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 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.

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 Reconstructing complex lineage trees from scRNA-seq data using MERLoT

2018 ◽  
Author(s):  
R. Gonzalo Parra ◽  
Nikolaos Papadopoulos ◽  
Laura Ahumada-Arranz ◽  
Jakob El Kholtei ◽  
Noah Mottelson ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Cell Fates ◽  
Tree Structures ◽  
Temporal Gene Expression ◽  
Transcriptomics Data ◽  
User Friendly ◽  
Lineage Trees

AbstractAdvances in single-cell transcriptomics techniques are revolutionizing studies of cellular differentiation and heterogeneity. Consequently, it becomes possible to track the trajectory of thousands of genes across the cellular lineage trees that represent the temporal emergence of cell types during dynamic processes. However, reconstruction of cellular lineage trees with more than a few cell fates has proved challenging. We present MERLoT (https://github.com/soedinglab/merlot), a flexible and user-friendly tool to reconstruct complex lineage trees from single-cell transcriptomics data and further impute temporal gene expression profiles along the reconstructed tree structures. We demonstrate MERLoT’s capabilities on various real cases and hundreds of simulated datasets.

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 Reconstructing complex lineage trees from scRNA-seq data using MERLoT

2019 ◽  
Vol 47 (17) ◽  
pp. 8961-8974 ◽  
Author(s):  
R Gonzalo Parra ◽  
Nikolaos Papadopoulos ◽  
Laura Ahumada-Arranz ◽  
Jakob El Kholtei ◽  
Noah Mottelson ◽  
...  
Keyword(s):  
Single Cell ◽  
Cellular Differentiation ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Cell Fates ◽  
Temporal Gene Expression ◽  
Transcriptomics Data ◽  
User Friendly ◽  
Lineage Trees

Abstract Advances in single-cell transcriptomics techniques are revolutionizing studies of cellular differentiation and heterogeneity. It has become possible to track the trajectory of thousands of genes across the cellular lineage trees that represent the temporal emergence of cell types during dynamic processes. However, reconstruction of cellular lineage trees with more than a few cell fates has proved challenging. We present MERLoT (https://github.com/soedinglab/merlot), a flexible and user-friendly tool to reconstruct complex lineage trees from single-cell transcriptomics data. It can impute temporal gene expression profiles along the reconstructed tree. We show MERLoT’s capabilities on various real cases and hundreds of simulated datasets.

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 Besca, a single-cell transcriptomics analysis toolkit to accelerate translational research

2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Sophia Clara Mädler ◽  
Alice Julien-Laferriere ◽  
Luis Wyss ◽  
Miroslav Phan ◽  
Anthony Sonrel ◽  
...  
Keyword(s):  
Best Practices ◽  
Translational Research ◽  
Single Cell ◽  
Tumor Tissue ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Supervised Machine Learning ◽  
Rna Seq ◽  
Disease Biology ◽  
Transcriptomics Data

Abstract Single-cell RNA sequencing (scRNA-seq) revolutionized our understanding of disease biology. The promise it presents to also transform translational research requires highly standardized and robust software workflows. Here, we present the toolkit Besca, which streamlines scRNA-seq analyses and their use to deconvolute bulk RNA-seq data according to current best practices. Beyond a standard workflow covering quality control, filtering, and clustering, two complementary Besca modules, utilizing hierarchical cell signatures and supervised machine learning, automate cell annotation and provide harmonized nomenclatures. Subsequently, the gene expression profiles can be employed to estimate cell type proportions in bulk transcriptomics data. Using multiple, diverse scRNA-seq datasets, some stemming from highly heterogeneous tumor tissue, we show how Besca aids acceleration, interoperability, reusability and interpretability of scRNA-seq data analyses, meeting crucial demands in translational research and beyond.

scholarly journals Single-cell transcriptomics of the early developing mouse cerebral cortex disentangles the spatial and temporal components of neuronal fate acquisition

2020 ◽  
Author(s):  
Matthieu X. Moreau ◽  
Yoann Saillour ◽  
Andrzej W. Cwetsch ◽  
Alessandra Pierani ◽  
Frédéric Causeret
Keyword(s):  
Cerebral Cortex ◽  
Single Cell ◽  
Spatial Information ◽  
Developmental Trajectories ◽  
Neuronal Fate ◽  
Mouse Cerebral Cortex ◽  
Neuronal Types ◽  
Key Driver ◽  
Cortical Regions ◽  
Neuronal Output

AbstractIn the developing cerebral cortex, how progenitors that seemingly display limited diversity end up in producing a vast array of neurons remains a puzzling question. The prevailing model that recently emerged suggests that temporal maturation of these progenitors is a key driver in the diversification of the neuronal output. However, temporal constrains are unlikely to account for all diversity across cortical regions, especially in the ventral and lateral domains where neuronal types significantly differ from their dorsal neocortical counterparts born at the same time. In this study, we implemented single-cell RNAseq to sample the diversity of progenitors and neurons along the dorso-ventral axis of the early developing pallium. We first identified neuronal types, mapped them on the tissue and performed genetic tracing to determine their origin. By characterising progenitor diversity, we disentangled the gene expression modules underlying temporal vs spatial regulations of neuronal specification. Finally, we reconstructed the developmental trajectories followed by ventral and dorsal pallial neurons to identify gene waves specific of each lineage. Our data suggest a model by which discrete neuronal fate acquisition from a continuous gradient of progenitors results from the superimposition of spatial information and temporal maturation.

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.

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