Cisternino, F. | Search bioRxiv

bioRxiv2026

Multiplexed high-content imaging uncovers morphological diversity of lymphocyte activation and dysfunction

Single-cell transcriptomic and proteomic technologies enable molecular profiling of immune cells at scale but provide limited access to cellular phenotypes shaped by spatial organisation, organelle architecture and cytoskeletal remodelling. Here we present TGlow, a scalable high-content imaging platform optimized for systematic single-cell phenotyping of primary human lymphocytes. TGlow integrates cyclic immunofluorescence, deep z-stack confocal imaging, and open-source data processing pipelines, including both classical and self-supervised vision transformer-based feature extraction, to jointly quantify cellular morphology, organelle organization, and immune activation states. Applied across over 400,000 primary human T cells spanning CD4+ activation time courses, drug perturbations, CRISPR knockouts and CD8+ T-cell exhaustion, TGlow resolves distinct and reproducible phenotypic states. We uncover dose-dependent and mechanism-specific drug phenotypes, such as defective endoplasmic reticulum polarisation under mycophenolic acid and tofacitinib. We show that mitochondrial clustering reveals activation- and cell-cycle-linked remodelling programs, CRISPR perturbations map gene-specific phenotypes that reposition cells along activation trajectories, and we identify a previously unrecognised collapse of cytoskeletal architecture in exhausted CD8+ T cells. TGlow provides a scalable framework for high-dimensional phenotyping of lymphocyte states advancing functional genomics, perturbation screening and population-level immune profiling by resolving the morphological and functional heterogeneity of lymphocytes and enabling systematic linkage of genetic and pharmacological perturbations to cellular function.

immunology↗

bioRxiv2023

Self-supervised learning for characterising histomorphological diversity and spatial RNA expression prediction across 23 human tissue types

As vast histological archives are digitised, there is a pressing need to be able to associate specific tissue substructures and incident pathology to disease outcomes without arduous annotation. Such automation provides an opportunity to learn fundamental biology about how tissue structure and function varies in a population. Recently, self-supervised learning has proven competitive to supervised machine learning approaches in classification, segmentation and representation learning. Here, we leverage self-supervised learning to generate histology feature representations using 1.7M images across 23 healthy tissues in 838 donors from GTEx. Using these representations, we demonstrate we can automatically segment tissues into their constituent tissue substructures and pathology proportions, and surpass the performance of conventionally used pre-trained models. We observe striking population variability in canonical tissue substructures, highlight examples of missing pathological diagnoses, incorrect assignment of target tissue and cross-tissue contamination. We demonstrate that this variability in tissue composition leads to a likely overestimation of eQTL tissue sharing and drives dramatic differential gene expression changes. We use derived tissue substructures to detect 284 tissue substructures and pathology specific eQTLs. As our derived histology representations are rich morphological descriptors of the underlying tissue, we introduce a multiple instance learning model that can predict and spatially localise individual RNA expression levels directly from histology to specific substructures and pathological features. We validate our RNA spatial predictions with matched ground truth immunohistochemistry (IHC) for several well characterised marker genes, recapitulating their known spatial specificity. Finally, we derive a gene expression spatial enrichment metric, allowing us to detect genes specifically expressed within sites of pathology (e.g. arterial calcification). Together, these results demonstrate the power of self-supervised machine learning when applied to vast histological datasets to allow researchers to pose and answer questions about tissue pathology, its spatial organisation and the interplay between morphological tissue variability and gene expression.

bioinformatics↗