scholarly journals A single-cell view on alga-virus interactions reveals sequential transcriptional programs and infection states

2020 ◽  
Vol 6 (21) ◽  
pp. eaba4137 ◽  
Author(s):  
Chuan Ku ◽  
Uri Sheyn ◽  
Arnau Sebé-Pedrós ◽  
Shifra Ben-Dor ◽  
Daniella Schatz ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Single Cells ◽  
Viral Transcript ◽  
Transcriptome Regulation ◽  
Evolution Of Viruses ◽  
Protein Encoding ◽  
Giant Viruses ◽  
Virus Interactions ◽  
Globally Distributed

The discovery of giant viruses infecting eukaryotes from diverse ecosystems has revolutionized our understanding of the evolution of viruses and their impact on protist biology, yet knowledge on their replication strategies and transcriptome regulation remains limited. Here, we profile single-cell transcriptomes of the globally distributed microalga Emiliania huxleyi and its specific giant virus during infection. We detected profound heterogeneity in viral transcript levels among individual cells. Clustering single cells based on viral expression profiles enabled reconstruction of the viral transcriptional trajectory. Reordering cells along this path unfolded highly resolved viral genetic programs composed of genes with distinct promoter elements that orchestrate sequential expression. Exploring host transcriptome dynamics across the viral infection states revealed rapid and selective shutdown of protein-encoding nuclear transcripts, while the plastid and mitochondrial transcriptomes persisted into later stages. Single-cell RNA-seq opens a new avenue to unravel the life cycle of giant viruses and their unique hijacking strategies.

scholarly journals Tracking infection dynamics at single-cell level reveals highly resolved expression programs of a large virus infecting algal blooms

2019 ◽  
Author(s):  
Chuan Ku ◽  
Uri Sheyn ◽  
Arnau Sebé-Pedrós ◽  
Shifra Ben-Dor ◽  
Daniella Schatz ◽  
...  
Keyword(s):  
Single Cell ◽  
Algal Blooms ◽  
Single Cells ◽  
Viral Transcript ◽  
Dna Viruses ◽  
Infection Dynamics ◽  
Protein Encoding ◽  
Large Virus ◽  
Nucleocytoplasmic Large Dna Viruses ◽  
Late Stages

AbstractNucleocytoplasmic large DNA viruses have the largest genomes among all viruses and infect diverse eukaryotes across various ecosystems, but their expression regulation and infection strategies are not well understood. We profiled single-cell transcriptomes of the worldwide-distributed microalga Emiliania huxleyi and its specific coccolithovirus responsible for massive bloom demise. Heterogeneity in viral transcript levels detected among single cells was used to reconstruct the viral transcriptional trajectory and to map cells along a continuum of infection states. This enabled identification of novel viral genetic programs, which are composed of five kinetic classes with distinct promoter elements. The infection substantially changed the host transcriptome, causing rapid shutdown of protein-encoding nuclear transcripts at the onset of infection, while the plastid and mitochondrial transcriptomes persisted to mid- and late stages, respectively. Single-cell transcriptomics thereby opens the way for tracking host-pathogen infection dynamics at high resolution within microbial communities in the marine environment.

scholarly journals Single cell transcriptome atlas of mouse mammary epithelial cells across development

2021 ◽  
Vol 23 (1) ◽  
Author(s):  
Bhupinder Pal ◽  
Yunshun Chen ◽  
Michael J. G. Milevskiy ◽  
François Vaillant ◽  
Lexie Prokopuk ◽  
...  
Keyword(s):  
Epithelial Cells ◽  
Single Cell ◽  
Developmental Stages ◽  
Mammary Epithelial Cells ◽  
Expression Profiles ◽  
Single Cells ◽  
Chromatin Accessibility ◽  
Mammary Epithelial ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

Abstract Background Heterogeneity within the mouse mammary epithelium and potential lineage relationships have been recently explored by single-cell RNA profiling. To further understand how cellular diversity changes during mammary ontogeny, we profiled single cells from nine different developmental stages spanning late embryogenesis, early postnatal, prepuberty, adult, mid-pregnancy, late-pregnancy, and post-involution, as well as the transcriptomes of micro-dissected terminal end buds (TEBs) and subtending ducts during puberty. Methods The single cell transcriptomes of 132,599 mammary epithelial cells from 9 different developmental stages were determined on the 10x Genomics Chromium platform, and integrative analyses were performed to compare specific time points. Results The mammary rudiment at E18.5 closely aligned with the basal lineage, while prepubertal epithelial cells exhibited lineage segregation but to a less differentiated state than their adult counterparts. Comparison of micro-dissected TEBs versus ducts showed that luminal cells within TEBs harbored intermediate expression profiles. Ductal basal cells exhibited increased chromatin accessibility of luminal genes compared to their TEB counterparts suggesting that lineage-specific chromatin is established within the subtending ducts during puberty. An integrative analysis of five stages spanning the pregnancy cycle revealed distinct stage-specific profiles and the presence of cycling basal, mixed-lineage, and 'late' alveolar intermediates in pregnancy. Moreover, a number of intermediates were uncovered along the basal-luminal progenitor cell axis, suggesting a continuum of alveolar-restricted progenitor states. Conclusions This extended single cell transcriptome atlas of mouse mammary epithelial cells provides the most complete coverage for mammary epithelial cells during morphogenesis to date. Together with chromatin accessibility analysis of TEB structures, it represents a valuable framework for understanding developmental decisions within the mouse mammary gland.

scholarly journals Genomic Architecture of Cells in Tissues (GeACT): Study of Human Mid-gestation Fetus

2020 ◽  
Author(s):  
Feng Tian ◽  
Fan Zhou ◽  
Xiang Li ◽  
Wenping Ma ◽  
Honggui Wu ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Single Cell ◽  
Human Cell ◽  
Expression Profiles ◽  
Single Cells ◽  
Cell Types ◽  
List Type ◽  
Cell Type ◽  
Genomic Architecture ◽  
Gene Modules

SummaryBy circumventing cellular heterogeneity, single cell omics have now been widely utilized for cell typing in human tissues, culminating with the undertaking of human cell atlas aimed at characterizing all human cell types. However, more important are the probing of gene regulatory networks, underlying chromatin architecture and critical transcription factors for each cell type. Here we report the Genomic Architecture of Cells in Tissues (GeACT), a comprehensive genomic data base that collectively address the above needs with the goal of understanding the functional genome in action. GeACT was made possible by our novel single-cell RNA-seq (MALBAC-DT) and ATAC-seq (METATAC) methods of high detectability and precision. We exemplified GeACT by first studying representative organs in human mid-gestation fetus. In particular, correlated gene modules (CGMs) are observed and found to be cell-type-dependent. We linked gene expression profiles to the underlying chromatin states, and found the key transcription factors for representative CGMs.HighlightsGenomic Architecture of Cells in Tissues (GeACT) data for human mid-gestation fetusDetermining correlated gene modules (CGMs) in different cell types by MALBAC-DTMeasuring chromatin open regions in single cells with high detectability by METATACIntegrating transcriptomics and chromatin accessibility to reveal key TFs for a CGM

scholarly journals P02.10 FocuSCOPE: a single cell, multi-omics solution to simultaneously analyze tumor variants and microenvironment

2021 ◽  
Vol 9 (Suppl 1) ◽  
pp. A12.1-A12
Author(s):  
Y Arjmand Abbassi ◽  
N Fang ◽  
W Zhu ◽  
Y Zhou ◽  
Y Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
High Throughput ◽  
Immune Cells ◽  
Genetic Variants ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
Single Cell Sequencing

Recent advances of high-throughput single cell sequencing technologies have greatly improved our understanding of the complex biological systems. Heterogeneous samples such as tumor tissues commonly harbor cancer cell-specific genetic variants and gene expression profiles, both of which have been shown to be related to the mechanisms of disease development, progression, and responses to treatment. Furthermore, stromal and immune cells within tumor microenvironment interact with cancer cells to play important roles in tumor responses to systematic therapy such as immunotherapy or cell therapy. However, most current high-throughput single cell sequencing methods detect only gene expression levels or epigenetics events such as chromatin conformation. The information on important genetic variants including mutation or fusion is not captured. To better understand the mechanisms of tumor responses to systematic therapy, it is essential to decipher the connection between genotype and gene expression patterns of both tumor cells and cells in the tumor microenvironment. We developed FocuSCOPE, a high-throughput multi-omics sequencing solution that can detect both genetic variants and transcriptome from same single cells. FocuSCOPE has been used to successfully perform single cell analysis of both gene expression profiles and point mutations, fusion genes, or intracellular viral sequences from thousands of cells simultaneously, delivering comprehensive insights of tumor and immune cells in tumor microenvironment at single cell resolution.Disclosure InformationY. Arjmand Abbassi: None. N. Fang: None. W. Zhu: None. Y. Zhou: None. Y. Chen: None. U. Deutsch: None.

scholarly journals Insights into the dynamics between viruses and their hosts in a hot spring microbial mat

2020 ◽  
Vol 14 (10) ◽  
pp. 2527-2541 ◽  
Author(s):  
Jessica K. Jarett ◽  
Mária Džunková ◽  
Frederik Schulz ◽  
Simon Roux ◽  
David Paez-Espino ◽  
...  
Keyword(s):  
Single Cell ◽  
Hot Spring ◽  
Current Knowledge ◽  
Single Cells ◽  
Metagenomic Data ◽  
Viral Genomes ◽  
Active Viral Replication ◽  
High Layer ◽  
Host Virus Interactions ◽  
Virus Interactions

Abstract Our current knowledge of host–virus interactions in biofilms is limited to computational predictions based on laboratory experiments with a small number of cultured bacteria. However, natural biofilms are diverse and chiefly composed of uncultured bacteria and archaea with no viral infection patterns and lifestyle predictions described to date. Herein, we predict the first DNA sequence-based host–virus interactions in a natural biofilm. Using single-cell genomics and metagenomics applied to a hot spring mat of the Cone Pool in Mono County, California, we provide insights into virus–host range, lifestyle and distribution across different mat layers. Thirty-four out of 130 single cells contained at least one viral contig (26%), which, together with the metagenome-assembled genomes, resulted in detection of 59 viruses linked to 34 host species. Analysis of single-cell amplification kinetics revealed a lack of active viral replication on the single-cell level. These findings were further supported by mapping metagenomic reads from different mat layers to the obtained host–virus pairs, which indicated a low copy number of viral genomes compared to their hosts. Lastly, the metagenomic data revealed high layer specificity of viruses, suggesting limited diffusion to other mat layers. Taken together, these observations indicate that in low mobility environments with high microbial abundance, lysogeny is the predominant viral lifestyle, in line with the previously proposed “Piggyback-the-Winner” theory.

scholarly journals Self-reporting transposons enable simultaneous readout of gene expression and transcription factor binding in single cells

2019 ◽  
Author(s):  
Arnav Moudgil ◽  
Michael N. Wilkinson ◽  
Xuhua Chen ◽  
June He ◽  
Alex J. Cammack ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Single Cell ◽  
Binding Sites ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
Cell Types ◽  
Specific Cell

AbstractIn situ measurements of transcription factor (TF) binding are confounded by cellular heterogeneity and represent averaged profiles in complex tissues. Single cell RNA-seq (scRNA-seq) is capable of resolving different cell types based on gene expression profiles, but no technology exists to directly link specific cell types to the binding pattern of TFs in those cell types. Here, we present self-reporting transposons (SRTs) and their use in single cell calling cards (scCC), a novel assay for simultaneously capturing gene expression profiles and mapping TF binding sites in single cells. First, we show how the genomic locations of SRTs can be recovered from mRNA. Next, we demonstrate that SRTs deposited by the piggyBac transposase can be used to map the genome-wide localization of the TFs SP1, through a direct fusion of the two proteins, and BRD4, through its native affinity for piggyBac. We then present the scCC method, which maps SRTs from scRNA-seq libraries, thus enabling concomitant identification of cell types and TF binding sites in those same cells. As a proof-of-concept, we show recovery of cell type-specific BRD4 and SP1 binding sites from cultured cells. Finally, we map Brd4 binding sites in the mouse cortex at single cell resolution, thus establishing a new technique for studying TF biology in situ.

scholarly journals A network regularized linear model to infer spatial expression pattern for single cells

2021 ◽  
Author(s):  
Chaohao Gu ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spatial Information ◽  
Expression Profiles ◽  
Single Cells ◽  
Expression Patterns ◽  
Capture Rate ◽  
Marker Genes ◽  
Spatial Expression ◽  
Cellular Expression

Abstract Spatial gene-expression is a crucial determinant of cell fate and behavior. Recent imaging and sequencing-technology advancements have enabled scientists to develop new tools that use spatial information to measure gene-expression at close to single-cell levels. Yet, while Fluorescence In-situ Hybridization (FISH) can quantify transcript numbers at single-cell resolution, it is limited to a small number of genes. Similarly, slide-seq was designed to measure spatial-expression profiles at the single-cell level but has a relatively low gene-capture rate. And although single-cell RNA-seq enables deep cellular gene-expression profiling, it loses spatial information during sample-collection. These major limitations have stymied these methods’ broader application in the field. To overcome spatio-omics technology’s limitations and better understand spatial patterns at single-cell resolution, we designed a computation algorithm that uses glmSMA to predict cell locations by integrating scRNA-seq data with a spatial-omics reference atlas. We treated cell-mapping as a convex optimization problem by minimizing the differences between cellular-expression profiles and location-expression profiles with an L1 regularization and graph Laplacian based L2 regularization to ensure a sparse and smooth mapping. We validated the mapping results by reconstructing spatial- expression patterns of well-known marker genes in complex tissues, like the mouse cerebellum and hippocampus. We used the biological literature to verify that the reconstructed patterns can recapitulate cell-type and anatomy structures. Our work thus far shows that, together, we can use glmSMA to accurately assign single cells to their original reference-atlas locations.

scholarly journals Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

2017 ◽  
Author(s):  
Junyue Cao ◽  
Jonathan S. Packer ◽  
Vijay Ramani ◽  
Darren A. Cusanovich ◽  
Chau Huynh ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Single Cells ◽  
Transcriptional Profiling ◽  
Cost Effective ◽  
Cell Types ◽  
Cell Capture ◽  
Rna Seq ◽  
Major Step ◽  
C Elegans

AbstractConventional methods for profiling the molecular content of biological samples fail to resolve heterogeneity that is present at the level of single cells. In the past few years, single cell RNA sequencing has emerged as a powerful strategy for overcoming this challenge. However, its adoption has been limited by a paucity of methods that are at once simple to implement and cost effective to scale massively. Here, we describe a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or single nuclei without requiring the physical isolation of each cell (Single cell Combinatorial Indexing RNA-seq or sci-RNA-seq). We show that sci-RNA-seq can be used to efficiently profile the transcriptomes of tens-of-thousands of single cells per experiment, and demonstrate that we can stratify cell types from these data. Key advantages of sci-RNA-seq over contemporary alternatives such as droplet-based single cell RNA-seq include sublinear cost scaling, a reliance on widely available reagents and equipment, the ability to concurrently process many samples within a single workflow, compatibility with methanol fixation of cells, cell capture based on DNA content rather than cell size, and the flexibility to profile either cells or nuclei. As a demonstration of sci-RNA-seq, we profile the transcriptomes of 42,035 single cells from C. elegans at the L2 stage, effectively 50-fold “shotgun cellular coverage” of the somatic cell composition of this organism at this stage. We identify 27 distinct cell types, including rare cell types such as the two distal tip cells of the developing gonad, estimate consensus expression profiles and define cell-type specific and selective genes. Given that C. elegans is the only organism with a fully mapped cellular lineage, these data represent a rich resource for future methods aimed at defining cell types and states. They will advance our understanding of developmental biology, and constitute a major step towards a comprehensive, single-cell molecular atlas of a whole animal.

scholarly journals Deep soft K-means clustering with self-training for single-cell RNA sequence data

2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Liang Chen ◽  
Weinan Wang ◽  
Yuyao Zhai ◽  
Minghua Deng
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Large Scale ◽  
Sequence Data ◽  
Dimensional Space ◽  
Expression Profiles ◽  
Single Cells ◽  
Clustering Algorithms ◽  
Training Procedure ◽  
Latent Space

Abstract Single-cell RNA sequencing (scRNA-seq) allows researchers to study cell heterogeneity at the cellular level. A crucial step in analyzing scRNA-seq data is to cluster cells into subpopulations to facilitate subsequent downstream analysis. However, frequent dropout events and increasing size of scRNA-seq data make clustering such high-dimensional, sparse and massive transcriptional expression profiles challenging. Although some existing deep learning-based clustering algorithms for single cells combine dimensionality reduction with clustering, they either ignore the distance and affinity constraints between similar cells or make some additional latent space assumptions like mixture Gaussian distribution, failing to learn cluster-friendly low-dimensional space. Therefore, in this paper, we combine the deep learning technique with the use of a denoising autoencoder to characterize scRNA-seq data while propose a soft self-training K-means algorithm to cluster the cell population in the learned latent space. The self-training procedure can effectively aggregate the similar cells and pursue more cluster-friendly latent space. Our method, called ‘scziDesk’, alternately performs data compression, data reconstruction and soft clustering iteratively, and the results exhibit excellent compatibility and robustness in both simulated and real data. Moreover, our proposed method has perfect scalability in line with cell size on large-scale datasets.

scholarly journals UniPath: a uniform approach for pathway and gene-set based analysis of heterogeneity in single-cell epigenome and transcriptome profiles

2020 ◽  
Author(s):  
Smriti Chawla ◽  
Sudhagar Samydurai ◽  
Say Li Kong ◽  
Zhenxun Wang ◽  
Wai Leong TAM ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Single Cells ◽  
Gene Expression Profiles ◽  
Transcriptome Profiling ◽  
Mouse Cell ◽  
Drop Out ◽  
Open Chromatin ◽  
Gene Set Enrichment ◽  
Gene Set

Abstract Recent advances in single-cell open-chromatin and transcriptome profiling have created a challenge of exploring novel applications with a meaningful transformation of read-counts, which often have high variability in noise and drop-out among cells. Here, we introduce UniPath, for representing single-cells using pathway and gene-set enrichment scores by a transformation of their open-chromatin or gene-expression profiles. The robust statistical approach of UniPath provides high accuracy, consistency and scalability in estimating gene-set enrichment scores for every cell. Its framework provides an easy solution for handling variability in drop-out rate, which can sometimes create artefact due to systematic patterns. UniPath provides an alternative approach of dimension reduction of single-cell open-chromatin profiles. UniPath's approach of predicting temporal-order of single-cells using their pathway enrichment scores enables suppression of covariates to achieve correct order of cells. Analysis of mouse cell atlas using our approach yielded surprising, albeit biologically-meaningful co-clustering of cell-types from distant organs. By enabling an unconventional method of exploiting pathway co-occurrence to compare two groups of cells, our approach also proves to be useful in inferring context-specific regulations in cancer cells. Available at https://reggenlab.github.io/UniPathWeb/.

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