scholarly journals Click-encoded rolling FISH for visualizing single-cell RNA polyadenylation and structures

2019 ◽  
Vol 47 (22) ◽  
pp. e145-e145 ◽  
Author(s):  
Feng Chen ◽  
Min Bai ◽  
Xiaowen Cao ◽  
Yue Zhao ◽  
Jing Xue ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Molecule ◽  
Rna Processing ◽  
Single Cells ◽  
Quantitative Information ◽  
Spatially Resolved ◽  
A Cell ◽  
Cell Type Specific ◽  
Order Structures ◽  
Rna Polyadenylation

Abstract Spatially resolved visualization of RNA processing and structures is important for better studying single-cell RNA function and landscape. However, currently available RNA imaging methods are limited to sequence analysis, and not capable of identifying RNA processing events and structures. Here, we developed click-encoded rolling FISH (ClickerFISH) for visualizing RNA polyadenylation and structures in single cells. In ClickerFISH, RNA 3′ polyadenylation tails, single-stranded and duplex regions are chemically labeled with different clickable DNA barcodes. These barcodes then initiate DNA rolling amplification, generating repetitive templates for FISH to image their subcellular distributions. Combined with single-molecule FISH, the proposed strategy can also obtain quantitative information of RNA of interest. Finally, we found that RNA poly(A) tailing and higher-order structures are spatially organized in a cell type-specific style with cell-to-cell heterogeneity. We also explored their spatiotemporal patterns during cell cycle stages, and revealed the highly dynamic organization especially in S phase. This method will help clarify the spatiotemporal architecture of RNA polyadenylation and structures.

scholarly journals Direct observation of frequency modulated transcription in single cells using light activation

eLife ◽  
2013 ◽  
Vol 2 ◽  
Author(s):  
Daniel R Larson ◽  
Christoph Fritzsch ◽  
Liang Sun ◽  
Xiuhau Meng ◽  
David S Lawrence ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Molecule ◽  
Single Cell Analysis ◽  
Cell Model ◽  
Single Cells ◽  
Single Gene ◽  
Gene Activation ◽  
Light Activation ◽  
Dynamic Synthesis ◽  
Single Cell Model

Single-cell analysis has revealed that transcription is dynamic and stochastic, but tools are lacking that can determine the mechanism operating at a single gene. Here we utilize single-molecule observations of RNA in fixed and living cells to develop a single-cell model of steroid-receptor mediated gene activation. We determine that steroids drive mRNA synthesis by frequency modulation of transcription. This digital behavior in single cells gives rise to the well-known analog dose response across the population. To test this model, we developed a light-activation technology to turn on a single steroid-responsive gene and follow dynamic synthesis of RNA from the activated locus.

scholarly journals Shaping development by stochasticity and dynamics in gene regulation

Open Biology ◽  
2017 ◽  
Vol 7 (5) ◽  
pp. 170030 ◽  
Author(s):  
Peng Dong ◽  
Zhe Liu
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Cell Fate ◽  
Single Molecule ◽  
Large Scale ◽  
Single Cells ◽  
Individual Gene ◽  
Functional Specification ◽  
Spatio Temporal

Animal development is orchestrated by spatio-temporal gene expression programmes that drive precise lineage commitment, proliferation and migration events at the single-cell level, collectively leading to large-scale morphological change and functional specification in the whole organism. Efforts over decades have uncovered two ‘seemingly contradictory’ mechanisms in gene regulation governing these intricate processes: (i) stochasticity at individual gene regulatory steps in single cells and (ii) highly coordinated gene expression dynamics in the embryo. Here we discuss how these two layers of regulation arise from the molecular and the systems level, and how they might interplay to determine cell fate and to control the complex body plan. We also review recent technological advancements that enable quantitative analysis of gene regulation dynamics at single-cell, single-molecule resolution. These approaches outline next-generation experiments to decipher general principles bridging gaps between molecular dynamics in single cells and robust gene regulations in the embryo.

scholarly journals Cell-to-cell diversification in ERBB-RAS-MAPK signal transduction that produces cell-type specific growth factor responses

2019 ◽  
Author(s):  
Hiraku Miyagi ◽  
Michio Hiroshima ◽  
Yasushi Sako
Keyword(s):  
Growth Factors ◽  
Single Cell ◽  
Cell Fate ◽  
Single Cells ◽  
Bet Hedging ◽  
Cell Type ◽  
Cell Responses ◽  
Single Cell Responses ◽  
Specific Growth Factor ◽  
A Cell

AbstractGrowth factors regulate cell fates, including their proliferation, differentiation, survival, and death, according to the cell type. Even when the response to a specific growth factor is deterministic for collective cell behavior, significant levels of fluctuation are often observed between single cells. Statistical analyses of single-cell responses provide insights into the mechanism of cell fate decisions but very little is known about the distributions of the internal states of cells responding to growth factors. Using multi-color immunofluorescent staining, we have here detected the phosphorylation of seven elements in the early response of the ERBB–RAS–MAPK system to two growth factors. Among these seven elements, five were analyzed simultaneously in distinct combinations in the same single cells. Although principle component analysis suggested cell-type and input specific phosphorylation patterns, cell-to-cell fluctuation was large. Mutual information analysis suggested that cells use multitrack (bush-like) signal transduction pathways under conditions in which clear cell fate changes have been reported. The clustering of single-cell response patterns indicated that the fate change in a cell population correlates with the large entropy of the response, suggesting a bet-hedging strategy is used in decision making. A comparison of true and randomized datasets further indicated that this large variation is not produced by simple reaction noise, but is defined by the properties of the signal-processing network.Author SummaryHow extracellular signals, such as growth factors (GFs), induce fate changes in biological cells is still not fully understood. Some GFs induce cell proliferation and others induce differentiation by stimulating a common reaction network. Although the response to each GF is reproducible for a cell population, not all single cells respond similarly. The question that arises is whether a certain GF conducts all the responding cells in the same direction during a fate change, or if it initially stimulates a variety of behaviors among single cells, from which the cells that move in the appropriate direction are later selected. Our current statistical analysis of single-cell responses suggests that the latter process, which is called a bet-hedging mechanism is plausible. The complex pathways of signal transmission seem to be responsible for this bet-hedging.

scholarly journals Single cell network analysis with a mixture of Nested Effects Models

2018 ◽  
Author(s):  
Martin Pirkl ◽  
Niko Beerenwinkel
Keyword(s):  
Single Cell ◽  
New Technologies ◽  
Single Cells ◽  
R Package ◽  
Supplementary Information ◽  
Data Sets ◽  
Cell Network ◽  
A Cell ◽  
Supplementary Material ◽  
Cell Data

AbstractMotivationNew technologies allow for the elaborate measurement of different traits of single cells. These data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous.ResultsWe developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular sub-populations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq.AvailabilityThe mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbgethz/mnem/[email protected], [email protected] informationSupplementary data are available.online.

scholarly journals Kronos scRT: a uniform framework for single-cell replication timing analysis

2021 ◽  
Author(s):  
Stefano Gnan ◽  
Joseph M. Josephides ◽  
Xia Wu ◽  
Manuela Spagnuolo ◽  
Dalila Saulebekova ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Sorting ◽  
Timing Analysis ◽  
Replication Timing ◽  
Genomic Region ◽  
Cell Replication ◽  
Specific Order ◽  
Mammalian Genomes ◽  
A Cell ◽  
Cell Type Specific

Mammalian genomes are replicated in a cell-type specific order and in coordination with transcription and chromatin organization. Although the field of replication is also entering the single-cell era, current studies require cell sorting, individual cell processing and have yielded a limited number (<100) of cells. Here, we have developed Kronos scRT (https://github.com/CL-CHEN-Lab/Kronos scRT), a software for single-cell Replication Timing (scRT) analysis. Kronos scRT does not require a specific platform nor cell sorting, allowing the investigation of large datasets obtained from asynchronous cells. Analysis of published available data and droplet-based scWGS data generated in the current study, allows exploitation of scRT data from thousands of cells for different mouse and human cell lines. Our results demonstrate that, although most cells replicate within a close timing range for a given genomic region, replication can also occur stochastically throughout S phase. Altogether, Kronos scRT allows investigating the RT program at a single-cell resolution for both homogeneous and heterogeneous cell populations in a fast and comprehensive manner.

scholarly journals Chronic opioid treatment arrests neurodevelopment and alters synaptic activity in human midbrain organoids

2021 ◽  
Author(s):  
Hye Sung Kim ◽  
Yang Xiao ◽  
Xuejing Chen ◽  
Siyu He ◽  
Jongwon Im ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Cell Lineage ◽  
Induced Pluripotent Stem Cell ◽  
Synaptic Activity ◽  
Gene Expressions ◽  
Opioid Treatment ◽  
Cell Type Specific ◽  
Induced Pluripotent ◽  
The Impact

SummaryThe impact of long-term opioid exposure on the embryonic brain is crucial to healthcare due to the surging number of pregnant mothers with an opioid dependency. Current studies on the neuronal effects are limited due to human brain inaccessibility and cross-species differences among animal models. Here, we report a model to assess cell-type specific responses to acute and chronic fentanyl treatment, as well as fentanyl withdrawal, using human induced pluripotent stem cell (hiPSC)-derived midbrain organoids. Single cell mRNA sequencing (25,510 single cells in total) results suggest that chronic fentanyl treatment arrests neuronal subtype specification during early midbrain development and alters the pathways associated with synaptic activities and neuron projection. Acute fentanyl treatment, however, increases dopamine release but does not induce significant changes in gene expressions of cell lineage development. To date, our study is the first unbiased examination of midbrain transcriptomics with synthetic opioid treatment at the single cell level.

scholarly journals ReadZS detects developmentally regulated RNA processing programs in single cell RNA-seq and defines subpopulations independent of gene expression

2021 ◽  
Author(s):  
Elisabeth Meyer ◽  
Roozbeh Dehghannasiri ◽  
Kaitlin Chaung ◽  
Julia Salzman
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Processing ◽  
Single Cells ◽  
Alternative Polyadenylation ◽  
Cell Type ◽  
Computationally Efficient ◽  
Developmentally Regulated ◽  
Eukaryotic Gene ◽  
Human Spermatogenesis

Post-transcriptional regulation of RNA processing (RNAP), including splicing and alternative polyadenylation (APA), controls eukaryotic gene function. Conservative estimates based on bulk tissue studies conclude that at least 50% of mammalian genes undergo APA. Single-cell RNA sequencing (scRNA-seq) could enable a near complete estimate of the extent, function, and regulation of these and other forms of RNA processing. Yet, statistical methods to detect regulated RNAP are limited in their detection power because they suffer from reliance on (a) incomplete annotations of 3' untranslated regions (3' UTRs), (b) peak calling heuristics, (c) analysis based on measurements collapsed over all cells in a cell type (pseudobulking), or (d) APA-specific detection. Here, we introduce ReadZS, a computationally-efficient, and annotation-free statistical approach to identify regulated RNAP, including but not limited to APA, in single cells. ReadZS rediscovers and substantially extends the scope of known cell type-specific RNAP in the human lung and during human spermatogenesis. The unique single-cell resolution and statistical properties of ReadZS enable discovery of new evolutionarily conserved, developmentally regulated RNAP and subpopulations of lung-resident macrophages, homogenous by gene expression alone.

Pulses and delays, anticipation and memory: seeing bacterial stress responses from a single-cell perspective

2020 ◽  
Vol 44 (5) ◽  
pp. 565-571
Author(s):  
Valentine Lagage ◽  
Stephan Uphoff
Keyword(s):  
Stress Response ◽  
Single Cell ◽  
Single Molecule ◽  
Stress Responses ◽  
Phenotypic Diversity ◽  
Single Cells ◽  
Super Resolution ◽  
Population Level ◽  
Protective Mechanisms ◽  
Single Cell Imaging

ABSTRACT Stress responses are crucial for bacteria to survive harmful conditions that they encounter in the environment. Although gene regulatory mechanisms underlying stress responses in bacteria have been thoroughly characterised for decades, recent advances in imaging technologies helped to uncover previously hidden dynamics and heterogeneity that become visible at the single-cell level. Despite the diversity of stress response mechanisms, certain dynamic regulatory features are frequently seen in single cells, such as pulses, delays, stress anticipation and memory effects. Often, these dynamics are highly variable across cells. While any individual cell may not achieve an optimal stress response, phenotypic diversity can provide a benefit at the population level. In this review, we highlight microscopy studies that offer novel insights into how bacteria sense stress, regulate protective mechanisms, cope with response delays and prepare for future environmental challenges. These studies showcase developments in the single-cell imaging toolbox including gene expression reporters, FRET, super-resolution microscopy and single-molecule tracking, as well as microfluidic techniques to manipulate cells and create defined stress conditions.

SIFTER: Electrophoretic complex fractionation protocol

2021 ◽  
Author(s):  
Julea Vlassakis ◽  
Louise L Hansen ◽  
Amy E Herr
Keyword(s):  
Single Cell ◽  
Protein Interaction ◽  
Gel Electrophoresis ◽  
Protein Complexes ◽  
Single Cells ◽  
Polyacrylamide Gel Electrophoresis ◽  
Simultaneous Detection ◽  
Single Cell Protein ◽  
Cell Protein ◽  
A Cell

Abstract We introduce micro-arrayed, differential detergent fractionation for the simultaneous detection of protein complexes in 100s of individual cells with SIFTER (Single-cell protein Interaction Fractionation Through Electrophoresis and immunoassay Readout). Size-based fractionation of protein complexes is accomplished with five assay steps. First, a cell suspension generated by trypsinization is introduced onto a microwell array, and single cells are settled into the microwells by gravity. Cells are lysed in F-actin stabilization buffer that is delivered by a hydrogel lid. Second, the protein complexes are fractionated from the smaller monomers by polyacrylamide gel electrophoresis. Monomers are electrophoresed into the gel and are immobilized using a UV-induced covalent reaction to benzophenone. Third, a protein-complex depolymerization buffer is introduced by another hydrogel lid. Fourth, the recently depolymerized complexes are electrophoresed into a region of the gel separate from the immobilized monomers, where the complex fraction are in turn immobilized. Fifth, in-gel immunoprobing detects the immobilized populations of monomer and depolymerized complexes. These general steps are built on previously published protocols for bulk actin studies, single-cell western blotting, and bidirectional separations1-4.

scholarly journals XYZeq: Spatially resolved single-cell RNA sequencing reveals expression heterogeneity in the tumor microenvironment

2021 ◽  
Vol 7 (17) ◽  
pp. eabg4755
Author(s):  
Youjin Lee ◽  
Derek Bogdanoff ◽  
Yutong Wang ◽  
George C. Hartoularos ◽  
Jonathan M. Woo ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Spatial Organization ◽  
Cell Types ◽  
Mouse Tumor ◽  
Spatially Resolved ◽  
Distinct Cell ◽  
Single Cell Rna Sequencing ◽  
A Cell ◽  
Anatomical Space

Single-cell RNA sequencing (scRNA-seq) of tissues has revealed remarkable heterogeneity of cell types and states but does not provide information on the spatial organization of cells. To better understand how individual cells function within an anatomical space, we developed XYZeq, a workflow that encodes spatial metadata into scRNA-seq libraries. We used XYZeq to profile mouse tumor models to capture spatially barcoded transcriptomes from tens of thousands of cells. Analyses of these data revealed the spatial distribution of distinct cell types and a cell migration-associated transcriptomic program in tumor-associated mesenchymal stem cells (MSCs). Furthermore, we identify localized expression of tumor suppressor genes by MSCs that vary with proximity to the tumor core. We demonstrate that XYZeq can be used to map the transcriptome and spatial localization of individual cells in situ to reveal how cell composition and cell states can be affected by location within complex pathological tissue.

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