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.

SIN-KNO: A method of gene regulatory network inference using single-cell transcription and gene knockout data

2019 ◽  
Vol 17 (06) ◽  
pp. 1950035
Author(s):  
Huiqing Wang ◽  
Yuanyuan Lian ◽  
Chun Li ◽  
Yue Ma ◽  
Zhiliang Yan ◽  
...  
Keyword(s):  
Gene Expression ◽  
Steady State ◽  
Single Cell ◽  
Gene Regulatory Network ◽  
Regulatory Network ◽  
Gene Knockout ◽  
Cell Heterogeneity ◽  
State Information ◽  
Gene Regulatory ◽  
Cell Data

As a tool of interpreting and analyzing genetic data, gene regulatory network (GRN) could reveal regulatory relationships between genes, proteins, and small molecules, as well as understand physiological activities and functions within biological cells, interact in pathways, and how to make changes in the organism. Traditional GRN research focuses on the analysis of the regulatory relationships through the average of cellular gene expressions. These methods are difficult to identify the cell heterogeneity of gene expression. Existing methods for inferring GRN using single-cell transcriptional data lack expression information when genes reach steady state, and the high dimensionality of single-cell data leads to high temporal and spatial complexity of the algorithm. In order to solve the problem in traditional GRN inference methods, including the lack of cellular heterogeneity information, single-cell data complexity and lack of steady-state information, we propose a method for GRN inference using single-cell transcription and gene knockout data, called SINgle-cell transcription data-KNOckout data (SIN-KNO), which focuses on combining dynamic and steady-state information of regulatory relationship contained in gene expression. Capturing cell heterogeneity information could help understand the gene expression difference in different cells. So, we could observe gene expression changes more accurately. Gene knockout data could observe the gene expression levels at steady-state of all other genes when one gene is knockout. Classifying the genes before analyzing the single-cell data could determine a large number of non-existent regulation, greatly reducing the number of regulation required for inference. In order to show the efficiency, the proposed method has been compared with several typical methods in this area including GENIE3, JUMP3, and SINCERITIES. The results of the evaluation indicate that the proposed method can analyze the diversified information contained in the two types of data, establish a more accurate gene regulation network, and improve the computational efficiency. The method provides a new thinking for dealing with large datasets and high computational complexity of single-cell data in the GRN inference.

scholarly journals Shaping development by stochasticity and dynamics in gene regulation

Open Biology ◽  
2017 ◽  
Vol 7 (5) ◽  
pp. 170030 ◽  
Author(s):  
Peng Dong ◽  
Zhe Liu
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Single Cell ◽  
Cell Fate ◽  
Single Molecule ◽  
Large Scale ◽  
Single Cells ◽  
Individual Gene ◽  
Functional Specification ◽  
Spatio Temporal

Animal development is orchestrated by spatio-temporal gene expression programmes that drive precise lineage commitment, proliferation and migration events at the single-cell level, collectively leading to large-scale morphological change and functional specification in the whole organism. Efforts over decades have uncovered two ‘seemingly contradictory’ mechanisms in gene regulation governing these intricate processes: (i) stochasticity at individual gene regulatory steps in single cells and (ii) highly coordinated gene expression dynamics in the embryo. Here we discuss how these two layers of regulation arise from the molecular and the systems level, and how they might interplay to determine cell fate and to control the complex body plan. We also review recent technological advancements that enable quantitative analysis of gene regulation dynamics at single-cell, single-molecule resolution. These approaches outline next-generation experiments to decipher general principles bridging gaps between molecular dynamics in single cells and robust gene regulations in the embryo.

scholarly journals Cellular heterogeneity during mouse pancreatic ductal adenocarcinoma progression at single-cell resolution

2019 ◽  
Author(s):  
Abdel Nasser Hosein ◽  
Huocong Huang ◽  
Zhaoning Wang ◽  
Kamalpreet Parmar ◽  
Wenting Du ◽  
...  
Keyword(s):  
Single Cell ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Cancer Cells ◽  
Genetically Engineered ◽  
Cellular Heterogeneity ◽  
Ductal Adenocarcinoma ◽  
Cell Populations ◽  
Specific Cell ◽  
Cell Heterogeneity ◽  
Mesenchymal Transition

AbstractBackground & AimsPancreatic ductal adenocarcinoma (PDA) is a major cause of cancer-related death with limited therapeutic options available. This highlights the need for improved understanding of the biology of PDA progression. The progression of PDA is a highly complex and dynamic process featuring changes in cancer cells and stromal cells; however, a comprehensive characterization of PDA cancer cell and stromal cell heterogeneity during disease progression is lacking. In this study, we aimed to profile cell populations and understand their phenotypic changes during PDA progression.MethodsWe employed single-cell RNA sequencing technology to agnostically profile cell heterogeneity during different stages of PDA progression in genetically engineered mouse models.ResultsOur data indicate that an epithelial-to-mesenchymal transition of cancer cells accompanies tumor progression. We also found distinct populations of macrophages with increasing inflammatory features during PDA progression. In addition, we noted the existence of three distinct molecular subtypes of fibroblasts in the normal mouse pancreas, which ultimately gave rise to two distinct populations of fibroblasts in advanced PDA, supporting recent reports on intratumoral fibroblast heterogeneity. Our data also suggest that cancer cells and fibroblasts are dynamically regulated by epigenetic mechanisms.ConclusionThis study systematically outlines the landscape of cellular heterogeneity during the progression of PDA. It strongly improves our understanding of the PDA biology and has the potential to aid in the development of therapeutic strategies against specific cell populations of the disease.

scholarly journals Single-cell analyses uncover granularity of muscle stem cells

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 31 ◽  
Author(s):  
John Saber ◽  
Alexander Y.T. Lin ◽  
Michael A. Rudnicki
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Satellite Cells ◽  
Muscle Regeneration ◽  
Single Cell Analysis ◽  
Cell Analysis ◽  
Cell Heterogeneity ◽  
Muscle Stem Cells ◽  
Cell Fate Decisions ◽  
Cell Groups

Satellite cells are the main muscle-resident cells responsible for muscle regeneration. Much research has described this population as being heterogeneous, but little is known about the different roles each subpopulation plays. Recent advances in the field have utilized the power of single-cell analysis to better describe and functionally characterize subpopulations of satellite cells as well as other cell groups comprising the muscle tissue. Furthermore, emerging technologies are opening the door to answering as-yet-unresolved questions pertaining to satellite cell heterogeneity and cell fate decisions.

scholarly journals Recent Advances in Understandings Towards Pathogenesis and Treatment for Intrauterine Adhesion and Disruptive Insights from Single-Cell Analysis

2020 ◽  
Author(s):  
Ross Ka-Kit Leung ◽  
Yixin Lin ◽  
Yanhui Liu
Keyword(s):  
Endothelial Cells ◽  
Stem Cell ◽  
Single Cell ◽  
Expression Profile ◽  
Cell Interaction ◽  
Endometrial Tissue ◽  
Cell Heterogeneity ◽  
Mesenchymal Transition ◽  
Intrauterine Adhesion ◽  
Cell Cell

Abstract Intrauterine adhesion is a major cause of menstrual irregularities, infertility, and recurrent pregnancy losses and the progress towards its amelioration and therapy is slow and unsatisfactory. We aim to summarize and evaluate the current treatment progress and research methods for intrauterine adhesion. We conducted literature review in January 2020 by searching articles at PubMed on prevention and treatment, pathogenesis, the repair of other tissues/organs, cell plasticity, and the stem cell–related therapies for intrauterine adhesion. A total of 110 articles were selected for review. Uterine cell heterogeneity, expression profile, and cell-cell interaction were investigated based on scRNA-seq of uterus provided by Human Cell Landscape (HCL) project. Previous knowledge on intrauterine adhesion (IUA) pathogenesis was mostly derived from correlation studies by differentially expressed genes between endometrial tissue of intrauterine adhesion patients/animal models and normal endometrial tissue. Although the TGF-β1/SMAD pathway was suggested as the key driver for IUA pathogenesis, uterine cell heterogeneity and distinct expression profile among different cell types highlighted the importance of single-cell investigations. Cell-cell interaction in the uterus revealed the central hub of endothelial cells interacting with other cells, with endothelial cells in endothelial to mesenchymal transition and fibroblasts as the strongest interaction partners. The potential of stem cell–related therapies appeared promising, yet suffers from largely animal studies and nonstandard study design. The need to dissect the roles of endometrial cells, endothelial cells, and fibroblasts and their interaction is evident in order to elucidate the molecular and cellular mechanisms in both intrauterine adhesion pathogenesis and treatment.

scholarly journals Cell-Type Transcriptome Atlas of Human Aortic Valves Reveal Cell Heterogeneity and Endothelial to Mesenchymal Transition Involved in Calcific Aortic Valve Disease

2020 ◽  
Vol 40 (12) ◽  
pp. 2910-2921
Author(s):  
Kang Xu ◽  
Shangbo Xie ◽  
Yuming Huang ◽  
Tingwen Zhou ◽  
Ming Liu ◽  
...  
Keyword(s):  
Aortic Valve ◽  
Single Cell ◽  
Aortic Valve Disease ◽  
Valve Disease ◽  
Cell Heterogeneity ◽  
Calcific Aortic Valve Disease ◽  
Mesenchymal Transition ◽  
Endothelial To Mesenchymal Transition ◽  
Human Aortic Valve ◽  
Insight Into

Objective: Leaflet thickening, fibrosis, and hardening are early pathological features of calcific aortic valve disease (CAVD). An inadequate understanding of the resident aortic valve cells involved in the pathological process may compromise the development of therapeutic strategies. We aim to construct a pattern of the human aortic valve cell atlas in healthy and CAVD clinical specimens, providing insight into the cellular origins of CAVD and the complex cytopathological differentiation process. Approach and Results: We used unbiased single-cell RNA sequencing for the high-throughput evaluation of cell heterogeneity in 34 632 cells isolated from 6 different human aortic valve leaflets. Cellular experiments, in situ localization, and bulk sequencing were performed to verify the differences between normal, healthy valves and those with CAVD. By comparing healthy and CAVD specimens, we identified 14 cell subtypes, including 3 heterogeneous subpopulations of resident valve interstitial cells, 3 types of immune-derived cells, 2 types of valve endothelial cells, and 6 novel valve-derived stromal cells found particularly in CAVD leaflets. Combining additional verification experiments with single-cell transcriptome profiling provided evidence of endothelial to mesenchymal transition involved in lesion thickening of the aortic valve leaflet. Conclusions: Our findings deconstructed the aortic valve cell atlas and suggested novel functional interactions among resident cell subpopulations. Our findings may provide insight into future targeted therapies to prevent CAVD.

scholarly journals Single-cell transcriptome reveals the redifferentiation trajectories of the early stage of de novo shoot regeneration in Arabidopsis thaliana

2022 ◽  
Author(s):  
Guangyu Liu ◽  
Jie Li ◽  
Jiming Li ◽  
Zhiyong Chen ◽  
Peisi Yuan ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Shoot Regeneration ◽  
De Novo ◽  
Developmental Process ◽  
Early Stage ◽  
Cell Populations ◽  
Cell Heterogeneity ◽  
Cell Transcriptome ◽  
Single Cell Transcriptome

De novo shoot regeneration from a callus plays a crucial role in both plant biotechnology and the fundamental research of plant cell totipotency. Recent studies have revealed many regulatory factors involved in this developmental process. However. our knowledge of the cell heterogeneity and cell fate transition during de novo shoot regeneration is still limited. Here, we performed time-series single-cell transcriptome experiments to reveal the cell heterogeneity and redifferentiation trajectories during the early stage of de novo shoot regeneration. Based on the single-cell transcriptome data of 35,669 cells at five-time points, we successfully determined seven major cell populations in this developmental process and reconstructed the redifferentiation trajectories. We found that all cell populations resembled root identities and undergone gradual cell-fate transitions. In detail, the totipotent callus cells differentiated into pluripotent QC-like cells and then gradually developed into less differentiated cells that have multiple root-like cell identities, such as pericycle-like cells. According to the reconstructed redifferentiation trajectories, we discovered that the canonical regeneration-related genes were dynamically expressed at certain stages of the redifferentiation process. Moreover, we also explored potential transcription factors and regulatory networks that might be involved in this process. The transcription factors detected at the initial stage, QC-like cells, and the end stage provided a valuable resource for future functional verifications. Overall, this dataset offers a unique glimpse into the early stages of de novo shoot regeneration, providing a foundation for a comprehensive analysis of the mechanism of de novo shoot regeneration.

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