Evaluating Bin2Cell for High-Resolution Morphological and Transcriptomic Integration*
Session Number
3
Advisor(s)
Dr. Wei Wu, University of California San Francisco
Location
A147
Discipline
Medical and Health Sciences
Start Date
15-4-2026 2:15 PM
End Date
15-4-2026 3:00 PM
Abstract
Accurately interpreting how cells interact within their respective tissue environments is integral for studying biological systems. Recent advances in spatial transcriptomics technologies have proved capable for this purpose, with platform VisiumHD capturing data at resolutions of roughly 2 micrometers. Despite this improvement, analyzing VisiumHD results has proved a challenge, as default analysis aggregates its extremely precise data into larger 8-micrometer squares, blurring true cell boundaries and leading to inaccurate cell location, identity, and gene expression profiles. Bin2Cell proves as a promising solution for this issue, using a deep learning model called StarDist to find cell nuclei within an image, then focusing nearby gene data into “reconstructed” cells by using the nuclei as anchor points. In this study, we analyze the effectivity of Bin2Cell as a computational method for reconstructing biologically meaningful cell-level representations from Visium HD spatial transcriptomic data using both benchmark datasets provided with the Bin2Cell pipeline and independent datasets processed through a similar workflow.
Evaluating Bin2Cell for High-Resolution Morphological and Transcriptomic Integration*
A147
Accurately interpreting how cells interact within their respective tissue environments is integral for studying biological systems. Recent advances in spatial transcriptomics technologies have proved capable for this purpose, with platform VisiumHD capturing data at resolutions of roughly 2 micrometers. Despite this improvement, analyzing VisiumHD results has proved a challenge, as default analysis aggregates its extremely precise data into larger 8-micrometer squares, blurring true cell boundaries and leading to inaccurate cell location, identity, and gene expression profiles. Bin2Cell proves as a promising solution for this issue, using a deep learning model called StarDist to find cell nuclei within an image, then focusing nearby gene data into “reconstructed” cells by using the nuclei as anchor points. In this study, we analyze the effectivity of Bin2Cell as a computational method for reconstructing biologically meaningful cell-level representations from Visium HD spatial transcriptomic data using both benchmark datasets provided with the Bin2Cell pipeline and independent datasets processed through a similar workflow.