Transcriptional regulatory network controlling the ontogeny of hematopoietic stem cells.

Hematopoietic stem cell (HSC) ontogeny is accompanied by dynamic changes in gene regulatory networks. We performed RNA-seq and histone mark ChIP-seq to define the transcriptomes and epigenomes of cells representing key developmental stages of HSC ontogeny in mice. The five populations analyzed were embryonic day 10.5 (E10.5) endothelium and hemogenic endothelium from the major arteries, an enriched population of prehematopoietic stem cells (pre-HSCs), fetal liver HSCs, and adult bone marrow HSCs. Using epigenetic signatures, we identified enhancers for each developmental stage. Only 12% of enhancers are primed, and 78% are active, suggesting the vast majority of enhancers are established de novo without prior priming in earlier stages. We constructed developmental stage-specific transcriptional regulatory networks by linking enhancers and predicted bound transcription factors to their target promoters using a novel computational algorithm, target inference via physical connection (TIPC). TIPC predicted known transcriptional regulators for the endothelial-to-hematopoietic transition, validating our overall approach, and identified putative novel transcription factors, including the broadly expressed transcription factors SP3 and MAZ. Finally, we validated a role for SP3 and MAZ in the formation of hemogenic endothelium. Our data and computational analyses provide a useful resource for uncovering regulators of HSC formation.

Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.

Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.

Epigenetic state determines inflammatory sensing in neuroblastoma.

Immunotherapy has revolutionized cancer treatment, but many cancers are not impacted by currently available immunotherapeutic strategies. Here, we investigated inflammatory signaling pathways in neuroblastoma, a classically "cold" pediatric cancer. By testing the functional response of a panel of 20 diverse neuroblastoma cell lines to three different inflammatory stimuli, we found that all cell lines have intact interferon signaling, and all but one lack functional cytosolic DNA sensing via cGAS-STING. However, double-stranded RNA (dsRNA) sensing via Toll-like receptor 3 (TLR3) was heterogeneous, as was signaling through other dsRNA sensors and TLRs more broadly. Seven cell lines showed robust response to dsRNA, six of which are in the mesenchymal epigenetic state, while all unresponsive cell lines are in the adrenergic state. Genetically switching adrenergic cell lines toward the mesenchymal state fully restored responsiveness. In responsive cells, dsRNA sensing results in the secretion of proinflammatory cytokines, enrichment of inflammatory transcriptomic signatures, and increased tumor killing by T cells in vitro. Using single-cell RNA sequencing data, we show that human neuroblastoma cells with stronger mesenchymal signatures have a higher basal inflammatory state, demonstrating intratumoral heterogeneity in inflammatory signaling that has significant implications for immunotherapeutic strategies in this aggressive childhood cancer.

Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data.

Single-cell DNA methylation data has become increasingly abundant and has uncovered many genes with a positive correlation between expression and promoter methylation, challenging the common dogma based on bulk data. However, computational tools for analyzing single-cell methylome data are lagging far behind. A number of tasks, including cell type calling and integration with transcriptome data, requires the construction of a robust gene activity matrix as the prerequisite but challenging task. The advent of multi-omics data enables measurement of both DNA methylation and gene expression for the same single cells. Although such data is rather sparse, they are sufficient to train supervised models that capture the complex relationship between DNA methylation and gene expression and predict gene activities at single-cell level. Here, we present methylome association by predictive linkage to expression (MAPLE), a computational framework that learns the association between DNA methylation and expression using both gene- and cell-dependent statistical features. Using multiple data sets generated with different experimental protocols, we show that using predicted gene activity values significantly improves several analysis tasks, including clustering, cell type identification, and integration with transcriptome data. Application of MAPLE revealed several interesting biological insights into the relationship between methylation and gene expression, including asymmetric importance of methylation signals around transcription start site for predicting gene expression, and increased predictive power of methylation signals in promoters located outside CpG islands and shores. With the rapid accumulation of single-cell epigenomics data, MAPLE provides a general framework for integrating such data with transcriptome data.

Endothelial MEKK3-KLF2/4 signaling integrates inflammatory and hemodynamic signals during definitive hematopoiesis.

The hematopoietic stem cells (HSCs) that produce blood for the lifetime of an animal arise from RUNX1+ hemogenic endothelial cells (HECs) in the embryonic vasculature through a process of endothelial-to-hematopoietic transition (EHT). Studies have identified inflammatory mediators and fluid shear forces as critical environmental stimuli for EHT, raising the question of how such diverse inputs are integrated to drive HEC specification. Endothelial cell MEKK3-KLF2/4 signaling can be activated by both fluid shear forces and inflammatory mediators, and it plays roles in cardiovascular development and disease that have been linked to both stimuli. Here we demonstrate that MEKK3 and KLF2/4 are required in endothelial cells for the specification of RUNX1+ HECs in both the yolk sac and dorsal aorta of the mouse embryo and for their transition to intraaortic hematopoietic cluster (IAHC) cells. The inflammatory mediators lipopolysaccharide and interferon-γ increase RUNX1+ HECs in an MEKK3-dependent manner. Maternal administration of catecholamines that stimulate embryo cardiac function and accelerate yolk sac vascular remodeling increases EHT by wild-type but not MEKK3-deficient endothelium. These findings identify MEKK-KLF2/4 signaling as an essential pathway for EHT and provide a molecular basis for the integration of diverse environmental inputs, such as inflammatory mediators and hemodynamic forces, during definitive hematopoiesis.

Single-cell multiomics reveals increased plasticity, resistant populations, and stem-cell-like blasts in KMT2A-rearranged leukemia.

KMT2A-rearranged (KMT2A-r) infant acute lymphoblastic leukemia (ALL) is a devastating malignancy with a dismal outcome, and younger age at diagnosis is associated with increased risk of relapse. To discover age-specific differences and critical drivers that mediate poor outcome in KMT2A-r ALL, we subjected KMT2A-r leukemias and normal hematopoietic cells from patients of different ages to single-cell multiomics analyses. We uncovered the following critical new insights: leukemia cells from patients <6 months have significantly increased lineage plasticity. Steroid response pathways are downregulated in the most immature blasts from younger patients. We identify a hematopoietic stem and progenitor-like (HSPC-like) population in the blood of younger patients that contains leukemic blasts and form an immunosuppressive signaling circuit with cytotoxic lymphocytes. These observations offer a compelling explanation for the ability of leukemias in young patients to evade chemotherapy and immune-mediated control. Our analysis also revealed preexisting lymphomyeloid primed progenitors and myeloid blasts at initial diagnosis of B-ALL. Tracking of leukemic clones in 2 patients whose leukemia underwent a lineage switch documented the evolution of such clones into frank acute myeloid leukemia (AML). These findings provide critical insights into KMT2A-r ALL and have clinical implications for molecularly targeted and immunotherapy approaches. Beyond infant ALL, our study demonstrates the power of single-cell multiomics to detect tumor intrinsic and extrinsic factors affecting rare but critical subpopulations within a malignant population that ultimately determines patient outcome.

Developmental trajectory of prehematopoietic stem cell formation from endothelium.

Hematopoietic stem and progenitor cells (HSPCs) in the bone marrow are derived from a small population of hemogenic endothelial (HE) cells located in the major arteries of the mammalian embryo. HE cells undergo an endothelial to hematopoietic cell transition, giving rise to HSPCs that accumulate in intra-arterial clusters (IAC) before colonizing the fetal liver. To examine the cell and molecular transitions between endothelial (E), HE, and IAC cells, and the heterogeneity of HSPCs within IACs, we profiled ∼40 000 cells from the caudal arteries (dorsal aorta, umbilical, vitelline) of 9.5 days post coitus (dpc) to 11.5 dpc mouse embryos by single-cell RNA sequencing and single-cell assay for transposase-accessible chromatin sequencing. We identified a continuous developmental trajectory from E to HE to IAC cells, with identifiable intermediate stages. The intermediate stage most proximal to HE, which we term pre-HE, is characterized by increased accessibility of chromatin enriched for SOX, FOX, GATA, and SMAD motifs. A developmental bottleneck separates pre-HE from HE, with RUNX1 dosage regulating the efficiency of the pre-HE to HE transition. A distal candidate Runx1 enhancer exhibits high chromatin accessibility specifically in pre-HE cells at the bottleneck, but loses accessibility thereafter. Distinct developmental trajectories within IAC cells result in 2 populations of CD45+ HSPCs; an initial wave of lymphomyeloid-biased progenitors, followed by precursors of hematopoietic stem cells (pre-HSCs). This multiomics single-cell atlas significantly expands our understanding of pre-HSC ontogeny.

Risk variants disrupting enhancers of TH1 and TREG cells in type 1 diabetes.

Genome-wide association studies (GWASs) have revealed 59 genomic loci associated with type 1 diabetes (T1D). Functional interpretation of the SNPs located in the noncoding region of these loci remains challenging. We perform epigenomic profiling of two enhancer marks, H3K4me1 and H3K27ac, using primary TH1 and TREG cells isolated from healthy and T1D subjects. We uncover a large number of deregulated enhancers and altered transcriptional circuitries in both cell types of T1D patients. We identify four SNPs (rs10772119, rs10772120, rs3176792, rs883868) in linkage disequilibrium (LD) with T1D-associated GWAS lead SNPs that alter enhancer activity and expression of immune genes. Among them, rs10772119 and rs883868 disrupt the binding of retinoic acid receptor α (RARA) and Yin and Yang 1 (YY1), respectively. Loss of binding by YY1 also results in the loss of long-range enhancer-promoter interaction. These findings provide insights into how noncoding variants affect the transcriptomes of two T-cell subtypes that play critical roles in T1D pathogenesis.

ER stress elicits non-canonical CASP8 (caspase 8) activation on autophagosomal membranes to induce apoptosis.

The VPS37A gene encodes a subunit of the endosomal sorting complex required for transport (ESCRT)-I complex that is frequently lost in a wide variety of human solid cancers. We have previously demonstrated the role of VPS37A in directing the ESCRT membrane scission machinery to seal the phagophore for autophagosome completion. Here, we report that VPS37A-deficient cells exhibit an accumulation of the apoptotic initiator CASP8 (caspase 8) on the phagophore and are primed to undergo rapid apoptosis through the intracellular death-inducing signaling complex (iDISC)-mediated CASP8 activation upon exposure to endoplasmic reticulum (ER) stress. Using CRISPR-Cas9 gene editing and comparative transcriptome analysis, we identified the ATF4-mediated stress response pathway as a crucial mediator to elicit iDISC-mediated apoptosis following the inhibition of autophagosome closure. Notably, ATF4-mediated iDISC activation occurred independently of the death receptor TNFRSF10B/DR5 upregulation but required the pro-apoptotic transcriptional factor DDIT3/CHOP to enhance the mitochondrial amplification pathway for full-activation of CASP8 in VPS37A-deficient cells stimulated with ER stress inducers. Our analysis also revealed the upregulation of NFKB/NF-kB signaling as a potential mechanism responsible for restraining iDISC activation and promoting cell survival upon VPS37A depletion. These findings have important implications for the future development of new strategies to treat human cancers, especially those with VPS37A loss.Abbreviations: ATG: autophagy related; BMS: BMS-345541; CASP: caspase; CHMP: charged multivesicular body protein; DKO: double knockout; Dox: doxycycline; ER: endoplasmic reticulum; ESCRT: endosomal sorting complex required for transport; gRNA: guide RNA; GSEA: gene set enrichment analysis; GSK157: GSK2656157; iDISC: intracellular death-inducing signaling complex; IKK: inhibitor of NFKB kinase; IPA: ingenuity pathway analysis; KO: knockout; MAP1LC3/LC3: microtubule associated protein 1 light chain 3; NFKB/NF-kB: nuclear factor kappa B; OZ: 5Z-7-oxozeaenol; RNA-seq: RNA sequencing; UPR: unfolded protein response; TFT: transcription factor target; THG: thapsigargin; TUN: tunicamycin; VPS: vacuolar protein sorting.

Integrating Single-Cell Methylome and Transcriptome Data with MAPLE.

As a mechanism of epigenetic gene regulation, DNA methylation has crucial roles in developmental and differentiation programs. Thanks to the recently introduced bisulfite-sequencing-based methods, it is possible to profile the entire methylome at single-cell resolution. However, analysis of single-cell methylome data is challenging due to data sparsity and moderate correlation with transcript level. Our recently developed computational framework, MAPLE, addresses these challenges using supervised learning models. Using both genomic sequence and methylation information as the input, MAPLE predicts activity for each gene, which can be used to integrate with transcriptome data from the same cell types. Here, we provide an overview of our method and detailed guidance on how to use it for the integration of methylome and transcriptome data.

IGF2BP family of RNA-binding proteins regulate innate and adaptive immune responses in cancer cells and tumor microenvironment.

Insulin-like growth factor 2 mRNA-binding proteins (IGF2BP1, IGF2BP2, and IGF2BP3) are a family of RNA-binding proteins that play an essential role in the development and disease by regulating mRNA stability and translation of critical regulators of cell division and metabolism. Genetic and chemical inhibition of these proteins slows down cancer cell proliferation, decreases invasiveness, and prolongs life span in a variety of animal models. The role of RNA-binding proteins in the induction of tissues' immunogenicity is increasingly recognized, but, the impact of the IGF2BPs family of proteins on the induction of innate and adaptive immune responses in cancer is not fully understood. Here we report that downregulation of IGF2BP1, 2, and 3 expression facilitates the expression of interferon beta-stimulated genes. IGF2BP1 has a greater effect on interferon beta and gamma signaling compared to IGF2BP2 and IGF2BP3 paralogs. We demonstrate that knockdown or knockout of IGF2BP1, 2, and 3 significantly potentiates inhibition of cell growth induced by IFNß and IFNγ. Mouse melanoma cells with Igf2bp knockouts demonstrate increased expression of MHC I (H-2) and induce intracellular Ifn-γ expression in syngeneic T-lymphocytes in vitro. Increased immunogenicity, associated with Igf2bp1 inhibition, "inflames" mouse melanoma tumors microenvironment in SM1/C57BL/6 and SW1/C3H mouse models measured by a two-fold increase of NK cells and tumor-associated myeloid cells. Finally, we demonstrate that the efficiency of anti-PD1 immunotherapy in the mouse melanoma model is significantly more efficient in tumors that lack Igf2bp1 expression. Our retrospective data analysis of immunotherapies in human melanoma patients indicates that high levels of IGF2BP1 and IGF2BP3 are associated with resistance to immunotherapies and poor prognosis. In summary, our study provides evidence of the role of IGF2BP proteins in regulating tumor immunogenicity and establishes those RBPs as immunotherapeutic targets in cancer.

Isolation of Epithelial and Stromal Cells from Colon Tissues in Homeostasis and Under Inflammatory Conditions.

Inflammation of the gastrointestinal tract is a prevalent pathology in diseases such as inflammatory bowel disease (IBD). Currently, there are no therapies to prevent IBD, and available therapies to treat IBD are often sub-optimal. Thus, an unmet need exists to better understand the molecular mechanisms underlying intestinal tissue responses to damage and regeneration. The recent development of single-cell RNA (sc-RNA) sequencing-based techniques offers a unique opportunity to shed light on novel signaling pathways and cellular states that govern tissue adaptation or maladaptation across a broad spectrum of diseases. These approaches require the isolation of high-quality cells from tissues for downstream transcriptomic analyses. In the context of intestinal biology, there is a lack of protocols that ensure the isolation of epithelial and non-epithelial compartments simultaneously with high-quality yield. Here, we report two protocols for the isolation of epithelial and stromal cells from mouse and human colon tissues under inflammatory conditions. Specifically, we tested the feasibility of the protocols in a mouse model of dextran sodium sulfate (DSS)-induced colitis and in human biopsies from Crohn's patients. We performed sc-RNA sequencing analysis and demonstrated that the protocol preserves most of the epithelial and stromal cell types found in the colon. Moreover, the protocol is suitable for immunofluorescence staining of surface markers for epithelial, stromal, and immune cell lineages for flow cytometry analyses. This optimized protocol will provide a new resource for scientists to study complex tissues such as the colon in the context of tissue damage and regeneration. Key features • This protocol allows the isolation of epithelial and stromal cells from colon tissues. • The protocol has been optimized for tissues under inflammatory conditions with compromised cell viability. • This protocol is suitable for experimental mouse models of colon inflammation and human biopsies.

Integrative Bulk and Single-Cell Profiling of Premanufacture T-cell Populations Reveals Factors Mediating Long-Term Persistence of CAR T-cell Therapy.

The adoptive transfer of chimeric antigen receptor (CAR) T cells represents a breakthrough in clinical oncology, yet both between- and within-patient differences in autologously derived T cells are a major contributor to therapy failure. To interrogate the molecular determinants of clinical CAR T-cell persistence, we extensively characterized the premanufacture T cells of 71 patients with B-cell malignancies on trial to receive anti-CD19 CAR T-cell therapy. We performed RNA-sequencing analysis on sorted T-cell subsets from all 71 patients, followed by paired Cellular Indexing of Transcriptomes and Epitopes (CITE) sequencing and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq) on T cells from six of these patients. We found that chronic IFN signaling regulated by IRF7 was associated with poor CAR T-cell persistence across T-cell subsets, and that the TCF7 regulon not only associates with the favorable naïve T-cell state, but is maintained in effector T cells among patients with long-term CAR T-cell persistence. These findings provide key insights into the underlying molecular determinants of clinical CAR T-cell function. SIGNIFICANCE: To improve clinical outcomes for CAR T-cell therapy, there is a need to understand the molecular determinants of CAR T-cell persistence. These data represent the largest clinically annotated molecular atlas in CAR T-cell therapy to date, and significantly advance our understanding of the mechanisms underlying therapeutic efficacy.This article is highlighted in the In This Issue feature, p. 2113.

Antioxidant and Genotoxic Effects of Aqueous and Methanol Extracts from Two Edible Mushrooms from Turkey in Human Peripheral Lymphocytes.

This study reported the genetic and oxidative effects of aqueous and methanol extracts from two edible mushrooms, Lepista nuda (Bull.) Cooke and Pleurotus ostreatus (Jacq.) P. Kummer, in cultured human lymphocytes. Chromosome aberration (CA) and micronucleus (MN) assays were used for genotoxic influences estimation. In addition, the changes of total antioxidant capacity (TAC) and total oxidative stress (TOS) in the cells were monitored. The fungal extracts at all applied concentrations did not indicate significant differences (p > 0.05) in CA and MN analyses. Furthermore, while the treatments with maximum concentration of aqueous extract of L. nuda statistically (p < 0.05) increased TAC especially, TOS levels in the cells were reduced by them in comparison with negative control. Based on TAC analysis, low IC50 values belonging to aqueous (5.43 mg/L) and methanol (10.88 mg/L) extracts of L. nuda were remarkable. Our data demonstrated that the extracts obtained from P. ostreatus and especially L. nuda can be a new resource for therapeutics with their nonmutagenic and antioxidant features.

scATAC-pro: a comprehensive workbench for single-cell chromatin accessibility sequencing data.

Single-cell chromatin accessibility sequencing has become a powerful technology for understanding epigenetic heterogeneity of complex tissues. However, there is a lack of open-source software for comprehensive processing, analysis, and visualization of such data generated using all existing experimental protocols. Here, we present scATAC-pro for quality assessment, analysis, and visualization of single-cell chromatin accessibility sequencing data. scATAC-pro computes a range of quality control metrics for several key steps of experimental protocols, with a flexible choice of methods. It generates summary reports for both quality assessment and downstream analysis. scATAC-pro is available at https://github.com/tanlabcode/scATAC-pro.

Identifying noncoding risk variants using disease-relevant gene regulatory networks.

Identifying noncoding risk variants remains a challenging task. Because noncoding variants exert their effects in the context of a gene regulatory network (GRN), we hypothesize that explicit use of disease-relevant GRNs can significantly improve the inference accuracy of noncoding risk variants. We describe Annotation of Regulatory Variants using Integrated Networks (ARVIN), a general computational framework for predicting causal noncoding variants. It employs a set of novel regulatory network-based features, combined with sequence-based features to infer noncoding risk variants. Using known causal variants in gene promoters and enhancers in a number of diseases, we show ARVIN outperforms state-of-the-art methods that use sequence-based features alone. Additional experimental validation using reporter assay further demonstrates the accuracy of ARVIN. Application of ARVIN to seven autoimmune diseases provides a holistic view of the gene subnetwork perturbed by the combinatorial action of the entire set of risk noncoding mutations.