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).
Google Scholar
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
Google Scholar
Liu, Y. et al. High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183, 1665–1681 (2020).
Google Scholar
Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792 (2022).
Google Scholar
Deng, Y. et al. Spatial-CUT&tag: spatially resolved chromatin modification profiling at the cellular level. Science 375, 681–686 (2022).
Google Scholar
Deng, Y. et al. Spatial profiling of chromatin accessibility in mouse and human tissues. Nature 609, 375–383 (2022).
Google Scholar
Lu, T., Ang, C. E. & Zhuang, X. Spatially resolved epigenomic profiling of single cells in complex tissues. Cell 185, 4448–4464 (2022).
Google Scholar
Goltsev, Y. et al. Deep profiling of mouse splenic architecture with codex multiplexed imaging. Cell 174, 968–981 (2018).
Google Scholar
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).
Google Scholar
Liu, Y. et al. High-plex protein and whole transcriptome co-mapping at cellular resolution with spatial CITE-seq. Nat. Biotechnol. 41, 1405–1409 (2023).
Google Scholar
Zhang, D. et al. Spatial epigenome–transcriptome co-profiling of mammalian tissues. Nature 616, 113–122 (2023).
Google Scholar
Jiang, F. et al. Simultaneous profiling of spatial gene expression and chromatin accessibility during mouse brain development. Nat. Methods 20, 1048–1057 (2023).
Google Scholar
Vandereyken, K., Sifrim, A., Thienpont, B. & Voet, T. Methods and applications for single-cell and spatial multi-omics. Nat. Rev. Genet. 24, 494–515 (2023).
Google Scholar
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).
Google Scholar
Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887 (2019).
Google Scholar
Dou, J. et al. Bi-order multimodal integration of single-cell data. Genome Biol. 23, 112 (2022).
Google Scholar
Cao, Z. J. & Gao, G. Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nat. Biotechnol. 40, 1458–1466 (2022).
Google Scholar
Xiong, L. et al. Online single-cell data integration through projecting heterogeneous datasets into a common cell-embedding space. Nat. Commun. 13, 6118 (2022).
Google Scholar
Chen, H., Ryu, J., Vinyard, M. E., Lerer, A. & Pinello, L. SIMBA: single-cell embedding along with features. Nat. Methods 21, 1003–1013 (2024).
Google Scholar
Chen, S. et al. Integration of spatial and single-cell data across modalities with weakly linked features. Nat. Biotechnol. 42, 1096–1106 (2024).
Google Scholar
Samaran, J., Peyré, G. & Cantini, L. scConfluence: single-cell diagonal integration with regularized inverse optimal transport on weakly connected features. Nat. Commun. 15, 7762 (2024).
Google Scholar
Tang, Z. et al. Modal-nexus auto-encoder for multi-modality cellular data integration and imputation. Nat. Commun. 15, 9021 (2024).
Google Scholar
You, Y. et al. Systematic comparison of sequencing-based spatial transcriptomic methods. Nat. Methods 21, 1743–1754 (2024).
Google Scholar
Cohen Kalafut, N., Huang, X. & Wang, D. Joint variational autoencoders for multimodal imputation and embedding. Nat. Mach. Intell. 5, 631–642 (2023).
Google Scholar
Ashuach, T. et al. MultiVI: deep generative model for the integration of multimodal data. Nat. Methods 20, 1222–1231 (2023).
Google Scholar
Cao, Y. et al. scButterfly: a versatile single-cell cross-modality translation method via dual-aligned variational autoencoders. Nat. Commun. 15, 2973 (2024).
Google Scholar
Wu, K. E., Yost, K. E., Chang, H. Y. & Zou, J. Babel enables cross-modality translation between multiomic profiles at single-cell resolution. Proc. Natl Acad. Sci. USA 118, e2023070118 (2021).
Google Scholar
Liu, J., Huang, Y., Singh, R., Vert, J. P. & Noble, W. S. Jointly embedding multiple single-cell omics measurements. Algorithms Bioinform. 143, 10 (2019).
Sunkin, S. M. et al. Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res. 41, D996–D1008 (2012).
Google Scholar
Ma, N. X., Puls, B. & Chen, G. Transcriptomic analyses of NeuroD1-mediated astrocyte-to-neuron conversion. Dev. Neurobiol. 82, 375–391 (2022).
Google Scholar
Zhou, Y. et al. Cooperative integration of spatially resolved multi-omics data with COSMOS. Nature Commun. 16, 27 (2025).
Google Scholar
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).
Google Scholar
Englund, C. et al. Pax6, Tbr2, and Tbr1 are expressed sequentially by radial glia, intermediate progenitor cells, and postmitotic neurons in developing neocortex. J. Neurosci. 25, 247–251 (2005).
Google Scholar
Bayam, E. et al. Genome-wide target analysis of NEUROD2 provides new insights into regulation of cortical projection neuron migration and differentiation. BMC Genom. 16, 681 (2015).
Google Scholar
Bormuth, I. et al. Neuronal basic helix–loop–helix proteins Neurod2/6 regulate cortical commissure formation before midline interactions. J. Neurosci. 33, 641–651 (2013).
Google Scholar
Hahn, M. A. et al. Reprogramming of DNA methylation at NEURO2-bound sequences during cortical neuron differentiation. Sci. Adv. 5, eaax0080 (2019).
Google Scholar
Zu, S. et al. Single-cell analysis of chromatin accessibility in the adult mouse brain. Nature 624, 378–389 (2023).
Google Scholar
Chai, H et al. Tri-omic single-cell mapping of the 3D epigenome and transcriptome in whole mouse brains throughout the lifespan. Nat. Methods 22, 994–1007 (2025).
Bogutz, A. B. et al. Transcription factor ASCL2 is required for development of the glycogen trophoblast cell lineage. PLoS Genet. 14, e1007587 (2018).
Google Scholar
Kunke, M. et al. SOX10-mediated regulation of enteric glial phenotype in vitro and its relevance for neuroinflammatory disorders. J. Mol. Neurosci. 75, 26 (2025).
Google Scholar
Forrest, M. P. et al. The psychiatric risk gene transcription factor 4 (TCF4) regulates neurodevelopmental pathways associated with schizophrenia, autism, and intellectual disability. Schizophr. Bull. 44, 1100–1110 (2018).
Google Scholar
Granja, J. M. et al. ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis. Nat. Genet. 53, 403–411 (2021).
Google Scholar
Li, Z. Benchmark datasets for SWITCH. Zenodo (2025).
Zhongzhan, L. SWITCH: a deep generative model for spatial multi-omics integration and cross-modal prediction. Zenodo (2025).
link
