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Detecting multiplets in single nucleus (sn)ATAC-seq data is challenging due to data sparsity and limited dynamic range. AMULET (ATAC-seq MULtiplet Estimation Tool) enumerates regions with greater than two uniquely aligned reads across the genome to effectively detect multiplets. We evaluate the method by generating snATAC-seq data in the human blood and pancreatic islet samples. AMULET has high precision, estimated via donor-based multiplexing, and high recall, estimated via simulated multiplets, compared to alternatives and identifies multiplets most effectively when a certain read depth of 25K median valid reads per nucleus is achieved.

Cis -Regulatory elements ( cis -REs) include promoters, enhancers, and insulators that regulate gene expression programs via binding of transcription factors. ATAC-seq technology effectively identifies active cis -REs in a given cell type (including from single cells) by mapping accessible chromatin at base-pair resolution. However, these maps are not immediately useful for inferring specific functions of cis -REs. For this purpose, we developed a deep learning framework (CoRE-ATAC) with novel data encoders that integrate DNA sequence (reference or personal genotypes) with ATAC-seq cut sites and read pileups. CoRE-ATAC was trained on 4 cell types (n = 6 samples/replicates) and accurately predicted known cis -RE functions from 7 cell types (n = 40 samples) that were not used in model training (mean average precision = 0.80, mean F1 score = 0.70). CoRE-ATAC enhancer predictions from 19 human islet samples coincided with genetically modulated gain/loss of enhancer activity, which was confirmed by massively parallel reporter assays (MPRAs). Finally, CoRE-ATAC effectively inferred cis -RE function from aggregate single nucleus ATAC-seq (snATAC) data from human blood-derived immune cells that overlapped with known functional annotations in sorted immune cells, which established the efficacy of these models to study cis -RE functions of rare cells without the need for cell sorting. ATAC-seq maps from primary human cells reveal individual- and cell-specific variation in cis -RE activity. CoRE-ATAC increases the functional resolution of these maps, a critical step for studying regulatory disruptions behind diseases.

Enhancers are cis -acting sequences that regulate transcription rates of their target genes in a cell-specific manner and harbor disease-associated sequence variants in cognate cell types. Many complex diseases are associated with enhancer malfunction, necessitating the discovery and study of enhancers from clinical samples. Assay for Transposase Accessible Chromatin (ATAC-seq) technology can interrogate chromatin accessibility from small cell numbers and facilitate studying enhancers in pathologies. However, on average, ~35% of open chromatin regions (OCRs) from ATAC-seq samples map to enhancers. We developed a neural network-based model, Predicting Enhancers from ATAC-Seq data (PEAS), to effectively infer enhancers from clinical ATAC-seq samples by extracting ATAC-seq data features and integrating these with sequence-related features (e.g., GC ratio). PEAS recapitulated ChromHMM-defined enhancers in CD14+ monocytes, CD4+ T cells, GM12878, peripheral blood mononuclear cells, and pancreatic islets. PEAS models trained on these 5 cell types effectively predicted enhancers in four cell types that are not used in model training (EndoC-βH1, naïve CD8+ T, MCF7, and K562 cells). Finally, PEAS inferred individual-specific enhancers from 19 islet ATAC-seq samples and revealed variability in enhancer activity across individuals, including those driven by genetic differences. PEAS is an easy-to-use tool developed to study enhancers in pathologies by taking advantage of the increasing number of clinical epigenomes.

Recent studies of the human genome have indicated that regulatory elements (e.g. promoters and enhancers) at distal genomic locations can interact with each other via chromatin folding and affect gene expression levels. Genomic technologies for mapping interactions between DNA regions, e.g., ChIA-PET and HiC, can generate genome-wide maps of interactions between regulatory elements. These interaction datasets are important resources to infer distal gene targets of non-coding regulatory elements and to facilitate prioritization of critical loci for important cellular functions. With the increasing diversity and complexity of genomic information and public ontologies, making sense of these datasets demands integrative and easy-to-use software tools. Moreover, network representation of chromatin interaction maps enables effective data visualization, integration, and mining. Currently, there is no software that can take full advantage of network theory approaches for the analysis of chromatin interaction datasets. To fill this gap, we developed a web-based application, QuIN, which enables: 1) building and visualizing chromatin interaction networks, 2) annotating networks with user-provided private and publicly available functional genomics and interaction datasets, 3) querying network components based on gene name or chromosome location, and 4) utilizing network based measures to identify and prioritize critical regulatory targets and their direct and indirect interactions. QuIN's web server is available at http://quin.jax.org QuIN is developed in Java and JavaScript, utilizing an Apache Tomcat web server and MySQL database and the source code is available under the GPLV3 license available on GitHub: https://github.com/UcarLab/QuIN/.

Broad domain promoters and super enhancers are regulatory elements that govern cell-specific functions and harbor disease-associated sequence variants. These elements are characterized by distinct epigenomic profiles, such as expanded deposition of histone marks H3K27ac for super enhancers and H3K4me3 for broad domains, however little is known about how they interact with each other and the rest of the genome in three-dimensional chromatin space. Using network theory methods, we studied chromatin interactions between broad domains and super enhancers in three ENCODE cell lines (K562, MCF7, GM12878) obtained via ChIA-PET, Hi-C, and Hi-CHIP assays. In these networks, broad domains and super enhancers interact more frequently with each other compared to their typical counterparts. Network measures and graphlets revealed distinct connectivity patterns associated with these regulatory elements that are robust across cell types and alternative assays. Machine learning models showed that these connectivity patterns could effectively discriminate broad domains from typical promoters and super enhancers from typical enhancers. Finally, targets of broad domains in these networks were enriched in disease-causing SNPs of cognate cell types. Taken together these results suggest a robust and unique organization of the chromatin around broad domains and super enhancers: loci critical for pathologies and cell-specific functions.

The human immune system undergoes continuous remodeling from infancy through old age, yet the timing and trajectory of these changes across the lifespan remain poorly defined. To address this, we profiled peripheral blood mononuclear cells from 95 healthy individuals (ages 2 months to 88 years), including infants (n=27), children (n=23), adults (n=18), and older adults (n=27) using scRNA-seq and snATAC-seq. MAIT and γδ T cells showed a \"Rise and fall\" pattern, which rise in childhood, peak in young adulthood, and decline with age. CD8 T cells were the most affected by aging with decreasing naïve T cells and increasing GzK CD8 T cells and TEMRA cells. Infants had lower myeloid/lymphoid ratio, with a distinct composition marked by increased frequencies of CD16 monocytes and plasmacytoid dendritic cells and reduced frequencies of CD14 monocytes and conventional DCs. Their adaptive immune compartment also displayed unique features, including constitutive interferon-stimulated gene expression in T and B cells, and an expanded SOX4 populations in naïve CD4 , naïve CD8 and γδ T cells, comprising ~30% of the naïve T cell pool. SOX4 naïve CD4 T cells displayed a Th2 epigenetic signature. This map provides critical insights into human immune system dynamics across the lifespan, emphasizing unique features of the infant immune system.

Cytomegalovirus (CMV), a common herpesvirus, establishes lifelong latency and increases in prevalence with age; yet its systemic impact on the aging immune system remains incompletely understood. We profiled circulating immune cells from healthy older adults (median age: 73) who were CMV(+) or CMV(-) using single-cell RNA-sequencing and validated key findings by flow cytometry. CMV(+) individuals exhibited significant expansion of adaptive immune cells: CD4⁺ and CD8⁺ TEMRA T, GZMK + CD8⁺ T, γδ T, and atypical B cells. Among innate immune cells, monocytes and dendritic cells remained largely unchanged while KLRC2 + (adaptive) NK cells increased and CD56 dim NK cells decreased. To facilitate CMV assessment in datasets with unknown CM serostatus, we developed CMVerify , a machine learning classifier that accurately predicts CMV serostatus from single-cell data across platforms and age groups (97% accuracy). These findings reveal extensive CMV-associated immune remodeling in older adults and underscore the importance of incorporating CMV status in studies of immune aging.Cytomegalovirus (CMV), a common herpesvirus, establishes lifelong latency and increases in prevalence with age; yet its systemic impact on the aging immune system remains incompletely understood. We profiled circulating immune cells from healthy older adults (median age: 73) who were CMV(+) or CMV(-) using single-cell RNA-sequencing and validated key findings by flow cytometry. CMV(+) individuals exhibited significant expansion of adaptive immune cells: CD4⁺ and CD8⁺ TEMRA T, GZMK + CD8⁺ T, γδ T, and atypical B cells. Among innate immune cells, monocytes and dendritic cells remained largely unchanged while KLRC2 + (adaptive) NK cells increased and CD56 dim NK cells decreased. To facilitate CMV assessment in datasets with unknown CM serostatus, we developed CMVerify , a machine learning classifier that accurately predicts CMV serostatus from single-cell data across platforms and age groups (97% accuracy). These findings reveal extensive CMV-associated immune remodeling in older adults and underscore the importance of incorporating CMV status in studies of immune aging.

Cytomegalovirus (CMV) is a common herpesvirus that establishes lifelong latency and becomes increasingly prevalent with age. We systematically characterized CMV-associated immune remodeling by analyzing six human cohorts (two newly built) using single-cell RNA sequencing, T cell receptor (TCR) sequencing, and flow cytometry. Beyond the well-known expansion of CD4 /CD8 TEMRA, adaptive NK, and γδ T cells, CMV(+) adults exhibited increased frequencies of GZMK CD8 T cells and atypical B cells, alongside a reduction of CD56 NK cells. Longitudinal profiling of an individual who seroconverted revealed rapid CMV-driven shifts in circulating immune cell frequencies. Single-cell TCR data analyzed using a large database of CMV-associated clones combined with predictive modelling (CMVerify), identified novel CMV-specific clonal expansions reproduced across two independent cohorts. In the CD8 lineage, CMV-specific clones were enriched in GZMK CD8 and CD8 TEMRA cells, while in the CD4 lineage, Th1 cells showed clonal expansion alongside CD4 TEMRA cells. This integrative study revealed how latent CMV alters the cellular and clonal landscape, defining GZMK CD8 and Th1 cells as newly recognized elements of response to CMV in humans.

Endoplasmic reticulum (ER) and inflammatory stress responses are two pathophysiologic factors contributing to islet dysfunction and failure in Type 2 Diabetes (T2D). However, how human islet cells respond to these stressors and whether T2D- associated genetic variants modulate these responses is unknown. To fill this knowledge gap, we profiled transcriptional (RNA-seq) and epigenetic (ATAC-seq) remodeling in human islets exposed to ex vivo ER (thapsigargin) or inflammatory (IL- 1β+IFN-γ) stress. 5,427 genes (~32%) were associated with stress responses; most were stressor-specific, including upregulation of genes mediating unfolded protein response (e.g. DDIT3, ATF4) and NFKB signaling (e.g. NFKB1, NFKBIA) in ER stress and cytokine-induced inflammation respectively. Islet single-cell RNA-seq profiling revealed strong but heterogeneous beta cell ER stress responses, including a distinct beta cell subset that highly expressed apoptotic genes. Epigenetic profiling uncovered 14,968 stress-responsive cis-regulatory elements (CREs; ~14%), the majority of which were stressor-specific, and revealed increased accessibility at binding sites of transcription factors that were induced upon stress (e.g. ATF4 for ER stress, IRF8 for cytokine-induced inflammation). Eighty-six stress-responsive CREs overlapped known T2D-associated variants, including 20 residing within CREs that were more accessible upon ER stress. Among these, we linked the rs6917676 T2D risk allele (T) to increased in vivo accessibility of an islet ER stress-responsive CRE and allele-specific beta cell nuclear factor binding in vitro. We showed that MAP3K5, the only ER stress- responsive gene in this locus, promotes beta cell apoptosis. Consistent with its pro- apoptotic and putative diabetogenic roles, MAP3K5 expression inversely correlated with beta cell abundance in human islets and was induced in beta cells from T2D donors. Together, this study provides new genome-wide insights into human islet stress responses and putative mechanisms of T2D genetic variants.Competing Interest StatementThe authors have declared no competing interest.

Cis-Regulatory elements (cis-REs) include promoters, enhancers, and insulators that regulate gene expression programs via binding of transcription factors. ATAC-seq technology effectively identifies active cis-REs in a given cell type (including from single cells) by mapping the accessible chromatin at base-pair resolution. However, these maps are not immediately useful for inferring specific functions of cis-REs. For this purpose, we developed a deep learning framework (CoRE-ATAC) with novel data encoders that integrate DNA sequence (reference or personal genotypes) and ATAC-seq read pileups. CoRE-ATAC was trained on 4 cell types (n=6 samples/replicates) and accurately predicted known cis-RE functions from 7 cell types (n=40 samples) that were not used in model training (average precision=0.80). CoRE-ATAC enhancer predictions from 19 human islets coincided with genetically modulated gain/loss of enhancer activity, which was confirmed by massively parallel reporter assays (MPRAs). Finally, CoRE-ATAC effectively inferred functionality of cis-REs from aggregate single nucleus ATAC-seq (snATAC) data from human blood-derived immune cells that overlapped well with known functional annotations in sorted immune cells. Performances on snATAC-seq data demonstrate CoRE-ATAC's ability to infer cis-RE function in rare cell populations that can be identified by unsupervised clustering of snATAC-seq cells but difficult to capture in bulk ATAC-seq. ATAC-seq maps from primary human cells reveal individual- and cell-specific variation in cis-RE activity. CoRE-ATAC increases the functional resolution of these maps, a critical step for studying regulatory disruptions behind diseases. Competing Interest Statement The authors have declared no competing interest. Footnotes * https://github.com/UcarLab/CoRE-ATAC/