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The mechanisms underlying haematopoietic lineage decisions remain disputed. Lineage-affiliated transcription factors with the capacity for lineage reprogramming, positive auto-regulation and mutual inhibition have been described as being expressed in uncommitted cell populations. This led to the assumption that lineage choice is cell-intrinsically initiated(More)
A toggle switch consists of two genes that mutually repress each other. This regulatory motif is active during cell differentiation and is thought to act as a memory device, being able to choose and maintain cell fate decisions. Commonly, this switch has been modeled in a deterministic framework where transcription and translation are lumped together. In(More)
Hematopoiesis is often pictured as a hierarchy of branching decisions, giving rise to all mature blood cell types from stepwise differentiation of a single cell, the hematopoietic stem cell. Various aspects of this process have been modeled using various experimental and theoretical techniques on different scales. Here we integrate the more common(More)
Time-lapse microscopy allows to monitor cell state transitions in a spatiotemporal context. Combined with single cell tracking and appropriate cell state markers, transition events can be observed within the genealogical relationship of a proliferating population. However, to infer the correlations between the spatiotemporal context and cell state(More)
Differentiation alters molecular properties of stem and progenitor cells, leading to changes in their shape and movement characteristics. We present a deep neural network that prospectively predicts lineage choice in differentiating primary hematopoietic progenitors using image patches from brightfield microscopy and cellular movement. Surprisingly, lineage(More)
Accessing gene expression at a single-cell level has unraveled often large heterogeneity among seemingly homogeneous cells, which remains obscured when using traditional population-based approaches. The computational analysis of single-cell transcriptomics data, however, still imposes unresolved challenges with respect to normalization, visualization and(More)
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