In vivo single-cell profiling of lncRNAs during Ebola virus infection

Long non-coding RNAs (lncRNAs) are pivotal mediators of systemic immune response to viral infection, yet most studies concerning their expression and functions upon immune stimulation are limited to in vitro bulk cell populations. This strongly constrains our understanding of how lncRNA expression varies at single-cell resolution, and how their cell-type specific immune regulatory roles may differ compared to protein-coding genes. Here, we perform the first in-depth characterization of lncRNA expression variation at single-cell resolution during Ebola virus (EBOV) infection in vivo. Using bulk RNA-sequencing from 119 samples and 12 tissue types, we significantly expand the current macaque lncRNA annotation. We then profile lncRNA expression variation in immune circulating single-cells during EBOV infection and find that lncRNAs' expression in fewer cells is a major differentiating factor from their protein-coding gene counterparts. Upon EBOV infection, lncRNAs present dynamic and mostly cell-type specific changes in their expression profiles especially in monocytes, the main cell type targeted by EBOV. Such changes are associated with gene regulatory modules related to important innate immune responses such as interferon response and purine metabolism. Within infected cells, several lncRNAs have positively and negatively correlated expression with viral load, suggesting that expression of some of these lncRNAs might be directly hijacked by EBOV to attack host cells. This study provides novel insights into the roles that lncRNAs play in the host response to acute viral infection and paves the way for future lncRNA studies at single-cell resolution.

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Single-cell RNA profiling links ncRNAs to spatiotemporal gene expression during C. elegans embryogenesis

Scientific Reports ◽

10.1038/s41598-020-75801-3 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Yan Sun ◽

Qichao Yu ◽

Lei Li ◽

Zhanlong Mei ◽

Biaofeng Zhou ◽

...

Keyword(s):

Single Cell ◽

Cell Types ◽

Specific Protein ◽

Embryonic Cells ◽

Cell Type ◽

Gene Expressions ◽

Protein Coding ◽

C Elegans ◽

Protein Coding Genes ◽

Cell Type Specific

Abstract Recent studies show that non-coding RNAs (ncRNAs) can regulate the expression of protein-coding genes and play important roles in mammalian development. Previous studies have revealed that during C. elegans (Caenorhabditis elegans) embryo development, numerous genes in each cell are spatiotemporally regulated, causing the cell to differentiate into distinct cell types and tissues. We ask whether ncRNAs participate in the spatiotemporal regulation of genes in different types of cells and tissues during the embryogenesis of C. elegans. Here, by using marker-free full-length high-depth single-cell RNA sequencing (scRNA-seq) technique, we sequence the whole transcriptomes from 1031 embryonic cells of C. elegans and detect 20,431 protein-coding genes, including 22 cell-type-specific protein-coding markers, and 9843 ncRNAs including 11 cell-type-specific ncRNA markers. We induce a ncRNAs-based clustering strategy as a complementary strategy to the protein-coding gene-based clustering strategy for single-cell classification. We identify 94 ncRNAs that have never been reported to regulate gene expressions, are co-expressed with 1208 protein-coding genes in cell type specific and/or embryo time specific manners. Our findings suggest that these ncRNAs could potentially influence the spatiotemporal expression of the corresponding genes during the embryogenesis of C. elegans.

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Single-cell profiling of Ebola virus infection in vivo reveals viral and host transcriptional dynamics

10.1101/2020.06.12.148957 ◽

2020 ◽

Cited By ~ 1

Author(s):

Dylan Kotliar ◽

Aaron E. Lin ◽

James Logue ◽

Travis K. Hughes ◽

Nadine M. Khoury ◽

...

Keyword(s):

Single Cell ◽

Ebola Virus ◽

Immune Cell ◽

Temporal Dynamics ◽

Cell Activity ◽

Case Fatality ◽

Protein Quantification ◽

Cell Protein ◽

Ebov Infection

SummaryEbola virus (EBOV) causes epidemics with high case fatality rates, yet remains understudied due to the challenge of experimentation in high-containment and outbreak settings. To better understand EBOV infection in vivo, we used single-cell transcriptomics and CyTOF-based single-cell protein quantification to characterize peripheral immune cell activity during EBOV infection in rhesus monkeys. We obtained 100,000 transcriptomes and 15,000,000 protein profiles, providing insight into pathogenesis. We find that immature, proliferative monocyte-lineage cells with reduced antigen presentation capacity replace conventional circulating monocyte subsets within days of infection, while lymphocytes upregulate apoptosis genes and decline in abundance. By quantifying viral RNA abundance in individual cells, we identify molecular determinants of tropism and examine temporal dynamics in viral and host gene expression. Within infected cells, we observe that EBOV down-regulates STAT1 mRNA and interferon signaling, and up-regulates putative pro-viral genes (e.g., DYNLL1 and HSPA5), nominating cellular pathways the virus manipulates for its replication. Overall, this study sheds light on EBOV tropism, replication dynamics, and elicited immune response, and provides a framework for characterizing interactions between hosts and emerging viruses in a maximum containment setting.

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Multiomics analysis reveals extensive epigenome remodeling during cortical development

10.1101/2020.08.07.241828 ◽

2020 ◽

Author(s):

Florian Noack ◽

Silvia Vangelisti ◽

Madalena Carido ◽

Faye Chong ◽

Boyan Bonev

Keyword(s):

Single Cell ◽

Transcriptional Activation ◽

Molecular Mechanisms ◽

Chromatin Accessibility ◽

Cell Type ◽

Lineage Specification ◽

3D Genome ◽

Epigenetic Remodeling ◽

Cell Type Specific

AbstractDespite huge advances in stem-cell, single-cell and epigenetic technologies, the precise molecular mechanisms that determine lineage specification remain largely unknown. Applying an integrative multiomics approach, e.g. combining single-cell RNA-seq, single-cell ATAC-seq together with cell-type-specific DNA methylation and 3D genome measurements, we systematically map the regulatory landscape in the mouse neocortex in vivo. Our analysis identifies thousands of novel enhancer-gene pairs associated with dynamic changes in chromatin accessibility and gene expression along the differentiation trajectory. Crucially, we provide evidence that epigenetic remodeling generally precedes transcriptional activation, yet true priming appears limited to a subset of lineage-determining enhancers. Notably, we reveal considerable heterogeneity in both contact strength and dynamics of the generally cell-type-specific enhancer-promoter contacts. Finally, our work suggests a so far unrecognized function of several key transcription factors which act as putative “molecular bridges” and facilitate the dynamic reorganization of the chromatin landscape accompanying lineage specification in the brain.

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ITag-RNA Allows in Vivo Cell-Type Specific Transcriptional Characterization and Tracking of Circulating Transcripts

SSRN Electronic Journal ◽

10.2139/ssrn.3229487 ◽

2018 ◽

Author(s):

J. Darr ◽

M. Lassi ◽

R. Gerlini ◽

F. Scheid ◽

M. Hrabě de Angelis ◽

...

Keyword(s):

Cell Type ◽

Cell Type Specific

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Conditional out-of-distribution generation for unpaired data using transfer VAE

Bioinformatics ◽

10.1093/bioinformatics/btaa800 ◽

2020 ◽

Vol 36 (Supplement_2) ◽

pp. i610-i617

Author(s):

Mohammad Lotfollahi ◽

Mohsen Naghipourfar ◽

Fabian J Theis ◽

F Alexander Wolf

Keyword(s):

Single Cell ◽

Generative Models ◽

Response To Treatment ◽

High Dimensional ◽

Compact Representation ◽

Hair Color ◽

Great Success ◽

Cell Type ◽

Style Transfer ◽

Cell Type Specific

Abstract Motivation While generative models have shown great success in sampling high-dimensional samples conditional on low-dimensional descriptors (stroke thickness in MNIST, hair color in CelebA, speaker identity in WaveNet), their generation out-of-distribution poses fundamental problems due to the difficulty of learning compact joint distribution across conditions. The canonical example of the conditional variational autoencoder (CVAE), for instance, does not explicitly relate conditions during training and, hence, has no explicit incentive of learning such a compact representation. Results We overcome the limitation of the CVAE by matching distributions across conditions using maximum mean discrepancy in the decoder layer that follows the bottleneck. This introduces a strong regularization both for reconstructing samples within the same condition and for transforming samples across conditions, resulting in much improved generalization. As this amount to solving a style-transfer problem, we refer to the model as transfer VAE (trVAE). Benchmarking trVAE on high-dimensional image and single-cell RNA-seq, we demonstrate higher robustness and higher accuracy than existing approaches. We also show qualitatively improved predictions by tackling previously problematic minority classes and multiple conditions in the context of cellular perturbation response to treatment and disease based on high-dimensional single-cell gene expression data. For generic tasks, we improve Pearson correlations of high-dimensional estimated means and variances with their ground truths from 0.89 to 0.97 and 0.75 to 0.87, respectively. We further demonstrate that trVAE learns cell-type-specific responses after perturbation and improves the prediction of most cell-type-specific genes by 65%. Availability and implementation The trVAE implementation is available via github.com/theislab/trvae. The results of this article can be reproduced via github.com/theislab/trvae_reproducibility.

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Differential pial and penetrating arterial responses examined by optogenetic activation of astrocytes and neurons

Journal of Cerebral Blood Flow & Metabolism ◽

10.1177/0271678x211010355 ◽

2021 ◽

pp. 0271678X2110103

Author(s):

Nao Hatakeyama ◽

Miyuki Unekawa ◽

Juri Murata ◽

Yutaka Tomita ◽

Norihiro Suzuki ◽

...

Keyword(s):

Cortical Neurons ◽

Acetylcholine Receptors ◽

Step Function ◽

Muscarinic Acetylcholine Receptors ◽

Cell Type ◽

Function Type ◽

Neuron Activation ◽

Cell Type Specific ◽

Arterial Responses

A variety of brain cells participates in neurovascular coupling by transmitting and modulating vasoactive signals. The present study aimed to probe cell type-dependent cerebrovascular (i.e., pial and penetrating arterial) responses with optogenetics in the cortex of anesthetized mice. Two lines of the transgenic mice expressing a step function type of light-gated cation channel (channelrhodopsine-2; ChR2) in either cortical neurons (muscarinic acetylcholine receptors) or astrocytes (Mlc1-positive) were used in the experiments. Photo-activation of ChR2-expressing astrocytes resulted in a widespread increase in cerebral blood flow (CBF), extending to the nonstimulated periphery. In contrast, photo-activation of ChR2-expressing neurons led to a relatively localized increase in CBF. The differences in the spatial extent of the CBF responses are potentially explained by differences in the involvement of the vascular compartments. In vivo imaging of the cerebrovascular responses revealed that ChR2-expressing astrocyte activation led to the dilation of both pial and penetrating arteries, whereas ChR2-expressing neuron activation predominantly caused dilation of the penetrating arterioles. Pharmacological studies showed that cell type-specific signaling mechanisms participate in the optogenetically induced cerebrovascular responses. In conclusion, pial and penetrating arterial vasodilation were differentially evoked by ChR2-expressing astrocytes and neurons.

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Comprehensive analysis of single cell ATAC-seq data with SnapATAC

Nature Communications ◽

10.1038/s41467-021-21583-9 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Rongxin Fang ◽

Sebastian Preissl ◽

Yang Li ◽

Xiaomeng Hou ◽

Jacinta Lucero ◽

...

Keyword(s):

Single Cell ◽

Single Cell Analysis ◽

Expression Patterns ◽

Regulatory Elements ◽

Cellular Heterogeneity ◽

Specific Gene ◽

Open Chromatin ◽

Cell Type ◽

Process Data ◽

Cell Type Specific

AbstractIdentification of the cis-regulatory elements controlling cell-type specific gene expression patterns is essential for understanding the origin of cellular diversity. Conventional assays to map regulatory elements via open chromatin analysis of primary tissues is hindered by sample heterogeneity. Single cell analysis of accessible chromatin (scATAC-seq) can overcome this limitation. However, the high-level noise of each single cell profile and the large volume of data pose unique computational challenges. Here, we introduce SnapATAC, a software package for analyzing scATAC-seq datasets. SnapATAC dissects cellular heterogeneity in an unbiased manner and map the trajectories of cellular states. Using the Nyström method, SnapATAC can process data from up to a million cells. Furthermore, SnapATAC incorporates existing tools into a comprehensive package for analyzing single cell ATAC-seq dataset. As demonstration of its utility, SnapATAC is applied to 55,592 single-nucleus ATAC-seq profiles from the mouse secondary motor cortex. The analysis reveals ~370,000 candidate regulatory elements in 31 distinct cell populations in this brain region and inferred candidate cell-type specific transcriptional regulators.

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