Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues

Duy Pham, Xiao Tan, Brad Balderson, Jun Xu, Laura F. Grice, Sohye Yoon, Emily F. Willis, Minh Tran, Pui Yeng Lam, Arti Raghubar, Priyakshi Kalita- de Croft, Sunil Lakhani, Jana Vukovic, Marc J. Ruitenberg () and Quan H. Nguyen ()
Additional contact information
Duy Pham: The University of Queensland
Xiao Tan: The University of Queensland
Brad Balderson: The University of Queensland
Jun Xu: The University of Queensland
Laura F. Grice: The University of Queensland
Sohye Yoon: The University of Queensland
Emily F. Willis: The University of Queensland
Minh Tran: The University of Queensland
Pui Yeng Lam: The University of Queensland
Arti Raghubar: The University of Queensland
Priyakshi Kalita- de Croft: The University of Queensland
Sunil Lakhani: The University of Queensland
Jana Vukovic: The University of Queensland
Marc J. Ruitenberg: The University of Queensland
Quan H. Nguyen: The University of Queensland

Nature Communications, 2023, vol. 14, issue 1, 1-25

Abstract: Abstract Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advance understanding of cellular processes. First, we present a spatial graph-based method, pseudo-time-space (PSTS), to model and uncover relationships between transcriptional states of cells across tissues undergoing dynamic change (e.g. neurodevelopment, brain injury and/or microglia activation, and cancer progression). We further developed a spatially-constrained two-level permutation (SCTP) test to study cell-cell interaction, finding highly interactive tissue regions across thousands of ligand-receptor pairs with markedly reduced false discovery rates. Finally, we present a spatial graph-based imputation method with neural network (stSME), to correct for technical noise/dropout and increase ST data coverage. Together, the algorithms that we developed, implemented in the comprehensive and fast stLearn software, allow for robust interrogation of biological processes within healthy and diseased tissues.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-43120-6

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DOI: 10.1038/s41467-023-43120-6

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