Language
English
Publication Date
5-4-2026
Journal
G3: Genes, Genomes, Genetics
DOI
10.1093/g3journal/jkag107
PMID
42081452
Abstract
Spatial analysis of gene expression patterns has been a key technique for revealing the potential functions of genes. Traditionally, these analyses conducted using in-situ hybridizations and other labor-intensive protocols were constrained to examining only a few candidate genes per sample. However, the advent of spatial transcriptomic techniques like Slide-seqV2 has transformed this field, enabling massively parallel exploration of gene expression patterns within their tissue contexts by pairing spatial locations with RNA sequencing. Despite its potential, Slide-seqV2 datasets often produce fewer usable reads than expected. We have identified that a significant source of errors in the technology stems from the chemical synthesis of barcodes used in Slide-seqV2. These errors are systematic, and in many cases, they can be bioinformatically identified and corrected. We have developed "Syrah, " an analysis pipeline that identifies and corrects barcode errors in Slide-SeqV2 and Curio seeker datasets. Syrah can dramatically enhance read numbers in Slide-seqV2 datasets, recovering up to 35% more reads, reassigning erroneous barcode matches, and removing improperly formed reads. Unlike other dataset improvement methods that rely on data driven imputation, Syrah uses a biochemical model and the barcode sequence data and does not, require additional datasets or intricate calculations. This innovative technique promises to transform the utility of Slide-seqV2 and Curio Seeker datasets by identifying usable reads that were discarded during previous analysis that required exact matching of barcode sequences.
Keywords
Curio Seeker, Slide-seqV2, bioinformatics resources, spatial transcriptomics
Published Open-Access
yes
Recommended Citation
Brewster, Carolyn; Mann, Frederick G; Benham-Pyle, Blair; et al., "Syrah: A Pipeline To Maximize Spatial Transcriptomics Data Output" (2026). Faculty, Staff and Students Publications. 7042.
https://digitalcommons.library.tmc.edu/baylor_docs/7042