Biology subjects

Turinsky, A.

Publications and source records attributed to Turinsky, A..

3 recordsLinked to original sources

bioRxiv2023

Early human fetal lung atlas reveals the temporal dynamics of epithelial cell plasticity.

While animal models have provided key insights into conserved mechanisms of how the lung forms during development, human-specific developmental mechanisms are not always captured. To fully appreciate how developmental defects and disease states alter the function of the lungs, studies in human lung models are important. Here, we sequenced >150,000 single single-cells from 19 healthy human fetal lung tissues from gestational weeks 10-19 and identified at least 58 unique cell types/states contributing to the developing lung. We captured novel dynamic developmental trajectories from various progenitor cells that give rise to club, ciliated, and pulmonary neuroendocrine cells. We also identified four CFTR-expressing progenitor cell types and pinpointed the temporal emergence of these cell types. These developmental dynamics reveal broader epithelial cell plasticity and novel lineage hierarchies that were not previously reported. Combined with spatial transcriptomics, we identified both cell autonomous and non-cell autonomous signalling pathways that may dictate the temporal and spatial emergence of cell lineages. Finally, we showed that human pluripotent stem cell-derived fetal lung models capture cell lineage trajectories specifically through CFTR-expressing progenitor cells, that were also observed in the native fetal tissue. Overall, this study provides a comprehensive single-cell atlas of the developing human lung, outlining the temporal and spatial complexities of cell lineage development. HighlightsO_LISingle-cell transcriptomics atlas from 19 human fetal lungs reveals cellular heterogeneity and previously unappreciated cellular plasticity in the epithelial compartment. C_LIO_LIIdentification of novel CFTR-expressing progenitor cells that gives rise to club, ciliated and PNEC. C_LIO_LINovel RNA velocity facilitated the identification of dynamic lineage trajectories in the epithelial compartment. C_LIO_LITemporally regulated cell signaling through promiscuous interactions between sender and receiving cells may dictate cell lineage fates. C_LIO_LIIntegration of human pluripotent stem cell (hPSC)-derived fetal lung cells and organoids with primary lung dataset show hPSC-differentiations captures key developmental trajectories of fetal epithelial cell states. C_LI

developmental biology↗

bioRxiv2020

MetaFusion: A high-confidence metacaller for filtering and prioritizing RNA-seq gene fusion candidates.

MotivationCurrent fusion detection tools use diverse calling approaches and provide varying results, making selection of the appropriate tool challenging. Ensemble fusion calling techniques appear promising; however, current options have limited accessibility and function. ResultsMetaFusion is a flexible meta-calling tool that amalgamates outputs from any number of fusion callers. Individual caller results are standardized by conversion into the new file type Common Fusion Format (CFF). Calls are annotated, merged using graph clustering, filtered, and ranked to provide a final output of high confidence candidates. MetaFusion consistently achieves higher precision and recall than individual callers on real and simulated datasets, and reaches up to 100% precision, indicating that ensemble calling is imperative for high confidence results. MetaFusion uses FusionAnnotator to annotate calls with information from cancer fusion databases, and is provided with a benchmarking toolkit to calibrate new callers. AvailabilityMetaFusion is freely available at https://github.com/ccmbioinfo/MetaFusion Contactarun.ramani@sickkids.ca Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

An Unsupervised Learning Method for Disease Classification Based on DNA Methylation Signatures

Recent work has shown that genome-wide DNA methylation (DNAm) profiles can be used to discern signatures that can identify specific genetic disorders. These methods are especially effective at identifying single gene (Mendelian) disease, and methods to identify such signatures have been built by comparing methylation profiles of known disease versus control samples. These methods, however, have to-date been supervised, precluding the application of these methods to diseases with as-yet-unknown genetic cause. In this work, we tackle the problem of unsupervised disease classification based on DNAm signatures. Our method combines pre-filtration of the data to identify most promising methylation sites, clustering to identify co-varying sites, and an iterative method to further refine the signatures to build an effective clustering framework. We validate the proposed method on four diseases with known DNAm signatures (CHARGE, Kabuki, Sotos, and Weaver syndromes) and show high accuracy at determining the correct disease using unsupervised analysis. We also experiment with our approach on a novel dataset of patients with a clinical diagnosis of Autism, and illustrate the de novo identification of a specific subtype.

bioinformatics↗