The adaptive immune system is equipped to eliminate both tumors and pathogenic microorganisms. It requires a series of complex and coordinated signals to drive the activation, proliferation and differentiation of appropriate T cell subsets. It is now established that changes in cellular activation are coupled to profound changes in cellular metabolism. In addition, emerging evidence now suggest that specific metabolic alterations associated with distinct T cell subsets may be ancillary to their differentiation and influential in their immune functions. The “Warburg effect" originally used to describe a phenomenon in which most cancer cells relied on aerobic glycolysis for their growth is a key process that sustain T cell activation and differentiation. Here we review how different aspects of metabolism in T cells influence their functions, focusing on the emerging role of key regulators of glucose metabolism such as HIF-1α. A thorough understanding of the role of metabolism in T cell function could provide insights into mechanisms involved in inflammatory-mediated conditions, with the potential for developing novel therapeutic approaches to treat these diseases.

Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advance understanding of cellular processes. First, we present a spatial graph-based method, pseudo-time-space (PSTS), to model and uncover relationships between transcriptional states of cells across tissues undergoing dynamic change (e.g. neurodevelopment, brain injury and/or microglia activation, and cancer progression). We further developed a spatially-constrained two-level permutation (SCTP) test to study cell-cell interaction, finding highly interactive tissue regions across thousands of ligand-receptor pairs with markedly reduced false discovery rates. Finally, we present a spatial graph-based imputation method with neural network (stSME), to correct for technical noise/dropout and increase ST data coverage. Together, the algorithms that we developed, implemented in the comprehensive and fast stLearn software, allow for robust interrogation of biological processes within healthy and diseased tissues.

Immune cells cycle between a resting and an activated state. Their metabolism is tightly linked to their activation status and, consequently, functions. Ag recognition induces T lymphocyte activation and proliferation and acquisition of effector functions that require and depend on cellular metabolic reprogramming. Likewise, recognition of pathogen-associated molecular patterns by monocytes and macrophages induces changes in cellular metabolism. As obligate intracellular parasites, viruses manipulate the metabolism of infected cells to meet their structural and functional requirements. For example, HIV-induced changes in immune cell metabolism and redox state are associated with CD4⁺ T cell depletion, immune activation, and inflammation. In this review, we highlight how HIV modifies immunometabolism with potential implications for cure research and pathogenesis of comorbidities observed in HIV-infected patients, including those with virologic suppression. In addition, we highlight recently described key methods that can be applied to study the metabolic dysregulation of immune cells in disease states.

The dermis and epidermis, crucial structural layers of the skin, encompass appendages, hair follicles (HFs), and intricate cellular heterogeneity. However, an integrated spatiotemporal transcriptomic atlas of embryonic skin has not yet been described and would be invaluable for studying skin‐related diseases in humans. Here, single‐cell and spatial transcriptomic analyses are performed on skin samples of normal and hairless fetal pigs across four developmental periods. The cross‐species comparison of skin cells illustrated that the pig epidermis is more representative of the human epidermis than mice epidermis. Moreover, Phenome‐wide association study analysis revealed that the conserved genes between pigs and humans are strongly associated with human skin‐related diseases. In the epidermis, two lineage differentiation trajectories describe hair follicle (HF) morphogenesis and epidermal development. By comparing normal and hairless fetal pigs, it is found that the hair placode (Pc), the most characteristic initial structure in HFs, arises from progenitor‐like OGN ⁺ / UCHL1 ⁺ cells. These progenitors appear earlier in development than the previously described early Pc cells and exhibit abnormal proliferation and migration during differentiation in hairless pigs. The study provides a valuable resource for in‐depth insights into HF development, which may serve as a key reference atlas for studying human skin disease etiology using porcine models.

The hypothalamus displays staggering cellular diversity, chiefly established during embryogenesis by the interplay of several signalling pathways and a battery of transcription factors. However, the contribution of epigenetic cues to hypothalamus development remains unclear. We mutated the polycomb repressor complex 2 gene Eed in the developing mouse hypothalamus, which resulted in the loss of H3K27me3, a fundamental epigenetic repressor mark. This triggered ectopic expression of posteriorly expressed regulators (e.g. Hox homeotic genes), upregulation of cell cycle inhibitors and reduced proliferation. Surprisingly, despite these effects, single cell transcriptomic analysis revealed that most neuronal subtypes were still generated in Eed mutants. However, we observed an increase in glutamatergic/GABAergic double-positive cells, as well as loss/reduction of dopamine, hypocretin and Tac2-Pax6 neurons. These findings indicate that many aspects of the hypothalamic gene regulatory flow can proceed without the key H3K27me3 epigenetic repressor mark, but points to a unique sensitivity of particular neuronal subtypes to a disrupted epigenomic landscape.

Identification of cell types using single-cell RNA-seq is revolutionizing the study of multicellular organisms. However, typical single-cell RNA-seq analysis often involves post hoc manual curation to ensure clusters are transcriptionally distinct, which is time-consuming, error-prone, and irreproducible. To overcome these obstacles, we developed Cytocipher, a bioinformatics method and scverse compatible software package that statistically determines significant clusters. Application of Cytocipher to normal tissue, development, disease, and large-scale atlas data reveals the broad applicability and power of Cytocipher to generate biological insights in numerous contexts. This included the identification of cell types not previously described in the datasets analysed, such as CD8+ T cell subtypes in human peripheral blood mononuclear cells; cell lineage intermediate states during mouse pancreas development; and subpopulations of luminal epithelial cells over-represented in prostate cancer. Cytocipher also scales to large datasets with high-test performance, as shown by application to the Tabula Sapiens Atlas representing >480 000 cells. Cytocipher is a novel and generalizable method that statistically determines transcriptionally distinct and programmatically reproducible clusters from single-cell data. The software version used for this manuscript has been deposited on Zenodo (https://doi.org/10.5281/zenodo.8089546), and is also available via github (https://github.com/BradBalderson/Cytocipher).