Learned morphological features guide cell type assignment of deconvolved spatial transcriptomics

Eduard Chelebian, Christophe Avenel, Julio Leon, Chung-Chau Hon, Carolina Wahlby

Keywords: self-supervised learning, spatial transcriptomics, cell type deconvolution

Abstract: Spatial transcriptomics enables to study the relationship between gene expression and tissue organization. Despite many recent advancements, existing sequencing-based methods have a spatial resolution that limits identification of individual cells. To address this, several cell type deconvolution methods have been proposed to integrate spatial gene expression with single-cell and single-nucleus RNA sequencing, producing per spot cell typing. However, these methods often overlook the contribution of morphology, which means cell identities are randomly assigned to the nuclei within a spot. In this paper, we introduce a hierarchical permutation-based framework which, guided by tissue morphology, efficiently reassigns cell types. We validate our method on simulated data, synthetic data, and a use case on the broadly used Tangram cell type deconvolution method with Visium data. We show that deconvolution-based cell typing using morphological tissue features from self-supervised deep learning lead to a more accurate annotation of the cells.

Submission Number: 319