scholarly journals CellTrans: An R Package to Quantify Stochastic Cell State Transitions

2017 ◽  
Vol 11 ◽  
pp. 117793221771224 ◽  
Author(s):  
Thomas Buder ◽  
Andreas Deutsch ◽  
Michael Seifert ◽  
Anja Voss-Böhme
Keyword(s):  
Cell Lines ◽  
Transition Probabilities ◽  
Cell Lineage ◽  
R Package ◽  
State Transitions ◽  
Cell State ◽  
Current State ◽  
Cancerous Cell ◽  
Distinct Cell ◽  
A Cell

Many normal and cancerous cell lines exhibit a stable composition of cells in distinct states which can, e.g., be defined on the basis of cell surface markers. There is evidence that such an equilibrium is associated with stochastic transitions between distinct states. Quantifying these transitions has the potential to better understand cell lineage compositions. We introduce CellTrans, an R package to quantify stochastic cell state transitions from cell state proportion data from fluorescence-activated cell sorting and flow cytometry experiments. The R package is based on a mathematical model in which cell state alterations occur due to stochastic transitions between distinct cell states whose rates only depend on the current state of a cell. CellTrans is an automated tool for estimating the underlying transition probabilities from appropriately prepared data. We point out potential analytical challenges in the quantification of these cell transitions and explain how CellTrans handles them. The applicability of CellTrans is demonstrated on publicly available data on the evolution of cell state compositions in cancer cell lines. We show that CellTrans can be used to (1) infer the transition probabilities between different cell states, (2) predict cell line compositions at a certain time, (3) predict equilibrium cell state compositions, and (4) estimate the time needed to reach this equilibrium. We provide an implementation of CellTrans in R, freely available via GitHub ( https://github.com/tbuder/CellTrans ).

scholarly journals Epigenetic mechanisms mediating cell state transitions in chondrocytes

2020 ◽  
Author(s):  
Manuela Wuelling ◽  
Christoph Neu ◽  
Andrea M. Thiesen ◽  
Simo Kitanovski ◽  
Yingying Cao ◽  
...  
Keyword(s):  
Metabolic Pathways ◽  
Cell Lineage ◽  
Cell Types ◽  
State Transitions ◽  
Epigenetic Mechanisms ◽  
Cell Type ◽  
Transition Analysis ◽  
Lineage Differentiation ◽  
A Cell ◽  
Cell Type Specific

AbstractEpigenetic modifications play critical roles in regulating cell lineage differentiation, but the epigenetic mechanisms guiding specific differentiation steps within a cell lineage have rarely been investigated. To decipher such mechanisms, we used the defined transition from proliferating (PC) into hypertrophic chondrocytes (HC) during endochondral ossification as a model. We established a map of activating and repressive histone modifications for each cell type. ChromHMM state transition analysis and Pareto-based integration of differential levels of mRNA and epigenetic marks revealed that differentiation associated gene repression is initiated by the addition of H3K27me3 to promoters still carrying substantial levels of activating marks. Moreover, the integrative analysis identified genes specifically expressed in cells undergoing the transition into hypertrophy.Investigation of enhancer profiles detected surprising differences in enhancer number, location, and transcription factor binding sites between the two closely related cell types. Furthermore, cell type-specific upregulation of gene expression was associated with a shift from low to high H3K27ac decoration. Pathway analysis identified PC-specific enhancers associated with chondrogenic genes, while HC-specific enhancers mainly control metabolic pathways linking epigenetic signature to biological functions.

scholarly journals scEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data

2018 ◽  
Vol 34 (12) ◽  
pp. 2077-2086 ◽  
Author(s):  
Suoqin Jin ◽  
Adam L MacLean ◽  
Tao Peng ◽  
Qing Nie
Keyword(s):  
Single Cell ◽  
Transition Probabilities ◽  
Energy Landscape ◽  
Control Cell ◽  
Robust Inference ◽  
Supplementary Information ◽  
Myoblast Differentiation ◽  
State Transitions ◽  
Marker Genes ◽  
Cell State

Abstract Motivation Single-cell RNA-sequencing (scRNA-seq) offers unprecedented resolution for studying cellular decision-making processes. Robust inference of cell state transition paths and probabilities is an important yet challenging step in the analysis of these data. Results Here we present scEpath, an algorithm that calculates energy landscapes and probabilistic directed graphs in order to reconstruct developmental trajectories. We quantify the energy landscape using ‘single-cell energy’ and distance-based measures, and find that the combination of these enables robust inference of the transition probabilities and lineage relationships between cell states. We also identify marker genes and gene expression patterns associated with cell state transitions. Our approach produces pseudotemporal orderings that are—in combination—more robust and accurate than current methods, and offers higher resolution dynamics of the cell state transitions, leading to new insight into key transition events during differentiation and development. Moreover, scEpath is robust to variation in the size of the input gene set, and is broadly unsupervised, requiring few parameters to be set by the user. Applications of scEpath led to the identification of a cell-cell communication network implicated in early human embryo development, and novel transcription factors important for myoblast differentiation. scEpath allows us to identify common and specific temporal dynamics and transcriptional factor programs along branched lineages, as well as the transition probabilities that control cell fates. Availability and implementation A MATLAB package of scEpath is available at https://github.com/sqjin/scEpath. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals The evolution of the cancer stem cell state in glioblastoma: emerging insights into the next generation of functional interactions

2020 ◽  
Author(s):  
Kelly Mitchell ◽  
Katie Troike ◽  
Daniel J Silver ◽  
Justin D Lathia
Keyword(s):  
Cellular Heterogeneity ◽  
Next Generation ◽  
Cell State ◽  
Functional Interactions ◽  
Current State ◽  
Key Concepts ◽  
Discrete Population ◽  
A Cell ◽  
Stem Cell State ◽  
Primary Malignant Brain Tumor

Abstract Cellular heterogeneity is a hallmark of advanced cancers and has been ascribed in part to a population of self-renewing, therapeutically resistant cancer stem cells (CSCs). Glioblastoma (GBM), the most common primary malignant brain tumor, has served as a platform for the study of CSCs. In addition to illustrating the complexities of CSC biology, these investigations have led to a deeper understanding of GBM pathogenesis, revealed novel therapeutic targets, and driven innovation towards the development of next-generation therapies. While there continues to be an expansion in our knowledge of how CSCs contribute to GBM progression, opportunities have emerged to revisit this conceptual framework. In this review, we will summarize the current state of CSCs in GBM using key concepts of evolution as a paradigm (variation, inheritance, selection, and time) to describe how the CSC state is subject to alterations of cell intrinsic and extrinsic interactions that shape their evolutionarily trajectory. We identify emerging areas for future consideration, including appreciating CSCs as a cell state that is subject to plasticity, as opposed to a discrete population. These future considerations will not only have an impact on our understanding of this ever-expanding field but will also provide an opportunity to inform future therapies to effectively treat this complex and devastating disease.

scholarly journals ZEB1 insufficiency causes corneal endothelial cell state transition and altered cellular processing

2019 ◽  
Author(s):  
Ricardo F. Frausto ◽  
Doug D. Chung ◽  
Payton M. Boere ◽  
Vinay S. Swamy ◽  
Huong N.V. Duong ◽  
...  
Keyword(s):  
Epithelial To Mesenchymal Transition ◽  
Cell Lineage ◽  
Corneal Dystrophy ◽  
State Transitions ◽  
Cellular Phenotype ◽  
Mesenchymal Transition ◽  
Cell State ◽  
Functional Studies ◽  
Mesenchymal To Epithelial Transition ◽  
Integral Role

ABSTRACTThe zinc finger e-box binding homeobox 1 (ZEB1) transcription factor is a master regulator of the epithelial to mesenchymal transition (EMT), and of the reverse mesenchymal to epithelial transition (MET) processes. ZEB1 plays an integral role in mediating cell state transitions during cell lineage specification, wound healing and disease. EMT/MET are characterized by distinct changes in molecular and cellular phenotype that are generally context-independent. Posterior polymorphous corneal dystrophy (PPCD), associated with ZEB1 insufficiency, provides a new biological context in which to understand and evaluate the classic EMT/MET paradigm. PPCD is characterized by a cadherin-switch and transition to an epithelial-like transcriptomic and cellular phenotype, which we study in a cell-based model of PPCD generated using CRISPR-Cas9-mediated ZEB1 knockout in corneal endothelial cells (CEnCs). Transcriptomic and functional studies support the hypothesis that CEnC undergo a MET-like transition in PPCD, termed endothelial to epithelial transition (EnET), and lead to the conclusion that EnET may be considered a corollary to the classic EMT/MET paradigm.

scholarly journals Single-cell analysis resolves the cell state transition and signaling dynamics associated with melanoma drug-induced resistance

2017 ◽  
Vol 114 (52) ◽  
pp. 13679-13684 ◽  
Author(s):  
Yapeng Su ◽  
Wei Wei ◽  
Lidia Robert ◽  
Min Xue ◽  
Jennifer Tsoi ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Lines ◽  
Single Cell Analysis ◽  
Acquired Resistance ◽  
Therapy Resistance ◽  
State Transitions ◽  
Markov Modeling ◽  
Drug Induced ◽  
Cell State ◽  
Braf Inhibition

Continuous BRAF inhibition of BRAF mutant melanomas triggers a series of cell state changes that lead to therapy resistance and escape from immune control before establishing acquired resistance genetically. We used genome-wide transcriptomics and single-cell phenotyping to explore the response kinetics to BRAF inhibition for a panel of patient-derived BRAFV600-mutant melanoma cell lines. A subset of plastic cell lines, which followed a trajectory covering multiple known cell state transitions, provided models for more detailed biophysical investigations. Markov modeling revealed that the cell state transitions were reversible and mediated by both Lamarckian induction and nongenetic Darwinian selection of drug-tolerant states. Single-cell functional proteomics revealed activation of certain signaling networks shortly after BRAF inhibition, and before the appearance of drug-resistant phenotypes. Drug targeting those networks, in combination with BRAF inhibition, halted the adaptive transition and led to prolonged growth inhibition in multiple patient-derived cell lines.

Cell state transitions: definitions and challenges

Development ◽  
2021 ◽  
Vol 148 (20) ◽  
Author(s):  
Carla Mulas ◽  
Agathe Chaigne ◽  
Austin Smith ◽  
Kevin J. Chalut
Keyword(s):  
Biological Systems ◽  
The State ◽  
Cell Junctions ◽  
State Transitions ◽  
Cell State ◽  
Experimental Systems ◽  
Fundamental Challenge ◽  
A Cell ◽  
Different Shapes ◽  
Cell Cell

ABSTRACT A fundamental challenge when studying biological systems is the description of cell state dynamics. During transitions between cell states, a multitude of parameters may change – from the promoters that are active, to the RNAs and proteins that are expressed and modified. Cells can also adopt different shapes, alter their motility and change their reliance on cell-cell junctions or adhesion. These parameters are integral to how a cell behaves and collectively define the state a cell is in. Yet, technical challenges prevent us from measuring all of these parameters simultaneously and dynamically. How, then, can we comprehend cell state transitions using finite descriptions? The recent Company of Biologists virtual workshop entitled ‘Cell State Transitions: Approaches, Experimental Systems and Models’ attempted to address this question. Here, we summarise some of the main points that emerged during the workshop's themed discussions. We also present examples of cell state transitions and describe models and systems that are pushing forward our understanding of how cells rewire their state.

In Vitro Evaluation of Chitosan Induced Cytotoxicity against Cancerous Cell lines: MCF-7, Saos-2 and HeLa

2019 ◽  
Vol 19 ◽  
Author(s):  
Zeinab Abedian ◽  
Niloofar Jenabian ◽  
Ali Akbar Moghadamnia ◽  
Ebrahim Zabihi ◽  
Roghayeh Pourbagher ◽  
...  
Keyword(s):  
Flow Cytometry ◽  
Cell Lines ◽  
Anticancer Agents ◽  
Fibroblast Cells ◽  
Apoptosis Induction ◽  
Cytotoxic Effects ◽  
Cancerous Cell ◽  
Significant Concentration ◽  
Mcf 7

Objective/ Background: Cancer is still the most common cause of morbidity in world and new powerful anticancer agents without severe side effects from natural sources is important. Methods: The evaluation of cytotoxicity and apoptosis induction was carried out in MCF-7,HeLa and Saos-2 as cancerous cell lines with different histological origin and human fibroblast served as control normal cell. The cells were treated with different concentrations of chitosan and the cytotoxicity was determined using MTT assay after 24, 48 and 72 h .The mode of death was evaluated by flow cytometry . Results: While both types of chitosan showed significant concentration-dependently cytotoxic effects against the three cancerous cell lines, fibroblast cells showed somehow more compatibility with chitosan. On the other hand, there were no significant differences between LMWC and HMWC cytotoxicity in all cell lines. The flow cytometry results showed the apoptosis pattern of death more in Saos-2 and HeLa while necrosis was more observable with MCF7. Also higher viability with both types of chitosan was seen in fibroblast as normal cells Conclusion: Chitosan shows anticancerous effect against 3 cancerous cell lines, while it is compatible with normal diploid fibroblast cells. Furthermore, it seems that the molecular weight of chitosan does not affect its anticancerous property.

scholarly journals DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

2021 ◽  
Vol 22 (3) ◽  
pp. 1399
Author(s):  
Salim Ghannoum ◽  
Waldir Leoncio Netto ◽  
Damiano Fantini ◽  
Benjamin Ragan-Kelley ◽  
Amirabbas Parizadeh ◽  
...  
Keyword(s):  
Single Cell ◽  
Biomarker Discovery ◽  
Enrichment Analysis ◽  
Myxoid Liposarcoma ◽  
R Package ◽  
Differential Analysis ◽  
A Cell ◽  
Reproducible Analysis ◽  
Transcriptomic Level ◽  
User Friendly

The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.

scholarly journals Stomatal Development in the Context of Epidermal Tissues

2021 ◽  
Author(s):  
Keiko U Torii
Keyword(s):  
Arabidopsis Thaliana ◽  
Molecular Genetic ◽  
Cell Lineage ◽  
Optimal Growth ◽  
Cell Types ◽  
State Transitions ◽  
Stomatal Development ◽  
Water Control ◽  
Pavement Cells ◽  
The Core

Abstract Background Stomata are adjustable pores on the surface of plant shoots for efficient gas exchange and water control. The presence of stomata is essential for plant growth and survival, and the evolution of stomata is considered as a key developmental innovation of the land plants, allowing colonization on land from aquatic environments some 450 million years ago. In the past two decades, molecular genetic studies using the model plant Arabidopsis thaliana identified key genes and signalling modules that regulate stomatal development: master-regulatory transcription factors that orchestrate cell-state transitions and peptide-receptor signal transduction pathways, which, together, enforce proper patterning of stomata within the epidermis. Studies in diverse plant species, ranging from bryophytes to angiosperm grasses, have begun to unravel the conservation and uniqueness of the core modules in stomatal development. Scope Here, I review the mechanisms of stomatal development in the context of epidermal tissue patterning. First, I introduce the core regulatory mechanisms of stomatal patterning and differentiation in the model species Arabidopsis thaliana. Subsequently, experimental evidence is presented supporting the idea that different cell types within the leaf epidermis, namely stomata, hydathodes pores, pavement cells, and trichomes, either share developmental origins or mutually influence each other’s gene regulatory circuits during development. Emphasis is taken on extrinsic and intrinsic signals regulating the balance between stomata and pavement cells, specifically by controlling the fate of Stomatal-Lineage Ground Cells (SLGCs) to remain within the stomatal-cell lineage or differentiate into pavement cells. Finally, I discuss the influence of inter-tissue-layer communication between the epidermis and underlying mesophyll/vascular tissues on stomatal differentiation. Understanding the dynamic behaviors of stomatal precursor cells and their differentiation in the broader context of tissue and organ development may help design plants tailored for optimal growth and productivity in specific agricultural applications and a changing environment.

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