Abstract
Molecular regulation of cell fate decisions underlies health and disease. To identify molecules that are active or regulated during a decision, and not before or after, the decision time point is crucial. However, cell fate markers are usually delayed and the time of decision therefore unknown. Fortunately, dividing cells induce temporal correlations in their progeny, which allow for retrospective inference of the decision time point. We present a computational method to infer decision time points from correlated marker signals in genealogies and apply it to differentiating hematopoietic stem cells. We find that myeloid lineage decisions happen generations before lineage marker onsets. Inferred decision time points are in agreement with data from colony assay experiments. The levels of the myeloid transcription factor PU.1 do not change during, but long after the predicted lineage decision event, indicating that the PU.1/GATA1 toggle switch paradigm cannot explain the initiation of early myeloid lineage choice.
Highlights
Molecular regulation of cell fate decisions underlies health and disease
We apply this method to a dataset of differentiating hematopoietic stem cell genealogies with annotated lineage marker onsets and find that myeloid/megakaryocytic-erythroid lineage decision happens several generations earlier than reported by lineage markers and that the dynamics of PU.[1] during the lineage decision is inconsistent with a PU.1-based toggle switch driving the lineage decision
One hematopoietic lineage decision is the choice of hematopoietic stem and progenitor cells (HSPCs) between the megakaryocyticerythroid (MegE) and the granulocyte-macrophage (GM) lineage[7]
Summary
Molecular regulation of cell fate decisions underlies health and disease. To identify molecules that are active or regulated during a decision, and not before or after, the decision time point is crucial. We parametrize the decision process and the marker delay in a computational model that combines a memory-less decision process and stochastic gene expression mimicking marker delay with graphical models and dynamic programming to cope with the computational complexity of genealogies This allows us to calculate the probabilities of different decision scenarios (called hidden trees, Fig. 2c) and determine the most likely time point of the unobserved lineage decision. We apply this method to a dataset of differentiating hematopoietic stem cell genealogies with annotated lineage marker onsets and find that myeloid/megakaryocytic-erythroid lineage decision happens several generations earlier than reported by lineage markers and that the dynamics of PU.[1] during the lineage decision is inconsistent with a PU.1-based toggle switch driving the lineage decision
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