scholarly journals BRAin INteractive Sequencing Analysis Tool (BRAIN-SAT); facilitating interactive transcriptome analyses (http://brainsat.eu/)

2019 ◽  
Author(s):  
M.L. Dubbelaar ◽  
M.L. Brummer ◽  
M. Meijer ◽  
B.J.L. Eggen ◽  
H.W.G.M. Boddeke
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
New Technologies ◽  
Easy Access ◽  
Published Data ◽  
Data Sets ◽  
Analysis Tool ◽  
Sequencing Analysis ◽  
Quantitative Expression

AbstractOver the last decade, a large number of glia transcriptome studies has been published. New technologies and platforms have been developed to allow access and interrogation of the published data. The increase in large transcriptomic data sets allows for innovative in silico analyses to address biological questions. Here we present BRAIN-SAT, the follow-up of our previous database GOAD, with several new features available on an interactive platform that enables access to recent, high quality bulk and single cell RNA-Seq data. The combination of several functions including gene searches, differential and quantitative expression analysis and a single cell expression analysis feature enables the exploration of published data sets at different levels. These different functionalities can be used for researchers and research companies in the neuroscience field to evaluate and visualize gene expression levels in a set of relevant publications. Here, we present a new platform with easy access to published gene expression studies for data exploration and gene of interest searches.

scholarly journals Using single-cell transcriptomics to understand functional states and interactions in microbial eukaryotes

2019 ◽  
Vol 374 (1786) ◽  
pp. 20190098 ◽  
Author(s):  
Chuan Ku ◽  
Arnau Sebé-Pedrós
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Interspecific Interactions ◽  
Single Cells ◽  
Life Cycles ◽  
Modern Biology ◽  
Microbial Eukaryotes ◽  
Eukaryotic Microorganisms

Understanding the diversity and evolution of eukaryotic microorganisms remains one of the major challenges of modern biology. In recent years, we have advanced in the discovery and phylogenetic placement of new eukaryotic species and lineages, which in turn completely transformed our view on the eukaryotic tree of life. But we remain ignorant of the life cycles, physiology and cellular states of most of these microbial eukaryotes, as well as of their interactions with other organisms. Here, we discuss how high-throughput genome-wide gene expression analysis of eukaryotic single cells can shed light on protist biology. First, we review different single-cell transcriptomics methodologies with particular focus on microbial eukaryote applications. Then, we discuss single-cell gene expression analysis of protists in culture and what can be learnt from these approaches. Finally, we envision the application of single-cell transcriptomics to protist communities to interrogate not only community components, but also the gene expression signatures of distinct cellular and physiological states, as well as the transcriptional dynamics of interspecific interactions. Overall, we argue that single-cell transcriptomics can significantly contribute to our understanding of the biology of microbial eukaryotes. This article is part of a discussion meeting issue ‘Single cell ecology’.

scholarly journals Resolution of Cell Fate Decisions Revealed by Single-Cell Gene Expression Analysis from Zygote to Blastocyst

2010 ◽  
Vol 18 (4) ◽  
pp. 675-685 ◽  
Author(s):  
Guoji Guo ◽  
Mikael Huss ◽  
Guo Qing Tong ◽  
Chaoyang Wang ◽  
Li Li Sun ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Fate ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Cell Fate Decisions ◽  
Cell Gene Expression ◽  
Cell Gene

scholarly journals Enhancing droplet-based single-nucleus RNA-seq resolution using the semi-supervised machine learning classifier DIEM

2019 ◽  
Author(s):  
Marcus Alvarez ◽  
Elior Rahmani ◽  
Brandon Jew ◽  
Kristina M. Garske ◽  
Zong Miao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Cell Types ◽  
Supervised Machine Learning ◽  
Data Sets ◽  
Rna Seq ◽  
Novel Approach ◽  
Single Nucleus ◽  
Downstream Analysis

AbstractSingle-nucleus RNA sequencing (snRNA-seq) measures gene expression in individual nuclei instead of cells, allowing for unbiased cell type characterization in solid tissues. Contrary to single-cell RNA seq (scRNA-seq), we observe that snRNA-seq is commonly subject to contamination by high amounts of extranuclear background RNA, which can lead to identification of spurious cell types in downstream clustering analyses if overlooked. We present a novel approach to remove debris-contaminated droplets in snRNA-seq experiments, called Debris Identification using Expectation Maximization (DIEM). Our likelihood-based approach models the gene expression distribution of debris and cell types, which are estimated using EM. We evaluated DIEM using three snRNA-seq data sets: 1) human differentiating preadipocytes in vitro, 2) fresh mouse brain tissue, and 3) human frozen adipose tissue (AT) from six individuals. All three data sets showed various degrees of extranuclear RNA contamination. We observed that existing methods fail to account for contaminated droplets and led to spurious cell types. When compared to filtering using these state of the art methods, DIEM better removed droplets containing high levels of extranuclear RNA and led to higher quality clusters. Although DIEM was designed for snRNA-seq data, we also successfully applied DIEM to single-cell data. To conclude, our novel method DIEM removes debris-contaminated droplets from single-cell-based data fast and effectively, leading to cleaner downstream analysis. Our code is freely available for use at https://github.com/marcalva/diem.

Analyzing Large Gene Expression Data Sets

2006 ◽  
Author(s):  
Soumya Raychaudhuri
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Data Sets ◽  
Expression Data ◽  
Clustering Methods ◽  
Biologically Relevant ◽  
Large Gene ◽  
Functional Coherence

The most interesting and challenging gene expression data sets to analyze are large multidimensional data sets that contain expression values for many genes across multiple conditions. In these data sets the use of scientific text can be particularly useful, since there are a myriad of genes examined under vastly different conditions, each of which may induce or repress expression of the same gene for different reasons. There is an enormous complexity to the data that we are examining—each gene is associated with dozens if not hundreds of expression values as well as multiple documents built up from vocabularies consisting of thousands of words. In Section 2.4 we reviewed common gene expression strategies, most of which revolve around defining groups of genes based on common profiles. A limitation of many gene expression analytic approaches is that they do not incorporate comprehensive background knowledge about the genes into the analysis. We present computational methods that leverage the peer-reviewed literature in the automatic analysis of gene expression data sets. Including the literature in gene expression data analysis offers an opportunity to incorporate background functional information about the genes when defining expression clusters. In Chapter 5 we saw how literature- based approaches could help in the analysis of single condition experiments. Here we will apply the strategies introduced in Chapter 6 to assess the coherence of groups of genes to enhance gene expression analysis approaches. The methods proposed here could, in fact, be applied to any multivariate genomics data type. The key concepts discussed in this chapter are listed in the frame box. We begin with a discussion of gene groups and their role in expression analysis; we briefly discuss strategies to assign keywords to groups and strategies to assess their functional coherence. We apply functional coherence measures to gene expression analysis; for examples we focus on a yeast expression data set. We first demonstrate how functional coherence can be used to focus in on the key biologically relevant gene groups derived by clustering methods such as self-organizing maps and k-means clustering.

scholarly journals An Integrated Preprocessing Approach for Exploring Single-Cell Gene Expression in Rare Cells

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Junyi Shang ◽  
David Welch ◽  
Manuela Buonanno ◽  
Brian Ponnaiya ◽  
Guy Garty ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Bystander Effect ◽  
Kinase Inhibitor ◽  
Digital Pcr ◽  
Lung Fibroblasts ◽  
Gene Expressions ◽  
Rare Cells

AbstractExploring the variability in gene expressions of rare cells at the single-cell level is critical for understanding mechanisms of differentiation in tissue function and development as well as for disease diagnostics and cancer treatment. Such studies, however, have been hindered by major difficulties in tracking the identity of individual cells. We present an approach that combines single-cell picking, lysing, reverse transcription and digital polymerase chain reaction to enable the isolation, tracking and gene expression analysis of rare cells. The approach utilizes a photocleavage bead-based microfluidic device to synthesize and deliver stable cDNA for downstream gene expression analysis, thereby allowing chip-based integration of multiple reactions and facilitating the minimization of sample loss or contamination. The utility of the approach was demonstrated with QuantStudio digital PCR by analyzing the radiation and bystander effect on individual IMR90 human lung fibroblasts. Expression levels of the Cyclin-dependent kinase inhibitor 1a (CDKN1A), Growth/differentiation factor 15 (GDF15), and Prostaglandin-endoperoxide synthase 2 (PTGS2) genes, previously shown to have different responses to direct and bystander irradiation, were measured across individual control, microbeam-irradiated or bystander IMR90 cells. In addition to the confirmation of accurate tracking of cell treatments through the system and efficient analysis of single-cell responses, the results enable comparison of activation levels of different genes and provide insight into signaling pathways within individual cells.

scholarly journals Spermatogonial Type 3 Deiodinase Regulates Thyroid Hormone Target Genes in Developing Testicular Somatic Cells

Endocrinology ◽  
2019 ◽  
Vol 160 (12) ◽  
pp. 2929-2945
Author(s):  
M Elena Martinez ◽  
Christine W Lary ◽  
Aldona A Karaczyn ◽  
Michael D Griswold ◽  
Arturo Hernandez
Keyword(s):  
Gene Expression ◽  
Thyroid Hormone ◽  
Target Genes ◽  
Somatic Cells ◽  
Published Data ◽  
Data Sets ◽  
Testicular Cells ◽  
Differentiation And Proliferation ◽  
Neonatal Testis ◽  
Type 3

Abstract Premature overexposure to thyroid hormone causes profound effects on testis growth, spermatogenesis, and male fertility. We used genetic mouse models of type 3 deiodinase (DIO3) deficiency to determine the genetic programs affected by premature thyroid hormone action and to define the role of DIO3 in regulating thyroid hormone economy in testicular cells. Gene expression profiling in the neonatal testis of DIO3-deficient mice identified 5699 differentially expressed genes. Upregulated and downregulated genes were, respectively, involved according to DAVID analysis with cell differentiation and proliferation. They included anti-Müllerian hormone and genes involved in the formation of the blood–testis barrier, which are specific to Sertoli cells (SCs). They also included steroidogenic genes, which are specific to Leydig cells. Comparison with published data sets of genes enriched in SCs and spermatogonia, and responsive to retinoic acid (RA), identified a subset of genes that were regulated similarly by RA and thyroid hormone. This subset of genes showed an expression bias, as they were downregulated when enriched in spermatogonia and upregulated when enriched in SCs. Furthermore, using a genetic approach, we found that DIO3 is not expressed in SCs, but spermatogonia-specific inactivation of DIO3 led to impaired testis growth, reduced SC number, decreased cell proliferation and, especially during neonatal development, altered gene expression specific to somatic cells. These findings indicate that spermatogonial DIO3 protects testicular cells from untimely thyroid hormone signaling and demonstrate a mechanism of cross-talk between somatic and germ cells in the neonatal testis that involves the regulation of thyroid hormone availability and action.

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