scholarly journals Modeling bi-modality improves characterization of cell cycle on gene expression in single cells

2014 ◽  
Author(s):  
Lucas Dennis ◽  
Andrew McDavid ◽  
Patrick Danaher ◽  
Greg Finak ◽  
Michael Krouse ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Single Cells ◽  
Cell Cycle Phase ◽  
Cycle Phase ◽  
Expression Variability ◽  
Cell Cycle Genes ◽  
Cell Expression ◽  
Cell Gene

Advances in high-throughput, single cell gene expression are allowing interrogation of cell heterogeneity. However, there is concern that the cell cycle phase of a cell might bias characterizations of gene expression at the single-cell level. We assess the effect of cell cycle phase on gene expression in single cells by measuring 333 genes in 930 cells across three phases and three cell lines. We determine each cell's phase non-invasively without chemical arrest and use it as a covariate in tests of differential expression. We observe bi-modal gene expression, a previously-described phenomenon, wherein the expression of otherwise abundant genes is either strongly positive, or undetectable within individual cells. This bi-modality is likely both biologically and technically driven. Irrespective of its source, we show that it should be modeled to draw accurate inferences from single cell expression experiments. To this end, we propose a semi-continuous modeling framework based on the generalized linear model, and use it to characterize genes with consistent cell cycle effects across three cell lines. Our new computational framework improves the detection of previously characterized cell-cycle genes compared to approaches that do not account for the bi-modality of single-cell data. We use our semi-continuous modelling framework to estimate single cell gene co-expression networks. These networks suggest that in addition to having phase-dependent shifts in expression (when averaged over many cells), some, but not all, canonical cell cycle genes tend to be co-expressed in groups in single cells. We estimate the amount of single cell expression variability attributable to the cell cycle. We find that the cell cycle explains only 5%-17% of expression variability, suggesting that the cell cycle will not tend to be a large nuisance factor in analysis of the single cell transcriptome.

scholarly journals Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

2019 ◽  
Author(s):  
Chiaowen Joyce Hsiao ◽  
PoYuan Tung ◽  
John D. Blischak ◽  
Jonathan E. Burnett ◽  
Kenneth A. Barr ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Cell Cycle Progression ◽  
Cell Types ◽  
Cell Cycle Phase ◽  
Cellular Heterogeneity ◽  
Cycle Phase ◽  
Cycle Progression ◽  
Cellular Environment

AbstractCellular heterogeneity in gene expression is driven by cellular processes such as cell cycle and cell-type identity, and cellular environment such as spatial location. The cell cycle, in particular, is thought to be a key driver of cell-to-cell heterogeneity in gene expression, even in otherwise homogeneous cell populations. Recent advances in single-cell RNA-sequencing (scRNA-seq) facilitate detailed characterization of gene expression heterogeneity, and can thus shed new light on the processes driving heterogeneity. Here, we combined fluorescence imaging with scRNA-seq to measure cell cycle phase and gene expression levels in human induced pluripotent stem cells (iPSCs). Using these data, we developed a novel approach to characterize cell cycle progression. While standard methods assign cells to discrete cell cycle stages, our method goes beyond this, and quantifies cell cycle progression on a continuum. We found that, on average, scRNA-seq data from only five genes predicted a cell’s position on the cell cycle continuum to within 14% of the entire cycle, and that using more genes did not improve this accuracy. Our data and predictor of cell cycle phase can directly help future studies to account for cell-cycle-related heterogeneity in iPSCs. Our results and methods also provide a foundation for future work to characterize the effects of the cell cycle on expression heterogeneity in other cell types.

scholarly journals CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

2022 ◽  
Author(s):  
Takaho Tsuchiya ◽  
Hiroki Hori ◽  
Haruka Ozaki
Keyword(s):  
Gene Expression ◽  
Single Cells ◽  
Cell Types ◽  
Regression Modeling ◽  
Transcriptome Data ◽  
Cell Type ◽  
Neighboring Cell ◽  
Expression Variability ◽  
Cell Expression ◽  
Cell Cell

Motivation: Cell-cell communications regulate internal cellular states of the cell, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still unexplored. The recent advent of spatial transcriptome measurement methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels that are influenced by neighboring cell types based on the spatial transcriptome data. However, limitations remain in the quantitativeness and interpretability: it neither focuses on HVGs, considers cooperation of neighboring cell types, nor quantifies the degree of regulation with each neighboring cell type. Results: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated effects of multiple neighboring cell types on HVGs. Furthermore, by applying CCPLS to the two real datasets, we demonstrate CCPLS can be used to extract biologically interpretable insights from the inferred cell-cell communications.

scholarly journals Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

2020 ◽  
Vol 30 (4) ◽  
pp. 611-621 ◽  
Author(s):  
Chiaowen Joyce Hsiao ◽  
PoYuan Tung ◽  
John D. Blischak ◽  
Jonathan E. Burnett ◽  
Kenneth A. Barr ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Data Analysis ◽  
Single Cell ◽  
Cell Cycle Phase ◽  
Cycle Phase ◽  
Rna Seq

scholarly journals The effects of proliferation status and cell cycle phase on the responses of single cells to chemotherapy

2020 ◽  
Vol 31 (8) ◽  
pp. 845-857 ◽  
Author(s):  
Adrián E. Granada ◽  
Alba Jiménez ◽  
Jacob Stewart-Ornstein ◽  
Nils Blüthgen ◽  
Simone Reber ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Information Theory ◽  
Tumor Cells ◽  
Single Cells ◽  
Residual Tumor ◽  
Cell Cycle Phase ◽  
Cycle Phase ◽  
Proliferating Cells ◽  
Probability Of Death

DNA-damaging chemotherapy often leaves residual tumor cells. Combining single-cell long-term live imaging with information theory, we found an unexpected effect: highly proliferative cells were more likely to arrest than to die, whereas more slowly proliferating cells showed a higher probability of death.

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 Microdroplet-based one-step RT-PCR for ultrahigh throughput single-cell multiplex gene expression analysis and rare cell detection

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jennifer Ma ◽  
Gary Tran ◽  
Alwin M. D. Wan ◽  
Edmond W. K. Young ◽  
Eugenia Kumacheva ◽  
...  
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Single Cells ◽  
Cell Populations ◽  
Cell Detection ◽  
Rt Pcr ◽  
One Step ◽  
Cell Gene

AbstractGene expression analysis of individual cells enables characterization of heterogeneous and rare cell populations, yet widespread implementation of existing single-cell gene analysis techniques has been hindered due to limitations in scale, ease, and cost. Here, we present a novel microdroplet-based, one-step reverse-transcriptase polymerase chain reaction (RT-PCR) platform and demonstrate the detection of three targets simultaneously in over 100,000 single cells in a single experiment with a rapid read-out. Our customized reagent cocktail incorporates the bacteriophage T7 gene 2.5 protein to overcome cell lysate-mediated inhibition and allows for one-step RT-PCR of single cells encapsulated in nanoliter droplets. Fluorescent signals indicative of gene expressions are analyzed using a probabilistic deconvolution method to account for ambient RNA and cell doublets and produce single-cell gene signature profiles, as well as predict cell frequencies within heterogeneous samples. We also developed a simulation model to guide experimental design and optimize the accuracy and precision of the assay. Using mixtures of in vitro transcripts and murine cell lines, we demonstrated the detection of single RNA molecules and rare cell populations at a frequency of 0.1%. This low cost, sensitive, and adaptable technique will provide an accessible platform for high throughput single-cell analysis and enable a wide range of research and clinical applications.

scholarly journals A comparison between single cell RNA sequencing and single molecule RNA FISH for rare cell analysis

2017 ◽  
Author(s):  
Eduardo Torre ◽  
Hannah Dueck ◽  
Sydney Shaffer ◽  
Janko Gospocic ◽  
Rohit Gupte ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Single Molecule ◽  
Single Cells ◽  
Cycle Phase ◽  
Expression Variability ◽  
Sequencing Technologies ◽  
Rna Fish ◽  
Single Cell Rna Sequencing ◽  
Sequencing Platforms

AbstractThe development of single cell RNA sequencing technologies has emerged as a powerful means of profiling the transcriptional behavior of single cells, leveraging the breadth of sequencing measurements to make inferences about cell type. However, there is still little understanding of how well these methods perform at measuring single cell variability for small sets of genes and what “transcriptome coverage” (e.g. genes detected per cell) is needed for accurate measurements. Here, we use single molecule RNA FISH measurements of 26 genes in thousands of melanoma cells to provide an independent reference dataset to assess the performance of the DropSeq and Fluidigm single cell RNA sequencing platforms. We quantified the Gini coefficient, a measure of rare-cell expression variability, and find that the correspondence between RNA FISH and single cell RNA sequencing for Gini, unlike for mean, increases markedly with per-cell library complexity up to a threshold of ∼2000 genes detected. A similar complexity threshold also allows for robust assignment of multi-genic cell states such as cell cycle phase. Our results provide guidelines for selecting sequencing depth and complexity thresholds for single cell RNA sequencing. More generally, our results suggest that if the number of genes whose expression levels are required to answer any given biological question is small, then greater transcriptome complexity per cell is likely more important than obtaining very large numbers of cells.

scholarly journals Single-cell transcriptomes of fluorescent, ubiquitination-based cell cycle indicator cells

2016 ◽  
Author(s):  
Michael Böttcher ◽  
Tsukasa Kouno ◽  
Elo Madissoon ◽  
Efthymios Motakis ◽  
Imad Abugessaisa ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Single Cells ◽  
Open Data ◽  
Image Data ◽  
Sequencing Data ◽  
Fluorescence Measurements ◽  
Starting Point ◽  
Public Repositories ◽  
Cell Expression

AbstractWe used a transgenic HeLa cell line that reports cell cycle phases through fluorescent, ubiquitination-based cell cycle indicators (Fucci), to produce a reference dataset of more than 270 curated single cells. Microscopic images were taken from each cell followed by RNA-sequencing, so that single-cell expression data is associated to the fluorescence intensity of the Fucci probes in the same cell. We developed an open data management and quality control workflow that enables users to replicate the processing of the sequence and microscopic image data that we deposited in public repositories. The workflow outputs a table with metadata, that is the starting point for further studies on these data. Beyond its use for cell cycle studies, We also expect that our workflow can be adapted to other single-cell projects using a similar combination of sequencing data and fluorescence measurements.

Single-cell analysis of the relationship among transferrin receptors, proliferation, and cell cycle phase in K562 cells

Cytometry ◽  
1985 ◽  
Vol 6 (2) ◽  
pp. 151-158 ◽  
Author(s):  
Natalie S. Rudolph ◽  
Betsy M. Ohlsson-Wilhelm ◽  
James F. Leary ◽  
Peter T. Rowley
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Single Cell Analysis ◽  
K562 Cells ◽  
Cell Cycle Phase ◽  
Cycle Phase ◽  
Transferrin Receptors ◽  
Cell Analysis ◽  
The Relationship
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