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

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.

2018 ◽  
Vol 11 (1) ◽  
Author(s):  
Anissa Guillemin ◽  
Angélique Richard ◽  
Sandrine Gonin-Giraud ◽  
Olivier Gandrillon

2017 ◽  
Vol 8 ◽  
Author(s):  
Soheila Dolatabadi ◽  
Julián Candia ◽  
Nina Akrap ◽  
Christoffer Vannas ◽  
Tajana Tesan Tomic ◽  
...  

Blood ◽  
2014 ◽  
Vol 124 (21) ◽  
pp. 2903-2903
Author(s):  
Chad D Sanada ◽  
Elizabeth Min ◽  
Siying Zou ◽  
Huiyan Jin ◽  
Ping-Xia Zhang ◽  
...  

Abstract Megakaryocyte-Erythroid Progenitors (MEPs) are bipotent cells capable of generating megakaryocytic (Mk) or erythroid (E) progeny. However, neither the cell fate-determining componentry nor the initial molecular consequences of lineage specification have been defined. To elucidate this, it is critical to rigorously purify MEP from primary cell sources. Unfortunately, existing purification strategies to do this fail to yield pure, bipotent cells. To improve upon existing approaches for the enrichment of primary human MEPs from G-CSF mobilized peripheral blood (MPB) and BM, we used the cell surface markers CD36 and CD110 in order to further enrich MEP from CD34+CD38+Lin-Flt3-CD45RA- cells. We then quantitated the Mk and E potential of those cells using single cell colony assays. Using this approach demonstrated that CD36/CD110 selection led to an increase of biphenotypic MEP (assessed as CFU-Mk/E) from ~15% to ~40% of colonies that grew. However, it was unclear from colony assay data alone whether or not the heterogeneity of the underlying population was accurately reflected. To address this, we subjected the FACS-sorted MEP-enriched population to single cell mRNA deep sequencing using the Fluidigm C1 platform. For comparison to MEP, we also performed single cell deep sequencing of CD34+CD38+CD41+Flt3- and CD34+CD38+Flt3-CD36+ cells, which are highly enriched for megakaryocyte progenitors (MkP) and erythroid progenitors (ErP), respectively. A total of 150 single cells were captured and sequenced with an average of 3 million reads per cell (1x100bp sequencing). The mRNA deep sequencing data was analyzed by a combination of gene and cell bi-clustering approach to identify both transcripts and cells that exhibited shared or differential patterning. Initial expression patterns and cell groups were identified using stringent expression filtering for transcripts that exhibited >10 FPKM in at least one cell, and iteratively defined and refined based on known E, Mk, and other hematopoietic genes, and then extended for all strongly expressed transcripts. For the MkP and ErP groups, the resulting clusters of cells expressed genes indicative of commitment to E or Mk differentiation. In contrast, within the MEP-enriched population, while a few cells clustered with MkP and ErP, the vast majority of cells fell into distinct subsets of uncommitted cells, supporting the idea that the MEP-enriched population was unique and distinct from MkP or ErP. Analysis of the gene expression patterns from the MEP, ErP and MkP revealed two remarkable trends. First, the transcription factors GATA1 and GATA2 showed distinct expression patterns in the different clusters of cells; there was a subset of MEP that had high GATA2 expression with little to no GATA1 expression (GATA2 cluster), and an opposite cluster containing high GATA1 expression and low or absent GATA2 expression (GATA1 cluster). The genes most positively correlated with GATA2 expression were also low or absent in the GATA 1 cluster. Closer analysis revealed that the GATA 1 cluster cells were predominantly erythroid and megakaryocyte committed, while the GATA2 cluster appeared uncommitted. A third cluster was present, containing intermediate expression of both GATA1 and GATA2. This cluster is as yet undefined, but appears to contain both MkP and MEP, suggesting a possible link between these two cell types. The second pattern we noted was that the genes in the GATA1 cluster correlated very strongly with cell cycle activity and cell cycle progression while the GATA2 cluster geneset had very low cell cycle activity. This suggested that the commitment of the MEP to E or Mk fates could not be unlinked from their cell cycling status. Such a finding could only be ascertained using single cell sequencing. Using single cell sequencing also provided us with a gene expression signature for primary human MkP, something which was not possible before because there is no reliable way to sort pure human MkP. Regarding GATA1 and GATA2 clusters, real time RT-PCR analysis of primary human ErP, MkP, and MEP point to a scenario where the ratio of GATA2/GATA1 is critical to determining the E vs. Mk fate decision. These findings will be further addressed in future studies aiming to understand the link between cell cycle and the MEP fate decision. Our new findings will help clarify genetic events critical for the E/Mk fate decision. Disclosures No relevant conflicts of interest to declare.


2007 ◽  
Vol 189 (19) ◽  
pp. 7127-7133 ◽  
Author(s):  
Tim J. Strovas ◽  
Linda M. Sauter ◽  
Xiaofeng Guo ◽  
Mary E. Lidstrom

ABSTRACT Cell-to-cell heterogeneity in gene expression and growth parameters was assessed in the facultative methylotroph Methylobacterium extorquens AM1. A transcriptional fusion between a well-characterized methylotrophy promoter (PmxaF ) and gfpuv (encoding a variant of green fluorescent protein [GFPuv]) was used to assess single-cell gene expression. Using a flowthrough culture system and laser scanning microscopy, data on fluorescence and cell size were obtained over time through several growth cycles for cells grown on succinate or methanol. Cells were grown continuously with no discernible lag between divisions, and high cell-to-cell variability was observed for cell size at division (2.5-fold range), division time, and growth rate. When individual cells were followed over multiple division cycles, no direct correlation was observed between the growth rate before a division and the subsequent growth rate or between the cell size at division and the subsequent growth rate. The cell-to-cell variability for GFPuv fluorescence from the PmxaF promoter was less, with a range on the order of 1.5-fold. Fluorescence and growth rate were also followed during a carbon shift experiment, in which cells growing on succinate were shifted to methanol. Variability of the response was observed, and the growth rate at the time of the shift from succinate to methanol was a predictor of the response. Higher growth rates at the time of the substrate shift resulted in greater decreases in growth rates immediately after the shift, but full induction of PmxaF -gfpuv was achieved faster. These results demonstrate that in M. extorquens, physiological heterogeneity at the single-cell level plays an important role in determining the population response to the metabolic shift examined.


2018 ◽  
Author(s):  
Malika Saint ◽  
François Bertaux ◽  
Wenhao Tang ◽  
Xi-Ming Sun ◽  
Laurence Game ◽  
...  

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.


2014 ◽  
Author(s):  
Lucas Dennis ◽  
Andrew McDavid ◽  
Patrick Danaher ◽  
Greg Finak ◽  
Michael Krouse ◽  
...  

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.


Methods ◽  
2018 ◽  
Vol 133 ◽  
pp. 81-90 ◽  
Author(s):  
Katja M. Piltti ◽  
Brian J. Cummings ◽  
Krystal Carta ◽  
Ayla Manughian-Peter ◽  
Colleen L. Worne ◽  
...  

eLife ◽  
2015 ◽  
Vol 4 ◽  
Author(s):  
Ryan A Kellogg ◽  
Chengzhe Tian ◽  
Tomasz Lipniacki ◽  
Stephen R Quake ◽  
Savaş Tay

Digital signaling enhances robustness of cellular decisions in noisy environments, but it is unclear how digital systems transmit temporal information about a stimulus. To understand how temporal input information is encoded and decoded by the NF-κB system, we studied transcription factor dynamics and gene regulation under dose- and duration-modulated inflammatory inputs. Mathematical modeling predicted and microfluidic single-cell experiments confirmed that integral of the stimulus (or area, concentration × duration) controls the fraction of cells that activate NF-κB in the population. However, stimulus temporal profile determined NF-κB dynamics, cell-to-cell variability, and gene expression phenotype. A sustained, weak stimulation lead to heterogeneous activation and delayed timing that is transmitted to gene expression. In contrast, a transient, strong stimulus with the same area caused rapid and uniform dynamics. These results show that digital NF-κB signaling enables multidimensional control of cellular phenotype via input profile, allowing parallel and independent control of single-cell activation probability and population heterogeneity.


2018 ◽  
Vol 92 (9) ◽  
pp. e00179-18 ◽  
Author(s):  
Xiu Xin ◽  
Hailong Wang ◽  
Lingling Han ◽  
Mingzhen Wang ◽  
Hui Fang ◽  
...  

ABSTRACTViral infection and replication are affected by host cell heterogeneity, but the mechanisms underlying the effects remain unclear. Using single-cell analysis, we investigated the effects of host cell heterogeneity, including cell size, inclusion, and cell cycle, on foot-and-mouth disease virus (FMDV) infection (acute and persistent infections) and replication. We detected various viral genome replication levels in FMDV-infected cells. Large cells and cells with a high number of inclusions generated more viral RNA copies and viral protein and a higher proportion of infectious cells than other cells. Additionally, we found that the viral titer was 10- to 100-fold higher in cells in G2/M than those in other cell cycle phases and identified a strong correlation between cell size, inclusion, and cell cycle heterogeneity, which all affected the infection and replication of FMDV. Furthermore, we demonstrated that host cell heterogeneity influenced the adsorption of FMDV due to differences in the levels of FMDV integrin receptors expression. Collectively, these results further our understanding of the evolution of a virus in a single host cell.IMPORTANCEIt is important to understand how host cell heterogeneity affects viral infection and replication. Using single-cell analysis, we found that viral genome replication levels exhibited dramatic variability in foot-and-mouth disease virus (FMDV)-infected cells. We also found a strong correlation between heterogeneity in cell size, inclusion number, and cell cycle status and that all of these characteristics affect the infection and replication of FMDV. Moreover, we found that host cell heterogeneity influenced the viral adsorption as differences in the levels of FMDV integrin receptors' expression. This study provided new ideas for the studies of correlation between FMDV infection mechanisms and host cells.


2017 ◽  
Author(s):  
Alice Moussy ◽  
Jérémie Cosette ◽  
Romuald Parmentier ◽  
Cindy da Silva ◽  
Guillaume Corre ◽  
...  

AbstractIndividual cells take lineage commitment decisions in a way that is not necessarily uniform. We address this issue by characterizing transcriptional changes in cord blood derived CD34+ cells at the single-cell level and integrating data with cell division history and morphological changes determined by time-lapse microscopy. We show, that major transcriptional changes leading to a multilineage-primed gene expression state occur very rapidly during the first cell cycle. One of the two stable lineage-primed patterns emerges gradually in each cell with variable timing. Some cells reach a stable morphology and molecular phenotype by the end of the first cell cycle and transmit it clonally. Others fluctuate between the two phenotypes over several cell cycles. Our analysis highlights the dynamic nature and variable timing of cell fate commitment in hematopoietic cells, links the gene expression pattern to cell morphology and identifies a new category of cells with fluctuating phenotypic characteristics, demonstrating the complexity of the fate decision process, away from a simple binary switch between two options as it is usually envisioned.


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