scholarly journals Microwell array-based opto-electrochemical detections revealing co-adaptation of rheological properties and oxygen metabolism in budding yeast

2021 ◽  
Author(s):  
Venkata Suresh Vajrala ◽  
Baptiste Alric ◽  
Adrian Laborde ◽  
Camille Colin ◽  
Emmanuel Suraniti ◽  
...  
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
Glucose Concentration ◽  
Budding Yeast ◽  
Single Cells ◽  
Oxygen Metabolism ◽  
Basal Respiration ◽  
Yeast Cells ◽  
Detection Analysis ◽  
The Impact

Microdevices composed of microwell arrays integrating nanoelectrodes (OptoElecWell) were developed to achieve dual high-resolution optical and electrochemical detections on single Saccharomyces cerevisiae budding yeast cells. Each array consists of 1.6x10^5 microwells of 8 um diameter and 5 um height, with a platinum nanoring electrode for in-situ electrochemistry, all integrated on a transparent thin wafer for further high- resolution live-cell imaging. After optimizing the filling rate, 32% of cells were effectively trapped within microwells. This allowed us to analyze S. cerevisiae metabolisms associated with basal respiration while simultaneously measuring optically other cellular parameters. In this study, we focused on the impact of glucose concentration on respiration and intracellular rheology. We found that while oxygen uptake rate decreased with increasing glucose concentration, diffusion of tracer nanoparticles increased. Our OptoElecWell based respiration methodology provided similar results compared to the commercial gold-standard Seahorse XF analyzer, while using 20 times lesser biological samples, paving the way to achieve single cell metabolomics. In addition, it facilitates an optical route to monitor the contents within single cells. The proposed device, in combination with the dual detection analysis, opens up new avenues for measuring cellular metabolism and relating it to various cellular physiological and rheological indicators at single cell level.

scholarly journals Morphodynamic signatures of MDA-MB-231 single cells and cell doublets undergoing invasion in confined microenvironments

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xingjian Zhang ◽  
Trevor Chan ◽  
Michael Mak
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Leading Edge ◽  
Cell Group ◽  
Collective State ◽  
Long Term Effects ◽  
Cell Metastasis ◽  
Geometric Confinement ◽  
Short And Long Term ◽  
The Impact

AbstractCancer cell metastasis is a major factor in cancer-related mortality. During the process of metastasis, cancer cells exhibit migratory phenotypes and invade through pores in the dense extracellular matrix. However, the characterization of morphological and subcellular features of cells in similar migratory phenotypes and the effects of geometric confinement on cell morphodynamics are not well understood. Here, we investigate the phenotypes of highly aggressive MDA-MB-231 cells in single cell and cell doublet (an initial and simplified collective state) forms in confined microenvironments. We group phenotypically similar single cells and cell doublets and characterize related morphological and subcellular features. We further detect two distinct migratory phenotypes, fluctuating and non-fluctuating, within the fast migrating single cell group. In addition, we demonstrate an increase in the number of protrusions formed at the leading edge of cells after invasion through geometric confinement. Finally, we track the short and long term effects of varied degrees of confinement on protrusion formation. Overall, our findings elucidate the underlying morphological and subcellular features associated with different single cell and cell doublet phenotypes and the impact of invasion through confined geometry on cell behavior.

scholarly journals Quality assessment of single-cell RNA sequencing data by coverage skewness analysis

2019 ◽  
Author(s):  
Imad Abugessaisa ◽  
Shuhei Noguchi ◽  
Melissa Cardon ◽  
Akira Hasegawa ◽  
Kazuhide Watanabe ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Single Cell ◽  
Rna Sequencing ◽  
Single Cells ◽  
Assessment Method ◽  
Poor Quality ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Gene Coverage ◽  
The Impact

AbstractAnalysis and interpretation of single-cell RNA-sequencing (scRNA-seq) experiments are compromised by the presence of poor quality cells. For meaningful analyses, such poor quality cells should be excluded to avoid biases and large variation. However, no clear guidelines exist. We introduce SkewC, a novel quality-assessment method to identify poor quality single-cells in scRNA-seq experiments. The method is based on the assessment of gene coverage for each single cell and its skewness as a quality measure. To validate the method, we investigated the impact of poor quality cells on downstream analyses and compared biological differences between typical and poor quality cells. Moreover, we measured the ratio of intergenic expression, suggesting genomic contamination, and foreign organism contamination of single-cell samples. SkewC is tested in 37,993 single-cells generated by 15 scRNA-seq protocols. We envision SkewC as an indispensable QC method to be incorporated into scRNA-seq experiment to preclude the possibility of scRNA-seq data misinterpretation.

scholarly journals μCB-seq: Microfluidic cell barcoding and sequencing for high-resolution imaging and sequencing of single cells

2020 ◽  
Author(s):  
Tyler N. Chen ◽  
Anushka Gupta ◽  
Mansi Zalavadia ◽  
Aaron M. Streets
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
Single Cell Analysis ◽  
Protein Localization ◽  
Single Cells ◽  
Optical Measurements ◽  
Detection Sensitivity ◽  
High Resolution Imaging ◽  
Cell Analysis ◽  
Resolution Imaging

AbstractSingle-cell RNA sequencing (scRNA-seq) enables the investigation of complex biological processes in multicellular organisms with high resolution. However, many phenotypic features that are critical to understanding the functional role of cells in a heterogeneous tissue or organ are not directly encoded in the genome and therefore cannot be profiled with scRNA-seq. Quantitative optical microscopy has long been a powerful approach for characterizing diverse cellular phenotypes including cell morphology, protein localization, and chemical composition. Combining scRNA-seq with optical imaging has the potential to provide comprehensive single-cell analysis, allowing for functional integration of gene expression profiling and cell-state characterization. However, it is difficult to track single cells through both measurements; therefore, coupling current scRNA-seq protocols with optical measurements remains a challenge. Here, we report Microfluidic Cell Barcoding and Sequencing (μCB-seq), a microfluidic platform that combines high-resolution imaging and sequencing of single cells. μCB-seq is enabled by a novel fabrication method that preloads primers with known barcode sequences inside addressable reaction chambers of a microfluidic device. In addition to enabling multi-modal single-cell analysis, μCB-seq improves gene detection sensitivity, providing a scalable and accurate method for information-rich characterization of single cells.

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.

Testing of solid oxide cells at high current densities

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
André Weber
Keyword(s):  
Single Cell ◽  
High Performance ◽  
High Efficiency ◽  
Electrochemical Impedance ◽  
Single Cells ◽  
Operating Conditions ◽  
Solid Oxide ◽  
Current Densities ◽  
Cell Testing ◽  
The Impact

Abstract Solid Oxide Cells (SOCs) have gained an increasing interest as electrochemical energy converters due to their high efficiency, fuel flexibility and ability of reversible fuel cell/electrolysis operation. During the development process as well as in quality assurance tests, the performance of single cells and cell stacks is commonly evaluated by means of current/voltage- (CV-) characteristics. Despite of the fact that the measurement of a CV-characteristic seems to be simple compared to more complex, dynamic methods as electrochemical impedance spectroscopy or current interrupt techniques, the resulting performance strongly depends on the test setup and the chosen operating conditions. In this paper, the impact of different single cell testing environments and operating conditions on the CV-characteristic of high performance cells is discussed. The influence of cell size, contacting and current collection, contact pressure, fuel flow rate and composition on the achievable cell performance is presented and limitations arising from the test bed and testing conditions will be pointed out. As today’s high performance cells are capable of delivering current densities of several ampere per cm2 a special emphasis will be laid on single cell testing in this current range.

scholarly journals Synchrotron macro ATR-FTIR microspectroscopy for high-resolution chemical mapping of single cells

The Analyst ◽  
2019 ◽  
Vol 144 (10) ◽  
pp. 3226-3238 ◽  
Author(s):  
Jitraporn Vongsvivut ◽  
David Pérez-Guaita ◽  
Bayden R. Wood ◽  
Philip Heraud ◽  
Karina Khambatta ◽  
...  
Keyword(s):  
High Resolution ◽  
Single Cell ◽  
Spatial Resolution ◽  
Environmental Science ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Cell Analysis ◽  
Chemical Mapping ◽  
Ftir Microspectroscopy ◽  
Resolution Single

Coupling synchrotron IR beam to an ATR element enhances spatial resolution suited for high-resolution single cell analysis in biology, medicine and environmental science.

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 Computation of Single-Cell Metabolite Distributions Using Mixture Models

2020 ◽  
Vol 8 ◽  
Author(s):  
Mona K. Tonn ◽  
Philipp Thomas ◽  
Mauricio Barahona ◽  
Diego A. Oyarzún
Keyword(s):  
Single Cell ◽  
Mixture Models ◽  
Single Cells ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Stochastic Simulations ◽  
Deterministic Models ◽  
Metabolic Heterogeneity ◽  
Cell Expression ◽  
The Impact

Metabolic heterogeneity is widely recognized as the next challenge in our understanding of non-genetic variation. A growing body of evidence suggests that metabolic heterogeneity may result from the inherent stochasticity of intracellular events. However, metabolism has been traditionally viewed as a purely deterministic process, on the basis that highly abundant metabolites tend to filter out stochastic phenomena. Here we bridge this gap with a general method for prediction of metabolite distributions across single cells. By exploiting the separation of time scales between enzyme expression and enzyme kinetics, our method produces estimates for metabolite distributions without the lengthy stochastic simulations that would be typically required for large metabolic models. The metabolite distributions take the form of Gaussian mixture models that are directly computable from single-cell expression data and standard deterministic models for metabolic pathways. The proposed mixture models provide a systematic method to predict the impact of biochemical parameters on metabolite distributions. Our method lays the groundwork for identifying the molecular processes that shape metabolic heterogeneity and its functional implications in disease.

scholarly journals Single-cell analysis of HIV-1 transcriptional activity reveals expression of proviruses in expanded clones during ART

2017 ◽  
Vol 114 (18) ◽  
pp. E3659-E3668 ◽  
Author(s):  
Ann Wiegand ◽  
Jonathan Spindler ◽  
Feiyu F. Hong ◽  
Wei Shao ◽  
Joshua C. Cyktor ◽  
...  
Keyword(s):  
Single Cell ◽  
Mononuclear Cells ◽  
Single Cells ◽  
Constitutive Expression ◽  
Multiple Time ◽  
Hiv Rna ◽  
Infected Cells ◽  
The Impact ◽  
Hiv 1

Little is known about the fraction of human immunodeficiency virus type 1 (HIV-1) proviruses that express unspliced viral RNA in vivo or about the levels of HIV RNA expression within single infected cells. We developed a sensitive cell-associated HIV RNA and DNA single-genome sequencing (CARD-SGS) method to investigate fractional proviral expression of HIV RNA (1.3-kb fragment of p6, protease, and reverse transcriptase) and the levels of HIV RNA in single HIV-infected cells from blood samples obtained from individuals with viremia or individuals on long-term suppressive antiretroviral therapy (ART). Spiking experiments show that the CARD-SGS method can detect a single cell expressing HIV RNA. Applying CARD-SGS to blood mononuclear cells in six samples from four HIV-infected donors (one with viremia and not on ART and three with viremia suppressed on ART) revealed that an average of 7% of proviruses (range: 2–18%) expressed HIV RNA. Levels of expression varied from one to 62 HIV RNA molecules per cell (median of 1). CARD-SGS also revealed the frequent expression of identical HIV RNA sequences across multiple single cells and across multiple time points in donors on suppressive ART consistent with constitutive expression of HIV RNA in infected cell clones. Defective proviruses were found to express HIV RNA at levels similar to those proviruses that had no obvious defects. CARD-SGS is a useful tool to characterize fractional proviral expression in single infected cells that persist despite ART and to assess the impact of experimental interventions on proviral populations and their expression.

scholarly journals Quantitative insights into age-associated DNA-repair inefficiency in single cells

2019 ◽  
Author(s):  
Thomas Z. Young ◽  
Ping Liu ◽  
Murat Acar
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Strand Break ◽  
Cellular Aging ◽  
Dsb Repair ◽  
Phase Duration ◽  
Age Related ◽  
Repair Efficiency ◽  
Single Strand Annealing ◽  
The Impact

ABSTRACTThe double strand break (DSB) is a highly toxic form of DNA damage that is thought to be both a driver and consequence of age-related dysfunction. Although DSB repair is essential for a cell’s survival, little is known about how DSB repair mechanisms are affected by cellular age. Here we characterize the impact of cellular aging on the efficiency of single-strand annealing (SSA), a repair mechanism for DSBs occurring between direct repeats. Using a single-cell reporter of SSA repair, we measure SSA repair efficiency in young and old cells, and report a 23.4% decline in repair efficiency. This decline is not due to increased usage of non-homologous end joining (NHEJ). Instead, we identify increased G1-phase duration in old cells as a factor responsible for the decreased SSA repair efficiency. We further explore how SSA repair efficiency is affected by sequence heterology and find that heteroduplex rejection remains high in old cells. Our work provides novel quantitative insights into the links between cellular aging and DSB repair efficiency at single-cell resolution in replicatively aging cells.

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