scholarly journals The Clinical Significance and Potential Molecular Mechanism of Upregulated CDC28 Protein Kinase Regulatory Subunit 1B in Osteosarcoma

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Chaohua Mo ◽  
Le Xie ◽  
Chang Chen ◽  
Jie Ma ◽  
Yingxin Huang ◽  
...  
Keyword(s):  
Protein Kinase ◽  
Single Cell ◽  
Molecular Mechanism ◽  
High Throughput ◽  
Regulatory Subunit ◽  
Sufficient Evidence ◽  
Mean Deviation ◽  
Rna Seq ◽  
Tissue Samples ◽  
Immune Infiltration

Background. CDC28 Protein Kinase Regulatory Subunit 1B (CKS1B) is a member of cyclin-dependent kinase subfamily and the relationship between CKS1B and osteosarcoma (OS) remains to be explored. Methods. 80 OS and 41 nontumor tissue samples were arranged to conduct immunohistochemistry (IHC) to evaluate CKS1B expression between OS and nontumor samples. The standard mean deviation (SMD) was calculated based on in-house IHC and tissue microarrays and exterior high-throughput datasets for further verification of CKS1B expression in OS. The effect of CKS1B expression on clinicopathological and overall survival of OS patients was measured through public high-throughput datasets, and analysis of immune infiltration and single-cell RNA-seq was applied to ascertain molecular mechanism of CKS1B in OS. Results. A total of 197 OS samples and 83 nontumor samples (including tissue and cell line) were obtained from in-house IHC, microarrays, and exterior high-throughput datasets. The analysis of integrated expression status demonstrated upregulation of CKS1B in OS (SMD = 1.38, 95% CI [0.52–2.25]) and the significant power of CKS1B expression in distinguishing OS samples from nontumor samples (Area under the Curve (AUC) = 0.89, 95% CI [0.86–0.91]). Clinicopathological and prognosis analysis indicated no remarkable significance but inference of immune infiltration and single-cell RNA-seq prompted that OS patients with overexpressed CKS1B were more likely to suffer OS metastasis while MYC Protooncogene may be the upstream regulon of CKS1B in proliferating osteoblastic OS cells. Conclusions. In this study, sufficient evidence was provided for upregulation of CKS1B in OS. The advanced effect of CKS1B on OS progression indicates a foreground of CKS1B as a biomarker for OS.

scholarly journals Systematic comparison of high-throughput single-cell RNA-seq methods for immune cell profiling

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Tracy M. Yamawaki ◽  
Daniel R. Lu ◽  
Daniel C. Ellwanger ◽  
Dev Bhatt ◽  
Paolo Manzanillo ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Cell Types ◽  
Data Interpretation ◽  
Detection Sensitivity ◽  
Rna Seq ◽  
Cell Recovery

Abstract Background Elucidation of immune populations with single-cell RNA-seq has greatly benefited the field of immunology by deepening the characterization of immune heterogeneity and leading to the discovery of new subtypes. However, single-cell methods inherently suffer from limitations in the recovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropout events. This issue is often compounded by limited sample availability and limited prior knowledge of heterogeneity, which can confound data interpretation. Results Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. We prepared 21 libraries under identical conditions of a defined mixture of two human and two murine lymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluated methods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expression signatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5′ v1 and 3′ v3 methods. We demonstrate that these methods have fewer dropout events, which facilitates the identification of differentially-expressed genes and improves the concordance of single-cell profiles to immune bulk RNA-seq signatures. Conclusion Overall, our characterization of immune cell mixtures provides useful metrics, which can guide selection of a high-throughput single-cell RNA-seq method for profiling more complex immune-cell heterogeneity usually found in vivo.

High-Throughput Single-Cell RNA-Seq of Large Cells and Nuclei

2017 ◽  
Vol 37 (17) ◽  
pp. 12-13
Author(s):  
Jennifer Chew ◽  
Adam Bemis ◽  
Ronald Lebofsky ◽  
Anna Quinlan ◽  
Kelly Kaihara
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Rna Seq

scholarly journals Sensitive high-throughput single-cell RNA-seq reveals within-clonal transcript correlations in yeast populations

2019 ◽  
Vol 4 (4) ◽  
pp. 683-692 ◽  
Author(s):  
Mariona Nadal-Ribelles ◽  
Saiful Islam ◽  
Wu Wei ◽  
Pablo Latorre ◽  
Michelle Nguyen ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Rna Seq

scholarly journals rPanglaoDB: an R package to download and merge labeled single-cell RNA-seq data from the PanglaoDB database

2021 ◽  
Author(s):  
Daniel Osorio ◽  
Marieke Lydia Kuijjer ◽  
James J. Cai
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
R Package ◽  
Rna Seq ◽  
Cell Type ◽  
Sequencing Data ◽  
Single Experiment ◽  
Tissue Samples ◽  
Molecular Phenotypes ◽  
Public Datasets

Motivation: Characterizing cells with rare molecular phenotypes is one of the promises of high throughput single-cell RNA sequencing (scRNA-seq) techniques. However, collecting enough cells with the desired molecular phenotype in a single experiment is challenging, requiring several samples preprocessing steps to filter and collect the desired cells experimentally before sequencing. Data integration of multiple public single-cell experiments stands as a solution for this problem, allowing the collection of enough cells exhibiting the desired molecular signatures. By increasing the sample size of the desired cell type, this approach enables a robust cell type transcriptome characterization. Results: Here, we introduce rPanglaoDB, an R package to download and merge the uniformly processed and annotated scRNA-seq data provided by the PanglaoDB database. To show the potential of rPanglaoDB for collecting rare cell types by integrating multiple public datasets, we present a biological application collecting and characterizing a set of 157 fibrocytes. Fibrocytes are a rare monocyte-derived cell type, that exhibits both the inflammatory features of macrophages and the tissue remodeling properties of fibroblasts. This constitutes the first fibrocytes' unbiased transcriptome profile report. We compared the transcriptomic profile of the fibrocytes against the fibroblasts collected from the same tissue samples and confirm their associated relationship with healing processes in tissue damage and infection through the activation of the prostaglandin biosynthesis and regulation pathway. Availability and Implementation: rPanglaoDB is implemented as an R package available through the CRAN repositories https://CRAN.R-project.org/package=rPanglaoDB.

scholarly journals Systematic Comparison of High-Throughput Single-Cell RNAseq Methods for Immune Cell Profiling

2020 ◽  
Author(s):  
Chi-Ming Kevin Li ◽  
Tracy M Yamawaki ◽  
Daniel R Lu ◽  
Daniel C Ellwanger ◽  
Dev Bhatt ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Cell Types ◽  
Drop Out ◽  
Detection Sensitivity ◽  
Rna Seq ◽  
Cell Recovery

Abstract Background: Elucidation of immune populations with single-cell RNA-seq has greatly benefited the fieldof immunology by deepening the characterization of immune heterogeneity and leading to thediscovery of new subtypes. However, single-cell methods inherently suffer from limitations in therecovery of complete transcriptomes due to the prevalence of cellular and transcriptional dropoutevents. This issue is often compounded by limited sample availability and limited prior knowledge ofheterogeneity, which can confound data interpretation.Results: Here, we systematically benchmarked seven high-throughput single-cell RNA-seq methods. Weprepared 21 libraries under identical conditions of a defined mixture of two human and two murinelymphocyte cell lines, simulating heterogeneity across immune-cell types and cell sizes. We evaluatemethods by their cell recovery rate, library efficiency, sensitivity, and ability to recover expressionsignatures for each cell type. We observed higher mRNA detection sensitivity with the 10x Genomics 5’v1 and 3’ v3 methods. We demonstrate that these methods have fewer drop-out events whichfacilitates the identification of differentially-expressed genes and improves the concordance of singlecellprofiles to immune bulk RNA-seq signatures.Conclusion: Overall, our characterization of immune cell mixtures provides useful metrics, which canguide selection of a high-throughput single-cell RNA-seq method for profiling more complex immunecellheterogeneity usually found in vivo.

scholarly journals Power in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues

2018 ◽  
Vol 52 (1) ◽  
pp. 203-221 ◽  
Author(s):  
Kenneth D. Birnbaum
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Genetic Screening ◽  
Developmental Trajectories ◽  
Fluorescent Microscopy ◽  
The Novel ◽  
Rna Seq ◽  
Single Cell Rna Sequencing ◽  
Developmental Dynamics

The growing scale and declining cost of single-cell RNA-sequencing (RNA-seq) now permit a repetition of cell sampling that increases the power to detect rare cell states, reconstruct developmental trajectories, and measure phenotype in new terms such as cellular variance. The characterization of anatomy and developmental dynamics has not had an equivalent breakthrough since groundbreaking advances in live fluorescent microscopy. The new resolution obtained by single-cell RNA-seq is a boon to genetics because the novel description of phenotype offers the opportunity to refine gene function and dissect pleiotropy. In addition, the recent pairing of high-throughput genetic perturbation with single-cell RNA-seq has made practical a scale of genetic screening not previously possible.

scholarly journals High-Throughput Single-Cell Labeling (Hi-SCL) for RNA-Seq Using Drop-Based Microfluidics

PLoS ONE ◽  
2015 ◽  
Vol 10 (5) ◽  
pp. e0116328 ◽  
Author(s):  
Assaf Rotem ◽  
Oren Ram ◽  
Noam Shoresh ◽  
Ralph A. Sperling ◽  
Michael Schnall-Levin ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Cell Labeling ◽  
Rna Seq

scholarly journals mDrop-seq: Massively parallel single-cell RNA-seq of Saccharomyces cerevisiae and Candida albicans

2021 ◽  
Author(s):  
Ryan Dohn ◽  
Bingqing Xie ◽  
Rebecca Back ◽  
Alan Selewa ◽  
Heather Eckart ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Candida Albicans ◽  
Stress Response ◽  
Single Cell ◽  
High Throughput ◽  
Yeast Species ◽  
Model Organism ◽  
Opportunistic Pathogen ◽  
Mrna Levels ◽  
Rna Seq

Advances in high-throughput single-cell mRNA sequencing (scRNA-seq) have been limited till date by technical challenges like tough cell walls and low RNA quantity that prevented transcriptomic profiling of microbial species at throughput. We present microbial Drop-seq or mDrop-seq, a high-throughput scRNA-seq technique that is used on two yeast species, Saccharomyces cerevisiae, a popular model organism and Candida albicans, a common opportunistic pathogen. We benchmarked mDrop-seq for sensitivity and specificity and used it to profile 35,109 S. cerevisiae cells to detect variation in mRNA levels between them. As a proof of concept, we quantified expression differences in heat-shocked S. cerevisiae using mDrop-seq. We detected differential activation of stress response genes within a seemingly homogenous population of S. cerevisiae under heat-shock. We also applied mDrop-seq to C. albicans cells, a polymorphic and clinically relevant yeast species with thicker cell wall compared to S. cerevisiae. Single cell transcriptomes in 39,705 C. albicans cells was characterized using mDrop-seq under different conditions, including exposure to fluconazole, a common anti-fungal drug. We noted differential regulation in stress response and drug target pathways between C. albicans cells, changes in cell cycle patterns and marked increases in histone activity. These experiments are among the first high throughput single cell RNA-seq on different yeast species and demonstrate mDrop-seq as an affordable, easily implementable, and scalable technique that can quantify the variability in gene expression in different yeast species. We hope that mDrop-seq will lead to better understanding of genetic variation in yeasts in response to stimuli and find immediate applications in investigating drug resistance and infection outcome.

scholarly journals Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning

2016 ◽  
Author(s):  
Bo Wang ◽  
Junjie Zhu ◽  
Emma Pierson ◽  
Daniele Ramazzotti ◽  
Serafim Batzoglou
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
High Throughput ◽  
Cell Populations ◽  
Gene Expression Measurement ◽  
Data Sets ◽  
Similarity Learning ◽  
Rna Seq ◽  
High Level ◽  
Cell Data

AbstractSingle-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical to identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. Here, we propose a novel similarity-learning framework, SIMLR (single-cell interpretation via multi-kernel learning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization applications. Benchmarking against state-of-the-art methods for these applications, we used SIMLR to re-analyse seven representative single-cell data sets, including high-throughput droplet-based data sets with tens of thousands of cells. We show that SIMLR greatly improves clustering sensitivity and accuracy, as well as the visualization and interpretability of the data.

scholarly journals Mitochondrial variant enrichment from high-throughput single-cell RNA-seq resolves clonal populations

2021 ◽  
Author(s):  
Tyler E. Miller ◽  
Caleb A. Lareau ◽  
Julia A. Verga ◽  
Daniel Ssozi ◽  
Leif S. Ludwig ◽  
...  
Keyword(s):  
Single Cell ◽  
High Throughput ◽  
Immune Cell ◽  
Rna Seq ◽  
High Confidence ◽  
Mitochondrial Mutations ◽  
Clonal Hematopoiesis ◽  
Single Cell Rna Sequencing ◽  
Variant Detection ◽  
Primary Human Cells

AbstractReconstructing lineage relationships in complex tissues can reveal mechanisms underlying development and disease. Recent methods combine single-cell transcriptomics with mitochondrial DNA variant detection to establish lineage relationships in primary human cells, but are not scalable to interrogate complex tissues. To overcome this limitation, here we develop a technology for high-confidence detection of mitochondrial mutations from high-throughput single-cell RNA-sequencing. We use the new method to identify skewed immune cell expansions in primary human clonal hematopoiesis.

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