Connecting the dots across time: Reconstruction of single cell signaling trajectories using time-stamped data

TL;DR

This paper introduces a novel method to reconstruct single cell signaling trajectories from time-stamped cytometry snapshot data by identifying invariant and slow variables, enabling trajectory inference in high-dimensional signaling space.

Contribution

It develops a new approach leveraging concepts from non-equilibrium physics to reconstruct signaling trajectories from snapshot data, overcoming limitations of live-cell imaging.

Findings

Successfully reconstructed trajectories from simulated data.

Validated approach with live-cell imaging measurements.

Provides a new framework for trajectory inference in high-dimensional data.

Abstract

Single cell responses are shaped by the geometry of signaling kinetic trajectories carved in a multidimensional space spanned by signaling protein abundances. It is however challenging to assay large number (>3) of signaling species in live-cell imaging which makes it difficult to probe single cell signaling kinetic trajectories in large dimensions. Flow and mass cytometry techniques can measure a large number (4 - >40) of signaling species but are unable to track single cells. Thus cytometry experiments provide detailed time stamped snapshots of single cell signaling kinetics. Is it possible to use the time stamped cytometry data to reconstruct single cell signaling trajectories? Borrowing concepts of conserved and slow variables from non-equilibrium statistical physics we develop an approach to reconstruct signaling trajectories using snapshot data by creating new variables that remain invariant or vary slowly during the signaling kinetics. We apply this approach to reconstruct trajectories using snapshot data obtained from in silico simulations and live-cell imaging measurements. The use of invariants and slow variables to reconstruct trajectories provides a radically different way to track object using snapshot data. The approach is likely to have implications for solving matching problems in a wide range of disciplines.