Regev, A. et al. The Human Cell Atlas. eLife (2017).
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).
Bressan, D., Battistoni, G. & Hannon, G. J. The dawn of spatial omics. Science (2023).
Wang, L. et al. Molecular and cellular dynamics of the developing human neocortex. Nature (2025).
Sun, E. D. et al. Spatial transcriptomic clocks reveal cell proximity effects in brain ageing. Nature 638, 160–171 (2024).
Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 19, 534–546 (2022).
Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).
Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. Bert: pre-training of deep bidirectional transformers for language understanding. In Proc. 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) 4171–4186 (Association for Computational Linguistics, 2019).
He, K. et al. Masked autoencoders are scalable vision learners. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 16000–16009 (IEEE, 2022).
Radford, A. et al. Learning transferable visual models from natural language supervision. In Proc. 38th International Conference on Machine Learning 8748–8763 (PMLR, 2021).
Cui, H. et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat. Methods 21, 1470–1480 (2024).
Hao, M. et al. Large-scale foundation model on single-cell transcriptomics. Nat. Methods 21, 1481–1491 (2024).
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).
Richter, T., Bahrami, M., Xia, Y., Fischer, D. S. & Theis, F. J. Delineating the effective use of self-supervised learning in single-cell genomics. Nat. Mach. Intell. 7, 68–78 (2024).
Fischer, F. et al. scTab: scaling cross-tissue single-cell annotation models. Nat. Commun.15, 6611 (2024).
Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. A simple framework for contrastive learning of visual representations. In Proc. 37th International Conference on Machine Learning 1597–1607 (PMLR, 2020).
Zbontar, J., Jing, L., Misra, I., LeCun, Y. & Deny, S. Barlow Twins: self-supervised learning via redundancy reduction. In Proc. 38th International Conference on Machine Learning 12310–12320 (PMLR, 2021).
Marx, V. Method of the year: spatially resolved transcriptomics. Nat. Methods 18, 9–14 (2021).
Carstens, J. L. et al. Spatial multiplexing and omics. Nat. Rev. Methods Prim. 4, 54 (2024).
Heumos, L. et al. Best practices for single-cell analysis across modalities. Nat. Rev. Genet. 24, 550–572 (2023).
Ergen, C. et al. Consensus prediction of cell type labels in single-cell data with popV. Nat. Genet. 56, 2731–2738 (2024).
Zormpas, E., Queen, R., Comber, A. & Cockell, S. J. Mapping the transcriptome: realizing the full potential of spatial data analysis. Cell 186, 5677–5689 (2023).
Yuan, Z. et al. Benchmarking spatial clustering methods with spatially resolved transcriptomics data. Nat. Methods 21, 712–722 (2024).
Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).
Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).
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).
Marshall, J. L. et al. High-resolution Slide-seqV2 spatial transcriptomics enables discovery of disease-specific cell neighborhoods and pathways. iScience 25, 104097 (2022).
Dong, K. & Zhang, S. Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nat. Commun. 13, 1739 (2022).
Long, Y. et al. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nat. Commun. 14, 1155 (2023).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
Zhang, M. et al. Molecularly defined and spatially resolved cell atlas of the whole mouse brain. Nature 624, 343–354 (2023).
Wu, B. et al. A spatiotemporal atlas of cholestatic injury and repair in mice. Nat. Genet. 56, 938–952 (2024).
Chen, X. et al. Whole-cortex in situ sequencing reveals input-dependent area identity. Nature (2024).
Greenwald, A. C. et al. Integrative spatial analysis reveals a multi-layered organization of glioblastoma. Cell 187, 2485–2501 (2024).
Lucas, C.-H. G. et al. Spatial genomic, biochemical and cellular mechanisms underlying meningioma heterogeneity and evolution. Nat. Genet. 56, 1121–1133 (2024).
Atta, L. & Fan, J. Computational challenges and opportunities in spatially resolved transcriptomic data analysis. Nat. Commun. 12, 5283 (2021).
Szałata, A. et al. Transformers in single-cell omics: a review and new perspectives. Nat. Methods 21, 1430–1443 (2024).
Wang, Y. et al. Spatial transcriptomics: technologies, applications and experimental considerations. Genomics 115, 110671 (2023).
Shah, S., Lubeck, E., Zhou, W. & Cai, L. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92, 342–357 (2016).
Wang, X. et al. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361, eaat5691 (2018).
Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).
Biancalani, T. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat. Methods 18, 1352–1362 (2021).
Benchmarking spatial and single-cell transcriptomics integration methods. Nat. Methods (2022).
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).
Ren, H., Walker, B. L., Cang, Z. & Nie, Q. Identifying multicellular spatiotemporal organization of cells with SpaceFlow. Nat. Commun. 13, 4076 (2022).
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).
Singhal, V. et al. BANKSY unifies cell typing and tissue domain segmentation for scalable spatial omics data analysis. Nat. Genet. 56, 431–441 (2024).
Ma, Y. & Zhou, X. Accurate and efficient integrative reference-informed spatial domain detection for spatial transcriptomics. Nat. Methods 21, 1231–1244 (2024).
Boiarsky, R. et al. Deeper evaluation of a single-cell foundation model. Nat. Mach. Intell. 6, 1443–1446 (2024).
Richter, T. & Bahrami, M. theislab/ssl_in_scg. GitHub (2025).
Richter, T. TillR/sc_pretrained. Hugging Face (2024).
Yoshida, M. et al. Local and systemic responses to SARS-CoV-2 infection in children and adults. Nature 602, 321–327 (2022).
Tabula Sapiens Consortium et al. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science 376, eabl4896 (2022).
Siletti, K. et al. Transcriptomic diversity of cell types across the adult human brain. Science 382, eadd7046 (2023).
Siletti, K. et al. Human Brain Cell Atlas v1.0. CELLxGENE cellxgene.cziscience.com/e/9f499d32-400d-4c42-ac9a-fb1481844fee.cxg (2023).
Jorstad, N. L. et al. Comparative transcriptomics reveals human-specific cortical features. Science 382, eade9516 (2023).
Bakken, T. E. et al. Comparative transcriptomics reveals human-specific cortical features. CELLxGENE cellxgene.cziscience.com/e/2bdd3a2c-2ff4-4314-adf3-8a06b797a33a.cxg (2023).
Wang, L. et al. Molecular and cellular dynamics of the developing human neocortex at single-cell resolution. CELLxGENE (2025).
Janesick, A. et al. High resolution mapping of the tumor microenvironment using integrated single-cell, spatial and in situ analysis. 10x Genomics (2023).
Marshall, J. L. et al. High resolution Slide-seqV2 spatial transcriptomics enables discovery of disease-specific cell neighborhoods and pathways. CELLxGENE (2022).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. (2018).
Biancalani, T., Scalia, G., Lu, Z., Gaddam, S. & Hupalowska, A. Tangram 0.4.0 documentation. TANGRAM (2021).
Biancalani, T. et al. broadinstitute/Tangram/tree/master. GitHub (2023).
Han, C. CSHCY/Reusability_SSL_in_SCG: release of the SSLBench. Zenodo (2025).
Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019).
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