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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Moffitt, J. R. et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Chen, A. et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell 185, 1777–1792.e21 (2022).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Zeng, H. et al. Integrative in situ mapping of single-cell transcriptional states and tissue histopathology in a mouse model of Alzheimer’s disease. Nat. Neurosci. 26, 430–446 (2023).CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Lu, T., Ang, C. E. & Zhuang, X. Spatially resolved epigenomic profiling of single cells in complex tissues. Cell 185, 4448–4464.e17 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Zeng, H. et al. Spatially resolved single-cell translatomics at molecular resolution. Science 380, eadd3067 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981.e15 (2018).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Zeira, R., Land, M., Strzalkowski, A. & Raphael, B. J. Alignment and integration of spatial transcriptomics data. Nat. Methods 19, 567–575 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Yuan, Y. & Bar-Joseph, Z. GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data. Genome Biol. 21, 300 (2020).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
PubMedÂ
Google ScholarÂ
Dong, K. & Zhang, S. Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nat. Commun. 13, 1739 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Fischer, D. S., Schaar, A. C. & Theis, F. J. Modeling intercellular communication in tissues using spatial graphs of cells. Nat. Biotechnol. 41, 332–336 (2023).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Palla, G., Fischer, D. S., Regev, A. & Theis, F. J. Spatial components of molecular tissue biology. Nat. Biotechnol. 40, 308–318 (2022).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Long, Y. et al. Spatially informed clustering, integration, and deconvolution of spatial transcriptomics with GraphST. Nat. Commun. 14, 1155 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Chen, M., Wei, Z., Huang, Z., Ding, B. & Li, Y. Simple and deep graph convolutional networks. in Proceedings of the 37th International Conference on Machine Learning 1725–1735 (PMLR, 2020).Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Wang, Q. et al. The Allen Mouse Brain Common Coordinate Framework: a 3D reference atlas. Cell 181, 936–953.e20 (2020).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Zhang, M. et al. Molecularly defined and spatially resolved cell atlas of the whole mouse brain. Nature 624, 343–354 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Langlieb, J. et al. The molecular cytoarchitecture of the adult mouse brain. Nature 624, 333–342 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Shi, H. et al. Spatial atlas of the mouse central nervous system at molecular resolution. Nature 622, 552–561 (2023).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Rood, J. E. et al. Toward a common coordinate framework for the human body. Cell 179, 1455–1467 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Armingol, E., Officer, A., Harismendy, O. & Lewis, N. E. Deciphering cell–cell interactions and communication from gene expression. Nat. Rev. Genet. 22, 71–88 (2021).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Yeh, F. L., Wang, Y., Tom, I., Gonzalez, L. C. & Sheng, M. TREM2 binds to apolipoproteins, including APOE and CLU/APOJ, and thereby facilitates uptake of amyloid-β by microglia. Neuron 91, 328–340 (2016).Krasemann, S. et al. The TREM2–APOE pathway drives the transcriptional phenotype of dysfunctional microglia in neurodegenerative diseases. Immunity 47, 566–581.e9 (2017).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Parhizkar, S. et al. Loss of TREM2 function increases amyloid seeding but reduces plaque-associated ApoE. Nat. Neurosci. 22, 191–204 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Wolfe, C. M., Fitz, N. F., Nam, K. N., Lefterov, I. & Koldamova, R. The role of APOE and TREM2 in Alzheimer’s disease-current understanding and perspectives. Int. J. Mol. Sci. 20, 81 (2019).ArticleÂ
Google ScholarÂ
Nandrot, E. F. et al. Essential role for MFG-E8 as ligand for αvβ5 integrin in diurnal retinal phagocytosis. Proc. Natl Acad. Sci. USA 104, 12005–12010 (2007).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Wei, X. et al. Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration. Science 377, eabp9444 (2022).Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).ArticleÂ
PubMedÂ
Google ScholarÂ
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Spitzer, M. H. et al. An interactive reference framework for modeling a dynamic immune system. Science 349, 1259425 (2015).Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360.e4 (2016).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Biancalani, T. et al. Deep learning and alignment of spatially resolved single-cell transcriptomes with Tangram. Nat. Methods 18, 1352–1362 (2021).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Kleshchevnikov, V. et al. Cell2location maps fine-grained cell types in spatial transcriptomics. Nat. Biotechnol. 40, 661–671 (2022).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Welch, J. D. et al. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177, 1873–1887.e17 (2019).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Virshup, I., Rybakov, S., Theis, F. J., Angerer, P. & Alexander Wolf, F. anndata: annotated data. Preprint at bioRxiv https://doi.org/10.1101/2021.12.16.473007 (2021).Wang, M. et al. Deep Graph Library: a graph-centric, highly-performant package for graph neural networks. Preprint at https://arxiv.org/abs/1909.01315 (2019).Zhang, H., Wu, Q., Yan, J., Wipf, D. & Yu, P. S. From canonical correlation analysis to self-supervised graph neural networks. in Advances in Neural Information Processing Systems 34, 76–89 (Curran Associates, 2021).Rueckert, D. et al. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18, 712–721 (1999).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).ArticleÂ
CASÂ
PubMedÂ
Google ScholarÂ
Palla, G. et al. Squidpy: a scalable framework for spatial omics analysis. Nat. Methods 19, 171–178 (2022).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).ArticleÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
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).ArticleÂ
CASÂ
PubMedÂ
PubMed CentralÂ
Google ScholarÂ
Tang, Z. et al. Search and match across spatial omics samples at single-cell resolution. Zenodo https://zenodo.org/doi/10.5281/zenodo.12215314 (2024).