scholarly journals Single-cell secretion analysis reveals a dual role for IL-10 in restraining and resolving the TLR4-induced inflammatory response

2020 ◽  
Author(s):  
Amanda F. Alexander ◽  
Hannah Forbes ◽  
Kathryn Miller-Jensen
Keyword(s):  
Inflammatory Response ◽  
Single Cell ◽  
Negative Feedback ◽  
Cytokine Secretion ◽  
Cell Secretion ◽  
Regulatory Motifs ◽  
Inflammatory Activation ◽  
Extensive Cell ◽  
Cell To Cell Variability ◽  
Cell Data

AbstractFollowing TLR4 stimulation of macrophages, negative feedback mediated by the anti-inflammatory cytokine IL-10 limits the inflammatory response. However, extensive cell-to-cell variability in TLR4-stimulated cytokine secretion raises questions about how negative feedback is robustly implemented. To explore this, we characterized the TLR4-stimulated secretion program in primary murine macrophages using a single-cell microwell assay that enabled evaluation of functional autocrine IL-10 signaling. High-dimensional analysis of single-cell data revealed three distinct tiers of TLR4-induced proinflammatory activation based on levels of cytokine secretion. Surprisingly, while IL-10 inhibits TLR4-induced activation in the highest tier, it also contributes to the TLR4-induced activation threshold by regulating which cells transition from non-secreting to secreting states. This role for IL-10 in restraining TLR4 inflammatory activation is largely mediated by intermediate IFN-β signaling, while TNF-a likely mediates response resolution by IL-10. Thus, cell-to-cell variability in cytokine regulatory motifs provides a means to tailor the TLR4-induced inflammatory response.

scholarly journals Information Theory and Stem Cell Biology

2017 ◽  
Author(s):  
Rosanna C. G. Smith ◽  
Ben D. MacArthur
Keyword(s):  
Information Theory ◽  
Stem Cell ◽  
Single Cell ◽  
Cell Biology ◽  
Cell Dynamics ◽  
Cell Behaviour ◽  
Single Cell Profiling ◽  
Cell To Cell Variability ◽  
Near Future ◽  
Cell Data

AbstractPurpose of ReviewTo outline how ideas from Information Theory may be used to analyze single cell data and better understand stem cell behaviour.Recent findingsRecent technological breakthroughs in single cell profiling have made it possible to interrogate cell-to-cell variability in a multitude of contexts, including the role it plays in stem cell dynamics. Here we review how measures from information theory are being used to extract biological meaning from the complex, high-dimensional and noisy datasets that arise from single cell profiling experiments. We also discuss how concepts linking information theory and statistical mechanics are being used to provide insight into cellular identity, variability and dynamics.SummaryWe provide a brief introduction to some basic notions from information theory and how they may be used to understand stem cell identities at the single cell level. We also discuss how work in this area might develop in the near future.

scholarly journals Mechanistic hierarchical population model identifies latent causes of cell-to-cell variability

2017 ◽  
Author(s):  
Carolin Loos ◽  
Katharina Moeller ◽  
Fabian Fröhlich ◽  
Tim Hucho ◽  
Jan Hasenauer
Keyword(s):  
Single Cell ◽  
Simulated Data ◽  
Biological Processes ◽  
Modeling Framework ◽  
Functional Implications ◽  
Multiple Levels ◽  
Underlying Mechanisms ◽  
Data Driven Modeling ◽  
Cell To Cell Variability ◽  
Cell Data

All biological systems exhibit cell-to-cell variability, and this variability often has functional implications. To gain a thorough understanding of biological processes, the latent causes and underlying mechanisms of this variability must be elucidated. Cell populations comprising multiple distinct subpopulations are commonplace in biology, yet no current methods allow the sources of variability between and within individual subpopulations to be identified. This limits the analysis of single-cell data, for example provided by flow cytometry and microscopy. In this study, we present a data-driven modeling framework for the analysis of populations comprising heterogeneous subpopulations. Our approach combines mixture modeling with frameworks for distribution approximation, facilitating the integration of multiple single-cell datasets and the detection of causal differences between and within subpopulations. The computational efficiency of our framework allows hundreds of competing hypotheses to be compared, giving unprecedented depth of a study. We demonstrated the ability of our method to capture multiple levels of heterogeneity in the analyzes of simulated data and data from highly heterogeneous sensory neurons involved in pain initiation. Our approach identified the sources of cell-to-cell variability and revealed mechanisms that underlie the modulation of nerve growth factor-induced Erk1/2 signaling by extracellular scaffolds.

scholarly journals Predicting the impact of sequence motifs on gene regulation using single-cell data

2020 ◽  
Author(s):  
Jacob Hepkema ◽  
Nicholas Keone Lee ◽  
Benjamin J. Stewart ◽  
Siwat Ruangroengkulrith ◽  
Varodom Charoensawan ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Single Cell ◽  
Human Kidney ◽  
Sequence Motifs ◽  
Cell Type ◽  
Regulatory Motifs ◽  
Mouse Tissues ◽  
The Impact ◽  
Cell Data ◽  
Proximal Promoters

AbstractBinding of transcription factors (TFs) at proximal promoters and distal enhancers is central to gene regulation. Yet, identification of TF binding sites, also known as regulatory motifs, and quantification of their impact remains challenging. Here we present scover, a convolutional neural network model that can discover putative regulatory motifs along with their cell type-specific importance from single-cell data. Analysis of scRNA-seq data from human kidney shows that ETS, YY1 and NRF1 are the most important motif families for proximal promoters. Using multiple mouse tissues we obtain for the first time a model with cell type resolution which explains 34% of the variance in gene expression. Finally, by applying scover to distal enhancers identified using scATAC-seq from the mouse cerebral cortex we highlight the emergence of layer specific regulatory patterns during development.

scholarly journals Analysis of cell size homeostasis at the single-cell and population level

2018 ◽  
Author(s):  
Philipp Thomas
Keyword(s):  
Single Cell ◽  
Cell Size ◽  
Single Cells ◽  
Population Level ◽  
Power Laws ◽  
Size Distributions ◽  
Unified Framework ◽  
Lineage Tree ◽  
Cell To Cell Variability ◽  
Cell Data

Growth pervades all areas of life from single cells to cell populations to tissues. However, cell size often fluctuates significantly from cell to cell and from generation to generation. Here we present a unified framework to predict the statistics of cell size variations within a lineage tree of a proliferating population. We analytically characterise (i) the distributions of cell size snapshots, (ii) the distribution within a population tree, and (iii) the distribution of lineages across the tree. Surprisingly, these size distributions differ significantly from observing single cells in isolation. In populations, cells seemingly grow to different sizes, typically exhibit less cell-to-cell variability and often display qualitatively different sensitivities to cell cycle noise and division errors. We demonstrate the key findings using recent single-cell data and elaborate on the implications for the ability of cells to maintain a narrow size distribution and the emergence of different power laws in these distributions.

scholarly journals Single-cell secretion analysis reveals a dual role for IL-10 in restraining and resolving the TLR4-induced inflammatory response

Cell Reports ◽  
2021 ◽  
Vol 36 (12) ◽  
pp. 109728
Author(s):  
Amanda F. Alexander ◽  
Ilana Kelsey ◽  
Hannah Forbes ◽  
Kathryn Miller-Jensen
Keyword(s):  
Inflammatory Response ◽  
Single Cell ◽  
Dual Role ◽  
Cell Secretion

Prioritization of cell types responsive to biological perturbations in single-cell data with Augur

2021 ◽  
Author(s):  
Jordan W. Squair ◽  
Michael A. Skinnider ◽  
Matthieu Gautier ◽  
Leonard J. Foster ◽  
Grégoire Courtine
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Cell Data

scholarly journals Differentiating and quantifying exosome secretion from a single cell using quasi-bound states in the continuum

Nanophotonics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 1081-1086 ◽  
Author(s):  
Abdoulaye Ndao ◽  
Liyi Hsu ◽  
Wei Cai ◽  
Jeongho Ha ◽  
Junhee Park ◽  
...  
Keyword(s):  
Single Cell ◽  
Optical Sensors ◽  
Figure Of Merit ◽  
Bound States ◽  
Single Cell Analysis ◽  
Optical Cavity ◽  
High Sensitivity ◽  
Cell Secretion ◽  
The Continuum ◽  
Refractive Index Detection

AbstractOne of the key challenges in biology is to understand how individual cells process information and respond to perturbations. However, most of the existing single-cell analysis methods can only provide a glimpse of cell properties at specific time points and are unable to provide cell secretion and protein analysis at single-cell resolution. To address the limits of existing methods and to accelerate discoveries from single-cell studies, we propose and experimentally demonstrate a new sensor based on bound states in the continuum to quantify exosome secretion from a single cell. Our optical sensors demonstrate high-sensitivity refractive index detection. Because of the strong overlap between the medium supporting the mode and the analytes, such an optical cavity has a figure of merit of 677 and sensitivity of 440 nm/RIU. Such results facilitate technological progress for highly conducive optical sensors for different biomedical applications.

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