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 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.

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 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.

scholarly journals 2P-222 Measurement of conduction velocity in cardiomyocytes at the single-cell level for a cell network model(The 46th Annual Meeting of the Biophysical Society of Japan)

2008 ◽  
Vol 48 (supplement) ◽  
pp. S109
Author(s):  
Tomoyuki Kaneko ◽  
Fumimasa Nomura ◽  
Yuki Tomoe ◽  
Ikurou Suzuki ◽  
Junko Hayashi ◽  
...  
Keyword(s):  
Single Cell ◽  
Annual Meeting ◽  
Conduction Velocity ◽  
Network Model ◽  
Single Cell Level ◽  
Cell Level ◽  
Cell Network ◽  
Biophysical Society ◽  
A Cell

scholarly journals COTAN: Co-expression Table Analysis for scRNA-seq data

2020 ◽  
Author(s):  
S. G. Galfrè ◽  
F. Morandin ◽  
M. Pietrosanto ◽  
F. Cremisi ◽  
M. Helmer-Citterich
Keyword(s):  
Single Cell ◽  
State Transition ◽  
Computational Method ◽  
Single Cell Level ◽  
Cell Level ◽  
Computational Approaches ◽  
Table Analysis ◽  
A Cell ◽  
Low Efficiency ◽  
Cell Gene

AbstractEstimating co-expression of cell identity factors in single-cell transcriptomes is crucial to decode new mechanisms of cell state transition. Due to the intrinsic low efficiency of single-cell mRNA profiling, novel computational approaches are required to accurately infer gene co-expression in a cell population. We introduce COTAN, a statistical and computational method to analyze the co-expression of gene pairs at single cell level, providing the foundation for single-cell gene interactome analysis.

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