Single-cell expression profiles of melanoma

Science ◽  
2016 ◽  
Vol 352 (6282) ◽  
pp. 183-185
Author(s):  
L. M. Zahn
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Cell Expression

scholarly journals Single-Cell Expression Landscape of SARS-CoV-2 Receptor ACE2 and Host Proteases in Normal and Malignant Lung Tissues from Pulmonary Adenocarcinoma Patients

Cancers ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1250
Author(s):  
Guangchun Han ◽  
Ansam Sinjab ◽  
Kieko Hara ◽  
Warapen Treekitkarnmongkol ◽  
Patrick Brennan ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Single Cell ◽  
Cancer Patients ◽  
Expression Profiles ◽  
Lung Cells ◽  
Alveolar Type ◽  
Specific Expression ◽  
Lung Cancer Patients ◽  
Normal Tissues ◽  
Cell Expression

The novel coronavirus SARS-CoV-2 is the causative agent of the COVID-19 pandemic. Severely symptomatic COVID-19 is associated with lung inflammation, pneumonia, and respiratory failure, thereby raising concerns of elevated risk of COVID-19-associated mortality among lung cancer patients. Angiotensin-converting enzyme 2 (ACE2) is the major receptor for SARS-CoV-2 entry into lung cells. The single-cell expression landscape of ACE2 and other SARS-CoV-2-related genes in pulmonary tissues of lung cancer patients remains unknown. We sought to delineate single-cell expression profiles of ACE2 and other SARS-CoV-2-related genes in pulmonary tissues of lung adenocarcinoma (LUAD) patients. We examined the expression levels and cellular distribution of ACE2 and SARS-CoV-2-priming proteases TMPRSS2 and TMPRSS4 in 5 LUADs and 14 matched normal tissues by single-cell RNA-sequencing (scRNA-seq) analysis. scRNA-seq of 186,916 cells revealed epithelial-specific expression of ACE2, TMPRSS2, and TMPRSS4. Analysis of 70,030 LUAD- and normal-derived epithelial cells showed that ACE2 levels were highest in normal alveolar type 2 (AT2) cells and that TMPRSS2 was expressed in 65% of normal AT2 cells. Conversely, the expression of TMPRSS4 was highest and most frequently detected (75%) in lung cells with malignant features. ACE2-positive cells co-expressed genes implicated in lung pathobiology, including COPD-associated HHIP, and the scavengers CD36 and DMBT1. Notably, the viral scavenger DMBT1 was significantly positively correlated with ACE2 expression in AT2 cells. We describe normal and tumor lung epithelial populations that express SARS-CoV-2 receptor and proteases, as well as major host defense genes, thus comprising potential treatment targets for COVID-19 particularly among lung cancer patients.

scholarly journals Single-cell transcriptomic catalog of mouse cortical development

2017 ◽  
Author(s):  
Lipin Loo ◽  
Jeremy M. Simon ◽  
Eric S. McCoy ◽  
Jesse K. Niehaus ◽  
Mark J. Zylka
Keyword(s):  
Cerebral Cortex ◽  
Single Cell ◽  
Neurological Disease ◽  
Expression Profiles ◽  
Cortical Development ◽  
Brain Diseases ◽  
Mouse Cerebral Cortex ◽  
Transcriptomics Data ◽  
Disease Associated Genes ◽  
Cell Expression

We generated a single-cell transcriptomic catalog of the developing mouse cerebral cortex that includes numerous classes of neurons, progenitors, and glia, their proliferation, migration, and activation states, and their relatedness within and across timepoints. Cell expression profiles stratified neurological disease-associated genes into distinct subtypes. Complex neurodevelopmental processes can be reconstructed with single-cell transcriptomics data, permitting a deeper understanding of cortical development and the cellular origins of brain diseases.

scholarly journals Surveying Brain Tumor Heterogeneity by Single-Cell RNA Sequencing of Multi-sector Biopsies

2020 ◽  
Author(s):  
Kai Yu ◽  
Yuqiong Hu ◽  
Fan Wu ◽  
Qiufang Guo ◽  
Zenghui Qian ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Chinese Patients ◽  
Rna Seq ◽  
Receptor Interactions ◽  
Cell Components ◽  
Dynamic Cell ◽  
Spatial Level ◽  
Cell Expression ◽  
Level Analysis

ABSTRACTBrain tumors are among the most challenging human tumors for which the mechanisms driving progression and heterogeneity remain poorly understood. We combined single-cell RNA-seq with multisector biopsies to sample and analyze single-cell expression profiles of gliomas from 13 Chinese patients. After classifying individual cells, we generated a spatial and temporal landscape of glioma that revealed the patterns of invasion between the different sub-regions of gliomas. We also used single-cell inferred CNVs and pseudotime trajectories to inform on the crucial branches that dominate tumor progression. The dynamic cell components of the multi-region biopsy analysis allowed us to spatially deconvolute with unprecedented accuracy the transcriptomic features of the core and those of the periphery of glioma at single cell level. Through this rich and geographically detailed dataset, we were also able to characterize and construct the chemokine and chemokine receptor interactions that exist among different tumor and non-tumor cells. This study provides the first spatial-level analysis of the cellular states that characterize human gliomas. It also presents an initial molecular map of the crosstalks between glioma cells and the surrounding microenvironment with single cell resolution.

scholarly journals Improving gene network inference with graph wavelets and making insights about ageing-associated regulatory changes in lungs

2020 ◽  
Author(s):  
Shreya Mishra ◽  
Divyanshu Srivastava ◽  
Vibhor Kumar
Keyword(s):  
Single Cell ◽  
Gene Network ◽  
Network Inference ◽  
Expression Profiles ◽  
Gene Network Inference ◽  
Regulatory Changes ◽  
Gene Regulatory ◽  
Cell Transcriptome ◽  
Cell Expression ◽  
Single Cell Transcriptome

Abstract Using gene-regulatory-networks-based approach for single-cell expression profiles can reveal unprecedented details about the effects of external and internal factors. However, noise and batch effect in sparse single-cell expression profiles can hamper correct estimation of dependencies among genes and regulatory changes. Here, we devise a conceptually different method using graphwavelet filters for improving gene network (GWNet)-based analysis of the transcriptome. Our approach improved the performance of several gene network-inference methods. Most Importantly, GWNet improved consistency in the prediction of gene regulatory network using single-cell transcriptome even in the presence of batch effect. The consistency of predicted gene network enabled reliable estimates of changes in the influence of genes not highlighted by differential-expression analysis. Applying GWNet on the single-cell transcriptome profile of lung cells, revealed biologically relevant changes in the influence of pathways and master regulators due to ageing. Surprisingly, the regulatory influence of ageing on pneumocytes type II cells showed noticeable similarity with patterns due to the effect of novel coronavirus infection in human lung.

scholarly journals Single-cell expression profiles of ACE2 and TMPRSS2 reveals potential vertical transmission and fetus infection of SARS-CoV-2

Aging ◽  
2020 ◽  
Vol 12 (20) ◽  
pp. 19880-19897
Author(s):  
Mengdie Lü ◽  
Li Qiu ◽  
Guangshuai Jia ◽  
Rongqun Guo ◽  
Qibin Leng
Keyword(s):  
Single Cell ◽  
Vertical Transmission ◽  
Expression Profiles ◽  
Cell Expression

scholarly journals SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data

GigaScience ◽  
2020 ◽  
Vol 9 (12) ◽  
Author(s):  
Matthew D Young ◽  
Sam Behjati
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Sequencing Data ◽  
Sequencing Technologies ◽  
Single Cell Rna Sequencing ◽  
Biological Interpretation ◽  
Downstream Analysis ◽  
Cell Expression ◽  
Rna Contamination

Abstract Background Droplet-based single-cell RNA sequence analyses assume that all acquired RNAs are endogenous to cells. However, any cell-free RNAs contained within the input solution are also captured by these assays. This sequencing of cell-free RNA constitutes a background contamination that confounds the biological interpretation of single-cell transcriptomic data. Results We demonstrate that contamination from this "soup" of cell-free RNAs is ubiquitous, with experiment-specific variations in composition and magnitude. We present a method, SoupX, for quantifying the extent of the contamination and estimating "background-corrected" cell expression profiles that seamlessly integrate with existing downstream analysis tools. Applying this method to several datasets using multiple droplet sequencing technologies, we demonstrate that its application improves biological interpretation of otherwise misleading data, as well as improving quality control metrics. Conclusions We present SoupX, a tool for removing ambient RNA contamination from droplet-based single-cell RNA sequencing experiments. This tool has broad applicability, and its application can improve the biological utility of existing and future datasets.

scholarly journals GapClust is a light-weight approach distinguishing rare cells from voluminous single cell expression profiles

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Botao Fa ◽  
Ting Wei ◽  
Yuan Zhou ◽  
Luke Johnston ◽  
Xin Yuan ◽  
...  
Keyword(s):  
Single Cell ◽  
Large Scale ◽  
State Of The Art ◽  
Expression Profiles ◽  
Cell Types ◽  
Superior Performance ◽  
Light Weight ◽  
Memory Efficiency ◽  
Rare Cells ◽  
Cell Expression

AbstractSingle cell RNA sequencing (scRNA-seq) is a powerful tool in detailing the cellular landscape within complex tissues. Large-scale single cell transcriptomics provide both opportunities and challenges for identifying rare cells playing crucial roles in development and disease. Here, we develop GapClust, a light-weight algorithm to detect rare cell types from ultra-large scRNA-seq datasets with state-of-the-art speed and memory efficiency. Benchmarking on diverse experimental datasets demonstrates the superior performance of GapClust compared to other recently proposed methods. When applying our algorithm to an intestine and 68 k PBMC datasets, GapClust identifies the tuft cells and a previously unrecognised subtype of monocyte, respectively.

scholarly journals Differential analysis of binarized single-cell RNA sequencing data captures biological variation

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Gerard A Bouland ◽  
Ahmed Mahfouz ◽  
Marcel J T Reinders
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Biological Variation ◽  
Expression Data ◽  
Differential Analysis ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Cell Expression ◽  
Zero Counts

Abstract Single-cell RNA sequencing data is characterized by a large number of zero counts, yet there is growing evidence that these zeros reflect biological variation rather than technical artifacts. We propose to use binarized expression profiles to identify the effects of biological variation in single-cell RNA sequencing data. Using 16 publicly available and simulated datasets, we show that a binarized representation of single-cell expression data accurately represents biological variation and reveals the relative abundance of transcripts more robustly than counts.

scholarly journals Efficient inference of single cell expression profiles with overlapping pooling and compressed sensing

2018 ◽  
Author(s):  
Xingzhao Wen ◽  
Weiqiang Xu ◽  
Xiao Sun ◽  
Jing Tu ◽  
Zuhong Lu
Keyword(s):  
Compressed Sensing ◽  
Single Cell ◽  
Expression Profile ◽  
Expression Profiles ◽  
Single Cells ◽  
Group Testing ◽  
Cell Types ◽  
Original Data ◽  
Computational Framework ◽  
Cell Expression

SUMMARYPlate-based single cell RNA-Seq (scRNA-seq) methods can detect a comprehensive profile for gene expression but suffers from high library cost of each single cell. Although cost can be reduced significantly by massively parallel scRNA-seq techniques, these approaches lose sensitivity for gene detection. Inspired by group testing and compressed sensing, here, we designed a computational framework to close the gap between sensitivity and library cost. In our framework, single cells were overlapped assigned into plenty of pools. Expression profile of each pool was then obtained by using plate-based sequence approach. The expression profile of all single cells was recovered based on the pool expression and the overlapped pooling design. The inferred expression profile showed highly consistency with the original data in both accuracy and cell types identification. A parallel computing scheme was designed to boost speed when processing the enormous single cells, and elastic net regression was combined with compressed sensing to auto-adapt for both sparsely and densely expressed genes.

scholarly journals Improving gene-network inference with graph-wavelets and making insights about ageing associated regulatory changes in lungs

2020 ◽  
Author(s):  
Shreya Mishra ◽  
Divyanshu Srivastava ◽  
Vibhor Kumar
Keyword(s):  
Single Cell ◽  
Gene Network ◽  
Network Inference ◽  
Expression Profiles ◽  
Batch Effect ◽  
Gene Network Inference ◽  
Regulatory Changes ◽  
Cell Transcriptome ◽  
Cell Expression ◽  
Single Cell Transcriptome

AbstractUsing gene-regulatory-networks based approach for single-cell expression profiles can reveal un-precedented details about the effects of external and internal factors. However, noise and batch effect in sparse single-cell expression profiles can hamper correct estimation of dependencies among genes and regulatory changes. Here we devise a conceptually different method using graph-wavelet filters for improving gene-network (GWNet) based analysis of the transcriptome. Our approach improved the performance of several gene-network inference methods. Most Importantly, GWNet improved consistency in the prediction of generegulatory-network using single-cell transcriptome even in presence of batch effect. Consistency of predicted gene-network enabled reliable estimates of changes in the influence of genes not highlighted by differential-expression analysis. Applying GWNet on the single-cell transcriptome profile of lung cells, revealed biologically-relevant changes in the influence of pathways and master-regulators due to ageing. Surprisingly, the regulatory influence of ageing on pneumocytes type II cells showed noticeable similarity with patterns due to effect of novel coronavirus infection in Human Lung.

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