Cell–cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia’s power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand–receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications.
Mapping cellular interactions from spatially resolved transcriptomics data / Zhu, J., Wang, Y., Chang, W.Y., Malewska, A., Napolitano, F., Gahan, J.C., Unni, N., Zhao, M., Yuan, R., Wu, F., Yue, L., Guo, L., Zhao, Z., Chen, D.Z., Hannan, R., Zhang, S., Xiao, G., Mu, P., Hanker, A.B., Strand, D., et al.. - In: NATURE METHODS. - ISSN 1548-7091. - 21:10(2024), pp. 1830-1842. [10.1038/s41592-024-02408-1]
Mapping cellular interactions from spatially resolved transcriptomics data
Napolitano F.;
2024
Abstract
Cell–cell communication (CCC) is essential to how life forms and functions. However, accurate, high-throughput mapping of how expression of all genes in one cell affects expression of all genes in another cell is made possible only recently through the introduction of spatially resolved transcriptomics (SRT) technologies, especially those that achieve single-cell resolution. Nevertheless, substantial challenges remain to analyze such highly complex data properly. Here, we introduce a multiple-instance learning framework, Spacia, to detect CCCs from data generated by SRTs, by uniquely exploiting their spatial modality. We highlight Spacia’s power to overcome fundamental limitations of popular analytical tools for inference of CCCs, including losing single-cell resolution, limited to ligand–receptor relationships and prior interaction databases, high false positive rates and, most importantly, the lack of consideration of the multiple-sender-to-one-receiver paradigm. We evaluated the fitness of Spacia for three commercialized single-cell resolution SRT technologies: MERSCOPE/Vizgen, CosMx/NanoString and Xenium/10x. Overall, Spacia represents a notable step in advancing quantitative theories of cellular communications.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
