References
Rao, A., Barkley, D., França, G. S. & Yanai, I. Exploring tissue architecture using spatial transcriptomics. Nature 596, 211–220 (2021).
Ståhl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).
Moses, L. & Pachter, L. Museum of spatial transcriptomics. Nat. Methods 19, 534–546 (2022).
Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).
Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).
Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792. e1721 (2022).
Maynard, K. R. et al. Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nat. Neurosci. 24, 425–436 (2021).
Cable, D. M. et al. Cell type-specific inference of differential expression in spatial transcriptomics. Nat. Methods 19, 1076–1087 (2022).
Fu, H. et al. Unsupervised spatially embedded deep representation of spatial transcriptomics. Preprint at bioRxiv https://doi.org/10.1101/2021.06.15.448542 (2021).
Hu, J. et al. SpaGCN: integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network. Nat. Methods 18, 1342–1351 (2021).
Zhao, E. et al. Spatial transcriptomics at subspot resolution with BayesSpace. Nat. Biotechnol. 39, 1375–1384 (2021).
Dong, K. & Zhang, S. Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nat. Commun. 13, 1739 (2022).
Sun, S., Zhu, J. & Zhou, X. Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies. Nat. Methods 17, 193–200 (2020).
Zhang, C., Dong, K., Aihara, K., Chen, L. & Zhang, S. STAMarker: determining spatial domain-specific variable genes with saliency maps in deep learning. Preprint at bioRxiv https://www.biorxiv.org/content/10.1101/2022.11.07.515535v1. (2022).
Shao, X. et al. Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk. Nat. Commun. 13, 4429 (2022).
Chen, S. et al. Spatially resolved transcriptomics reveals unique gene signatures associated with human temporal cortical architecture and Alzheimer’s pathology. Acta Neuropathol Commun 10, 188 (2022).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e1821 (2019).
Zeira, R., Land, M., Strzalkowski, A. & Raphael, B. Alignment and integration of spatial transcriptomics data. Nat. Methods 19, 567–575 (2022).
Villacampa, E. G. et al. Genome-wide spatial expression profiling in formalin-fixed tissues. Cell Genomics 1, 100065 (2021).
Yao, Z. et al. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell 184, 3222–3241.e3226 (2021).
Kadowaki, K. et al. Phosphohippolin expression in the rat central nervous system. Mol. Brain. Res. 125, 105–112 (2004).
Zacharias, D. A. & Kappen, C. Developmental expression of the four plasma membrane calcium ATPase (Pmca) genes in the mouse. Biochim. Biophys. Acta Gen. Subj. 1428, 397–405 (1999).
Mamoor, S. The α1 subunit of the γ-aminobutyric acid receptor, Gabra1, is differentially expressed in the brains of patients with schizophrenia. Preprint at OSF https://doi.org/10.31219/osf.io/m93ya. (2020).
Richardson, L. et al. EMAGE mouse embryo spatial gene expression database: 2014 update. Nucleic Acids Res. 42, D835–D844 (2014).
Savolainen, S. M., Foley, J. F. & Elmore, S. A. Histology atlas of the developing mouse heart with emphasis on E11. 5 to E18. 5. Toxicol. Pathol. 37, 395–414 (2009).
Haddad-Tóvolli, R., Szabó, N.-E., Zhou, X. & Alvarez-Bolado, G. Genetic manipulation of the mouse developing hypothalamus through in utero electroporation. J. Vis. Exp. 77, e50412 (2013).
Díaz-Guerra, E., Pignatelli, J., Nieto-Estévez, V. & Vicario-Abejón, C. Transcriptional regulation of olfactory bulb neurogenesis. Anat. Rec. 296, 1364–1382 (2013).
Chen, V. S. et al. Histology atlas of the developing prenatal and postnatal mouse central nervous system, with emphasis on prenatal days E7. 5 to E18. 5. Toxicol. Pathol. 45, 705–744 (2017).
Xu, Y. et al. A single-cell transcriptome atlas profiles early organogenesis in human embryos. Nat. Cell Biol. 25, 604–615 (2023).
Siddiqui, T. J. et al. An LRRTM4–HSPG complex mediates excitatory synapse development on dentate gyrus granule cells. Neuron 79, 680–695 (2013).
Elmore, M. R. et al. Colony-stimulating factor 1 receptor signaling is necessary for microglia viability, unmasking a microglia progenitor cell in the adult brain. Neuron 82, 380–397 (2014).
Zhang, B. et al. Integrated systems approach identifies genetic nodes and networks in late-onset Alzheimer’s disease. Cell 153, 707–720 (2013).
Zhao, Y. et al. TREM2 is a receptor for β-amyloid that mediates microglial function. Neuron 97, 1023–1031. e1027 (2018).
Hong, S. et al. Complement and microglia mediate early synapse loss in Alzheimer mouse models. Science 352, 712–716 (2016).
Salceda, S. et al. The immunomodulatory protein B7-H4 is overexpressed in breast and ovarian cancers and promotes epithelial cell transformation. Exp. Cell. Res. 306, 128–141 (2005).
Zheng, Z.-Y. et al. Wild-type N-Ras, overexpressed in basal-like breast cancer, promotes tumor formation by inducing IL-8 secretion via JAK2 activation. Cell Rep. 12, 511–524 (2015).
Schelker, M. et al. Estimation of immune cell content in tumour tissue using single-cell RNA-seq data. Nat. Commun. 8, 2032 (2017).
Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014. e1022 (2018).
Tzingounis, A. V., Kobayashi, M., Takamatsu, K. & Nicoll, R. A. Hippocalcin gates the calcium activation of the slow afterhyperpolarization in hippocampal pyramidal cells. Neuron 53, 487–493 (2007).
Yan, C., Costa, R., Darnell, J. E. Jr, Chen, J. & Van Dyke, T. Distinct positive and negative elements control the limited hepatocyte and choroid plexus expression of transthyretin in transgenic mice. EMBO J. 9, 869–878 (1990).
Strange, B. A., Witter, M. P., Lein, E. S. & Moser, E. I. Functional organization of the hippocampal longitudinal axis. Nat. Rev. Neurosci. 15, 655–669 (2014).
Zeidman, P. & Maguire, E. A. Anterior hippocampus: the anatomy of perception, imagination and episodic memory. Nat. Rev. Neurosci. 17, 173–182 (2016).
Rood, J. E. et al. Toward a common coordinate framework for the human body. Cell 179, 1455–1467 (2019).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Salehi, A. & Davulcu, H. Graph attention auto-encoders. Preprint at https://arxiv.org/abs/1905.10715 (2019).
Simon, L. M., Wang, Y.-Y. & Zhao, Z. Integration of millions of transcriptomes using batch-aware triplet neural networks. Nat. Mach. Intell. 3, 705–715 (2021).
Dong, Z. et al. Registration of large-scale terrestrial laser scanner point clouds: a review and benchmark. ISPRS J. Photogramm. Remote Sens. 163, 327–342 (2020).
Umeyama, S. Least-squares estimation of transformation parameters between two point patterns. IEEE Trans. Pattern Anal. Mach. Intell. 13, 376–380 (1991).
Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. Preprint at https://arxiv.org/abs/1412.6980 (2014).
Clevert, D.-A., Unterthiner, T. & Hochreiter, S. Fast and accurate deep network learning by exponential linear units (ELUs). Preprint at https://arxiv.org/abs/1511.07289 (2015).
Fey, M. & Lenssen, J. E. Fast graph representation learning with PyTorch Geometric. Preprint at https://arxiv.org/abs/1903.02428 (2019).
Scrucca, L., Fop, M., Murphy, T. B. & Raftery, A. Mclust 5: clustering, classification and density estimation using Gaussian finite mixture models. R Journal 8, 205–233 (2016).
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics 16, 284–287 (2012).
Tran, H. T. N. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020).
Zhou, X., Dong, K. & Zhang, S. Integrating spatial transcriptomics data across different conditions, technologies, and developmental stages. Zenodo https://doi.org/10.5281/zenodo.8315415 (2023).
