Description:

Imaging-based spatial transcriptomics (ST) 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. 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 multiplexed error-robust fluorescence in situ hybridization (MERFISH) single-molecule FISH (smFISH) data, probabilistic cell typing by In situ Sequencing (pciSeq) and spatially-resolved transcript amplicon readout mapping (STARmap). 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. (https://www.biorxiv.org/content/10.1101/2021.02.13.431099v1). The project was partially funded by Python Software Foundation (PSF)

Target Audience:

Intermediate and Advanced researchers and data scientists

Areas:

Computational Biology, Multiplexed Imaging, Machine Learning, High-Throughput, New Tools

Background knowledge:

An interest to know about cutting-edge work in Computational Biology field

Code availability:

The open-source, platform independent software implementation of Sparcle is available on Github: https://github.com/sandhya212/Sparcle_for_spot_reassignments

Talk roadmap:

• 10 mins: Introduction to spatial transcriptomics (ST) and single-cell RNA sequeqnced data
• 10 mins: Motivation for Sparcle, what Sparcle does and the computational workflow of Sparcle.
• 8-10 mins: Results and Discussion

The takeaway for the audience

• To understand the basic structure of a spatial transcriptomics image and single-cell RNA sequenced data
• What are the computational challenges in the ST field
• How Sparcle addresses these crucial challenges and future directions in field of spatial transcriptomics especially when combining imaging with single-cell RNA sequenced data.