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Connecting the dots across time: reconstruction of single-cell signalling trajectories using time-stamped data
Single-cell responses are shaped by the geometry of signalling kinetic trajectories carved in a multidimensional space spanned by signalling protein abundances. It is, however, challenging to assay a large number (more than 3) of signalling species in live-cell imaging, which makes it difficult to p...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society Publishing
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5579131/ https://www.ncbi.nlm.nih.gov/pubmed/28879015 http://dx.doi.org/10.1098/rsos.170811 |
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author | Mukherjee, Sayak Stewart, David Stewart, William Lanier, Lewis L. Das, Jayajit |
author_facet | Mukherjee, Sayak Stewart, David Stewart, William Lanier, Lewis L. Das, Jayajit |
author_sort | Mukherjee, Sayak |
collection | PubMed |
description | Single-cell responses are shaped by the geometry of signalling kinetic trajectories carved in a multidimensional space spanned by signalling protein abundances. It is, however, challenging to assay a large number (more than 3) of signalling species in live-cell imaging, which makes it difficult to probe single-cell signalling kinetic trajectories in large dimensions. Flow and mass cytometry techniques can measure a large number (4 to more than 40) of signalling species but are unable to track single cells. Thus, cytometry experiments provide detailed time-stamped snapshots of single-cell signalling kinetics. Is it possible to use the time-stamped cytometry data to reconstruct single-cell signalling trajectories? Borrowing concepts of conserved and slow variables from non-equilibrium statistical physics we develop an approach to reconstruct signalling trajectories using snapshot data by creating new variables that remain invariant or vary slowly during the signalling kinetics. We apply this approach to reconstruct trajectories using snapshot data obtained from in silico simulations, live-cell imaging measurements, and, synthetic flow cytometry datasets. The application of invariants and slow variables to reconstruct trajectories provides a radically different way to track objects using snapshot data. The approach is likely to have implications for solving matching problems in a wide range of disciplines. |
format | Online Article Text |
id | pubmed-5579131 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | The Royal Society Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-55791312017-09-06 Connecting the dots across time: reconstruction of single-cell signalling trajectories using time-stamped data Mukherjee, Sayak Stewart, David Stewart, William Lanier, Lewis L. Das, Jayajit R Soc Open Sci Biochemistry and Biophysics Single-cell responses are shaped by the geometry of signalling kinetic trajectories carved in a multidimensional space spanned by signalling protein abundances. It is, however, challenging to assay a large number (more than 3) of signalling species in live-cell imaging, which makes it difficult to probe single-cell signalling kinetic trajectories in large dimensions. Flow and mass cytometry techniques can measure a large number (4 to more than 40) of signalling species but are unable to track single cells. Thus, cytometry experiments provide detailed time-stamped snapshots of single-cell signalling kinetics. Is it possible to use the time-stamped cytometry data to reconstruct single-cell signalling trajectories? Borrowing concepts of conserved and slow variables from non-equilibrium statistical physics we develop an approach to reconstruct signalling trajectories using snapshot data by creating new variables that remain invariant or vary slowly during the signalling kinetics. We apply this approach to reconstruct trajectories using snapshot data obtained from in silico simulations, live-cell imaging measurements, and, synthetic flow cytometry datasets. The application of invariants and slow variables to reconstruct trajectories provides a radically different way to track objects using snapshot data. The approach is likely to have implications for solving matching problems in a wide range of disciplines. The Royal Society Publishing 2017-08-23 /pmc/articles/PMC5579131/ /pubmed/28879015 http://dx.doi.org/10.1098/rsos.170811 Text en © 2017 The Authors. http://creativecommons.org/licenses/by/4.0/ Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Biochemistry and Biophysics Mukherjee, Sayak Stewart, David Stewart, William Lanier, Lewis L. Das, Jayajit Connecting the dots across time: reconstruction of single-cell signalling trajectories using time-stamped data |
title | Connecting the dots across time: reconstruction of single-cell signalling trajectories using time-stamped data |
title_full | Connecting the dots across time: reconstruction of single-cell signalling trajectories using time-stamped data |
title_fullStr | Connecting the dots across time: reconstruction of single-cell signalling trajectories using time-stamped data |
title_full_unstemmed | Connecting the dots across time: reconstruction of single-cell signalling trajectories using time-stamped data |
title_short | Connecting the dots across time: reconstruction of single-cell signalling trajectories using time-stamped data |
title_sort | connecting the dots across time: reconstruction of single-cell signalling trajectories using time-stamped data |
topic | Biochemistry and Biophysics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5579131/ https://www.ncbi.nlm.nih.gov/pubmed/28879015 http://dx.doi.org/10.1098/rsos.170811 |
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