Kausar, S. | bioRxiv Science

bioRxiv2024

Multi-modal refinement of the human heart atlas during the first gestational trimester

1.Forty first-trimester human hearts were studied to lay groundwork for further studies of principles underlying congenital heart defects. We first sampled 49,227 cardiac nuclei from three fetuses at 8.6, 9.0, and 10.7 post-conceptional weeks (pcw) for single-nucleus RNA sequencing, enabling distinction of six classes comprising 21 cell types. Improved resolution led to identification of novel cardiomyocytes and minority autonomic and lymphatic endothelial transcriptomes, among others. After integration with 5-7 pcw heart single-cell RNAseq, we identified a human cardiomyofibroblast progenitor preceding diversification of cardiomyocyte and stromal lineages. Analysis of six Visium sections from two additional hearts was aided by deconvolution, and key spatial markers validated on sectioned and whole hearts in two- and three-dimensional space and over time. Altogether, anatomical-positional features including innervation, conduction and subdomains of the atrioventricular septum translate latent molecular identity into specialized cardiac functions. This atlas adds unprecedented spatial and temporal resolution to the characterization of human-specific aspects of early heart formation.

developmental biology↗

bioRxiv2023

scRNAseq_KNIME workflow: A Customizable, Locally Executable, Interactive and Automated KNIME workflow for single-cell RNA seq

SummarySingle-cell RNA sequencing (scRNA-seq) is nowadays widely used to measure gene expression in individual cells, but meaningful biological interpretation of the generated scRNA-seq data remains a complicated task. Indeed, expertise in both the biological domain under study, statistics, and computer programming are prerequisite for thorough analysis of scRNA-seq data. However, biological experts may lack data science expertise, and bioinformaticians limited understanding of the biology may lead to time-consuming iterations. A user-friendly and automated workflow with possibility for customization is hence of a wide interest for both the biological and bioinformatics communities, and for their fruitful collaborations. Here, we propose a locally installable, user-friendly, interactive, and automated workflow that allows the users to perform the main steps of scRNA-seq data analysis. The interface is composed of graphical entities dedicated to specific and modifiable tasks. It can easily be used by biologists and can also serve as a customizable basis for bioinformaticians. Availability and implementationThe workflow is developed in KNIME; its tasks were defined by R scripts using KNIME R nodes. The workflow is publicly available at https://github.com/Saminakausar/scRNAseq_KNIME. Contact: anais.baudot@univ-amu.fr; muhasif123@gmail.com

bioinformatics↗

bioRxiv2022

DGH-GO: Dissecting the Genetic Heterogeneity of complex diseases using Gene Ontology

Complex diseases such as neurodevelopmental disorders (NDDs) lack biological markers for their diagnosis and are phenotypically heterogeneous, which makes them difficult to diagnose at early-age. The genetic heterogeneity corresponds to their clinical phenotype variability and, because of this, complex diseases exhibit multiple etiologies. The multi-etiological aspects of complex-diseases emerge from distinct but functionally similar group of genes. Different diseases sharing genes of such groups show related clinical outcomes that further restrict our understanding of disease mechanisms, thus, limiting the applications of personalized medicine or systems biomedicine approaches to complex genetic disorders. Here, we present an interactive and user-friendly application, DGH-GO that allows biologists to dissect the genetic heterogeneity of complex diseases by stratifying the putative disease-causing genes into clusters that may lead to or contribute to a specific disease traits development. The application can also be used to study the shared etiology of complex-diseases. DGH-GO creates a semantic similarity matrix of putative disease-causing genes or known-disease genes for multiple disorders using Gene Ontology (GO). The resultant matrix can be visualized in a 2D space using different dimension reduction methods (T-SNE, Principal component analysis and Principal coordinate analysis). Functional similarities assessed through GO and semantic similarity measure can be used to identify clusters of functionally similar genes that may generate a disease specific traits. This can be achieved by employing four different clustering methods (K-means, Hierarchical, Fuzzy and PAM). The user may change the clustering parameters and see their effect on stratification results immediately. DGH-GO was applied to genes disrupted by rare genetic variants in Autism Spectrum Disorder (ASD) patients. The analysis confirmed the multi-etiological nature of ASD by identifying the four clusters that were enriched for distinct biological mechanisms and phenotypic terms. In the second case study, the analysis of genes shared by different NDDs showed that genes involving in multiple disorders tend to aggregate in similar clusters, indicating a possible shared etiology. In summary, functional similarities, dimension reduction and clustering methods, coupled with interactive visualization and control over analysis allows biologists to explore and analyze their datasets without requiring expert knowledge on these methods. The source code of proposed application is available at https://github.com/Muh-Asif/DGH-GO Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=101 SRC="FIGDIR/small/513077v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1a79e61org.highwire.dtl.DTLVardef@18ef52dorg.highwire.dtl.DTLVardef@827df4org.highwire.dtl.DTLVardef@11b4049_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗