Optimization-based decoding of Imaging Spatial Transcriptomics data.

Bioinformatics (Oxford, England)
Authors
Abstract

MOTIVATION: Imaging Spatial Transcriptomics techniques characterize gene expression in cells in their native context by imaging barcoded probes for mRNA with single molecule resolution. However, the need to acquire many rounds of high-magnification imaging data limits the throughput and impact of existing methods.

RESULTS: We describe the Joint Sparse method for Imaging Transcriptomics, an algorithm for decoding lower magnification Imaging Spatial Transcriptomics data than that used in standard experimental workflows. Joint Sparse method for Imaging Transcriptomics incorporates codebook knowledge and sparsity assumptions into an optimization problem, which is less reliant on well separated optical signals than current pipelines. Using experimental data obtained by performing Multiplexed Error-Robust Fluorescence in situ Hybridization on tissue from mouse brain, we demonstrate that Joint Sparse method for Imaging Transcriptomics enables improved throughput and recovery performance over standard decoding methods.

AVAILABILITY AND IMPLEMENTATION: Software implementation of JSIT, together with example files, is available at https://github.com/jpbryan13/JSIT.

Year of Publication
2023
Journal
Bioinformatics (Oxford, England)
Volume
39
Issue
6
Date Published
06/2023
ISSN
1367-4811
DOI
10.1093/bioinformatics/btad362
PubMed ID
37267161
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