Syrah: a pipeline to maximize spatial transcriptomics data output.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Spatial analysis of gene expression is crucial for understanding gene function.
• Traditional methods were limited to a few genes per sample.
• Spatial transcriptomics, like Slide-seqV2, enables large-scale gene expression analysis in tissue context.

Purpose of the Study:

• To address the issue of low usable read counts in Slide-seqV2 datasets.
• To identify and correct systematic barcode synthesis errors in spatial transcriptomic data.
• To develop a bioinformatic tool for enhancing the quality of Slide-seqV2 and Curio seeker datasets.

Main Methods:

• Developed the Syrah analysis pipeline.
• Utilized a biochemical model based on barcode sequence data.
• Identified and corrected systematic errors in chemical synthesis of barcodes.

Main Results:

• Syrah recovers up to 35% more reads in Slide-seqV2 datasets.
• The pipeline reassigns erroneous barcode matches and removes improperly formed reads.
• Syrah enhances data utility without requiring imputation or additional datasets.

Conclusions:

• Syrah effectively identifies and corrects barcode errors in spatial transcriptomic data.
• The pipeline significantly improves read numbers and data quality for Slide-seqV2 and Curio seeker.
• Syrah transforms the utility of spatial transcriptomic datasets by recovering discarded reads.