scholarly journals Analysis of cell size homeostasis at the single-cell and population level

2018 ◽  
Author(s):  
Philipp Thomas
Keyword(s):  
Single Cell ◽  
Cell Size ◽  
Single Cells ◽  
Population Level ◽  
Power Laws ◽  
Size Distributions ◽  
Unified Framework ◽  
Lineage Tree ◽  
Cell To Cell Variability ◽  
Cell Data

Growth pervades all areas of life from single cells to cell populations to tissues. However, cell size often fluctuates significantly from cell to cell and from generation to generation. Here we present a unified framework to predict the statistics of cell size variations within a lineage tree of a proliferating population. We analytically characterise (i) the distributions of cell size snapshots, (ii) the distribution within a population tree, and (iii) the distribution of lineages across the tree. Surprisingly, these size distributions differ significantly from observing single cells in isolation. In populations, cells seemingly grow to different sizes, typically exhibit less cell-to-cell variability and often display qualitatively different sensitivities to cell cycle noise and division errors. We demonstrate the key findings using recent single-cell data and elaborate on the implications for the ability of cells to maintain a narrow size distribution and the emergence of different power laws in these distributions.

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 RNA velocity in single cells

2017 ◽  
Author(s):  
Gioele La Manno ◽  
Ruslan Soldatov ◽  
Hannah Hochgerner ◽  
Amit Zeisel ◽  
Viktor Petukhov ◽  
...  
Keyword(s):  
Single Cell ◽  
Cellular Differentiation ◽  
Single Cells ◽  
Time Derivative ◽  
Dynamic Processes ◽  
Embryonic Brain ◽  
Future State ◽  
Single Cell Rna Sequencing ◽  
Mouse Hippocampus ◽  
Lineage Tree

AbstractRNA abundance is a powerful indicator of the state of individual cells, but does not directly reveal dynamic processes such as cellular differentiation. Here we show that RNA velocity—the time derivative of RNA abundance—can be estimated by distinguishing unspliced and spliced mRNAs in standard single-cell RNA sequencing protocols. We show that RNA velocity is a vector that predicts the future state of individual cells on a timescale of hours. We validate the accuracy of RNA velocity in the neural crest lineage, demonstrate its use on multiple technical platforms, reconstruct the branching lineage tree of the mouse hippocampus, and measure RNA kinetics in human embryonic brain. We expect RNA velocity to greatly aid the analysis of developmental lineages and cellular dynamics, particularly in humans.

scholarly journals Single-Cell Mechanophenotyping in Microfluidics to Evaluate Behavior of U87 Glioma Cells

Micromachines ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 845
Author(s):  
Esra Sengul ◽  
Meltem Elitas
Keyword(s):  
Single Cell ◽  
Glioma Cell ◽  
Single Cells ◽  
Glioma Cells ◽  
Population Level ◽  
Glioma Cell Line ◽  
Scientific Discipline ◽  
Regular Growth ◽  
Mesenchymal Transition ◽  
Deformation Properties

Integration of microfabricated, single-cell resolution and traditional, population-level biological assays will be the future of modern techniques in biology that will enroll in the evolution of biology into a precision scientific discipline. In this study, we developed a microfabricated cell culture platform to investigate the indirect influence of macrophages on glioma cell behavior. We quantified proliferation, morphology, motility, migration, and deformation properties of glioma cells at single-cell level and compared these results with population-level data. Our results showed that glioma cells obtained slightly slower proliferation, higher motility, and extremely significant deformation capability when cultured with 50% regular growth medium and 50% macrophage-depleted medium. When the expression levels of E-cadherin and Vimentin proteins were measured, it was verified that observed mechanophenotypic alterations in glioma cells were not due to epithelium to mesenchymal transition. Our results were consistent with previously reported enormous heterogeneity of U87 glioma cell line. Herein, for the first time, we quantified the change of deformation indexes of U87 glioma cells using microfluidic devices for single-cells analysis.

scholarly journals Noise-driven growth rate gain in clonal cellular populations

2016 ◽  
Vol 113 (12) ◽  
pp. 3251-3256 ◽  
Author(s):  
Mikihiro Hashimoto ◽  
Takashi Nozoe ◽  
Hidenori Nakaoka ◽  
Reiko Okura ◽  
Sayo Akiyoshi ◽  
...  
Keyword(s):  
Growth Rate ◽  
Population Growth ◽  
Single Cell ◽  
Single Cells ◽  
Population Level ◽  
Growth Conditions ◽  
Cell Dynamics ◽  
Generation Times ◽  
Complex Population ◽  
The Mean

Cellular populations in both nature and the laboratory are composed of phenotypically heterogeneous individuals that compete with each other resulting in complex population dynamics. Predicting population growth characteristics based on knowledge of heterogeneous single-cell dynamics remains challenging. By observing groups of cells for hundreds of generations at single-cell resolution, we reveal that growth noise causes clonal populations of Escherichia coli to double faster than the mean doubling time of their constituent single cells across a broad set of balanced-growth conditions. We show that the population-level growth rate gain as well as age structures of populations and of cell lineages in competition are predictable. Furthermore, we theoretically reveal that the growth rate gain can be linked with the relative entropy of lineage generation time distributions. Unexpectedly, we find an empirical linear relation between the means and the variances of generation times across conditions, which provides a general constraint on maximal growth rates. Together, these results demonstrate a fundamental benefit of noise for population growth, and identify a growth law that sets a “speed limit” for proliferation.

scholarly journals Symposium on single cell analysis and genomic approaches, Experimental Biology 2017 Chicago, Illinois, April 23, 2017

2017 ◽  
Vol 49 (9) ◽  
pp. 491-495
Author(s):  
Hilary A. Coller
Keyword(s):  
Single Cell ◽  
Single Cell Analysis ◽  
Single Cells ◽  
Experimental Biology ◽  
Intratumor Heterogeneity ◽  
Cell Analysis ◽  
Single Cell Genomics ◽  
Critical Issues ◽  
Cell To Cell Variability ◽  
External Stimuli

Emerging technologies for the analysis of genome-wide information in single cells have the potential to transform many fields of biology, including our understanding of cell states, the response of cells to external stimuli, mosaicism, and intratumor heterogeneity. At Experimental Biology 2017 in Chicago, Physiological Genomics hosted a symposium in which five leaders in the field of single cell genomics presented their recent research. The speakers discussed emerging methodologies in single cell analysis and critical issues for the analysis of single cell data. Also discussed were applications of single cell genomics to understanding the different types of cells within an organism or tissue and the basis for cell-to-cell variability in response to stimuli.

scholarly journals Single Cell Viewer (SCV): An interactive visualization data portal for single cell RNA sequence data

2019 ◽  
Author(s):  
Shuoguo Wang ◽  
Constance Brett ◽  
Mohan Bolisetty ◽  
Ryan Golhar ◽  
Isaac Neuhaus ◽  
...  
Keyword(s):  
Single Cell ◽  
Sequence Data ◽  
Single Cells ◽  
Link Type ◽  
Technological Advances ◽  
R Shiny ◽  
Data Volume ◽  
Exploratory Data ◽  
Cell Data ◽  
Shiny Application

AbstractMotivationThanks to technological advances made in the last few years, we are now able to study transcriptomes from thousands of single cells. These have been applied widely to study various aspects of Biology. Nevertheless, comprehending and inferring meaningful biological insights from these large datasets is still a challenge. Although tools are being developed to deal with the data complexity and data volume, we do not have yet an effective visualizations and comparative analysis tools to realize the full value of these datasets.ResultsIn order to address this gap, we implemented a single cell data visualization portal called Single Cell Viewer (SCV). SCV is an R shiny application that offers users rich visualization and exploratory data analysis options for single cell datasets.AvailabilitySource code for the application is available online at GitHub (http://www.github.com/neuhausi/single-cell-viewer) and there is a hosted exploration application using the same example dataset as this publication at http://periscopeapps.org/[email protected]; [email protected]

scholarly journals CIM-seq

2021 ◽  
Author(s):  
Nathanael Andrews ◽  
Martin Enge
Keyword(s):  
Single Cell ◽  
Single Cells ◽  
Likelihood Estimation ◽  
Cell Types ◽  
Data Sets ◽  
Target Tissue ◽  
Data Set ◽  
Rnaseq Data ◽  
The Given ◽  
Cell Data

Abstract CIM-seq is a tool for deconvoluting RNA-seq data from cell multiplets (clusters of two or more cells) in order to identify physically interacting cell in a given tissue. The method requires two RNAseq data sets from the same tissue: one of single cells to be used as a reference, and one of cell multiplets to be deconvoluted. CIM-seq is compatible with both droplet based sequencing methods, such as Chromium Single Cell 3′ Kits from 10x genomics; and plate based methods, such as Smartseq2. The pipeline consists of three parts: 1) Dissociation of the target tissue, FACS sorting of single cells and multiplets, and conventional scRNA-seq 2) Feature selection and clustering of cell types in the single cell data set - generating a blueprint of transcriptional profiles in the given tissue 3) Computational deconvolution of multiplets through a maximum likelihood estimation (MLE) to determine the most likely cell type constituents of each multiplet.

scholarly journals Automated cell cycle and cell size measurements for single-cell gene expression studies

2017 ◽  
Author(s):  
Anissa Guillemin ◽  
Angelique Richard ◽  
Sandrine Gonin-Giraud ◽  
Olivier Gandrillon
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Cell Size ◽  
Cell Tracking ◽  
Erythroid Progenitors ◽  
Expression Variability ◽  
Unbiased Gene ◽  
Transcriptomic Level ◽  
Cell To Cell Variability

AbstractRecent rise of single-cell studies revealed the importance of understanding the role of cell-to-cell variability, especially at the transcriptomic level. One of the numerous sources of cell-to-cell variation in gene expression is the heterogeneity in cell proliferation state. How cell cycle and cell size influences gene expression variability at single-cell level is not yet clearly understood. To deconvolute such influences, most of the single-cell studies used dedicated methods that could include some bias. Here, we provide a universal and automatic toxic-free label method, compatible with single-cell high-throughput RT-qPCR. This led to an unbiased gene expression analysis and could be also used for improving single-cell tracking and imaging when combined with cell isolation. As an application for this technique, we showed that cell-to-cell variability in chicken erythroid progenitors was negligibly influenced by cell size nor cell cycle.

scholarly journals Accurate sub-population detection and mapping across single cell experiments with PopCorn

2018 ◽  
Author(s):  
Yijie Wang ◽  
Jan Hoinka ◽  
Teresa M Przytycka
Keyword(s):  
Comparative Analysis ◽  
Single Cell ◽  
Single Cells ◽  
Novel Method ◽  
Cell Data

The identification of sub-populations of cells present in a sample and the comparison of such sub-populations across samples are among the most frequently performed analyzes of single-cell data. Current tools for these kinds of data, however, fall short in their ability to adequately perform these tasks. We introduce a novel method, PopCorn (single cell sub-Populations Comparison), allowing for the identification of sub-populations of cells present within individual experiments while simultaneously performing sub-populations mapping across these experiments. PopCorn utilizes several novel algorithmic solutions enabling the execution of these tasks with unprecedented precision. As such, PopCorn provides a much-needed tool for comparative analysis of populations of single cells.

scholarly journals Single-cell intracellular pH measurements reveal cell-cycle linked pH dynamics

2021 ◽  
Author(s):  
Julia S Spear ◽  
Katharine A White
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Intracellular Ph ◽  
Cell Cycle Progression ◽  
Single Cells ◽  
Population Level ◽  
Growth Factor Signaling ◽  
Cycle Progression ◽  
Cell Behaviors ◽  
Sinusoidal Pattern

Transient changes in intracellular pH (pHi) have been shown to regulate normal cell behaviors like migration and cell-cycle progression, while dysregulated pHi dynamics are a hallmark of cancer. However, little is known about how pHi heterogeneity and dynamics influence population-level measurements or single-cell behaviors. Here, we present and characterize single-cell pHi heterogeneity distributions in both normal and cancer cells and measure dynamic pHi increases in single cells in response to growth factor signaling. Next, we measure pHi dynamics in single cells during cell cycle progression. We determined that single-cell pHi is significantly decreased at the G1/S boundary, increases from S phase to the G2/M transition, rapidly acidifies during mitosis, and recovers in daughter cells. This sinusoidal pattern of pHi dynamics was linked to cell cycle timing regardless of synchronization method. This work confirms prior work at the population level and reveals distinct advantages of single-cell pHi measurements in capturing pHi heterogeneity across a population and dynamics within single cells.

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