Biology subjects

DePasquale, E. A. K.

Publications and source records attributed to DePasquale, E. A. K..

4 recordsLinked to original sources

bioRxiv2026

HLiCA: An integrated cell atlas of the healthy human liver

The human liver is composed of a heterogeneous mix of cell types. How these distinct populations contribute individually and collectively to liver function remains poorly understood. Although single-cell technologies have advanced our understanding of liver biology, individual studies have often been limited by small donor cohorts and inconsistent cell type annotations. Integrating multiple datasets can overcome these challenges and better capture biological variability. We present the Human Liver Cell Atlas (HLiCA), an integrated reference of non-disease liver cells assembled from eight datasets across six research centers, encompassing more than 525,000 cells from 110 donors. Developed in collaboration with the Human Cell Atlas Liver Bionetwork, the HLiCA incorporates expert-curated cell annotations refined through community feedback and dedicated cell type annotation meetings. The HLiCA classifies cells into six lineages and expands the cell type resolution to include 47 distinct cell types. Starting from raw sequencing reads, we realigned all data and performed rigorous benchmarking to ensure robust integration across technical and biological variables. Genetic ancestry was inferred for all samples to evaluate the range of ancestral backgrounds represented in the atlas. The expanded cell type annotation enabled identification of previously unrecognized liver cell types, including NRXN1+ stromal cells. Their presence was validated using spatial transcriptomics, which localized NRXN1+ stromal cells to periportal regions. With the number of donors included in the HLiCA we were able to examine cell type specific associations with demographic covariates. In hepatocytes, drug metabolism genes showed differential expression between sexes, and in cholangiocytes, mucus-production genes varied with age. As the largest and most genetically diverse human liver cell atlas to date, the HLiCA provides a comprehensive, well-annotated reference for the field, annotated by expert consensus. This resource will enable deeper interrogation of liver cellular diversity, architecture, and function in the healthy human liver and serve as a reference to understand changes that occur with disease.

genomics↗

bioRxiv2024

Translation Initiation Represents an Acute Myeloid Leukemia Cell Vulnerability That Can Be Co-Targeted With BCL-2 Inhibition

Targeted therapies like Venetoclax have increased the options available to acute myeloid leukemia (AML) patients, but survival remains poor due to drug resistance and disease relapse. We found that the translation initiation factor EIF4A1, which unwinds complex mRNA structures in the 5 UTR of oncogenic transcripts, is highly expressed in AML stem- and progenitor-like cells. Inhibiting eIF4A with the small molecule Zotatifin reduces translation of transcripts related to the cell cycle and survival. This results in downregulation of AKT, STAT-5, and MCL-1 and underlies synergy of Zotatifin with Venetoclax. The drug combination promotes apoptosis across AML genotypes, while the effect on healthy blood cells is limited. Using in vivo relapsed and refractory AML patient-derived xenograft models, the combination significantly suppressed tumor burden and prolonged survival of xenografted mice. These results support eIF4A-mediated protein translation as a therapeutic target in AML. SIGNIFICANCEDespite advances in targeted therapies, the 5-year survival rate for acute myeloid leukemia remains around 32%. The efficacy of existing treatments may be improved with the addition of Zotatifin, an inhibitor targeting the translation initiation factor eIF4A.

cancer biology↗

bioRxiv2022

WAT3R: Recovery of T-Cell Receptor Variable Regions From 3' Single-Cell RNA-Sequencing

SummaryDiversity of the T-cell receptor (TCR) repertoire is central to adaptive immunity. The TCR is composed of and {beta} chains, encoded by the TRA and TRB genes, of which the variable regions determine antigen specificity. To generate novel biological insights into the complex functioning of immune cells, combined capture of variable regions and single-cell transcriptomes provides a compelling approach. Recent developments enable the enrichment of TRA and TRB variable regions from widely used technologies for 3-biased single-cell RNA-sequencing (scRNA-seq). However, a comprehensive computational pipeline to process TCR-enriched data from 3 scRNA-seq is not available. Here we present an analysis pipeline to process TCR variable regions enriched from 3 scRNA-seq cDNA. The tool reports TRA and TRB nucleotide and amino acid sequences linked to cell barcodes, enabling the reconstruction of T-cell clonotypes with associated transcriptomes. We demonstrate the software using peripheral blood mononuclear cells (PBMCs) from a healthy donor and detect TCR sequences in a high proportion of single T-cells. Detection of TCR sequences is negligible in non-T-cell populations, demonstrating specificity. Finally, we show that TCR clones are larger in CD8 Memory T-cells than other T-cell types, indicating an association between T-cell clonotypes and differentiation states. Availability and implementationThe Workflow for Association of T-cell receptors from 3 single-cell RNA-seq (WAT3R), including test data, is available on GitHub (https://github.com/mainciburu/WAT3R), Docker Hub (https://hub.docker.com/r/mainciburu/wat3r), and a workflow on the Terra platform (https://app.terra.bio). The test dataset is available on GEO (accession number pending).

immunology↗

bioRxiv2018

DoubletDecon: Cell-State Aware Removal of Single-Cell RNA-Seq Doublets

Methods for single-cell RNA sequencing (scRNA-Seq) have greatly advanced in recent years. While droplet- and well-based methods have increased the capture frequency of cells for scRNA-Seq, these technologies readily produce technical artifacts, such as doublet-cell and multiplet-cell captures. Doublets occurring between distinct cell-types can appear as hybrid scRNA-Seq profiles, but do not have distinct transcriptomes from individual cell states. We introduce DoubletDecon, an approach that detects doublets with a combination of deconvolution analyses and the identification of unique cell-state gene expression. We demonstrate the ability of DoubletDecon to identify synthetic and cell-hashing cell singlets and doublets from scRNA-Seq datasets of varying cellular complexity. DoubletDecon is able to account for cell-cycle effects and is compatible with diverse species and unsupervised population detection algorithms (e.g., ICGS, Seurat). We believe this approach has the potential to become a standard quality control step for the accurate delineation of cell states.

bioinformatics↗