Biology subjects

Hayat, S.

Publications and source records attributed to Hayat, S..

9 recordsLinked to original sources

bioRxiv2023

GraphComm: A Graph-based Deep Learning Method to Predict Cell-Cell Communication in single-cell RNAseq data

Interactions between cells coordinate various functions across cell-types in health and disease states. Novel single-cell techniques enable deep investigation of cellular crosstalk at single-cell resolution. Cell-cell communication (CCC) is mediated by underlying gene-gene networks, however most current methods are unable to account for complex interactions within the cell as well as incorporate the effect of pathway and protein complexes on interactions. This results in the inability to infer overarching signalling patterns within a dataset as well as limit the ability to successfully explore other data types such as spatial cell dimension. Therefore, to represent transcriptomic data as intricate networks, complementing gene expression with information from cells to ligands and receptors for relevant cell-cell communication inference, we present GraphComm - a new graph-based deep learning method for predicting cell-cell communication in single-cell RNAseq datasets. GraphComm improves CCC inference by capturing detailed information such as cell location and intracellular signalling patterns from a database of more than 30,000 protein interaction pairs. With this framework, GraphComm is able to predict biologically relevant results in datasets previously validated for CCC, datasets that have undergone chemical or genetic perturbations and datasets with spatial cell information.

cell biology↗

bioRxiv2023

Repeated exposure to high-THC Cannabis smoke during gestation alters sex ratio, behavior, and amygdala gene expression of Sprague Dawley rat offspring

Due to the recent legalization of Cannabis in many jurisdictions and the consistent trend of increasing THC content in Cannabis products, there is an urgent need to understand the impact of Cannabis use during pregnancy on fetal neurodevelopment and behavior. To this end, we repeatedly exposed female Sprague-Dawley rats to Cannabis smoke from gestational days 6 to 20 (n=12; Aphria Mohawk; 19.51% THC, <0.07% cannabidiol) or room-air as a control (n=10) using a commercially available system. Maternal reproductive parameters, behavior of the adult offspring, and gene expression in the offspring amygdala were assessed. Body temperature was decreased in dams following smoke exposure and more fecal boli were observed in the chambers before and after smoke exposure in those dams exposed to smoke. Maternal weight gain, food intake, gestational length, litter number, and litter weight were not altered by exposure to Cannabis smoke. A significant increase in the male-to-female ratio was noted in the Cannabis-exposed litters. In adulthood, both male and female Cannabis smoke-exposed offspring explored the inner zone of an open field significantly less than control offspring. Gestational Cannabis smoke exposure did not affect behavior on the elevated plus maze test or social interaction test in the offspring. Cannabis offspring were better at visual pairwise discrimination and reversal learning tasks conducted in touchscreen-equipped operant conditioning chambers. Analysis of gene expression in the adult amygdala using RNAseq revealed subtle changes in genes related to development, cellular function, and nervous system disease in a subset of the male offspring. These results demonstrate that repeated exposure to high-THC Cannabis smoke during gestation alters maternal physiological parameters, sex ratio, and anxiety-like behaviors in the adulthood offspring. Significance statementCannabis use by pregnant women has increased alongside increased THC content in recent years. As smoking Cannabis is the most common method of use, we used a validated model of Cannabis smoke exposure to repeatedly expose pregnant rats to combusted high-THC Cannabis smoke. Our results show alterations in litter sex ratio, anxiety-like behavior, and decision making in the offspring which may relate to subtle changes in expression of amygdala genes related to development, cellular function, and nervous system disease. Thus, we believe this gestational Cannabis exposure model may be useful in delineating long-term effects on the offspring.

neuroscience↗

bioRxiv2023

Minuteman - A versatile cloud computational platform for collaborative research

Secure platforms for bio-computation are critical to foster increasingly complex and data-intensive collaborations involving biomedical data. Here, we present Minuteman - an open-source cloud computing platform that can be securely used across organizations. Minuteman can be used for hosting data sources and running computational pipelines in an organized way. The platform consists of three fundamental features, 1) data operations including collaborative data processing and analytics, 2) customizable user access management for secure dissemination of the data, and 3) interactive exploration of the data through 3rd party (e.g. shiny, dashboard etc.) applications that can be scaled using docker containers. Strict data access rules and user-specific roles are applied across the whole platform to maintain data security. Minuteman is ideal for scenarios where data security, and privileged access are critical, such as industry-academia collaborations, and multi-institution consortiums. Using single-cell transcriptomics preprocessing, analyses and visualization pipelines across labs, we showcase the utility of the Minuteman platform for biomedical data analyses. Minuteman code is available at (https://github.com/hayatlab/minuteman)

bioinformatics↗

bioRxiv2022

Targeting the Immune-Fibrosis Axis in Myocardial Infarction and Heart Failure

Cardiac fibrosis is causally linked to heart failure pathogenesis and adverse clinical outcomes. However, the precise fibroblast populations that drive fibrosis in the human heart and the mechanisms that govern their emergence remain incompletely defined. Here, we performed Cellular Indexing of Transcriptomes and Epitomes by sequencing (CITE-seq) in 22 explanted human hearts from healthy donors, acute myocardial infarction (MI), and chronic ischemic and non-ischemic cardiomyopathy patients. We identified a fibroblast trajectory marked by fibroblast activator protein (FAP) and periostin (POSTN) expression that was independent of myofibroblasts, peaked early after MI, remained elevated in chronic heart failure, and displayed a transcriptional signature consistent with fibrotic activity. We assessed the applicability of cardiac fibrosis models and demonstrated that mouse MI, angiotensin II/phenylephrine infusion, and pressure overload models were superior compared to cultured human heart and dermal fibroblasts in recapitulating cardiac fibroblast diversity including pathogenic cell states. Ligand-receptor analysis and spatial transcriptomics predicted interactions between macrophages, T cells, and fibroblasts within spatially defined niches. CCR2+ monocyte and macrophage states were the dominant source of ligands targeting fibroblasts. Inhibition of IL-1{beta} signaling to cardiac fibroblasts was sufficient to suppress fibrosis, emergence, and maturation of FAP+POSTN+ fibroblasts. Herein, we identify a human fibroblast trajectory marked by FAP and POSTN expression that is associated with cardiac fibrosis and identify macrophage-fibroblast crosstalk mediated by IL-1{beta} signaling as a key regulator of pathologic fibroblast differentiation and fibrosis.

immunology↗

bioRxiv2022

Fast model-free standardization and integration of single-cell transcriptomics data

Single-cell transcriptomics datasets from the same anatomical sites generated by different research labs are becoming mainstream. However, fast, and computationally inexpensive tools for standardization of cell-type annotation and data integration are still needed to increase research inclusivity. To standardize cell-type annotation and integrate single-cell transcriptomics datasets, we have built a fast, model-free integration method called MASI (Marker-Assisted Standardization and Integration). MASI can run integrative annotation on a personal laptop for approximately one million cells, providing a cheap computational alternative for the single-cell data analysis community. MASI has an average macro F1/overall accuracy of 0.79/0.89 over the 4 benchmark datasets. We demonstrate that MASI outperforms other methods based on speed, and its performance for the tasks of data integration and cell-type annotation is comparable or even superior to other existing methods. We apply MASI for integrative lineage analysis and show that it preserves the underlying biological signal in datasets tested. Finally, to harness knowledge from single-cell atlases, we demonstrate three case studies that cover integration across research groups, biological conditions, and surveyed participants, respectively.

bioinformatics↗

bioRxiv2022

SciViewer- An interactive browser for visualizing single cell datasets

Single-cell sequencing improves our ability to understand biological systems at single-cell resolution and can be used to identify novel drug targets and optimal cell-types for target validation. However, tools that can interactively visualize and provide target-centric views of these large datasets are limited. We present SciViewer (Single-cell Interactive Viewer), a novel tool to interactively visualize, annotate and share single-cell datasets. SciViewer allows visualization of cluster, gene and pathway level information such as clustering annotation, differential expression, pathway enrichment, cell-type specificity, cellular composition, normalized gene expression and comparison across datasets. Further, we provide APIs for SciViewer to interact with publicly available pharmacogenomics databases for systematic evaluation of potential novel drug targets. We provide a module for non-programmatic upload of single-cell datasets. SciViewer will be a useful tool for data exploration and target discovery from single-cell datasets. It is available on GitHub (https://github.com/Dhawal-Jain/SciViewer).

bioinformatics↗

bioRxiv2021

MACA: Marker-based automatic cell-type annotation for single cell expression data

SummaryAccurately identifying cell-types is a critical step in single-cell sequencing analyses. Here, we present marker-based automatic cell-type annotation (MACA), a new tool for annotating single-cell transcriptomics datasets. We developed MACA by testing 4 cell-type scoring methods with 2 public cell-marker databases as reference in 6 single-cell studies. MACA compares favorably to 4 existing marker-based cell-type annotation methods in terms of accuracy and speed. We show that MACA can annotate a large single-nuclei RNA-seq study in minutes on human hearts with ~290k cells. MACA scales easily to large datasets and can broadly help experts to annotate cell types in single-cell transcriptomics datasets, and we envision MACA provides a new opportunity for integration and standardization of cell-type annotation across multiple datasets. Availability and implementationMACA is written in python and released under GNU General Public License v3.0. The source code is available at https://github.com/ImXman/MACA. ContactYang Xu (yxu71@vols.utk.edu), Sikander Hayat (hayat221@gmail.com)

bioinformatics↗

bioRxiv2021

Multiscale interactome analysis coupled with off-target drug predictions reveals drug repurposing candidates for human coronavirus disease

The COVID-19 pandemic has led to an urgent need for the identification of new antiviral drug therapies that can be rapidly deployed to treat patients with this disease. COVID-19 is caused by infection with the human coronavirus SARS-CoV-2. We developed a computational approach to identify new antiviral drug targets and repurpose clinically-relevant drug compounds for the treatment of COVID-19. Our approach is based on graph convolutional networks (GCN) and involves multiscale host-virus interactome analysis coupled to off-target drug predictions. Cellbased experimental assessment reveals several clinically-relevant repurposing drug candidates predicted by the in silico analyses to have antiviral activity against human coronavirus infection. In particular, we identify the MET inhibitor capmatinib as having potent and broad antiviral activity against several coronaviruses in a MET-independent manner, as well as novel roles for host cell proteins such as IRAK1/4 in supporting human coronavirus infection, which can inform further drug discovery studies.

cell biology↗

bioRxiv2018

Evaluation of colorectal cancer subtypes and cell lines using deep learning

Colorectal cancer (CRC) is a common cancer with a high mortality rate and a rising incidence rate in the developed world. The disease shows variable drug response and outcome. Molecular profiling techniques have been used to better understand the variability between tumours as well as cancer models such as cell lines. Drug discovery programs use cell lines as a proxy for human cancers to characterize their molecular makeup and drug response, identify relevant indications and discover biomarkers. In order to maximize the translatability and the clinical relevance of in vitro studies, selection of optimal cancer models is imperative. We have developed a deep learning based method to measure the similarity between CRC tumors and other tumors or disease models such as cancer cell lines. Our method efficiently leverages multi-omics data sets containing copy number alterations, gene expression and point mutations, and learns latent factors that describe the data in lower dimension. These latent factors represent the patterns across gene expression, copy number, and mutational profiles which are clinically relevant and explain the variability of molecular profiles across tumours and cell lines. Using these, we propose a refined colorectal cancer sample classification and provide best-matching cell lines in terms of multi-omics for the different subtypes. These findings are relevant for patient stratification and selection of cell lines for early stage drug discovery pipelines, biomarker discovery, and target identification.

bioinformatics↗