Aging profoundly affects immune system function, rendering the elderly more susceptible to pathogens, cancers and chronic inflammation. We previously identified a population of IL-10-producing, T follicular helper-like cells ("Tfh10"), linked to suppressed vaccine responses in aged mice. Here, we use the power of single-cell (sc)genomics and genome-scale modeling to characterize Tfh10 — and the full CD4+memory T cell (CD4+TM) compartment — in young and old mice. Unprecedented scRNA-seq coverage of the CD4+TM compartment and parallel chromatin accessibility measurements (scATAC-seq) enabled identification of 13 CD4+TM populations, which we validated as a reference through comprehensive cross-comparison to aging cell atlases and scRNA-seq studies reporting Tfh10 in other contexts. Beyond robust characterization of age- and cell-type-dependent transcriptional landscapes, we used integrative computational modeling to predict the underlying regulatory mechanisms: We inferred gene regulatory networks (GRNs) that describe transcription-factor control of gene expression in each T-cell population and how these circuits change with age. Furthermore, we integrated our data with prior, pan-cell scRNA-seq studies to identify intercellular-signaling networks driving age-dependent changes in CD4+TM. Our atlas of finely resolved CD4+TM subsets, GRNs and cell-cell communication networks is a critical resource for analysis of biologic processes operative in memory T cells in youth and old age. The resource presents new opportunities to manipulate regulatory circuits in CD4+TM, which, long-term, could improve immune responses in the elderly.

Transcription factors read the genome, fundamentally connecting DNA sequence to gene expression across diverse cell types. Determining how, where, and when TFs bind chromatin will advance our understanding of gene regulatory networks and cellular behavior. The 2017 ENCODE-DREAM in vivo Transcription-Factor Binding Site (TFBS) Prediction Challenge highlighted the value of chromatin accessibility data to TFBS prediction, establishing state-of-the-art methods for TFBS prediction from DNase-seq. However, the more recent Assay-for-Transposase-Accessible-Chromatin (ATAC)-seq has surpassed DNase-seq as the most widely-used chromatin accessibility profiling method. Furthermore, ATAC-seq is the only such technique available at single-cell resolution from standard commercial platforms. While ATAC-seq datasets grow exponentially, suboptimal motif scanning is unfortunately the most common method for TFBS prediction from ATAC-seq. To enable community access to state-of-the-art TFBS prediction from ATAC-seq, we (1) curated an extensive benchmark dataset (127 TFs) for ATAC-seq model training and (2) built “maxATAC”, a suite of user-friendly, deep neural network models for genome-wide TFBS prediction from ATAC-seq in any cell type. With models available for 127 human TFs, maxATAC is the largest collection of high-performance TFBS prediction models for ATAC-seq. maxATAC performance extends to primary cells and single-cell ATAC-seq, enabling improved TFBS prediction in vivo. We demonstrate maxATAC’s capabilities by identifying TFBS associated with allele-dependent chromatin accessibility at atopic dermatitis genetic risk loci.

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Transcription factors read the genome, fundamentally connecting DNA sequence to gene expression across diverse cell types. Determining how, where, and when TFs bind chromatin will advance our understanding of gene regulatory networks and cellular behavior. The 2017 ENCODE-DREAM in vivo Transcription-Factor Binding Site (TFBS) Prediction Challenge highlighted the value of chromatin accessibility data to TFBS prediction, establishing state-of-the- art methods. Yet, while Assay-for-Transposase-Accessible-Chromatin (ATAC)-seq datasets grow exponentially, suboptimal motif scanning is commonly used for TFBS prediction from ATAC-seq. Here, we present "maxATAC", a suite of user-friendly, deep neural network models for genome- wide TFBS prediction from ATAC-seq in any cell type. With models available for 127 human TFs, maxATAC is the largest collection of state-of-the-art TFBS models to date. maxATAC performance extends to primary cells and single-cell ATAC-seq, enabling state-of-the-art TFBS prediction in vivo. We demonstrate maxATAC's capabilities by identifying TFBS associated with allele-dependent chromatin accessibility at atopic dermatitis genetic risk loci.

Our recent data showed that an aberrant IL-10-producing T follicular helper population (Tfh10) accumulates dramatically with age and is associated with age-related declines in vaccine responsiveness. Through single cell RNA sequencing and chromatin accessibility analysis of IL-10+ and IL-10- memory CD4+ T cells from young and aged mice, we identified increased expression of CD153 on aged Tfh and Tfh10 cells. Mechanistically, we linked inflammaging (increased IL-6 levels) to elevated CD153 expression of Tfh cells through c-Maf. Surprisingly, blockade of CD153 in aged mice significantly reduced their vaccine-driven antibody response, which was associated with decreased expression of ICOS on antigen-specific Tfh cells. Combined, these data show that an IL-6/c-Maf/CD153 circuit is critical for maintaining ICOS expression. Thus, although overall Tfh-mediated B cell responses are reduced in the context of vaccines and aging, our data suggest that elevated expression of CD153 on Tfh cells potentiates the remaining Tfh function in aged mice.

Myocardial infarction remains a leading cause of morbidity and death. Insufficient delivery of oxygen to the myocardium sets into play a complicated process of repair that involves the temporal recruitment of different immune cells so as to remove debris and necrotic cells expeditiously and to form effective scar tissue. Clearly defined and overlapping phases have been identified in the process, which transitions from an overall proinflammatory to anti-inflammatory phenotype with time. Variations in the strength of the phases as well as in the co-ordination among them have profound consequences. Too strong of an inflammatory phase can result in left ventricular wall thinning and eventual rupture, whereas too strong of an anti-inflammatory phase can lead to cardiac stiffening, arrhythmias, or ventricular aneurisms. In both cases, heart failure is an intermediate consequence with death being the likely outcome. Here, we summarize the role of key immune cells in the repair process of the heart after left ventricular myocardial infarction, along with the associated cytokines and chemokines. A better understanding of the immune response ought to lead hopefully to improved therapies that exploit the natural repair process for mending the infarcted heart.

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