Transcript-indexed ATAC-seq for precision immune profiling.

T cells create vast amounts of diversity in the genes that encode their T cell receptors (TCRs), which enables individual clones to recognize specific peptide-major histocompatibility complex (MHC) ligands. Here we combined sequencing of the TCR-encoding genes with assay for transposase-accessible chromatin with sequencing (ATAC-seq) analysis at the single-cell level to provide information on the TCR specificity and epigenomic state of individual T cells. By using this approach, termed transcript-indexed ATAC-seq (T-ATAC-seq), we identified epigenomic signatures in immortalized leukemic T cells, primary human T cells from healthy volunteers and primary leukemic T cells from patient samples. In peripheral blood CD4+ T cells from healthy individuals, we identified cis and trans regulators of naive and memory T cell states and found substantial heterogeneity in surface-marker-defined T cell populations. In patients with a leukemic form of cutaneous T cell lymphoma, T-ATAC-seq enabled identification of leukemic and nonleukemic regulatory pathways in T cells from the same individual by allowing separation of the signals that arose from the malignant clone from the background T cell noise. Thus, T-ATAC-seq is a new tool that enables analysis of epigenomic landscapes in clonal T cells and should be valuable for studies of T cell malignancy, immunity and immunotherapy

Systematic identification of post-transcriptional regulatory modules

In our cells, a limited number of RNA binding proteins (RBPs) are responsible for all aspects of RNA metabolism across the entire transcriptome. To accomplish this, RBPs form regulatory units that act on specific target regulons. However, the landscape of RBP combinatorial interactions remains poorly explored. Here, we perform a systematic annotation of RBP combinatorial interactions via multimodal data integration. We build a large-scale map of RBP protein neighborhoods by generating in vivo proximity-dependent biotinylation datasets of 50 human RBPs. In parallel, we use CRISPR interference with single-cell readout to capture transcriptomic changes upon RBP knockdowns. By combining these physical and functional interaction readouts, along with the atlas of RBP mRNA targets from eCLIP assays, we generate an integrated map of functional RBP interactions. We then use this map to match RBPs to their context-specific functions and validate the predicted functions biochemically for four RBPs. This study provides a detailed map of RBP interactions and deconvolves them into distinct regulatory modules with annotated functions and target regulons. This multimodal and integrative framework provides a principled approach for studying post-transcriptional regulatory processes and enriches our understanding of their underlying mechanisms

Preleukemic Hematopoietic Stem Cells in Human Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is an aggressive malignancy of the bone marrow characterized by an uncontrolled proliferation of undifferentiated myeloid lineage cells. Decades of research have demonstrated that AML evolves from the sequential acquisition of genetic alterations within a single lineage of hematopoietic cells. More recently, the advent of high-throughput sequencing has enabled the identification of a premalignant phase of AML termed preleukemia. Multiple studies have demonstrated that AML can arise from the accumulation of mutations within hematopoietic stem cells (HSCs). These HSCs have been termed “preleukemic HSCs” as they represent the evolutionary ancestors of the leukemia. Through examination of the biological and clinical characteristics of these preleukemic HSCs, this review aims to shed light on some of the unexplored questions in the field. We note that some of the material discussed is speculative in nature and is presented in order to motivate future work

Chromatin accessibility profiling by ATAC-seq

The assay for transposase-accessible chromatin using sequencing (ATAC-seq) provides a simple and scalable way to detect the unique chromatin landscape associated with a cell type and how it may be altered by perturbation or disease. ATAC-seq requires a relatively small number of input cells and does not require a priori knowledge of the epigenetic marks or transcription factors governing the dynamics of the system. Here we describe an updated and optimized protocol for ATAC-seq, called Omni-ATAC, that is applicable across a broad range of cell and tissue types. The ATAC-seq workflow has five main steps: sample preparation, transposition, library preparation, sequencing and data analysis. This protocol details the steps to generate and sequence ATAC-seq libraries, with recommendations for sample preparation and downstream bioinformatic analysis. ATAC-seq libraries for roughly 12 samples can be generated in 10 h by someone familiar with basic molecular biology, and downstream sequencing analysis can be implemented using benchmarked pipelines by someone with basic bioinformatics skills and with access to a high-performance computing environment

Insulin signaling and dietary restriction differentially influence the decline of learning and memory with age

Of all the age-related declines, memory loss is one of the most devastating. While conditions that increase longevity have been identified, the effects of these longevity-promoting factors on learning and memory are unknown. Here we show that the C. elegans Insulin/IGF-1 receptor mutant daf-2 improves memory performance early in adulthood and maintains learning ability better with age but, surprisingly, demonstrates no extension in long-term memory with age. By contrast, eat-2 mutants, a model of Dietary Restriction (DR), exhibit impaired long-term memory in young adulthood but maintain this level of memory longer with age. We find that crh-1, the C. elegans homolog of the CREB transcription factor, is required for longterm associative memory, but not for learning or short-term memory. The expression of crh-1 declines with age and differs in the longevity mutants, and CREB expression and activity correlate with memory performance. Our results suggest that specific longevity treatments have acute and long-term effects on cognitive functions that decline with age through thei

Convergent epigenetic evolution drives relapse in acute myeloid leukemia

Relapse of acute myeloid leukemia (AML) is highly aggressive and often treatment refractory. We analyzed previously published AML relapse cohorts and found that 40% of relapses occur without changes in driver mutations, suggesting that non-genetic mechanisms drive relapse in a large proportion of cases. We therefore characterized epigenetic patterns of AML relapse using 26 matched diagnosis-relapse samples with ATAC-seq. This analysis identified a relapse-specific chromatin accessibility signature for mutationally stable AML, suggesting that AML undergoes epigenetic evolution at relapse independent of mutational changes. Analysis of leukemia stem cell (LSC) chromatin changes at relapse indicated that this leukemic compartment underwent significantly less epigenetic evolution than non-LSCs, while epigenetic changes in non-LSCs reflected overall evolution of the bulk leukemia. Finally, we used single-cell ATAC-seq paired with mitochondrial sequencing (mtscATAC) to map clones from diagnosis into relapse along with their epigenetic features. We found that distinct mitochondrially-defined clones exhibit more similar chromatin accessibility at relapse relative to diagnosis, demonstrating convergent epigenetic evolution in relapsed AML. These results demonstrate that epigenetic evolution is a feature of relapsed AML and that convergent epigenetic evolution can occur following treatment with induction chemotherapy

ArchR is a scalable software package for integrative single-cell chromatin accessibility analysis.

The advent of single-cell chromatin accessibility profiling has accelerated the ability to map gene regulatory landscapes but has outpaced the development of scalable software to rapidly extract biological meaning from these data. Here we present a software suite for single-cell analysis of regulatory chromatin in R (ArchR; https://www.archrproject.com/ ) that enables fast and comprehensive analysis of single-cell chromatin accessibility data. ArchR provides an intuitive, user-focused interface for complex single-cell analyses, including doublet removal, single-cell clustering and cell type identification, unified peak set generation, cellular trajectory identification, DNA element-to-gene linkage, transcription factor footprinting, mRNA expression level prediction from chromatin accessibility and multi-omic integration with single-cell RNA sequencing (scRNA-seq). Enabling the analysis of over 1.2 million single cells within 8 h on a standard Unix laptop, ArchR is a comprehensive software suite for end-to-end analysis of single-cell chromatin accessibility that will accelerate the understanding of gene regulation at the resolution of individual cells