Single-cell atlas of healthy human blood unveils age-related loss of NKG2C+GZMB-CD8+ memory T cells and accumulation of type 2 memory T cells.

Extensive, large-scale single-cell profiling of healthy human blood at different ages is one of the critical pending tasks required to establish a framework for the systematic understanding of human aging. Here, using single-cell RNA/T cell receptor (TCR)/BCR-seq with protein feature barcoding, we profiled 317 samples from 166 healthy individuals aged 25-85 years old. From this, we generated a dataset from ∼2 million cells that described 55 subpopulations of blood immune cells. Twelve subpopulations changed with age, including the accumulation of GZMK+CD8+ T cells and HLA-DR+CD4+ T cells. In contrast to other T cell memory subsets, transcriptionally distinct NKG2C+GZMB-CD8+ T cells counterintuitively decreased with age. Furthermore, we found a concerted age-associated increase in type 2/interleukin (IL)4-expressing memory subpopulations across CD4+ and CD8+ T cell compartments (CCR4+CD8+ Tcm and Th2 CD4+ Tmem), suggesting a systematic functional shift in immune homeostasis with age. Our work provides novel insights into healthy human aging and a comprehensive annotated resource.

Enhanced epigenetic profiling of classical human monocytes reveals a specific signature of healthy aging in the DNA methylome.

The impact of healthy aging on molecular programming of immune cells is poorly understood. Here, we report comprehensive characterization of healthy aging in human classical monocytes, with a focus on epigenomic, transcriptomic, and proteomic alterations, as well as the corresponding proteomic and metabolomic data for plasma, using healthy cohorts of 20 young and 20 older males (~27 and ~64 years old on average). For each individual, we performed eRRBS-based DNA methylation profiling, which allowed us to identify a set of age-associated differentially methylated regions (DMRs) - a novel, cell-type specific signature of aging in DNA methylome. Hypermethylation events were associated with H3K27me3 in the CpG islands near promoters of lowly-expressed genes, while hypomethylated DMRs were enriched in H3K4me1 marked regions and associated with age-related increase of expression of the corresponding genes, providing a link between DNA methylation and age-associated transcriptional changes in primary human cells.

Semi-supervised peak calling with SPAN and JBR genome browser.

The widespread application of ChIP-seq led to a growing need for consistent analysis of multiple epigenetics profiles, for instance, in human studies where multiple replicates are a common element of design. Such multi-samples experimental designs introduced analytical and computational challenges. For example, when peak calling is done independently for each sample, small differences in signal strength/quality lead to a very different number of peaks for individual samples, making group-level analysis difficult. On the other side, when samples are pooled together for joint analysis, individual-level statistical differences are averaged out. Recently, we have demonstrated that a semi-supervised peak calling approach (SPAN) allows for robust analysis of multiple epigenetic profiles while preserving individual sample statistics. Here, we present this approach's implementation, centered around the JBR genome browser, a stand-alone tool that allows for accessible and streamlined annotation, analysis and visualization. Specifically, JBR supports graphical interactive manual region selection and annotation, thereby addressing supervised learning's key procedural challenge. Furthermore, JBR includes the capability for peak optimization, i.e. calibration of sample-specific peak calling parameters by leveraging manual annotation. This procedure can be applied to a broad range of ChIP-seq datasets of different quality and chromatin accessibility ATAC-seq, including single-cell experiments. JBR was designed for efficient data processing, resulting in fast viewing and analysis of multiple replicates, up to thousands of tracks. Accelerated execution and integrated semi-supervised peak calling make JBR and SPAN next-generation visualization and analysis tools for multi-sample epigenetic data. AVAILABILITY AND IMPLEMENTATION: SPAN and JBR run on Linux, Mac OS and Windows, and is freely available at https://research.jetbrains.org/groups/biolabs/tools/span-peak-analyzer and https://research.jetbrains.org/groups/biolabs/tools/jbr-genome-browser. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.