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 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 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 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.

An artificial intelligent platform for live cell identification and the detection of cross-contamination (Preprint)

2019 ◽  
Author(s):  
Ruixin Wang ◽  
Dongni Wang ◽  
Dekai Kang ◽  
Xusen Guo ◽  
Chong Guo ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Line ◽  
Cell Lines ◽  
Cell Morphology ◽  
Single Cell Level ◽  
Cross Contamination ◽  
Cell Level ◽  
Cell Identification ◽  
Pure Cell ◽  
Microscopy Images

BACKGROUND In vitro human cell line models have been widely used for biomedical research to predict clinical response, identify novel mechanisms and drug response. However, one-fifth to one-third of cell lines have been cross-contaminated, which can seriously result in invalidated experimental results, unusable therapeutic products and waste of research funding. Cell line misidentification and cross-contamination may occur at any time, but authenticating cell lines is infrequent performed because the recommended genetic approaches are usually require extensive expertise and may take a few days. Conversely, the observation of live-cell morphology is a direct and real-time technique. OBJECTIVE The purpose of this study was to construct a novel computer vision technology based on deep convolutional neural networks (CNN) for “cell face” recognition. This was aimed to improve cell identification efficiency and reduce the occurrence of cell-line cross contamination. METHODS Unstained optical microscopy images of cell lines were obtained for model training (about 334 thousand patch images), and testing (about 153 thousand patch images). The AI system first trained to recognize the pure cell morphology. In order to find the most appropriate CNN model，we explored the key image features in cell morphology classification tasks using the classical CNN model-Alexnet. After that, a preferred fine-grained recognition model BCNN was used for the cell type identification (seven classifications). Next, we simulated the situation of cell cross-contamination and mixed the cells in pairs at different ratios. The detection of the cross-contamination was divided into two levels, whether the cells are mixed and what the contaminating cell is. The specificity, sensitivity, and accuracy of the model were tested separately by external validation. Finally, the segmentation model DialedNet was used to present the classification results at the single cell level. RESULTS The cell texture and density were the influencing factors that can be better recognized by the bilinear convolutional neural network (BCNN) comparing to AlexNet. The BCNN achieved 99.5% accuracy in identifying seven pure cell lines and 86.3% accuracy for detecting cross-contamination (mixing two of the seven cell lines). DilatedNet was applied to the semantic segment for analyzing in single-cell level and achieved an accuracy of 98.2%. CONCLUSIONS This study successfully demonstrated that cell lines can be morphologically identified using deep learning models. Only light-microscopy images and no reagents are required, enabling most labs to routinely perform cell identification tests.

scholarly journals Quality control of direct cell–mineral adhesion measurements in air and liquid using inverse AFM imaging

RSC Advances ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 5384-5392
Author(s):  
Abd Alaziz Abu Quba ◽  
Gabriele E. Schaumann ◽  
Mariam Karagulyan ◽  
Doerte Diehl
Keyword(s):  
Quality Control ◽  
Single Cell ◽  
Single Cell Level ◽  
Cell Level ◽  
C Cell ◽  
Afm Imaging ◽  
Direct Cell ◽  
Mineral Interaction

Setup for a reliable cell-mineral interaction at the single-cell level, (a) study of the mineral by a sharp tip, (b) study of the bacterial modified probe by a characterizer, (c) cell-mineral interaction, (d) subsequent check of the modified probe.

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