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...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
The Royal Society
2017-01-01
|
| Series: | Royal Society Open Science |
| Subjects: | |
| Online Access: | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.170811 |
| _version_ | 1818838906042318848 |
|---|---|
| author | Sayak Mukherjee David Stewart William Stewart Lewis L. Lanier Jayajit Das |
| author_facet | Sayak Mukherjee David Stewart William Stewart Lewis L. Lanier Jayajit Das |
| author_sort | Sayak Mukherjee |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-19T03:45:50Z |
| format | Article |
| id | doaj.art-13a691768d794e15a7a2d347fb63ab67 |
| institution | Directory Open Access Journal |
| issn | 2054-5703 |
| language | English |
| last_indexed | 2024-12-19T03:45:50Z |
| publishDate | 2017-01-01 |
| publisher | The Royal Society |
| record_format | Article |
| series | Royal Society Open Science |
| spelling | doaj.art-13a691768d794e15a7a2d347fb63ab672022-12-21T20:37:07ZengThe Royal SocietyRoyal Society Open Science2054-57032017-01-014810.1098/rsos.170811170811Connecting the dots across time: reconstruction of single-cell signalling trajectories using time-stamped dataSayak MukherjeeDavid StewartWilliam StewartLewis L. LanierJayajit DasSingle-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.https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.170811single-cell signalling kineticsflow cytometrymass cytometrytrajectory reconstructioninvariantspair-matching |
| spellingShingle | Sayak Mukherjee David Stewart William Stewart Lewis L. Lanier Jayajit Das Connecting the dots across time: reconstruction of single-cell signalling trajectories using time-stamped data Royal Society Open Science single-cell signalling kinetics flow cytometry mass cytometry trajectory reconstruction invariants pair-matching |
| 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 | single-cell signalling kinetics flow cytometry mass cytometry trajectory reconstruction invariants pair-matching |
| url | https://royalsocietypublishing.org/doi/pdf/10.1098/rsos.170811 |
| work_keys_str_mv | AT sayakmukherjee connectingthedotsacrosstimereconstructionofsinglecellsignallingtrajectoriesusingtimestampeddata AT davidstewart connectingthedotsacrosstimereconstructionofsinglecellsignallingtrajectoriesusingtimestampeddata AT williamstewart connectingthedotsacrosstimereconstructionofsinglecellsignallingtrajectoriesusingtimestampeddata AT lewisllanier connectingthedotsacrosstimereconstructionofsinglecellsignallingtrajectoriesusingtimestampeddata AT jayajitdas connectingthedotsacrosstimereconstructionofsinglecellsignallingtrajectoriesusingtimestampeddata |