Integrated time-lapse and single-cell transcription studies highlight the variable and dynamic nature of human hematopoietic cell fate commitment

Abstract

Individual cells take lineage commitment decisions in a way that is not necessarily uniform. We address this issue by characterising transcriptional changes in cord blood-derived CD34+ cells at the single-cell level and integrating data with cell division history and morphological changes determined by time-lapse microscopy. We show that major transcriptional changes leading to a multilineage-primed gene expression state occur very rapidly during the first cell cycle. One of the 2 stable lineage-primed patterns emerges gradually in each cell with variable timing. Some cells reach a stable morphology and molecular phenotype by the end of the first cell cycle and transmit it clonally. Others fluctuate between the 2 phenotypes over several cell cycles. Our analysis highlights the dynamic nature and variable timing of cell fate commitment in hematopoietic cells, links the gene expression pattern to cell morphology, and identifies a new category of cells with fluctuating phenotypic characteristics, demonstrating the complexity of the fate decision process (which is different from a simple binary switch between 2 options, as it is usually envisioned).

Multiple Myeloma Exemplifies a Model of Cancer Based on Tissue Disruption as the Initiator Event

“…Variation in gene expression arising from transcriptional noise and network fluctuation and the associated phenotypic heterogeneity accounts for stochasticity of cell fate decisions in stem and progenitor cells ( 20 ). Moreover, the degree of SGE is modulated during development and differentiation: many studies now showed that following a phase of highly and widespread SGE, cells progressively transit toward a more homogeneous, coordinated and restricted gene expression patterns ( 21 – 23 ) associated with a more restrictive chromatin ( 24 ). Of note, when hematopoietic stem or progenitors cells are induced to differentiate, a transient state is characterized by an increased in SGE ( 22 , 23 ).…”

“…Moreover, the degree of SGE is modulated during development and differentiation: many studies now showed that following a phase of highly and widespread SGE, cells progressively transit toward a more homogeneous, coordinated and restricted gene expression patterns ( 21 – 23 ) associated with a more restrictive chromatin ( 24 ). Of note, when hematopoietic stem or progenitors cells are induced to differentiate, a transient state is characterized by an increased in SGE ( 22 , 23 ). Highly variable expression seems necessary for the necessarily large developmental “choices” of stem cells and in contrast as differentiation progresses, expression patterns become more tightly constrained, well-defined and less diverse ( 25 ).…”

Noise-driven cell differentiation and the emergence of spatiotemporal patterns

“…While a population of cells has, on average, certain properties relevant to differentiation, e.g., the mean number of key proteins, the average position of cell division plane, and etc., these average values do not tell the whole story. Instead, the variance in these values, i.e., the non-genetic variation present amongst individuals, is the key to understand differentiation (as observed in studies such as in [12,50]). This noise in the population is essentially the fuel that propels cellular differentiation, be it in the reversible differentiation in prokaryotes or the more complicated irreversible ones in higher organisms.…”

Time Dependent Stochastic mRNA and Protein Synthesis in Piecewise-Deterministic Models of Gene Networks

“…Quantifying intrinsic fluctuations requires single cell measurements of several genes. A variety of technologies are now ready to take this challenge-single cell sequencing [50], single-cell RNA microscopy [10], various versions of time-lapse microscopy [51], fluorescence correlation microscopy [12]. It is therefore important to test the propensity of expression fluctuations to discriminate between various gene network architectures.…”