Oncogenic transcription factors instruct promoter-enhancer hubs in individual triple negative breast cancer cells.

Sequencing-based mapping of ensemble pairwise interactions among regulatory elements support the existence of topological assemblies known as promoter-enhancer hubs or cliques in cancer. Yet, prevalence, regulators, and functions of promoter-enhancer hubs in individual cancer cells remain unclear. Here, we systematically integrated functional genomics, transcription factor screening, and optical mapping of promoter-enhancer interactions to identify key promoter-enhancer hubs, examine heterogeneity of their assembly, determine their regulators, and elucidate their role in gene expression control in individual triple negative breast cancer (TNBC) cells. Optical mapping of individual SOX9 and MYC alleles revealed the existence of frequent multiway interactions among promoters and enhancers within spatial hubs. Our single-allele studies further demonstrated that lineage-determining SOX9 and signaling-dependent NOTCH1 transcription factors compact MYC and SOX9 hubs. Together, our findings suggest that promoter-enhancer hubs are dynamic and heterogeneous topological assemblies, which are controlled by oncogenic transcription factors and facilitate subtype-restricted gene expression in cancer.

Enhancer-promoter hubs organize transcriptional networks promoting oncogenesis and drug resistance.

Recent advances in high-resolution mapping of spatial interactions among regulatory elements support the existence of complex topological assemblies of enhancers and promoters known as enhancer-promoter hubs or cliques. Yet, organization principles of these multi-interacting enhancer-promoter hubs and their potential role in regulating gene expression in cancer remains unclear. Here, we systematically identified enhancer-promoter hubs in breast cancer, lymphoma, and leukemia. We found that highly interacting enhancer-promoter hubs form at key oncogenes and lineage-associated transcription factors potentially promoting oncogenesis of these diverse cancer types. Genomic and optical mapping of interactions among enhancer and promoter elements further showed that topological alterations in hubs coincide with transcriptional changes underlying acquired resistance to targeted therapy in T cell leukemia and B cell lymphoma. Together, our findings suggest that enhancer-promoter hubs are dynamic and heterogeneous topological assemblies with the potential to control gene expression circuits promoting oncogenesis and drug resistance.

AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics.

Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs for atlas-scale datasets like Human Pancreas Analysis Program (HPAP), we develop AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX shows the higher performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulates known islet pathobiology and shows differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8+ T cells infiltration in islets during type 1 diabetes progression.

Modeling type 1 diabetes progression using machine learning and single-cell transcriptomic measurements in human islets.

Type 1 diabetes (T1D) is a chronic condition in which beta cells are destroyed by immune cells. Despite progress in immunotherapies that could delay T1D onset, early detection of autoimmunity remains challenging. Here, we evaluate the utility of machine learning for early prediction of T1D using single-cell analysis of islets. Using gradient-boosting algorithms, we model changes in gene expression of single cells from pancreatic tissues in T1D and non-diabetic organ donors. We assess if mathematical modeling could predict the likelihood of T1D development in non-diabetic autoantibody-positive donors. While most autoantibody-positive donors are predicted to be non-diabetic, select donors with unique gene signatures are classified as T1D. Our strategy also reveals a shared gene signature in distinct T1D-associated models across cell types, suggesting a common effect of the disease on transcriptional outputs of these cells. Our study establishes a precedent for using machine learning in early detection of T1D.

Intrinsically disordered domain of transcription factor TCF-1 is required for T cell developmental fidelity.

In development, pioneer transcription factors access silent chromatin to reveal lineage-specific gene programs. The structured DNA-binding domains of pioneer factors have been well characterized, but whether and how intrinsically disordered regions affect chromatin and control cell fate is unclear. Here, we report that deletion of an intrinsically disordered region of the pioneer factor TCF-1 (termed L1) leads to an early developmental block in T cells. The few T cells that develop from progenitors expressing TCF-1 lacking L1 exhibit lineage infidelity distinct from the lineage diversion of TCF-1-deficient cells. Mechanistically, L1 is required for activation of T cell genes and repression of GATA2-driven genes, normally reserved to the mast cell and dendritic cell lineages. Underlying this lineage diversion, L1 mediates binding of TCF-1 to its earliest target genes, which are subject to repression as T cells develop. These data suggest that the intrinsically disordered N terminus of TCF-1 maintains T cell lineage fidelity.

Spatial promoter-enhancer hubs in cancer: organization, regulation, and function.

Transcriptional dysregulation is a hallmark of cancer and can be driven by altered enhancer landscapes. Recent studies in genome organization have revealed that multiple enhancers and promoters can spatially coalesce to form dynamic topological assemblies, known as promoter-enhancer hubs, which strongly correlate with elevated gene expression. In this review, we discuss the structure and complexity of promoter-enhancer hubs recently identified in multiple cancer types. We further discuss underlying mechanisms driving dysregulation of promoter-enhancer hubs and speculate on their functional role in pathogenesis. Understanding the role of promoter-enhancer hubs in transcriptional dysregulation can provide insight into new therapeutic approaches to target these complex features of genome organization.

The pseudokinase Trib1 regulates the transition of exhausted T cells to a KLR+ CD8+ effector state, and its deletion improves checkpoint blockade.

CD8+ T cell exhaustion (TEX) impairs the ability of T cells to clear chronic infection or cancer. While TEX are hypofunctional, some TEX retain effector gene signatures, a feature associated with killer lectin-like receptor (KLR) expression. Although KLR+ TEX (TKLR) may improve control of chronic antigen, the signaling molecules regulating this population are poorly understood. Using single-cell RNA sequencing (scRNA-seq), flow cytometry, RNA velocity, and single-cell T cell receptor sequencing (scTCR-seq), we demonstrate that deleting the pseudokinase Trib1 shifts TEX toward CX3CR1+ intermediates with robust enrichment of TKLR via clonal T cell expansion. Adoptive transfer studies demonstrate this shift toward CD8+ TKLR in Trib1-deficient cells is CD8 intrinsic, while CD4-depletion studies demonstrate CD4+ T cells are required for improved viral control in Trib1 conditional knockout mice. Further, Trib1 loss augments anti-programmed death-ligand 1 (PD-L1) blockade to improve viral clearance. These data identify Trib1 as an important regulator of CD8+ TEX whose targeting enhances the TKLR effector state and improves checkpoint inhibitor therapy.

The pseudokinase Trib1 regulates the transition of exhausted T cells to a KLR + CD8 + effector state and its deletion improves checkpoint blockade.

T cell exhaustion (T EX ) impairs the ability of T cells to clear chronic infection or cancer. While exhausted T cells are hypofunctional, some exhausted T cells retain effector gene signatures, a feature that is associated with expression of KLRs (killer lectin-like receptors). Although KLR + T cells may improve control of chronic antigen, the signaling molecules regulating this population are poorly understood. Using scRNA-seq, flow cytometry, RNA velocity, and scTCR-seq, we demonstrate that deleting the pseudokinase Trib1 shifts T EX towards CX3CR1 + intermediates (T INT ) with robust enrichment of KLR + CD8 + T cells (T KLR ) via clonal T cell expansion. These changes are associated with globally increased KLR gene expression throughout the exhaustion program. Further, Trib1 loss augments anti-PD-L1 blockade to improve viral clearance by expanding the T KLR population. Together, these data identify Trib1 as an important regulator of T cell exhaustion whose targeting enhances the KLR + effector state and improves the response to checkpoint inhibitor therapy.

Computational workflow and interactive analysis of single-cell expression profiling of islets generated by the Human Pancreas Analysis Program.

Type 1 and Type 2 diabetes are distinct genetic diseases of the pancreas which are defined by the abnormal level of blood glucose. Understanding the initial molecular perturbations that occur during the pathogenesis of diabetes is of critical importance in understanding these disorders. The inability to biopsy the human pancreas of living donors hampers insights into early detection, as the majority of diabetes studies have been performed on peripheral leukocytes from the blood, which is not the site of pathogenesis. Therefore, efforts have been made by various teams including the Human Pancreas Analysis Program (HPAP) to collect pancreatic tissues from deceased organ donors with different clinical phenotypes. HPAP is designed to define the molecular pathogenesis of islet dysfunction by generating detailed datasets of functional, cellular, and molecular information in pancreatic tissues of clinically well-defined organ donors with Type 1 and Type 2 diabetes. Moreover, data generated by HPAP continously become available through a centralized database, PANC-DB, thus enabling the diabetes research community to access these multi-dimensional data prepublication. Here, we present the computational workflow for single-cell RNA-seq data analysis of 258,379 high-quality cells from the pancreatic islets of 67 human donors generated by HPAP, the largest existing scRNA-seq dataset of human pancreatic tissues. We report various computational steps including preprocessing, doublet removal, clustering and cell type annotation across single-cell RNA-seq data from islets of four distintct classes of organ donors, i.e. non-diabetic control, autoantibody positive but normoglycemic, Type 1 diabetic, and Type 2 diabetic individuals. Moreover, we present an interactive tool, called CellxGene developed by the Chan Zuckerberg initiative, to navigate these high-dimensional datasets. Our data and interactive tools provide a reliable reference for singlecell pancreatic islet biology studies, especially diabetes-related conditions.

AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics.

Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs, we developed AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX show the superior performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulated known islet pathobiology and showed differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8+ T cells infiltration in islets during type 1 diabetes progression.

Single-cell multi-omics analysis of human pancreatic islets reveals novel cellular states in type 1 diabetes.

Type 1 diabetes (T1D) is an autoimmune disease in which immune cells destroy insulin-producing beta cells. The aetiology of this complex disease is dependent on the interplay of multiple heterogeneous cell types in the pancreatic environment. Here, we provide a single-cell atlas of pancreatic islets of 24 T1D, autoantibody-positive and nondiabetic organ donors across multiple quantitative modalities including ~80,000 cells using single-cell transcriptomics, ~7,000,000 cells using cytometry by time of flight and ~1,000,000 cells using in situ imaging mass cytometry. We develop an advanced integrative analytical strategy to assess pancreatic islets and identify canonical cell types. We show that a subset of exocrine ductal cells acquires a signature of tolerogenic dendritic cells in an apparent attempt at immune suppression in T1D donors. Our multimodal analyses delineate cell types and processes that may contribute to T1D immunopathogenesis and provide an integrative procedure for exploration and discovery of human pancreatic function.

EBF1 nuclear repositioning instructs chromatin refolding to promote therapy resistance in T leukemic cells.

Chromatin misfolding has been implicated in cancer pathogenesis; yet, its role in therapy resistance remains unclear. Here, we systematically integrated sequencing and imaging data to examine the spatial and linear chromatin structures in targeted therapy-sensitive and -resistant human T cell acute lymphoblastic leukemia (T-ALL). We found widespread alterations in successive layers of chromatin organization including spatial compartments, contact domain boundaries, and enhancer positioning upon the emergence of targeted therapy resistance. The reorganization of genome folding structures closely coincides with the restructuring of chromatin activity and redistribution of architectural proteins. Mechanistically, the derepression and repositioning of the B-lineage-determining transcription factor EBF1 from the heterochromatic nuclear envelope to the euchromatic interior instructs widespread genome refolding and promotes therapy resistance in leukemic T cells. Together, our findings suggest that lineage-determining transcription factors can instruct changes in genome topology as a driving force for epigenetic adaptations in targeted therapy resistance.

TooManyPeaks identifies drug-resistant-specific regulatory elements from single-cell leukemic epigenomes.

Emerging single-cell epigenomic assays are used to investigate the heterogeneity of chromatin activity and its function. However, identifying cells with distinct regulatory elements and clearly visualizing their relationships remains challenging. To this end, we introduce TooManyPeaks to address the need for the simultaneous study of chromatin state heterogeneity in both rare and abundant subpopulations. Our analyses of existing data from three widely used single-cell assays for transposase-accessible chromatin using sequencing (scATAC-seq) show the superior performance of TooManyPeaks in delineating and visualizing pure clusters of rare and abundant subpopulations. Furthermore, the application of TooManyPeaks to new scATAC-seq data from drug-naive and drug-resistant leukemic T cells clearly visualizes relationships among these cells and stratifies a rare "resistant-like" drug-naive sub-clone with distinct cis-regulatory elements.

ZMYND8-regulated IRF8 transcription axis is an acute myeloid leukemia dependency.

The transformed state in acute leukemia requires gene regulatory programs involving transcription factors and chromatin modulators. Here, we uncover an IRF8-MEF2D transcriptional circuit as an acute myeloid leukemia (AML)-biased dependency. We discover and characterize the mechanism by which the chromatin "reader" ZMYND8 directly activates IRF8 in parallel with the MYC proto-oncogene through their lineage-specific enhancers. ZMYND8 is essential for AML proliferation in vitro and in vivo and associates with MYC and IRF8 enhancer elements that we define in cell lines and in patient samples. ZMYND8 occupancy at IRF8 and MYC enhancers requires BRD4, a transcription coactivator also necessary for AML proliferation. We show that ZMYND8 binds to the ET domain of BRD4 via its chromatin reader cassette, which in turn is required for proper chromatin occupancy and maintenance of leukemic growth in vivo. Our results rationalize ZMYND8 as a potential therapeutic target for modulating essential transcriptional programs in AML.

ALC1 links chromatin accessibility to PARP inhibitor response in homologous recombination-deficient cells.

The response to poly(ADP-ribose) polymerase inhibitors (PARPi) is dictated by homologous recombination (HR) DNA repair and the abundance of lesions that trap PARP enzymes. It remains unclear, however, if the established role of PARP in promoting chromatin accessibility impacts viability in these settings. Using a CRISPR-based screen, we identified the PAR-binding chromatin remodeller ALC1/CHD1L as a key determinant of PARPi toxicity in HR-deficient cells. ALC1 loss reduced viability of breast cancer gene (BRCA)-mutant cells and enhanced sensitivity to PARPi by up to 250-fold, while overcoming several resistance mechanisms. ALC1 deficiency reduced chromatin accessibility concomitant with a decrease in the association of base damage repair factors. This resulted in an accumulation of replication-associated DNA damage, increased PARP trapping and a reliance on HR. These findings establish PAR-dependent chromatin remodelling as a mechanistically distinct aspect of PARPi responses and therapeutic target in HR-deficient cancers.

The vascular landscape of human cancer.

Tumors depend on a blood supply to deliver oxygen and nutrients, making tumor vasculature an attractive anticancer target. However, only a fraction of patients with cancer benefit from angiogenesis inhibitors. Whether antiangiogenic therapy would be more effective if targeted to individuals with specific tumor characteristics is unknown. To better characterize the tumor vascular environment both within and between cancer types, we developed a standardized metric - the endothelial index (EI) - to estimate vascular density in over 10,000 human tumors, corresponding to 31 solid tumor types, from transcriptome data. We then used this index to compare hyper- and hypovascular tumors, enabling the classification of human tumors into 6 vascular microenvironment signatures (VMSs) based on the expression of a panel of 24 vascular "hub" genes. The EI and VMS correlated with known tumor vascular features and were independently associated with prognosis in certain cancer types. Retrospective testing of clinical trial data identified VMS2 classification as a powerful biomarker for response to bevacizumab. Thus, we believe our studies provide an unbiased picture of human tumor vasculature that may enable more precise deployment of antiangiogenesis therapy.

Trib1 regulates T cell differentiation during chronic infection by restraining the effector program.

In chronic infections, the immune response fails to control virus, leading to persistent antigen stimulation and the progressive development of T cell exhaustion. T cell effector differentiation is poorly understood in the context of exhaustion, but targeting effector programs may provide new strategies for reinvigorating T cell function. We identified Tribbles pseudokinase 1 (Trib1) as a central regulator of antiviral T cell immunity, where loss of Trib1 led to a sustained enrichment of effector-like KLRG1+ T cells, enhanced function, and improved viral control. Single-cell profiling revealed that Trib1 restrains a population of KLRG1+ effector CD8 T cells that is transcriptionally distinct from exhausted cells. Mechanistically, we identified an interaction between Trib1 and the T cell receptor (TCR) signaling activator, MALT1, which disrupted MALT1 signaling complexes. These data identify Trib1 as a negative regulator of TCR signaling and downstream function, and reveal a link between Trib1 and effector versus exhausted T cell differentiation that can be targeted to improve antiviral immunity.

TooManyCells identifies and visualizes relationships of single-cell clades.

Identifying and visualizing transcriptionally similar cells is instrumental for accurate exploration of the cellular diversity revealed by single-cell transcriptomics. However, widely used clustering and visualization algorithms produce a fixed number of cell clusters. A fixed clustering 'resolution' hampers our ability to identify and visualize echelons of cell states. We developed TooManyCells, a suite of graph-based algorithms for efficient and unbiased identification and visualization of cell clades. TooManyCells introduces a visualization model built on a concept intentionally orthogonal to dimensionality-reduction methods. TooManyCells is also equipped with an efficient matrix-free divisive hierarchical spectral clustering different from prevalent single-resolution clustering methods. TooManyCells enables multiresolution and multifaceted exploration of single-cell clades. An advantage of this paradigm is the immediate detection of rare and common populations that outperforms popular clustering and visualization algorithms, as demonstrated using existing single-cell transcriptomic data sets and new data modeling drug-resistance acquisition in leukemic T cells.

Genetic Variation in Type 1 Diabetes Reconfigures the 3D Chromatin Organization of T Cells and Alters Gene Expression.

Genetics is a major determinant of susceptibility to autoimmune disorders. Here, we examined whether genome organization provides resilience or susceptibility to sequence variations, and how this would contribute to the molecular etiology of an autoimmune disease. We generated high-resolution maps of linear and 3D genome organization in thymocytes of NOD mice, a model of type 1 diabetes (T1D), and the diabetes-resistant C57BL/6 mice. Multi-enhancer interactions formed at genomic regions harboring genes with prominent roles in T cell development in both strains. However, diabetes risk-conferring loci coalesced enhancers and promoters in NOD, but not C57BL/6 thymocytes. 3D genome mapping of NODxC57BL/6 F1 thymocytes revealed that genomic misfolding in NOD mice is mediated in cis. Moreover, immune cells infiltrating the pancreas of humans with T1D exhibited increased expression of genes located on misfolded loci in mice. Thus, genetic variation leads to altered 3D chromatin architecture and associated changes in gene expression that may underlie autoimmune pathology.