EasyFlow: An open source, user friendly cytometry analyzer with graphic user interface (GUI).

Flow cytometry enables quantitative measurements of fluorescence in single cells. The technique was widely used for immunology to identify populations with different surface protein markers. More recently, the usage of flow cytometry has been extended to additional readouts, including intracellular proteins and fluorescent protein transgenes, and is widely utilized to study development, systems biology, microbiology, and many other fields. A common file format (FCS format, defined by International Society for Advancement of Cytometry (ISAC)) has been universally adopted, facilitating data exchange between different machines. A diverse spectrum of software packages have been developed for analysis of flow cytometry data. However, those are either 1) costly proprietary softwares, 2) open source packages with prerequisite installation of R or Python and sometimes require users to have experience in coding or 3) online tools that are limiting for analysis of large data sets. Here we present EasyFlow, an open source flow cytometry analysis GUI based on Matlab or Python, that can be installed and run locally cross-platform-ly (Windows and MacOS), without prerequisite user having previous knowledge on coding. The python version (EasyFlowQ) is also developed on a popular plotting framework (Matplotlib) and modern user interface (UI) toolkit (Qt), allowing more advanced users to customize and keep contributing to the software, as well as its tutorials. Overall, EasyFlow serves as a simple-to-use tool for inexperienced users with little coding experience to use locally, as well as a platform for advanced users to further customize for their own needs.

The computational capabilities of many-to-many protein interaction networks.

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.

From pseudo to real-time dynamics of T cell thymic differentiation.

Numerous methods have recently emerged for ordering single cells along developmental trajectories. However, accurate depiction of developmental dynamics can only be achieved after rescaling the trajectory according to the relative time spent at each developmental point. We formulate a model which estimates local cell densities and fluxes, and incorporates cell division and apoptosis rates, to infer the real-time dimension of the developmental trajectory. We validate the model using mathematical simulations and apply it to experimental high dimensional cytometry data obtained from the mouse thymus to construct the true time profile of the thymocyte developmental process. Our method can easily be implemented in any of the existing tools for trajectory inference.

The context-dependent, combinatorial logic of BMP signaling.

Cell-cell communication systems typically comprise families of ligand and receptor variants that function together in combinations. Pathway activation depends on the complex way in which ligands are presented extracellularly and receptors are expressed by the signal-receiving cell. To understand the combinatorial logic of such a system, we systematically measured pairwise bone morphogenetic protein (BMP) ligand interactions in cells with varying receptor expression. Ligands could be classified into equivalence groups based on their profile of positive and negative synergies with other ligands. These groups varied with receptor expression, explaining how ligands can functionally replace each other in one context but not another. Context-dependent combinatorial interactions could be explained by a biochemical model based on the competitive formation of alternative signaling complexes with distinct activities. Together, these results provide insights into the roles of BMP combinations in developmental and therapeutic contexts and establish a framework for analyzing other combinatorial, context-dependent signaling systems.

Ligand-receptor promiscuity enables cellular addressing.

In multicellular organisms, secreted ligands selectively activate, or "address," 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.

Decoupling of degradation from deadenylation reshapes poly(A) tail length in yeast meiosis.

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.

Deciphering the principles of the RNA editing code via large-scale systematic probing.

Adenosine-to-inosine editing is catalyzed by ADAR1 at thousands of sites transcriptome-wide. Despite intense interest in ADAR1 from physiological, bioengineering, and therapeutic perspectives, the rules of ADAR1 substrate selection are poorly understood. Here, we used large-scale systematic probing of ∼2,000 synthetic constructs to explore the structure and sequence context determining editability. We uncover two structural layers determining the formation and propagation of A-to-I editing, independent of sequence. First, editing is robustly induced at fixed intervals of 35 bp upstream and 30 bp downstream of structural disruptions. Second, editing is symmetrically introduced on opposite sites on a double-stranded structure. Our findings suggest a recursive model for RNA editing, whereby the structural alteration induced by the editing at one site iteratively gives rise to the formation of an additional editing site at a fixed periodicity, serving as a basis for the propagation of editing along and across both strands of double-stranded RNA structures.

An operational view of intercellular signaling pathways.

Animal cells use a conserved repertoire of intercellular signaling pathways to communicate with one another. These pathways are well-studied from a molecular point of view. However, we often lack an "operational" understanding that would allow us to use these pathways to rationally control cellular behaviors. This requires knowing what dynamic input features each pathway perceives and how it processes those inputs to control downstream processes. To address these questions, researchers have begun to reconstitute signaling pathways in living cells, analyzing their dynamic responses to stimuli, and developing new functional representations of their behavior. Here we review important insights obtained through these new approaches, and discuss challenges and opportunities in understanding signaling pathways from an operational point of view.

Combinatorial Signal Perception in the BMP Pathway.

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.

Inferring Cell-State Transition Dynamics from Lineage Trees and Endpoint Single-Cell Measurements.

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.

Dynamics of epigenetic regulation at the single-cell level.

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.

Paradoxical signaling by a secreted molecule leads to homeostasis of cell levels.

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.

Mapping differentiation under mixed culture conditions reveals a tunable continuum of T cell fates.

Cell differentiation is typically directed by external signals that drive opposing regulatory pathways. Studying differentiation under polarizing conditions, with only one input signal provided, is limited in its ability to resolve the logic of interactions between opposing pathways. Dissection of this logic can be facilitated by mapping the system's response to mixtures of input signals, which are expected to occur in vivo, where cells are simultaneously exposed to various signals with potentially opposing effects. Here, we systematically map the response of naïve T cells to mixtures of signals driving differentiation into the Th1 and Th2 lineages. We characterize cell state at the single cell level by measuring levels of the two lineage-specific transcription factors (T-bet and GATA3) and two lineage characteristic cytokines (IFN-γ and IL-4) that are driven by these transcription regulators. We find a continuum of mixed phenotypes in which individual cells co-express the two lineage-specific master regulators at levels that gradually depend on levels of the two input signals. Using mathematical modeling we show that such tunable mixed phenotype arises if autoregulatory positive feedback loops in the gene network regulating this process are gradual and dominant over cross-pathway inhibition. We also find that expression of the lineage-specific cytokines follows two independent stochastic processes that are biased by expression levels of the master regulators. Thus, cytokine expression is highly heterogeneous under mixed conditions, with subpopulations of cells expressing only IFN-γ, only IL-4, both cytokines, or neither. The fraction of cells in each of these subpopulations changes gradually with input conditions, reproducing the continuous internal state at the cell population level. These results suggest a differentiation scheme in which cells reflect uncertainty through a continuously tuneable mixed phenotype combined with a biased stochastic decision rather than a binary phenotype with a deterministic decision.

Monitoring the dynamics of primary T cell activation and differentiation using long term live cell imaging in microwell arrays.

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.

Balancing speed and accuracy of polyclonal T cell activation: a role for extracellular feedback.

BACKGROUND: Extracellular feedback is an abundant module of intercellular communication networks, yet a detailed understanding of its role is still lacking. Here, we study interactions between polyclonal activated T cells that are mediated by IL-2 extracellular feedback as a model system. RESULTS: Using mathematical modeling we show that extracellular feedback can give rise to opposite outcomes: competition or cooperation between interacting T cells, depending on their relative levels of activation. Furthermore, the outcome of the interaction also depends on the relative timing of activation of the cells. A critical time window exists after which a cell that has been more strongly activated nevertheless cannot exclude an inferior competitor. CONCLUSIONS: In a number of experimental studies of polyclonal T-cell systems, outcomes ranging from cooperation to competition as well as time dependent competition were observed. Our model suggests that extracellular feedback can contribute to these observed behaviors as it translates quantitative differences in T cells' activation strength and in their relative activation time into qualitatively different outcomes. We propose extracellular feedback as a general mechanism that can balance speed and accuracy - choosing the most suitable responders out of a polyclonal population under the clock of an escalating threat.

Design principles of cell circuits with paradoxical components.

Biological systems display complex networks of interactions both at the level of molecules inside the cell and at the level of interactions between cells. Networks of interacting molecules, such as transcription networks, have been shown to be composed of recurring circuits called network motifs, each with specific dynamical functions. Much less is known about the possibility of such circuit analysis in networks made of communicating cells. Here, we study models of circuits in which a few cell types interact by means of signaling molecules. We consider circuits of cells with architectures that seem to recur in immunology. An intriguing feature of these circuits is their use of signaling molecules with a pleiotropic or paradoxical role, such as cytokines that increase both cell growth and cell death. We find that pleiotropic signaling molecules can provide cell circuits with systems-level functions. These functions include for different circuits maintenance of homeostatic cell concentrations, robust regulation of differentiation processes, and robust pulses of cells or cytokines.

The ePetri dish, an on-chip cell imaging platform based on subpixel perspective sweeping microscopy (SPSM).

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.

CTLs respond with activation and granule secretion when serving as targets for T-cell recognition.

Cytotoxic T lymphocytes (CTLs) suppress T cell responses directed against their antigens regardless of their own T cell receptor (TCR) specificity. This makes the use of CTLs promising for tolerance induction in autoimmunity and transplantation. It has been established that binding of the CTL CD8 molecule to the major histocompatibility complex (MHC) class I α3 domain of the recognizing T cell must be permitted for death of the latter cell to ensue. However, the signaling events triggered in the CTL by this molecular interaction in the absence of TCR recognition have never been clarified. Here we use single-cell imaging to study the events occurring in CTLs serving as targets for recognition by specific T cells. We demonstrate that CTLs actively respond to recognition by polarizing their cytotoxic granules to the contact area, releasing their lethal cargo, and vigorously proliferating. Using CTLs from perforin knockout (KO) mice and lymphocyte specific kinase (Lck) knockdown with specific small interfering RNA (siRNA), we show that the killing of the recognizing CD8 T cell is perforin dependent and is initiated by Lck signaling in the CTL. Collectively, these data suggest a novel mechanism in which the entire cascade generally triggered by TCR engagement is "hijacked" in CTLs serving as targets for T cell recognition without TCR ligation.

Deletion of alloreactive T cells by veto cytotoxic T lymphocytes is mediated through extracellular signal-regulated kinase phosphorylation.

BACKGROUND: Anti third-party cytotoxic T lymphocytes (CTLs) were shown to exhibit marked veto activity, thereby inducing transplantation tolerance across major histocompatibility antigens. Elimination of effector cells requires co-expression of CD8 and FasL on the veto cells and is mediated through CD8-major histocompatibility complex (MHC) class I interaction and Fas-Fas ligand signaling. METHODS: To further interrogate the signaling events induced in the effector cells on their interaction with veto cell populations, effector cells from 2C transgenic mice were preincubated with different signaling inhibitors and were subject to fluorescence-activated cell sorting and western blot analysis. RESULTS: Screening with inhibitors revealed specific inhibition only with the map kinase (MEK)/extracellular signal regulated kinase (ERK) inhibitor, U0126. Accordingly, fluorescence-activated cell sorting and western blot analysis showed that ERK phosphorylation is induced in the effector cells within 1 hr of incubation with the veto cells. ERK phosphorylation had no effect on the Fas expression level, nor was it reduced when using effector cells from Fas KO mice. Examination of ERK phosphorylation in high and low MHC-I expressing effectors revealed marked differences, suggesting that the interaction between CD8 on the veto CTL, and MHC-I on the effector cells is likely responsible for ERK phosphorylation. Furthermore, XIAP in 2C cells is specifically reduced on binding to the cognate veto cells during the mixed lymphocyte reaction but before the appearance of Annexin V reactivity. CONCLUSIONS: These results suggest that the interaction between CD8 on veto CTL and the MHC class I alpha3 domain on the effector cell, leads to phosphorylation of MEK/ERK in the latter cell, associated with a significant reduction of XIAP levels which, in turn, enables potent triggering of Fas-FasL mediated apoptosis on cognate binding of the veto CTLs.