Chromatin regulators play a major role in establishing and maintaining gene expression states. Yet how they control gene expression in single cells, quantitatively and over time, remains unclear. We used time-lapse microscopy to analyze the dynamic effects of four silencers associated with diverse modifications: DNA methylation, histone deacetylation, and histone methylation. For all regulators, silencing and reactivation occurred in all-or-none events, enabling the regulators to modulate the fraction of cells silenced rather than the amount of gene expression. These dynamics could be described by a three-state model involving stochastic transitions between active, reversibly silent, and irreversibly silent states. Through their individual transition rates, these regulators operate over different time scales and generate distinct types of epigenetic memory. Our results provide a framework for understanding and engineering mammalian chromatin regulation and epigenetic memory.

Summary The Bone Morphogenetic Protein (BMP) signaling pathway comprises multiple ligands and receptors that interact promiscuously with one another, and typically appear in combinations. This feature is often explained in terms of redundancy and regulatory flexibility, but it has remained unclear what signal processing capabilities it provides. Here, we show that the BMP pathway processes multi-ligand inputs using a specific repertoire of computations, including ratiometric sensing, balance detection and imbalance detection. These computations operate on the relative levels of different ligands, and can arise directly from competitive receptor-ligand interactions. Furthermore, cells can select different computations to perform on the same ligand combination through expression of alternative sets of receptor variants. These results provide a direct signal processing role for promiscuous receptor-ligand interactions, and establish operational principles for quantitatively controlling cells with BMP ligands. Similar principles could apply to other promiscuous signaling pathways.

We report a chip-scale lensless wide-field-of-view microscopy imaging technique, subpixel perspective sweeping microscopy, which can render microscopy images of growing or confluent cell cultures autonomously. We demonstrate that this technology can be used to build smart Petri dish platforms, termed ePetri, for cell culture experiments. This technique leverages the recent broad and cheap availability of high performance image sensor chips to provide a low-cost and automated microscopy solution. Unlike the two major classes of lensless microscopy methods, optofluidic microscopy and digital in-line holography microscopy, this new approach is fully capable of working with cell cultures or any samples in which cells may be contiguously connected. With our prototype, we demonstrate the ability to image samples of area 6 mm × 4 mm at 660-nm resolution. As a further demonstration, we showed that the method can be applied to image color stained cell culture sample and to image and track cell culture growth directly within an incubator. Finally, we showed that this method can track embryonic stem cell differentiations over the entire sensor surface. Smart Petri dish based on this technology can significantly streamline and improve cell culture experiments by cutting down on human labor and contamination risks.lensless imaging | time-lapse microscopy | on-chip cellular imaging | stem cell differentiation tracking | superresolution algorithm

Summary As they proliferate, living cells undergo transitions between specific molecularly and developmentally distinct states. Despite the functional centrality of these transitions in multicellular organisms, it has remained challenging to determine which transitions occur and at what rates without perturbations and cell engineering. Here, we introduce Kin Correlation Analysis (KCA) and show that quantitative cell state transition dynamics can be inferred without direct, molecular-level observation from the clustering of cell states on pedigrees (lineage trees). Combining KCA with pedigrees obtained from time-lapse imaging and end-point single-molecule RNA-FISH measurements of gene expression, we determined the cell state transition network of mouse embryonic stem (ES) cells. This analysis revealed that mouse ES cells exhibit stochastic and reversible transitions along a linear chain of states ranging from 2C-like to epiblast-like. Our approach is broadly applicable and may be applied to systems with irreversible transitions and non-stationary dynamics, such as in cancer and development.

A widespread feature of extracellular signaling in cell circuits is paradoxical pleiotropy: the same secreted signaling molecule can induce opposite effects in the responding cells. For example, the cytokine IL-2 can promote proliferation and death of T cells. The role of such paradoxical signaling remains unclear. To address this, we studied CD4(+) T cell expansion in culture. We found that cells with a 30-fold difference in initial concentrations reached a homeostatic concentration nearly independent of initial cell levels. Below an initial threshold, cell density decayed to extinction (OFF-state). We show that these dynamics relate to the paradoxical effect of IL-2, which increases the proliferation rate cooperatively and the death rate linearly. Mathematical modeling explained the observed cell and cytokine dynamics and predicted conditions that shifted cell fate from homeostasis to the OFF-state. We suggest that paradoxical signaling provides cell circuits with specific dynamical features that are robust to environmental perturbations.