Biology subjects

Le, D. D.

Publications and source records attributed to Le, D. D..

4 recordsLinked to original sources

bioRxiv2021

isoformant: A visual toolkit for reference-free long-read isoform analysis at single-read resolution

isoformant is an analytical toolkit for isoform characterization of Oxford Nanopore Technologies (ONT) long-transcript sequencing data (i.e. direct RNA and cDNA). Deployment of these tools using Jupyter Notebook enables interactive analysis of user-defined region-of-interest (ROI), typically a gene. The core module of isoformant clusters sequencing reads by k-mer density to generate isoform consensus sequences without the requirement for a reference genome or prior annotations. The inclusion of differential isoform usage hypothesis testing based on read distribution among clusters enables comparison across multiple samples. Here, as proof-of-principle, we demonstrate the utility of isoformant for analyzing isoform diversity of commercially-available isoform standard mixtures. isoformant is available here: https://github.com/danledinh/isoformant.

bioinformatics↗

bioRxiv2019

Heterogeneity and targeted therapy-induced adaptations in lung cancer revealed by longitudinal single-cell RNA sequencing

Lung cancer, the leading cause of cancer mortality, exhibits heterogeneity that enables adaptability, limits therapeutic success, and remains incompletely understood. Single-cell RNA sequencing (scRNAseq) of metastatic lung cancer was performed using 44 tumor biopsies obtained longitudinally from 27 patients before and during targeted therapy. Over 20,000 cancer and tumor microenvironment (TME) single-cell profiles exposed a rich and dynamic tumor ecosystem. scRNAseq of cancer cells illuminated targetable oncogenes beyond those detected clinically. Cancer cells surviving therapy as residual disease (RD) expressed an alveolar-regenerative cell signature suggesting a therapy-induced primitive cell state transition, whereas those present at on-therapy progressive disease (PD) upregulated kynurenine, plasminogen, and gap junction pathways. Active T-lymphocytes and decreased macrophages were present at RD and immunosuppressive cell states characterized PD. Biological features revealed by scRNAseq were biomarkers of clinical outcomes in independent cohorts. This study highlights how therapy-induced adaptation of the multi-cellular ecosystem of metastatic cancer shapes clinical outcomes.

cancer biology↗

bioRxiv2019

Single Cell RNAseq Reveals A Critical Role of Chloride Channels in Airway Development

The conducting airway forms a protective mucosal barrier and is the primary target of airway disorders. To better understand how airway developmental programs are established to support air breathing and barrier functions, we constructed a single-cell atlas of the human and mouse developing trachea. In this study, we uncover hitherto unrecognized heterogeneity of cell states with distinct differentiation programs and immune features of the developing airway. In addition, we find ubiquitous expression of CFTR and ANO1/TMEM16A chloride channels in the embryonic airway epithelium. We show that genetic inactivation of TMEM16A leads to airway defects commonly seen in cystic fibrosis patients with deficient CFTR, alters the differentiation trajectory of airway basal progenitors, and results in mucus cell hyperplasia and aberrant epithelial antimicrobial expression. Together, our study illuminates conserved developmental features of the mammalian airway and implicates chloride homeostasis as a key player in regulating mucosal barrier formation and function relevant to early onset airway diseases.

developmental biology↗

bioRxiv2017

Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding

Transcription factors (TFs) are primary regulators of gene expression in cells, where they bind specific genomic target sites to control transcription. Quantitative measurements of TF-DNA binding energies can improve the accuracy of predictions of TF occupancy and downstream gene expression in vivo and further shed light on how transcriptional networks are rewired throughout evolution. Here, we present a novel sequencing-based TF binding assay and analysis pipeline capable of providing quantitative estimates of binding energies for more than one million DNA sequences in parallel at high energetic resolution. Using this platform, we measured the binding energies associated with all possible combinations of 10 nucleotides flanking the known consensus DNA target for two model yeast TFs, Pho4 and Cbf1. A large fraction of these flanking mutations change overall binding energies by an amount equal to or greater than consensus site mutations, suggesting that current definitions of TF binding sites may be too restrictive. By systematically comparing estimates of binding energies output by deep neural networks (NN) and biophysical models trained on these data, we establish that dinucleotide specificities are sufficient to explain essentially all variance in observed binding behavior, with Cbf1 binding exhibiting significantly more epistasis than Pho4. NN-derived binding energies agree with orthogonal biochemical measurements and reveal that dynamically occupied sites in vivo are both energetically and mutationally distant from the highest-affinity sites.

biophysics↗