Robust estimation of cancer and immune cell-type proportions from bulk tumor ATAC-Seq data.

Assay for Transposase-Accessible Chromatin sequencing (ATAC-Seq) is a widely used technique to explore gene regulatory mechanisms. For most ATAC-Seq data from healthy and diseased tissues such as tumors, chromatin accessibility measurement represents a mixed signal from multiple cell types. In this work, we derive reliable chromatin accessibility marker peaks and reference profiles for most non-malignant cell types frequently observed in the microenvironment of human tumors. We then integrate these data into the EPIC deconvolution framework (Racle et al., 2017) to quantify cell-type heterogeneity in bulk ATAC-Seq data. Our EPIC-ATAC tool accurately predicts non-malignant and malignant cell fractions in tumor samples. When applied to a human breast cancer cohort, EPIC-ATAC accurately infers the immune contexture of the main breast cancer subtypes.

Analysis of chromatin accessibility in peripheral blood mononuclear cells from patients with early-stage breast cancer.

Objective: This study was aimed at exploring a specific open region of chromatin in the peripheral blood mononuclear cells (PBMCs) of patients with breast cancer and evaluating its feasibility as a biomarker for diagnosing and predicting breast cancer prognosis. Methods: We obtained PBMCs from breast cancer patients and healthy people for the assay for transposase-accessible chromatin (ATAC) sequencing (n = 3) and obtained the GSE27562 chip sequencing data for secondary analyses. Through bioinformatics analysis, we mined the pattern changes for chromatin accessibility in the PBMCs of breast cancer patients. Results: A total of 1,906 differentially accessible regions (DARs) and 1,632 differentially expressed genes (DEGs) were identified via ATAC sequencing. The upregulated DEGs in the disease group were mainly distributed in the cells, organelles, and cell-intima-related structures and were mainly responsible for biological functions such as cell nitrogen complex metabolism, macromolecular metabolism, and cell communication, in addition to functions such as nucleic acid binding, enzyme binding, hydrolase reaction, and transferase activity. Combined with microarray data analysis, the following set of nine DEGs showed intersection between the ATAC and microarray data: JUN, MSL2, CDC42, TRIB1, SERTAD3, RAB14, RHOB, RAB40B, and PRKDC. HOMER predicted and identified five transcription factors that could potentially bind to these peak sites, namely NFY, Sp 2, GFY, NRF, and ELK 1. Conclusion: Chromatin accessibility analysis of the PBMCs from patients with early-stage breast cancer underscores its potential as a significant avenue for biomarker discovery in breast cancer diagnostics and treatment. By screening the transcription factors and DEGs related to breast cancer, this study provides a comprehensive theoretical foundation that is expected to guide future clinical applications and therapeutic developments.

Multi-omic analysis of longitudinal acute myeloid leukemia patient samples reveals potential prognostic markers linked to disease progression.

Relapse remains a determinant of treatment failure and contributes significantly to mortality in acute myeloid leukemia (AML) patients. Despite efforts to understand AML progression and relapse mechanisms, findings on acquired gene mutations in relapse vary, suggesting inherent genetic heterogeneity and emphasizing the role of epigenetic modifications. We conducted a multi-omic analysis using Omni-C, ATAC-seq, and RNA-seq on longitudinal samples from two adult AML patients at diagnosis and relapse. Herein, we characterized genetic and epigenetic changes in AML progression to elucidate the underlying mechanisms of relapse. Differential interaction analysis showed significant 3D chromatin landscape reorganization between relapse and diagnosis samples. Comparing global open chromatin profiles revealed that relapse samples had significantly fewer accessible chromatin regions than diagnosis samples. In addition, we discovered that relapse-related upregulation was achieved either by forming new active enhancer contacts or by losing interactions with poised enhancers/potential silencers. Altogether, our study highlights the impact of genetic and epigenetic changes on AML progression, underlining the importance of multi-omic approaches in understanding disease relapse mechanisms and guiding potential therapeutic interventions.

Sex-biased human thymic architecture guides T cell development through spatially defined niches.

Within the thymus, regulation of the cellular crosstalk directing T cell development depends on spatial interactions within specialized niches. To create a spatially defined map of tissue niches guiding human postnatal T cell development, we employed the multidimensional imaging platform co-detection by indexing (CODEX) as well as cellular indexing of transcriptomes and epitopes sequencing (CITE-seq) and assay for transposase accessible chromatin sequencing (ATAC-seq). We generated age-matched 4- to 5-month-old human postnatal thymus datasets for male and female donors, identifying significant sex differences in both T cell and thymus biology. We demonstrate a possible role for JAG ligands in directing thymic-like dendritic cell development, identify important functions of a population of extracellular matrix (ECM)- fibroblasts, and characterize the medullary niches surrounding Hassall's corpuscles. Together, these data represent an age-matched spatial multiomic resource to investigate how sex-based differences in thymus regulation and T cell development arise, providing an essential resource to understand the mechanisms underlying immune function and dysfunction in males and females.

Dissecting the Single-Cell Diversity and Heterogeneity Underlying Cervical Precancerous Lesions and Cancer Tissues.

The underlying cellular diversity and heterogeneity from cervix precancerous lesions to cervical squamous cell carcinoma (CSCC) is investigated. Four single-cell datasets including normal tissues, normal adjacent tissues, precancerous lesions, and cervical tumors were integrated to perform disease stage analysis. Single-cell compositional data analysis (scCODA) was utilized to reveal the compositional changes of each cell type. Differentially expressed genes (DEGs) among cell types were annotated using BioCarta. An assay for transposase-accessible chromatin sequencing (ATAC-seq) analysis was performed to correlate epigenetic alterations with gene expression profiles. Lastly, a logistic regression model was used to assess the similarity between the original and new cohort data (HRA001742). After global annotation, seven distinct cell types were categorized. Eight consensus-upregulated DEGs were identified in B cells among different disease statuses, which could be utilized to predict the overall survival of CSCC patients. Inferred copy number variation (CNV) analysis of epithelial cells guided disease progression classification. Trajectory and ATAC-seq integration analysis identified 95 key transcription factors (TF) and one immunohistochemistry (IHC) testified key-node TF (YY1) involved in epithelial cells from CSCC initiation to progression. The consistency of epithelial cell subpopulation markers was revealed with single-cell sequencing, bulk sequencing, and RT-qPCR detection. KRT8 and KRT15, markers of Epi6, showed progressively higher expression with disease progression as revealed by IHC detection. The logistic regression model testified the robustness of the resemblance of clusters among the various datasets utilized in this study. Valuable insights into CSCC cellular diversity and heterogeneity provide a foundation for future targeted therapy.

Differential chromatin accessibility and Gene Expression Associated with Backfat Deposition in pigs.

BACKGROUND: Backfat serves as a vital fat reservoir in pigs, and its excessive accumulation will adversely impact pig growth performance, farming efficiency, and pork quality. The aim of this research is to integrate assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq) and RNA sequencing (RNA-seq) to explore the molecular mechanisms underlying porcine backfat deposition. RESULTS: ATAC-seq analysis identified 568 genes originating from 698 regions exhibiting differential accessibility, which were significantly enriched in pathways pertinent to adipocyte differentiation and lipid metabolism. Besides, a total of 283 transcription factors (TFs) were identified by motif analysis. RNA-seq analysis revealed 978 differentially expressed genes (DEGs), which were enriched in pathways related to energy metabolism, cell cycle and signal transduction. The integration of ATAC-seq and RNA-seq data indicates that DEG expression levels are associated with chromatin accessibility. This comprehensive study highlights the involvement of critical pathways, including the Wnt signaling pathway, Jak-STAT signaling pathway, and fatty acid degradation, in the regulation of backfat deposition. Through rigorous analysis, we identified several candidate genes (LEP, CTBP2, EHHADH, OSMR, TCF7L2, BCL2, FGF1, UCP2, CCND1, TIMP1, and VDR) as potentially significant contributors to backfat deposition. Additionally, we constructed TF-TF and TF-target gene regulatory networks and identified a series of potential TFs related to backfat deposition (FOS, STAT3, SMAD3, and ESR1). CONCLUSIONS: This study represents the first application of ATAC-seq and RNA-seq, affording a novel perspective into the mechanisms underlying backfat deposition and providing invaluable resources for the enhancement of pig breeding programs.

Evaluating Methods for the Prediction of Cell Type-Specific Enhancers in the Mammalian Cortex.

Identifying cell type-specific enhancers in the brain is critical to building genetic tools for investigating the mammalian brain. Computational methods for functional enhancer prediction have been proposed and validated in the fruit fly and not yet the mammalian brain. We organized the 'Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi-Omics' to assess machine learning and feature-based methods designed to nominate enhancer DNA sequences to target cell types in the mouse cortex. Methods were evaluated based on in vivo validation data from hundreds of cortical cell type-specific enhancers that were previously packaged into individual AAV vectors and retro-orbitally injected into mice. We find that open chromatin was a key predictor of functional enhancers, and sequence models improved prediction of non-functional enhancers that can be deprioritized as opposed to pursued for in vivo testing. Sequence models also identified cell type-specific transcription factor codes that can guide designs of in silico enhancers. This community challenge establishes a benchmark for enhancer prioritization algorithms and reveals computational approaches and molecular information that are crucial for the identification of functional enhancers for mammalian cortical cell types. The results of this challenge bring us closer to understanding the complex gene regulatory landscape of the mammalian brain and help us design more efficient genetic tools and potential gene therapies for human neurological diseases.

Review and Evaluate the Bioinformatics Analysis Strategies of ATAC-seq and CUT&Tag Data.

Efficient and reliable profiling methods are essential to study epigenetics. Tn5, one of the first identified prokaryotic transposases with high DNA-binding and tagmentation efficiency, is widely adopted in different genomic and epigenomic protocols for high-throughputly exploring the genome and epigenome. Based on Tn5, the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) and the Cleavage Under Targets and Tagmentation (CUT&Tag) were developed to measure chromatin accessibility and detect DNA-protein interactions. These methodologies can be applied to large amounts of biological samples with low-input levels, such as rare tissues, embryos, and sorted single cells. However, fast and proper processing of these epigenomic data has become a bottleneck because massive data production continues to increase quickly. Furthermore, inappropriate data analysis can generate biased or misleading conclusions. Therefore, it is essential to evaluate the performance of Tn5-based ATAC-seq and CUT&Tag data processing bioinformatics tools, many of which were developed mostly for analyzing chromatin immunoprecipitation followed by sequencing (ChIP-seq) data. Here, we conducted a comprehensive benchmarking analysis to evaluate the performance of eight popular software for processing ATAC-seq and CUT&Tag data. We compared the sensitivity, specificity, and peak width distribution for both narrow-type and broad-type peak calling. We also tested the influence of the availability of control IgG input in CUT&Tag data analysis. Finally, we evaluated the differential analysis strategies commonly used for analyzing the CUT&Tag data. Our study provided comprehensive guidance for selecting bioinformatics tools and recommended analysis strategies, which were implemented into Docker/Singularity images for streamlined data analysis.

An Improved Chromosome-scale Genome Assembly and Population Genetics resource for Populus tremula.

Aspen (Populus tremula L.) is a keystone species and a model system for forest tree genomics. We present an updated resource comprising a chromosome-scale assembly, population genetics and genomics data. Using the resource, we explore the genetic basis of natural variation in leaf size and shape, traits with complex genetic architecture. We generated the genome assembly using long-read sequencing, optical and high-density genetic maps. We conducted whole-genome resequencing of the Umeå Aspen (UmAsp) collection. Using the assembly and re-sequencing data from the UmAsp, Swedish Aspen (SwAsp) and Scottish Aspen (ScotAsp) collections we performed genome-wide association analyses (GWAS) using Single Nucleotide Polymorphisms (SNPs) for 26 leaf physiognomy phenotypes. We conducted Assay of Transposase Accessible Chromatin sequencing (ATAC-Seq), identified genomic regions of accessible chromatin, and subset SNPs to these regions, improving the GWAS detection rate. We identified candidate long non-coding RNAs in leaf samples, quantified their expression in an updated co-expression network, and used this to explore the functions of candidate genes identified from the GWAS. A GWAS found SNP associations for seven traits. The associated SNPs were in or near genes annotated with developmental functions, which represent candidates for further study. Of particular interest was a ~177-kbp region harbouring associations with several leaf phenotypes in ScotAsp. We have incorporated the assembly, population genetics, genomics, and GWAS data into the PlantGenIE.org web resource, including updating existing genomics data to the new genome version, to enable easy exploration and visualisation. We provide all raw and processed data to facilitate reuse in future studies.

CXCR4 orchestrates the TOX-programmed exhausted phenotype of CD8+ T cells via JAK2/STAT3 pathway.

Evidence from clinical trials suggests that CXCR4 antagonists enhance immunotherapy effectiveness in several cancers. However, the specific mechanisms through which CXCR4 contributes to immune cell phenotypes are not fully understood. Here, we employed single-cell transcriptomic analysis and identified CXCR4 as a marker gene in T cells, with CD8+PD-1high exhausted T (Tex) cells exhibiting high CXCR4 expression. By blocking CXCR4, the Tex phenotype was attenuated in vivo. Mechanistically, CXCR4-blocking T cells mitigated the Tex phenotype by regulating the JAK2-STAT3 pathway. Single-cell RNA/TCR/ATAC-seq confirmed that Cxcr4-deficient CD8+ T cells epigenetically mitigated the transition from functional to exhausted phenotypes. Notably, clinical sample analysis revealed that CXCR4+CD8+ T cells showed higher expression in patients with a non-complete pathological response. Collectively, these findings demonstrate the mechanism by which CXCR4 orchestrates CD8+ Tex cells and provide a rationale for combining CXCR4 antagonists with immunotherapy in clinical trials.

Mir221/222 drive synovial hyperplasia and arthritis by targeting cell cycle inhibitors and chromatin remodeling components.

miRNAs constitute fine-tuners of gene expression and are implicated in a variety of diseases spanning from inflammation to cancer. miRNA expression is deregulated in rheumatoid arthritis (RA); however, their specific role in key arthritogenic cells such as the synovial fibroblast (SF) remains elusive. Previous studies have shown that Mir221/222 expression is upregulated in RA SFs. Here, we demonstrate that TNF and IL-1ß but not IFN-γ activated Mir221/222 gene expression in murine SFs. SF-specific overexpression of Mir221/222 in huTNFtg mice led to further expansion of SFs and disease exacerbation, while its total ablation led to reduced SF expansion and attenuated disease. Mir221/222 overexpression altered the SF transcriptional profile igniting pathways involved in cell cycle and ECM (extracellular matrix) regulation. Validation of targets of Mir221/222 revealed cell cycle inhibitors Cdkn1b and Cdkn1c, as well as the epigenetic regulator Smarca1. Single-cell ATAC-seq data analysis revealed increased Mir221/222 gene activity in pathogenic SF subclusters and transcriptional regulation by Rela, Relb, Junb, Bach1, and Nfe2l2. Our results establish an SF-specific pathogenic role of Mir221/222 in arthritis and suggest that its therapeutic targeting in specific subpopulations could lead to novel fibroblast-targeted therapies.

Gene regulatory dynamics during the development of a paleopteran insect, the mayfly Cloeon dipterum.

The evolution of insects has been marked by the appearance of key body plan innovations that promoted the outstanding ability of this lineage to adapt to new habitats, boosting the most successful radiation in animals. To understand the evolution of these new structures, it is essential to investigate which genes and gene regulatory networks participate during the embryonic development of insects. Great efforts have been made to fully understand gene expression and gene regulation during the development of holometabolous insects, in particular Drosophila melanogaster. Conversely, functional genomics resources and databases in other insect lineages are scarce. To provide a new platform to study gene regulation in insects, we generated ATAC-seq for the first time during the development of the mayfly Cloeon dipterum, which belongs to Paleoptera, the sister group to all other winged insects. With these comprehensive datasets along six developmental stages, we characterized pronounced changes in accessible chromatin between early and late embryogenesis. The application of ATAC-seq in mayflies provides a fundamental resource to understand the evolution of gene regulation in insects.

Proteomic Differences in Colonic Epithelial Cells in Ulcerative Colitis Have an Epigenetic Basis.

Background and Aims: The colonic epithelium serves as both a barrier to lumenal contents and a gatekeeper of inflammatory responses. In ulcerative colitis (UC), epithelial dysfunction is a core feature, but little is known about the cellular changes that may underlie disease pathology. We therefore evaluated how the chromatin epigenetics and proteome of epithelial cells differs between health and UC. Methods: We sorted live CD326+ epithelial cells from colon biopsies of healthy control (HC) screening colonoscopy recipients and from inflamed or uninflamed colon segments of UC patients on no biologic nor immunomodulator therapy (n = 5-7 subjects per group). Cell lysates were analyzed by proteomic evaluation and nuclei were analyzed for open chromatin with assay for transposase-accessible chromatin using sequencing. Results: Proteins most highly elevated in inflamed UC biopsies relative to HC were those encoded by the HLA-DRA (P = 3.1 × 10-33) and CD74 (P = 1.6 × 10-27), genes associated with antigen presentation, and the antimicrobial dual oxidase 2 (DUOX2) (P = 3.2 × 10-28) and lipocalin-2 (P = 2.2 × 10-26) genes. Conversely, the water channel aquaporin 8 was strikingly less common with inflammation (P = 1.9 × 10-18). Assay for transposase-accessible chromatin using sequencing revealed more open chromatin around the aquaporin 8 gene in HCs (P = 2.0 × 10-2) and more around the DUOX2/DUOXA2 locus in inflamed UC colon (P = 5.7 × 10-4), suggesting an epigenetic basis for differential protein expression by epithelial cells in health and disease. Conclusion: Numerous differences exist between the proteome and chromatin of colonic epithelial cells in UC patients and HCs, some of which correlate to suggest specific epigenetic mechanisms regulating the epithelial proteome.

Identification of a transcription factor network regulating anti-TNF mediated IL10 expression in human CD4+ T cells.

CD4+ T cells are key players in immune-mediated inflammatory diseases (IMIDs) through the production of inflammatory mediators including tumour necrosis factor (TNF). Anti-TNF therapy has revolutionized the treatment of several IMIDs and we previously demonstrated that in vitro treatment of human CD4+ T cells with anti-TNF promotes anti-inflammatory IL-10 expression in multiple subpopulations of CD4+ T cells. Here we investigated the transcriptional mechanisms underlying the IL-10 induction by TNF-blockade in CD4+ T cells, isolated from PBMCs of healthy volunteers, stimulated in vitro for 3 days with anti-CD3/CD28 mAb in the absence or presence of anti-TNF. After culture, CD45RA+ cells were depleted before performing gene expression profiling and chromatin accessibility analysis. Gene expression analysis of CD45RA-CD4+ T cells showed a distinct anti-TNF specific gene signature of 183 genes (q-value < 0.05). Pathway enrichment analysis of differentially expressed genes revealed multiple pathways related to cytokine signalling and regulation of cytokine production; in particular, IL10 was the most upregulated gene by anti-TNF, while the proinflammatory cytokines and chemokines IFNG, IL9, IL22, and CXCL10 were significantly downregulated (q-value < 0.05). Transcription factor motif analysis at the differentially open chromatin regions, after anti-TNF treatment, revealed 58 transcription factor motifs enriched at the IL10 locus. We identified seven transcription factor candidates for the anti-TNF mediated regulation of IL-10, which were either differentially expressed or whose locus was differentially accessible upon anti-TNF treatment. Correlation analysis between the expression of these transcription factors and IL10 suggests a role for MAF, PRDM1, and/or EOMES in regulating IL10 expression in CD4+ T cells upon anti-TNF treatment.

HOXD12 defines an age-related aggressive subtype of oligodendroglioma.

Oligodendroglioma, IDH-mutant and 1p/19q-codeleted has highly variable outcomes that are strongly influenced by patient age. The distribution of oligodendroglioma age is non-Gaussian and reportedly bimodal, which motivated our investigation of age-associated molecular alterations that may drive poorer outcomes. We found that elevated HOXD12 expression was associated with both older patient age and shorter survival in the TCGA (FDR < 0.01, FDR = 1e-5) and the CGGA (p = 0.03, p < 1e-3). HOXD12 gene body hypermethylation was associated with older age, higher WHO grade, and shorter survival in the TCGA (p < 1e-6, p < 0.001, p < 1e-3) and with older age and higher WHO grade in Capper et al. (p < 0.002, p = 0.014). In the TCGA, HOXD12 gene body hypermethylation and elevated expression were independently prognostic of NOTCH1 and PIK3CA mutations, loss of 15q, MYC activation, and standard histopathological features. Single-nucleus RNA and ATAC sequencing data showed that HOXD12 activity was elevated in neoplastic tissue, particularly within cycling and OPC-like cells, and was associated with a stem-like phenotype. A pan-HOX DNA methylation analysis revealed an age and survival-associated HOX-high signature that was tightly associated with HOXD12 gene body methylation. Overall, HOXD12 expression and gene body hypermethylation were associated with an older, atypically aggressive subtype of oligodendroglioma.

Muscle growth differences in Lijiang pigs revealed by ATAC-seq multi-omics.

As one of the largest tissues in the animal body, skeletal muscle plays a pivotal role in the production and quality of pork. Consequently, it is of paramount importance to investigate the growth and developmental processes of skeletal muscle. Lijiang pigs, which naturally have two subtypes, fast-growing and slow-growing, provide an ideal model for such studies by eliminating breed-related influences. In this study, we selected three fast-growing and three slow-growing 6-month-old Lijiang pigs as subjects. We utilized assay for transposase-accessible chromatin with sequencing (ATAC-seq) combined with genomics, RNA sequencing, and proteomics to screen for differentially expressed genes and transcription factors linked to increased longissimus dorsi muscle volume in Lijiang pigs. We identified 126 genes through ATAC-seq, including PPARA, TNRC6B, NEDD1, and FKBP5, that exhibited differential expression patterns during muscle growth. Additionally, we identified 59 transcription factors, including Foxh1, JunB, Mef2 family members (Mef2a/b/c/d), NeuroD1, and TEAD4. By examining open chromatin regions (OCRs) with significant genetic differentiation, genes such as SAV1, CACNA1H, PRKCG, and FGFR4 were found. Integrating ATAC-seq with transcriptomics and transcriptomics with proteomics, we identified differences in open chromatin regions, transcription, and protein levels of FKBP5 and SCARB2 genes in fast-growing and slow-growing Lijiang pigs. Utilizing multi-omics analysis with R packages, we jointed ATAC-seq, transcriptome, and proteome datasets, identifying enriched pathways related to glycogen metabolism and skeletal muscle cell differentiation. We pinpointed genes such as MYF6 and HABP2 that exhibit strong correlations across these diverse data types. This study provides a multi-faceted understanding of the molecular mechanisms that lead to differences in pig muscle fiber growth.

Mitochondrial dysfunction and increased reactive oxygen species production in MECP2 mutant astrocytes and their impact on neurons.

Studies on MECP2 function and its implications in Rett Syndrome (RTT) have traditionally centered on neurons. Here, using human embryonic stem cell (hESC) lines, we modeled MECP2 loss-of-function to explore its effects on astrocyte (AST) development and dysfunction in the brain. Ultrastructural analysis of RTT hESC-derived cerebral organoids revealed significantly smaller mitochondria compared to controls (CTRs), particularly pronounced in glia versus neurons. Employing a multiomics approach, we observed increased gene expression and accessibility of a subset of nuclear-encoded mitochondrial genes upon mutation of MECP2 in ASTs compared to neurons. Analysis of hESC-derived ASTs showed reduced mitochondrial respiration and altered key proteins in the tricarboxylic acid cycle and electron transport chain in RTT versus CTRs. Additionally, RTT ASTs exhibited increased cytosolic amino acids under basal conditions, which were depleted upon increased energy demands. Notably, mitochondria isolated from RTT ASTs exhibited increased reactive oxygen species and influenced neuronal activity when transferred to cortical neurons. These findings underscore MECP2 mutation's differential impact on mitochondrial and metabolic pathways in ASTs versus neurons, suggesting that dysfunctional AST mitochondria may contribute to RTT pathophysiology by affecting neuronal health.

Discovery of therapeutic targets in cancer using chromatin accessibility and transcriptomic data.

Most cancer types lack targeted therapeutic options, and when first-line targeted therapies are available, treatment resistance is a huge challenge. Recent technological advances enable the use of assay for transposase-accessible chromatin with sequencing (ATAC-seq) and RNA sequencing (RNA-seq) on patient tissue in a high-throughput manner. Here, we present a computational approach that leverages these datasets to identify drug targets based on tumor lineage. We constructed gene regulatory networks for 371 patients of 22 cancer types using machine learning approaches trained with three-dimensional genomic data for enhancer-to-promoter contacts. Next, we identified the key transcription factors (TFs) in these networks, which are used to find therapeutic vulnerabilities, by direct targeting of either TFs or the proteins that they interact with. We validated four candidates identified for neuroendocrine, liver, and renal cancers, which have a dismal prognosis with current therapeutic options.

A molecular pathway for cancer cachexia-induced muscle atrophy revealed at single-nucleus resolution.

Cancer cachexia is a prevalent and often fatal wasting condition that cannot be fully reversed with nutritional interventions. Muscle atrophy is a central component of the syndrome, but the mechanisms whereby cancer leads to skeletal muscle atrophy are not well understood. We performed single-nucleus multi-omics on skeletal muscles from a mouse model of cancer cachexia and profiled the molecular changes in cachexic muscle. Our results revealed the activation of a denervation-dependent gene program that upregulates the transcription factor myogenin. Further studies showed that a myogenin-myostatin pathway promotes muscle atrophy in response to cancer cachexia. Short hairpin RNA inhibition of myogenin or inhibition of myostatin through overexpression of its endogenous inhibitor follistatin prevented cancer cachexia-induced muscle atrophy in mice. Our findings uncover a molecular basis of muscle atrophy associated with cancer cachexia and highlight potential therapeutic targets for this disorder.

A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data.

Eukaryotic gene regulation is a combinatorial, dynamic, and quantitative process that plays a vital role in development and disease and can be modeled at a systems level in gene regulatory networks (GRNs). The wealth of multi-omics data measured on the same samples and even on the same cells has lifted the field of GRN inference to the next stage. Combinations of (single-cell) transcriptomics and chromatin accessibility allow the prediction of fine-grained regulatory programs that go beyond mere correlation of transcription factor and target gene expression, with enhancer GRNs (eGRNs) modeling molecular interactions between transcription factors, regulatory elements, and target genes. In this review, we highlight the key components for successful (e)GRN inference from (sc)RNA-seq and (sc)ATAC-seq data exemplified by state-of-the-art methods as well as open challenges and future developments. Moreover, we address preprocessing strategies, metacell generation and computational omics pairing, transcription factor binding site detection, and linear and three-dimensional approaches to identify chromatin interactions as well as dynamic and causal eGRN inference. We believe that the integration of transcriptomics together with epigenomics data at a single-cell level is the new standard for mechanistic network inference, and that it can be further advanced with integrating additional omics layers and spatiotemporal data, as well as with shifting the focus towards more quantitative and causal modeling strategies.