Detecting and profiling tissue-selective genes

2006 ◽  
Vol 26 (2) ◽  
pp. 158-162 ◽  
Author(s):  
Shuang Liang ◽  
Yizheng Li ◽  
Xiaobing Be ◽  
Steve Howes ◽  
Wei Liu
Keyword(s):  
Drug Targets ◽  
Tissue Specificity ◽  
Statistical Approach ◽  
Cell Types ◽  
Expression Data ◽  
Data Set ◽  
Normal Human ◽  
Tissue Selectivity ◽  
Error Rate Control ◽  
Shed Light

The widespread use of DNA microarray technologies has generated large amounts of data from various tissue and/or cell types. These data set the stage to answer the question of tissue specificity of human transcriptome in a comprehensive manner. Our focus is to uncover the tissue-gene relationship by identifying genes that are preferentially expressed in a small number of tissue types. The tissue selectivity would shed light on the potential physiological functions of these genes and provides an indispensable reference to compare against disease pathophysiology and to identify or validate tissue-specific drug targets. Here we describe a systematic computational and statistical approach to profile gene expression data to identify tissue-selective genes with the use of a more extensive data set and a well-established multiple-comparison procedure with error rate control. Expression data of 35,152 probe sets in 97 normal human tissue types were analyzed, and 3,919 genes were identified to be selective to one or a few tissue types. We presented results of these tissue-selective genes and compared them to those identified by other studies.

scholarly journals Transcriptional and Cellular Diversity of the Human Heart

Circulation ◽  
2020 ◽  
Vol 142 (5) ◽  
pp. 466-482 ◽  
Author(s):  
Nathan R. Tucker ◽  
Mark Chaffin ◽  
Stephen J. Fleming ◽  
Amelia W. Hall ◽  
Victoria A. Parsons ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Human Heart ◽  
Large Scale ◽  
Cell Types ◽  
Matrix Remodeling ◽  
Data Set ◽  
Cellular Specificity ◽  
Normal Human ◽  
Differential Gene ◽  
Association Data

Background: The human heart requires a complex ensemble of specialized cell types to perform its essential function. A greater knowledge of the intricate cellular milieu of the heart is critical to increase our understanding of cardiac homeostasis and pathology. As recent advances in low-input RNA sequencing have allowed definitions of cellular transcriptomes at single-cell resolution at scale, we have applied these approaches to assess the cellular and transcriptional diversity of the nonfailing human heart. Methods: Microfluidic encapsulation and barcoding was used to perform single nuclear RNA sequencing with samples from 7 human donors, selected for their absence of overt cardiac disease. Individual nuclear transcriptomes were then clustered based on transcriptional profiles of highly variable genes. These clusters were used as the basis for between-chamber and between-sex differential gene expression analyses and intersection with genetic and pharmacologic data. Results: We sequenced the transcriptomes of 287 269 single cardiac nuclei, revealing 9 major cell types and 20 subclusters of cell types within the human heart. Cellular subclasses include 2 distinct groups of resident macrophages, 4 endothelial subtypes, and 2 fibroblast subsets. Comparisons of cellular transcriptomes by cardiac chamber or sex reveal diversity not only in cardiomyocyte transcriptional programs but also in subtypes involved in extracellular matrix remodeling and vascularization. Using genetic association data, we identified strong enrichment for the role of cell subtypes in cardiac traits and diseases. Intersection of our data set with genes on cardiac clinical testing panels and the druggable genome reveals striking patterns of cellular specificity. Conclusions: Using large-scale single nuclei RNA sequencing, we defined the transcriptional and cellular diversity in the normal human heart. Our identification of discrete cell subtypes and differentially expressed genes within the heart will ultimately facilitate the development of new therapeutics for cardiovascular diseases.

scholarly journals ADAPTS: Automated Deconvolution Augmentation of Profiles for Tissue Specific cells

2019 ◽  
Author(s):  
Samuel A Danziger ◽  
David L Gibbs ◽  
Ilya Shmulevich ◽  
Mark McConnell ◽  
Matthew WB Trotter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Gene Expression Data ◽  
Immune Cell ◽  
De Novo ◽  
Cell Types ◽  
Expression Data ◽  
Cell Type ◽  
Data Set ◽  
Rnaseq Data

AbstractImmune cell infiltration of tumors can be an important component for determining patient outcomes, e.g. by inferring immune cell presence by deconvolving gene expression data drawn from a heterogenous mix of cell types. One particularly powerful family of deconvolution techniques uses signature matrices of genes that uniquely identify each cell type as determined from cell type purified gene expression data. Many methods of this type have been recently published, often including new signature matrices appropriate for a single purpose, such as investigating a specific type of tumor. The package ADAPTS helps users make the most of this expanding knowledge base by introducing a framework for cell type deconvolution. ADAPTS implements modular tools for customizing signature matrices for new tissue types by adding custom cell types or building new matrices de novo, including from single cell RNAseq data. It includes a common interface to several popular deconvolution algorithms that use a signature matrix to estimate the proportion of cell types present in heterogenous samples. ADAPTS also implements a novel method for clustering cell types into groups that are hard to distinguish by deconvolution and then re-splitting those clusters using hierarchical deconvolution. We demonstrate that the techniques implemented in ADAPTS improve the ability to reconstruct the cell types present in a single cell RNAseq data set in a blind predictive analysis. ADAPTS is currently available for use in R on CRAN and GitHub.

HCVS: Pinpointing Chromatin States Through Hierarchical Clustering and Visualization Scheme

2019 ◽  
Vol 14 (2) ◽  
pp. 148-156
Author(s):  
Nighat Noureen ◽  
Sahar Fazal ◽  
Muhammad Abdul Qadir ◽  
Muhammad Tanvir Afzal
Keyword(s):  
Hierarchical Clustering ◽  
Real Data ◽  
Cell Types ◽  
Computational Scheme ◽  
Data Set ◽  
Chromatin States ◽  
Functional Regions ◽  
Visualization Strategy ◽  
Hidden States ◽  
Next Generation Sequencing Ngs

Background: Specific combinations of Histone Modifications (HMs) contributing towards histone code hypothesis lead to various biological functions. HMs combinations have been utilized by various studies to divide the genome into different regions. These study regions have been classified as chromatin states. Mostly Hidden Markov Model (HMM) based techniques have been utilized for this purpose. In case of chromatin studies, data from Next Generation Sequencing (NGS) platforms is being used. Chromatin states based on histone modification combinatorics are annotated by mapping them to functional regions of the genome. The number of states being predicted so far by the HMM tools have been justified biologically till now. Objective: The present study aimed at providing a computational scheme to identify the underlying hidden states in the data under consideration. </P><P> Methods: We proposed a computational scheme HCVS based on hierarchical clustering and visualization strategy in order to achieve the objective of study. Results: We tested our proposed scheme on a real data set of nine cell types comprising of nine chromatin marks. The approach successfully identified the state numbers for various possibilities. The results have been compared with one of the existing models as well which showed quite good correlation. Conclusion: The HCVS model not only helps in deciding the optimal state numbers for a particular data but it also justifies the results biologically thereby correlating the computational and biological aspects.

scholarly journals The Cellular Choreography of Osteoblast Angiotropism in Bone Development and Homeostasis

2021 ◽  
Vol 22 (14) ◽  
pp. 7253
Author(s):  
Georgiana Neag ◽  
Melissa Finlay ◽  
Amy J. Naylor
Keyword(s):  
Endothelial Cells ◽  
Bone Formation ◽  
Drug Targets ◽  
Bone Development ◽  
Cell Types ◽  
Regulatory Relationship ◽  
Sequencing Technologies ◽  
Resolution Imaging ◽  
Molecular Relationships ◽  
Form Type

Interaction between endothelial cells and osteoblasts is essential for bone development and homeostasis. This process is mediated in large part by osteoblast angiotropism, the migration of osteoblasts alongside blood vessels, which is crucial for the homing of osteoblasts to sites of bone formation during embryogenesis and in mature bones during remodeling and repair. Specialized bone endothelial cells that form “type H” capillaries have emerged as key interaction partners of osteoblasts, regulating osteoblast differentiation and maturation and ensuring their migration towards newly forming trabecular bone areas. Recent revolutions in high-resolution imaging methodologies for bone as well as single cell and RNA sequencing technologies have enabled the identification of some of the signaling pathways and molecular interactions that underpin this regulatory relationship. Similarly, the intercellular cross talk between endothelial cells and entombed osteocytes that is essential for bone formation, repair, and maintenance are beginning to be uncovered. This is a relatively new area of research that has, until recently, been hampered by a lack of appropriate analysis tools. Now that these tools are available, greater understanding of the molecular relationships between these key cell types is expected to facilitate identification of new drug targets for diseases of bone formation and remodeling.

EXPRESS: How Does Bonus Payment Affect the Demand for Auto Loans and Their Delinquency?

2021 ◽  
pp. 002224372110092
Author(s):  
Zhenling Jiang ◽  
Dennis J. Zhang ◽  
Tat Chan
Keyword(s):  
Consumer Products ◽  
Data Set ◽  
Financial Decisions ◽  
Consumer Panel ◽  
Loan Demand ◽  
Bonus Payments ◽  
Base Salary ◽  
Long Term Impact ◽  
Shed Light

This paper studies how receiving a bonus changes the consumers’ demand for auto loans and the risk of future delinquency. Unlike traditional consumer products, auto loans have a long-term impact on consumers’ financial state because of the monthly payment obligation. Using a large consumer panel data set of credit and employment information, the authors find that receiving a bonus increases auto loan demand by 21 percent. These loans, however, are associated with higher risk, as the delinquency rate increases by 18.5 −31.4 percent depending on different measures. In contrast, an increase in consumers’ base salary will increase the demand for auto loans but not the delinquency. By comparing consumers with bonuses with those without bonuses, the authors find that bonus payments lead to both demand expansion and demand shifting on auto loans. The empirical findings help shed light on how consumers make financial decisions and have important implications for financial institutions on when demand for auto loans and the associated risk arise.

scholarly journals Is the Genotype-Phenotype Map Modular?: A Statistical Approach Using Mouse Quantitative Trait Loci Data

Genetics ◽  
2000 ◽  
Vol 156 (1) ◽  
pp. 305-311 ◽  
Author(s):  
Jason G Mezey ◽  
James M Cheverud ◽  
Günter P Wagner
Keyword(s):  
Quantitative Trait Loci ◽  
Quantitative Trait ◽  
Statistical Approach ◽  
Genetic Effects ◽  
Statistical Distribution ◽  
Data Set ◽  
Alveolar Region ◽  
Phenotypic Characters ◽  
Statistical Support ◽  
Complex Characters

Abstract Various theories about the evolution of complex characters make predictions about the statistical distribution of genetic effects on phenotypic characters, also called the genotype-phenotype map. With the advent of QTL technology, data about these distributions are becoming available. In this article, we propose simple tests for the prediction that functionally integrated characters have a modular genotype-phenotype map. The test is applied to QTL data on the mouse mandible. The results provide statistical support for the notion that the ascending ramus region of the mandible is modularized. A data set comprising the effects of QTL on a more extensive portion of the phenotype is required to determine if the alveolar region of the mandible is also modularized.

scholarly journals Selecting single cell clustering parameter values using subsampling-based robustness metrics

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ryan B. Patterson-Cross ◽  
Ariel J. Levine ◽  
Vilas Menon
Keyword(s):  
Single Cell ◽  
Optimal Parameter ◽  
Clustering Algorithms ◽  
Cell Types ◽  
Parameter Selection ◽  
Data Set ◽  
Biologically Relevant ◽  
Cell Clustering ◽  
Parameter Values ◽  
Robustness Metrics

Abstract Background Generating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types with shared transcriptomic signatures. However, many of these clustering workflows rely on user-tuned parameter values, tailored to each dataset, to identify a set of biologically relevant clusters. Whereas users often develop their own intuition as to the optimal range of parameters for clustering on each data set, the lack of systematic approaches to identify this range can be daunting to new users of any given workflow. In addition, an optimal parameter set does not guarantee that all clusters are equally well-resolved, given the heterogeneity in transcriptomic signatures in most biological systems. Results Here, we illustrate a subsampling-based approach (chooseR) that simultaneously guides parameter selection and characterizes cluster robustness. Through bootstrapped iterative clustering across a range of parameters, chooseR was used to select parameter values for two distinct clustering workflows (Seurat and scVI). In each case, chooseR identified parameters that produced biologically relevant clusters from both well-characterized (human PBMC) and complex (mouse spinal cord) datasets. Moreover, it provided a simple “robustness score” for each of these clusters, facilitating the assessment of cluster quality. Conclusion chooseR is a simple, conceptually understandable tool that can be used flexibly across clustering algorithms, workflows, and datasets to guide clustering parameter selection and characterize cluster robustness.

Production, crystallization and preliminary X-ray analysis of the human integrin \alpha_1 I domain

1999 ◽  
Vol 55 (7) ◽  
pp. 1365-1367 ◽  
Author(s):  
Tiina A. Salminen ◽  
Yvonne Nymalm ◽  
Jussi Kankare ◽  
Jarmo Käpylä ◽  
Jyrki Heino ◽  
...  
Keyword(s):  
Large Scale ◽  
Cell Types ◽  
Diffusion Method ◽  
Scale Production ◽  
Data Set ◽  
Cell Parameters ◽  
Collagen Receptors ◽  
Large Scale Production ◽  
Peg 4000 ◽  
Reservoir Solution

Integrin α1β1 is one of the main collagen receptors in many cell types. A fast large-scale production, purification and crystallization method for the integrin α1 I domain is reported here. The α1 I domain was crystallized using the vapour-diffusion method with a reservoir solution containing a mixture of PEG 4000, sodium acetate, glycerol and Tris–HCl buffer. The crystals beong to the C2 space group, with unit-cell parameters a = 74.5, b = 81.9, c = 37.3 Å, α = γ = 90.0, β = 90.8°. The crystals diffract to 2.0 Å and a 94.2% complete data set to 2.2 Å has been collected from a single crystal with an R merge of 5.8%.

scholarly journals The transcriptional tissue specificity of the human proα1(I) collagen gene is determined by a negative cis-regulatory element in the promoter

1992 ◽  
Vol 286 (1) ◽  
pp. 179-185 ◽  
Author(s):  
C P Simkevich ◽  
J P Thompson ◽  
H Poppleton ◽  
R Raghow
Keyword(s):  
Tissue Specificity ◽  
Regulatory Element ◽  
Mesenchymal Cell ◽  
Cell Types ◽  
Regulatory Elements ◽  
Negative Influence ◽  
Collagen Gene ◽  
Specific Expression ◽  
Cis Acting ◽  
First Intron

The transcriptional activity of plasmid pCOL-KT, in which human pro alpha 1 (I) collagen gene upstream sequences up to -804 and most of the first intron (+474 to +1440) drive expression of the chloramphenicol acetyltransferase (CAT) gene [Thompson, Simkevich, Holness, Kang & Raghow (1991) J. Biol. Chem. 266, 2549-2556], was tested in a number of mesenchymal and non-mesenchymal cells. We observed that pCOL-KT was readily expressed in fibroblasts of human (IMR-90 and HFL-1), murine (NIH 3T3) and avian (SL-29) origin and in a human rhabdomyosarcoma cell line (A204), but failed to be expressed in human erythroleukaemia (K562) and rat pheochromocytoma (PC12) cells, indicating that the regulatory elements required for appropriate tissue-specific expression of the human pro alpha 1 (I) collagen gene were present in pCOL-KT. To delineate the nature of cis-acting sequences which determine the tissue specificity of pro alpha 1 (I) collagen gene expression, functional consequences of deletions in the promoter and first intron of pCOL-KT were tested in various cell types by transient expression assays. Cis elements in the promoter-proximal and intronic sequences displayed either a positive or a negative influence depending on the cell type. Thus deletion of fragments using EcoRV (nt -625 to -442 deleted), XbaI (-804 to -331) or SstII (+670 to +1440) resulted in 2-10-fold decreased expression in A204 and HFL-1 cells. The negative influences of deletions in the promoter-proximal sequences was apparently considerably relieved by deleting sequences in the first intron, and the constructs containing the EcoRV/SstII or XbaI/SstII double deletions were expressed to a much greater extent than either of the single deletion constructs. In contrast, the XbaI* deletion (nt -804 to -609), either alone or in combination with the intronic deletion, resulted in very high expression in all cells regardless of their collagen phenotype; the XbaI*/(-SstII) construct, which contained the intronic SstII fragment (+670 to +1440) in the reverse orientation, was not expressed in either mesenchymal or nonmesenchymal cells. Based on these results, we conclude that orientation-dependent interactions between negatively acting 5′-upstream sequences and the first intron determine the mesenchymal cell specificity of human pro alpha 1 (I) collagen gene transcription.

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