Biology subjects

Parks, B.

Publications and source records attributed to Parks, B..

4 recordsLinked to original sources

bioRxiv2025

Scalable high-performance single cell data analysis with BPCells

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.

bioinformatics↗

bioRxiv2021

NEAT-seq: Simultaneous profiling of intra-nuclear proteins, chromatin accessibility, and gene expression in single cells

Oligonucleotide-conjugated antibodies1 have allowed for joint measurement of surface protein abundance and the transcriptome in single cells using high-throughput sequencing. Extending these measurements to gene regulatory proteins in the nucleus would provide a powerful means to link changes in abundance of trans-acting TFs to changes in activity of cis-acting elements and expression of target genes. Here, we introduce Nuclear protein Epitope, chromatin Accessibility, and Transcriptome sequencing (NEAT-seq), a technique to simultaneously measure nuclear protein abundance, chromatin accessibility, and the transcriptome in single cells. We apply this technique to profile CD4 memory T cells using a panel of master transcription factors (TFs) that drive distinct helper T cell subsets and regulatory T cells (Tregs) and identify examples of TFs with regulatory activity gated by three distinct mechanisms: transcription, translation, and regulation of chromatin binding. Furthermore, we identify regulatory elements and target genes associated with each TF, which we use to link a non-coding GWAS SNP within a GATA motif to both strong allele-specific chromatin accessibility in cells expressing high levels of GATA3 protein, and a putative target gene.

genomics↗

bioRxiv2020

Multi-omic profiling reveals widespread dysregulation of innate immunity and hematopoiesis in COVID-19

Our understanding of protective vs. pathologic 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 reveal widespread dysfunction of peripheral innate immunity in severe and fatal COVID-19, with the most profound disturbances including a prominent neutrophil hyperactivation signature and monocytes with anti-inflammatory features. We further demonstrate 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. One Sentence SummarySingle-cell profiling demonstrates multifarious dysregulation of innate immune phenotype associated with COVID-19 severity.

immunology↗

bioRxiv2019

A single cell framework for multi-omic analysis of disease identifies malignant regulatory signatures in mixed phenotype acute leukemia

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.

genomics↗