scholarly journals Charting Tissue Expression Anatomy by Spatial Transcriptome Decomposition

2018 ◽  
Author(s):  
Jonas Maaskola ◽  
Ludvig Bergenstråhle ◽  
Aleksandra Jurek ◽  
José Fernández Navarro ◽  
Jens Lagergren ◽  
...  
Keyword(s):  
Gene Expression ◽  
Negative Binomial ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Negative Binomial Regression ◽  
Cell Types ◽  
Tissue Expression ◽  
Spatial Activity ◽  
Cell Layers ◽  
Transcriptomics Data

We create data-driven maps of transcriptomic anatomy with a probabilistic framework for unsupervised pattern discovery in spatial gene expression data. With convolved negative binomial regression we discover patterns which correspond to cell types, microenvironments, or tissue components, and that consist of gene expression profiles and spatial activity maps. Expression profiles quantify how strongly each gene is expressed in a given pattern, and spatial activity maps reflect where in space each pattern is active. Arbitrary covariates and prior hierarchies are supported to leverage complex experimental designs.We demonstrate the method with Spatial Transcriptomics data of mouse brain and olfactory bulb. The discovered transcriptomic patterns correspond to neuroanatomically distinct cell layers. Moreover, batch effects are successfully addressed, leading to consistent pattern inference for multi-sample analyses. On this basis, we identify known and uncharacterized genes that are spatially differentially expressed in the hippocampal field between Ammon’s horn and the dentate gyrus.

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

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Bing He ◽  
Ping Chen ◽  
Sonia Zambrano ◽  
Dina Dabaghie ◽  
Yizhou Hu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Human Kidney ◽  
Cell Types ◽  
Model Organisms ◽  
Mural Cell ◽  
Glomerular Cell ◽  
Individual Gene ◽  
The Individual

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.

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

2013 ◽  
pp. 76-94 ◽  
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.

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

2020 ◽  
Vol 7 (5) ◽  
pp. 881-896 ◽  
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.

scholarly journals GEDS: A Gene Expression Display Server for mRNAs, miRNAs and Proteins

Cells ◽  
2019 ◽  
Vol 8 (7) ◽  
pp. 675 ◽  
Author(s):  
Xia ◽  
Liu ◽  
Zhang ◽  
Guo
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Gene Expression Data ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cancer Cell Line ◽  
Tissue Expression ◽  
The Cancer Genome Atlas ◽  
Expression Data ◽  
Protein Levels

High-throughput technologies generate a tremendous amount of expression data on mRNA, miRNA and protein levels. Mining and visualizing the large amount of expression data requires sophisticated computational skills. An easy to use and user-friendly web-server for the visualization of gene expression profiles could greatly facilitate data exploration and hypothesis generation for biologists. Here, we curated and normalized the gene expression data on mRNA, miRNA and protein levels in 23315, 9009 and 9244 samples, respectively, from 40 tissues (The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GETx)) and 1594 cell lines (Cancer Cell Line Encyclopedia (CCLE) and MD Anderson Cell Lines Project (MCLP)). Then, we constructed the Gene Expression Display Server (GEDS), a web-based tool for quantification, comparison and visualization of gene expression data. GEDS integrates multiscale expression data and provides multiple types of figures and tables to satisfy several kinds of user requirements. The comprehensive expression profiles plotted in the one-stop GEDS platform greatly facilitate experimental biologists utilizing big data for better experimental design and analysis. GEDS is freely available on http://bioinfo.life.hust.edu.cn/web/GEDS/.

ASSOCIATION OF FEATURE GENE EXPRESSION WITH STRUCTURAL FINGERPRINTS OF CHEMICAL COMPOUNDS

2011 ◽  
Vol 09 (04) ◽  
pp. 503-519 ◽  
Author(s):  
YUN LI ◽  
KANG TU ◽  
SIYUAN ZHENG ◽  
JINGFANG WANG ◽  
YIXUE LI ◽  
...  
Keyword(s):  
Gene Expression ◽  
Structural Information ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Structural Difference ◽  
Structural Features ◽  
Molecular Structures ◽  
Transcriptomics Data ◽  
Function Relationship ◽  
Relationship Of

Exploring the relationship between a chemical structure and its biological function is of great importance for drug discovery. For understanding the mechanisms of drug action, researchers traditionally focused on the molecular structures in the context of interactions with targets. The newly emerged high-throughput "omics" technology opened a new dimension to study the structure–function relationship of chemicals. Previous studies made attempts to introduce transcriptomics data into chemical function investigation. But little effort has been made to link structural fingerprints of compounds with defined intracellular functions, i.e. expression of particular genes and altered pathways. By integrating the chemical structural information with the gene expression profiles of chemical-treated cells, we developed a novel method to associate the structural difference between compounds with the expression of a definite set of genes, which were called feature genes. A subtraction protocol was designed to extract a minimum gene set related to chemical structural features, which can be utilized in practice as markers for drug screening. Case studies demonstrated that our approach is capable of finding feature genes associated with chemical structural fingerprints.

scholarly journals Gene Expression in Porphyromonas gingivalis after Contact with Human Epithelial Cells

2005 ◽  
Vol 73 (4) ◽  
pp. 2327-2335 ◽  
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.

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

2019 ◽  
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.

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

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.

scholarly journals Distinct properties in ventral pallidum projection neuron subtypes

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.

scholarly journals Semi-soft Clustering of Single Cell Data

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.

Sign in / Sign up

Export Citation Format

Share Document