Cell–cell interaction networks regulate blood stem and progenitor cell fate

Kirouac, Daniel C.; Madlambayan, Gerard J.; Yu, Mei‐Ching; Sykes, Edward A.; Ito, Caryn Y.; Zandstra, Peter W.

doi:10.1038/msb.2009.49

Cited by 111 publications

(123 citation statements)

References 62 publications

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“…Recent studies have identified a variety of negative-feedback strategies for achieving robust control of tissue size and cell number (Shraiman, 2005;Kirouac et al, 2009;Lander et al, 2009). The olfactory epithelium (OE) of the mouse has been a particularly useful model system in this regard .…”

Section: Introductionmentioning

confidence: 99%

Activin and GDF11 collaborate in feedback control of neuroepithelial stem cell proliferation and fate

et al. 2011

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SUMMARYStudies of the olfactory epithelium model system have demonstrated that production of neurons is regulated by negative feedback. Previously, we showed that a locally produced signal, the TGF superfamily ligand GDF11, regulates the genesis of olfactory receptor neurons by inhibiting proliferation of the immediate neuronal precursors (INPs) that give rise to them. GDF11 is antagonized by follistatin (FST), which is also produced locally. Here, we show that Fst -/-mice exhibit dramatically decreased neurogenesis, a phenotype that can only be partially explained by increased GDF11 activity. Instead, a second FST-binding factor, activin B (ACTB), inhibits neurogenesis by a distinct mechanism: whereas GDF11 inhibits expansion of INPs, ACTB inhibits expansion of stem and early progenitor cells. We present data supporting the concept that these latter cells, previously considered two distinct types, constitute a dynamic stem/progenitor population in which individual cells alternate expression of Sox2 and/or Ascl1. In addition, we demonstrate that interplay between ACTB and GDF11 determines whether stem/progenitor cells adopt a glial versus neuronal fate. Altogether, the data indicate that the transition between stem cells and committed progenitors is neither sharp nor irreversible and that GDF11, ACTB and FST are crucial components of a circuit that controls both total cell number and the ratio of neuronal versus glial cells in this system. Thus, our findings demonstrate a close connection between the signals involved in the control of tissue size and those that regulate the proportions of different cell types.

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Section: Introductionmentioning

confidence: 99%

Activin and GDF11 collaborate in feedback control of neuroepithelial stem cell proliferation and fate

et al. 2011

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“…For instance, two emulsions with droplets 20 containing single cells could pass through a sorter and then be reinjected and coalesced in our device for the study of cell-cell interactions. 17 We plan to use the method to create droplets with controlled numbers of various kinds of Brownian particles to study colloidal self-assembly. …”

Section: Resultsmentioning

confidence: 99%

Synchronized reinjection and coalescence of droplets in microfluidics

et al. 2014

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Coalescence of two kinds of pre-processed droplets is necessary to perform chemical and biological assays in droplet-based microfluidics. However, a robust technique to accomplish this does not exist. Here we present a microfluidic device to synchronize the reinjection of two different kinds of droplets and coalesce them, using hydrostatic pressure in conjunction with a conventional syringe pump. We use a device consisting of two opposing T-junctions for reinjecting two kinds of droplets and control the flows 10 of the droplets by applying gravity-driven hydrostatic pressure. The hydrostatic-pressure operation facilitates balancing the droplet reinjection rates and allows us to synchronize the reinjection. Furthermore, we present a simple but robust module to coalesce two droplets that sequentially come into the module, regardless of their arrival times. These re-injection and coalescence techniques might be used in lab-on-chip applications requiring droplets with controlled numbers of solid materials, which can be 15 made by coalescing two pre-processed droplets that are formed and sorted in devices.

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“…According to the authors' statement [8] this sloppiness is a potential explanation to "[...] the difficulty of extracting precise parameter estimates from collective fits, even from comprehensive data." Nowadays, the term sloppiness is often found in the literature and sometimes erroneously used for concluding that parameters cannot be estimated and identified and thereby is sometimes utilized as an excuse that the parameter estimation behaves poorly [27][28][29].…”

Section: Sloppinessmentioning

confidence: 99%

Cause and cure of sloppiness in ordinary differential equation models

2014

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Data-based mathematical modeling of biochemical reaction networks, e.g. by nonlinear ordinary differential equation (ODE) models, has been successfully applied. In this context, parameter estimation and uncertainty analysis is a major task in order to assess the quality of the description of the system by the model. Recently, a broadened eigenvalue spectrum of the Hessian matrix of the objective function covering orders of magnitudes was observed and has been termed as sloppiness. In this work, we investigate the origin of sloppiness from structures in the sensitivity matrix arising from the properties of the model topology and the experimental design. Furthermore, we present strategies using optimal experimental design methods in order to circumvent the sloppiness issue and present non-sloppy designs for a benchmark model.

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Cell–cell interaction networks regulate blood stem and progenitor cell fate

Cited by 111 publications

References 62 publications

Activin and GDF11 collaborate in feedback control of neuroepithelial stem cell proliferation and fate

Activin and GDF11 collaborate in feedback control of neuroepithelial stem cell proliferation and fate

Synchronized reinjection and coalescence of droplets in microfluidics

Cause and cure of sloppiness in ordinary differential equation models

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