Nathan J. Palpant

Efficient discovery of regulatory genes and elements is essential for understanding cell identity, differentiation, and disease mechanisms. The TRIAGE methods are a set of well‐established computational approaches that identify context‐specific regulatory genes and prioritize regulatory elements across the genome. Previous publications have described the development of these algorithms, their benchmarking, and biological applications. Here, we provide step‐by‐step protocols for applying the TRIAGE methods to identify regulatory drivers from diverse input types, including gene expression matrices, gene lists, and genomic loci. It covers analyses of both bulk and single‐cell RNA‐seq datasets and enables genome‐wide interrogation of regulatory elements at single‐base resolution. The analysis is efficient, typically requiring Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1 : Prioritization of regulatory genes from bulk RNA‐seq data Basic Protocol 2 : Identification of cell populations and regulatory genes in single‐cell RNA‐seq data Basic Protocol 3 : Prioritization of regulatory long noncoding RNAs Basic Protocol 4 : Prioritization of functional genetic variants from eQTL data Alternate Protocol : Python‐based implementation of the TRIAGE workflow for regulatory gene and element prioritization Support Protocol : Preparing a normalized expression matrix from bulk RNA‐seq count data

Deciphering cell identity remains a central challenge in biology, as experimental profiling can only capture a fraction of the molecular diversity across human cell states. Computational modelling fills this gap by offering scalable predictions beyond what experimental assays can measure. These models play key roles in classifying cell type, discovering previously unknown states, interpreting perturbation responses, and generating hypotheses for contexts that are experimentally inaccessible. The rapid expansion of consortium-level data has enabled models to learn generalisable genomic features of cell identity. In the present review, we examine 43 representative computational methods utilising information extracted from genomic regulatory elements, marker genes, or reference atlases and traditional machine learning or transformer-based approaches for cell identity inference. By outlining biological rationales, applications, and limitations of these methods in a minimally technical tone, the review serves as a practical guide for biologists to choose appropriate methods for their specific analytical needs.

Low‐grade serous ovarian carcinoma (LGSOC) is a rare, indolent ovarian cancer subtype with limited effective therapies. Approximately 40% of cases lack canonical MAPK/ERK or PI3K/AKT/mTOR pathway alterations and are classified as having no specific molecular profile (NSMP). These patients have poor responses to chemotherapy, MEK inhibitors, and hormonal therapies, highlighting the need for alternative strategies. This study aimed to identify novel therapeutic targets in NSMP LGSOC. A high‐throughput drug screen of 3,436 compounds (including FDA‐approved, clinically tested, and investigational agents) was conducted across 12 LGSOC and one control ovarian epithelial cell line. EGFR inhibitors emerged as selective hits in NSMP cell lines and were further tested in two NSMP and two MAPK‐mutant lines in combination with standard‐of‐care chemotherapy agents, carboplatin and paclitaxel. EGFR expression was assessed using RNA sequencing, DNA methylation profiling, and immunohistochemistry in primary tumors, followed by survival analysis based on expression levels. Unsupervised clustering of drug response data revealed subtype‐specific vulnerabilities, with EGFR inhibitors showing marked cytotoxicity in all five NSMP lines (robust Z ‐score ≤ −2), and minimal activity in MAPK‐ and USP9X ‐mutant lines. EGFR inhibitors (avitinib, AV‐412) showed selective, low‐dose synergy with standard‐of‐care chemotherapy in NSMP models, with minimal and inconsistent effects in MAPK‐mutant lines. NSMP tumors showed elevated EGFR mRNA and EGFR protein expression, associated with poor survival, advanced disease stage, and peritoneal involvement, and inversely correlated with MAPK mutations. These findings position EGFR overexpression as a defining and targetable feature of NSMP LGSOC and support further preclinical validation of EGFR inhibitors as a treatment strategy for this understudied cancer subtype. © 2026 The Pathological Society of Great Britain and Ireland.

Disease is a heterogeneous process that involves multiple organs and cell types. Understanding how genomic variation contributes to disease requires approaches that move beyond the linear assumptions of additive models and resolve underlying disease pathways. While genome-wide association studies have catalogued hundreds of thousands of genomic variants linked to disease, our understanding of their cell-type specific roles remains largely limited, restricting our ability to translate genetic findings into targeted interventions. Here, we analyse consortium-scale epigenomic data spanning 833 biological samples across 8 epigenetic features to develop a generalisable machine learning framework that models the modular architecture of genome regulation. We define 720 epigenomic signatures, Epigenetically Co-Modulated Patterns (EpiCops), that capture co-regulated genomic regions with tissue and cell-specific regulatory activity. Using EpiCops, we effectively segregate functional genomic loci of mixed biological contexts, including cell-type specific enhancers, variants of complex traits and diseases. Applied to type-2-diabetes, EpiCops identify variant clusters associated with distinct biological pathways and organs, including clusters of opposing cardiovascular risk profiles driven by divergent organ-specific regulatory mechanisms. By integrating EpiCops with partitioned polygenic risk score, we further validate robustness of these variant clusters in independent cohort studies. Collectively, our study demonstrates EpiCops as a scalable framework for resolving the cell-type specific regulatory architecture of complex disease and advancing mechanistic understanding of disease processes.

Modern biology increasingly relies on large-scale screening to generate high dimensional datasets with potential to accelerate discovery. However, analysing these complex datasets remains challenging, due to hierarchical biological structure, uncertainty in the true number of groups, and high dimensional noise that confounds genuine biological signal. Here we present an unsupervised consensus clustering tool, Untangled, that aggregates clustering solutions across granularities to construct a stability-based representation, followed by cluster number optimization and systematic evaluation of cluster robustness. Through extensive benchmarking on simulated datasets, ground truth datasets and diverse high-dimensional screening applications, we demonstrate that Untangled reliably recovers underlying relationships, resolves stable meaningful substructure, and more effectively prioritizes robust clusters with shared biological mechanisms and conserved phenotypic responses than alternative clustering approaches. These results establish Untangled as a scalable framework for cluster discovery to guide efficient follow-up investigation from high-dimensional biological datasets.

Heart disease is the leading cause of morbidity and mortality in individuals with diabetes, due largely to risks associated with ischaemic injuries such as myocardial infarction (MI). We use human population genetic data to demonstrate that classical cardiovascular disease risk biomarkers, including common measures of hyperglycaemia, do not fully account for the increased risk of post-MI mortality in patients with diabetes. This study therefore systematically evaluates glycaemic stress underpinning cardiovascular risk in diabetes. Here, we show using in vivo studies in adult male mice and in vitro models that glycaemic variability, rather than sustained hyperglycaemia alone, is a key risk factor for cardiomyocyte dysfunction and increased susceptibility to myocardial injury in diabetes. We further demonstrate that patient plasma assays can elucidate the predictive potential of glycaemic variability as a primary contributor to cardiomyocyte dysfunction and subclinical cardiac injury in diabetes. These findings provide preclinical models for mechanistic and drug discovery studies and inform strategies for managing cardiovascular outcomes in patients with diabetes.

Human induced pluripotent stem cell–derived cardiomyocytes (iPSC-CMs) are widely used to model cardiovascular disease, yet numerous differentiation protocols generate cardiomyocytes with heterogeneous molecular and functional properties, complicating experimental design. Here we systematically compare sixteen commonly used cardiomyocyte differentiation protocols and characterize their resulting cell states using single-nucleus RNA sequencing, functional phenotyping and computational integration with human genetic data. Despite similar cardiomyocyte yields, protocols produced distinct transcriptional programs, subtype compositions and physiological properties. By integrating protocol-specific gene expression signatures with genome-wide association studies of cardiovascular traits, we identify cardiomyocyte states enriched for genetic architectures underlying specific diseases. These analyses accurately predict protocols most suitable for modelling particular disease contexts, including electrophysiological defects associated with Brugada syndrome and metabolic vulnerability relevant to myocardial infarction. Our results demonstrate that differentiation protocols encode biologically distinct cardiomyocyte states with differential disease relevance and establish a framework for aligning stem-cell differentiation strategies with human complex trait genetics to guide model selection. This approach enables rational design of iPSC-based disease models and highlights how population-scale genetic data can inform experimental systems in stem cell biology.

Mechanisms by which macrophages deploy antibacterial zinc toxicity are poorly understood. To gain insight into this antimicrobial pathway, we developed bacterial reporter–paired single-cell RNA sequencing of human monocyte-derived macrophages (HMDM) infected with an Escherichia coli zinc-stress reporter strain. We identified HMDM subpopulations harboring zinc-stressed E. coli and corresponding mammalian genes predicted to be associated with either zinc toxicity or survival of zinc-stressed bacteria. Consistent with the latter, SLC30A4 that encodes zinc exporter ZNT4 was enriched in one subpopulation of HMDM containing zinc-stressed E. coli and its overexpression in human macrophages increased intracellular E. coli survival. At a population level, SLC30A4 expression was rapidly downregulated in human macrophages responding to E. coli and its ectopic expression in macrophages attenuated zinc starvation of intracellular E. coli . This is consistent with a model in which macrophages switch off SLC30A4 to engage zinc starvation, while also deploying zinc toxicity against bacteria adapting to a low-zinc environment. Consistent with this, intramacrophage E. coli rapidly upregulated znuA messenger RNA (mRNA) that is induced during zinc limitation, with zntA mRNA that is induced during zinc stress peaking later. Moreover, E. coli cultured under conditions of zinc limitation displayed greatly enhanced zinc sensitivity. Susceptibility of zinc-sensitive E. coli to killing by macrophages was also attenuated when zinc uptake by E. coli was inactivated, confirming the coordinated actions of zinc starvation and zinc toxicity in macrophage antibacterial responses. Strategies that enhance zinc starvation of intracellular bacteria could be exploited in the design of host-directed therapeutics that amplify macrophage-mediated antibacterial zinc toxicity.

Gene expression analysis has evolved substantially over the past 25 years, from early transcript surveys using expressed sequence tags and microarrays to RNA sequencing, and more recently to single-cell and spatial transcriptomics. These successive waves have expanded measurement scale and resolution, enabling systematic discovery of transcriptional programmes, inference of gene regulatory networks, and increasingly direct links between transcriptomic insight and therapeutic strategies that modulate gene expression. In this Perspective, we synthesize major methodological milestones with bibliometric trends in leading bioinformatics journals to describe four revolutions that redefined gene expression analysis. We also map widely used computational tools onto a common timeline by analysing 70 78 831 open-access full-text articles, illustrating how enduring statistical frameworks coexist with rapidly growing end-to-end analysis ecosystems. We highlight current challenges and emerging directions in core bioinformatics approaches for gene expression analysis. Looking ahead, we argue that the next era will be defined less by generating new datasets and more by organizing, searching, and reusing transcriptomic and multimodal information at scale. We propose three future directions: consortium-scale searchable transcriptomic knowledgebases, foundation models for gene expression analysis, and programmable regulatory design for engineered control of gene expression. The landscape of gene expression analysis is shifting from descriptive measurement towards queryable, predictive, and programmable gene expression biology.

BACKGROUND: Myocardial infarction (MI) is a leading cause of death worldwide and can eliminate up to a third of the cardiomyocytes within the human heart. Although cardiomyocytes undergo mitosis during early development, most cardiomyocytes cease cell cycling soon after birth. In contrast, rodent MI models have shown that cardiomyocytes increase mitosis in response to ischemia; however, this has not been shown in humans. METHODS: Using a unique premortem post-MI human heart, immunostaining, bulk RNA sequencing, proteomics, metabolomics, single-nucleus RNA sequencing and a novel post-MI human biopsy method, we investigated human cardiomyocyte mitosis post-MI. RESULTS: We show that adult human cardiomyocytes exhibit increased mitosis and cytokinesis in response to ischemia. CONCLUSIONS: Future development of therapeutics to enhance this intrinsic mitotic potential could lead to new treatments that reverse heart failure via cardiac regeneration.