Biology subjects

Mahurkar, A.

Publications and source records attributed to Mahurkar, A..

11 recordsLinked to original sources

bioRxiv2025

Integrative analysis of genomic and transcriptomic data informs precancer progression in the pancreas

Pancreatic ductal adenocarcinoma (PDAC) arises from heterogeneous precursor lesions, including intraductal papillary mucinous neoplasms (IPMNs), but the features distinguishing indolent from progressive lesions remain unclear. We performed an integrative analysis of transcriptomic, genomic, and microenvironmental profiles of IPMNs to define multi-omic phenotypes. Using transfer learning, we projected IPMN-derived transcriptional programs onto spatial transcriptomic datasets from IPMNs and pancreatic intraepithelial neoplasias (PanINs). We identified two major phenotypes: one associated with cancer-associated fibroblasts and epithelial-to-mesenchymal transition, shared across IPMN, PanIN, and PDAC; and a second, glycolysis-enriched phenotype with a unique somatic mutation profile specific to IPMN. Spatial mapping further revealed grade-specific enrichment of transcriptional programs and distinct interactions with stromal and immune subtypes, underscoring the role of the precancer microenvironment in progression. These findings establish multi-omic phenotypes that unify genetic, transcriptional, and microenvironmental heterogeneity, providing a framework for distinguishing progressive from indolent precancers and a web-based public atlas for future exploration of these data and transcriptional phenotypes.

bioinformatics↗

bioRxiv2024

in silico transcriptome dissection of neocortical excitatory neurogenesis via joint matrix decomposition and transfer learning

Vast quantities of multi-omic data have been produced to characterize the development and diversity of cell types in the cerebral cortex of humans and other mammals. To more fully harness the collective discovery potential of these data, we have assembled gene-level transcriptomic data from 188 published studies of neocortical development, including the transcriptomes of [~]30 million single-cells, extensive spatial transcriptomic experiments and RNA sequencing of sorted cells and bulk tissues: nemoanalytics.org/landing/neocortex. Applying joint matrix decomposition (SJD) to mouse, macaque and human data in this collection, we defined transcriptome dynamics that are conserved across mammalian neurogenesis and which elucidate the evolution of outer, or basal, radial glial cells. Decomposition of adult human neocortical data identified layer-specific signatures in mature neurons and, in combination with transfer learning methods in NeMO Analytics, enabled the charting of their early developmental emergence and protracted maturation across years of postnatal life. Interrogation of data from cerebral organoids demonstrated that while broad molecular elements of in vivo development are recapitulated in vitro, many layer-specific transcriptomic programs in neuronal maturation are absent. We invite computational biologists and cell biologists without coding expertise to use NeMO Analytics in their research and to fuel it with emerging data (carlocolantuoni.org).

neuroscience↗

bioRxiv2023

Single neuron analysis of aging associated changes in learning reveals progressive impairmentsin transcriptional plasticity

Molecular mechanisms underlying aging associated impairments in learning and long-term memory storage are poorly understood. Here we leveraged the single identified motor neuron L7 in Aplysia, which mediates a form of non-associative learning, sensitization of the siphon-withdraw reflex, to assess the transcriptomic correlates of aging associated changes in learning. RNAseq analysis of the single L7 motor neuron isolated following short-term or long-term sensitization training of 8,10 and 12 months old Aplysia, corresponding to mature, late mature and senescent stages, has revealed progressive impairments in transcriptional plasticity during aging. Specifically, we observed modulation of the expression of multiple lncRNAs and mRNAs encoding transcription factors, regulators of translation, RNA methylation, and cytoskeletal rearrangements during learning and their deficits during aging. Our comparative gene expression analysis also revealed the recruitment of specific transcriptional changes in two other neurons, the motor neuron L11 and the giant cholinergic neuron R2 whose roles in long-term sensitization were previously not known. Taken together, our analyses establish cell type specific progressive impairments in the expression of learning- and memory-related components of the transcriptome during aging.

neuroscience↗

bioRxiv2023

Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data

A large number of genomic and imaging datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While much effort has been devoted to capturing information related to biospecimen information and experimental procedures, the metadata standards that describe data matrices and the analysis workflows that produced them are relatively lacking. Detailed metadata schema related to data analysis are needed to facilitate sharing and interoperability across groups and to promote data provenance for reproducibility. To address this need, we developed the Matrix and Analysis Metadata Standards (MAMS) to serve as a resource for data coordinating centers and tool developers. We first curated several simple and complex "use cases" to characterize the types of featureobservation matrices (FOMs), annotations, and analysis metadata produced in different workflows. Based on these use cases, metadata fields were defined to describe the data contained within each matrix including those related to processing, modality, and subsets. Suggested terms were created for the majority of fields to aid in harmonization of metadata terms across groups. Additional provenance metadata fields were also defined to describe the software and workflows that produced each FOM. Finally, we developed a simple listlike schema that can be used to store MAMS information and implemented in multiple formats. Overall, MAMS can be used as a guide to harmonize analysis-related metadata which will ultimately facilitate integration of datasets across tools and consortia. MAMS specifications, use cases, and examples can be found at https://github.com/single-cell-mams/mams/.

bioinformatics↗

bioRxiv2020

gEAR: gene Expression Analysis Resource portal for community-driven, multi-omic data exploration

The gEAR portal (gene Expression Analysis Resource, umgear.org) is an open access community-driven tool for multi-omic and multi-species data visualization, analysis and sharing. The gEAR supports visualization of multiple RNA-seq data types (bulk, sorted, single cell/nucleus) and epigenomics data, from multiple species, time points and tissues in a single-page, user-friendly browsable format. An integrated scRNA-seq workbench provides access to raw data of scRNA-seq datasets for de novo analysis, as well as marker-gene and cluster comparisons of pre-assigned clusters. Users can upload, view, analyze and privately share their own data in the context of previously published datasets. Short, permanent URLs can be generated for dissemination of individual or collections of datasets in published manuscripts. While the gEAR is currently curated for auditory research with over 90 high-value datasets organized in thematic profiles, the gEAR also supports the BRAIN initiative (via nemoanalytics.org) and is easily adaptable for other research domains.

bioinformatics↗

bioRxiv2020

Evolution of cellular diversity in primary motor cortex of human, marmoset monkey, and mouse

The primary motor cortex (M1) is essential for voluntary fine motor control and is functionally conserved across mammals. Using high-throughput transcriptomic and epigenomic profiling of over 450,000 single nuclei in human, marmoset monkey, and mouse, we demonstrate a broadly conserved cellular makeup of this region, whose similarity mirrors evolutionary distance and is consistent between the transcriptome and epigenome. The core conserved molecular identity of neuronal and non-neuronal types allowed the generation of a cross-species consensus cell type classification and inference of conserved cell type properties across species. Despite overall conservation, many species specializations were apparent, including differences in cell type proportions, gene expression, DNA methylation, and chromatin state. Few cell type marker genes were conserved across species, providing a short list of candidate genes and regulatory mechanisms responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allowed the Patch-seq identification of layer 5 (L5) corticospinal Betz cells in non-human primate and human and characterization of their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell type diversity in M1 across mammals and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

neuroscience↗

bioRxiv2020

An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types

Single cell transcriptomics has transformed the characterization of brain cell identity by providing quantitative molecular signatures for large, unbiased samples of brain cell populations. With the proliferation of taxonomies based on individual datasets, a major challenge is to integrate and validate results toward defining biologically meaningful cell types. We used a battery of single-cell transcriptome and epigenome measurements generated by the BRAIN Initiative Cell Census Network (BICCN) to comprehensively assess the molecular signatures of cell types in the mouse primary motor cortex (MOp). We further developed computational and statistical methods to integrate these multimodal data and quantitatively validate the reproducibility of the cell types. The reference atlas, based on more than 600,000 high quality single-cell or -nucleus samples assayed by six molecular modalities, is a comprehensive molecular account of the diverse neuronal and non-neuronal cell types in MOp. Collectively, our study indicates that the mouse primary motor cortex contains over 55 neuronal cell types that are highly replicable across analysis methods, sequencing technologies, and modalities. We find many concordant multimodal markers for each cell type, as well as thousands of genes and gene regulatory elements with discrepant transcriptomic and epigenomic signatures. These data highlight the complex molecular regulation of brain cell types and will directly enable design of reagents to target specific MOp cell types for functional analysis.

neuroscience↗

bioRxiv2018

Cost Effective, Experimentally Robust Differential Expression Analysis for Human/Mammalian, Pathogen, and Dual-Species Transcriptomics

As sequencing read length has increased, researchers have quickly adopted longer reads for their experiments. Here, we examine host-pathogen interaction studies to assess if using longer reads is warranted. Six diverse datasets encountered in studies of host-pathogen interactions were used to assess what genomic attributes might affect the outcome of differential gene expression analysis including: gene density, operons, gene length, number of introns/exons, and intron length. Principal components analysis, hierarchical clustering with bootstrap support, and regression analyses of pairwise comparisons were undertaken on the same reads, looking at all combinations of paired and unpaired reads trimmed to 36,54,72, and 101-bp. For E coli, 36-bp single end reads performed as well as any other read length and as well as paired end reads. For all other comparisons, 54-bp and 72-bp reads were typically equivalent and different from 36-bp and 101-bp reads. Read pairing improved the outcome in several, but not all, comparisons in no discernable pattern, such that using paired reads is recommended in most scenarios. No specific genome attribute appeared to influence the data. However, experiments with an a priori expected greater biological complexity had more variable results with all read lengths relative to those with decreased complexity. When combined with cost, 54-bp paired end reads provided the most robust, internally reproducible results across all comparisons. However, using 36-bp single end reads may be desirable for bacterial samples, although possibly only if the transcriptional response is expected a priori to be robust.\n\nDATA SUMMARYO_LIThe human only CSHL Encode data set (1) was downloaded from ftp://hgdownload.cse.ucsc.edu/goldenPath/hgl9/encodeDCC/wgEncodeCshlLongRnaSeq/.\nC_LIO_LIThe data from mice vaginas infected with Candida albicans (2) was downloaded from the SRA (url - https://trace.ncbi.nlm.nih.gov/Traces/sra/?study=SRP057050).\nC_LIO_LIThe data from Aspergillus fumigatus cells in contact with human cells was downloaded from the SRA (url - https://www.ncbi.nlm.nih.gov/bioproject/399754).\nC_LIO_LIThe data from a strand-specific library from a study comparing C. albicans cells in contact with human cells with those in media (3) was downloaded from the SRA (url - https://trace.ncbi.nlm.nih.gov/Traces/sra/?study=SRP011085).\nC_LIO_LIThe data from C. albicans in culture media (3) was downloaded from the SRA (url - https://trace.ncbi.nlm.nih.gov/Traces/sra/?study=SRP011085).\nC_LIO_LIThe data from Escherichia coli grown in different media (4) was downloaded from the SRA (url - https://trace.ncbi.nlm.nih.gov/Traces/sra/?study=SRP056578).\nC_LI\n\nI/We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files. {boxtimes}\n\nIMPACT STATEMENTAs sequencing technologies improve, sequencing costs decrease and read lengths increase. We examine host-pathogen interaction studies to assess if using these longer reads is warranted given their increased cost relative to using the same number of shorter reads. To this end we compared the use of various read lengths and read pairing for six diverse host-pathogen datasets with varying genomic attributes including: gene density, operons, gene length, number of introns/exons, and intron length. We find that in the bacterial sample, 36-bp single end reads performed as well as any other read length and as well as paired end reads. When combined with cost, 54-bp paired end reads provided the most robust, internally reproducible results for all other comparisons. Read pairing improved the outcome in several, but not all, comparisons in no discernable pattern, such that using paired reads is recommended in most scenarios. No specific genome attribute appeared to influence the data.

genomics↗

bioRxiv2018

FADU: A Feature Counting Tool for Prokaryotic RNA-Seq Analysis

MotivationThe major algorithms for quantifying transcriptomics data for differential gene expression analysis were designed for analyzing data from human or human-like genomes, specifically those with single gene transcripts and distinct transcriptional boundaries that extend beyond the coding sequence (CDS) as identified through expressed sequence tags (ESTs) or EST-like sequence data. Some eukaryotic genomes and all, or nearly all, bacterial genomes require alternate methods of quantification since they lack annotation of transcriptional boundaries with EST or EST-like data, have overlapping transcriptional boundaries, and/or have polycistronic transcripts.\n\nResultsAn algorithm was developed and tested that better quantifies transcriptomics data for differential gene expression analysis in organisms with overlapping transcriptional units and polycistronic transcripts. Using data from standard libraries originating from Escherichia coli and Ehrlichia chaffeensis, and strand-specific libraries from the Wolbachia endosymbiont wBm, FADU can derive counts for genes that are missed by HTSeq and featurecounts. Using the default parameters with the E. coli data, FADU can detect transcription of 51 more genes than HTSeq in union mode and 21 genes more than featurecounts, with 42 and 18 of these features being <300 bp, respectively. Due to its ability to derive counts for otherwise unrepresented genes without overstating their abundance, we believe FADU to be an improved tool for quantifying transcripts in prokaryotic systems for RNA-Seq analyses.\n\nAvailability and implementationFADU is available at https://github.com/adkinsrs/FADU. FADU was implemented using Python3 and requires the PySAM module (version 0.12.0.1 or later).\n\nContactjdhotopp@som.umaryland.edu

genomics↗

bioRxiv2017

Metaviz: interactive statistical and visual analysis of metagenomic data

Along with the survey techniques of 16S rRNA amplicon and whole-metagenome shotgun sequencing, an array of tools exists for clustering, taxonomic annotation, normalization, and statistical analysis of microbiome sequencing results. Integrative and interactive visualization that enables researchers to perform exploratory analysis in this feature rich hierarchical data is an area of need. In this work, we present Metaviz, a web browser-based tool for interactive exploratory metagenomic data analysis. Metaviz can visualize abundance data served from an R session or a Python web service that queries a graph database. As metagenomic sequencing features have a hierarchy, we designed a novel navigation mechanism to explore this feature space. We visualize abundance counts with heatmaps and stacked bar plots that are dynamically updated as a user selects taxonomic features to inspect. Metaviz also supports common data exploration techniques, including PCA scatter plots to interpret variability in the dataset and alpha diversity boxplots for examining ecological community composition. The Metaviz application and documentation is hosted at http://www.metaviz.org.

bioinformatics↗

bioRxiv2017

Dual RNA sequencing (dRNA-Seq) of bacteria and their host cells

Bacterial pathogens subvert host cells by manipulating cellular pathways for survival and replication; in turn, host cells respond to the invading pathogen through cascading changes in gene expression. Deciphering these complex temporal and spatial dynamics to identify novel bacterial virulence factors or host response pathways is crucial for improved diagnostics and therapeutics. Dual RNA sequencing (dRNA-Seq) has recently been developed to simultaneously capture host and bacterial transcriptomes from an infected cell. This approach builds on the high sensitivity and resolution of RNA-Seq technology and is applicable to any bacteria that interact with eukaryotic cells, encompassing parasitic, commensal or mutualistic lifestyles. We pioneered dRNA-Seq to simultaneously capture prokaryotic and eukaryotic expression profiles of cells infected with bacteria, using in vitro Chlamydia-infected epithelial cells as proof of principle. Here we provide a detailed laboratory and bioinformatics protocol for dRNA-seq that is readily adaptable to any host-bacteria system of interest.

genomics↗