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Title: Inferring the structures of signaling motifs from paired dynamic traces of single cells
Individual cells show variability in their signaling dynamics that often correlates with phenotypic responses, indicating that cell-to-cell variability is not merely noise but can have functional consequences. Based on this observation, we reasoned that cell-to-cell variability under the same treatment condition could be explained in part by a single signaling motif that maps different upstream signals into a corresponding set of downstream responses. If this assumption holds, then repeated measurements of upstream and downstream signaling dynamics in a population of cells could provide information about the underlying signaling motif for a given pathway, even when no prior knowledge of that motif exists. To test these two hypotheses, we developed a computer algorithm called MISC (Motif Inference from Single Cells) that infers the underlying signaling motif from paired time-series measurements from individual cells. When applied to measurements of transcription factor and reporter gene expression in the yeast stress response, MISC predicted signaling motifs that were consistent with previous mechanistic models of transcription. The ability to detect the underlying mechanism became less certain when a cell’s upstream signal was randomly paired with another cell’s downstream response, demonstrating how averaging time-series measurements across a population obscures information about the underlying signaling mechanism. In some cases, motif predictions improved as more cells were added to the analysis. These results provide evidence that mechanistic information about cellular signaling networks can be systematically extracted from the dynamical patterns of single cells.  more » « less
Award ID(s):
1845796
PAR ID:
10302251
Author(s) / Creator(s):
;
Editor(s):
Asthagiri, Anand R.
Date Published:
Journal Name:
PLOS Computational Biology
Volume:
17
Issue:
2
ISSN:
1553-7358
Page Range / eLocation ID:
e1008657
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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