Segmentation-free integration of nuclei morphology and spatial transcriptomics for retinal images

TL;DR

SEFI is a segmentation-free method that integrates nuclear morphology with spatial transcriptomics data using self-supervised learning, improving clustering in complex tissue images without requiring cell segmentation.

Contribution

It introduces a novel segmentation-free approach combining morphology and gene expression data via self-supervised learning, specifically addressing challenges in dense tissue analysis.

Findings

Effective clustering of retinal gene expression data

Operates without cell segmentation, reducing complexity

Applicable to densely packed tissue regions

Abstract

This study introduces SEFI (SEgmentation-Free Integration), a novel method for integrating morphological features of cell nuclei with spatial transcriptomics data. Cell segmentation poses a significant challenge in the analysis of spatial transcriptomics data, as tissue-specific structural complexities and densely packed cells in certain regions make it difficult to develop a universal approach. SEFI addresses this by utilizing self-supervised learning to extract morphological features from fluorescent nuclear staining images, enhancing the clustering of gene expression data without requiring segmentation. We demonstrate SEFI on spatially resolved gene expression profiles of the developing retina, acquired using multiplexed single molecule Fluorescence In Situ Hybridization (smFISH). SEFI is publicly available at https://github.com/eduardchelebian/sefi.