Scaling up spatial transcriptomics for large-sized tissues: uncovering cellular-level tissue architecture beyond conventional platforms with iSCALE.

Abstract

Recent advances in spatial transcriptomics (ST) technologies have transformed our ability to profile gene expression while retaining the crucial spatial context within tissues. However, existing ST platforms suffer from high costs, long turnaround times, low resolution, limited gene coverage, and small tissue capture areas, which hinder their broad applications. Here we present iSCALE, a method that predicts super-resolution gene expression and automatically annotates cellular-level tissue architecture for large-sized tissues that exceed the capture areas of standard ST platforms. The accuracy of iSCALE were validated by comprehensive evaluations, involving benchmarking experiments, immunohistochemistry staining, and manual annotation by pathologists. When applied to multiple sclerosis human brain samples, iSCALE uncovered lesion associated cellular characteristics that were undetectable by conventional ST experiments. Our results demonstrate iSCALE's utility in analyzing large-sized tissues with automatic and unbiased tissue annotation, inferring cell type composition, and pinpointing regions of interest for features not discernible through human visual assessment.