scholarly journals Inference and multiscale model of epithelial-to-mesenchymal transition via single-cell transcriptomic data

2020 ◽  
Vol 48 (17) ◽  
pp. 9505-9520 ◽  
Author(s):  
Yutong Sha ◽  
Shuxiong Wang ◽  
Peijie Zhou ◽  
Qing Nie
Keyword(s):  
Unsupervised Learning ◽  
Single Cell ◽  
Cell Fate ◽  
Epithelial To Mesenchymal Transition ◽  
Multiscale Model ◽  
Integrative Approach ◽  
Intermediate Cell ◽  
Mesenchymal Transition ◽  
Transcriptomic Data ◽  
Fate Decision

Abstract Rapid growth of single-cell transcriptomic data provides unprecedented opportunities for close scrutinizing of dynamical cellular processes. Through investigating epithelial-to-mesenchymal transition (EMT), we develop an integrative tool that combines unsupervised learning of single-cell transcriptomic data and multiscale mathematical modeling to analyze transitions during cell fate decision. Our approach allows identification of individual cells making transition between all cell states, and inference of genes that drive transitions. Multiscale extractions of single-cell scale outputs naturally reveal intermediate cell states (ICS) and ICS-regulated transition trajectories, producing emergent population-scale models to be explored for design principles. Testing on the newly designed single-cell gene regulatory network model and applying to twelve published single-cell EMT datasets in cancer and embryogenesis, we uncover the roles of ICS on adaptation, noise attenuation, and transition efficiency in EMT, and reveal their trade-off relations. Overall, our unsupervised learning method is applicable to general single-cell transcriptomic datasets, and our integrative approach at single-cell resolution may be adopted for other cell fate transition systems beyond EMT.

scholarly journals Cytoplasmic NOTCH and membrane-derived β-catenin link cell fate choice to epithelial-mesenchymal transition during myogenesis

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Daniel Sieiro ◽  
Anne C Rios ◽  
Claire E Hirst ◽  
Christophe Marcelle
Keyword(s):  
Cell Fate ◽  
Epithelial To Mesenchymal Transition ◽  
Epithelial Mesenchymal Transition ◽  
Mesenchymal Transition ◽  
Epithelial Plasticity ◽  
Cellular Environment ◽  
Muscle Formation ◽  
Fate Decision ◽  
Gsk 3Β ◽  
Coupling Cell

How cells in the embryo coordinate epithelial plasticity with cell fate decision in a fast changing cellular environment is largely unknown. In chick embryos, skeletal muscle formation is initiated by migrating Delta1-expressing neural crest cells that trigger NOTCH signaling and myogenesis in selected epithelial somite progenitor cells, which rapidly translocate into the nascent muscle to differentiate. Here, we uncovered at the heart of this response a signaling module encompassing NOTCH, GSK-3β, SNAI1 and β-catenin. Independent of its transcriptional function, NOTCH profoundly inhibits GSK-3β activity. As a result SNAI1 is stabilized, triggering an epithelial to mesenchymal transition. This allows the recruitment of β-catenin from the membrane, which acts as a transcriptional co-factor to activate myogenesis, independently of WNT ligand. Our results intimately associate the initiation of myogenesis to a change in cell adhesion and may reveal a general principle for coupling cell fate changes to EMT in many developmental and pathological processes.

Volumetric compression develops noise-driven single-cell heterogeneity

2021 ◽  
Vol 118 (51) ◽  
pp. e2110550118
Author(s):  
Xing Zhao ◽  
Jiliang Hu ◽  
Yiwei Li ◽  
Ming Guo
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Single Molecule ◽  
Regulatory Network ◽  
Network Architecture ◽  
Cell Heterogeneity ◽  
Cell Lung Carcinoma ◽  
Homogeneous Population ◽  
Mesenchymal Transition ◽  
Signature Genes

Recent studies have revealed that extensive heterogeneity of biological systems arises through various routes ranging from intracellular chromosome segregation to spatiotemporally varying biochemical stimulations. However, the contribution of physical microenvironments to single-cell heterogeneity remains largely unexplored. Here, we show that a homogeneous population of non–small-cell lung carcinoma develops into heterogeneous subpopulations upon application of a homogeneous physical compression, as shown by single-cell transcriptome profiling. The generated subpopulations stochastically gain the signature genes associated with epithelial–mesenchymal transition (EMT; VIM, CDH1, EPCAM, ZEB1, and ZEB2) and cancer stem cells (MKI67, BIRC5, and KLF4), respectively. Trajectory analysis revealed two bifurcated paths as cells evolving upon the physical compression, along each path the corresponding signature genes (epithelial or mesenchymal) gradually increase. Furthermore, we show that compression increases gene expression noise, which interplays with regulatory network architecture and thus generates differential cell-fate outcomes. The experimental observations of both single-cell sequencing and single-molecule fluorescent in situ hybridization agrees well with our computational modeling of regulatory network in the EMT process. These results demonstrate a paradigm of how mechanical stimulations impact cell-fate determination by altering transcription dynamics; moreover, we show a distinct path that the ecology and evolution of cancer interplay with their physical microenvironments from the view of mechanobiology and systems biology, with insight into the origin of single-cell heterogeneity.

scholarly journals Learning time-varying information flow from single-cell epithelial to mesenchymal transition data

PLoS ONE ◽  
2018 ◽  
Vol 13 (10) ◽  
pp. e0203389 ◽  
Author(s):  
Smita Krishnaswamy ◽  
Nevena Zivanovic ◽  
Roshan Sharma ◽  
Dana Pe’er ◽  
Bernd Bodenmiller
Keyword(s):  
Single Cell ◽  
Information Flow ◽  
Epithelial To Mesenchymal Transition ◽  
Time Varying ◽  
Mesenchymal Transition ◽  
Learning Time

scholarly journals Topographer Reveals Dynamic Mechanisms of Cell Fate Decisions from Single-Cell Transcriptomic Data

2018 ◽  
Author(s):  
Jiajun Zhang ◽  
Qing Nie ◽  
Tianshou Zhou
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Branch Point ◽  
State Transition ◽  
Transition Probabilities ◽  
Marker Gene ◽  
Complex Cell ◽  
Cell Fate Decisions ◽  
Transcriptomic Data ◽  
Cell State

AbstractCell fate decisions play a pivotal role in development but technologies for dissecting them are limited. We developed a multifunction new method, Topographer to construct a ‘quantitative’ Waddington’s landscape of single-cell transcriptomic data. This method is able to identify complex cell-state transition trajectories and to estimate complex cell-type dynamics characterized by fate and transition probabilities. It also infers both marker gene networks and their dynamic changes as well as dynamic characteristics of transcriptional bursting along the cell-state transition trajectories. Applying this method to single-cell RNA-seq data on the differentiation of primary human myoblasts, we not only identified three known cell types but also estimated both their fate probabilities and transition probabilities among them. We found that the percent of genes expressed in a bursty manner is significantly higher at (or near) the branch point (∼97%) than before or after branch (below 80%), and that both gene-gene and cell-cell correlation degrees are apparently lower near the branch point than away from the branching. Topographer allows revealing of cell fate mechanisms in a coherent way at three scales: cell lineage (macroscopic), gene network (mesoscopic) and gene expression (microscopic).

scholarly journals Analyses of Multi-dimensional Single Cell Trajectories Quantify Transition Paths Between Nonequilibrium Steady States

2020 ◽  
Author(s):  
Weikang Wang ◽  
Jianhua Xing
Keyword(s):  
Single Cell ◽  
Epithelial To Mesenchymal Transition ◽  
A549 Cells ◽  
Time Lapse ◽  
State Theory ◽  
Nonequilibrium Steady States ◽  
Transition Path ◽  
Mesenchymal Transition ◽  
Transition Path Theory ◽  
Cell Trajectories

ABSTRACTA problem ubiquitous in almost all scientific areas is escape from a metastable state, or relaxation from one stationary distribution to a new one1. More than a century of studies lead to celebrated theoretical and computational developments such as the transition state theory and reactive flux formulation. Modern transition path sampling and transition path theory focus on an ensemble of trajectories that connect the initial and final states in a state space2, 3. However, it is generally unfeasible to experimentally observe these trajectories in multiple dimensions and compare to theoretical results. Here we report and analyze single cell trajectories of human A549 cells undergoing TGF-β induced epithelial-to-mesenchymal transition (EMT) in a combined morphology and protein texture space obtained through time lapse imaging. From the trajectories we identify parallel reaction paths with corresponding reaction coordinates and quasi-potentials. Studying cell phenotypic transition dynamics will provide testing grounds for nonequilibrium reaction rate theories.

scholarly journals Integrated time-lapse and single-cell transcription studies highlight the variable and dynamic nature of human hematopoietic cell fate commitment

2017 ◽  
Author(s):  
Alice Moussy ◽  
Jérémie Cosette ◽  
Romuald Parmentier ◽  
Cindy da Silva ◽  
Guillaume Corre ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Single Cell ◽  
Cell Fate ◽  
Morphological Changes ◽  
Gene Expression Pattern ◽  
Time Lapse ◽  
Dynamic Nature ◽  
Transcriptional Changes ◽  
Fate Decision

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.

scholarly journals Distinct Cytosine Modification Profiles Define Epithelial-to-Mesenchymal Cell-State Transitions

2021 ◽  
Author(s):  
Min Kyung Lee ◽  
Meredith S. Brown ◽  
Owen Wilkins ◽  
Diwakar R. Pattabiraman ◽  
Brock C. Christensen
Keyword(s):  
Gene Expression ◽  
Cytosine Methylation ◽  
Epithelial To Mesenchymal Transition ◽  
Mesenchymal Cell ◽  
State Transitions ◽  
Open Chromatin ◽  
Intermediate Cell ◽  
Mesenchymal Transition ◽  
Accessible Chromatin ◽  
Cytosine Modification

Abstract Background: Epithelial-to-mesenchymal transition (EMT) is an early step in the invasion-metastasis cascade, involving progression through a number of cell intermediate states. Due to challenges with isolating intermediate cell states in EMT, genome-wide cytosine modification mechanisms that define transition through EMT states are not completely understood. We measured multiple DNA cytosine methylation modification marks, complemented with chromatin accessibility and gene expression, across clonal populations residing in specific EMT states. Results: Clones exhibiting intermediate EMT phenotypes demonstrated increased global 5-hydroxymethylcytosine (5hmC), decreased 5-methylcytosine (5mC), and more accesible chromatin. Open chromatin regions containing CpG loci with abundant 5hmC were enriched in motifs of key EMT transcription factors, ZEB1 and Snail. The magnitude of altered gene expression in intermediate cell states was higher for genes both with increased gene promoter 5hmC and differentially accessible chromatin compared with genes that exhibited differentially accessible chromatin alone, implicating functional epigenetic duality in regulation of EMT.Conclusion: Our results indicate the importance of both distinct and shared epigenetic profiles at the cytosine and chromatin level associated with EMT processes that contribute to gene regulation and which may be targeted to prevent the progression of EMT.

scholarly journals CellRank for directed single-cell fate mapping

2020 ◽  
Author(s):  
Marius Lange ◽  
Volker Bergen ◽  
Michal Klein ◽  
Manu Setty ◽  
Bernhard Reuter ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Normal Development ◽  
Biological Process ◽  
Single Cells ◽  
Intermediate Cell ◽  
Directional Information ◽  
Stochastic Nature ◽  
Single Cell Rna Sequencing

AbstractComputational trajectory inference enables the reconstruction of cell-state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for mapping the fate of single cells in diverse scenarios, including perturbations such as regeneration or disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, derived from ratios of spliced to unspliced reads. CellRank takes into account both the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in RNA velocity vectors. On data from pancreas development, we show that it automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. CellRank also predicts a novel dedifferentiation trajectory during regeneration after lung injury, which we follow up experimentally by confirming the existence of previously unknown intermediate cell states.

scholarly journals Identification of Gene Signatures and Expression Patterns During Epithelial-to-Mesenchymal Transition From Single-Cell Expression Atlas

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiangtian Yu ◽  
XiaoYong Pan ◽  
ShiQi Zhang ◽  
Yu-Hang Zhang ◽  
Lei Chen ◽  
...  
Keyword(s):  
Drug Resistance ◽  
Single Cell ◽  
Epithelial To Mesenchymal Transition ◽  
Expression Patterns ◽  
Cancer Recurrence ◽  
Functional Enrichment ◽  
Support Vector ◽  
Pathogenic Potential ◽  
Mesenchymal Transition ◽  
Cell Expression

Cancer, which refers to abnormal cell proliferative diseases with systematic pathogenic potential, is one of the leading threats to human health. The final causes for patients’ deaths are usually cancer recurrence, metastasis, and drug resistance against continuing therapy. Epithelial-to-mesenchymal transition (EMT), which is the transformation of tumor cells (TCs), is a prerequisite for pathogenic cancer recurrence, metastasis, and drug resistance. Conventional biomarkers can only define and recognize large tissues with obvious EMT markers but cannot accurately monitor detailed EMT processes. In this study, a systematic workflow was established integrating effective feature selection, multiple machine learning models [Random forest (RF), Support vector machine (SVM)], rule learning, and functional enrichment analyses to find new biomarkers and their functional implications for distinguishing single-cell isolated TCs with unique epithelial or mesenchymal markers using public single-cell expression profiling. Our discovered signatures may provide an effective and precise transcriptomic reference to monitor EMT progression at the single-cell level and contribute to the exploration of detailed tumorigenesis mechanisms during EMT.

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