Author
Rojesh Shrestha
Other affiliations: University of Maryland, Baltimore
Bio: Rojesh Shrestha is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Kidney & Cellular differentiation. The author has an hindex of 7, co-authored 14 publications receiving 911 citations. Previous affiliations of Rojesh Shrestha include University of Maryland, Baltimore.
Topics: Kidney, Cellular differentiation, Cell type, Population, Progenitor cell
Papers
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TL;DR: It is inferred that inherited kidney diseases that arise from distinct genetic mutations but share the same phenotypic manifestation originate from the same differentiated cell type, and that the collecting duct in kidneys of adult mice generates a spectrum of cell types through a newly identified transitional cell.
Abstract: Our understanding of kidney disease pathogenesis is limited by an incomplete molecular characterization of the cell types responsible for the organ’s multiple homeostatic functions. To help fill this knowledge gap, we characterized 57,979 cells from healthy mouse kidneys by using unbiased single-cell RNA sequencing. On the basis of gene expression patterns, we infer that inherited kidney diseases that arise from distinct genetic mutations but share the same phenotypic manifestation originate from the same differentiated cell type. We also found that the collecting duct in kidneys of adult mice generates a spectrum of cell types through a newly identified transitional cell. Computational cell trajectory analysis and in vivo lineage tracing revealed that intercalated cells and principal cells undergo transitions mediated by the Notch signaling pathway. In mouse and human kidney disease, these transitions were shifted toward a principal cell fate and were associated with metabolic acidosis.
751 citations
Vanderbilt University Medical Center1, Vanderbilt University2, University of Pennsylvania3, Queen Mary University of London4, National Institute for Health Research5, Veterans Health Administration6, Emory University7, VA Boston Healthcare System8, University of Ioannina9, Imperial College London10, University of Leicester11, University of Bordeaux12, University of Michigan13, University of Cambridge14, McMaster University15, COMSATS Institute of Information Technology16, University of Dundee17, University of Newcastle18, Lund University19, Leiden University Medical Center20, University Medical Center Groningen21, University of Edinburgh22, Guy's and St Thomas' NHS Foundation Trust23, King's College London24, University of Texas Health Science Center at Houston25, University of Liverpool26, Broad Institute27, Boston University28, University of London29, University of Bristol30, Washington University in St. Louis31, university of lille32, Wellcome Trust Centre for Human Genetics33, University of Eastern Finland34, Wellcome Trust Sanger Institute35, National Institutes of Health36, Population Health Research Institute37, Brigham and Women's Hospital38, University of Sassari39, Wellcome Trust40, University of Oxford41, Harokopio University42, University of Washington43, Harvard University44, Stanford University45, VA Palo Alto Healthcare System46
TL;DR: Analysis of blood pressure data from the Million Veteran Program trans-ethnic cohort identifies common and rare variants, and genetically predicted gene expression across multiple tissues associated with systolic, diastolic and pulse pressure in over 775,000 individuals.
Abstract: In this trans-ethnic multi-omic study, we reinterpret the genetic architecture of blood pressure to identify genes, tissues, phenomes and medication contexts of blood pressure homeostasis. We discovered 208 novel common blood pressure SNPs and 53 rare variants in genome-wide association studies of systolic, diastolic and pulse pressure in up to 776,078 participants from the Million Veteran Program (MVP) and collaborating studies, with analysis of the blood pressure clinical phenome in MVP. Our transcriptome-wide association study detected 4,043 blood pressure associations with genetically predicted gene expression of 840 genes in 45 tissues, and mouse renal single-cell RNA sequencing identified upregulated blood pressure genes in kidney tubule cells.
310 citations
TL;DR: Ablation of STING ameliorated kidney fibrosis in mouse models of chronic kidney disease, demonstrating how TFAM sequesters mtDNA to limit the inflammation leading to fibrosis.
261 citations
TL;DR: By employing large scale single cell transcriptome analysis, computationally defined mesenchymal progenitors at different stages and delineated their bi-lineage differentiation paths in young, adult and aging mice, it is concluded that marrow adipogenic lineage precursors are a newly identified component of marrow adipose tissue.
Abstract: Bone marrow mesenchymal lineage cells are a heterogeneous cell population involved in bone homeostasis and diseases such as osteoporosis. While it is long postulated that they originate from mesenchymal stem cells, the true identity of progenitors and their in vivo bifurcated differentiation routes into osteoblasts and adipocytes remain poorly understood. Here, by employing large scale single cell transcriptome analysis, we computationally defined mesenchymal progenitors at different stages and delineated their bi-lineage differentiation paths in young, adult and aging mice. One identified subpopulation is a unique cell type that expresses adipocyte markers but contains no lipid droplets. As non-proliferative precursors for adipocytes, they exist abundantly as pericytes and stromal cells that form a ubiquitous 3D network inside the marrow cavity. Functionally they play critical roles in maintaining marrow vasculature and suppressing bone formation. Therefore, we name them marrow adipogenic lineage precursors (MALPs) and conclude that they are a newly identified component of marrow adipose tissue.
168 citations
TL;DR: In this article, the authors profile open chromatin and gene expression in developing and adult mouse kidneys at single cell resolution and reveal key cell type-specific transcription factors and major gene-regulatory circuits for kidney cells.
Abstract: Determining the epigenetic program that generates unique cell types in the kidney is critical for understanding cell-type heterogeneity during tissue homeostasis and injury response. Here, we profile open chromatin and gene expression in developing and adult mouse kidneys at single cell resolution. We show critical reliance of gene expression on distal regulatory elements (enhancers). We reveal key cell type-specific transcription factors and major gene-regulatory circuits for kidney cells. Dynamic chromatin and expression changes during nephron progenitor differentiation demonstrates that podocyte commitment occurs early and is associated with sustained Foxl1 expression. Renal tubule cells follow a more complex differentiation, where Hfn4a is associated with proximal and Tfap2b with distal fate. Mapping single nucleotide variants associated with human kidney disease implicates critical cell types, developmental stages, genes, and regulatory mechanisms. The single cell multi-omics atlas reveals key chromatin remodeling events and gene expression dynamics associated with kidney development.
93 citations
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TL;DR: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascul...
Abstract: Background: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascul...
3,034 citations
01 Mar 2017
TL;DR: Recent advances in understanding of mTOR function, regulation, and importance in mammalian physiology are reviewed and how the mTOR-signaling network contributes to human disease is highlighted.
Abstract: The mechanistic target of rapamycin (mTOR) coordinates eukaryotic cell growth and metabolism with environmental inputs, including nutrients and growth factors. Extensive research over the past two decades has established a central role for mTOR in regulating many fundamental cell processes, from protein synthesis to autophagy, and deregulated mTOR signaling is implicated in the progression of cancer and diabetes, as well as the aging process. Here, we review recent advances in our understanding of mTOR function, regulation, and importance in mammalian physiology. We also highlight how the mTOR signaling network contributes to human disease and discuss the current and future prospects for therapeutically targeting mTOR in the clinic.
2,014 citations
TL;DR: The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update as discussed by the authors .
Abstract: The American Heart Association, in conjunction with the National Institutes of Health, annually reports the most up-to-date statistics related to heart disease, stroke, and cardiovascular risk factors, including core health behaviors (smoking, physical activity, diet, and weight) and health factors (cholesterol, blood pressure, and glucose control) that contribute to cardiovascular health. The Statistical Update presents the latest data on a range of major clinical heart and circulatory disease conditions (including stroke, congenital heart disease, rhythm disorders, subclinical atherosclerosis, coronary heart disease, heart failure, valvular disease, venous disease, and peripheral artery disease) and the associated outcomes (including quality of care, procedures, and economic costs).The American Heart Association, through its Statistics Committee, continuously monitors and evaluates sources of data on heart disease and stroke in the United States to provide the most current information available in the annual Statistical Update. The 2022 Statistical Update is the product of a full year's worth of effort by dedicated volunteer clinicians and scientists, committed government professionals, and American Heart Association staff members. This year's edition includes data on the monitoring and benefits of cardiovascular health in the population and an enhanced focus on social determinants of health, adverse pregnancy outcomes, vascular contributions to brain health, and the global burden of cardiovascular disease and healthy life expectancy.Each of the chapters in the Statistical Update focuses on a different topic related to heart disease and stroke statistics.The Statistical Update represents a critical resource for the lay public, policymakers, media professionals, clinicians, health care administrators, researchers, health advocates, and others seeking the best available data on these factors and conditions.
1,483 citations
TL;DR: This study developed Microwell-seq, a high-throughput and low-cost scRNA-seq platform using simple, inexpensive devices, and built a web-based "single-cell MCA analysis" pipeline that accurately defines cell types based on single-cell digital expression.
1,234 citations
TL;DR: A computational doublet detection tool-DoubletFinder-that identifies doublets using only gene expression data is presented, allowing its application across scRNA-seq datasets with diverse distributions of cell types.
Abstract: Single-cell RNA sequencing (scRNA-seq) data are commonly affected by technical artifacts known as "doublets," which limit cell throughput and lead to spurious biological conclusions. Here, we present a computational doublet detection tool-DoubletFinder-that identifies doublets using only gene expression data. DoubletFinder predicts doublets according to each real cell's proximity in gene expression space to artificial doublets created by averaging the transcriptional profile of randomly chosen cell pairs. We first use scRNA-seq datasets where the identity of doublets is known to show that DoubletFinder identifies doublets formed from transcriptionally distinct cells. When these doublets are removed, the identification of differentially expressed genes is enhanced. Second, we provide a method for estimating DoubletFinder input parameters, allowing its application across scRNA-seq datasets with diverse distributions of cell types. Lastly, we present "best practices" for DoubletFinder applications and illustrate that DoubletFinder is insensitive to an experimentally validated kidney cell type with "hybrid" expression features.
1,148 citations