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Heberle, B. A.

Publications and source records attributed to Heberle, B. A..

3 recordsLinked to original sources

Decoding the human PBMC isonome: Isoform-level resolution with single-cell long-read transcriptomics

Long-read single-cell RNA sequencing provides an opportunity to understand human health and disease at a level difficult to resolve with bulk or short-read methods. This approach enables isoform-level investigation of cellular diversity and disease mechanisms and definition of cell-types, rather than using genes alone. Using a modified, microfluidic-free PIPseq workflow and computational pipeline adapted for Oxford Nanopore long-read sequencing, we generated the largest long-read single-cell dataset of human peripheral blood mononuclear cells (PBMCs) to date. This study profiled isoform usage across immune cells, integrating marker expression and isoform discovery. We identified 128 novel isoforms from known and new genes, several with distinct cell-type-specific patterns, and characterized marker gene isoform expression across cell-types. Non-canonical protein-coding variants of GZMB and CD3G were enriched in unexpected cell-types, including megakaryocytes and monocyte-derived populations. We also discovered novel transcripts from CMC1 and LYAR with cell-type-specific signatures that were the predominantly expressed transcript within the gene. This study expands versatility of long-read single-cell studies to not only relay changes in isoform signatures, but to position them within the functional context of the biology they impact. These results demonstrate the power of long-read single-cell sequencing for mapping the isoform landscape--the isonome--across tissues and disease contexts.

genomics↗

RNApysoforms: Fast rendering interactive visualization of RNA isoform structure and expression in Python

MotivationAlternative splicing generates multiple RNA isoforms from a single gene, enriching genetic diversity and impacting gene function. Effective visualization of these isoforms and their expression patterns is crucial but challenging due to limitations in existing tools. Traditional genome browsers lack programmability, while other tools offer limited customization, produce static plots, or cannot simultaneously display structures and expression levels. RNApysoforms was developed to address these gaps by providing a Python-based package that enables concurrent visualization of RNA isoform structures and expression data. Leveraging plotly and polars libraries, it offers an interactive, customizable, and faster-rendering framework suitable for web applications, enhancing the analysis and dissemination of RNA isoform research. Availability and implementationRNApysoforms is a Python package available at (https://github.com/UK-SBCoA-EbbertLab/RNApysoforms) via an open-source MIT license. It can be easily installed using the piip package installer for Python. Thorough documentation and usage vignettes are available at: https://rna-pysoforms.readthedocs.io/en/latest/.

bioinformatics↗

Systematic review and meta-analysis of bulk RNAseq studies in human Alzheimer's disease brain tissue

ObjectiveTo systematically review and meta-analyze bulk RNA sequencing studies comparing Alzheimers disease (AD) patients with controls in human brain tissue, assessing study quality and identifying key genes and pathways. MethodsWe searched PubMed, Web of Science, and Scopus on September 23, 2023, for studies using bulk RNAseq on primary human brain tissue from AD patients and controls. Excluded were non-primary tissue, re-analyses without new data, limited RNA types and gene panels. Quality was assessed with a 10-category tool. Meta-analysis used high-quality datasets. ResultsFrom 3,266 records, 24 studies met criteria. Meta-analysis found 571 differentially expressed genes (DEGs) in temporal lobe and 189 in frontal lobe; overlapping pathways included "Tube morphogenesis" and "Neuroactive ligand-receptor interaction." LimitationsStudy heterogeneity and limited data tables constrained the review. ConclusionsRigorous methods are vital in AD transcriptomic studies. Findings enhance understanding of transcriptomic changes, aiding biomarker and therapeutic development. RegistrationPROSPERO (CRD42023466522).

neuroscience↗