scholarly journals scSLAM-seq reveals core features of transcription dynamics in single cells

2019 ◽  
Author(s):  
Florian Erhard ◽  
Marisa A.P. Baptista ◽  
Tobias Krammer ◽  
Thomas Hennig ◽  
Marius Lange ◽  
...  
Keyword(s):  
Single Cell ◽  
Temporal Dynamics ◽  
Single Cells ◽  
Transcriptional Response ◽  
Gene Promoter ◽  
Viral Gene Expression ◽  
Viral Gene ◽  
Stochastic Nature ◽  
Rna Labeling ◽  
Induced Changes

AbstractCurrent single-cell RNA sequencing approaches gives a snapshot of a cellular phenotype but convey no information on the temporal dynamics of transcription. Moreover, the stochastic nature of transcription at molecular level is not recovered. Here, we present single-cell SLAM-seq (scSLAM-seq), which integrates metabolic RNA labeling, biochemical nucleoside conversion and single-cell RNA-seq to directly measure total transcript levels and transcriptional activity by differentiating newly synthesized from pre-existing RNA for thousands of genes per single cell. scSLAM-seq recovers the earliest virus-induced changes in cytomegalovirus infection and reveals a so far hidden phase of viral gene expression comprising promiscuous transcription of all kinetic classes. It depicts the stochastic nature of transcription and demonstrates extensive gene-specific differences. These range from stable transcription rates to on-off dynamics which coincide with gene-/promoter-intrinsic features (Tbp-TATA-box interactions and DNA methylation). Gene but not cell-specific features thus explain the heterogeneity in transcriptomes between individual cells and the transcriptional response to perturbations.

scholarly journals Massively parallel and time-resolved RNA sequencing in single cells with scNT-Seq

2021 ◽  
Author(s):  
Qi Qiu ◽  
Peng Hu ◽  
Hao Wu
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Temporal Dynamics ◽  
Single Cells ◽  
Chemical Conversion ◽  
Droplet Microfluidics ◽  
Massively Parallel ◽  
One Pot ◽  
Time Resolved ◽  
Rna Biogenesis

Abstract Single-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal dynamics of RNA biogenesis and decay. Here we present single-cell metabolically labeled new RNA tagging sequencing (scNT-Seq), a method for massively parallel analysis of newly-transcribed and pre-existing RNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking newly-transcribed RNAs with T-to-C substitutions. The steps of the protocol are (1) metabolically labeling of cells with 4sU, (2) co-encapsulating individual cell with a barcoded oligo-dT primer coated bead in a nanoliter-scale droplet, (3) performing one-pot 4sU chemical conversion on pooled barcoded beads, and (4) reverse transcription, cDNA amplification, tagmentation, indexing PCR, and sequencing. scNT-Seq provides a broadly applicable strategy to investigate dynamic biological systems at single-cell resolution.

scholarly journals CellRank for directed single-cell fate mapping

2020 ◽  
Author(s):  
Marius Lange ◽  
Volker Bergen ◽  
Michal Klein ◽  
Manu Setty ◽  
Bernhard Reuter ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Normal Development ◽  
Biological Process ◽  
Single Cells ◽  
Intermediate Cell ◽  
Directional Information ◽  
Stochastic Nature ◽  
Single Cell Rna Sequencing

AbstractComputational trajectory inference enables the reconstruction of cell-state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for mapping the fate of single cells in diverse scenarios, including perturbations such as regeneration or disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, derived from ratios of spliced to unspliced reads. CellRank takes into account both the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in RNA velocity vectors. On data from pancreas development, we show that it automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. CellRank also predicts a novel dedifferentiation trajectory during regeneration after lung injury, which we follow up experimentally by confirming the existence of previously unknown intermediate cell states.

scholarly journals CellRank for directed single-cell fate mapping

2020 ◽  
Author(s):  
Fabian Theis ◽  
Marius Lange ◽  
Volker Bergen ◽  
Michal Klein ◽  
Manu Setty ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Normal Development ◽  
Biological Process ◽  
Single Cells ◽  
Intermediate Cell ◽  
Directional Information ◽  
Stochastic Nature ◽  
Single Cell Rna Sequencing

Abstract Computational trajectory inference enables the reconstruction of cell-state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for mapping the fate of single cells in diverse scenarios, including perturbations such as regeneration or disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, derived from ratios of spliced to unspliced reads. CellRank takes into account both the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in RNA velocity vectors. On data from pancreas development, we show that it automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. CellRank also predicts a novel dedifferentiation trajectory during regeneration after lung injury, which we follow up experimentally by confirming the existence of previously unknown intermediate cell states.

scholarly journals Comprehensive benchmarking of single cell RNA sequencing technologies for characterizing cellular perturbation

2020 ◽  
Author(s):  
Verboom Karen ◽  
Alemu T Assefa ◽  
Nurten Yigit ◽  
Jasper Anckaert ◽  
Niels Vandamme ◽  
...  
Keyword(s):  
Single Cell ◽  
Differentially Expressed Genes ◽  
Single Cells ◽  
Neuroblastoma Cells ◽  
Transcriptional Response ◽  
Differentially Expressed ◽  
Cellular Heterogeneity ◽  
Rna Seq ◽  
Library Size ◽  
Cell Subpopulations

ABSTRACTTechnological advances in transcriptome sequencing of single cells continues to provide an unprecedented view on tissue composition and cellular heterogeneity. While several studies have compared different single cell RNA-seq methods with respect to data quality and their ability to distinguish cell subpopulations, none of these studies investigated the heterogeneity of the cellular transcriptional response upon a chemical perturbation. In this study, we evaluated the transcriptional response of NGP neuroblastoma cells upon nutlin-3 treatment using the C1, ddSeq and Chromium single cell systems. These devices and library preparation methods are representative for the wide variety of platforms, ranging from microfluid chips to droplet-based systems and from full transcript sequencing to 3-prime end sequencing. In parallel, we used bulk RNA-seq for molecular characterization of the transcriptional response. Two complementary metrics to evaluate performance were applied: the first is the number and identity of differentially expressed genes as defined in consensus by two statistical models, and the second is the enrichment analysis of biological signals. Where relevant, to make the data more comparable, we downsampled sequencing library size, selected cell subpopulations based on specific RNA abundance features, or created pseudobulk samples. While the C1 detects the highest number of genes per cell and better resembles bulk RNA-seq, the Chromium identifies most differentially expressed genes, albeit still substantially fewer than bulk RNA-seq. Gene set enrichment analyses reveals that detection of a limited set of the most abundant genes in single cell RNA-seq experiments is sufficient for molecular phenotyping. Finally, single cell RNA-seq reveals a heterogeneous response of NGP neuroblastoma cells upon nutlin-3 treatment, revealing putative late-responder or resistant cells, both undetected in bulk RNA-seq experiments.

scholarly journals DeLTA 2.0: A deep learning pipeline for quantifying single-cell spatial and temporal dynamics

2021 ◽  
Author(s):  
Owen M. O'Connor ◽  
Razan N. Alnahhas ◽  
Jean-Baptiste Lugagne ◽  
Mary Dunlop
Keyword(s):  
Single Cell ◽  
Temporal Dynamics ◽  
Single Cells ◽  
Time Lapse ◽  
Error Rates ◽  
Two Dimensions ◽  
Age And Growth ◽  
Two Dimensional ◽  
Spatial Effects ◽  
Deep Convolutional Neural Networks

Improvements in microscopy software and hardware have dramatically increased the pace of image acquisition, making analysis a major bottleneck in generating quantitative, single-cell data. Although tools for segmenting and tracking bacteria within time-lapse images exist, most require human input, are specialized to the experimental set up, or lack accuracy. Here, we introduce DeLTA 2.0, a purely Python workflow that can rapidly and accurately analyze single cells on two-dimensional surfaces to quantify gene expression and cell growth. The algorithm uses deep convolutional neural networks to extract single-cell information from time-lapse images, requiring no human input after training. DeLTA 2.0 retains all the functionality of the original version, which was optimized for bacteria growing in the mother machine microfluidic device, but extends results to two-dimensional growth environments. Two-dimensional environments represent an important class of data because they are more straightforward to implement experimentally, they offer the potential for studies using co-cultures of cells, and they can be used to quantify spatial effects and multi-generational phenomena. However, segmentation and tracking are significantly more challenging tasks in two-dimensions due to exponential increases in the number of cells that must be tracked. To showcase this new functionality, we analyze mixed populations of antibiotic resistant and susceptible cells, and also track pole age and growth rate across generations. In addition to the two-dimensional capabilities, we also introduce several major improvements to the code that increase accessibility, including the ability to accept many standard microscopy file formats and arbitrary image sizes as inputs. DeLTA 2.0 is rapid, with run times of less than 10 minutes for complete movies with hundreds of cells, and is highly accurate, with error rates around 1%, making it a powerful tool for analyzing time-lapse microscopy data.

scholarly journals Single cell analysis reveals human cytomegalovirus drives latently infected cells towards an anergic-like monocyte state

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Miri Shnayder ◽  
Aharon Nachshon ◽  
Batsheva Rozman ◽  
Biana Bernshtein ◽  
Michael Lavi ◽  
...  
Keyword(s):  
Immune Response ◽  
Single Cell ◽  
Human Cytomegalovirus ◽  
Single Cell Analysis ◽  
Viral Gene Expression ◽  
Viral Gene ◽  
Viral Transcript ◽  
Hematopoietic Stem ◽  
Latently Infected ◽  
Stem And Progenitor Cells

Human cytomegalovirus (HCMV) causes a lifelong infection through establishment of latency. Although reactivation from latency can cause life-threatening disease, our molecular understanding of HCMV latency is incomplete. Here we use single cell RNA-seq analysis to characterize latency in monocytes and hematopoietic stem and progenitor cells (HSPCs). In monocytes, we identify host cell surface markers that enable enrichment of latent cells harboring higher viral transcript levels, which can reactivate more efficiently, and are characterized by reduced intrinsic immune response that is important for viral gene expression. Significantly, in latent HSPCs, viral transcripts could be detected only in monocyte progenitors and were also associated with reduced immune-response. Overall, our work indicates that regardless of the developmental stage in which HCMV infects, HCMV drives hematopoietic cells towards a weaker immune-responsive monocyte state and that this anergic-like state is crucial for the virus ability to express its transcripts and to eventually reactivate.

scholarly journals HSV-1 single cell analysis reveals anti-viral and developmental programs activation in distinct sub-populations

2019 ◽  
Author(s):  
Nir Drayman ◽  
Parthiv Patel ◽  
Luke Vistain ◽  
Savaş Tay
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Viral Gene Expression ◽  
Population Level ◽  
Cellular Reprogramming ◽  
Viral Gene ◽  
Beta Catenin ◽  
Infected Cells ◽  
Hsv 1

ABSTRACTViral infection is usually studied at the population level by averaging over millions of cells. However, infection at the single-cell level is highly heterogeneous. Here, we combine live-cell imaging and single-cell RNA sequencing to characterize viral and host transcriptional heterogeneity during HSV-1 infection of primary human cells. We find extreme variability in the level of viral gene expression among individually infected cells and show that they cluster into transcriptionally distinct sub-populations. We find that anti-viral signaling is initiated in a rare group of abortively infected cells, while highly infected cells undergo cellular reprogramming to an embryonic-like transcriptional state. This reprogramming involves the recruitment of beta-catenin to the host nucleus and viral replication compartments and is required for late viral gene expression and progeny production. These findings uncover the transcriptional differences in cells with variable infection outcomes and shed new light on the manipulation of host pathways by HSV-1.

scholarly journals Single-Cell Virus Sequencing of Influenza Infections That Trigger Innate Immunity

2019 ◽  
Vol 93 (14) ◽  
Author(s):  
Alistair B. Russell ◽  
Elizaveta Elshina ◽  
Jacob R. Kowalsky ◽  
Aartjan J. W. te Velthuis ◽  
Jesse D. Bloom
Keyword(s):  
Gene Expression ◽  
Influenza Virus ◽  
Genetic Variation ◽  
Immune Activation ◽  
Single Cells ◽  
Viral Gene Expression ◽  
Innate Immune ◽  
Viral Gene ◽  
Viral Genes ◽  
Infected Cells

ABSTRACTInfluenza virus-infected cells vary widely in their expression of viral genes and only occasionally activate innate immunity. Here, we develop a new method to assess how the genetic variation in viral populations contributes to this heterogeneity. We do this by determining the transcriptome and full-length sequences of all viral genes in single cells infected with a nominally “pure” stock of influenza virus. Most cells are infected by virions with defects, some of which increase the frequency of innate-immune activation. These immunostimulatory defects are diverse and include mutations that perturb the function of the viral polymerase protein PB1, large internal deletions in viral genes, and failure to express the virus’s interferon antagonist NS1. However, immune activation remains stochastic in cells infected by virions with these defects and occasionally is triggered even by virions that express unmutated copies of all genes. Our work shows that the diverse spectrum of defects in influenza virus populations contributes to—but does not completely explain—the heterogeneity in viral gene expression and immune activation in single infected cells.IMPORTANCEBecause influenza virus has a high mutation rate, many cells are infected by mutated virions. But so far, it has been impossible to fully characterize the sequence of the virion infecting any given cell, since conventional techniques such as flow cytometry and single-cell transcriptome sequencing (scRNA-seq) only detect if a protein or transcript is present, not its sequence. Here we develop a new approach that uses long-read PacBio sequencing to determine the sequences of virions infecting single cells. We show that viral genetic variation explains some but not all of the cell-to-cell variability in viral gene expression and innate immune induction. Overall, our study provides the first complete picture of how viral mutations affect the course of infection in single cells.

scholarly journals Defining the Transcriptional Landscape during Cytomegalovirus Latency with Single-Cell RNA Sequencing

2017 ◽  
Author(s):  
Miri Shnayder ◽  
Aharon Nachshon ◽  
Benjamin Krishna ◽  
Emma Poole ◽  
Alina Boshkov ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Latent Infection ◽  
Viral Gene Expression ◽  
Gene Expression Program ◽  
Viral Reservoir ◽  
Viral Gene ◽  
Rna Seq ◽  
Expression Program ◽  
Transcriptional Landscape

AbstractPrimary infection with human cytomegalovirus (HCMV) results in a lifelong infection due to its ability to establish latent infection, one characterized viral reservoir being hematopoietic cells. Although reactivation from latency causes serious disease in immunocompromised individuals, our molecular understanding of latency is limited. Here, we delineate viral gene expression during natural HCMV persistent infection by analyzing the massive RNA-seq atlas generated by the Genotype-Tissue Expression (GTEx) project. This systematic analysis reveals that HCMV persistencein-vivois prevalent in diverse tissues. Unexpectedly, we find only viral transcripts that resemble gene expression during stages of lytic infection with no evidence of any highly restricted latency-associated viral gene expression program. To further define the transcriptional landscape during HCMV latent infection, we also used single cell RNA-seq and a tractable experimental latency model. In contrast to current views on latency, we also find no evidence for a specific restricted latency-associated viral gene expression program. Instead, we reveal that latency-associated gene expression largely mirrors a late lytic viral program albeit at much lower levels of expression. Overall, our work has the potential to revolutionize our understanding of HCMV persistence and suggests that latency is governed mainly by quantitative changes, with a limited number of qualitative changes, in viral gene expression.

scholarly journals Multidimensional analysis of Gammaherpesvirus RNA expression reveals unexpected heterogeneity of gene expression

2018 ◽  
Author(s):  
Lauren M. Oko ◽  
Abigail K. Kimball ◽  
Rachael E. Kaspar ◽  
Ashley N. Knox ◽  
Carrie B. Coleman ◽  
...  
Keyword(s):  
Gene Expression ◽  
Virus Infection ◽  
Single Cell ◽  
Viral Gene Expression ◽  
Lytic Replication ◽  
Viral Gene ◽  
Single Cell Level ◽  
Cell Level ◽  
Non Coding Rnas ◽  
Cell Variation

ABSTRACTVirus-host interactions are frequently studied in bulk cell populations, obscuring cell-to-cell variation. Here we investigate endogenous herpesvirus gene expression at the single-cell level, combining a sensitive and robust fluorescent in situ hybridization platform with multiparameter flow cytometry, to study the expression of gammaherpesvirus non-coding RNAs (ncRNAs) during lytic replication, latent infection and reactivation in vitro. This method allowed robust detection of viral ncRNAs of murine gammaherpesvirus 68 (γHV68), Kaposi’s sarcoma associated herpesvirus and Epstein-Barr virus, revealing variable expression at the single-cell level. By quantifying the inter-relationship of viral ncRNA, viral mRNA, viral protein and host mRNA regulation during γHV68 infection, we find heterogeneous and asynchronous gene expression during latency and reactivation, with reactivation from latency identified by a distinct gene expression profile within rare cells. Further, during lytic replication with γHV68, we find many cells have limited viral gene expression, with only a fraction of cells showing robust gene expression, dynamic RNA localization, and progressive infection. Lytic viral gene expression was enhanced in primary fibroblasts and by conditions associated with enhanced viral replication, with multiple subpopulations of cells present in even highly permissive infection conditions. These findings, powered by single-cell analysis integrated with automated clustering algorithms, suggest inefficient or abortive γHV infection in many cells, and identify substantial heterogeneity in viral gene expression at the single-cell level.AUTHOR SUMMARYThe gammaherpesviruses are a group of DNA tumor viruses that establish lifelong infection. How these viruses infect and manipulate cells has frequently been studied in bulk populations of cells. While these studies have been incredibly insightful, there is limited understanding of how virus infection proceeds within a single cell. Here we present a new approach to quantify gammaherpesvirus gene expression at the single-cell level. This method allows us to detect cell-to-cell variation in the expression of virus non-coding RNAs, an important and understudied class of RNAs which do not encode for proteins. By examining multiple features of virus gene expression, this method further reveals significant variation in infection between cells across multiple stages of infection, even in conditions generally thought to be highly uniform. These studies emphasize that gammaherpesvirus infection can be surprisingly heterogeneous when viewed at the level of the individual cell. Because this approach can be broadly applied across diverse viruses, this study affords new opportunities to understand the complexity of virus infection within single cells.

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