RT Journal Article
SR Electronic
T1 Sparcle: assigning transcripts to cells in multiplexed images
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.02.13.431099
DO 10.1101/2021.02.13.431099
A1 Sandhya Prabhakaran
A1 Tal Nawy
A1 Dana Pe’er’
YR 2021
UL http://biorxiv.org/content/early/2021/02/14/2021.02.13.431099.abstract
AB Background Imaging-based spatial transcriptomics has the power to reveal patterns of single-cell gene expression by detecting mRNA transcripts as individually resolved spots in multiplexed images. However, molecular quantification has been severely limited by the computational challenges of segmenting poorly outlined, overlapping cells, and of overcoming technical noise; the majority of transcripts are routinely discarded because they fall outside the segmentation boundaries. This lost information leads to less accurate gene count matrices and weakens downstream analyses, such as cell type or gene program identification.Results Here, we present Sparcle, a probabilistic model that reassigns transcripts to cells based on gene covariation patterns and incorporates spatial features such as distance to nucleus. We demonstrate its utility on both multiplexed error-robust fluorescence in situ hybridization (MERFISH) and single-molecule FISH (smFISH) data.Conclusions Sparcle improves transcript assignment, providing more realistic per-cell quantification of each gene, better delineation of cell boundaries, and improved cluster assignments. Critically, our approach does not require an accurate segmentation and is agnostic to technological platform.Competing Interest StatementThe authors have declared no competing interest.