Distinct transcriptomic and epigenomic modalities underpin human memory T cell subsets and their activation potential

scPLAN: a hierarchical computational framework for single transcriptomics data annotation, integration and cell-type label refinement

MOTIVATION

In the past decade, single-cell RNA sequencing (scRNA-seq) has emerged as a pivotal method for transcriptomic profiling in biomedical research. Precise cell-type identification is crucial for subsequent analysis of single-cell data. And the integration and refinement of annotated data are essential for building comprehensive databases. However, prevailing annotation techniques often overlook the hierarchical organization of cell types, resulting in inconsistent annotations. Meanwhile, most existing integration approaches fail to integrate datasets with different annotation depths and none of them can enhance the labels of outdated data with lower annotation resolutions using more intricately annotated datasets or novel biological findings.

RESULTS

Here, we introduce scPLAN, a hierarchical computational framework designed for scRNA-seq data analysis. scPLAN excels in annotating unlabeled scRNA-seq data using a reference dataset structured along a hierarchical cell-type tree. It identifies potential novel cell types in a systematic, layer-by-layer manner. Additionally, scPLAN effectively integrates annotated scRNA-seq datasets with varying levels of annotation depth, ensuring consistent refinement of cell-type labels across datasets with lower resolutions. Through extensive annotation and novel cell detection experiments, scPLAN has demonstrated its efficacy. Two case studies have been conducted to showcase how scPLAN integrates datasets with diverse cell-type label resolutions and refine their cell-type labels.

AVAILABILITY

https://github.com/michaelGuo1204/scPLAN.

SifiNet: a robust and accurate method to identify feature gene sets and annotate cells

SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multi-omic cellular profiles. It is conveniently available as an open-source R package.

SifiNet: A robust and accurate method to identify feature gene sets and annotate cells

SifiNet is a robust and accurate computational pipeline for identifying distinct gene sets, extracting and annotating cellular subpopulations, and elucidating intrinsic relationships among these subpopulations. Uniquely, SifiNet bypasses the cell clustering stage, commonly integrated into other cellular annotation pipelines, thereby circumventing potential inaccuracies in clustering that may compromise subsequent analyses. Consequently, SifiNet has demonstrated superior performance in multiple experimental datasets compared with other state-of-the-art methods. SifiNet can analyze both single-cell RNA and ATAC sequencing data, thereby rendering comprehensive multiomic cellular profiles. It is conveniently available as an open-source R package.

Cytotoxic CD8+ Temra cells show loss of chromatin accessibility at genes associated with T cell activation

As humans age, their memory T cell compartment expands due to the lifelong exposure to antigens. This expansion is characterized by terminally differentiated CD8+ T cells (Temra), which possess NK cell-like phenotype and are associated with chronic inflammatory conditions. Temra cells are predominantly driven by the sporadic reactivation of cytomegalovirus (CMV), yet their epigenomic patterns and cellular heterogeneity remain understudied. To address this gap, we correlated their gene expression profiles with chromatin openness and conducted single-cell transcriptome analysis, comparing them to other CD8+ subsets and CMV-responses. We confirmed that Temra cells exhibit high expression of genes associated with cytotoxicity and lower expression of costimulatory and chemokine genes. The data revealed that CMV-responsive CD8+ T cells (Tcmv) were predominantly derived from a mixed population of Temra and memory cells (Tcm/em) and shared their transcriptomic profiles. Using ATAC-seq analysis, we identified 1449 differentially accessible chromatin regions between CD8+ Temra and Tcm/em cells, of which only 127 sites gained chromatin accessibility in Temra cells. We further identified 51 gene loci, including costimulatory CD27, CD28, and ICOS genes, whose chromatin accessibility correlated with their gene expression. The differential chromatin regions Tcm/em cells were enriched in motifs that bind multiple transcriptional activators, such as Jun/Fos, NFkappaB, and STAT, whereas the open regions in Temra cells mainly contained binding sites of T-box transcription factors. Our single-cell analysis of CD8+CCR7loCD45RAhi sorted Temra population showed several subsets of Temra and NKT-like cells and CMC1+ Temra populations in older individuals that were shifted towards decreased cytotoxicity. Among CD8+CCR7loCD45RAhi sorted cells, we found a decreased proportion of IL7R+ Tcm/em-like and MAIT cells in individuals with high levels of CMV antibodies (CMVhi). These results shed new light on the molecular and cellular heterogeneity of CD8+ Temra cells and their relationship to aging and CMV infection.

Unraveling the Phenotypic States of Human innate-like T Cells: Comparative Insights with Conventional T Cells and Mouse Models

The "innate-like" T cell compartment, known as Tinn, represents a diverse group of T cells that straddle the boundary between innate and adaptive immunity, having the ability to mount rapid responses following activation. In mice, this ability is acquired during thymic development. We explored the transcriptional landscape of Tinn compared to conventional T cells (Tconv) in the human thymus and blood using single cell RNA sequencing and flow cytometry. We reveal that in human blood, the majority of Tinn cells, including iNKT, MAIT, and Vδ2+Vγ9+ T cells, share an effector program characterized by the expression of unique chemokine and cytokine receptors, and cytotoxic molecules. This program is driven by specific transcription factors, distinct from those governing Tconv cells. Conversely, only a fraction of thymic Tinn cells displays an effector phenotype, while others share transcriptional features with developing Tconv cells, indicating potential divergent developmental pathways. Unlike the mouse, human Tinn cells do not differentiate into multiple effector subsets but develop a mixed type I/type III effector potential. To conduct a comprehensive cross-species analysis, we constructed a murine Tinn developmental atlas and uncovered additional species-specific distinctions, including the absence of type II Tinn cells in humans, which implies distinct immune regulatory mechanisms across species. The study provides insights into the development and functionality of Tinn cells, emphasizing their role in immune responses and their potential as targets for therapeutic interventions.

Unveiling the growing significance of metabolism in modulating immune cell function: exploring mechanisms and implications; a review

Immunometabolism has emerged as a rapidly growing field of research, holding significant promise for personalised medicine and precision immunotherapy. This review explores the intricate relationship between immune function and metabolic processes, emphasising their profound impact on various immune-related disorders. Understanding how metabolic dysregulation contributes to the pathogenesis of these disorders remains a critical research gap. Therefore, this review aims to bridge that gap by examining the key metabolic pathways involved and their specific implications in immune cell function. Key metabolic pathways, including glycolysis, mitochondrial metabolism, fatty acid metabolism, and amino acid metabolism, are discussed in the context of immune cell function. Dysregulation of these pathways can disrupt immune cell activation, differentiation, and overall function, contributing to disease pathogenesis. Understanding these metabolic alterations' molecular mechanisms is essential for developing targeted therapeutic interventions. The review also emphasises the importance of personalised medicine in immune-related disorders. The unique metabolic profiles of individuals can influence treatment outcomes, highlighting the need for tailored approaches. Integrating metabolic profiling into clinical practice can enhance treatment efficacy and improve patient outcomes. Investigating the clinical significance of immunometabolism in diverse disease contexts will facilitate the translation of research findings into clinical practice. Moreover, refining treatment strategies based on individual metabolic profiles will contribute to advancing precision immunotherapy.