Search and match across spatial omics samples at single-cell resolution

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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Moffitt, J. R. et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
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 
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 
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).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lu, T., Ang, C. E. & Zhuang, X. Spatially resolved epigenomic profiling of single cells in complex tissues. Cell 185, 4448–4464.e17 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zeng, H. et al. Spatially resolved single-cell translatomics at molecular resolution. Science 380, eadd3067 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Goltsev, Y. et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174, 968–981.e15 (2018).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Zeira, R., Land, M., Strzalkowski, A. & Raphael, B. J. Alignment and integration of spatial transcriptomics data. Nat. Methods 19, 567–575 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Yuan, Y. & Bar-Joseph, Z. GCNG: graph convolutional networks for inferring gene interaction from spatial transcriptomics data. Genome Biol. 21, 300 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
PubMed 

Google Scholar 
Dong, K. & Zhang, S. Deciphering spatial domains from spatially resolved transcriptomics with an adaptive graph attention auto-encoder. Nat. Commun. 13, 1739 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
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 
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 
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).Article 
CAS 
PubMed 

Google Scholar 
Wang, Q. et al. The Allen Mouse Brain Common Coordinate Framework: a 3D reference atlas. Cell 181, 936–953.e20 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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 
Langlieb, J. et al. The molecular cytoarchitecture of the adult mouse brain. Nature 624, 333–342 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Shi, H. et al. Spatial atlas of the mouse central nervous system at molecular resolution. Nature 622, 552–561 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Rood, J. E. et al. Toward a common coordinate framework for the human body. Cell 179, 1455–1467 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Parhizkar, S. et al. Loss of TREM2 function increases amyloid seeding but reduces plaque-associated ApoE. Nat. Neurosci. 22, 191–204 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
PubMed 

Google Scholar 
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 
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).Article 
CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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 
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).Article 
CAS 
PubMed 

Google Scholar 
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 
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).Article 
CAS 
PubMed 

Google Scholar 
Palla, G. et al. Squidpy: a scalable framework for spatial omics analysis. Nat. Methods 19, 171–178 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
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).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
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).

Hot Topics

Related Articles