scholarly journals Show me your neighbours, and I’ll tell you what you are – cellular microenvironment matters

2017 ◽  
Author(s):  
Timea Toth ◽  
Tamas Balassa ◽  
Norbert Bara ◽  
Ferenc Kovacs ◽  
Andras Kriston ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
Cell Cultures ◽  
Cell Biology ◽  
Single Cell Level ◽  
Cell Level ◽  
Phenotypic Analysis ◽  
Additional Information ◽  
A Cell ◽  
Local Properties

AbstractTo answer major questions of cell biology, it is essential to understand cellular complexity. Modern automated microscopes produce vast amounts of images routinely, making manual analysis nearly impossible. Due to their efficiency, machine learning-based analysis software have become essential tools to perform single-cell-level phenotypic analysis of large imaging datasets. However, an important limitation of such methods is that they do not use the information gained from the cellular micro- and macroenvironment: the algorithmic decision is based solely on the local properties of the cell of interest. Here, we present how various microenvironmental features contribute to identifying a cell and how such additional information can improve single-cell-level phenotypic image analysis. The proposed methodology was tested for different sizes of Euclidean and nearest neighbour-based cellular environments both on tissue sections and cell cultures. Our experimental data verify that the microenvironment of a cell largely determines its entity. This effect was found to be especially strong for established tissues, while it was somewhat weaker in the case of cell cultures. Our analysis shows that combining local cellular features with the properties of the cell's microenvironment significantly improves the accuracy of machine learning-based phenotyping.

Sequential glycan profiling at single cell level with the microfluidic lab-in-a-trench platform: a new era in experimental cell biology

Lab on a Chip ◽  
2014 ◽  
Vol 14 (18) ◽  
pp. 3629-3639 ◽  
Author(s):  
Tríona M. O'Connell ◽  
Damien King ◽  
Chandra K. Dixit ◽  
Brendan O'Connor ◽  
Dermot Walls ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Single Cell Level ◽  
Cell Level ◽  
New Era ◽  
Experimental Cell ◽  
A Cell ◽  
Glycan Profiling

It is now widely recognised that the earliest changes that occur on a cell when it is stressed or becoming diseased are alterations in its surface glycosylation.

scholarly journals Bacterial glycocalyx integrity drives multicellular swarm biofilm dynamism

2020 ◽  
Author(s):  
Fares Saïdi ◽  
Nicolas Y. Jolivet ◽  
David J. Lemon ◽  
Arnaldo Nakamura ◽  
Anthony G. Garza ◽  
...  
Keyword(s):  
Single Cell ◽  
Expression Profiles ◽  
Single Cell Level ◽  
Cell Level ◽  
Bacterial Surface ◽  
Type Iv ◽  
The Social ◽  
A Cell ◽  
Cell Changes ◽  
Cell Community

ABSTRACTBacterial surface exopolysaccharide (EPS) layers are key determinants of biofilm establishment and maintenance, leading to the formation of higher-order 3D structures conferring numerous survival benefits to a cell community. In addition to a specific EPS glycocalyx, we recently revealed that the social δ-proteobacterium Myxococcus xanthus secretes a novel biosurfactant polysaccharide (BPS), with both EPS and BPS polymers required for type IV pilus (T4P)-dependent swarm expansion via spatio-specific biofilm expression profiles. Thus the synergy between EPS and BPS secretion somehow modulates the multicellular lifecycle of M. xanthus. Herein, we demonstrate that BPS secretion functionally-activates the EPS glycocalyx via its destabilization, fundamentally altering the characteristics of the cell surface. This impacts motility behaviours at the single-cell level as well as the aggregative capacity of cells in groups via EPS fibril formation and T4P assembly. These changes modulate structuration of swarm biofilms via cell layering, likely contributing to the formation of internal swarm polysaccharide architecture. Together, these data reveal the manner by which the interplay between two secreted polymers induces single-cell changes that modulate swarm biofilm communities.

scholarly journals Accurate and efficient discretizations for stochastic models providing near agent-based spatial resolution at low computational cost

2019 ◽  
Vol 16 (159) ◽  
pp. 20190421 ◽  
Author(s):  
Nabil T. Fadai ◽  
Ruth E. Baker ◽  
Matthew J. Simpson
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Computational Cost ◽  
Kolmogorov Equation ◽  
Single Cell Level ◽  
Agent Based Model ◽  
Computationally Efficient ◽  
Agent Based Models ◽  
Cell Level ◽  
Agent Based

Understanding how cells proliferate, migrate and die in various environments is essential in determining how organisms develop and repair themselves. Continuum mathematical models, such as the logistic equation and the Fisher–Kolmogorov equation, can describe the global characteristics observed in commonly used cell biology assays, such as proliferation and scratch assays. However, these continuum models do not account for single-cell-level mechanics observed in high-throughput experiments. Mathematical modelling frameworks that represent individual cells, often called agent-based models, can successfully describe key single-cell-level features of these assays but are computationally infeasible when dealing with large populations. In this work, we propose an agent-based model with crowding effects that is computationally efficient and matches the logistic and Fisher–Kolmogorov equations in parameter regimes relevant to proliferation and scratch assays, respectively. This stochastic agent-based model allows multiple agents to be contained within compartments on an underlying lattice, thereby reducing the computational storage compared to existing agent-based models that allow one agent per site only. We propose a systematic method to determine a suitable compartment size. Implementing this compartment-based model with this compartment size provides a balance between computational storage, local resolution of agent behaviour and agreement with classical continuum descriptions.

scholarly journals Convolutional Neural Network Can Recognize Drug Resistance of Single Cancer Cells

2020 ◽  
Vol 21 (9) ◽  
pp. 3166
Author(s):  
Kiminori Yanagisawa ◽  
Masayasu Toratani ◽  
Ayumu Asai ◽  
Masamitsu Konno ◽  
Hirohiko Niioka ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Single Cell ◽  
Tumor Cells ◽  
Antitumor Drugs ◽  
Single Cell Level ◽  
Cell Level ◽  
Single Cancer ◽  
Deep Learning Model

It is known that single or isolated tumor cells enter cancer patients’ circulatory systems. These circulating tumor cells (CTCs) are thought to be an effective tool for diagnosing cancer malignancy. However, handling CTC samples and evaluating CTC sequence analysis results are challenging. Recently, the convolutional neural network (CNN) model, a type of deep learning model, has been increasingly adopted for medical image analyses. However, it is controversial whether cell characteristics can be identified at the single-cell level by using machine learning methods. This study intends to verify whether an AI system could classify the sensitivity of anticancer drugs, based on cell morphology during culture. We constructed a CNN based on the VGG16 model that could predict the efficiency of antitumor drugs at the single-cell level. The machine learning revealed that our model could identify the effects of antitumor drugs with ~0.80 accuracies. Our results show that, in the future, realizing precision medicine to identify effective antitumor drugs for individual patients may be possible by extracting CTCs from blood and performing classification by using an AI system.

scholarly journals Accurate and efficient discretisations for stochastic models providing near agent-based spatial resolution at low computational cost

2019 ◽  
Author(s):  
Nabil T. Fadai ◽  
Ruth E. Baker ◽  
Matthew J. Simpson
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Computational Cost ◽  
Kolmogorov Equation ◽  
Single Cell Level ◽  
Agent Based Model ◽  
Computationally Efficient ◽  
Agent Based Models ◽  
Cell Level ◽  
Agent Based

AbstractUnderstanding how cells proliferate, migrate, and die in various environments is essential in determining how organisms develop and repair themselves. Continuum mathematical models, such as the logistic equation and the Fisher-Kolmogorov equation, can describe the global characteristics observed in commonly-used cell biology assays, such as proliferation and scratch assays. However, these continuum models do not account for single-cell-level mechanics observed in high-throughput experiments. Mathematical modelling frameworks that represent individual cells, often called agent-based models, can successfully describe key single-cell-level features of these assays, but are computationally infeasible when dealing with large populations. In this work, we propose an agent-based model with crowding effects that is computationally efficient and matches the logistic and Fisher-Kolmogorov equations in parameter regimes relevant to proliferation and scratch assays, respectively. This stochastic agent-based model allows multiple agents to be contained within compartments on an underlying lattice, thereby reducing the computational storage compared to existing agent-based models that allow one agent per site only. We propose a systematic method to determine a suitable compartment size. Implementing this compartment-based model with this compartment size provides a balance between computational storage, local resolution of agent behaviour, and agreement with classical continuum descriptions.

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.

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