The growth of single-cell datasets to multi-million cell atlases has uncovered major scalability problems for single-cell analysis software. Here, we present BPCells, a package for high-performance single-cell analysis of RNA-seq and ATAC-seq datasets. BPCells uses disk-backed streaming compute algorithms to reduce memory requirements by nearly 70-fold compared to in-memory workflows with little to no loss of execution speed. BPCells also introduces high-performance compressed formats based on bitpacking compression for ATAC-seq fragment files and single-cell sparse matrices. These novel compression algorithms help to accelerate disk-backed analysis by reducing data transfer from disk, while providing the lowest computational overhead of all compression algorithms tested. Using BPCells, we perform normalization and PCA of a 44 million cell dataset on a laptop, demonstrating that BPCells makes working with the largest contemporary single-cell datasets feasible on modest hardware, while leaving headroom on servers for future datasets an order of magnitude larger.

The binding of multiple transcription factors (TFs) to genomic enhancers activates gene expression in mammalian cells. However, the molecular details that link enhancer sequence to TF binding, promoter state, and gene expression levels remain opaque. We applied single-molecule footprinting (SMF) to measure the simultaneous occupancy of TFs, nucleosomes, and components of the transcription machinery on engineered enhancer/promoter constructs with variable numbers of TF binding sites for both a synthetic and an endogenous TF. We find that activation domains enhance a TF’s capacity to compete with nucleosomes for binding to DNA in a BAF-dependent manner, TF binding on nucleosome-free DNA is consistent with independent binding between TFs, and average TF occupancy linearly contributes to promoter activation rates. We also decompose TF strength into separable binding and activation terms, which can be tuned and perturbed independently. Finally, we develop thermodynamic and kinetic models that quantitatively predict both the binding microstates observed at the enhancer and subsequent time-dependent gene expression. This work provides a template for quantitative dissection of distinct contributors to gene activation, including the activity of chromatin remodelers, TF activation domains, chromatin acetylation, TF concentration, TF binding affinity, and TF binding site configuration.

The binding of multiple transcription factors (TFs) to genomic enhancers drives gene expression in mammalian cells1. However, the molecular details that link enhancer sequence to TF binding, promoter state and transcription levels remain unclear. Here we applied single-molecule footprinting2,3 to measure the simultaneous occupancy of TFs, nucleosomes and other regulatory proteins on engineered enhancer-promoter constructs with variable numbers of TF binding sites for both a synthetic TF and an endogenous TF involved in the type I interferon response. Although TF binding events on nucleosome-free DNA are independent, activation domains recruit cofactors that destabilize nucleosomes, driving observed TF binding cooperativity. Average TF occupancy linearly determines promoter activity, and we decompose TF strength into separable binding and activation terms. Finally, we develop thermodynamic and kinetic models that quantitatively predict both the enhancer binding microstates and gene expression dynamics. This work provides a template for the quantitative dissection of distinct contributors to gene expression, including TF activation domains, concentration, binding affinity, binding site configuration and recruitment of chromatin regulators.

Adaptive immunity relies on specialized effector functions elicited by lymphocytes, yet how antigen recognition activates appropriate effector responses through nonspecific signaling intermediates is unclear. Here we examined the role of chromatin priming in specifying the functional outputs of effector T cells and found that most of the cis-regulatory landscape active in effector T cells was poised early in development before the expression of the T cell antigen receptor. We identified two principal mechanisms underpinning this poised landscape: the recruitment of the nucleosome remodeler mammalian SWItch/Sucrose Non-Fermentable (mSWI/SNF) by the transcription factors RUNX1 and PU.1 to establish chromatin accessibility at T effector loci; and a ‘relay’ whereby the transcription factor BCL11B succeeded PU.1 to maintain occupancy of the chromatin remodeling complex mSWI/SNF together with RUNX1, after PU.1 silencing during lineage commitment. These mechanisms define modes by which T cells acquire the potential to elicit specialized effector functions early in their ontogeny and underscore the importance of integrating extrinsic cues to the developmentally specified intrinsic program. Koh et al. show that loci active in differentiated effector T cells are poised in early T precursors before the expression of T cell antigen receptors in a manner dependent on the chromatin remodeling complex mammalian SWItch/Sucrose Non-Fermentable and the PU.1–RUNX1 and BCL11B–RUNX1 complexes.

In this work, we describe NEAT-seq (sequencing of nuclear protein epitope abundance, chromatin accessibility and the transcriptome in single cells), enabling interrogation of regulatory mechanisms spanning the central dogma. We apply this technique to profile CD4 memory T cells using a panel of master transcription factors (TFs) that drive T cell subsets and identify examples of TFs with regulatory activity gated by transcription, translation and regulation of chromatin binding. We also link a noncoding genome-wide association study single-nucleotide polymorphism (SNP) within a GATA motif to a putative target gene, using NEAT-seq data to internally validate SNP impact on GATA3 regulation.

Our understanding of protective versus pathological immune responses to SARS-CoV-2, the virus that causes coronavirus disease 2019 (COVID-19), is limited by inadequate profiling of patients at the extremes of the disease severity spectrum. Here, we performed multi-omic single-cell immune profiling of 64 COVID-19 patients across the full range of disease severity, from outpatients with mild disease to fatal cases. Our transcriptomic, epigenomic, and proteomic analyses revealed widespread dysfunction of peripheral innate immunity in severe and fatal COVID-19, including prominent hyperactivation signatures in neutrophils and NK cells. We also identified chromatin accessibility changes at NF-κB binding sites within cytokine gene loci as a potential mechanism for the striking lack of pro-inflammatory cytokine production observed in monocytes in severe and fatal COVID-19. We further demonstrated that emergency myelopoiesis is a prominent feature of fatal COVID-19. Collectively, our results reveal disease severity–associated immune phenotypes in COVID-19 and identify pathogenesis-associated pathways that are potential targets for therapeutic intervention.

In order to identify the molecular determinants of human diseases, such as cancer, that arise from a diverse range of tissue, it is necessary to accurately distinguish normal and pathogenic cellular programs. 1–3 Here we present a novel approach for single-cell multi-omic deconvolution of healthy and pathological molecular signatures within phenotypically heterogeneous malignant cells. By first creating immunophenotypic, transcriptomic and epigenetic single-cell maps of hematopoietic development from healthy peripheral blood and bone marrow mononuclear cells, we identify cancer-specific transcriptional and chromatin signatures from single cells in a cohort of mixed phenotype acute leukemia (MPAL) clinical samples. MPALs are a high-risk subtype of acute leukemia characterized by a heterogeneous malignant cell population expressing both myeloid and lymphoid lineage-specific markers. 4, 5 Our results reveal widespread heterogeneity in the pathogenetic gene regulatory and expression programs across patients, yet relatively consistent changes within patients even across malignant cells occupying diverse portions of the hematopoietic lineage. An integrative analysis of transcriptomic and epigenetic maps identifies 91,601 putative gene-regulatory interactions and classifies a number of transcription factors that regulate leukemia specific genes, including RUNX1 -linked regulatory elements proximal to CD69 . This work provides a template for integrative, multi-omic analysis for the interpretation of pathogenic molecular signatures in the context of developmental origin.

Identifying the causes of human diseases requires deconvolution of abnormal molecular phenotypes spanning DNA accessibility, gene expression and protein abundance(1-3). We present a single-cell framework that integrates highly multiplexed protein quantification, transcriptome profiling and analysis of chromatin accessibility. Using this approach, we establish a normal epigenetic baseline for healthy blood development, which we then use to deconvolve aberrant molecular features within blood from patients with mixed-phenotype acute leukemia(4,5). Despite widespread epigenetic heterogeneity within the patient cohort, we observe common malignant signatures across patients as well as patient-specific regulatory features that are shared across phenotypic compartments of individual patients. Integrative analysis of transcriptomic and chromatin-accessibility maps identified 91,601 putative peak-to-gene linkages and transcription factors that regulate leukemia-specific genes, such as RUNX1-linked regulatory elements proximal to the marker gene CD69. These results demonstrate how integrative, multiomic analysis of single cells within the framework of normal development can reveal both distinct and shared molecular mechanisms of disease from patient samples.