Origins of regulated cell-to-cell variability

Mammary epithelial morphogenesis in 3D combinatorial microenvironments

Human mammary epithelial cells can proliferate and reorganize into polarized multi-cellular constructs in-vitro, thereby functioning as an important model system in recapitulating key steps of in-vivo morphogenesis. Current approaches to constructing such three-dimensional mimics of the in-vivo microenvironment have involved the use of complex and ill-defined naturally derived matrices, whose properties are difficult to manipulate independently, and which have therefore limited our ability to understand the extrinsic regulation of morphogenesis. Here, we employ an automated, high-throughput approach to array modular building blocks of synthetic components, and develop a systematic approach to analyze colonies resulting from these varied microenvironmental combinations. This methodology allows us to systematically map the relationship between microenvironmental properties and ensuing morphogenetic phenotypes. Our analysis reveals that apico-basal polarity of mammary epithelial cells occurs within a narrow range of matrix stiffness, and that phenotypic homogeneity is favored in matrices which are insensitive to MMP-mediated degradation. Furthermore, combinations of extracellular proteins in the matrix finely tune the morphology of the mammary colonies, suggesting that subtle disregulations of the microenvironment may play a significant role in pathological disease states. This approach, which leverages the combinatorial possibilities of modular synthetic artificial extracellular matrices with an automated technology platform, demonstrates how morphogenesis can be assessed systematically in 3D, and provides new insights into mammary epithelial multicellularity.

Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes

Artificial intelligence (AI) digital health systems have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI systems, and the barriers facing their implementation into medical practice. The development of second-generation AI systems is discussed with a focus on overcoming some of these obstacles. Second-generation systems are aimed at focusing on a single subject and on improving patients' clinical outcomes. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation systems are expected to assist patients and providers in adopting and implementing of these systems into everyday care.

A quantitative analysis of the interplay of environment, neighborhood, and cell state in 3D spheroids

Cells react to their microenvironment by integrating external stimuli into phenotypic decisions via an intracellular signaling network. To analyze the interplay of environment, local neighborhood, and internal cell state effects on phenotypic variability, we developed an experimental approach that enables multiplexed mass cytometric imaging analysis of up to 240 pooled spheroid microtissues. We quantified the contributions of environment, neighborhood, and intracellular state to marker variability in single cells of the spheroids. A linear model explained on average more than half of the variability of 34 markers across four cell lines and six growth conditions. The contributions of cell-intrinsic and environmental factors to marker variability are hierarchically interdependent, a finding that we propose has general implications for systems-level studies of single-cell phenotypic variability. By the overexpression of 51 signaling protein constructs in subsets of cells, we also identified proteins that have cell-intrinsic and cell-extrinsic effects. Our study deconvolves factors influencing cellular phenotype in a 3D tissue and provides a scalable experimental system, analytical principles, and rich multiplexed imaging datasets for future studies.

Measuring and Modeling Single-Cell Heterogeneity and Fate Decision in Mouse Embryos

Cellular heterogeneity is a property of any living system; however, its relationship with cellular fate decision remains an open question. Recent technological advances have enabled valuable insights, especially in complex systems such as the mouse embryo. In this review, we discuss recent studies that characterize cellular heterogeneity at different levels during mouse development, from the two-cell stage up to gastrulation. In addition to key experimental findings, we review mathematical modeling approaches that help researchers interpret these findings. Disentangling the role of heterogeneity in cell fate decision will likely rely on the refined integration of experiments, large-scale omics data, and mathematical modeling, complemented by the use of synthetic embryos and gastruloids as promising in vitro models.

3D Printed Microneedle Array for Electroporation

In-vitro transfection of cells by electroporation is a widely used approach in cell biology and medicine. The transfection method is highly dependent on the cell culture's electrical resistance, which is strongly determined by differences in the membranes, but also on the morphology of the electrodes. Microneedle (MN)-based electrodes have been used to concentrate the electrical field during electroporation, and therefore maximize its effect on cell membrane permeability. So far, the methods used for the fabrication of MN electrodes have been relatively limited with respect to the needle design. In this work, we provide a method to fabricate MNs using 3D printing, which is a technology that provides a high degree of flexibility with respect to geometry and dimensions. Pyramidal-shaped MN designs were fabricated and tested on HCT116 cancer cells. Customization of the tips of the pyramids permits tailoring of the electrical field in the vicinity of the cell membranes. The fabricated device enables low-voltage (2 V) electroporation, eliminating the need for the use of specialized chemical buffers. The results show the potential of this method, which can be exploited and optimized for many different applications, and offer a very accessible approach for in-vitro electroporation and cell studies. The MNs can be customized to create complex structures, for example, for a multi-culture cell environment.

Abiotic and past climatic conditions drive protein abundance variation among natural populations of the caddisfly Crunoecia irrorata

Deducing impacts of environmental change on species and the populations they form in nature is an important goal in contemporary ecology. Achieving this goal is hampered by our limited understanding of the influence of naturally occurring environmental variation on the molecular systems of ecologically relevant species, as the pathways underlying fitness-affecting plastic responses have primarily been studied in model organisms and under controlled laboratory conditions. Here, to test the hypothesis that proteome variation systematically relates to variation in abiotic conditions, we establish such relationships by profiling the proteomes of 24 natural populations of the spring-dwelling caddisfly Crunoecia irrorata. We identified protein networks whose abundances correlated with environmental (abiotic) gradients such as in situ pH, oxygen- and nitrate concentrations but also climatic data such as past thermal minima and temperature seasonality. Our analyses suggest that variations in abiotic conditions induce discrete proteome responses such as the differential abundance of proteins associated with cytoskeletal function, heat-shock proteins and proteins related to post-translational modification. Identifying these drivers of proteome divergence characterizes molecular "noise", and positions it as a background against which molecular signatures of species' adaptive responses to stressful conditions can be identified.

A systematic approach to decipher crosstalk in the p53 signaling pathway using single cell dynamics

The transcription factors NF-κB and p53 are key regulators in the genotoxic stress response and are critical for tumor development. Although there is ample evidence for interactions between both networks, a comprehensive understanding of the crosstalk is lacking. Here, we developed a systematic approach to identify potential interactions between the pathways. We perturbed NF-κB signaling by inhibiting IKK2, a critical regulator of NF-κB activity, and monitored the altered response of p53 to genotoxic stress using single cell time lapse microscopy. Fitting subpopulation-specific computational p53 models to this time-resolved single cell data allowed to reproduce in a quantitative manner signaling dynamics and cellular heterogeneity for the unperturbed and perturbed conditions. The approach enabled us to untangle the integrated effects of IKK/ NF-κB perturbation on p53 dynamics and thereby derive potential interactions between both networks. Intriguingly, we find that a simultaneous perturbation of multiple processes is necessary to explain the observed changes in the p53 response. Specifically, we show interference with the activation and degradation of p53 as well as the degradation of Mdm2. Our results highlight the importance of the crosstalk and its potential implications in p53-dependent cellular functions.

Cells can respond to external and internal inputs by transducing information to the nucleus where transcription factors initiate corresponding cellular responses. Cellular signaling is mediated by several pathways; molecular networks that can interact with each other, which alters signal processing and modulates cellular responses. As deregulated signaling can lead to the development of tumors it is important to understand not only how signaling pathways function but also the contribution of their interaction on the signaling dynamics. Here, we analyzed the interplay of the IKK/ NF-κB and p53 pathway, which are both critical for the cellular response to DNA damage and have been implicated in tumor development. To systematically identify interaction points between both pathways, we perturbed IKK/ NF-κB signaling and tracked the changes in the response of p53 to DNA damage. Using computational methods, we show that several reactions in the p53 pathway are simultaneously affected by NF-κB signaling and that this combined action is necessary to explain altered behaviour of the p53 pathway. Hence, our results provide new insights into the interplay between the NF-κB and p53 pathway and help to gain a more comprehensive understanding of the crosstalk.

Rapidly dynamic host cell heterogeneity in bacterial adhesion governs susceptibility to infection by Listeria monocytogenes

Interactions between host cells and individual pathogenic bacteria determine the clinical severity of disease during systemic infection in humans. Vascular endothelial cells, which line the lumen of blood vessels, represent a critical barrier for a bacterium in the bloodstream. These cells adopt a myriad of phenotypes that may modulate their susceptibility to infection; however, the precise determinants of their heterogeneity in susceptibility are not known. Here, we show that heterogeneity in susceptibility to Listeria monocytogenes infection among primary human vascular endothelial cells can be attributed entirely to robust, preexisting host cell heterogeneity in bacterial adhesion, and we find no evidence for significant heterogeneity in later steps of infection. High susceptibility to adhesion decays rapidly, within 30–60 min. Thus, rapidly fluctuating, nongenetic variability in bacterial adhesion diversifies susceptibility to infection, both among host cells and within individual cells over time.

Isolating the sources of heterogeneity in nano-engineered particle-cell interactions

Nano-engineered particles have the potential to enhance therapeutic success and reduce toxicity-based treatment side effects via the targeted delivery of drugs to cells. This delivery relies on complex interactions between numerous biological, chemical and physical processes. The intertwined nature of these processes has thus far hindered attempts to understand their individual impact. Variation in experimental data, such as the number of particles inside each cell, further inhibits understanding. Here, we present a mathematical framework that is capable of examining the impact of individual processes during particle delivery. We demonstrate that variation in experimental particle uptake data can be explained by three factors: random particle motion; variation in particle-cell interactions; and variation in the maximum particle uptake per cell. Without all three factors, the experimental data cannot be explained. This work provides insight into biological mechanisms that cause heterogeneous responses to treatment, and enables precise identification of treatment-resistant cell subpopulations.

Microraft array-based platform for sorting of viable microcolonies based on cell-lethal immunoassay of intracellular proteins in microcolony biopsies

The majority of bioassays are cell-lethal and thus cannot be used for cell assay and selection prior to live-cell sorting. A quad microraft array-based platform was developed to perform semi-automated cell sampling, bioassay, and banking on ultra-small sample sizes. The system biopsies and collects colony fragments, quantifies intracellular protein levels via immunostaining, and then retrieves the living mother colonies based on the fragments' immunoassay outcome. To accomplish this, a magnetic, microwell-based plate was developed to mate directly above the microraft array and capture colony fragments with a one-to-one spatial correspondence to their mother colonies. Using the Signal Transducer and Activator of Transcription 3 (STAT3) model pathway in basophilic leukemia cells, the system was used to sort cells based on the amount of intracellular STAT3 protein phosphorylation (pSTAT3). Colonies were detected on quad arrays using bright field microscopy with 96 ± 20% accuracy (true-positive rate), 49 ± 3% of the colonies were identified as originating from a single cell, and the majority (95 ± 3%) of biopsied clonal fragments were successfully collected into the microwell plate for immunostaining. After assay, biopsied fragments were matched back to their mother colonies and mother colonies with fragments possessing the greatest and least pSTAT3/STAT3 were resampled for expansion and downstream biological assays for pSTAT3/STAT3 and immune granule exocytosis. This approach has the potential to enable colony screening and sorting based on assays not compatible with cell viability, greatly expanding the cell selection criteria available to identify cells with unique phenotypes for subsequent biomedical research.

The effects of proliferation status and cell cycle phase on the responses of single cells to chemotherapy

DNA-damaging chemotherapeutics are widely used in cancer treatments, but for solid tumors they often leave a residual tumor-cell population. Here we investigated how cellular states might affect the response of individual cells in a clonal population to cisplatin, a DNA-damaging chemotherapeutic agent. Using a live-cell reporter of cell cycle phase and long-term imaging, we monitored single-cell proliferation before, at the time of, and after treatment. We found that in response to cisplatin, cells either arrested or died, and the ratio of these outcomes depended on the dose. While we found that the cell cycle phase at the time of cisplatin addition was not predictive of outcome, the proliferative history of the cell was: highly proliferative cells were more likely to arrest than to die, whereas slowly proliferating cells showed a higher probability of death. Information theory analysis revealed that the dose of cisplatin had the greatest influence on the cells’ decisions to arrest or die, and that the proliferation status interacted with the cisplatin dose to further guide this decision. These results show an unexpected effect of proliferation status in regulating responses to cisplatin and suggest that slowly proliferating cells within tumors may be acutely vulnerable to chemotherapy.

Host factor prioritization for pan-viral genetic perturbation screens using random intercept models and network propagation

Genetic perturbation screens using RNA interference (RNAi) have been conducted successfully to identify host factors that are essential for the life cycle of bacteria or viruses. So far, most published studies identified host factors primarily for single pathogens. Furthermore, often only a small subset of genes, e.g., genes encoding kinases, have been targeted. Identification of host factors on a pan-pathogen level, i.e., genes that are crucial for the replication of a diverse group of pathogens has received relatively little attention, despite the fact that such common host factors would be highly relevant, for instance, for devising broad-spectrum anti-pathogenic drugs. Here, we present a novel two-stage procedure for the identification of host factors involved in the replication of different viruses using a combination of random effects models and Markov random walks on a functional interaction network. We first infer candidate genes by jointly analyzing multiple perturbations screens while at the same time adjusting for high variance inherent in these screens. Subsequently the inferred estimates are spread across a network of functional interactions thereby allowing for the analysis of missing genes in the biological studies, smoothing the effect sizes of previously found host factors, and considering a priori pathway information defined over edges of the network. We applied the procedure to RNAi screening data of four different positive-sense single-stranded RNA viruses, Hepatitis C virus, Chikungunya virus, Dengue virus and Severe acute respiratory syndrome coronavirus, and detected novel host factors, including UBC, PLCG1, and DYRK1B, which are predicted to significantly impact the replication cycles of these viruses. We validated the detected host factors experimentally using pharmacological inhibition and an additional siRNA screen and found that some of the predicted host factors indeed influence the replication of these pathogens.

Disentangling the behavioural variability of confined cell migration

Cell-to-cell variability is inherent to numerous biological processes, including cell migration. Quantifying and characterizing the variability of migrating cells is challenging, as it requires monitoring many cells for long time windows under identical conditions. Here, we observe the migration of single human breast cancer cells (MDA-MB-231) in confining two-state micropatterns. To describe the stochastic dynamics of this confined migration, we employ a dynamical systems approach. We identify statistics to measure the behavioural variance of the migration, which significantly exceeds that predicted by a population-averaged stochastic model. This additional variance can be explained by the combination of an ‘ageing’ process and population heterogeneity. To quantify population heterogeneity, we decompose the cells into subpopulations of slow and fast cells, revealing the presence of distinct classes of dynamical systems describing the migration, ranging from bistable to limit cycle behaviour. Our findings highlight the breadth of migration behaviours present in cell populations.

Stochastic fluctuations in apoptotic threshold of tumour cells can enhance apoptosis and combat fractional killing

Fractional killing, which is a significant impediment to successful chemotherapy, is observed even in a population of genetically identical cancer cells exposed to apoptosis-inducing agents. This phenomenon arises not from genetic mutation but from cell-to-cell variation in the activation timing and level of the proteins that regulates apoptosis. To understand the mechanism behind the phenomenon, we formulate complex fractional killing processes as a first-passage time (FPT) problem with a stochastically fluctuating boundary. Analytical calculations are performed for the FPT distribution in a toy model of stochastic p53 gene expression, where the cancer cell is killed only when the p53 expression level crosses an active apoptotic threshold. Counterintuitively, we find that threshold fluctuations can effectively enhance cellular killing by significantly decreasing the mean time that the p53 protein reaches the threshold level for the first time. Moreover, faster fluctuations lead to the killing of more cells. These qualitative results imply that fluctuations in threshold are a non-negligible stochastic source, and can be taken as a strategy for combating fractional killing of cancer cells.

Regulation of Cell Cycle Entry and Exit: A Single Cell Perspective

The transition between proliferating and quiescent states must be carefully regulated to ensure that cells divide to create the cells an organism needs only at the appropriate time and place. Cyclin-dependent kinases (CDKs) are critical for both transitioning cells from one cell cycle state to the next, and for regulating whether cells are proliferating or quiescent. CDKs are regulated by association with cognate cyclins, activating and inhibitory phosphorylation events, and proteins that bind to them and inhibit their activity. The substrates of these kinases, including the retinoblastoma protein, enforce the changes in cell cycle status. Single cell analysis has clarified that competition among factors that activate and inhibit CDK activity leads to the cell's decision to enter the cell cycle, a decision the cell makes before S phase. Signaling pathways that control the activity of CDKs regulate the transition between quiescence and proliferation in stem cells, including stem cells that generate muscle and neurons. © 2020 American Physiological Society. Compr Physiol 10:317-344, 2020.

Accurate delineation of cell cycle phase transitions in living cells with PIP-FUCCI

Cell cycle phase transitions are tightly orchestrated to ensure efficient cell cycle progression and genome stability. Interrogating these transitions is important for understanding both normal and pathological cell proliferation. By quantifying the dynamics of the popular FUCCI reporters relative to the transitions into and out of S phase, we found that their dynamics are substantially and variably offset from true S phase boundaries. To enhance detection of phase transitions, we generated a new reporter whose oscillations are directly coupled to DNA replication and combined it with the FUCCI APC/C reporter to create "PIP-FUCCI". The PIP degron fusion protein precisely marks the G1/S and S/G2 transitions; shows a rapid decrease in signal in response to large doses of DNA damage only during G1; and distinguishes cell type-specific and DNA damage source-dependent arrest phenotypes. We provide guidance to investigators in selecting appropriate fluorescent cell cycle reporters and new analysis strategies for delineating cell cycle transitions.

Mass Cytometry Imaging for the Study of Human Diseases-Applications and Data Analysis Strategies

High parameter imaging is an important tool in the life sciences for both discovery and healthcare applications. Imaging Mass Cytometry (IMC) and Multiplexed Ion Beam Imaging (MIBI) are two relatively recent technologies which enable clinical samples to be simultaneously analyzed for up to 40 parameters at subcellular resolution. Importantly, these "Mass Cytometry Imaging" (MCI) modalities are being rapidly adopted for studies of the immune system in both health and disease. In this review we discuss, first, the various applications of MCI to date. Second, due to the inherent challenge of analyzing high parameter spatial data, we discuss the various approaches that have been employed for the processing and analysis of data from MCI experiments.

Network inference performance complexity: a consequence of topological, experimental and algorithmic determinants

MOTIVATION

Network inference algorithms aim to uncover key regulatory interactions governing cellular decision-making, disease progression and therapeutic interventions. Having an accurate blueprint of this regulation is essential for understanding and controlling cell behavior. However, the utility and impact of these approaches are limited because the ways in which various factors shape inference outcomes remain largely unknown.

RESULTS

We identify and systematically evaluate determinants of performance-including network properties, experimental design choices and data processing-by developing new metrics that quantify confidence across algorithms in comparable terms. We conducted a multifactorial analysis that demonstrates how stimulus target, regulatory kinetics, induction and resolution dynamics, and noise differentially impact widely used algorithms in significant and previously unrecognized ways. The results show how even if high-quality data are paired with high-performing algorithms, inferred models are sometimes susceptible to giving misleading conclusions. Lastly, we validate these findings and the utility of the confidence metrics using realistic in silico gene regulatory networks. This new characterization approach provides a way to more rigorously interpret how algorithms infer regulation from biological datasets.

AVAILABILITY AND IMPLEMENTATION

Code is available at http://github.com/bagherilab/networkinference/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Transcriptional profiling of single fiber cells in a transgenic paradigm of an inherited childhood cataract reveals absence of molecular heterogeneity

Our recent single-cell transcriptomic analysis has demonstrated that heterogeneous transcriptional activity attends molecular transition from the nascent to terminally differentiated fiber cells in the developing mouse lens. To understand the role of transcriptional heterogeneity in terminal differentiation and the functional phenotype (transparency) of this tissue, here we present a single-cell analysis of the developing lens, in a transgenic paradigm of an inherited pathology, known as the lamellar cataract. Cataracts hinder transmission of light into the eye. Lamellar cataract is the most prevalent bilateral childhood cataract. In this disease of early infancy, initially, the opacities remain confined to a few fiber cells, thus presenting an opportunity to investigate early molecular events that lead to cataractogenesis. We used a previously established paradigm that faithfully recapitulates this disease in transgenic mice. About 500 single fiber cells, manually isolated from a 2-day-old transgenic lens were interrogated individually for the expression of all known 17 crystallins and 78 other relevant genes using a Biomark HD (Fluidigm). We find that fiber cells from spatially and developmentally discrete regions of the transgenic (cataract) lens show remarkable absence of the heterogeneity of gene expression. Importantly, the molecular variability of cortical fiber cells, the hallmark of the WT lens, is absent in the transgenic cataract, suggesting absence of specific cell-type(s). Interestingly, we find a repetitive pattern of gene activity in progressive states of differentiation in the transgenic lens. This molecular dysfunction portends pathology much before the physical manifestations of the disease.

The Lung Microbiota of Healthy Mice Are Highly Variable, Cluster by Environment, and Reflect Variation in Baseline Lung Innate Immunity

RATIONALE

The "gut-lung axis" is commonly invoked to explain the microbiome's influence on lung inflammation. Yet the lungs harbor their own microbiome, which is altered in respiratory disease. The relative influence of gut and lung bacteria on lung inflammation is unknown.

OBJECTIVES

To determine whether baseline lung immune tone reflects local (lung-lung) or remote (gut-lung) microbe-host interactions.

METHODS

We compared lung, tongue, and cecal bacteria in 40 healthy, genetically identical, 10-week-old mice, using 16S ribosomal RNA gene quantification and sequencing. We measured inflammatory cytokines, using a multiplex assay of homogenized lung tissue. We compared lung bacteria in healthy mice treated with varied durations of systemic antibiotics.

MEASUREMENTS AND MAIN RESULTS

Lung bacterial communities are highly variable among mice, cluster strongly by cage, shipment, and vendor, and are altered by antibiotics in a microbiologically predictable manner. Baseline lung concentrations of two key inflammatory cytokines (IL-1α and IL-4) are correlated with the diversity and community composition of lung bacterial communities. Lung concentrations of these inflammatory cytokines correlate more strongly with variation in lung bacterial communities than with that of the gut or mouth.

CONCLUSIONS

In the lungs of healthy mice, baseline innate immune tone more strongly reflects local (lung-lung) microbe-host interactions than remote (gut-lung) microbe-host interactions. Our results independently confirm the existence and immunologic significance of the murine lung microbiome, even in health. Variation in lung microbiota is likely an important, underappreciated source of experimental and clinical variability. The lung microbiome is an unexplored therapeutic target for the prevention and treatment of inflammatory lung disease.

Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting

Cellular microscopy images contain rich insights about biology. To extract this information, researchers use features, or measurements of the patterns of interest in the images. Here, we introduce a convolutional neural network (CNN) to automatically design features for fluorescence microscopy. We use a self-supervised method to learn feature representations of single cells in microscopy images without labelled training data. We train CNNs on a simple task that leverages the inherent structure of microscopy images and controls for variation in cell morphology and imaging: given one cell from an image, the CNN is asked to predict the fluorescence pattern in a second different cell from the same image. We show that our method learns high-quality features that describe protein expression patterns in single cells both yeast and human microscopy datasets. Moreover, we demonstrate that our features are useful for exploratory biological analysis, by capturing high-resolution cellular components in a proteome-wide cluster analysis of human proteins, and by quantifying multi-localized proteins and single-cell variability. We believe paired cell inpainting is a generalizable method to obtain feature representations of single cells in multichannel microscopy images.

To understand the cell biology captured by microscopy images, researchers use features, or measurements of relevant properties of cells, such as the shape or size of cells, or the intensity of fluorescent markers. Features are the starting point of most image analysis pipelines, so their quality in representing cells is fundamental to the success of an analysis. Classically, researchers have relied on features manually defined by imaging experts. In contrast, deep learning techniques based on convolutional neural networks (CNNs) automatically learn features, which can outperform manually-defined features at image analysis tasks. However, most CNN methods require large manually-annotated training datasets to learn useful features, limiting their practical application. Here, we developed a new CNN method that learns high-quality features for single cells in microscopy images, without the need for any labeled training data. We show that our features surpass other comparable features in identifying protein localization from images, and that our method can generalize to diverse datasets. By exploiting our method, researchers will be able to automatically obtain high-quality features customized to their own image datasets, facilitating many downstream analyses, as we highlight by demonstrating many possible use cases of our features in this study.

Single-cell kinetics of siRNA-mediated mRNA degradation

RNA interference (RNAi) enables the therapeutic use of small interfering RNAs (siRNAs) to silence disease-related genes. The efficiency of silencing is commonly assessed by measuring expression levels of the target protein at a given time point post-transfection. Here, we determine the siRNA-induced fold change in mRNA degradation kinetics from single-cell fluorescence time-courses obtained using live-cell imaging on single-cell arrays (LISCA). After simultaneous transfection of mRNAs encoding eGFP (target) and CayRFP (reference), the eGFP expression is silenced by siRNA. The single-cell time-courses are fitted using a mathematical model of gene expression. Analysis yields best estimates of related kinetic rate constants, including mRNA degradation constants. We determine the siRNA-induced changes in kinetic rates and their correlations between target and reference protein expression. Assessment of mRNA degradation constants using single-cell time-lapse imaging is fast (<30 h) and returns an accurate, time-independent measure of siRNA-induced silencing, thus allowing the exact evaluation of siRNA therapeutics.

Short-term cellular memory tunes the signaling responses of the chemokine receptor CXCR4

The chemokine receptor CXCR4 regulates fundamental processes in development, normal physiology, and diseases, including cancer. Small subpopulations of CXCR4-positive cells drive the local invasion and dissemination of malignant cells during metastasis, emphasizing the need to understand the mechanisms controlling responses at the single-cell level to receptor activation by the chemokine ligand CXCL12. Using single-cell imaging, we discovered that short-term cellular memory of changes in environmental conditions tuned CXCR4 signaling to Akt and ERK, two kinases activated by this receptor. Conditioning cells with growth stimuli before CXCL12 exposure increased the number of cells that initiated CXCR4 signaling and the amplitude of Akt and ERK activation. Data-driven, single-cell computational modeling revealed that growth factor conditioning modulated CXCR4-dependent activation of Akt and ERK by decreasing extrinsic noise (preexisting cell-to-cell differences in kinase activity) in PI3K and mTORC1. Modeling established mTORC1 as critical for tuning single-cell responses to CXCL12-CXCR4 signaling. Our single-cell model predicted how combinations of extrinsic noise in PI3K, Ras, and mTORC1 superimposed on different driver mutations in the ERK and/or Akt pathways to bias CXCR4 signaling. Computational experiments correctly predicted that selected kinase inhibitors used for cancer therapy shifted subsets of cells to states that were more permissive to CXCR4 activation, suggesting that such drugs may inadvertently potentiate pro-metastatic CXCR4 signaling. Our work establishes how changing environmental inputs modulate CXCR4 signaling in single cells and provides a framework to optimize the development and use of drugs targeting this signaling pathway.

From observing to predicting single-cell structure and function with high-throughput/high-content microscopy

In the past 15 years, cell-based microscopy has evolved its focus from observing cell function to aiming to predict it. In particular-powered by breakthroughs in computer vision, large-scale image analysis and machine learning-high-throughput and high-content microscopy imaging have enabled to uniquely harness single-cell information to systematically discover and annotate genes and regulatory pathways, uncover systems-level interactions and causal links between cellular processes, and begin to clarify and predict causal cellular behaviour and decision making. Here we review these developments, discuss emerging trends in the field, and describe how single-cell 'omics and single-cell microscopy are imminently in an intersecting trajectory. The marriage of these two fields will make possible an unprecedented understanding of cell and tissue behaviour and function.

Exploring single cells in space and time during tissue development, homeostasis and regeneration

Complex 3D tissues arise during development following tightly organized events in space and time. In particular, gene regulatory networks and local interactions between single cells lead to emergent properties at the tissue and organism levels. To understand the design principles of tissue organization, we need to characterize individual cells at given times, but we also need to consider the collective behavior of multiple cells across different spatial and temporal scales. In recent years, powerful single cell methods have been developed to characterize cells in tissues and to address the challenging questions of how different tissues are formed throughout development, maintained in homeostasis, and repaired after injury and disease. These approaches have led to a massive increase in data pertaining to both mRNA and protein abundances in single cells. As we review here, these new technologies, in combination with in toto live imaging, now allow us to bridge spatial and temporal information quantitatively at the single cell level and generate a mechanistic understanding of tissue development.

Hyperosmotic Stress Response Memory is Modulated by Gene Positioning in Yeast

Cellular memory is a critical ability that allows microorganisms to adapt to potentially detrimental environmental fluctuations. In the unicellular eukaryote Saccharomyces cerevisiae, cellular memory can take the form of faster or slower responses within the cell population to repeated stresses. Using microfluidics and fluorescence time-lapse microscopy, we studied how yeast responds to short, pulsed hyperosmotic stresses at the single-cell level by analyzing the dynamic behavior of the stress-responsive STL1 promoter (pSTL1) fused to a fluorescent reporter. We established that pSTL1 exhibits variable successive activation patterns following two repeated short stresses. Despite this variability, most cells exhibited a memory of the first stress as decreased pSTL1 activity in response to the second stress. Notably, we showed that genomic location is important for the memory effect, since displacement of the promoter to a pericentromeric chromatin domain decreased the transcriptional strength of pSTL1 and led to a loss of memory. This study provides a quantitative description of a cellular memory that includes single-cell variability and highlights the contribution of chromatin structure to stress memory.

Self-organization and symmetry breaking in intestinal organoid development

Intestinal organoids are complex three-dimensional structures that mimic the cell-type composition and tissue organization of the intestine by recapitulating the self-organizing ability of cell populations derived from a single intestinal stem cell. Crucial in this process is a first symmetry-breaking event, in which only a fraction of identical cells in a symmetrical sphere differentiate into Paneth cells, which generate the stem-cell niche and lead to asymmetric structures such as the crypts and villi. Here we combine single-cell quantitative genomic and imaging approaches to characterize the development of intestinal organoids from single cells. We show that their development follows a regeneration process that is driven by transient activation of the transcriptional regulator YAP1. Cell-to-cell variability in YAP1, emerging in symmetrical spheres, initiates Notch and DLL1 activation, and drives the symmetry-breaking event and formation of the first Paneth cell. Our findings reveal how single cells exposed to a uniform growth-promoting environment have the intrinsic ability to generate emergent, self-organized behaviour that results in the formation of complex multicellular asymmetric structures.

Memory and relatedness of transcriptional activity in mammalian cell lineages

Phenotypically identical mammalian cells often display considerable variability in transcript levels of individual genes. How transcriptional activity propagates in cell lineages, and how this varies across genes is poorly understood. Here we combine live-cell imaging of short-lived transcriptional reporters in mouse embryonic stem cells with mathematical modelling to quantify the propagation of transcriptional activity over time and across cell generations in phenotypically homogenous cells. In sister cells we find mean transcriptional activity to be strongly correlated and transcriptional dynamics tend to be synchronous; both features control how quickly transcriptional levels in sister cells diverge in a gene-specific manner. Moreover, mean transcriptional activity is transmitted from mother to daughter cells, leading to multi-generational transcriptional memory and causing inter-family heterogeneity in gene expression.

Counting growth factors in single cells with infrared quantum dots to measure discrete stimulation distributions

The distribution of single-cell properties across a population of cells can be measured using diverse tools, but no technology directly quantifies the biochemical stimulation events regulating these properties. Here we report digital counting of growth factors in single cells using fluorescent quantum dots and calibrated three-dimensional deconvolution microscopy (QDC-3DM) to reveal physiologically relevant cell stimulation distributions. We calibrate the fluorescence intensities of individual compact quantum dots labeled with epidermal growth factor (EGF) and demonstrate the necessity of near-infrared emission to overcome intrinsic cellular autofluoresence at the single-molecule level. When applied to human triple-negative breast cancer cells, we observe proportionality between stimulation and both receptor internalization and inhibitor response, reflecting stimulation heterogeneity contributions to intrinsic variability. We anticipate that QDC-3DM can be applied to analyze any peptidic ligand to reveal single-cell correlations between external stimulation and phenotypic variability, cell fate, and drug response.

Measuring growth factors in single cells at physiologically relevant stimulation doses is challenging. Here the authors use fluorescent quantum dots and calibrated three-dimensional deconvolution microscopy to digitally count growth factors in single cells and reveal stimulation distributions in cancer cells.

A Two-Pulse Cellular Stimulation Test Elucidates Variability and Mechanisms in Signaling Pathways

Mammalian cells respond in a variable manner when provided with physiological pulses of ligand, such as low concentrations of acetylcholine present for just tens of seconds or TNFα for just tens of minutes. For a two-pulse stimulation, some cells respond to both pulses, some do not respond, and yet others respond to only one or the other pulse. Are these different response patterns the result of the small number of ligands being able to only stochastically activate the pathway at random times or an output pattern from a deterministic algorithm responding differently to different stimulation intervals? If the response is deterministic in nature, what parameters determine whether a response is generated or skipped? To answer these questions, we developed a two-pulse test that utilizes different rest periods between stimulation pulses. This "rest-period test" revealed that cells skip responses predictably as the rest period is shortened. By combining these experimental results with a mathematical model of the pathway, we further obtained mechanistic insight into potential sources of response variability. Our analysis indicates that in both intracellular calcium and NFκB signaling, response variability is consistent with extrinsic noise (cell-to-cell variability in protein levels), a short-term memory of stimulation, and high Hill coefficient processes. Furthermore, these results support recent works that have emphasized the role of deterministic processes for explaining apparently stochastic cellular response variability and indicate that even weak stimulations likely guide mammalian cells to appropriate fates rather than leaving outcomes to chance. We envision that the rest-period test can be applied to other signaling pathways to extract mechanistic insight.

Delays in Fitness Adjustment Can Lead to Coexistence of Hierarchically Interacting Species

Organisms that exploit different environments may experience a stochastic delay in adjusting their fitness when they switch habitats. We study two such organisms whose fitness is determined by the species composition of the local environment, as they interact through a public good. We show that a delay in the fitness adjustment can lead to the coexistence of the two species in a metapopulation, although the faster-growing species always wins in well-mixed competition experiments. Coexistence is favored over wide parameter ranges and is independent of spatial clustering. It arises when species are heterogeneous in their fitness and can keep each other balanced.

Multi-experiment nonlinear mixed effect modeling of single-cell translation kinetics after transfection

Single-cell time-lapse studies have advanced the quantitative understanding of cellular pathways and their inherent cell-to-cell variability. However, parameters retrieved from individual experiments are model dependent and their estimation is limited, if based on solely one kind of experiment. Hence, methods to integrate data collected under different conditions are expected to improve model validation and information content. Here we present a multi-experiment nonlinear mixed effect modeling approach for mechanistic pathway models, which allows the integration of multiple single-cell perturbation experiments. We apply this approach to the translation of green fluorescent protein after transfection using a massively parallel read-out of micropatterned single-cell arrays. We demonstrate that the integration of data from perturbation experiments allows the robust reconstruction of cell-to-cell variability, i.e., parameter densities, while each individual experiment provides insufficient information. Indeed, we show that the integration of the datasets on the population level also improves the estimates for individual cells by breaking symmetries, although each of them is only measured in one experiment. Moreover, we confirmed that the suggested approach is robust with respect to batch effects across experimental replicates and can provide mechanistic insights into the nature of batch effects. We anticipate that the proposed multi-experiment nonlinear mixed effect modeling approach will serve as a basis for the analysis of cellular heterogeneity in single-cell dynamics.

Spatial Analysis of Single Fiber Cells of the Developing Ocular Lens Reveals Regulated Heterogeneity of Gene Expression

The developing eye lens presents an exceptional paradigm for spatial transcriptomics. It is composed of highly organized long, slender transparent fiber cells, which differentiate from the edges of the anterior epithelium of the lens (equator), attended by high expression of crystallins, which generates transparency. Every fiber cell, therefore, is an optical unit whose refractive properties derive from its gene activity. Here, we probe this tangible relationship between the gene activity and the phenotype by studying the expression of all known 17 crystallins and 77 other non-crystallin genes in single fiber cells isolated from three states/regions of differentiation, allowing us to follow molecular progression at the single-cell level. The data demonstrate highly variable gene activity in cortical fibers, interposed between the nascent and the terminally differentiated fiber cell transcription. These data suggest that the so-called stochastic, highly heterogeneous gene activity is a regulated intermediate in the realization of a functional phenotype.

Microstencil-based spatial immobilization of individual cells for single cell analysis

Cells exhibit biologically heterogeneous phenotypes, particularly in pathogenic states. To study cell behavior at the single cell level, a variety of micropatterning techniques have been proposed that allow the spatial organization of cells with great control over cell volume, morphology, and intercellular interactions. Among these strategies, microstencil patterning has traditionally been eschewed due to fragility of membranes and lack of control over cell configurations within patterns. Here, we present a simple and reproducible strategy to create robust microstencils and achieve consistent and efficient cell patterns requiring less than 4 μl of cell solution. Polydimethylsiloxane microstencils fabricated with this technique can be used dozens of times over the course of several months with minimal wear or degradation. Characterization of pattern size, cell suspension density, and droplet volume allows on-demand configurations of singlets, doublets, triplets, or multiple cells per individual space. In addition, a novel technique to suppress evaporative convection provides precise and repeatable results, with a twofold increase in patterning efficacy. Selective dual surface modification to create hydrophilic islands on a hydrophobic substrate facilitates a significantly longer and healthier lifespan of cells without crossover of pattern boundaries. The ability to pattern individual cells with or without an extracellular matrix substrate and to control the magnitude of cell-cell contact as well as spread area provides a powerful approach to monitoring cell functions such as proliferation and intercellular signaling.

Accurate analytic solution of chemical master equations for gene regulation networks in a single cell

Studying gene regulation networks in a single cell is an important, interesting, and hot research topic of molecular biology. Such process can be described by chemical master equations (CMEs). We propose a Hamilton-Jacobi equation method with finite-size corrections to solve such CMEs accurately at the intermediate region of switching, where switching rate is comparable to fast protein production rate. We applied this approach to a model of self-regulating proteins [H. Ge et al., Phys. Rev. Lett. 114, 078101 (2015)PRLTAO0031-900710.1103/PhysRevLett.114.078101] and found that as a parameter related to inducer concentration increases the probability of protein production changes from unimodal to bimodal, then to unimodal, consistent with phenotype switching observed in a single cell.

A systematic study of the determinants of protein abundance memory in cell lineage

Proteins are essential players of life activities. Intracellular protein levels directly affect cellular functions and cell fate. Upon cell division, the proteins in the mother cell are inherited by the daughters. However, what factors and by how much they affect this epigenetic inheritance of protein abundance remains unclear. Using both computational and experimental approaches, we systematically investigated this problem. We derived an analytical expression for the dependence of protein inheritance on various factors and showed that it agreed with numerical simulations of protein production and experimental results. Our work provides a framework for quantitative studies of protein inheritance and for the potential application of protein memory manipulation.

A dataset of images and morphological profiles of 30 000 small-molecule treatments using the Cell Painting assay

Background

Large-scale image sets acquired by automated microscopy of perturbed samples enable a detailed comparison of cell states induced by each perturbation, such as a small molecule from a diverse library. Highly multiplexed measurements of cellular morphology can be extracted from each image and subsequently mined for a number of applications.

Findings

This microscopy dataset includes 919 265 five-channel fields of view, representing 30 616 tested compounds, available at “The Cell Image Library” (CIL) repository. It also includes data files containing morphological features derived from each cell in each image, both at the single-cell level and population-averaged (i.e., per-well) level; the image analysis workflows that generated the morphological features are also provided. Quality-control metrics are provided as metadata, indicating fields of view that are out-of-focus or containing highly fluorescent material or debris. Lastly, chemical annotations are supplied for the compound treatments applied.

Conclusions

Because computational algorithms and methods for handling single-cell morphological measurements are not yet routine, the dataset serves as a useful resource for the wider scientific community applying morphological (image-based) profiling. The dataset can be mined for many purposes, including small-molecule library enrichment and chemical mechanism-of-action studies, such as target identification. Integration with genetically perturbed datasets could enable identification of small-molecule mimetics of particular disease- or gene-related phenotypes that could be useful as probes or potential starting points for development of future therapeutics.

Probing Intercell Variability Using Bulk Measurements

The measurement of noise is critical when assessing the design and function of synthetic biological systems. Cell-to-cell variability can be quantified experimentally using single-cell measurement techniques such as flow cytometry and fluorescent microscopy. However, these approaches are costly and impractical for high-throughput parallelized experiments, which are frequently conducted using plate-reader devices. In this paper we describe reporter systems that allow estimation of the cell-to-cell variability in a biological system's output using only measurements of a cell culture's bulk properties. We analyze one potential implementation of such a system that is based upon a fluorescent protein FRET reporter pair, finding that with typical parameters from the literature it is able to reliably estimate variability. We also briefly describe an alternate implementation based upon an activating sRNA circuit. The feasible region of parameter values for which the reporter system can function is assessed, and the dependence of its performance on both extrinsic and intrinsic noise is investigated. Experimental realization of these constructs can yield novel reporter systems that allow measurement of a synthetic gene circuit's output, as well as the intrapopulation variability of this output, at little added cost.

Infection by bacteriophage lambda: an evolving paradigm for cellular individuality

Since the earliest days of molecular biology, bacteriophage lambda has served to illuminate cellular function. Among its many roles, lambda infection is a paradigm for phenotypic heterogeneity among genetically identical cells. Early studies attributed this cellular individuality to random biochemical fluctuations, or 'noise'. More recently, however, attention has turned to the role played by deterministic hidden variables in driving single-cell behavior. Here, I briefly describe how studies in lambda are driving the shift in our understanding of cellular heterogeneity, allowing us to better appreciate the precision at which cells function.

Tracing the origin of heterogeneity and symmetry breaking in the early mammalian embryo

A fundamental question in developmental and stem cell biology concerns the origin and nature of signals that initiate asymmetry leading to pattern formation and self-organization. Instead of having prominent pre-patterning determinants as present in model organisms (worms, sea urchin, frog), we propose that the mammalian embryo takes advantage of more subtle cues such as compartmentalized intracellular reactions that generate micro-scale inhomogeneity, which is gradually amplified over several cellular generations to drive pattern formation while keeping developmental plasticity. It is therefore possible that by making use of compartmentalized information followed by its amplification, mammalian embryos would follow general principle of development found in other organisms in which the spatial cue is more robustly presented.

Spatially Correlated Gene Expression in Bacterial Groups: The Role of Lineage History, Spatial Gradients, and Cell-Cell Interactions

Gene expression levels in clonal bacterial groups have been found to be spatially correlated. These correlations can partly be explained by the shared lineage history of nearby cells, although they could also arise from local cell-cell interactions. Here, we present a quantitative framework that allows us to disentangle the contributions of lineage history, long-range spatial gradients, and local cell-cell interactions to spatial correlations in gene expression. We study pathways involved in toxin production, SOS stress response, and metabolism in Escherichia coli microcolonies and find for all pathways that shared lineage history is the main cause of spatial correlations in gene expression levels. However, long-range spatial gradients and local cell-cell interactions also contributed to spatial correlations in SOS response, amino acid biosynthesis, and overall metabolic activity. Together, our data show that the phenotype of a cell is influenced by its lineage history and population context, raising the question of whether bacteria can arrange their activities in space to perform functions they cannot achieve alone.

Integrating Analysis of Cellular Heterogeneity in High-Content Dose-Response Studies

Heterogeneity is a complex property of cellular systems and therefore presents challenges to the reliable identification and characterization. Large-scale biology projects may span many months, requiring a systematic approach to quality control to track reproducibility and correct for instrumental variation and assay drift that could mask biological heterogeneity and preclude comparisons of heterogeneity between runs or even between plates. However, presently there is no standard approach to the tracking and analysis of heterogeneity. Previously, we demonstrated the use of the Kolmogorov-Smirnov statistic as a metric for monitoring the reproducibility of heterogeneity in a screen and described the use of three heterogeneity indices as a means to characterize, filter, and browse cellular heterogeneity in big data sets (Gough et al., Methods 96:12-26, 2016). In this chapter, we present a detailed method for integrating the analysis of cellular heterogeneity in assay development, validation, screening, and post screen. Importantly, we provide a detailed method for quality control, to normalize cellular data, track heterogeneity over time, and analyze heterogeneity in big data sets, along with software tools to assist in that process. The example screen for this method is from an HCS project, but the approach applies equally to other experimental methods that measure populations of cells.

Antisense transcription-dependent chromatin signature modulates sense transcript dynamics

Antisense transcription is widespread in genomes. Despite large differences in gene size and architecture, we find that yeast and human genes share a unique, antisense transcription-associated chromatin signature. We asked whether this signature is related to a biological function for antisense transcription. Using quantitative RNA-FISH, we observed changes in sense transcript distributions in nuclei and cytoplasm as antisense transcript levels were altered. To determine the mechanistic differences underlying these distributions, we developed a mathematical framework describing transcription from initiation to transcript degradation. At GAL1, high levels of antisense transcription alter sense transcription dynamics, reducing rates of transcript production and processing, while increasing transcript stability. This relationship with transcript stability is also observed as a genome-wide association. Establishing the antisense transcription-associated chromatin signature through disruption of the Set3C histone deacetylase activity is sufficient to similarly change these rates even in the absence of antisense transcription. Thus, antisense transcription alters sense transcription dynamics in a chromatin-dependent manner.

p53 elevation in human cells halt SV40 infection by inhibiting T-ag expression

SV40 large T-antigen (T-ag) has been known for decades to inactivate the tumor suppressor p53 by sequestration and additional mechanisms. Our present study revealed that the struggle between p53 and T-ag begins very early in the infection cycle. We found that p53 is activated early after SV40 infection and defends the host against the infection. Using live cell imaging and single cell analyses we found that p53 dynamics are variable among individual cells, with only a subset of cells activating p53 immediately after SV40 infection. This cell-to-cell variabilty had clear consequences on the outcome of the infection. None of the cells with elevated p53 at the beginning of the infection proceeded to express T-ag, suggesting a p53-dependent decision between abortive and productive infection. In addition, we show that artificial elevation of p53 levels prior to the infection reduces infection efficiency, supporting a role for p53 in defending against SV40. We further found that the p53-mediated host defense mechanism against SV40 is not facilitated by apoptosis nor via interferon-stimulated genes. Instead p53 binds to the viral DNA at the T-ag promoter region, prevents its transcriptional activation by Sp1, and halts the progress of the infection. These findings shed new light on the long studied struggle between SV40 T-ag and p53, as developed during virus-host coevolution. Our studies indicate that the fate of SV40 infection is determined as soon as the viral DNA enters the nucleus, before the onset of viral gene expression.

Mitochondrial levels determine variability in cell death by modulating apoptotic gene expression

Fractional killing is the main cause of tumour resistance to chemotherapy. This phenomenon is observed even in genetically identical cancer cells in homogeneous microenvironments. To understand this variable resistance, here we investigate the individual responses to TRAIL in a clonal population of HeLa cells using live-cell microscopy and computational modelling. We show that the cellular mitochondrial content determines the apoptotic fate and modulates the time to death, cells with higher mitochondrial content are more prone to die. We find that all apoptotic protein levels are modulated by the mitochondrial content. Modelling the apoptotic network, we demonstrate that these correlations, and especially the differential control of anti- and pro-apoptotic protein pairs, confer mitochondria a powerful discriminatory capacity of apoptotic fate. We find a similar correlation between the mitochondria and apoptotic proteins in colon cancer biopsies. Our results reveal a different role of mitochondria in apoptosis as the global regulator of apoptotic protein expression.

It is unclear what causes variation in cell death in response to chemotherapy. Here, the authors show that cellular mitochondrial content modulates apoptotic protein levels, which in turn regulates response to agents such as TRAIL.

Cell-specific responses to the cytokine TGFβ are determined by variability in protein levels

The cytokine TGFβ provides important information during embryonic development, adult tissue homeostasis, and regeneration. Alterations in the cellular response to TGFβ are involved in severe human diseases. To understand how cells encode the extracellular input and transmit its information to elicit appropriate responses, we acquired quantitative time-resolved measurements of pathway activation at the single-cell level. We established dynamic time warping to quantitatively compare signaling dynamics of thousands of individual cells and described heterogeneous single-cell responses by mathematical modeling. Our combined experimental and theoretical study revealed that the response to a given dose of TGFβ is determined cell specifically by the levels of defined signaling proteins. This heterogeneity in signaling protein expression leads to decomposition of cells into classes with qualitatively distinct signaling dynamics and phenotypic outcome. Negative feedback regulators promote heterogeneous signaling, as a SMAD7 knock-out specifically affected the signal duration in a subpopulation of cells. Taken together, we propose a quantitative framework that allows predicting and testing sources of cellular signaling heterogeneity.

Robust control of the adaptive immune system

The adaptive immune system continually faces unpredictable circumstances yet reproducibly counteracts invading pathogens while limiting damage to self. However, the system is dynamic in nature: many of its internal components are not fixed, but rather, fluctuate over time. This concept is exemplified by αβ T lymphocytes, which vary significantly from cell-to-cell in their spatiotemporal dynamics, antigen-binding receptors, and subcellular protein concentrations. How are reproducible immune functions achieved in the face of such variability? This design principle is known as robustness and requires the system to employ layered control schemes that both buffer and exploit different facets of cellular variation. In this article, we discuss these schemes and their applications to individual αβ T cell responses as well as integrated population level behaviours.