scholarly journals Mechanistic models of signaling pathways deconvolute the glioblastoma single-cell functional landscape

NAR Cancer ◽  
2020 ◽  
Vol 2 (2) ◽  
Author(s):  
Matías M Falco ◽  
María Peña-Chilet ◽  
Carlos Loucera ◽  
Marta R Hidalgo ◽  
Joaquín Dopazo
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Large Degree ◽  
Mechanistic Modeling ◽  
Therapeutic Interventions ◽  
Single Cell Level ◽  
Cell Level ◽  
Cell Fate Decisions ◽  
Cancer Hallmarks ◽  
Definition Of

Abstract Single-cell RNA sequencing is revealing an unexpectedly large degree of heterogeneity in gene expression levels across cell populations. However, little is known on the functional consequences of this heterogeneity and the contribution of individual cell fate decisions to the collective behavior of the tissues these cells are part of. Here, we use mechanistic modeling of signaling circuits, which reveals a complex functional landscape at single-cell level. Different clusters of neoplastic glioblastoma cells have been defined according to their differences in signaling circuit activity profiles triggering specific cancer hallmarks, which suggest different functional strategies with distinct degrees of aggressiveness. Moreover, mechanistic modeling of effects of targeted drug inhibitions at single-cell level revealed, how in some cells, the substitution of VEGFA, the target of bevacizumab, by other expressed proteins, like PDGFD, KITLG and FGF2, keeps the VEGF pathway active, insensitive to the VEGFA inhibition by the drug. Here, we describe for the first time mechanisms that individual cells use to avoid the effect of a targeted therapy, providing an explanation for the innate resistance to the treatment displayed by some cells. Our results suggest that mechanistic modeling could become an important asset for the definition of personalized therapeutic interventions.

scholarly journals Mechanistic models of signaling pathways deconvolute the functional landscape of glioblastoma at single cell resolution

2019 ◽  
Author(s):  
Matías M. Falco ◽  
María Peña-Chilet ◽  
Carlos Loucera ◽  
Marta R. Hidalgo ◽  
Joaquín Dopazo
Keyword(s):  
Single Cell ◽  
Signaling Pathways ◽  
Rapid Development ◽  
Mechanistic Modeling ◽  
Mechanistic Models ◽  
Single Cell Level ◽  
Glioblastoma Cells ◽  
Cell Level ◽  
Cell Fate Decisions ◽  
Degree Of Heterogeneity

AbstractThe rapid development of single cell RNA-sequencing (scRNA-seq) technologies is revealing an unexpectedly large degree of heterogeneity in gene expression levels across the different cells that compose the same tissue sample. However, little is known on the functional consequences of this heterogeneity and the contribution of individual cell-fate decisions to the collective behavior of the tissues these cells are part of. Mechanistic models of signaling pathways have already proven to be useful tools for understanding relevant aspects of cell functionality. Here we propose to use this mechanistic modeling strategy to deconvolute the complexity of the functional behavior of a tissue by dissecting it into the individual functional landscapes of its component cells by using a single-cell RNA-seq experiment of glioblastoma cells. This mechanistic modeling analysis revealed a high degree of heterogeneity at the scale of signaling circuits, suggesting the existence of a complex functional landscape at single cell level. Different clusters of neoplastic glioblastoma cells have been characterized according to their differences in signaling circuit activity profiles, which only partly overlap with the conventional glioblastoma subtype classification. The activity of signaling circuits that trigger cell functionalities which can easily be assimilated to cancer hallmarks reveals different functional strategies with different degrees of aggressiveness followed by any of the clusters.In addition, mechanistic modeling allows simulating the effect of interventions on the components of the signaling circuits, such as drug inhibitions. Thus, effects of drug inhibitions at single cell level can be dissected, revealing for the first time the mechanisms that individual cells use to avoid the effect of a targeted therapy which explain why and how a small proportion of cells display, in fact, different degrees of resistance to the treatment. The results presented here strongly suggest that mechanistic modeling at single cell level not only allows uncovering the molecular mechanisms of the tumor progression but also can predict the success of a treatment and can contribute to a better definition of therapeutic targets in the future.

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.

T Cell Fate at the Single-Cell Level

2016 ◽  
Vol 34 (1) ◽  
pp. 65-92 ◽  
Author(s):  
Veit R. Buchholz ◽  
Ton N.M. Schumacher ◽  
Dirk H. Busch
Keyword(s):  
T Cell ◽  
Single Cell ◽  
Cell Fate ◽  
Single Cell Level ◽  
Cell Level

In vivo MRI of cancer cell fate at the single-cell level in a mouse model of breast cancer metastasis to the brain

2006 ◽  
Vol 56 (5) ◽  
pp. 1001-1010 ◽  
Author(s):  
Chris Heyn ◽  
John A. Ronald ◽  
Soha S. Ramadan ◽  
Jonatan A. Snir ◽  
Andrea M. Barry ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Mouse Model ◽  
Single Cell ◽  
Cell Fate ◽  
Cancer Metastasis ◽  
Breast Cancer Metastasis ◽  
Single Cell Level ◽  
Cell Level ◽  
The Brain

scholarly journals Revealing cell fate decisions during reprogramming by scRNA-seq

2020 ◽  
Vol 145 ◽  
pp. 01033
Author(s):  
Yu Liang
Keyword(s):  
Small Molecules ◽  
Single Cell ◽  
Cell Fate ◽  
Pluripotent Stem Cells ◽  
Rapid Development ◽  
Underlying Mechanism ◽  
Cellular Heterogeneity ◽  
Cell Level ◽  
Cell Fate Decisions ◽  
Cell Fate Conversion

Single-cell RNA sequencing (scRNA-seq) technologies serve as powerful tools to dissect cellular heterogeneity comprehensively. With the rapid development of scRNA-seq, many previously unsolved questions were answered by using scRNA-seq. Cell reprogramming allows to reprogram the somatic cell into pluripotent stem cells by specific transcription factors or small molecules. However, the underlying mechanism for the reprogramming progress remains unclear in some aspects for it is a highly heterogeneous process. By using scRNA-seq, it is of great value for better understanding the mechanism of reprogramming process by analyzing cell fate conversion at single-cell level. In this review, we will introduce the methods of scRNA-seq and generation of iPSCs by reprogramming, and summarize the main researches that revealing reprogramming mechanism with the use scRNA-seq.

scholarly journals Single cell transcriptomics view of cell lineage, cell fate and cellular differentiation

2017 ◽  
Author(s):  
Wenfa Ng
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Fate ◽  
Cellular Differentiation ◽  
Cell Types ◽  
Rna Degradation ◽  
Single Cell Level ◽  
Cell Level ◽  
Single Cell Rna Sequencing ◽  
A Cell

Single cell studies increasing reveal myriad cellular subtypes beyond those postulated or observed through optical and fluorescence microscopy as well as DNA sequencing studies. While gene sequencing at the single cell level offer a path towards illuminating, in totality, the different subtypes of cells present, the technique nevertheless does not offer answers concerning the functional repertoire of the cell, which is defined by the collection of RNA transcribed from the genome. Known as the transcriptome, transcribed RNA defines the function of the cell as proteins or effector RNA molecules, while the genome is the collection of all information endowed in the cell type, expressed or not. Thus, a particular cell state, lineage, cell fate or cellular differentiation is more fully depicted by transcriptomic analysis compared to delineating the genomic context at the single cell level. While conceptually sound and could be analysed by contemporary single cell RNA sequencing technology and data analysis pipelines, the relative instability of RNA in view of RNase in the environment would make sample preparation particularly challenging, where degradation of cellular RNA by extraneous factors could provide a misinterpretation of specific functions available to a cell type. Hence, RNA as the de facto functional molecule of the cell defining the proteomics landscape as well as effector RNA repertoire, meant that RNA transcriptomics at the single cell level is the way forward if the goal is to understand all available cell types, lineage, cell fate and cellular differentiation. Given that a cell state is defined by the functions encoded by functional molecules such as proteins and RNA, single cell RNA sequencing offers a larger contextual basis for understanding cellular decision making and functions, for example, proteins are increasingly known to work in concert with RNA effector molecules in enabling a function. Hence, providing a view of the diverse cell types and lineages present in a body, single cell RNA sequencing is only hampered by the high sensitivity required to analyse the small amount of RNA available in single cells, as well as the perennial problem of RNA studies: how to prevent or reduce RNA degradation by environmental RNase enzymes. Ability to reduce RNA degradation would provide the cell biologist a unique view of the functional landscape of different cells in the body through the language of RNA.

scholarly journals Single cell transcriptomics view of cell lineage, cell fate and cellular differentiation

2017 ◽  
Author(s):  
Wenfa Ng
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Fate ◽  
Cellular Differentiation ◽  
Cell Types ◽  
Rna Degradation ◽  
Single Cell Level ◽  
Cell Level ◽  
Single Cell Rna Sequencing ◽  
A Cell

Single cell studies increasing reveal myriad cellular subtypes beyond those postulated or observed through optical and fluorescence microscopy as well as DNA sequencing studies. While gene sequencing at the single cell level offer a path towards illuminating, in totality, the different subtypes of cells present, the technique nevertheless does not offer answers concerning the functional repertoire of the cell, which is defined by the collection of RNA transcribed from the genome. Known as the transcriptome, transcribed RNA defines the function of the cell as proteins or effector RNA molecules, while the genome is the collection of all information endowed in the cell type, expressed or not. Thus, a particular cell state, lineage, cell fate or cellular differentiation is more fully depicted by transcriptomic analysis compared to delineating the genomic context at the single cell level. While conceptually sound and could be analysed by contemporary single cell RNA sequencing technology and data analysis pipelines, the relative instability of RNA in view of RNase in the environment would make sample preparation particularly challenging, where degradation of cellular RNA by extraneous factors could provide a misinterpretation of specific functions available to a cell type. Hence, RNA as the de facto functional molecule of the cell defining the proteomics landscape as well as effector RNA repertoire, meant that RNA transcriptomics at the single cell level is the way forward if the goal is to understand all available cell types, lineage, cell fate and cellular differentiation. Given that a cell state is defined by the functions encoded by functional molecules such as proteins and RNA, single cell RNA sequencing offers a larger contextual basis for understanding cellular decision making and functions, for example, proteins are increasingly known to work in concert with RNA effector molecules in enabling a function. Hence, providing a view of the diverse cell types and lineages present in a body, single cell RNA sequencing is only hampered by the high sensitivity required to analyse the small amount of RNA available in single cells, as well as the perennial problem of RNA studies: how to prevent or reduce RNA degradation by environmental RNase enzymes. Ability to reduce RNA degradation would provide the cell biologist a unique view of the functional landscape of different cells in the body through the language of RNA.

scholarly journals Defining the Emerging Blood System During Development at Single-Cell Resolution

2021 ◽  
Vol 9 ◽  
Author(s):  
Göran Karlsson ◽  
Mikael N. E. Sommarin ◽  
Charlotta Böiers
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Blood Disorders ◽  
Microfluidic Platforms ◽  
Hematopoietic Stem ◽  
Cell Fate Decisions ◽  
Wide Range ◽  
Targeted Gene Expression ◽  
Definition Of

Developmental hematopoiesis differs from adult and is far less described. In the developing embryo, waves of lineage-restricted blood precede the ultimate emergence of definitive hematopoietic stem cells (dHSCs) capable of maintaining hematopoiesis throughout life. During the last two decades, the advent of single-cell genomics has provided tools to circumvent previously impeding characteristics of embryonic hematopoiesis, such as cell heterogeneity and rare cell states, allowing for definition of lineage trajectories, cellular hierarchies, and cell-type specification. The field has rapidly advanced from microfluidic platforms and targeted gene expression analysis, to high throughput unbiased single-cell transcriptomic profiling, single-cell chromatin analysis, and cell tracing—offering a plethora of tools to resolve important questions within hematopoietic development. Here, we describe how these technologies have been implemented to address a wide range of aspects of embryonic hematopoiesis ranging from the gene regulatory network of dHSC formation via endothelial to hematopoietic transition (EHT) and how EHT can be recapitulated in vitro, to hematopoietic trajectories and cell fate decisions. Together, these studies have important relevance for regenerative medicine and for our understanding of genetic blood disorders and childhood leukemias.

Persistent RNA virus infection is short-lived at the single-cell level but leaves transcriptomic footprints

2021 ◽  
Vol 218 (10) ◽  
Author(s):  
Peter Reuther ◽  
Katrin Martin ◽  
Mario Kreutzfeldt ◽  
Matias Ciancaglini ◽  
Florian Geier ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Fate ◽  
Persistent Infection ◽  
Rna Virus ◽  
Intrinsic Clearance ◽  
Viral Clearance ◽  
Type I ◽  
Matrix Remodeling ◽  
Single Cell Level ◽  
Cell Level

Several RNA viruses can establish life-long persistent infection in mammalian hosts, but the fate of individual virus-infected cells remains undefined. Here we used Cre recombinase–encoding lymphocytic choriomeningitis virus to establish persistent infection in fluorescent cell fate reporter mice. Virus-infected hepatocytes underwent spontaneous noncytolytic viral clearance independently of type I or type II interferon signaling or adaptive immunity. Viral clearance was accompanied by persistent transcriptomic footprints related to proliferation and extracellular matrix remodeling, immune responses, and metabolism. Substantial overlap with persistent epigenetic alterations in HCV-cured patients suggested a universal RNA virus-induced transcriptomic footprint. Cell-intrinsic clearance occurred in cell culture, too, with sequential infection, reinfection cycles separated by a period of relative refractoriness to infection. Our study reveals that systemic persistence of a prototypic noncytolytic RNA virus depends on continuous spread and reinfection. Yet undefined cell-intrinsic mechanisms prevent viral persistence at the single-cell level but give way to profound transcriptomic alterations in virus-cleared cells.

scholarly journals Fundamental trade-offs between information flow in single cells and cellular populations

2017 ◽  
Vol 114 (22) ◽  
pp. 5755-5760 ◽  
Author(s):  
Ryan Suderman ◽  
John A. Bachman ◽  
Adam Smith ◽  
Peter K. Sorger ◽  
Eric J. Deeds
Keyword(s):  
Single Cell ◽  
Information Transfer ◽  
Single Cells ◽  
Population Level ◽  
Signaling Networks ◽  
Single Cell Level ◽  
Cell Level ◽  
Cell Fate Decisions ◽  
Trade Offs ◽  
Low Levels

Signal transduction networks allow eukaryotic cells to make decisions based on information about intracellular state and the environment. Biochemical noise significantly diminishes the fidelity of signaling: networks examined to date seem to transmit less than 1 bit of information. It is unclear how networks that control critical cell-fate decisions (e.g., cell division and apoptosis) can function with such low levels of information transfer. Here, we use theory, experiments, and numerical analysis to demonstrate an inherent trade-off between the information transferred in individual cells and the information available to control population-level responses. Noise in receptor-mediated apoptosis reduces information transfer to approximately 1 bit at the single-cell level but allows 3–4 bits of information to be transmitted at the population level. For processes such as eukaryotic chemotaxis, in which single cells are the functional unit, we find high levels of information transmission at a single-cell level. Thus, low levels of information transfer are unlikely to represent a physical limit. Instead, we propose that signaling networks exploit noise at the single-cell level to increase population-level information transfer, allowing extracellular ligands, whose levels are also subject to noise, to incrementally regulate phenotypic changes. This is particularly critical for discrete changes in fate (e.g., life vs. death) for which the key variable is the fraction of cells engaged. Our findings provide a framework for rationalizing the high levels of noise in metazoan signaling networks and have implications for the development of drugs that target these networks in the treatment of cancer and other diseases.

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