Decoding division: single-cell insights into the transcriptional, proteomic and metabolic regulation of proliferating CD4+ T-cells

Abstract

Understanding the immune system is central to developing effective treatments for immune-mediated diseases (IMDs). CD4+ T-cells, as key orchestrators of adaptive immunity, encompass a diverse range of subtypes that coordinate and regulate the immune response. Advances in immunogenomics have increasingly linked CD4+ T-cell activation to IMD pathogenesis, highlighting the importance of this process in disease. Upon activation, these cells undergo extensive and tightly regulated changes across multiple molecular layers, including their transcriptome, proteome, metabolism and morphology, that enable their proliferation and differentiation into specialised effector subsets. Capturing these dynamic changes requires high-resolution approaches. Single-cell technologies have transformed our ability to study the heterogeneity of T-cell responses, revealing subtype- and context-specific gene expression programmes. However, most studies have overlooked proliferation status, despite reported division-dependent proteomic changes in naive CD8+ T-cells, showing expression can change markedly across division states. This project aims to expand current understanding of CD4+ T-cell biology by investigating how proliferation shapes gene and protein expression. In the first part of this thesis, I present a pilot study proposing a strategy to analyse CD4+ T-cells by division state, revealing early evidence of division-dependent transcriptional and proteomic changes. Building on these findings, I generate a larger-scale dataset profiling naive, memory and regulatory CD4+ T-cell subtypes. Subsequent chapters explore how activation induces division-dependent expression patterns at both the gene and protein level. I examine the overlap between division-dependent genes and known IMD-associated loci, proposing a potential role for division state in disease pathogenesis. Additionally, I infer metabolic state from transcriptomic data to uncover division-dependent metabolic reprogramming. Finally, I compare gene and protein expression dynamics to identify regulatory divergence and discordance. By integrating division-state resolution into the analysis of CD4+ T-cell activation, this work provides novel insights into the transcriptional and functional programmes that govern T-cell responses, and their potential links to IMD.