scholarly journals Corrigendum to “Functional Virtual Flow Cytometry: A Visual Analytic Approach for Characterizing Single-Cell Gene Expression Patterns”

2017 ◽  
Vol 2017 ◽  
pp. 1-1
Author(s):  
Zhi Han ◽  
Travis Johnson ◽  
Jie Zhang ◽  
Xuan Zhang ◽  
Kun Huang
Keyword(s):  
Gene Expression ◽  
Flow Cytometry ◽  
Single Cell ◽  
Expression Patterns ◽  
Analytic Approach ◽  
Gene Expression Patterns ◽  
Cell Gene Expression ◽  
Cell Gene

scholarly journals Single-cell gene expression patterns in lupus monocytes independently indicate disease activity, interferon and therapy

2017 ◽  
Vol 4 (1) ◽  
pp. e000202 ◽  
Author(s):  
Zhongbo Jin ◽  
Wei Fan ◽  
Mark A Jensen ◽  
Jessica M Dorschner ◽  
George F Bonadurer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Disease Activity ◽  
Single Cell ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Cell Gene Expression ◽  
Cell Gene

scholarly journals Human bone marrow-derived mesenchymal stem cell gene expression patterns vary with culture conditions

2013 ◽  
Vol 48 (2) ◽  
pp. 107 ◽  
Author(s):  
Myoung Woo Lee ◽  
Dae Seong Kim ◽  
Keon Hee Yoo ◽  
Hye Ryung Kim ◽  
In Keun Jang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bone Marrow ◽  
Stem Cell ◽  
Mesenchymal Stem Cell ◽  
Expression Patterns ◽  
Culture Conditions ◽  
Gene Expression Patterns ◽  
Cell Gene Expression ◽  
Cell Gene ◽  
Stem Cell Gene

scholarly journals scHLAcount: allele-specific HLA expression from single-cell gene expression data

2020 ◽  
Vol 36 (12) ◽  
pp. 3905-3906 ◽  
Author(s):  
Charlotte A Darby ◽  
Michael J T Stubbington ◽  
Patrick J Marks ◽  
Álvaro Martínez Barrio ◽  
Ian T Fiddes
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Human Leukocyte ◽  
Supplementary Information ◽  
Hla Genes ◽  
Sequencing Studies ◽  
Cell Gene Expression ◽  
Allele Specific ◽  
Cell Gene

Abstract Summary Bulk RNA sequencing studies have demonstrated that human leukocyte antigen (HLA) genes may be expressed in a cell type-specific and allele-specific fashion. Single-cell gene expression assays have the potential to further resolve these expression patterns, but currently available methods do not perform allele-specific quantification at the molecule level. Here, we present scHLAcount, a post-processing workflow for single-cell RNA-seq data that computes allele-specific molecule counts of the HLA genes based on a personalized reference constructed from the sample’s HLA genotypes. Availability and implementation scHLAcount is available under the MIT license at https://github.com/10XGenomics/scHLAcount. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Blood mononuclear cell gene expression patterns denote the onset of persisting post-partum depressive disorder

2009 ◽  
Vol 15 (1) ◽  
pp. 2-2 ◽  
Author(s):  
R H Segman ◽  
T Goltser-Dubner ◽  
I Weiner ◽  
L Canetti ◽  
E Galili-Weisstub ◽  
...  
Keyword(s):  
Gene Expression ◽  
Depressive Disorder ◽  
Mononuclear Cell ◽  
Post Partum ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Cell Gene Expression ◽  
Cell Gene

scholarly journals scINSIGHT for interpreting single-cell gene expression from biologically heterogeneous data

2021 ◽  
Author(s):  
Kun Qian ◽  
Shiwei Fu ◽  
Hongwei Li ◽  
Wei Vivian Li
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Patterns ◽  
Real Data ◽  
Heterogeneous Data ◽  
Biological Processes ◽  
Cell Gene Expression ◽  
Improved Performance ◽  
Cell Gene ◽  
Clinical Problems

The increasing number of scRNA-seq data emphasizes the need for integrative analysis to interpret similarities and differences between single-cell samples. Even though different batch effect removal methods have been developed, none of the existing methods is suitable for heterogeneous single-cell samples coming from multiple biological conditions. To address this challenge, we propose a method named scINSIGHT to learn coordinated gene expression patterns that are common among or specific to different biological conditions, offering a unique chance to identify cellular identities and key biological processes across single-cell samples. We have evaluated scINSIGHT in comparison with state-of-the-art methods using simulated and real data, which consistently demonstrate its improved performance. In addition, our results show the applicability of scINSIGHT in diverse biomedical and clinical problems.

scholarly journals Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning

2020 ◽  
Vol 48 (20) ◽  
pp. 11335-11346
Author(s):  
Nikolaos-Kosmas Chlis ◽  
Lisa Rausch ◽  
Thomas Brocker ◽  
Jan Kranich ◽  
Fabian J Theis
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
High Throughput ◽  
Imaging Flow Cytometry ◽  
Myeloid Progenitor ◽  
Myeloid Progenitor Cells ◽  
Cell Gene Expression ◽  
Cell Gene

Abstract High-content imaging and single-cell genomics are two of the most prominent high-throughput technologies for studying cellular properties and functions at scale. Recent studies have demonstrated that information in large imaging datasets can be used to estimate gene mutations and to predict the cell-cycle state and the cellular decision making directly from cellular morphology. Thus, high-throughput imaging methodologies, such as imaging flow cytometry can potentially aim beyond simple sorting of cell-populations. We introduce IFC-seq, a machine learning methodology for predicting the expression profile of every cell in an imaging flow cytometry experiment. Since it is to-date unfeasible to observe single-cell gene expression and morphology in flow, we integrate uncoupled imaging data with an independent transcriptomics dataset by leveraging common surface markers. We demonstrate that IFC-seq successfully models gene expression of a moderate number of key gene-markers for two independent imaging flow cytometry datasets: (i) human blood mononuclear cells and (ii) mouse myeloid progenitor cells. In the case of mouse myeloid progenitor cells IFC-seq can predict gene expression directly from brightfield images in a label-free manner, using a convolutional neural network. The proposed method promises to add gene expression information to existing and new imaging flow cytometry datasets, at no additional cost.

scholarly journals Single-cell RNA-seq analysis of human CSF microglia and myeloid cells in neuroinflammation

2020 ◽  
Vol 7 (4) ◽  
pp. e732 ◽  
Author(s):  
Ekaterina Esaulova ◽  
Claudia Cantoni ◽  
Irina Shchukina ◽  
Konstantin Zaitsev ◽  
Robert C. Bucelli ◽  
...  
Keyword(s):  
Gene Expression ◽  
Flow Cytometry ◽  
High Resolution ◽  
Single Cell ◽  
Gene Expression Analysis ◽  
Myeloid Cell ◽  
Cell Populations ◽  
Cell Gene Expression ◽  
Cell Gene ◽  
Resolution Single

ObjectiveTo identify and characterize myeloid cell populations within the CSF of patients with MS and anti-myelin oligodendrocyte glycoprotein (MOG) disorder by high-resolution single-cell gene expression analysis.MethodsSingle-cell RNA sequencing (scRNA-seq) was used to profile individual cells of CSF and blood from 2 subjects with relapsing-remitting MS (RRMS) and one with anti-MOG disorder. Publicly available scRNA-seq data from the blood and CSF of 2 subjects with HIV were also analyzed. An informatics pipeline was used to cluster cell populations by transcriptomic profiling. Based on gene expression by CSF myeloid cells, a flow cytometry panel was devised to examine myeloid cell populations from the CSF of 11 additional subjects, including individuals with RRMS, anti-MOG disorder, and control subjects without inflammatory demyelination.ResultsCommon myeloid populations were identified within the CSF of subjects with RRMS, anti-MOG disorder, and HIV. These included monocytes, conventional and plasmacytoid dendritic cells, and cells with a transcriptomic signature matching microglia. Microglia could be discriminated from other myeloid cell populations in the CSF by flow cytometry.ConclusionsHigh-resolution single-cell gene expression analysis clearly distinguishes distinct myeloid cell types present within the CSF of subjects with neuroinflammation. A population of microglia exists within the human CSF, which is detectable by surface protein expression. The function of these cells during immunity and disease requires further investigation.

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