scholarly journals Cell explosions: single cell death triggers an avoidance response in local populations of the ecologically prominent phytoplankton genus Micromonas

2019 ◽  
Author(s):  
Richard Henshaw ◽  
Jonathan Roberts ◽  
Marco Polin
Keyword(s):  
Cell Death ◽  
Avoidance Response ◽  
Single Cell ◽  
Single Cells ◽  
Local Environment ◽  
Warning Signal ◽  
Predator Prey ◽  
A Cell ◽  
Observed Behaviour ◽  
Micromonas Pusilla

The global phytoplankton community, comprised of aquatic photosynthetic organisms, is acknowledged for being responsible for half of the global oxygen production Prominent among these is the pico-eukaryote Micromonas commoda (formally Micromonas pusilla of the genus Micromonas), which can be found in marine and coastal environments across the globe. Cell death of phytoplankton has been identified as contributing to the largest carbon transfers on the planet moving 109 tonnes of carbon in the oceans every day. During a cell death organic matter is released into the local environment which can act as both a food source and a warning signal for nearby organisms. Here we present a novel motility response to single cell death in populations of Micromonas sp., where the death of a single cell releases a chemical patch triggers surrounding cells to escape the immediate affected area. These so-called “burst events” are then modelled and compared with a spherically symmetric diffusing patch which is found to faithfully reproduce the observed behaviour. Finally, laser ablation of single cells reproduces the observed avoidance response, confirming that Micromonas sp. has evolved a specific motility response in order to escape harmful environments for example nearby predator-prey interactions or virus lysis induced cell death.

scholarly journals Cell wound repair in Drosophila occurs through three distinct phases of membrane and cytoskeletal remodeling

2011 ◽  
Vol 193 (3) ◽  
pp. 455-464 ◽  
Author(s):  
Maria Teresa Abreu-Blanco ◽  
Jeffrey M. Verboon ◽  
Susan M. Parkhurst
Keyword(s):  
Cell Death ◽  
Plasma Membrane ◽  
Single Cell ◽  
Wound Repair ◽  
Single Cells ◽  
Repair Process ◽  
Wound Edge ◽  
Cytoskeletal Remodeling ◽  
Tissue Integrity ◽  
E Cadherin

When single cells or tissues are injured, the wound must be repaired quickly in order to prevent cell death, loss of tissue integrity, and invasion by microorganisms. We describe Drosophila as a genetically tractable model to dissect the mechanisms of single-cell wound repair. By analyzing the expression and the effects of perturbations of actin, myosin, microtubules, E-cadherin, and the plasma membrane, we define three distinct phases in the repair process—expansion, contraction, and closure—and identify specific components required during each phase. Specifically, plasma membrane mobilization and assembly of a contractile actomyosin ring are required for this process. In addition, E-cadherin accumulates at the wound edge, and wound expansion is excessive in E-cadherin mutants, suggesting a role for E-cadherin in anchoring the actomyosin ring to the plasma membrane. Our results show that single-cell wound repair requires specific spatial and temporal cytoskeleton responses with distinct components and mechanisms required at different stages of the process.

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.

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 Multiplexed biochemical imaging reveals caspase activation patterns underlying single cell fate

2018 ◽  
Author(s):  
Maximilian W. Fries ◽  
Kalina T. Haas ◽  
Suzan Ber ◽  
John Saganty ◽  
Emma K. Richardson ◽  
...  
Keyword(s):  
Cell Death ◽  
Single Cell ◽  
Cell Fate ◽  
Genetic Heterogeneity ◽  
Cellular Response ◽  
Single Cells ◽  
Caspase Activation ◽  
Cell Fate Decisions ◽  
Network Activation ◽  
Biochemical Imaging

The biochemical activities underlying cell-fate decisions vary profoundly even in genetically identical cells. But such non-genetic heterogeneity remains refractory to current imaging methods, because their capacity to monitor multiple biochemical activities in single living cells over time remains limited1. Here, we deploy a family of newly designed GFP-like sensors (NyxBits) with fast photon-counting electronics and bespoke analytics (NyxSense) in multiplexed biochemical imaging, to define a network determining the fate of single cells exposed to the DNA-damaging drug cisplatin. By simultaneously imaging a tri-nodal network comprising the cell-death proteases Caspase-2, -3 and -92, we reveal unrecognized single-cell heterogeneities in the dynamics and amplitude of caspase activation that signify survival versus cell death via necrosis or apoptosis. Non-genetic heterogeneity in the pattern of caspase activation recapitulates traits of therapy resistance previously ascribed solely to genetic causes3,4. Chemical inhibitors that alter these patterns can modulate in a predictable manner the phenotypic landscape of the cellular response to cisplatin. Thus, multiplexed biochemical imaging reveals cellular populations and biochemical states, invisible to other methods, underlying therapeutic responses to an anticancer drug. Our work develops widely applicable tools to monitor the dynamic activation of biochemical networks at single-cell resolution. It highlights the necessity to resolve patterns of network activation in single cells, rather than the average state of individual nodes, to define, and potentially control, mechanisms underlying cellular decisions in health and disease.

scholarly journals LTMG: A novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data

2018 ◽  
Author(s):  
Changlin Wan ◽  
Wennan Chang ◽  
Yu Zhang ◽  
Fenil Shah ◽  
Xiaoyu Lu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Single Cells ◽  
Cell Types ◽  
R Package ◽  
Data Sets ◽  
Rna Seq ◽  
Cell Functions ◽  
Transcriptional Regulatory ◽  
A Cell

ABSTRACTA key challenge in modeling single-cell RNA-seq (scRNA-seq) data is to capture the diverse gene expression states regulated by different transcriptional regulatory inputs across single cells, which is further complicated by a large number of observed zero and low expressions. We developed a left truncated mixture Gaussian (LTMG) model that stems from the kinetic relationships between the transcriptional regulatory inputs and metabolism of mRNA and gene expression abundance in a cell. LTMG infers the expression multi-modalities across single cell entities, representing a gene’s diverse expression states; meanwhile the dropouts and low expressions are treated as left truncated, specifically representing an expression state that is under suppression. We demonstrated that LTMG has significantly better goodness of fitting on an extensive number of single-cell data sets, comparing to three other state of the art models. In addition, our systems kinetic approach of handling the low and zero expressions and correctness of the identified multimodality are validated on several independent experimental data sets. Application on data of complex tissues demonstrated the capability of LTMG in extracting varied expression states specific to cell types or cell functions. Based on LTMG, a differential gene expression test and a co-regulation module identification method, namely LTMG-DGE and LTMG-GCR, are further developed. We experimentally validated that LTMG-DGE is equipped with higher sensitivity and specificity in detecting differentially expressed genes, compared with other five popular methods, and that LTMG-GCR is capable to retrieve the gene co-regulation modules corresponding to perturbed transcriptional regulations. A user-friendly R package with all the analysis power is available at https://github.com/zy26/LTMGSCA.

scholarly journals Sci-Hi-C: a single-cell Hi-C method for mapping 3D genome organization in large number of single cells

2019 ◽  
Author(s):  
Vijay Ramani ◽  
Xinxian Deng ◽  
Ruolan Qiu ◽  
Choli Lee ◽  
Christine M Disteche ◽  
...  
Keyword(s):  
Single Cell ◽  
Genome Organization ◽  
Single Cells ◽  
Three Dimensional ◽  
Population Based ◽  
Order Structure ◽  
Chromosome Conformation ◽  
3D Genome ◽  
A Cell ◽  
Cell To Cell Variability

AbstractThe highly dynamic nature of chromosome conformation and three-dimensional (3D) genome organization leads to cell-to-cell variability in chromatin interactions within a cell population, even if the cells of the population appear to be functionally homogeneous. Hence, although Hi-C is a powerful tool for mapping 3D genome organization, this heterogeneity of chromosome higher order structure among individual cells limits the interpretive power of population based bulk Hi-C assays. Moreover, single-cell studies have the potential to enable the identification and characterization of rare cell populations or cell subtypes in a heterogeneous population. However, it may require surveying relatively large numbers of single cells to achieve statistically meaningful observations in single-cell studies. By applying combinatorial cellular indexing to chromosome conformation capture, we developed single-cell combinatorial indexed Hi-C (sci-Hi-C), a high throughput method that enables mapping chromatin interactomes in large number of single cells. We demonstrated the use of sci-Hi-C data to separate cells by karytoypic and cell-cycle state differences and to identify cellular variability in mammalian chromosomal conformation. Here, we provide a detailed description of method design and step-by-step working protocols for sci-Hi-C.

Abstract 388: High-throughput Single Cell Tracking of Mitochondrial Function in Cardiomyocytes

2015 ◽  
Vol 117 (suppl_1) ◽  
Author(s):  
Giovanni Fajardo ◽  
Kristi Bezold ◽  
Tobias Meyer ◽  
Daria Mochly-Rosen ◽  
Daniel Bernstein
Keyword(s):  
Oxidative Stress ◽  
Hydrogen Peroxide ◽  
Cell Death ◽  
Membrane Potential ◽  
Intracellular Calcium ◽  
Single Cell ◽  
Mitochondrial Function ◽  
Single Cells ◽  
Cell Populations ◽  
Over Time

Mitochondria play a significant role in the regulation of multiple functions in the heart, ranging from metabolism to cell death. Most mitochondrial assays require either isolating the organelle, thus disrupting the intracellular signaling or using intact cells with average measurements for the entire population neglecting the ability to distinguish cell heterogeneity. Here we describe a novel method for tracking single cell mitochondrial function in cell populations over time. Adult mouse myocytes were exposed to the mitochondrial uncoupler FCCP and hydrogen peroxide to induce changes in membrane potential and oxidative stress, respectively. TMRM and mitosox fluorescence were used to quantify mitochondrial membrane potential and ROS production, Fluo-4 fluorescence was used to assess intracellular calcium. Tracking of single cells was performed using Matlab and Imaris software. After FCCP exposure TMRM signal intensity decreased before there was a significant change in myocyte length that led to hypercontracture and cell death within 10 minutes. To better understand the dynamics of mitochondrial function and its relationship with cell death a dose response curve was established for hydrogen peroxide at 100, 500 and 1000 μM. The higher doses of hydrogen peroxide induced hypercontracture faster than lower doses. For further studies 100 μM was used to assess how homogenous the response to hydrogen peroxide was in cell populations. We found 3 distinct populations of cells responding at different times, a population of cells hypercontracted between 7-10 min, another between 10-15 min and a third population only after 15 min. Changes in membrane potential, oxidative stress and intracellular calcium were simultaneously assessed for every single cell during these time points. In addition, given the organized structure of the mitochondria in the myocyte we have adapted our technique to track individual mitochondria to study heterogeneous responses at the single mitochondrion level. This method provides a unique tool to simultaneously assess multiple parameters of mitochondrial function in single cells over time. In doing so, we have unmasked a complex heterogeneity of single cell behavior that is lost in methods than only average cell populations.

scholarly journals Why yeast cells can undergo apoptosis: death in times of peace, love, and war

2006 ◽  
Vol 175 (4) ◽  
pp. 521-525 ◽  
Author(s):  
Sabrina Büttner ◽  
Tobias Eisenberg ◽  
Eva Herker ◽  
Didac Carmona-Gutierrez ◽  
Guido Kroemer ◽  
...  
Keyword(s):  
Cell Death ◽  
Single Cells ◽  
Yeast Cells ◽  
Unicellular Organism ◽  
Killer Toxins ◽  
Cell Biol ◽  
Cell Death Program ◽  
A Cell ◽  
Multicellular Organisms ◽  
Cell Suicide

The purpose of apoptosis in multicellular organisms is obvious: single cells die for the benefit of the whole organism (for example, during tissue development or embryogenesis). Although apoptosis has also been shown in various microorganisms, the reason for this cell death program has remained unexplained. Recently published studies have now described yeast apoptosis during aging, mating, or exposure to killer toxins (Fabrizio, P., L. Battistella, R. Vardavas, C. Gattazzo, L.L. Liou, A. Diaspro, J.W. Dossen, E.B. Gralla, and V.D. Longo. 2004. J. Cell Biol. 166:1055–1067; Herker, E., H. Jungwirth, K.A. Lehmann, C. Maldener, K.U. Frohlich, S. Wissing, S. Buttner, M. Fehr, S. Sigrist, and F. Madeo. 2004. J. Cell Biol. 164:501–507, underscoring the evolutionary benefit of a cell suicide program in yeast and, thus, giving a unicellular organism causes to die for.

scholarly journals Cyclic Microchip Assay for Measurement of Hundreds of Functional Proteins in Single Neurons

2021 ◽  
Author(s):  
Liwei Yang ◽  
Avery Ball ◽  
Jesse Liu ◽  
Tanya Jain ◽  
Yueming Li ◽  
...  
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Protein Detection ◽  
Cellular Processes ◽  
Cell Functions ◽  
Cellular Life ◽  
5Xfad Mouse ◽  
A Cell ◽  
Mouse Cells ◽  
Neuron Function

Proteins are responsible for nearly all cell functions throughout cellular life. To date, the molecular functions of hundreds of proteins have been studied as they are critical to cellular processes. Those proteins are varied dramatically at different statuses and differential stages of the cells even in the same tissue. The existing single-cell tools can only analyze dozens of proteins and thus have not been able to fully characterize a cell yet. Herein, we present a single-cell cyclic multiplex in situ tagging (CycMIST) technology that affords the comprehensive functional proteome profiling of single cells. It permits multiple, separate rounds of multiplex assays of the same single cells on a microchip where each round detects 40-50 proteins. A decoding process is followed to assign protein identities and quantify protein detection signals. We demonstrate the technology on a neuron cell line by detecting 182 proteins that includes surface makers, neuron function proteins, neurodegeneration markers, signaling pathway proteins and transcription factors. Further study on 5XFAD mouse, an Alzheimer s Disease (AD) model, cells validate the utility of our technology which reveals the deep heterogeneity of brain cells. Through comparison with control mouse cells, the differentially expressed proteins in the AD mouse model have been detected. The single-cell CycMIST technology can potentially analyze the entire functional proteome spectrum, and thus it may offer new insights into cell machinery and advance many fields including systems biology, drug discovery, molecular diagnostics, and clinical studies.

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