Journal
MOLECULAR SYSTEMS BIOLOGY
Volume 17, Issue 6, Pages -Publisher
WILEY
DOI: 10.15252/msb.202010108
Keywords
cell segmentation and annotation; scRNAseq; single cell multiomics integration; spatial differentially expressed genes; spatial transcriptomics
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Funding
- NIH [R01NS117148, T32CA201160]
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The study developed a new computational framework JSTA for joint cell segmentation and cell type annotation, utilizing prior knowledge of cell type-specific gene expression to improve RNA assignment accuracy. The results showed that leveraging existing cell type taxonomy can increase RNA assignment accuracy by more than 45%.
RNA hybridization-based spatial transcriptomics provides unparalleled detection sensitivity. However, inaccuracies in segmentation of image volumes into cells cause misassignment of mRNAs which is a major source of errors. Here, we develop JSTA, a computational framework for joint cell segmentation and cell type annotation that utilizes prior knowledge of cell type-specific gene expression. Simulation results show that leveraging existing cell type taxonomy increases RNA assignment accuracy by more than 45%. Using JSTA, we were able to classify cells in the mouse hippocampus into 133 (sub)types revealing the spatial organization of CA1, CA3, and Sst neuron subtypes. Analysis of within cell subtype spatial differential gene expression of 80 candidate genes identified 63 with statistically significant spatial differential gene expression across 61 (sub)types. Overall, our work demonstrates that known cell type expression patterns can be leveraged to improve the accuracy of RNA hybridization-based spatial transcriptomics while providing highly granular cell (sub)type information. The large number of newly discovered spatial gene expression patterns substantiates the need for accurate spatial transcriptomic measurements that can provide information beyond cell (sub)type labels.
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