Sparcle: assigning transcripts to cells in multiplexed images

• Published in: Bioinformatics Advances Vol. 2; no. 1; p. vbac048
• Main Author: Prabhakaran, Sandhya
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 2022
• Subjects: Application Note; Bioinformatics; Fluorescence in situ hybridization; Gene expression; Segmentation; Transcriptomics
• ISSN: 1367-4803; 2635-0041; 2635-0041
• DOI: 10.1093/bioadv/vbac048

Motivation 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, single-molecule FISH data, probabilistic cell typing in situ sequencing, spatially resolved transcript amplicon readout mapping and MERFISH from Vizgen. 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. Availability and implementation The code is available at: https://github.com/sandhya212/Sparcle_for_spot_reassignments Contact sandhya.prabhakaran@moffitt.org Supplementary information Supplementary data are available at Bioinformatics Advances online.