stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues

By D. Pham, Xiao Tan, Jun Xu, Laura F Grice, P. Lam, A. Raghubar, Jana Vukovic, M. Ruitenberg, Quan Nguyen. bioRxiv, 2020

332 citations recorded by the source corpus.

Spatial Transcriptomics is an emerging technology that adds spatial dimensionality and tissue morphology to the genome-wide transcriptional profile of cells in an undissociated tissue. Integrating these three types of data creates a vast potential for deciphering novel biology of cell types in their native morphological context. Here we developed innovative integrative analysis approaches to utilise all three data types to first find cell types, then reconstruct cell type evolution within a tissue, and search for tissue regions with high cell-to-cell interactions. First, for normalisation of gene expression, we compute a distance measure using morphological similarity and neighbourhood smoothing. The normalised data is then used to find clusters that represent transcriptional profiles of specific cell types and cellular phenotypes. Clusters are further sub-clustered if cells are spatially separated. Analysing anatomical regions in three mouse brain sections and 12 human brain datasets, we found the spatial clustering method more accurate and sensitive than other methods. Second, we introduce a method to calculate transcriptional states by pseudo-space-time (PST) distance. PST distance is a function of physical distance (spatial distance) and gene expression distance (pseudotime distance) to estimate the pairwise similarity between transcriptional profiles among cells within a tissue. We reconstruct spatial transition gradients within and between cell types that are connected locally within a cluster, or globally between clusters, by a directed minimum spanning tree optimisation approach for PST distance. The PST algorithm could model spatial transition from non-invasive to invasive cells within a breast cancer dataset. Third, we utilise spatial information and gene expression profiles to identify locations in the tissue where there is both high ligand-receptor interaction activity and diverse cell type co-localisation. These tissue locations are predicted to be hotspots where cell-cell interactions are more likely to occur. We detected tissue regions and ligand-receptor pairs significantly enriched compared to background distribution across a breast cancer tissue. Together, these three algorithms, implemented in a comprehensive Python software stLearn, allow for the elucidation of biological processes within healthy and diseased tissues.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/448b40a14cd8b8a934b8a14d8c1e31455ad3c4f4

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Citations in the corpus

Screening cell–cell communication in spatial transcriptomics via collective optimal transport (2023, methodology): “…defined based on spatial information 25 ; stLearn relates the co-expression of ligand and receptor genes to the spatial diversity of cell types 24 ; SVCA 26 and MISTy 27 use probabilistic and machine learning models, respectively, to identify the spatially constrained intercellular gene–gene…”
Best practices for single-cell analysis across modalities (2023, methodology): “GCNG 237 , Misty 238 and NCEM 236 formulate this task in terms of spatial graphs of cells and graph neural networks, SpaOTsc 239 uses optimal transport, and SVCA 240 quantifies the effect of cell–cell communication events on gene expression profiles with spatial variance component analysis.”
Spatial components of molecular tissue biology (2022, neutral): “Most of the existing software for (spatial) single-cell analysis should accommodate such data types associated with segmented cells in its existing framework 59,108,122–128 .”
An introduction to spatial transcriptomics for biomedical research (2022, methodology): “Also, in contrast to standard normalization, spatial and morphological expression (SME) from the stLearn python package presents a novel method for normalization which smooths library sizes in spots or segmented cells based on the library size of nearby spots within a radius d and their morphologica”