Publications

Many biological circuits comprise sets of protein variants that interact with one another in a many-to-many, or promiscuous, fashion. These architectures can provide powerful computational capabilities that are especially critical in multicellular organisms. Understanding the principles of biochemical computations in these circuits could allow more precise control of cellular behaviors. However, these systems are inherently difficult to analyze, due to their large number of interacting molecular components, partial redundancies, and cell context dependence. Here, we discuss recent experimental and theoretical advances that are beginning to reveal how promiscuous circuits compute, what roles those computations play in natural biological contexts, and how promiscuous architectures can be applied for the design of synthetic multicellular behaviors.

In multicellular organisms, secreted ligands selectively activate, or \u201caddress,\u201d specific target cell populations to control cell fate decision-making and other processes. Key cell-cell communication pathways use multiple promiscuously interacting ligands and receptors, provoking the question of how addressing specificity can emerge from molecular promiscuity. To investigate this issue, we developed a general mathematical modeling framework based on the bone morphogenetic protein (BMP) pathway architecture. We find that promiscuously interacting ligand-receptor systems allow a small number of ligands, acting in combinations, to address a larger number of individual cell types, defined by their receptor expression profiles. Promiscuous systems outperform seemingly more specific one-to-one signaling architectures in addressing capability. Combinatorial addressing extends to groups of cell types, is robust to receptor expression noise, grows more powerful with increases in the number of receptor variants, and is maximized by specific biochemical parameter relationships. Together, these results identify design principles governing cellular addressing by ligand combinations.

Nascent messenger RNA is endowed with a poly(A) tail that is subject to gradual deadenylation and subsequent degradation in the cytoplasm. Deadenylation and degradation rates are typically correlated, rendering it difficult to dissect the determinants governing each of these processes and the mechanistic basis of their coupling. Here we developed an approach that allows systematic, robust and multiplexed quantification of poly(A) tails in Saccharomyces cerevisiae. Our results suggest that mRNA deadenylation and degradation rates are decoupled during meiosis, and that transcript length is a major determinant of deadenylation rates and a key contributor to reshaping of poly(A) tail lengths. Meiosis-specific decoupling also leads to unique positive associations between poly(A) tail length and gene expression. The decoupling is associated with a focal localization pattern of the RNA degradation factor Xrn1, and can be phenocopied by Xrn1 deletion under nonmeiotic conditions. Importantly, the association of transcript length with deadenylation rates is conserved across eukaryotes. Our study uncovers a factor that shapes deadenylation rate and reveals a unique context in which degradation is decoupled from deadenylation.

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.

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 observation, from the clustering of cell states on pedigrees (lineage trees). Combining KCA with pedigrees obtained from time-lapse imaging and endpoint single-molecule RNA-fluorescence in situ hybridization (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.

Methods that allow monitoring of individual cells over time, using live cell imaging, are essential for studying dynamical cellular processes in heterogeneous cell populations such as primary T lymphocytes. However, applying single cell time-lapse microscopy to study activation and differentiation of these cells was limited due to a number of reasons. First, primary naïve T cells are non-adherent and become highly motile upon activation through their antigen receptor. Second, CD4⁺ T cell differentiation is a relatively slow process which takes 3-4 days. As a result, long-term dynamic monitoring of individual cells during the course of activation and differentiation is challenging as cells rapidly escape out of the microscope field of view. Here we present and characterize a platform which enables capture and growth of primary T lymphocytes with minimal perturbation, allowing for long-term monitoring of cell activation and differentiation. We use standard cell culture plates combined with PDMS based arrays containing thousands of deep microwells in which primary CD4⁺ T cells are trapped and activated by antigen coated microbeads. We demonstrate that this system allows for live cell imaging of individual T cells for up to 72 h, providing quantitative data on cell proliferation and death times. In addition, we continuously monitor dynamics of gene expression in those cells, of either intracellular proteins using cells from transgenic mice expressing fluorescent reporter proteins, or cell surface proteins using fluorescently labeled antibodies. Finally, we show how intercellular interactions between different cell types can be investigated using our device. This system provides a new platform in which dynamical processes and intercellular interactions within heterogeneous populations of primary T cells can be studied at the single cell level.

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.

We study the conformal field theory dual of the type IIA flux compactification model of DeWolfe, Giryavets, Kachru and Taylor, with all moduli stabilized. We find its central charge and properties of its operator spectrum. We concentrate on the moduli space of the conformal field theory, which we investigate through domain walls in the type IIA string theory. The moduli space turns out to consist of many different branches. We use Bezout's theorem and Bernstein's theorem to enumerate the different branches of the moduli space and estimate their dimension.