Single-cell RNA sequencing reveals the mesangial identity and species diversity of glomerular cell transcriptomes

AbstractMolecular characterization of the individual cell types in human kidney as well as model organisms are critical in defining organ function and understanding translational aspects of biomedical research. Previous studies have uncovered gene expression profiles of several kidney glomerular cell types, however, important cells, including mesangial (MCs) and glomerular parietal epithelial cells (PECs), are missing or incompletely described, and a systematic comparison between mouse and human kidney is lacking. To this end, we use Smart-seq2 to profile 4332 individual glomerulus-associated cells isolated from human living donor renal biopsies and mouse kidney. The analysis reveals genetic programs for all four glomerular cell types (podocytes, glomerular endothelial cells, MCs and PECs) as well as rare glomerulus-associated macula densa cells. Importantly, we detect heterogeneity in glomerulus-associated Pdgfrb-expressing cells, including bona fide intraglomerular MCs with the functionally active phagocytic molecular machinery, as well as a unique mural cell type located in the central stalk region of the glomerulus tuft. Furthermore, we observe remarkable species differences in the individual gene expression profiles of defined glomerular cell types that highlight translational challenges in the field and provide a guide to design translational studies.

Download Full-text

Gene Expression Regulation underlying Osteo-, Adipo-, and Chondro-Genic Lineage Commitment of Human Mesenchymal Stem Cells

Medical Advancements in Aging and Regenerative Technologies - Advances in Medical Technologies and Clinical Practice ◽

10.4018/978-1-4666-2506-8.ch004 ◽

2013 ◽

pp. 76-94 ◽

Cited By ~ 1

Author(s):

Ana M. Sotoca ◽

Michael Weber ◽

Everardus J. J. van Zoelen

Keyword(s):

Gene Expression ◽

Stem Cells ◽

Mesenchymal Stem Cells ◽

Gene Expression Regulation ◽

Expression Profiles ◽

Human Mesenchymal Stem Cells ◽

Gene Expression Profiles ◽

Stem Cell Differentiation ◽

Cell Types ◽

Lineage Commitment

Human mesenchymal stem cells have a high potential in regenerative medicine. They can be isolated from a variety of adult tissues, including bone marrow, and can be differentiated into multiple cell types of the mesodermal lineage, including adipocytes, osteocytes, and chondrocytes. Stem cell differentiation is controlled by a process of interacting lineage-specific and multipotent genes. In this chapter, the authors use full genome microarrays to explore gene expression profiles in the process of Osteo-, Adipo-, and Chondro-Genic lineage commitment of human mesenchymal stem cells.

Download Full-text

Aortic heterogeneity across segments and under high fat/salt/glucose conditions at the single-cell level

National Science Review ◽

10.1093/nsr/nwaa038 ◽

2020 ◽

Vol 7 (5) ◽

pp. 881-896 ◽

Cited By ~ 1

Author(s):

Dongxu He ◽

Aiqin Mao ◽

Chang-Bo Zheng ◽

Hao Kan ◽

Ka Zhang ◽

...

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cell Types ◽

The Body ◽

Dietary Salt ◽

High Fat ◽

High Blood Glucose ◽

Thoracic And Abdominal

Abstract The aorta, with ascending, arch, thoracic and abdominal segments, responds to the heartbeat, senses metabolites and distributes blood to all parts of the body. However, the heterogeneity across aortic segments and how metabolic pathologies change it are not known. Here, a total of 216 612 individual cells from the ascending aorta, aortic arch, and thoracic and abdominal segments of mouse aortas under normal conditions or with high blood glucose levels, high dietary salt, or high fat intake were profiled using single-cell RNA sequencing. We generated a compendium of 10 distinct cell types, mainly endothelial (EC), smooth muscle (SMC), stromal and immune cells. The distributions of the different cells and their intercommunication were influenced by the hemodynamic microenvironment across anatomical segments, and the spatial heterogeneity of ECs and SMCs may contribute to differential vascular dilation and constriction that were measured by wire myography. Importantly, the composition of aortic cells, their gene expression profiles and their regulatory intercellular networks broadly changed in response to high fat/salt/glucose conditions. Notably, the abdominal aorta showed the most dramatic changes in cellular composition, particularly involving ECs, fibroblasts and myeloid cells with cardiovascular risk factor-related regulons and gene expression networks. Our study elucidates the nature and range of aortic cell diversity, with implications for the treatment of metabolic pathologies.

Download Full-text

CancerLivER: a database of liver cancer gene expression resources and biomarkers

Database ◽

10.1093/database/baaa012 ◽

2020 ◽

Vol 2020 ◽

Author(s):

Harpreet Kaur ◽

Sherry Bhalla ◽

Dilraj Kaur ◽

Gajendra PS Raghava

Keyword(s):

Gene Expression ◽

Liver Cancer ◽

Expression Profiles ◽

Expression Patterns ◽

Gene Expression Profiles ◽

Full Potential ◽

Individual Gene ◽

Comprehensive Information ◽

Tremendous Amount ◽

Disease Specific

Abstract Liver cancer is the fourth major lethal malignancy worldwide. To understand the development and progression of liver cancer, biomedical research generated a tremendous amount of transcriptomics and disease-specific biomarker data. However, dispersed information poses pragmatic hurdles to delineate the significant markers for the disease. Hence, a dedicated resource for liver cancer is required that integrates scattered multiple formatted datasets and information regarding disease-specific biomarkers. Liver Cancer Expression Resource (CancerLivER) is a database that maintains gene expression datasets of liver cancer along with the putative biomarkers defined for the same in the literature. It manages 115 datasets that include gene-expression profiles of 9611 samples. Each of incorporated datasets was manually curated to remove any artefact; subsequently, a standard and uniform pipeline according to the specific technique is employed for their processing. Additionally, it contains comprehensive information on 594 liver cancer biomarkers which include mainly 315 gene biomarkers or signatures and 178 protein- and 46 miRNA-based biomarkers. To explore the full potential of data on liver cancer, a web-based interactive platform was developed to perform search, browsing and analyses. Analysis tools were also integrated to explore and visualize the expression patterns of desired genes among different types of samples based on individual gene, GO ontology and pathways. Furthermore, a dataset matrix download facility was provided to facilitate the users for their extensive analysis to elucidate more robust disease-specific signatures. Eventually, CancerLivER is a comprehensive resource which is highly useful for the scientific community working in the field of liver cancer.Availability: CancerLivER can be accessed on the web at https://webs.iiitd.edu.in/raghava/cancerliver.

Download Full-text

Gene Expression in Porphyromonas gingivalis after Contact with Human Epithelial Cells

Infection and Immunity ◽

10.1128/iai.73.4.2327-2335.2005 ◽

2005 ◽

Vol 73 (4) ◽

pp. 2327-2335 ◽

Cited By ~ 44

Author(s):

Yumiko Hosogi ◽

Margaret J. Duncan

Keyword(s):

Gene Expression ◽

Epithelial Cells ◽

Porphyromonas Gingivalis ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cell Types ◽

Adverse Conditions ◽

Human Epithelial Cells ◽

Host Epithelium ◽

Shock Proteins

ABSTRACT Porphyromonas gingivalis, a gram-negative oral anaerobe, is strongly associated with adult periodontitis. The adherence of the organism to host epithelium signals changes in both cell types as bacteria initiate infection and colonization and epithelial cells rally their defenses. We hypothesized that the expression of a defined set of P. gingivalis genes would be consistently up-regulated during infection of HEp-2 human epithelial cells. P. gingivalis genome microarrays were used to compare the gene expression profiles of bacteria that adhered to HEp-2 cells and bacteria that were incubated alone. Genes whose expression was temporally up-regulated included those involved in the oxidative stress response and those encoding heat shock proteins that are essential to maintaining cell viability under adverse conditions. The results suggest that contact with epithelial cells induces in P. gingivalis stress-responsive pathways that promote the survival of the bacterium.

Download Full-text

Application of the fractional-stable distributions for approximation of the gene expression profiles

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2014-0094 ◽

2015 ◽

Vol 14 (3) ◽

Cited By ~ 2

Author(s):

Viacheslav Saenko ◽

Yury Saenko

Keyword(s):

Gene Expression ◽

Power Law ◽

Limit Distribution ◽

Probability Distributions ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Stable Distributions ◽

Scientific Data ◽

Microarray Experiments ◽

Individual Gene

AbstractNowadays, there are reliable scientific data highlighting that the probability density function of the gene expression demonstrates a number of universal features commonly observed in the microarray experiments. First of all, these distributions demonstrate the power-law asymptotics and, secondly, the shape of these distributions is inherent for all organisms and tissues. This fact leads to appearance of a number of works where authors investigate various probability distributions for an approximation of the empirical distributions of the gene expression. Nevertheless, all these distributions are not a limit distribution and are not a solution of any equation. These facts, in our opinion, are essential shortcoming of these probability laws. Besides, the expression of the individual gene is not an accidental phenomenon and it depends on the expression of the other genes. This suggests an existence of the genic regulatory net in a cell. The present work describes the class of the fractional-stable distributions. This class of the distributions is a limit distribution of the sums of independent identically distributed random variables. Due to the power-law asymptotics, these distributions are applicable for the approximation of the experimental densities of the gene expression for microarray experiments. The parameters of the fractional stable distributions are statistically estimated by experimental data and the functions of the empirical and theoretical densities are compared. Here we describe algorithms for simulation of the fractional-stable variables and estimation of the parameters of the the fractional stable densities. The results of such a comparison allow to conclude that the empirical densities of the gene expression can be approximated by the fractional-stable distributions.

Download Full-text

Self-reporting transposons enable simultaneous readout of gene expression and transcription factor binding in single cells

10.1101/538553 ◽

2019 ◽

Cited By ~ 3

Author(s):

Arnav Moudgil ◽

Michael N. Wilkinson ◽

Xuhua Chen ◽

June He ◽

Alex J. Cammack ◽

...

Keyword(s):

Gene Expression ◽

Transcription Factor ◽

Single Cell ◽

Binding Sites ◽

Expression Profiles ◽

Single Cells ◽

Gene Expression Profiles ◽

Cell Types ◽

Specific Cell

AbstractIn situ measurements of transcription factor (TF) binding are confounded by cellular heterogeneity and represent averaged profiles in complex tissues. Single cell RNA-seq (scRNA-seq) is capable of resolving different cell types based on gene expression profiles, but no technology exists to directly link specific cell types to the binding pattern of TFs in those cell types. Here, we present self-reporting transposons (SRTs) and their use in single cell calling cards (scCC), a novel assay for simultaneously capturing gene expression profiles and mapping TF binding sites in single cells. First, we show how the genomic locations of SRTs can be recovered from mRNA. Next, we demonstrate that SRTs deposited by the piggyBac transposase can be used to map the genome-wide localization of the TFs SP1, through a direct fusion of the two proteins, and BRD4, through its native affinity for piggyBac. We then present the scCC method, which maps SRTs from scRNA-seq libraries, thus enabling concomitant identification of cell types and TF binding sites in those same cells. As a proof-of-concept, we show recovery of cell type-specific BRD4 and SP1 binding sites from cultured cells. Finally, we map Brd4 binding sites in the mouse cortex at single cell resolution, thus establishing a new technique for studying TF biology in situ.

Download Full-text

Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data

Nature Communications ◽

10.1038/s41467-021-26328-2 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Bárbara Andrade Barbosa ◽

Saskia D. van Asten ◽

Ji Won Oh ◽

Arantza Farina-Sarasqueta ◽

Joanne Verheij ◽

...

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cell Types ◽

Expression Data ◽

Cell Type ◽

Expression Variability ◽

Variable Nature ◽

Log Normal

AbstractDeconvolution of bulk gene expression profiles into the cellular components is pivotal to portraying tissue’s complex cellular make-up, such as the tumor microenvironment. However, the inherently variable nature of gene expression requires a comprehensive statistical model and reliable prior knowledge of individual cell types that can be obtained from single-cell RNA sequencing. We introduce BLADE (Bayesian Log-normAl Deconvolution), a unified Bayesian framework to estimate both cellular composition and gene expression profiles for each cell type. Unlike previous comprehensive statistical approaches, BLADE can handle > 20 types of cells due to the efficient variational inference. Throughout an intensive evaluation with > 700 simulated and real datasets, BLADE demonstrated enhanced robustness against gene expression variability and better completeness than conventional methods, in particular, to reconstruct gene expression profiles of each cell type. In summary, BLADE is a powerful tool to unravel heterogeneous cellular activity in complex biological systems from standard bulk gene expression data.

Download Full-text

Distinct properties in ventral pallidum projection neuron subtypes

10.1101/2021.11.15.468637 ◽

2021 ◽

Author(s):

Nimrod Bernat ◽

Rianne Campbell ◽

Hyungwoo Nam ◽

Mahashweta Basu ◽

Tal Odesser ◽

...

Keyword(s):

Gene Expression ◽

Expression Profiles ◽

Expression Patterns ◽

Critical Role ◽

Gene Expression Profiles ◽

Cell Types ◽

Projection Neuron ◽

Brain Regions ◽

Ventral Pallidum ◽

Lateral Habenula

The ventral pallidum (VP), a major component of the basal ganglia, plays a critical role in motivational disorders. It sends projections to many different brain regions but it is not yet known whether and how these projections differ in their cellular properties, gene expression patterns, connectivity and role in reward seeking. In this study, we focus on four major outputs of the VP - to the lateral hypothalamus (LH), ventral tegmental area (VTA), mediodorsal thalamus (MDT), and lateral habenula (LHb) - and examine the differences between them in 1) baseline gene expression profiles using projection-specific RNA-sequencing; 2) physiological parameters using whole-cell patch clamp; and 3) their influence on cocaine reward using chemogenetic tools. We show that these four VP efferents differ in all three aspects and highlight specifically differences between the projections to the LH and the VTA. These two projections originate largely from separate populations of neurons, express distinct sets of genes related to neurobiological functions, and show opposite physiological and behavioral properties. Collectively, our data demonstrates for the first time that VP neurons exhibit distinct molecular and cellular profiles in a projection-specific manner, suggesting that they represent different cell types.

Download Full-text

Semi-soft Clustering of Single Cell Data

10.1101/285056 ◽

2018 ◽

Author(s):

Lingxue Zhu ◽

Jing Lei ◽

Bernie Devlin ◽

Kathryn Roeder

Keyword(s):

Gene Expression ◽

Single Cell ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Pairwise Comparison ◽

Cell Types ◽

Intermediate Cell ◽

Soft Clustering ◽

Membership Matrix ◽

Cell Data

AbstractMotivated by the dynamics of development, in which cells of recognizable types, or pure cell types, transition into other types over time, we propose a method of semi-soft clustering that can classify both pure and intermediate cell types from data on gene expression or protein abundance from individual cells. Called SOUP, for Semi-sOft clUstering with Pure cells, this novel algorithm reveals the clustering structure for both pure cells, which belong to one single cluster, as well as transitional cells with soft memberships. SOUP involves a two-step process: identify the set of pure cells and then estimate a membership matrix. To find pure cells, SOUP uses the special block structure the K cell types form in a similarity matrix, devised by pairwise comparison of the gene expression profiles of individual cells. Once pure cells are identified, they provide the key information from which the membership matrix can be computed. SOUP is applicable to general clustering problems as well, as long as the unrestrictive modeling assumptions hold. The performance of SOUP is documented via extensive simulation studies. Using SOUP to analyze two single cell data sets from brain shows it produce sensible and interpretable results.

Download Full-text

Global Modulation in DNA Epigenetics during Pro-Inflammatory Macrophage Activation

10.1101/587311 ◽

2019 ◽

Author(s):

Nikhil Jain ◽

Tamar Shahal ◽

Tslil Gabrieli ◽

Noa Gilat ◽

Dmitry Torchinsky ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Single Molecule ◽

Transcriptional Control ◽

Excision Repair ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Cell Types ◽

Inflammatory Activation ◽

Methylation Patterns

AbstractDNA methylation patterns create distinct gene expression profiles. These patterns are maintained after cell division, thus enabling the differentiation and maintenance of multiple cell types from the same genome sequence. The advantage of this mechanism for transcriptional control is that chemical-encoding allows to rapidly establish new epigenetic patterns “on-demand” through enzymatic methylation and de-methylation of DNA. Here we show that this feature is associated with the fast response of macrophages during their pro-inflammatory activation. By using a combination of mass spectroscopy and single-molecule imaging to quantify global epigenetic changes in the genomes of primary macrophages, we followed three distinct DNA marks (methylated, hydroxymethylated and unmethylated), involved in establishing new DNA methylation patterns during pro-inflammatory activation. The observed epigenetic modulation together with gene expression data generated for the involved enzymatic machinery, may suggest that de-methylation upon LPS-activation starts with oxidation of methylated CpGs, followed by excision-repair of these oxidized bases and their replacement with unmodified cytosine.

Download Full-text