. Comput. Biol. Bioinform. 19, 3255–3262 (2022).

    Article  CAS  PubMed  Google Scholar 

  143. Kang, M. et al. Mapping single-cell developmental potential in health and disease with interpretable deep learning. Preprint at bioRxiv https://doi.org/10.1101/2024.03.19.585637 (2024).

  144. Palla, G., Fischer, D. S., Regev, A. & Theis, F. J. Spatial components of molecular tissue biology. Nat. Biotechnol. 40, 308–318 (2022).

    Article  CAS  PubMed  Google Scholar 

  145. Lohoff, T. et al. Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis. Nat. Biotechnol. 40, 74–85 (2022).

    Article  CAS  PubMed  Google Scholar 

  146. Hickey, J. W. et al. Organization of the human intestine at single-cell resolution. Nature 619, 572–584 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  147. Greenwald, A. C. et al. Integrative spatial analysis reveals a multi-layered organization of glioblastoma. Cell 187, 2485–2501.e26 (2024).

    Article  CAS  PubMed  Google Scholar 

  148. Liu, J. et al. Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing. Life Sci. Alliance 6, e202201701 (2023).

    Article  CAS  PubMed  Google Scholar 

  149. He, S. et al. High-plex imaging of RNA and proteins at subcellular resolution in fixed tissue by spatial molecular imaging. Nat. Biotechnol. 40, 1794–1806 (2022).

    Article  CAS  PubMed  Google Scholar 

  150. Eng, C. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  151. Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  152. Janesick, A. et al. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. Nat. Commun. 14, 8353 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Liang, S. et al. Single-cell manifold-preserving feature selection for detecting rare cell populations. Nat. Comput. Sci. 1, 374–384 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  154. Missarova, A. et al. geneBasis: an iterative approach for unsupervised selection of targeted gene panels from scRNA-seq. Genome Biol. 22, 333 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  155. Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).

    Article  CAS  PubMed  Google Scholar 

  156. Wei, X. et al. Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration. Science 377, eabp9444 (2022).

    Article  CAS  PubMed  Google Scholar 

  157. Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792.e21 (2022).

    Article  CAS  PubMed  Google Scholar 

  158. Nagendran, M. et al. 1457 Visium HD enables spatially resolved, single-cell scale resolution mapping of FFPE human breast cancer tissue. J. Immunother. Cancer 11, A1620 (2023).

    Google Scholar 

  159. Lee, Y. et al. XYZeq: spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment. Sci. Adv. 7, eabg4755 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  160. Srivatsan, S. R. et al. Embryo-scale, single-cell spatial transcriptomics. Science 373, 111–117 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  161. Russell, A. J. C. et al. Slide-tags enables single-nucleus barcoding for multimodal spatial genomics. Nature 625, 101–109 (2024).

    Article  CAS  PubMed  Google Scholar 

  162. Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  163. Jung, N. & Kim, T.-K. Spatial transcriptomics in neuroscience. Exp. Mol. Med. 55, 2105–2115 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 19, 534–546 (2022).

    Article  CAS  PubMed  Google Scholar 

  165. Magoulopoulou, A. et al. Padlock probe-based targeted in situ sequencing: overview of methods and applications. Annu. Rev. Genom. Hum. Genet. 24, 133–150 (2023).

    Article  CAS  Google Scholar 

  166. Williams, C. G., Lee, H. J., Asatsuma, T., Vento-Tormo, R. & Haque, A. An introduction to spatial transcriptomics for biomedical research. Genome Med. 14, 68 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  167. Chu, T., Wang, Z., Pe’er, D. & Danko, C. G. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nat. Cancer 3, 505–517 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. Newman, A. M. et al. Determining cell type abundance and expression from bulk tissues with digital cytometry. Nat. Biotechnol. 37, 773–782 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  169. Li, B. et al. Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution. Nat. Methods 19, 662–670 (2022).

    Article  CAS  PubMed  Google Scholar 

  170. Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. 40, 661–671 (2022).

    Article  CAS  PubMed  Google Scholar 

  171. Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat. Biotechnol. 40, 517–526 (2022).

    Article  CAS  PubMed  Google Scholar 

  172. Vahid, M. R. et al. High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE. Nat. Biotechnol. 41, 1543–1548 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  173. Biancalani, T. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat. Methods 18, 1352–1362 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  174. Wei, R. et al. Spatial charting of single-cell transcriptomes in tissues. Nat. Biotechnol. 40, 1190–1199 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. Zhao, E. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol. 39, 1375–1384 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. Bergenstråhle, L. et al. Super-resolved spatial transcriptomics by deep data fusion. Nat. Biotechnol. 40, 476–479 (2022).

    Article  PubMed  Google Scholar 

  177. Hu, J. et al. Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA. Cell Syst. 14, 404–417.e4 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  178. Zhang, D. et al. Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology. Nat. Biotechnol. https://doi.org/10.1038/s41587-023-02019-9 (2024).

  179. Miller, B. F., Huang, F., Atta, L., Sahoo, A. & Fan, J. Reference-free cell type deconvolution of multi-cellular pixel-resolution spatially resolved transcriptomics data. Nat. Commun. 13, 2339 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  180. Garcia-Alonso, L. et al. Single-cell roadmap of human gonadal development. Nature 607, 540–547 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  181. Fawkner-Corbett, D. et al. Spatiotemporal analysis of human intestinal development at single-cell resolution. Cell 184, 810–826.e23 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  182. Moor, A. E. et al. Spatial reconstruction of single enterocytes uncovers broad zonation along the intestinal villus axis. Cell 175, 1156–1167.e15 (2018).

    Article  CAS  PubMed  Google Scholar 

  183. Bahar Halpern, K. et al. Lgr5+ telocytes are a signaling source at the intestinal villus tip. Nat. Commun. 11, 1936 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  184. Valdeolivas, A. et al. Profiling the heterogeneity of colorectal cancer consensus molecular subtypes using spatial transcriptomics. NPJ Precis. Oncol. 8, 10 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  185. Sibai, M. et al. The spatial landscape of Cancer Hallmarks reveals patterns of tumor ecology. Preprint at bioRxiv https://doi.org/10.1101/2022.06.18.496114 (2023).

  186. Heiser, C. N. et al. Molecular cartography uncovers evolutionary and microenvironmental dynamics in sporadic colorectal tumors. Cell 186, 5620–5637.e16 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  187. Ren, Y. et al. Spatial transcriptomics reveals niche-specific enrichment and vulnerabilities of radial glial stem-like cells in malignant gliomas. Nat. Commun. 14, 1028 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  188. Arora, R. et al. Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response. Nat. Commun. 14, 5029 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  189. Meylan, M. et al. Tertiary lymphoid structures generate and propagate anti-tumor antibody-producing plasma cells in renal cell cancer. Immunity 55, 527–541.e5 (2022).

    Article  CAS  PubMed  Google Scholar 

  190. Haviv, D. et al. The covariance environment defines cellular niches for spatial inference. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02193-4 (2024).

  191. Abdelaal, T., Mourragui, S., Mahfouz, A. & Reinders, M. J. T. SpaGE: spatial gene enhancement using scRNA-seq. Nucleic Acids Res. 48, e107 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  192. Sun, E. D., Ma, R., Navarro Negredo, P., Brunet, A. & Zou, J. TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses. Nat. Methods 21, 444–454 (2024).

    Article  CAS  PubMed  Google Scholar 

  193. Clifton, K. et al. STalign: alignment of spatial transcriptomics data using diffeomorphic metric mapping. Nat. Commun. 14, 8123 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  194. Jones, A., Townes, F. W., Li, D. & Engelhardt, B. E. Alignment of spatial genomics data using deep Gaussian processes. Nat. Methods 20, 1379–1387 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  195. Zhang, M. et al. Molecularly defined and spatially resolved cell atlas of the whole mouse brain. Nature 624, 343–354 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  196. Preibisch, S., Saalfeld, S. & Tomancak, P. Globally optimal stitching of tiled 3D microscopic image acquisitions. Bioinformatics 25, 1463–1465 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  197. Varrone, M., Tavernari, D., Santamaria-Martínez, A., Walsh, L. A. & Ciriello, G. CellCharter reveals spatial cell niches associated with tissue remodeling and cell plasticity. Nat. Genet. 56, 74–84 (2024).

    Article  CAS  PubMed  Google Scholar 

  198. Rajachandran, S. et al. Dissecting the spermatogonial stem cell niche using spatial transcriptomics. Cell Rep. 42, 112737 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  199. Walsh, L. A. & Quail, D. F. Decoding the tumor microenvironment with spatial technologies. Nat. Immunol. 24, 1982–1993 (2023).

    Article  CAS  PubMed  Google Scholar 

  200. Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E. & Swarup, V. hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data. Cell Rep. Methods 3, 100498 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  201. Choi, J. et al. QuadST: a powerful and robust approach for identifying cell-cell interaction-changed genes on spatially resolved transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2023.12.04.570019 (2023).

  202. Pentimalli, T. M. et al. High-resolution molecular atlas of a lung tumor in 3D. Preprint at bioRxiv https://doi.org/10.1101/2023.05.10.539644 (2023).

  203. Schürch, C. M. et al. Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182, 1341–1359.e19 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  204. Qiu, X. et al. Spateo: multidimensional spatiotemporal modeling of single-cell spatial transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2022.12.07.519417 (2022).

  205. Wu, Z. et al. Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens. Nat. Biomed. Eng. 6, 1435–1448 (2022).

    Article  CAS  PubMed  Google Scholar 

  206. Farah, E. N. et al. Spatially organized cellular communities form the developing human heart. Nature 627, 854–864 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  207. Bhate, S. S., Barlow, G. L., Schürch, C. M. & Nolan, G. P. Tissue schematics map the specialization of immune tissue motifs and their appropriation by tumors. Cell Syst. 13, 109–130.e6 (2022).

    Article  CAS  PubMed  Google Scholar 

  208. Kim, J. et al. Unsupervised discovery of tissue architecture in multiplexed imaging. Nat. Methods 19, 1653–1661 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  209. Long, Y. et al. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nat. Commun. 14, 1155 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  210. Ren, H., Walker, B. L., Cang, Z. & Nie, Q. Identifying multicellular spatiotemporal organization of cells with SpaceFlow. Nat. Commun. 13, 4076 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  211. Li, J., Chen, S., Pan, X., Yuan, Y. & Shen, H.-B. Cell clustering for spatial transcriptomics data with graph neural networks. Nat. Comput. Sci. 2, 399–408 (2022).

    Article  CAS  PubMed  Google Scholar 

  212. Pham, D. et al. Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues. Nat. Commun. 14, 7739 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  213. Cang, Z. et al. Screening cell-cell communication in spatial transcriptomics via collective optimal transport. Nat. Methods 20, 218–228 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  214. Andersson, A. et al. Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions. Nat. Commun. 12, 6012 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  215. Birk, S. et al. Large-scale characterization of cell niches in spatial atlases using bio-inspired graph learning. Preprint at bioRxiv https://doi.org/10.1101/2024.02.21.581428 (2024).

  216. Turesson, G. The genotypical response of the plant species to the habitat. Hereditas 3, 211–350 (1922).

    Article  Google Scholar 

  217. Ortiz, R. Göte Turesson’s research legacy to Hereditas: from the ecotype concept in plants to the analysis of landraces’ diversity in crops. Hereditas 157, 44 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  218. Luca, B. A. et al. Atlas of clinically distinct cell states and ecosystems across human solid tumors. Cell 184, 5482–5496.e28 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  219. Steen, C. B. et al. The landscape of tumor cell states and ecosystems in diffuse large B cell lymphoma. Cancer Cell 39, 1422–1437.e10 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  220. Luca, B. A. et al. Atlas of clinically-distinct cell states and cellular ecosystems across human solid tumors. Cancer Res. 80, abstr. 3443 (2020).

    Article  Google Scholar 

  221. Wu, S. Z. et al. A single-cell and spatially resolved atlas of human breast cancers. Nat. Genet. 53, 1334–1347 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  222. Jerby-Arnon, L. & Regev, A. DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data. Nat. Biotechnol. 40, 1467–1477 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  223. Bill, R. et al. CXCL9:SPP1 macrophage polarity identifies a network of cellular programs that control human cancers. Science 381, 515–524 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  224. Pelka, K. et al. Spatially organized multicellular immune hubs in human colorectal cancer. Cell 184, 4734–4752.e20 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  225. Ji, A. L. et al. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 182, 497–514.e22 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  226. He, S. et al. Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor-immune hubs. Nat. Biotechnol. https://doi.org/10.1038/s41587-024-02173-8 (2024).

  227. Liu, C. et al. Spatiotemporal mapping of gene expression landscapes and developmental trajectories during zebrafish embryogenesis. Dev. Cell 57, 1284–1298.e5 (2022).

    Article  CAS  PubMed  Google Scholar 

  228. Gu, Y., Liu, J., Li, C. & Welch, J. D. Mapping cell fate transition in space and time. Preprint at bioRxiv https://doi.org/10.1101/2024.02.12.579941 (2024).

  229. Zhao, T. et al. Spatial genomics enables multi-modal study of clonal heterogeneity in tissues. Nature 601, 85–91 (2022).

    Article  CAS  PubMed  Google Scholar 

  230. Xue, Y. et al. Single-cell mitochondrial variant enrichment resolved clonal tracking and spatial architecture in human embryonic hematopoiesis. Preprint at bioRxiv https://doi.org/10.1101/2023.09.18.558215 (2023).

  231. Ratz, M. et al. Clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics. Nat. Neurosci. 25, 285–294 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  232. Erickson, A. et al. Spatially resolved clonal copy number alterations in benign and malignant tissue. Nature 608, 360–367 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  233. Lomakin, A. et al. Spatial genomics maps the structure, nature and evolution of cancer clones. Nature 611, 594–602 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  234. Househam, J. et al. Phenotypic plasticity and genetic control in colorectal cancer evolution. Nature 611, 744–753 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  235. Lim, J. et al. Transitioning single-cell genomics into the clinic. Nat. Rev. Genet. 24, 573–584 (2023).

    Article  CAS  PubMed  Google Scholar 

  236. Van de Sande, B. et al. Applications of single-cell RNA sequencing in drug discovery and development. Nat. Rev. Drug Discov. 22, 496–520 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  237. Lozano, A. X. et al. T cell characteristics associated with toxicity to immune checkpoint blockade in patients with melanoma. Nat. Med. 28, 353–362 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  238. Kwon, M. et al. Determinants of response and intrinsic resistance to PD-1 blockade in microsatellite instability-high gastric cancer. Cancer Discov. 11, 2168–2185 (2021).

    Article  CAS  PubMed  Google Scholar 

  239. Zhou, Y. et al. Single-cell multiomics sequencing reveals prevalent genomic alterations in tumor stromal cells of human colorectal cancer. Cancer Cell 38, 818–828.e5 (2020).

    Article  CAS  PubMed  Google Scholar 

  240. Abe, Y. et al. A single-cell atlas of non-haematopoietic cells in human lymph nodes and lymphoma reveals a landscape of stromal remodelling. Nat. Cell Biol. 24, 565–578 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  241. Ajani, J. A. et al. YAP1 mediates gastric adenocarcinoma peritoneal metastases that are attenuated by YAP1 inhibition. Gut 70, 55–66 (2021).

    Article  CAS  PubMed  Google Scholar 

  242. Beneyto-Calabuig, S. et al. Clonally resolved single-cell multi-omics identifies routes of cellular differentiation in acute myeloid leukemia. Cell Stem Cell 30, 706–721.e8 (2023).

    Article  CAS  PubMed  Google Scholar 

  243. Miyamoto, D. T., Ting, D. T., Toner, M., Maheswaran, S. & Haber, D. A. Single-cell analysis of circulating tumor cells as a window into tumor heterogeneity. Cold Spring Harb. Symp. Quant. Biol. 81, 269–274 (2016).

    Article  PubMed  Google Scholar 

  244. Loh, K. M. et al. Mapping the pairwise choices leading from pluripotency to human bone, heart, and other mesoderm cell types. Cell 166, 451–467 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  245. Karimi, E. et al. Single-cell spatial immune landscapes of primary and metastatic brain tumours. Nature 614, 555–563 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  246. Wang, X. Q. et al. Spatial predictors of immunotherapy response in triple-negative breast cancer. Nature 621, 868–876 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  247. Lin, J. R. et al. High-plex immunofluorescence imaging and traditional histology of the same tissue section for discovering image-based biomarkers. Nat. Cancer 4, 1036–1052 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  248. Sorin, M. et al. Single-cell spatial landscapes of the lung tumour immune microenvironment. Nature 614, 548–554 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  249. Lin, J. R. et al. Multiplexed 3D atlas of state transitions and immune interaction in colorectal cancer. Cell 186, 363–381.e19 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  250. Digre, A. & Lindskog, C. The human protein atlas-Integrated omics for single cell mapping of the human proteome. Protein Sci. 32, e4562 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  251. Cui, H. et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat. Methods https://doi.org/10.1038/s41592-024-02201-0 (2024).

  252. Carpenter, A. E. & Singh, S. Bringing computation to biology by bridging the last mile. Nat. Cell Biol. 26, 5–7 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  253. Hu, Y. et al. Unsupervised and supervised discovery of tissue cellular neighborhoods from cell phenotypes. Nat. Methods 21, 267–278 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  254. Heimberg, G. et al. Scalable querying of human cell atlases via a foundational model reveals commonalities across fibrosis-associated macrophages. Preprint at bioRxiv https://doi.org/10.1101/2023.07.18.549537 (2023).

  255. Bommasani, R. et al. On the opportunities and risks of foundation models. Preprint at https://doi.org/10.48550/arXiv.2108.07258 (2022).

  256. Yang, F. et al. scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data. Nat. Mach. Intell. 4, 852–866 (2022).

    Article  Google Scholar 

  257. Bian, H. et al. scMulan: a multitask generative pre-trained language model for single-cell analysis. Preprint at bioRxiv https://doi.org/10.1101/2024.01.25.577152 (2024).

  258. Hao, M. et al. Large-scale foundation model on single-cell transcriptomics. Nat. Methods https://doi.org/10.1038/s41592-024-02305-7 (2024).

  259. Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618, 616–624 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  260. Shen, H. et al. Generative pretraining from large-scale transcriptomes for single-cell deciphering. iScience 26, 106536 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  261. Yang, X. et al. GeneCompass: deciphering universal gene regulatory mechanisms with knowledge-informed cross-species foundation model. Preprint at bioRxiv https://doi.org/10.1101/2023.09.26.559542 (2023).

  262. Rosen, Y. et al. Universal cell embeddings: a foundation model for cell biology. Preprint at bioRxiv https://doi.org/10.1101/2023.11.28.568918 (2023).

  263. Zhang, R., Luo, Y., Ma, J., Zhang, M. & Wang, S. scPretrain: multi-task self-supervised learning for cell-type classification. Bioinformatics 38, 1607–1614 (2022).

    Article  CAS  PubMed  Google Scholar 

  264. Alsabbagh, A. R. et al. Foundation models meet imbalanced single-cell data when learning cell type annotations. Preprint at bioRxiv https://doi.org/10.1101/2023.10.24.563625 (2023).

  265. Boiarsky, R., Singh, N., Buendia, A., Getz, G. & Sontag, D. A deep dive into single-cell RNA sequencing foundation models. Preprint at bioRxiv https://doi.org/10.1101/2023.10.19.563100 (2023).

  266. Kedzierska, K. Z., Crawford, L., Amini, A. P. & Lu, A. X. Assessing the limits of zero-shot foundation models in single-cell biology. Preprint at bioRxiv https://doi.org/10.1101/2023.10.16.561085 (2023).

  267. Khan, S. A. et al. Reusability report: learning the transcriptional grammar in single-cell RNA-sequencing data using transformers. Nat. Mach. Intell. 5, 1437–1446 (2023).

    Article  Google Scholar 

  268. Schaar, A. C. et al. Nicheformer: a foundation model for single-cell and spatial omics. Preprint at bioRxiv https://doi.org/10.1101/2024.04.15.589472 (2024).

  269. Roohani, Y., Huang, K. & Leskovec, J. Predicting transcriptional outcomes of novel multigene perturbations with GEARS. Nat. Biotechnol. 42, 927–935 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  270. Zhang, Y., Tiňo, P., Leonardis, A. & Tang, K. A survey on neural network interpretability. IEEE Trans. Emerg. Top. Comput. Intell. 5, 726–742 (2021).

    Article  Google Scholar 

  271. Swanson, K., Wu, E., Zhang, A., Alizadeh, A. A. & Zou, J. From patterns to patients: advances in clinical machine learning for cancer diagnosis, prognosis, and treatment. Cell 186, 1772–1791 (2023).

    Article  CAS  PubMed  Google Scholar 

  272. Xu, C. et al. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Mol. Syst. Biol. 17, e9620 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  273. Zimmerman, K. D., Espeland, M. A. & Langefeld, C. D. A practical solution to pseudoreplication bias in single-cell studies. Nat. Commun. 12, 738 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  274. Crowell, H. L. et al. muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat. Commun. 11, 6077 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  275. He, L. et al. NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data. Commun. Biol. 4, 629 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  276. Pullin, J. M. & McCarthy, D. J. A comparison of marker gene selection methods for single-cell RNA sequencing data. Genome Biol. 25, 56 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  277. Wang, X. et al. An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection. Bioinformatics 28, 2534–2536 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  278. Hao, Y. et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat. Biotechnol. 42, 293–304 (2024).

    Article  CAS  PubMed  Google Scholar 

  279. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  280. Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genom. 19, 477 (2018).

    Article  Google Scholar 

  281. Ellwanger, D. C., Scheibinger, M., Dumont, R. A., Barr-Gillespie, P. G. & Heller, S. Transcriptional dynamics of hair-bundle morphogenesis revealed with CellTrails. Cell Rep. 23, 2901–2914.e13 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  282. Cannoodt, R. et al. SCORPIUS improves trajectory inference and identifies novel modules in dendritic cell development. Preprint at bioRxiv https://doi.org/10.1101/079509 (2016).

  283. Dong, R. & Yuan, G. C. SpatialDWLS: accurate deconvolution of spatial transcriptomic data. Genome Biol. 22, 145 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  284. Wan, X. et al. Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope. Nat. Commun. 14, 7848 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  285. Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  286. Wang, X., Park, J., Susztak, K., Zhang, N. R. & Li, M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat. Commun. 10, 380 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  287. Menden, K. et al. Deep learning-based cell composition analysis from tissue expression profiles. Sci. Adv. 6, eaba2619 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  288. Garmire, L. X. et al. Challenges and perspectives in computational deconvolution of genomics data. Nat. Methods 21, 391–400 (2024).

    Article  CAS  PubMed  Google Scholar 

  289. Li, H. et al. DeconPeaker, a deconvolution model to identify cell types based on chromatin accessibility in ATAC-seq data of mixture samples. Front. Genet. 11, 392 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  290. Hutson, M. Hunting for the best bioscience software tool? Check this database. Nature https://doi.org/10.1038/d41586-023-00053-w (2023).

  291. Decamps, C. et al. Guidelines for cell-type heterogeneity quantification based on a comparative analysis of reference-free DNA methylation deconvolution software. BMC Bioinform. 21, 16 (2020).

    Article  CAS  Google Scholar 

  292. Gohil, S. H., Iorgulescu, J. B., Braun, D. A., Keskin, D. B. & Livak, K. J. Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy. Nat. Rev. Clin. Oncol. 18, 244–256 (2021).

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors are grateful to the members of the Newman and Wang laboratories for the valuable discussions and feedback. The original figures were created with Biorender.com. This work was supported by the National Science Foundation (J.P.D., Graduate Research Fellowship DGE-1656518), the National Cancer Institute (L.W., R01CA266280 and U24CA274274; A.M.N., R01CA255450), the Cancer Prevention and Research Institute of Texas (L.W., RP200385), the Break Through Cancer Foundation (L.W.), the University Cancer Foundation via the Institutional Research Grant Program (L.W.), the Melanoma Research Alliance (A.M.N., grant number 926521), and the Virginia and D. K. Ludwig Fund for Cancer Research (A.M.N.). L.W. is an Andrew Sabin Family Foundation Fellow. A.M.N. is a Chan Zuckerberg Biohub – San Francisco Investigator.

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Author notes

  1. These authors contributed equally: Gunsagar S. Gulati, Jeremy Philip D’Silva.

Authors and Affiliations

  1. Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA

    Gunsagar S. Gulati

  2. Department of Biomedical Data Science, Stanford University, Stanford, CA, USA

    Jeremy Philip D’Silva & Aaron M. Newman

  3. Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA

    Yunhe Liu & Linghua Wang

  4. The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA

    Linghua Wang

  5. Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA

    Aaron M. Newman

  6. Stanford Cancer Institute, Stanford University, Stanford, CA, USA

    Aaron M. Newman

  7. Chan Zuckerberg Biohub – San Francisco, San Francisco, CA, USA

    Aaron M. Newman

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  1. Gunsagar S. Gulati
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  2. Jeremy Philip D’Silva
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  4. Linghua Wang
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Contributions

All authors discussed the content of the article, contributed to writing or editing, and reviewed the manuscript before submission. L.W. and A.M.N. jointly supervised the work.

Corresponding author

Correspondence to Aaron M. Newman.

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Competing interests

A.M.N. holds patents related to digital cytometry and cancer biomarkers and has ownership interests in CiberMed, Inc., LiquidCell Dx, Inc. and CytoTrace Biosciences, Inc. The other authors declare no competing interests.

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Related links

Gene Ontology: https://geneontology.org/

Human Cell Atlas: https://www.humancellatlas.org/

Human Protein Atlas: http://www.proteinatlas.org

Human Tumor Atlas Network: https://humantumoratlas.org/

Glossary

Ambient RNA

Extraneous RNA molecules arising from lysed cells during sample processing that can contaminate gene expression measurements.

Cell atlases

Comprehensive references of cell types and states, typically generated using single-cell omics technologies.

Clustering

A technique for grouping elements of a dataset (for example, cells) by a similarity measure.

Correlative biomarkers

Quantitative phenotypes (for example, the abundance of a particular cell state or type within a tissue) that are statistically associated with a health outcome (known as ‘prognostic’ biomarkers) or the likelihood of responding to a given treatment (‘predictive’ biomarker).

Digital cytometry

A computational technique typically applied to bulk RNA admixtures to infer the proportions and characteristics of specific cell types within a complex tissue sample using gene expression signatures.

High-dimensional gene expression data

A dataset containing information on the expression levels of numerous genes across multiple samples, which in single-cell RNA-seq data are single cells.

Linear adjustment models

Statistical methods to correct data by accounting for the effect of one or more linear relationships between variables.

Low-dimensional embedding

A representation of high-dimensional data (for example, expression matrix with thousands of genes) that reduces the number of variables, while preserving important structures and relationships in the data for simplified analysis and visualization.

Multicellular neighbourhood

A local multicellular microenvironment, with an exact spatial resolution defined based on the assay and the studied tissue, for example, cells within a radius of 50 µm, or the 200 nearest neighbours of a cell.

Optimal transport problem

A problem solved by determining a mapping between two distributions that minimizes a cost function; in the context of single-cell time series data, the distributions can be cell populations at different time points, and the solution finds a mapping that relates cells at a later time point to their inferred antecedents at one or more earlier time points.

Spatial ecotypes

Recurrent sets of multicellular neighbourhoods characterized by a co-occurring set of phenotypic states (for example, transcriptional programmes) in one or more cell types.

Subtle cell state

A cellular phenotype characterized by small or nuanced differences in gene expression compared to other states of the same cell type.

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Gulati, G.S., D’Silva, J.P., Liu, Y. et al. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nat Rev Mol Cell Biol 26, 11–31 (2025). https://doi.org/10.1038/s41580-024-00768-2

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