scholarly journals Extensive Regulation of Metabolism and Growth during the Cell Division Cycle

2014 ◽  
Author(s):  
Nikolai Slavov ◽  
David Botstein ◽  
Amy Caudy
Keyword(s):  
Gene Expression ◽  
Oxygen Consumption ◽  
Cell Division ◽  
Single Cell ◽  
Single Cells ◽  
Emergent Behavior ◽  
Yeast Cells ◽  
Physiological Data ◽  
Synchronized Cultures ◽  
Metabolic Cycle

Yeast cells grown in culture can spontaneously synchronize their respiration, metabolism, gene expression and cell division. Such metabolic oscillations in synchronized cultures reflect single-cell oscillations, but the relationship between the oscillations in single cells and synchronized cultures is poorly understood. To understand this relationship and the coordination between metabolism and cell division, we collected and analyzed DNA-content, gene-expression and physiological data, at hundreds of time-points, from cultures metabolically-synchronized at different growth rates, carbon sources and biomass densities. The data enabled us to extend and generalize our mechanistic model, based on ensemble average over phases (EAP), connecting the population-average gene-expression of asynchronous cultures to the gene-expression dynamics in the single-cells comprising the cultures. The extended model explains the carbon-source specific growth-rate responses of hundreds of genes. Our physiological data demonstrate that the frequency of metabolic cycling in synchronized cultures increases with the biomass density, suggesting that this cycling is an emergent behavior, resulting from the entraining of the single-cell metabolic cycle by a quorum-sensing mechanism, and thus underscoring the difference between metabolic cycling in single cells and in synchronized cultures. Measurements of constant levels of residual glucose across metabolically synchronized cultures indicate that storage carbohydrates are required to fuel not only the G1/S transition of the division cycle but also the metabolic cycle. Despite the large variation in profiled conditions and in the scale of their dynamics, most genes preserve invariant dynamics of coordination with each other and with the rate of oxygen consumption. Similarly, the G1/S transition always occurs at the beginning, middle or end of the high oxygen consumption phases, analogous to observations in human and drosophila cells. These results highlight evolutionary conserved coordination among metabolism, cell growth and division.

scholarly journals A conserved cell growth cycle can account for the environmental stress responses of divergent eukaryotes

2012 ◽  
Vol 23 (10) ◽  
pp. 1986-1997 ◽  
Author(s):  
Nikolai Slavov ◽  
Edoardo M. Airoldi ◽  
Alexander van Oudenaarden ◽  
David Botstein
Keyword(s):  
Gene Expression ◽  
Growth Rate ◽  
Oxygen Consumption ◽  
Heat Stress ◽  
Cell Division ◽  
Environmental Stress ◽  
Fission Yeast ◽  
Budding Yeast ◽  
Cell Division Cycle ◽  
Metabolic Cycle

The respiratory metabolic cycle in budding yeast (Saccharomyces cerevisiae) consists of two phases that are most simply defined phenomenologically: low oxygen consumption (LOC) and high oxygen consumption (HOC). Each phase is associated with the periodic expression of thousands of genes, producing oscillating patterns of gene expression found in synchronized cultures and in single cells of slowly growing unsynchronized cultures. Systematic variation in the durations of the HOC and LOC phases can account quantitatively for well-studied transcriptional responses to growth rate differences. Here we show that a similar mechanism—transitions from the HOC phase to the LOC phase—can account for much of the common environmental stress response (ESR) and for the cross-protection by a preliminary heat stress (or slow growth rate) to subsequent lethal heat stress. Similar to the budding yeast metabolic cycle, we suggest that a metabolic cycle, coupled in a similar way to the ESR, in the distantly related fission yeast, Schizosaccharomyces pombe, and in humans can explain gene expression and respiratory patterns observed in these eukaryotes. Although metabolic cycling is associated with the G0/G1 phase of the cell division cycle of slowly growing budding yeast, transcriptional cycling was detected in the G2 phase of the division cycle in fission yeast, consistent with the idea that respiratory metabolic cycling occurs during the phases of the cell division cycle associated with mass accumulation in these divergent eukaryotes.

scholarly journals Single-cell phenotyping and RNA sequencing reveal novel patterns of gene expression heterogeneity and regulation during growth and stress adaptation in a unicellular eukaryote

2018 ◽  
Author(s):  
Malika Saint ◽  
François Bertaux ◽  
Wenhao Tang ◽  
Xi-Ming Sun ◽  
Laurence Game ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Cell Size ◽  
Single Cells ◽  
Yeast Cells ◽  
Stress Adaptation ◽  
Unicellular Eukaryote ◽  
Gene Expression Heterogeneity ◽  
Cell To Cell Variability

Cell-to-cell variability is central for microbial populations and contributes to cell function, stress adaptation and drug resistance. Gene-expression heterogeneity underpins this variability, but has been challenging to study genome-wide. Here, we report an integrated approach for imaging of individual fission yeast cells followed by single-cell RNA sequencing (scRNA-seq) and novel Bayesian normalisation. We analyse >2000 single cells and >700 matching RNA controls in various environmental conditions and identify sets of highly variable genes. Combining scRNA-seq with cell-size measurements provides unique insights into genes regulated during cell growth and division in single cells, including genes whose expression does not scale with cell size. We further analyse the heterogeneity and dynamics of gene expression during adaptive and acute responses to changing environments. Entry into stationary phase is preceded by a gradual, synchronised adaptation in gene regulation, followed by highly variable gene expression when growth decreases. Conversely, a sudden and acute heat-shock leads to a stronger and coordinated response and adaptation across cells. This analysis reveals that the extent and dynamics of global gene-expression heterogeneity is regulated in response to different physiological conditions within populations of a unicellular eukaryote. In summary, this works illustrates the potential of combined transcriptomics and imaging analysis in single cells to provide comprehensive and unbiased mechanistic understanding of cell-to-cell variability in microbial communities.

scholarly journals Using single-cell transcriptomics to understand functional states and interactions in microbial eukaryotes

2019 ◽  
Vol 374 (1786) ◽  
pp. 20190098 ◽  
Author(s):  
Chuan Ku ◽  
Arnau Sebé-Pedrós
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Interspecific Interactions ◽  
Single Cells ◽  
Life Cycles ◽  
Modern Biology ◽  
Microbial Eukaryotes ◽  
Eukaryotic Microorganisms

Understanding the diversity and evolution of eukaryotic microorganisms remains one of the major challenges of modern biology. In recent years, we have advanced in the discovery and phylogenetic placement of new eukaryotic species and lineages, which in turn completely transformed our view on the eukaryotic tree of life. But we remain ignorant of the life cycles, physiology and cellular states of most of these microbial eukaryotes, as well as of their interactions with other organisms. Here, we discuss how high-throughput genome-wide gene expression analysis of eukaryotic single cells can shed light on protist biology. First, we review different single-cell transcriptomics methodologies with particular focus on microbial eukaryote applications. Then, we discuss single-cell gene expression analysis of protists in culture and what can be learnt from these approaches. Finally, we envision the application of single-cell transcriptomics to protist communities to interrogate not only community components, but also the gene expression signatures of distinct cellular and physiological states, as well as the transcriptional dynamics of interspecific interactions. Overall, we argue that single-cell transcriptomics can significantly contribute to our understanding of the biology of microbial eukaryotes. This article is part of a discussion meeting issue ‘Single cell ecology’.

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 ZipSeq : Barcoding for Real-time Mapping of Single Cell Transcriptomes

2020 ◽  
Author(s):  
Kenneth H. Hu ◽  
John P. Eichorst ◽  
Chris S. McGinnis ◽  
David M. Patterson ◽  
Eric D. Chow ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Real Time ◽  
Dimensional Space ◽  
Single Cells ◽  
Expression Patterns ◽  
Live Cells ◽  
Glass Substrates ◽  
Complete Mapping

ABSTRACTSpatial transcriptomics seeks to integrate single-cell transcriptomic data within the 3-dimensional space of multicellular biology. Current methods use glass substrates pre-seeded with matrices of barcodes or fluorescence hybridization of a limited number of probes. We developed an alternative approach, called ‘ZipSeq’, that uses patterned illumination and photocaged oligonucleotides to serially print barcodes (Zipcodes) onto live cells within intact tissues, in real-time and with on-the-fly selection of patterns. Using ZipSeq, we mapped gene expression in three settings: in-vitro wound healing, live lymph node sections and in a live tumor microenvironment (TME). In all cases, we discovered new gene expression patterns associated with histological structures. In the TME, this demonstrated a trajectory of myeloid and T cell differentiation, from periphery inward. A variation of ZipSeq efficiently scales to the level of single cells, providing a pathway for complete mapping of live tissues, subsequent to real-time imaging or perturbation.

scholarly journals Dissecting heterogeneous cell populations across drug and disease conditions with PopAlign

2020 ◽  
Vol 117 (46) ◽  
pp. 28784-28794
Author(s):  
Sisi Chen ◽  
Paul Rivaud ◽  
Jong H. Park ◽  
Tiffany Tsou ◽  
Emeric Charles ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Large Scale ◽  
Probabilistic Models ◽  
Single Cells ◽  
Measurement Techniques ◽  
Patient Specific ◽  
Cell Populations ◽  
Cell Measurement ◽  
The Impact

Single-cell measurement techniques can now probe gene expression in heterogeneous cell populations from the human body across a range of environmental and physiological conditions. However, new mathematical and computational methods are required to represent and analyze gene-expression changes that occur in complex mixtures of single cells as they respond to signals, drugs, or disease states. Here, we introduce a mathematical modeling platform, PopAlign, that automatically identifies subpopulations of cells within a heterogeneous mixture and tracks gene-expression and cell-abundance changes across subpopulations by constructing and comparing probabilistic models. Probabilistic models provide a low-error, compressed representation of single-cell data that enables efficient large-scale computations. We apply PopAlign to analyze the impact of 40 different immunomodulatory compounds on a heterogeneous population of donor-derived human immune cells as well as patient-specific disease signatures in multiple myeloma. PopAlign scales to comparisons involving tens to hundreds of samples, enabling large-scale studies of natural and engineered cell populations as they respond to drugs, signals, or physiological change.

scholarly journals Cell cycle Start is coupled to entry into the yeast metabolic cycle across diverse strains and growth rates

2016 ◽  
Vol 27 (1) ◽  
pp. 64-74 ◽  
Author(s):  
Anthony J. Burnetti ◽  
Mert Aydin ◽  
Nicolas E. Buchler
Keyword(s):  
Cell Cycle ◽  
Oxygen Consumption ◽  
Cell Division ◽  
Dna Replication ◽  
Growth Rates ◽  
Quantitative Relationship ◽  
High Temporal Resolution ◽  
Different Strains ◽  
Yeast Metabolic Cycle ◽  
Metabolic Cycle

Cells have evolved oscillators with different frequencies to coordinate periodic processes. Here we studied the interaction of two oscillators, the cell division cycle (CDC) and the yeast metabolic cycle (YMC), in budding yeast. Previous work suggested that the CDC and YMC interact to separate high oxygen consumption (HOC) from DNA replication to prevent genetic damage. To test this hypothesis, we grew diverse strains in chemostat and measured DNA replication and oxygen consumption with high temporal resolution at different growth rates. Our data showed that HOC is not strictly separated from DNA replication; rather, cell cycle Start is coupled with the initiation of HOC and catabolism of storage carbohydrates. The logic of this YMC–CDC coupling may be to ensure that DNA replication and cell division occur only when sufficient cellular energy reserves have accumulated. Our results also uncovered a quantitative relationship between CDC period and YMC period across different strains. More generally, our approach shows how studies in genetically diverse strains efficiently identify robust phenotypes and steer the experimentalist away from strain-specific idiosyncrasies.

Effector T-cell cytolytic activity modules derived from CD3+ single cells from human primary triple-negative breast cancer (TNBC) in multiple solid tumors to predict response to immune checkpoint blockade therapy (ICB).

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. 1073-1073
Author(s):  
Chaitanya Ramanuj Acharya ◽  
Herbert K Lyerly
Keyword(s):  
Gene Expression ◽  
T Cell ◽  
Single Cell ◽  
Single Cells ◽  
Progression Free Survival ◽  
Cytolytic Activity ◽  
Enrichment Score ◽  
P Value ◽  
Free Survival ◽  
Tumor Types

1073 Background: The prognostic and predictive value of tumor infiltrating lymphocytes for ICB has been recognized in a variety of tumor types, including TNBC. Nonetheless, our understanding of the mechanistic aspects of T cell activation remains incomplete. We hypothesize that a specific effector phenotype of T cell cytolytic activity (ECA) is a consistent feature of epithelial tumors, possibly varying by tumor types with a range of inflammatory features. Methods: We evaluated 6,311 purified CD3+ single cells from human primary TNBC and computed sample set enrichment scores of a set of previously published immune metagenes. Following unsupervised clustering of the enrichment scores of the entire single cell population, two subgroups of cells with highest and lowest average enrichment score of T cell cytolytic activity formed a basis for detecting functional gene expression modules. Spectral decomposition and Jackstraw analysis estimated eight modules with overlapping sets of genes. Each gene expression module was then used to train a Random Forest classifier of ECA phenotype. Results: We discovered that our module-derived classifiers were prognostic not only in TNBC samples obtained from both TCGA (N = 150) and METABRIC (N = 320) datasets but also in 14 other tumor types encompassing 6,000 samples. For example, patient samples from TCGA dataset predicted to be in group ECA ‘High’ have better progression-free survival (p-value: 0.0098l; HR: 0.30) and better overall survival (p-value: 0.0066; HR: 0.17). In both breast datasets, gene within the classifier are relatively under-expressed in ER+ tumors as opposed to HER2+ and TNBC (p-value < 2.2e-16). In a dataset of normal, pure DCIS and mixed DCIS (GSE26304;N = 114), the same genes were relatively under-expressed in DCIS samples relative to invasive tumors (p-value < 2.2e-16). Additionally, in a pre-therapy tumor dataset of fifty-one advanced melanoma patients treated with Nivolumab, who previously either progressed on ipilimumab or were ipilimumab-naïve, our module-derived classifier was able to classify responders and non-responders with 77% accuracy (p-value = 0.02) and was associated with progression-free survival (p-value = 0.03; HR: 0.28). Conclusions: Our study highlights one important application of single-cell genomics in our understanding of immune microenvironment and potentially identify new immunotherapy targets.

scholarly journals Coupling among growth rate response, metabolic cycle, and cell division cycle in yeast

2011 ◽  
Vol 22 (12) ◽  
pp. 1997-2009 ◽  
Author(s):  
Nikolai Slavov ◽  
David Botstein
Keyword(s):  
Growth Rate ◽  
Oxygen Consumption ◽  
Cell Division ◽  
Carbon Source ◽  
Oxidative Metabolism ◽  
Sole Source ◽  
Cell Division Cycle ◽  
Steady State Responses ◽  
Yeast Metabolic Cycle ◽  
Metabolic Cycle

We studied the steady-state responses to changes in growth rate of yeast when ethanol is the sole source of carbon and energy. Analysis of these data, together with data from studies where glucose was the carbon source, allowed us to distinguish a “universal” growth rate response (GRR) common to all media studied from a GRR specific to the carbon source. Genes with positive universal GRR include ribosomal, translation, and mitochondrial genes, and those with negative GRR include autophagy, vacuolar, and stress response genes. The carbon source–specific GRR genes control mitochondrial function, peroxisomes, and synthesis of vitamins and cofactors, suggesting this response may reflect the intensity of oxidative metabolism. All genes with universal GRR, which comprise 25% of the genome, are expressed periodically in the yeast metabolic cycle (YMC). We propose that the universal GRR may be accounted for by changes in the relative durations of the YMC phases. This idea is supported by oxygen consumption data from metabolically synchronized cultures with doubling times ranging from 5 to 14 h. We found that the high oxygen consumption phase of the YMC can coincide exactly with the S phase of the cell division cycle, suggesting that oxidative metabolism and DNA replication are not incompatible.

scholarly journals Perfusion double-channel micropipette probes for oxygen flux mapping with single-cell resolution

2018 ◽  
Vol 9 ◽  
pp. 850-860 ◽  
Author(s):  
Yang Gao ◽  
Bin Li ◽  
Riju Singhal ◽  
Adam Fontecchio ◽  
Ben Pelleg ◽  
...  
Keyword(s):  
Oxygen Consumption ◽  
Single Cell ◽  
Oxygen Concentration ◽  
Spatial Resolution ◽  
Single Cells ◽  
Element Model ◽  
Cellular Respiration ◽  
System Parameters ◽  
Measurement Signal ◽  
Double Channel

Measuring cellular respiration with single-cell spatial resolution is a significant challenge, even with modern tools and techniques. Here, a double-channel micropipette is proposed and investigated as a probe to achieve this goal by sampling fluid near the point of interest. A finite element model (FEM) of this perfusion probe is validated by comparing simulation results with experimental results of hydrodynamically confined fluorescent molecule diffusion. The FEM is then used to investigate the dependence of the oxygen concentration variation and the measurement signal on system parameters, including the pipette’s shape, perfusion velocity, position of the oxygen sensors within the pipette, and proximity of the pipette to the substrate. The work demonstrates that the use of perfusion double-barrel micropipette probes enables the detection of oxygen consumption signals with micrometer spatial resolution, while amplifying the signal, as compared to sensors without the perfusion system. In certain flow velocity ranges (depending on pipette geometry and configuration), the perfusion flow increases oxygen concentration gradients formed due to cellular oxygen consumption. An optimal perfusion velocity for respiratory measurements on single cells can be determined for different system parameters (e.g., proximity of the pipette to the substrate). The optimum perfusion velocities calculated in this paper range from 1.9 to 12.5 μm/s. Finally, the FEM model is used to show that the spatial resolution of the probe may be varied by adjusting the pipette tip diameter, which may allow oxygen consumption mapping of cells within tissue, as well as individual cells at subcellular resolution.

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