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

A synthetic protein-level neural network in mammalian cells

Artificial neural networks provide a powerful paradigm for nonbiological information processing. To understand whether similar principles could enable computation within living cells, we combined de novo-designed protein heterodimers and engineered viral proteases to implement a synthetic protein circuit that performs winner-take-all neural network classification. This "perceptein" circuit combines weighted input summation through reversible binding interactions with self-activation and mutual inhibition through irreversible proteolytic cleavage. These interactions collectively generate a large repertoire of distinct protein species stemming from up to eight coexpressed starting protein species. The complete system achieves multi-output signal classification with tunable decision boundaries in mammalian cells and can be used to conditionally control cell death. These results demonstrate how engineered protein-based networks can enable programmable signal classification in living cells.

Autism spectrum disorder variation as a computational trade-off via dynamic range of neuronal population responses

Individuals diagnosed with autism spectrum disorder (ASD) show neural and behavioral characteristics differing from the neurotypical population. This may stem from a computational principle that relates inference and computational dynamics to the dynamic range of neuronal population responses, reflecting the signal levels for which the system is responsive. In the present study, we showed that an increased dynamic range (IDR), indicating a gradual response of a neuronal population to changes in input, accounts for neural and behavioral variations in individuals diagnosed with ASD across diverse tasks. We validated the model with data from finger-tapping synchronization, orientation reproduction and global motion coherence tasks. We suggested that increased heterogeneity in the half-activation point of individual neurons may be the biological mechanism underlying the IDR in ASD. Taken together, this model provides a proof of concept for a new computational principle that may account for ASD and generates new testable and distinct predictions regarding its behavioral, neural and biological foundations.

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 developmental biology, systems biology, microbiology, and many other fields. A common file format (FCS format, defined by the International Society for Advancement of Cytometry (ISAC)) has been universally adopted, facilitating data exchange between different machines. A diverse spectrum of software packages has been developed for the 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 graphic user interface (GUI) based on Matlab or Python, that can be installed and run locally across platforms (Windows, MacOS, and Linux) without requiring previous coding knowledge. The Python version (EasyFlowQ) is also developed on a popular plotting framework (Matplotlib) and modern user interface 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.

Network switches and their role in circadian clocks

Circadian rhythms are generated by complex interactions among genes and proteins. Self-sustained ∼24 h oscillations require negative feedback loops and sufficiently strong nonlinearities that are the product of molecular and network switches. Here, we review common mechanisms to obtain switch-like behavior, including cooperativity, antagonistic enzymes, multisite phosphorylation, positive feedback, and sequestration. We discuss how network switches play a crucial role as essential components in cellular circadian clocks, serving as integral parts of transcription-translation feedback loops that form the basis of circadian rhythm generation. The design principles of network switches and circadian clocks are illustrated by representative mathematical models that include bistable systems and negative feedback loops combined with Hill functions. This work underscores the importance of negative feedback loops and network switches as essential design principles for biological oscillations, emphasizing how an understanding of theoretical concepts can provide insights into the mechanisms generating biological rhythms.

Macroscopic quorum sensing sustains differentiating embryonic stem cells

Cells can secrete molecules that help each other's replication. In cell cultures, chemical signals might diffuse only within a cell colony or between colonies. A chemical signal's interaction length-how far apart interacting cells are-is often assumed to be some value without rigorous justifications because molecules' invisible paths and complex multicellular geometries pose challenges. Here we present an approach, combining mathematical models and experiments, for determining a chemical signal's interaction length. With murine embryonic stem (ES) cells as a testbed, we found that differentiating ES cells secrete FGF4, among others, to communicate over many millimeters in cell culture dishes and, thereby, form a spatially extended, macroscopic entity that grows only if its centimeter-scale population density is above a threshold value. With this 'macroscopic quorum sensing', an isolated macroscopic, but not isolated microscopic, colony can survive differentiation. Our integrated approach can determine chemical signals' interaction lengths in generic multicellular communities.

Distinct human skeletal muscle-derived CD90 progenitor subsets for myo-fibro-adipogenic disease modeling and treatment in multiplexed conditions

Chronic muscle injuries, such as massive rotator cuff tears, are associated with progressive muscle wasting, fibrotic scarring, and intramuscular fat accumulation. While progenitor cell subsets are usually studied in culture conditions that drive either myogenic, fibrogenic, or adipogenic differentiation, it is still unknown how combined myo-fibro-adipogenic signals, which are expected to occur in vivo, modulate progenitor differentiation. We therefore evaluated the differentiation potential of retrospectively generated subsets of primary human muscle mesenchymal progenitors in multiplexed conditions in the presence or absence of 423F drug, a modulator of gp130 signaling. We identified a novel CD90+CD56- non-adipogenic progenitor subset that maintained a lack of adipogenic potential in single and multiplexed myo-fibro-adipogenic culture conditions. CD90-CD56- demarcated fibro-adipogenic progenitors (FAP) and CD56+CD90+ progenitors were typified as myogenic. These human muscle subsets exhibited varying degrees of intrinsically regulated differentiation in single and mixed induction cultures. Modulation of gp130 signaling via 423F drug mediated muscle progenitor differentiation in a dose-, induction-, and cell subset-dependent manner and markedly decreased fibro-adipogenesis of CD90-CD56- FAP. Conversely, 423F promoted myogenesis of CD56+CD90+ myogenic subset, indicated by increased myotube diameter and number of nuclei per myotube. 423F treatment eliminated FAP-derived mature adipocytes from mixed adipocytes-FAP cultures but did not modify the growth of non-differentiated FAP in these cultures. Collectively, these data demonstrate that capability of myogenic, fibrogenic, or adipogenic differentiation is largely dependent on the intrinsic features of cultured subsets, and that the degree of lineage differentiation varies when signals are multiplexed. Moreover, our tests performed in primary human muscle cultures reveal and confirm the potential triple-therapeutic effects of 423F drug which simultaneously attenuates degenerative fibrosis, fat accumulation and promotes myo-regeneration.

Lineage tracing reveals B cell antibody class switching is stochastic, cell-autonomous, and tuneable

To optimize immunity to pathogens, B lymphocytes generate plasma cells with functionally diverse antibody isotypes. By lineage tracing single cells within differentiating B cell clones, we identified the heritability of discrete fate controlling mechanisms to inform a general mathematical model of B cell fate regulation. Founder cells highly influenced clonal plasma-cell fate, whereas class switch recombination (CSR) was variegated within clones. In turn, these CSR patterns resulted from independent all-or-none expression of both activation-induced cytidine deaminase (AID) and IgH germline transcription (GLT), with the latter being randomly re-expressed after each cell division. A stochastic model premised on these molecular transition rules accurately predicted antibody switching outcomes under varied conditions in vitro and during an immune response in vivo. Thus, the generation of functionally diverse antibody types follows rules of autonomous cellular programming that can be adapted and modeled for the rational control of antibody classes for potential therapeutic benefit.

Metabolic regulation and function of T helper cells in neuroinflammation

Neuroinflammatory conditions such as multiple sclerosis (MS) are initiated by pathogenic immune cells invading the central nervous system (CNS). Autoreactive CD4⁺ T helper cells are critical players that orchestrate the immune response both in MS and in other neuroinflammatory autoimmune diseases including animal models that have been developed for MS. T helper cells are classically categorized into different subsets, but heterogeneity exists within these subsets. Untangling the more complex regulation of these subsets will clarify their functional roles in neuroinflammation. Here, we will discuss how differentiation, immune checkpoint pathways, transcriptional regulation and metabolic factors determine the function of CD4⁺ T cell subsets in CNS autoimmunity. T cells rely on metabolic reprogramming for their activation and proliferation to meet bioenergetic demands. This includes changes in glycolysis, glutamine metabolism and polyamine metabolism. Importantly, these pathways were recently also implicated in the fine tuning of T cell fate decisions during neuroinflammation. A particular focus of this review will be on the Th17/Treg balance and intra-subset functional states that can either promote or dampen autoimmune responses in the CNS and thus affect disease outcome. An increased understanding of factors that could tip CD4⁺ T cell subsets and populations towards an anti-inflammatory phenotype will be critical to better understand neuroinflammatory diseases and pave the way for novel treatment paradigms.

Simplifying cell fate map by determining lineage history of core pathway activation during fate specification

A fundamental question in developmental biology is how a single genome gives rise to the diversity of cell fates. In essence, each cell fate in the human body is a unique but stable output state of the genome, maintained by positive and negative feedbacks from both inside and outside the cell (a stable cell state). Traditionally, defining a cell fate means identifying a unique combination of transcriptional factors expressed by the specific cell type. The hundreds of transcriptional factors in the genome, however, have complicated the task of simplifying cell fate representation and obtaining insights into its regulation. Moreover, results from this approach provides only a mostly static picture, with each cell fate/state disconnected from one another. An alternative approach instead defines cell fates by determining their relationship to each other, through identifying the signaling pathways that control each step of their lineage transition from a common progenitor during development. Decades of studies have shown only a handful of signaling pathways are sufficient to specify all cell fates in the body, simplifying the execution of such a strategy. In this review, I will argue this alternative approach is not only feasible but also has the potential of simplifying the cell fate landscape as well as facilitating the engineering of different cell fates for regenerative medicine.

Potential Landscapes, Bifurcations, and Robustness of Tristable Networks

Bistable switches that produce all-or-none responses have been found to regulate a number of natural cellular decision making processes, and subsequently synthetic switches were designed to exploit their potential. However, an increasing number of studies, particularly in the context of cellular differentiation, highlight the existence of a mixed state that can be explained by tristable switches. The criterion for designing robust tristable switches still remains to be understood from the perspective of network topology. To address such a question, we calculated the robustness of several 2- and 3-component network motifs, connected via only two positive feedback loops, in generating tristable signal response curves. By calculating the effective potential landscape and following its modifications with the bifurcation parameter, we constructed one-parameter bifurcation diagrams of these models in a high-throughput manner for a large combinations of parameters. We report here that introduction of a self-activatory positive feedback loop, directly or indirectly, into a mutual inhibition loop leads to generating the most robust tristable response. The high-throughput approach of our method further allowed us to determine the robustness of four types of tristable responses that originate from the relative locations of four bifurcation points. Using the method, we also analyzed the role of additional mutual inhibition loops in stabilizing the mixed state.

Multi-stability in cellular differentiation enabled by a network of three mutually repressing master regulators

Identifying the design principles of complex regulatory networks driving cellular decision-making remains essential to decode embryonic development as well as enhance cellular reprogramming. A well-studied network motif involved in cellular decision-making is a toggle switch-a set of two opposing transcription factors A and B, each of which is a master regulator of a specific cell fate and can inhibit the activity of the other. A toggle switch can lead to two possible states-(high A, low B) and (low A, high B)-and drives the 'either-or' choice between these two cell fates for a common progenitor cell. However, the principles of coupled toggle switches remain unclear. Here, we investigate the dynamics of three master regulators A, B and C inhibiting each other, thus forming three-coupled toggle switches to form a toggle triad. Our simulations show that this toggle triad can lead to co-existence of cells into three differentiated 'single positive' phenotypes-(high A, low B, low C), (low A, high B, low C) and (low A, low B, high C). Moreover, the hybrid or 'double positive' phenotypes-(high A, high B, low C), (low A, high B, high C) and (high A, low B, high C)-can coexist together with 'single positive' phenotypes. Including self-activation loops on A, B and C can increase the frequency of 'double positive' states. Finally, we apply our results to understand cellular decision-making in terms of differentiation of naive CD4⁺ T cells into Th1, Th2 and Th17 states, where hybrid Th1/Th2 and hybrid Th1/Th17 cells have been reported in addition to the Th1, Th2 and Th17 ones. Our results offer novel insights into the design principles of a multi-stable network topology and provide a framework for synthetic biology to design tristable systems.

Quorum sensing via dynamic cytokine signaling comprehensively explains divergent patterns of effector choice among helper T cells

In the animal kingdom, various forms of swarming enable groups of autonomous individuals to transform uncertain information into unified decisions which are probabilistically beneficial. Crossing scales from individual to group decisions requires dynamically accumulating signals among individuals. In striking parallel, the mammalian immune system is also a group of decentralized autonomous units (i.e. cells) which collectively navigate uncertainty with the help of dynamically accumulating signals (i.e. cytokines). Therefore, we apply techniques of understanding swarm behavior to a decision-making problem in the mammalian immune system, namely effector choice among CD4+ T helper (Th) cells. We find that incorporating dynamic cytokine signaling into a simple model of Th differentiation comprehensively explains divergent observations of this process. The plasticity and heterogeneity of individual Th cells, the tunable mixtures of effector types that can be generated in vitro, and the polarized yet updateable group effector commitment often observed in vivo are all explained by the same set of underlying molecular rules. These rules reveal that Th cells harness dynamic cytokine signaling to implement a system of quorum sensing. Quorum sensing, in turn, may confer adaptive advantages on the mammalian immune system, especially during coinfection and during coevolution with manipulative parasites. This highlights a new way of understanding the mammalian immune system as a cellular swarm, and it underscores the power of collectives throughout nature.

A Quantitative Multivariate Model of Human Dendritic Cell-T Helper Cell Communication

Cell-cell communication involves a large number of molecular signals that function as words of a complex language whose grammar remains mostly unknown. Here, we describe an integrative approach involving (1) protein-level measurement of multiple communication signals coupled to output responses in receiving cells and (2) mathematical modeling to uncover input-output relationships and interactions between signals. Using human dendritic cell (DC)-T helper (Th) cell communication as a model, we measured 36 DC-derived signals and 17 Th cytokines broadly covering Th diversity in 428 observations. We developed a data-driven, computationally validated model capturing 56 already described and 290 potentially novel mechanisms of Th cell specification. By predicting context-dependent behaviors, we demonstrate a new function for IL-12p70 as an inducer of Th17 in an IL-1 signaling context. This work provides a unique resource to decipher the complex combinatorial rules governing DC-Th cell communication and guide their manipulation for vaccine design and immunotherapies.

Modulation of T helper 1 and T helper 2 immune balance in a murine stress model during Chlamydia muridarum genital infection

A murine model to study the effect of cold-induced stress (CIS) on Chlamydia muridarum genital infection and immune response has been developed in our laboratory. Previous results in the lab show that CIS increases the intensity of chlamydia genital infection, but little is known about the effects and mechanisms of CIS on the differentiation and activities of CD4+ T cell subpopulations and bone marrow-derived dendritic cells (BMDCs). The factors that regulate the production of T helper 1 (Th1) or T helper 2 (Th2) cytokines are not well defined. In this study, we examined whether CIS modulates the expressions of beta-adrenergic receptor (β-AR), transcription factors, hallmark cytokines of Th1 and Th2, and differentiation of BMDCs during C. muridarum genital infection in the murine model. Our results show that the mRNA level of the beta2-adrenergic receptor (β2-AR) compared to β1-AR and β3-AR was high in the mixed populations of CD4+ T cells and BMDCs. Furthermore, we observed decreased expression of T-bet, low level of Interferon-gamma (IFN-γ) production, increased expression of GATA-3, and Interleukin-4 (IL-4) production in CD4+ T cells of stressed mice. Exposure of BMDCs to Fenoterol, β2-AR agonist, or ICI118,551, β2-AR antagonist, revealed significant β2-AR stimulation or inhibition, respectively, in stressed mice. Moreover, co-culturing of mature BMDCs and naïve CD4+ T cells increased the production of IL-4, IL-10, L-17, and IL-23 cytokines, suggesting that stimulation of β2-AR leads to the increased production of Th2 cytokines. Overall, our results show for the first time that CIS promotes the switching from a Th1 to Th2 cytokine environment. This was evidenced in the murine stress model by the overexpression of GATA-3 concurrent with elevated IL-4 production, reduced T-bet expression, and IFN-γ secretion.

Modes of Communication between T Cells and Relevance for Immune Responses

T cells are essential mediators of the adaptive immune system, which constantly patrol the body in search for invading pathogens. During an infection, T cells that recognise the pathogen are recruited, expand and differentiate into subtypes tailored to the infection. In addition, they differentiate into subsets required for short and long-term control of the pathogen, i.e., effector or memory. T cells have a remarkable degree of plasticity and heterogeneity in their response, however, their overall response to a given infection is consistent and robust. Much research has focused on how individual T cells are activated and programmed. However, in order to achieve a critical level of population-wide reproducibility and robustness, neighbouring cells and surrounding tissues have to provide or amplify relevant signals to tune the overall response accordingly. The characteristics of the immune response-stochastic on the individual cell level, robust on the global level-necessitate coordinated responses on a system-wide level, which facilitates the control of pathogens, while maintaining self-tolerance. This global coordination can only be achieved by constant cellular communication between responding cells, and faults in this intercellular crosstalk can potentially lead to immunopathology or autoimmunity. In this review, we will discuss how T cells mount a global, collective response, by describing the modes of T cell-T cell (T-T) communication they use and highlighting their physiological relevance in programming and controlling the T cell response.

Quantitative genetic analysis deciphers the impact of cis and trans regulation on cell-to-cell variability in protein expression levels

Identifying the factors that shape protein expression variability in complex multi-cellular organisms has primarily focused on promoter architecture and regulation of single-cell expression in cis. However, this targeted approach has to date been unable to identify major regulators of cell-to-cell gene expression variability in humans. To address this, we have combined single-cell protein expression measurements in the human immune system using flow cytometry with a quantitative genetics analysis. For the majority of proteins whose variability in expression has a heritable component, we find that genetic variants act in trans, with notably fewer variants acting in cis. Furthermore, we highlight using Mendelian Randomization that these variability-Quantitative Trait Loci might be driven by the cis regulation of upstream genes. This indicates that natural selection may balance the impact of gene regulation in cis with downstream impacts on expression variability in trans.

Order by chance: origins and benefits of stochasticity in immune cell fate control

To protect against diverse challenges, the immune system must continuously generate an arsenal of specialized cell types, each of which can mount a myriad of effector responses upon detection of potential threats. To do so, it must generate multiple differentiated cell populations with defined sizes and proportions, often from rare starting precursor cells. Here, we discuss the emerging view that inherently probabilistic mechanisms, involving rare, rate-limiting regulatory events in single cells, control fate decisions and population sizes and fractions during immune development and function. We first review growing evidence that key fate control points are gated by stochastic signaling and gene regulatory events that occur infrequently over decision-making timescales, such that initially homogeneous cells can adopt variable outcomes in response to uniform signals. We next discuss how such stochastic control can provide functional capabilities that are harder to achieve with deterministic control strategies, and may be central to robust immune system function.

Challenges in measuring and understanding biological noise

Biochemical reactions are intrinsically stochastic, leading to variation in the production of mRNAs and proteins within cells. In the scientific literature, this source of variation is typically referred to as 'noise'. The observed variability in molecular phenotypes arises from a combination of processes that amplify and attenuate noise. Our ability to quantify cell-to-cell variability in numerous biological contexts has been revolutionized by recent advances in single-cell technology, from imaging approaches through to 'omics' strategies. However, defining, accurately measuring and disentangling the stochastic and deterministic components of cell-to-cell variability is challenging. In this Review, we discuss the sources, impact and function of molecular phenotypic variability and highlight future directions to understand its role.

Characterization of cell fate probabilities in single-cell data with Palantir

Single-cell RNA sequencing studies of differentiating systems have raised fundamental questions regarding the discrete versus continuous nature of both differentiation and cell fate. Here we present Palantir, an algorithm that models trajectories of differentiating cells by treating cell fate as a probabilistic process and leverages entropy to measure cell plasticity along the trajectory. Palantir generates a high-resolution pseudo-time ordering of cells and, for each cell state, assigns a probability of differentiating into each terminal state. We apply our algorithm to human bone marrow single-cell RNA sequencing data and detect important landmarks of hematopoietic differentiation. Palantir's resolution enables the identification of key transcription factors that drive lineage fate choice and closely track when cells lose plasticity. We show that Palantir outperforms existing algorithms in identifying cell lineages and recapitulating gene expression trends during differentiation, is generalizable to diverse tissue types, and is well-suited to resolving less-studied differentiating systems.

Negative Co-stimulation Constrains T Cell Differentiation by Imposing Boundaries on Possible Cell States

Co-stimulation regulates T cell activation, but it remains unclear whether co-stimulatory pathways also control T cell differentiation. We used mass cytometry to profile T cells generated in the genetic absence of the negative co-stimulatory molecules CTLA-4 and PD-1. Our data indicate that negative co-stimulation constrains the possible cell states that peripheral T cells can acquire. CTLA-4 imposes major boundaries on CD4⁺ T cell phenotypes, whereas PD-1 subtly limits CD8⁺ T cell phenotypes. By computationally reconstructing T cell differentiation paths, we identified protein expression changes that underlied the abnormal phenotypic expansion and pinpointed when lineage choice events occurred during differentiation. Similar alterations in T cell phenotypes were observed after anti-CTLA-4 and anti-PD-1 antibody blockade. These findings implicate negative co-stimulation as a key regulator and determinant of T cell differentiation and suggest that checkpoint blockade might work in part by altering the limits of T cell phenotypes.

Limit-cycle oscillatory coexpression of cross-inhibitory transcription factors: a model mechanism for lineage promiscuity

Lineage switches are genetic regulatory motifs that govern and maintain the commitment of a developing cell to a particular cell fate. A canonical example of a lineage switch is the pair of transcription factors PU.1 and GATA-1, of which the former is affiliated with the myeloid and the latter with the erythroid lineage within the haematopoietic system. On a molecular level, PU.1 and GATA-1 positively regulate themselves and antagonize each other via direct protein-protein interactions. Here we use mathematical modelling to identify a novel type of dynamic behaviour that can be supported by such a regulatory architecture. Guided by the specifics of the PU.1-GATA-1 interaction, we formulate, using the law of mass action, a system of differential equations for the key molecular concentrations. After a series of systematic approximations, the system is reduced to a simpler one, which is tractable to phase-plane and linearization methods. The reduced system formally resembles, and generalizes, a well-known model for competitive species from mathematical ecology. However, in addition to the qualitative regimes exhibited by a pair of competitive species (exclusivity, bistable exclusivity, stable-node coexpression) it also allows for oscillatory limit-cycle coexpression. A key outcome of the model is that, in the context of cell-fate choice, such oscillations could be harnessed by a differentiating cell to prime alternately for opposite outcomes; a bifurcation-theory approach is adopted to characterize this possibility.

The Multifaceted Interface Between Cytokines and microRNAs: An Ancient Mechanism to Regulate the Good and the Bad of Inflammation

MicroRNAs (miRNAs) are evolutionary conserved small non-coding RNA molecules that affect gene expression by binding to target messenger RNAs and play a role in biological processes like cell growth, differentiation, and death. Different CD4⁺ T cell subsets such as Th1, Th2, Th17, and T regulatory cells, exert a distinct role in effector and regulatory-type immune responses. miRNAs have been shown to respond to dynamic micro-environmental cues and regulate multiple functions of T cell subsets including their development, survival and activation. Thus, miRNA functions contribute to immune homeostasis, on the one side, and to the control of immune tolerance, on the other. Among the most important proteins whose expression is targeted by miRNAs, there are the cytokines, that act as both key upstream signals and major functional outputs, and that, in turn, can affect miRNA level. Here, we analyze what is known about the regulatory circuit of miRNAs and cytokines in CD4⁺ T lymphocytes, and how this bidirectional system is dysregulated in conditions of pathological inflammation and autoimmunity. Furthermore, we describe how different T cell subsets release distinct fingerprints of miRNAs that modify the extracellular milieu and the inter-cellular communication between immune cells at the autocrine, paracrine, and endocrine level. In conclusion, a deeper knowledge of the interplay between miRNAs and cytokines in T cells may have pivotal implications for finding novel therapeutic strategies to target inflammation and autoimmune disorders.

Towards a Quantitative Understanding of Cell Identity

Cells have traditionally been characterized using expression levels of a few proteins that are thought to specify phenotype. This requires a priori selection of proteins, which can introduce descriptor bias, and neglects the wealth of additional molecular information nested within each cell in a population, which often makes these sparse descriptors qualitative. Recently, more unbiased and quantitative cell characterization has been made possible by new high-throughput, information-dense experimental approaches and data-driven computational methods. This review discusses such quantitative descriptors in the context of three central concepts of cell identity: definition, creation, and stability. Collectively, these concepts are essential for constructing quantitative phenotypic landscapes, which will enhance our understanding of cell biology and facilitate cell engineering for research and clinical applications.

Statistical Dynamics of Spatial-Order Formation by Communicating Cells

Communicating cells can coordinate their gene expressions to form spatial patterns, generating order from disorder. Ubiquitous "secrete-and-sense cells" secrete and sense the same molecule to do so. Here we present a modeling framework-based on cellular automata and mimicking approaches of statistical mechanics-for understanding how secrete-and-sense cells with bistable gene expression, from disordered beginnings, can become spatially ordered by communicating through rapidly diffusing molecules. Classifying lattices of cells by two "macrostate" variables-"spatial index," measuring degree of order, and average gene-expression level-reveals a conceptual picture: a group of cells behaves as a single particle, in an abstract space, that rolls down on an adhesive "pseudo-energy landscape" whose shape is determined by cell-cell communication and an intracellular gene-regulatory circuit. Particles rolling down the landscape represent cells becoming more spatially ordered. We show how to extend this framework to more complex forms of cellular communication.

A geometric approach to characterize the functional identity of single cells

Single-cell transcriptomic data has the potential to radically redefine our view of cell-type identity. Cells that were previously believed to be homogeneous are now clearly distinguishable in terms of their expression phenotype. Methods for automatically characterizing the functional identity of cells, and their associated properties, can be used to uncover processes involved in lineage differentiation as well as sub-typing cancer cells. They can also be used to suggest personalized therapies based on molecular signatures associated with pathology. We develop a new method, called ACTION, to infer the functional identity of cells from their transcriptional profile, classify them based on their dominant function, and reconstruct regulatory networks that are responsible for mediating their identity. Using ACTION, we identify novel Melanoma subtypes with differential survival rates and therapeutic responses, for which we provide biomarkers along with their underlying regulatory networks.

The regulation of lymphocyte activation and proliferation

Activation induced proliferation and clonal expansion of antigen specific lymphocytes is a hallmark of the adaptive immune response to pathogens. Recent studies identify two distinct control phases. In the first T and B lymphocytes integrate antigen and additional costimuli to motivate a programmed proliferative burst that ceases with a return to cell quiescence and eventual death. This proliferative burst is autonomously timed, ensuring an appropriate response magnitude whilst preventing uncontrolled expansion. This initial response is subject to further modification and extension by a range of signals that modify, expand and direct the emergence of a rich array of new cell types. Thus, both robust clonal expansion of a small number of antigen specific T cells, and the concurrent emergence of extensive cellular diversity, confers immunity to a vast array of different pathogens. The in vivo response to a given pathogen is made up by the sum of all responding clones and is reproducible and pathogen specific. Thus, a precise description of the regulatory principles governing lymphocyte proliferation, differentiation and survival is essential to a unified understanding of the immune system.

A cellular and molecular view of T helper 17 cell plasticity in autoimmunity

Since the original identification of the T helper 17 (Th17) subset in 2005, it has become evident that these cells do not only contribute to host defence against pathogens, such as bacteria and fungi, but that they are also critically involved in the pathogenesis of many autoimmune diseases. In contrast to the classic Th1 and Th2 cells, which represent rather stably polarized subsets, Th17 cells display remarkable heterogeneity and plasticity. This has been attributed to the characteristics of the key transcription factor that guides Th17 differentiation, retinoic acid receptor-related orphan nuclear receptor gamma (RORγ). Unlike the 'master regulators' T-bet and GATA3 that orchestrate Th1 and Th2 differentiation, respectively, RORγ controls transcription at relatively few loci in Th17 cells. Moreover, its expression is not stabilized by positive feedback loops but rather influenced by environmental cues, allowing for substantial functional plasticity. Importantly, a subset of IL-17/IFNγ double-producing Th17 cells was identified in both human and mouse models. Evidence is accumulating that these IL-17/IFNγ double-producing cells are pathogenic drivers in autoimmune diseases, including rheumatoid arthritis, multiple sclerosis and inflammatory bowel disease. In addition, IL-17/IFNγ double-producing cells have been identified in disorders in which the role of autoimmunity remains unclear, such as sarcoidosis. The observed plasticity of Th17 cells towards the Th1 phenotype can be explained by extensive epigenetic priming of the IFNG locus in Th17 cells. In fact, Th17 cells display an IFNG chromatin landscape that is remarkably similar to that of Th1 cells. On the other hand, pathogenic capabilities of Th17 cells can be restrained by stimulating IL-10 production and transdifferentiation into IL-10 producing T regulatory type 1 (Tr1) cells. In this review, we discuss recent advances in our knowledge on the cellular and molecular mechanisms involved in Th17 differentiation, heterogeneity and plasticity. We focus on transcriptional regulation of the Th17 expression program, the epigenetic dynamics involved, and how genetic variants associated with autoimmunity may affect immune responses through distal gene regulatory elements. Finally, the implications of Th17 cell plasticity for the pathogenesis and treatment of human autoimmune diseases will be discussed.

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

The Human Cell Atlas

The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

T-cell immunology in sarcoidosis: Disruption of a delicate balance between helper and regulatory T-cells

PURPOSE OF REVIEW

Although the aetiology of sarcoidosis is not yet completely understood, immunological changes within the T-cell compartment are characteristic for an exaggerated antigen-driven immune response. In this review, we describe the most recent findings on T-cell subset responses and regulation in sarcoidosis. We discuss how future immunological research can advance the field to unravel pathobiological mechanisms of this intriguingly complex disease.

RECENT FINDINGS

Research into the field of T-cell plasticity has recently challenged the long-held T helper type 1 (Th1) paradigm in sarcoidosis and striking parallels with autoimmune disorders and common variable immunodeficiency were recognized. For instance, it was demonstrated that Th17.1-cells rather than Th1-cells are responsible for the exaggerated IFN-γ production in pulmonary sarcoidosis. Furthermore, impaired regulatory T-cell function and alterations within the expression of co-inhibitory receptors that control T-cell responses, such as PD-1, CTLA-4 and BTNL2, raise new questions regarding T-cell regulation in pulmonary sarcoidosis.

SUMMARY

It becomes increasingly clear that Th17(.1)-cells and regulatory T-cells are key players in sarcoidosis T-cell immunology. New findings on plasticity and co-inhibitory receptor expression by these subsets help build a more comprehensive model for T-cell regulation in sarcoidosis and will finally shed light on the potential of new treatment modalities.

The Innate Lymphoid Cell Precursor

The discovery of tissue-resident innate lymphoid cell populations effecting different forms of type 1, 2, and 3 immunity; tissue repair; and immune regulation has transformed our understanding of mucosal immunity and allergy. The emerging complexity of these populations along with compounding issues of redundancy and plasticity raise intriguing questions about their precise lineage relationship. Here we review advances in mapping the emergence of these lineages from early lymphoid precursors. We discuss the identification of a common innate lymphoid cell precursor characterized by transient expression of the transcription factor PLZF, and the lineage relationships of innate lymphoid cells with conventional natural killer cells and lymphoid tissue inducer cells. We also review the rapidly growing understanding of the network of transcription factors that direct the development of these lineages.

Effector/memory CD4 T cells making either Th1 or Th2 cytokines commonly co-express T-bet and GATA-3

Naïve CD4 T (NCD4T) cells post-activation undergo programming for inducible production of cytokines leading to generation of memory cells with various functions. Based on cytokine based polarization of NCD4T cells in vitro, programming for either ‘Th1’ (interferon-gamma [IFNg]) or ‘Th2’ (interleukin [IL]-4/5/13) cytokines is thought to occur via mutually exclusive expression and functioning of T-bet or GATA-3 transcription factors (TFs). However, we show that a high proportion of mouse and human memory-phenotype CD4 T (MCD4T) cells generated in vivo which expressed either Th1 or Th2 cytokines commonly co-expressed T-bet and GATA-3. While T-bet levels did not differ between IFNg-expressing and IL-4/5/13-expressing MCD4T cells, GATA-3 levels were higher in the latter. These observations were also confirmed in MCD4T cells from FVB/NJ or aged C57BL/6 or IFNg-deficient mice. While MCD4T cells from these strains showed greater Th2 commitment than those from young C57BL/6 mice, pattern of co-expression of TF was similar. Effector T cells generated in vivo following immunization also showed TF co-expression in Th1 or Th2 cytokine producing cells. We speculated that the difference in TF expression pattern of MCD4T cells generated in vivo and those generated in cytokine polarized cultures in vitro could be due to relative absence of polarizing conditions during activation in vivo. We tested this by NCD4T cell activation in non-polarizing conditions in vitro. Anti-CD3 and anti-CD28-mediated priming of polyclonal NCD4T cells in vitro without polarizing milieu generated cells that expressed either IFNg or IL-4/5/13 but not both, yet both IFNg- and IL-4/5/13-expressing cells showed upregulation of both TFs. We also tested monoclonal T cell populations activated in non-polarizing conditions. TCR-transgenic NCD4T cells primed in vitro by cognate peptide in non-polarizing conditions which expressed either IFNg or IL-4/5/13 also showed a high proportion of cells co-expressing TFs, and their cytokine commitment varied depending on genetic background or priming conditions, without altering pattern of TF co-expression. Thus, the model of mutually antagonistic differentiation programs driven by mutually exclusively expressed T-bet or GATA-3 does not completely explain natural CD4 T cell priming outcomes.

Expression Diversity Adds Richness to T Cell Populations

Variation in protein expression is a feature of all cell populations. Using T cell subsets as a proof-of-concept, Lu et al. (2016) develop a framework for dissecting out the contributors to this cell-to-cell expression variation from high-parameter flow cytometry studies.

Instructed subsets or agile swarms: how T-helper cells may adaptively counter uncertainty with variability and plasticity

Over recent years, extensive phenotypic variability and plasticity have been revealed among the T-helper cells of the mammalian adaptive immune system, even within clonal lineages of identical antigen specificity. This challenges the conventional view that T-helper cells assort into functionally distinct subsets following differential instruction by the innate immune system. We argue that the adaptive value of coping with uncertainty can reconcile the 'instructed subset' framework with T-helper variability and plasticity. However, we also suggest that T-helper cells might better be understood as agile swarms engaged in collective decision-making to promote host fitness. With rigorous testing, the 'agile swarms' framework may illuminate how variable and plastic individual T-helper cells interact to create coherent immunity.

Systematic Analysis of Cell-to-Cell Expression Variation of T Lymphocytes in a Human Cohort Identifies Aging and Genetic Associations

Cell-to-cell expression variation (CEV) is a prevalent feature of even well-defined cell populations, but its functions, particularly at the organismal level, are not well understood. Using single-cell data obtained via high-dimensional flow cytometry of T cells as a model, we introduce an analysis framework for quantifying CEV in primary cell populations and studying its functional associations in human cohorts. Analyses of 840 CEV phenotypes spanning multiple baseline measurements of 14 proteins in 28 T cell subpopulations suggest that the quantitative extent of CEV can exhibit substantial subject-to-subject differences and yet remain stable within healthy individuals over months. We linked CEV to age and disease-associated genetic polymorphisms, thus implicating CEV as a biomarker of aging and disease susceptibility and suggesting that it might play an important role in health and disease. Our dataset, interactive figures, and software for computing CEV with flow cytometry data provide a resource for exploring CEV functions.

A kinetic model of multiple phenotypic states for breast cancer cells

Quantitative modeling of microscopic genes regulatory mechanisms in an individual cell is a crucial step towards understanding various macroscopic physiological phenomena of cell populations. Based on the regulatory mechanisms of genes zeb1 and cdh1 in the growth and development of breast cancer cells, we propose a kinetic model at the level of single cell. By constructing the effective landscape of underlying stationary probability for the genes expressions, it is found that (i) each breast cancer cell has three phenotypic states (i.e., the stem-like, basal, and luminal states) which correspond to three attractions of the probability landscape. (ii) The interconversions between phenotypic states can be induced by the noise intensity and the property of phenotypic switching is quantified by the mean first-passage time. (iii) Under certain conditions, the probabilities of each cancer cell appearing in the three states are consistent with the macroscopic phenotypic equilibrium proportions in the breast cancer SUM159 cell line. (iv) Our kinetic model involving the TGF-β signal can also qualitatively explain several macroscopic physiological phenomena of breast cancer cells, such as the "TGF-β paradox" in tumor therapy, the five clinical subtypes of breast cancer cells, and the effects of transient TGF-β on breast cancer metastasis.

Diverse continuum of CD4+ T-cell states is determined by hierarchical additive integration of cytokine signals

During cell differentiation, progenitor cells integrate signals from their environment that guide their development into specialized phenotypes. The ways by which cells respond to complex signal combinations remain difficult to analyze and model. To gain additional insight into signal integration, we systematically mapped the response of CD4⁺ T cells to a large number of input cytokine combinations that drive their differentiation. We find that, in response to varied input combinations, cells differentiate into a continuum of cell fates as opposed to a limited number of discrete phenotypes. Input cytokines hierarchically influence the cell population, with TGFβ being most dominant followed by IL-6 and IL-4. Mathematical modeling explains these results using additive signal integration within hierarchical groups of input cytokine combinations and correctly predicts cell population response to new input conditions. These findings suggest that complex cellular responses can be effectively described using a segmented linear approach, providing a framework for prediction of cellular responses to new cytokine combinations and doses, with implications to fine-tuned immunotherapies.

Revealing the vectors of cellular identity with single-cell genomics

Single-cell genomics has now made it possible to create a comprehensive atlas of human cells. At the same time, it has reopened definitions of a cell's identity and of the ways in which identity is regulated by the cell's molecular circuitry. Emerging computational analysis methods, especially in single-cell RNA sequencing (scRNA-seq), have already begun to reveal, in a data-driven way, the diverse simultaneous facets of a cell's identity, from discrete cell types to continuous dynamic transitions and spatial locations. These developments will eventually allow a cell to be represented as a superposition of 'basis vectors', each determining a different (but possibly dependent) aspect of cellular organization and function. However, computational methods must also overcome considerable challenges-from handling technical noise and data scale to forming new abstractions of biology. As the scale of single-cell experiments continues to increase, new computational approaches will be essential for constructing and characterizing a reference map of cell identities.

A Tunable Diffusion-Consumption Mechanism of Cytokine Propagation Enables Plasticity in Cell-to-Cell Communication in the Immune System

Immune cells communicate by exchanging cytokines to achieve a context-appropriate response, but the distances over which such communication happens are not known. Here, we used theoretical considerations and experimental models of immune responses in vitro and in vivo to quantify the spatial extent of cytokine communications in dense tissues. We established that competition between cytokine diffusion and consumption generated spatial niches of high cytokine concentrations with sharp boundaries. The size of these self-assembled niches scaled with the density of cytokine-consuming cells, a parameter that gets tuned during immune responses. In vivo, we measured interactions on length scales of 80-120 μm, which resulted in a high degree of cell-to-cell variance in cytokine exposure. Such heterogeneous distributions of cytokines were a source of non-genetic cell-to-cell variability that is often overlooked in single-cell studies. Our findings thus provide a basis for understanding variability in the patterning of immune responses by diffusible factors.

Demystifying the cytokine network: Mathematical models point the way

Cytokines provide the means by which immune cells communicate with each other and with parenchymal cells. There are over one hundred cytokines and many exist in families that share receptor components and signal transduction pathways, creating complex networks. Reductionist approaches to understanding the role of specific cytokines, through the use of gene-targeted mice, have revealed further complexity in the form of redundancy and pleiotropy in cytokine function. Creating an understanding of the complex interactions between cytokines and their target cells is challenging experimentally. Mathematical and computational modeling provides a robust set of tools by which complex interactions between cytokines can be studied and analyzed, in the process creating novel insights that can be further tested experimentally. This review will discuss and provide examples of the different modeling approaches that have been used to increase our understanding of cytokine networks. This includes discussion of knowledge-based and data-driven modeling approaches and the recent advance in single-cell analysis. The use of modeling to optimize cytokine-based therapies will also be discussed.

Regulation of the IL-10-driven macrophage phenotype under incoherent stimuli

Macrophages are ubiquitous innate immune cells that play a central role in health and disease by adopting distinct phenotypes, which are broadly divided into classical inflammatory responses and alternative responses that promote immune suppression and wound healing. Although macrophages are attractive therapeutic targets, incomplete understanding of this functional choice limits clinical manipulation. While individual stimuli, pathways, and genes involved in macrophage functional responses have been identified, how macrophages evaluate complex in vivo milieus comprising multiple divergent stimuli remains poorly understood. Here, we used combinations of "incoherent" stimuli-those that individually promote distinct macrophage phenotypes-to elucidate how the immunosuppressive, IL-10-driven macrophage phenotype is induced, maintained, and modulated under such combinatorial stimuli. The IL-10-induced immunosuppressive phenotype was largely insensitive to co-administered IL-12, which has been reported to modulate macrophage phenotype, but maintaining the immunosuppressive phenotype required sustained exposure to IL-10. Our data implicate the intracellular protein, BCL3, as a key mediator of the IL-10-driven phenotype. Notably, co-administration of IFN-γ disrupted an IL-10-mediated positive feedback loop that may reinforce the immunosuppressive phenotype. This novel combinatorial perturbation approach thus generated new insights into macrophage decision making and local immune network function.

Regulation of macrophage polarization and plasticity by complex activation signals

Macrophages are versatile cells of the immune system that play an important role in both advancing and resolving inflammation. Macrophage activation has been described as a continuum, and different stimuli lead to M1, M2, or mixed phenotypes. In addition, macrophages expressing markers associated with both M1 and M2 function are observed in vivo. Using flow cytometry, we examine how macrophage populations respond to combined M1 and M2 activation signals, presented either simultaneously or sequentially. We demonstrate that macrophages exposed to a combination of LPS, IFN-γ, IL-4, and IL-13 acquire a mixed activation state, with individual cells expressing both M1 marker CD86 and M2 marker CD206 instead of polarizing to discrete phenotypes. Over time, co-stimulated macrophages lose expression of CD86 and display increased expression of CD206. In addition, we find that exposure to LPS/IFN-γ potentiates the subsequent response to IL-4/IL-13, whereas pre-polarization with IL-4/IL-13 inhibits the response to LPS/IFN-γ. Mathematical modeling of candidate regulatory networks indicates that a complex inter-dependence of M1- and M2-associated pathways underlies macrophage activation. Specifically, a mutual inhibition motif was not by itself sufficient to reproduce the temporal marker expression data; incoherent feed-forward of M1 activation as well as both inhibition and activation of M2 by M1 were required. Together these results corroborate a continuum model of macrophage activation and demonstrate that phenotypic markers evolve with time and with exposure to complex signals.

Rapid Discrimination Among Putative Mechanistic Models of Biochemical Systems

An overarching goal in molecular biology is to gain an understanding of the mechanistic basis underlying biochemical systems. Success is critical if we are to predict effectively the outcome of drug treatments and the development of abnormal phenotypes. However, data from most experimental studies is typically noisy and sparse. This allows multiple potential mechanisms to account for experimental observations, and often devising experiments to test each is not feasible. Here, we introduce a novel strategy that discriminates among putative models based on their repertoire of qualitatively distinct phenotypes, without relying on knowledge of specific values for rate constants and binding constants. As an illustration, we apply this strategy to two synthetic gene circuits exhibiting anomalous behaviors. Our results show that the conventional models, based on their well-characterized components, cannot account for the experimental observations. We examine a total of 40 alternative hypotheses and show that only 5 have the potential to reproduce the experimental data, and one can do so with biologically relevant parameter values.

Human amnion favours tissue repair by inducing the M1-to-M2 switch and enhancing M2 macrophage features

Human amniotic mesenchymal cells (hAMTCs) possess interesting immunomodulatory properties, making them attractive candidates for regenerative medicine applications. Recent in vivo reports argue in favour of an important role for macrophages as targets of hAMTC‐mediated suppression of inflammation and the enhancement of tissue repair. However, a comprehensive study of the effects of hAMTCs and their conditioned medium (CM) on human macrophage differentiation and function is unavailable. In the present study we found that hAMTCs and CM induce the differentiation of myeloid cells (U937 and monocytes) towards macrophages. We then investigated their effects on monocytes differentiated toward pro‐inflammatory M1 and anti‐inflammatory M2 macrophages. Monocytes treated under M1 conditions in the presence of hAMTCs or CMs shifted towards M2‐like macrophages, which expressed CD14, CD209, CD23, CD163 and PM‐2 K, possessed higher phagocytic activity and produced higher IL‐10 and lower pro‐inflammatory cytokines. They were also poor T cell stimulators and Th1 inducers, while they were able to increase activated and naïve suppressive Treg subsets. We show that prostaglandins, and not IL‐6, play a role in determining the M2 activation status. Instead, monocytes treated under M2 conditions in the presence of hAMTCs or CM retained M2‐like features, but with an enhanced anti‐inflammatory profile, having a reduced expression of the co‐stimulatory molecule CD80, reduced phagocytosis activity and decreased the secretion of inflammatory chemokines. Importantly, we provide evidence that macrophages re‐educated by CM improve tissue regeneration/repair in wound‐healing models. In conclusion, we identified new cell targets of hAMTCs and their bioactive factors and here provide insight into the beneficial effects observed when these cells are used in therapeutic approaches in vivo. © 2016 The Authors Journal of Tissue Engineering and Regenerative Medicine Published by John Wiley & Sons Ltd.

Logical Modeling and Dynamical Analysis of Cellular Networks

The logical (or logic) formalism is increasingly used to model regulatory and signaling networks. Complementing these applications, several groups contributed various methods and tools to support the definition and analysis of logical models. After an introduction to the logical modeling framework and to several of its variants, we review here a number of recent methodological advances to ease the analysis of large and intricate networks. In particular, we survey approaches to determine model attractors and their reachability properties, to assess the dynamical impact of variations of external signals, and to consistently reduce large models. To illustrate these developments, we further consider several published logical models for two important biological processes, namely the differentiation of T helper cells and the control of mammalian cell cycle.

Single-cell analysis defines the divergence between the innate lymphoid cell lineage and lymphoid tissue-inducer cell lineage

The precise lineage relationship between innate lymphoid cells (ILCs) and lymphoid tissue-inducer (LTi) cells is poorly understood. Using single-cell multiplex transcriptional analysis of 100 lymphoid genes and single-cell cultures of fetal liver precursor cells, we identified the common proximal precursor to these lineages and found that its bifurcation was marked by differential induction of the transcription factors PLZF and TCF1. Acquisition of individual effector programs specific to the ILC subsets ILC1, ILC2 and ILC3 was initiated later, at the common ILC precursor stage, by transient expression of mixed ILC1, ILC2 and ILC3 transcriptional patterns, whereas, in contrast, the development of LTi cells did not go through multilineage priming. Our findings provide insight into the divergent mechanisms of the differentiation of the ILC lineage and LTi cell lineage and establish a high-resolution 'blueprint' of their development.

Translational opportunities for targeting the Th17 axis in acute graft-vs.-host disease

Allogeneic stem cell transplantation (allo-SCT) is a curative therapy for different life-threatening malignant and non-malignant hematologic disorders. Acute graft-vs.-host disease (aGVHD) and particularly gastrointestinal aGVHD remains a major source of morbidity and mortality following allo-SCT, which limits the use of this treatment in a broader spectrum of patients. Better understanding of aGVHD pathophysiology is indispensable to identify new therapeutic targets for aGVHD prevention and therapy. Growing amount of data suggest a role for T helper (Th)17 cells in aGVHD pathophysiology. In this review, we will discuss the current knowledge in this area in animal models and in humans. We will then describe new potential treatments for aGVHD along the Th17 axis.

Single-cell technologies to study the immune system

The immune system is composed of a variety of cells that act in a coordinated fashion to protect the organism against a multitude of different pathogens. The great variability of existing pathogens corresponds to a similar high heterogeneity of the immune cells. The study of individual immune cells, the fundamental unit of immunity, has recently transformed from a qualitative microscopic imaging to a nearly complete quantitative transcriptomic analysis. This shift has been driven by the rapid development of multiple single‐cell technologies. These new advances are expected to boost the detection of less frequent cell types and transient or intermediate cell states. They will highlight the individuality of each single cell and greatly expand the resolution of current available classifications and differentiation trajectories. In this review we discuss the recent advancement and application of single‐cell technologies, their limitations and future applications to study the immune system.