TY - JOUR
T1 - Mapping cellular interactions from spatially resolved transcriptomics data
AU - Zhu, James
AU - Wang, Yunguan
AU - Chang, Woo Yong
AU - Malewska, Alicia
AU - Napolitano, Fabiana
AU - Gahan, Jeffrey
AU - Unni, Nisha
AU - Zhao, Min
AU - Yuan, Rongqing
AU - Wu, Fangjiang
AU - Yue, Lauren
AU - Guo, Lei
AU - Zhao, Zhuo
AU - Chen, Danny Z.
AU - Hannan, Raquibul
AU - Zhang, Siyuan
AU - Xiao, Guanghua
AU - Mu, Ping
AU - Hanker, Ariella B.
AU - Strand, Douglas
AU - Arteaga, Carlos L.
AU - Desai, Neil
AU - Wang, Xinlei
AU - Xie, Yang
AU - Wang, Tao
N1 - Publisher Copyright:
© The Author(s), under exclusive licence to Springer Nature America, Inc. 2024.
PY - 2024/10
Y1 - 2024/10
N2 - 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.
AB - 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.
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U2 - 10.1038/s41592-024-02408-1
DO - 10.1038/s41592-024-02408-1
M3 - Article
C2 - 39227721
AN - SCOPUS:85203054286
SN - 1548-7091
VL - 21
SP - 1830
EP - 1842
JO - Nature methods
JF - Nature methods
IS - 10
ER -