Project description:Cellular barcoding using heritable synthetic barcodes coupled to high throughput sequencing is a powerful technique for the accurate tracing of clonal lineages in a wide variety of biological contexts. Recent studies have integrated cellular barcoding with a single-cell transcriptomics readout, extending the capabilities of these lineage tracing methods to the single-cell level. However there remains a lack of scalable and standardised open-source tools to pre-process and visualise both bulk and single-cell level cellular barcoding datasets. Here, we describe bartools, an open-source R-based toolkit that streamlines the pre-processing, analysis and visualisation of synthetic cellular barcoding datasets. In addition, we developed BARtab, a portable and scalable Nextflow pipeline that automates upstream barcode extraction, quality control, filtering and enumeration from high throughput sequencing data. In addition to population-level cellular barcoding datasets, BARtab and bartools contain methods for the extraction, annotation, and visualisation of transcribed barcodes from single-cell RNA-seq and spatial transcriptomics experiments, thus extending the analytical toolbox to also support novel expressed cellular barcoding methodologies. We showcase the integrated BARtab and bartools workflow through the analysis of bulk, single-cell, and spatial transcriptomics cellular barcoding datasets.
Project description:Cellular senescence is a complex multifactorial biological phenomenon that plays essential roles in aging, and aging-related diseases. During this process, the senescent cells undergo gene expression altering and chromatin structure remodeling. However, studies on the epigenetic landscape of senescence using integrated multi-omics approaches are limited. In this research, we performed ATAC-seq, RNA-seq, and ChIP-seq on different senescent types to reveal the landscape of senescence and identify the prime regulatory elements.
Project description:The ability to assign expression patterns to individual cell types that constitute a tissue is a major challenge in RNA expression analysis. This especially applies to brain given the plethora of different cells coexisting in that tissue. Here, we derived cell-type specific transcriptome signatures from existing single cell RNA data and integrated these signatures with a newly generated dataset of expression (bulk RNA-seq) of the postnatal developing hippocampus. This integrated analysis allowed us to provide a comprehensive and unbiased prediction of the differentiation drivers for 10 different hippocampal cell types and describe how the different cell types interact to support crucial developmental stages. Our integrated analysis provides a reliable resource of predicted differentiation drivers and insight into the multifaceted aspects of the cells in hippocampus during development.
Project description:Drug Toxicity Signature Generation Center (DToxS) at the Icahn School of Medicine at Mount Sinai is an integral part of the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. A key aim of DToxS is to generate both proteomic and transcriptomic signatures that cab predict adverse effects, especially cardiotoxicity, of drugs approved by the Food and Drug Administration. Towards this goal, high throughput shot-gun proteomics experiments (308 cell line/drug combinations + 64 HeLa control lysates + 9 auxiliary treatment samples) have been conducted at the Center for Advanced Proteomics Research at Rutgers-New Jersey Medical School. The integrated proteomic and transcriptomic signatures have been used for computational network analysis to identify cellular signatures of cardiotoxicity that may predict drug-induced toxicity and possible mitigation of such toxicities by mixing different drugs. Both raw and processed proteomics data have been carefully controlled for quality and have been made publicly available via the PRoteomics IDEntifications (PRIDE) database. As such, this broad drug-stimulated proteomic dataset is valuable for the prediction drug toxicities and their mitigation.