Biology subjects

Engel, K. L.

Publications and source records attributed to Engel, K. L..

5 recordsLinked to original sources

bioRxiv2025

scExploreR: a flexible platform for democratized analysis of multimodal single-cell data by non-programmers

Single-cell sequencing has revolutionized biomedical research by uncovering cellular heterogeneity in disease mechanisms, with significant potential for advancing personalized medicine. However, participation in single-cell data analysis is limited by the programming experience required to access data. Several existing browsers allow the interrogation of single-cell data through a point-and-click interface accessible to non-programmers, but many of these browsers are limited in the depth of analysis that can be performed, or the flexibility of input data formats accepted. Thus, programming experience is still required for comprehensive data analysis. We developed scExploreR to address these limitations and extend the range of analysis tasks that can be performed by non-programmers. scExploreR is implemented as a packaged R Shiny app that can be run locally or easily deployed for multiple users on a server. scExploreR offers extensive customization options for plots, allowing users to generate publication quality figures. Leveraging our SCUBA package, scExploreR seamlessly handles multimodal data, providing identical plotting capabilities regardless of input format. By empowering researchers to directly explore and analyze single-cell data, scExploreR bridges communication gaps between biological and computational scientists, streamlining insight generation.

bioinformatics↗

bioRxiv2022

MuVEH and mitoMuVEH improve discovery of genetic variation from single cells.

Understanding the genetic underpinnings and clonal structure of malignancies at single-cell resolution is critical to accurately predicting drug response and understanding mechanisms of drug resistance and disease evolution in heterogeneous populations of cells. Here, we introduce an accessible, multiplexable, targeted mutation enrichment approach and end-to-end analysis pipeline called MuVEH (Multiplexed Variant Enrichment by Hybridization) that increases the resolution of variant detection in scRNA-seq analysis. When applied specifically to the mitochondrial chromosome ("mitoMuVEH"), this technique can also be used to reconstruct and trace clonal relationships between individual cells. We applied both approaches to two pairs of primary bone marrow specimens from acute myelogenous leukemia (AML) patients collected at diagnosis and after relapse following Venetoclax+Azacitidine (Ven/Aza) therapy. Used together, MuVEH and mitoMuVEH reveal clonal evolution and changing mutational burden in response to treatment at single-cell resolution in these patients. Ultimately, these approaches have the potential to extract additional biological insights from precious patient samples and provide insight into the contributions clonality and genotype have during disease progression.

cancer biology↗

bioRxiv2021

Analysis of subcellular transcriptomes by RNA proximity labeling with Halo-seq

Thousands of RNA species display nonuniform distribution within cells. However, quantification of the spatial patterns adopted by individual RNAs remains difficult, in part by a lack of quantitative tools for subcellular transcriptome analysis. In this study, we describe an RNA proximity labeling method that facilitates the quantification of subcellular RNA populations with high spatial specificity. This method, termed Halo-seq, pairs a light-activatable, radical generating small molecule with highly efficient Click chemistry to efficiently label and purify spatially defined RNA samples. We compared Halo-seq with previously reported similar methods and found that Halo-seq displayed a higher efficiency of RNA labeling, indicating that it is well suited to the investigation of small, precisely localized RNA populations. We then used Halo-seq to quantify nuclear, nucleolar, and cytoplasmic transcriptomes, characterize their dynamic nature following perturbation, and identify RNA sequence features associated with their composition. Specifically, we found that RNAs containing AU-rich elements are relatively enriched in the nucleus. This enrichment becomes stronger upon treatment with the nuclear export inhibitor leptomycin B, both expanding the role of HuR in RNA export and generating a comprehensive set of transcripts whose export from the nucleus depends on HuR.

molecular biology↗

bioRxiv2020

LABRAT reveals association of alternative polyadenylation with transcript localization, RNA binding protein expression, transcription speed, and cancer survival

The sequence content of the 3' UTRs of many mRNA transcripts is regulated through alternative polyadenylation (APA). The study of this process using RNAseq data, though, has been historically challenging. To combat this problem, we developed LABRAT, an APA quantification method. LABRAT takes advantage of newly developed transcriptome quantification techniques to accurately determine relative APA site usage and how it varies across conditions. Using LABRAT, we found consistent relationships between gene-distal APA and subcellular RNA localization in multiple cell types. We also observed connections between transcription speed and APA site choice as well as tumor-specific transcriptome-wide shifts in APA in hundreds of patient-derived tumor samples that were associated with patient prognosis. We investigated the effects of APA on transcript expression and found a weak overall relationship, although many individual genes showed strong correlations between APA and expression. We interrogated the roles of 191 RNA-binding proteins in the regulation of APA, finding that dozens promote broad, directional shifts in relative APA isoform abundance both in vitro and in patient-derived samples. Finally, we find that APA site shifts in the two classes of APA, tandem UTRs and alternative last exons, are strongly correlated across many contexts, suggesting that they are coregulated.

genomics↗

bioRxiv2016

Genomic diagnosis for children with intellectual disability and/or developmental delay

BackgroundDevelopmental disabilities have diverse genetic causes that must be identified to facilitate precise diagnoses. We describe genomic data from 371 affected individuals, 309 of which were sequenced as proband-parent trios.\n\nMethodsWhole exome sequences (WES) were generated for 365 individuals (127 affected) and whole genome sequences (WGS) were generated for 612 individuals (244 affected).\n\nResultsPathogenic or likely pathogenic variants were found in 100 individuals (27%), with variants of uncertain significance in an additional 42 (11.3%). We found that a family history of neurological disease, especially the presence of an affected 1st degree relative, reduces the pathogenic/likely pathogenic variant identification rate, reflecting both the disease relevance and ease of interpretation of de novo variants. We also found that improvements to genetic knowledge facilitated interpretation changes in many cases. Through systematic reanalyses we have thus far reclassified 15 variants, with 11.3% of families who initially were found to harbor a VUS, and 4.7% of families with a negative result, eventually found to harbor a pathogenic or likely pathogenic variant. To further such progress, the data described here are being shared through ClinVar, GeneMatcher, and dbGAP.\n\nConclusionOur data strongly support the value of large-scale sequencing, especially WGS within proband-parent trios, as both an effective first-choice diagnostic tool and means to advance clinical and research progress related to pediatric neurological disease.

genomics↗