Automated co-culture system for spatiotemporal analysis of cell-to-cell communication

Frank, Tino; Tay, Savaş

doi:10.1039/c5lc00182j

Cited by 29 publications

(34 citation statements)

References 40 publications

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“…Here, we showed that MEFs, a model of a nonimmune constituent of connective tissue, might consume large amounts of TNF- (having a limited ability to produce TNF- themselves). This suggests a one-directional signal propagation from macrophages to nonimmune cells, which is consistent with another study (45). We also showed that TNF- uptake involved internalization of the cytokine by TNRF1 (Fig.…”

Section: Discussionsupporting

confidence: 93%

Quantitative analysis of competitive cytokine signaling predicts tissue thresholds for the propagation of macrophage activation

et al. 2018

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Toll-like receptor (TLR) signaling regulates macrophage activation and effector cytokine propagation in the constrained environment of a tissue. In macrophage populations, TLR4 stimulates the dose-dependent transcription of nuclear factor κB (NF-κB) target genes. However, using single-RNA counting, we found that individual cells exhibited a wide range (three orders of magnitude) of expression of the gene encoding the proinflammatory cytokine tumor necrosis factor-α (TNF-α). The TLR4-induced transcriptional response correlated with the extent of NF-κB signaling in the cells and their size. We compared the rates of TNF-α production and uptake in macrophages and mouse embryonic fibroblasts and generated a mathematical model to explore the heterogeneity in the response of macrophages to TLR4 stimulation and the propagation of the TNF-α signal in the tissue. The model predicts that the local propagation of the TLR4-dependent TNF-α response and cellular NF-κB signaling are limited to small distances of a few cell diameters between neighboring tissue-resident macrophages. In our predictive model, TNF-α propagation was constrained by competitive uptake of TNF-α from the environment, rather than by heterogeneous production of the cytokine. We propose that the highly constrained architecture of tissues enables effective localized propagation of inflammatory cues while avoiding out-of-context responses at longer distances.

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Section: Discussionsupporting

confidence: 93%

Quantitative analysis of competitive cytokine signaling predicts tissue thresholds for the propagation of macrophage activation

et al. 2018

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“…27,28 To this end, we first tested the standard culture medium (DMEM+FBS) on 3T3 mouse fibroblasts cultured on chip under constant flow. Under this condition cells started to round up and finally died after 3-4 hours of constant flow.…”

Section: Methodsmentioning

confidence: 99%

“…29 Nevertheless, the signal generator could easily be integrated in cell culture chambers with diffusive medium exchange allowing to study cell types that are shear sensitive. 27,28 …”

Section: Methodsmentioning

confidence: 99%

Universal signal generator for dynamic cell stimulation

et al. 2017

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Dynamic cell stimulation is a powerful technique for probing gene networks and for applications in stem cell differentiation, immunomodulation and signaling. We developed a robust and flexible method and associated microfluidic devices to generate a wide-range of precisely formulated dynamic chemical signals to stimulate live cells and measure their dynamic response. This signal generator is capable of digital to analog conversion (DAC) through combinatoric selection of discrete input concentrations, and outperforms existing methods by both achievable resolution, dynamic range and simplicity in design. It requires no calibration, has minimal space requirements and can be easily integrated into microfluidic cell culture devices. The signal generator hardware and software we developed allows to choose the waveform, period and amplitude of chemical input signals and features addition of well-defined chemical noise to study the role of stochasticity in cellular information processing.

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“…Nevertheless, there are known ''master regulators,'' transcription factors like NF-kB, IRF3, and AP-1 that control the expression of genes studied here, and incorporating time-lapse microscopy by tagging these proteins in live cells could reveal more information on pathogen-host interactions (Tay et al, 2010;Kellogg and Tay, 2015;Selimkhanov et al, 2014). This kind of work would also benefit from new microfluidic and optogenetic methods, allowing better control of cell-cell interactions and creating more precise and realistic conditions in vitro (Kellogg et al, 2014;Frank and Tay, 2015;Toettcher et al, 2013). These tools would When infected with single Salmonella cells, the macrophage population exhibits heterogeneous infection outcomes, with cells expressing either high (red) or low (blue) levels of interferon (IFN) genes.…”

mentioning

confidence: 97%