Biology subjects

Bais, A. S.

Publications and source records attributed to Bais, A. S..

5 recordsLinked to original sources

bioRxiv2023

Pathogenic mechanisms underlying adverse neurodevelopmental outcome in congenital heart disease

Poor neurodevelopment is often observed with congenital heart disease (CHD), especially with mutations in chromatin modifiers. Here analysis of mice with hypoplastic left heart syndrome (HLHS) arising from mutations in Sin3A associated chromatin modifier Sap130, and adhesion protein Pcdha9, revealed neurodevelopmental and neurobehavioral deficits reminiscent of those in HLHS patients. Microcephaly was associated with impaired cortical neurogenesis, mitotic block, and increased apoptosis. Transcriptional profiling indicated dysregulated neurogenesis by REST, altered CREB signaling regulating memory and synaptic plasticity, and impaired neurovascular coupling modulating cerebral blood flow. Many neurodevelopmental/neurobehavioral disease pathways were recovered, including autism and cognitive impairment. These same pathways emerged from genome-wide DNA methylation and Sap130 chromatin immunoprecipitation sequencing analyses, suggesting epigenetic perturbation. Mice with Pcdha9 mutation or forebrain-specific Sap130 deletion without CHD showed learning/memory deficits and autism-like behavior. These novel findings provide mechanistic insights indicating the adverse neurodevelopment in HLHS may involve cell autonomous/nonautonomous defects and epigenetic dysregulation and suggest new avenues for therapy.

developmental biology↗

bioRxiv2021

iPSC modeling shows uncompensated mitochondrial mediated oxidative stress underlies early heart failure in hypoplastic left heart syndrome

Hypoplastic left heart syndrome (HLHS) is a severe congenital heart defect with 30% mortality from heart failure (HF) in the first year of life, but why only some patients suffer early-HF and its cause remain unknown. Modeling using induced pluripotent stem cell-derived cardiomyocytes (iPSC-CM) showed early-HF patient iPSC-CM have increased apoptosis, redox stress, and failed antioxidant response. This was associated with mitochondrial permeability transition pore (mPTP) opening, mitochondrial hyperfusion and respiration defects. Whereas iPSC-CM from patients without early-HF had hyper-elevated antioxidant response with increased mitochondrial fission and mitophagy. Single cell transcriptomics showed dichotomization by HF outcome, with mitochondrial dysfunction and endoplasmic reticulum (ER) stress associated with early-HF. Importantly, oxidative stress and apoptosis associated with early HF were rescued by sildenafil inhibition of mPTP opening or TUDCA suppression of ER stress. Together these findings demonstrate a new paradigm for modeling clinical outcome in iPSC-CM, demonstrating uncompensated mitochondrial oxidative stress underlies early HF in HLHS.

cell biology↗

bioRxiv2020

Computational profiling of hiPSC-derived heart organoids reveals chamber defects associated with Ebstein's anomaly

Heart organoids have the potential to generate primary heart-like anatomical structures and hold great promise as in vitro models for cardiac disease. However, their properties have not yet been carefully studied, which hinders a wider spread application. Here we report the development of differentiation systems for ventricular and atrial heart organoids, enabling the study of heart disease with chamber defects. We show that our systems generate organoids comprising of major cardiac cell types, and we used single cell RNA sequencing together with sample multiplexing to characterize the cells we generate. To that end, we also developed a machine learning label transfer approach lever-aging cell type, chamber, and laterality annotations available for primary human fetal heart cells. We then used this model to analyze organoid cells from an isogeneic line carrying an Ebsteins anomaly associated genetic variant, and we successfully recapitulated the diseases atrialized ventricular defects. In summary, we have established a workflow integrating heart organoids and computational analysis to model heart development in normal and disease states.

developmental biology↗

bioRxiv2020

Single cell RNA sequencing reveals differential cell cycle activity in key cell populations during nephrogenesis

The kidney is a complex organ composed of more than 30 terminally differentiated cell types that all are required to perform its numerous homeostatic functions. Defects in kidney development are a significant cause of chronic kidney disease in children, which can lead to kidney failure that can only be treated by transplant or dialysis. A better understanding of molecular mechanisms that drive kidney development is important for designing strategies to enhance renal repair and regeneration. In this study, we profiled gene expression in the developing mouse kidney at embryonic day 14.5 at single cell resolution. Consistent with previous studies, clusters with distinct transcriptional signatures clearly identify major compartments and cell types of the developing kidney. Cell cycle activity distinguishes between the "primed" and "self-renewing" sub-populations of nephron progenitors, with increased expression of the cell cycle related genes Birc5, Cdca3, Smc2 and Smc4 in "primed" nephron progenitors. Augmented Birc5 expression was also detected in immature distal tubules and a sub-set of ureteric bud cells, suggesting that Birc5 might be a novel key molecule required for early events of nephron patterning and tubular fusion between the distal nephron and the collecting duct epithelia.

genomics↗

bioRxiv2019

scds: Computational Annotation of Doublets in Single Cell RNA Sequencing Data

MotivationSingle cell RNA sequencing (scRNA-seq) technologies enable the study of transcriptional heterogeneity at the resolution of individual cells and have an increasing impact on biomedical research. Specifically, high-throughput approaches that employ micro-fluidics in combination with unique molecular identifiers (UMIs) are capable of assaying many thousands of cells per experiment and are rapidly becoming commonplace. However, it is known that these methods sometimes wrongly consider two or more cells as single cells, and that a number of so-called doublets is present in the output of such experiments. Treating doublets as single cells in downstream analyses can severely bias a studys conclusions, and therefore computational strategies for the identification of doublets are needed. Here we present single cell doublet scoring (scds), a software tool for the in silico identification of doublets in scRNA-seq data. ResultsWith scds, we propose two new and complementary approaches for doublet identification: Co-expression based doublet scoring (cxds) and binary classification based doublet scoring (bcds). The co-expression based approach, cxds, utilizes binarized (absence/presence) gene expression data and employs a binomial model for the co-expression of pairs of genes and yields interpretable doublet annotations. bcds, on the other hand, uses a binary classification approach to discriminate artificial doublets from the original data. We apply our methods and existing doublet identification approaches to four data sets with experimental doublet annotations and find that our methods perform at least as well as the state of the art, but at comparably little computational cost. We also find appreciable differences between methods and across data sets, that no approach dominates all others, and we believe there is room for improvement in computational doublet identification as more data with experimental annotations becomes available. In the meanwhile, scds presents a scalable, competitive approach that allows for doublet annotations in thousands of cells in a matter of seconds. Availability and Implementationscds is implemented as an R package and freely available at https://github.com/kostkalab/scds. Contactkostka@pitt.edu

bioinformatics↗