scholarly journals Quantitative single-cell live imaging links HES5 dynamics with cell-state and fate in murine neurogenesis

2018 ◽  
Author(s):  
Cerys S Manning ◽  
Veronica Biga ◽  
James Boyd ◽  
Jochen Kursawe ◽  
Bodvar Ymisson ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Live Imaging ◽  
Protein Quantification ◽  
Expression Change ◽  
Dynamic View ◽  
Transient Period ◽  
Gene Expression Dynamics ◽  
Transcription Factor Expression ◽  
Protein Fluctuations

AbstractDuring embryogenesis cells make fate decisions within complex tissue environments. The levels and dynamics of transcription factor expression regulate these decisions. Here we use single cell live imaging of an endogenous HES5 reporter and absolute protein quantification to gain a dynamic view of neurogenesis in the embryonic mammalian spinal cord. We report that dividing neural progenitors show both aperiodic and periodic HES5 protein fluctuations. Mathematical modelling suggests that in progenitor cells the HES5 oscillator operates close to its bifurcation boundary where stochastic conversions between dynamics are possible. HES5 expression becomes more frequently periodic as cells transition to differentiation which, coupled with an overall decline in HES5 expression, creates a transient period of oscillations with higher fold expression change. This increases the decoding capacity of HES5 oscillations and correlates with interneuron versus motor neuron cell fate. Thus, HES5 undergoes complex changes in gene expression dynamics as cells differentiate.

scholarly journals Cell lineage and cell cycling analyses of the 4d micromere using live imaging in the marine annelid Platynereis dumerilii

eLife ◽  
2017 ◽  
Vol 6 ◽  
Author(s):  
B Duygu Özpolat ◽  
Mette Handberg-Thorsager ◽  
Michel Vervoort ◽  
Guillaume Balavoine
Keyword(s):  
Cell Cycle ◽  
Single Cell ◽  
Cell Fate ◽  
Cell Lineage ◽  
Growth Zone ◽  
Live Imaging ◽  
In Vivo Studies ◽  
Platynereis Dumerilii ◽  
Cell Cycling

Cell lineage, cell cycle, and cell fate are tightly associated in developmental processes, but in vivo studies at single-cell resolution showing the intricacies of these associations are rare due to technical limitations. In this study on the marine annelid Platynereis dumerilii, we investigated the lineage of the 4d micromere, using high-resolution long-term live imaging complemented with a live-cell cycle reporter. 4d is the origin of mesodermal lineages and the germline in many spiralians. We traced lineages at single-cell resolution within 4d and demonstrate that embryonic segmental mesoderm forms via teloblastic divisions, as in clitellate annelids. We also identified the precise cellular origins of the larval mesodermal posterior growth zone. We found that differentially-fated progeny of 4d (germline, segmental mesoderm, growth zone) display significantly different cell cycling. This work has evolutionary implications, sets up the foundation for functional studies in annelid stem cells, and presents newly established techniques for live imaging marine embryos.

scholarly journals Inferring kinetic parameters of oscillatory gene regulation from single cell time series data

2021 ◽  
Author(s):  
Joshua Burton ◽  
Cerys S Manning ◽  
Magnus Rattray ◽  
Nancy Papalopulu ◽  
Jochen Kursawe
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Single Cell ◽  
Kinetic Parameters ◽  
Cell Fate ◽  
Time Series Data ◽  
Single Cells ◽  
Population Heterogeneity ◽  
Series Data ◽  
Gene Expression Dynamics

Gene expression dynamics, such as stochastic oscillations and aperiodic fluctuations, have been associated with cell fate changes in multiple contexts, including development and cancer. Single cell live imaging of protein expression with endogenous reporters is widely used to observe such gene expression dynamics. However, the experimental investigation of regulatory mechanisms underlying the observed dynamics is challenging, since these mechanisms include complex interactions of multiple processes, including transcription, translation, and protein degradation. Here, we present a Bayesian method to infer kinetic parameters of oscillatory gene expression regulation using an auto-negative feedback motif with delay. Specifically, we use a delay-adapted nonlinear Kalman filter within a Metropolis-adjusted Langevin algorithm to identify posterior probability distributions. Our method can be applied to time series data on gene expression from single cells and is able to infer multiple parameters simultaneously. We apply it to published data on murine neural progenitor cells and show that it outperforms alternative methods. We further analyse how parameter uncertainty depends on the duration and time resolution of an imaging experiment, to make experimental design recommendations. This work demonstrates the utility of parameter inference on time course data from single cells and enables new studies on cell fate changes and population heterogeneity.

scholarly journals Inferring kinetic parameters of oscillatory gene regulation from single cell time-series data

2021 ◽  
Vol 18 (182) ◽  
Author(s):  
Joshua Burton ◽  
Cerys S. Manning ◽  
Magnus Rattray ◽  
Nancy Papalopulu ◽  
Jochen Kursawe
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Single Cell ◽  
Kinetic Parameters ◽  
Cell Fate ◽  
Time Series Data ◽  
Single Cells ◽  
Population Heterogeneity ◽  
Series Data ◽  
Gene Expression Dynamics

Gene expression dynamics, such as stochastic oscillations and aperiodic fluctuations, have been associated with cell fate changes in multiple contexts, including development and cancer. Single cell live imaging of protein expression with endogenous reporters is widely used to observe such gene expression dynamics. However, the experimental investigation of regulatory mechanisms underlying the observed dynamics is challenging, since these mechanisms include complex interactions of multiple processes, including transcription, translation and protein degradation. Here, we present a Bayesian method to infer kinetic parameters of oscillatory gene expression regulation using an auto-negative feedback motif with delay. Specifically, we use a delay-adapted nonlinear Kalman filter within a Metropolis-adjusted Langevin algorithm to identify posterior probability distributions. Our method can be applied to time-series data on gene expression from single cells and is able to infer multiple parameters simultaneously. We apply it to published data on murine neural progenitor cells and show that it outperforms alternative methods. We further analyse how parameter uncertainty depends on the duration and time resolution of an imaging experiment, to make experimental design recommendations. This work demonstrates the utility of parameter inference on time course data from single cells and enables new studies on cell fate changes and population heterogeneity.

scholarly journals Single cell RNA-seq identifies the origins of heterogeneity in efficient cell transdifferentiation and reprogramming

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Mirko Francesconi ◽  
Bruno Di Stefano ◽  
Clara Berenguer ◽  
Luisa de Andrés-Aguayo ◽  
Marcos Plana-Carmona ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Cell Types ◽  
Rna Seq ◽  
Wide Range ◽  
Cell Fate Conversion ◽  
Induced Pluripotent ◽  
Cell Expression ◽  
Computational Analyses ◽  
Transcription Factor Expression

Forced transcription factor expression can transdifferentiate somatic cells into other specialised cell types or reprogram them into induced pluripotent stem cells (iPSCs) with variable efficiency. To better understand the heterogeneity of these processes, we used single-cell RNA sequencing to follow the transdifferentation of murine pre-B cells into macrophages as well as their reprogramming into iPSCs. Even in these highly efficient systems, there was substantial variation in the speed and path of fate conversion. We predicted and validated that these differences are inversely coupled and arise in the starting cell population, with Mychigh large pre-BII cells transdifferentiating slowly but reprogramming efficiently and Myclow small pre-BII cells transdifferentiating rapidly but failing to reprogram. Strikingly, differences in Myc activity predict the efficiency of reprogramming across a wide range of somatic cell types. These results illustrate how single cell expression and computational analyses can identify the origins of heterogeneity in cell fate conversion processes.

scholarly journals Single-Cell Transcriptome Analysis as a Promising Tool to Study Pluripotent Stem Cell Reprogramming

2021 ◽  
Vol 22 (11) ◽  
pp. 5988
Author(s):  
Hyun Kyu Kim ◽  
Tae Won Ha ◽  
Man Ryul Lee
Keyword(s):  
Stem Cells ◽  
Single Cell ◽  
Cell Fate ◽  
Human Cell ◽  
Transcriptome Analysis ◽  
Single Cell Analysis ◽  
Embryonic Stem ◽  
Single Cell Level ◽  
Cell Analysis ◽  
Cell Level

Cells are the basic units of all organisms and are involved in all vital activities, such as proliferation, differentiation, senescence, and apoptosis. A human body consists of more than 30 trillion cells generated through repeated division and differentiation from a single-cell fertilized egg in a highly organized programmatic fashion. Since the recent formation of the Human Cell Atlas consortium, establishing the Human Cell Atlas at the single-cell level has been an ongoing activity with the goal of understanding the mechanisms underlying diseases and vital cellular activities at the level of the single cell. In particular, transcriptome analysis of embryonic stem cells at the single-cell level is of great importance, as these cells are responsible for determining cell fate. Here, we review single-cell analysis techniques that have been actively used in recent years, introduce the single-cell analysis studies currently in progress in pluripotent stem cells and reprogramming, and forecast future studies.

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