Redefining Signaling Pathways with an Expanding Single-Cell Toolbox

Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives

Overlaying omics data onto spatial biological dimensions has been a promising technology to provide high-resolution insights into the interactome and cellular heterogeneity relative to the organization of the molecular microenvironment of tissue samples in normal and disease states. Spatial omics can be categorized into three major modalities: (a) next-generation sequencing-based assays, (b) imaging-based spatially resolved transcriptomics approaches including in situ hybridization/in situ sequencing, and (c) imaging-based spatial proteomics. These modalities allow assessment of transcripts and proteins at a cellular level, generating large and computationally challenging datasets. The lack of standardized computational pipelines to analyze and integrate these nonuniform structured data has made it necessary to apply artificial intelligence and machine learning strategies to best visualize and translate their complexity. In this review, we summarize the currently available techniques and computational strategies, highlight their advantages and limitations, and discuss their future prospects in the scientific field.

Robust Surfactant-Assisted One-Pot Sample Preparation for Label-Free Single-Cell and Nanoscale Proteomics

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk cells. However, such bulk measurement obscures cell-to-cell heterogeneity, precluding proteome profiling of single cells and small numbers of cells of interest. To address this issue, in the recent 5 years, there has been a surge of small sample preparation methods developed for robust and effective collection and processing of single cells and small numbers of cells for in-depth MS-based proteome profiling. Based on their broad accessibility, they can be categorized into two types: methods based on specific devices and those based on standard PCR tubes or multi-well plates. In this chapter, we describe the detailed protocol of our recently developed, easily adoptable, Surfactant-assisted One-Pot (SOP) sample preparation coupled with MS method termed SOP-MS for label-free single-cell and nanoscale proteomics. SOP-MS capitalizes on the combination of an MS-compatible surfactant, n-dodecyl-β-D-maltoside (DDM), and standard low-bind PCR tube or multi-well plate for "all-in-one" one-pot sample preparation without sample transfer. With its robust and convenient features, SOP-MS can be readily implemented in any MS laboratory for single-cell and nanoscale proteomics. With further improvements in MS detection sensitivity and sample throughput, we believe that SOP-MS could open an avenue for single-cell proteomics with broad applicability in biological and biomedical research.

Modeling Cellular Signaling Variability Based on Single-Cell Data: The TGFβ-SMAD Signaling Pathway

Nongenetic heterogeneity is key to cellular decisions, as even genetically identical cells respond in very different ways to the same external stimulus, e.g., during cell differentiation or therapeutic treatment of disease. Strong heterogeneity is typically already observed at the level of signaling pathways that are the first sensors of external inputs and transmit information to the nucleus where decisions are made. Since heterogeneity arises from random fluctuations of cellular components, mathematical models are required to fully describe the phenomenon and to understand the dynamics of heterogeneous cell populations. Here, we review the experimental and theoretical literature on cellular signaling heterogeneity, with special focus on the TGFβ/SMAD signaling pathway.

Machine learning phenomics (MLP) combining deep learning with time-lapse-microscopy for monitoring colorectal adenocarcinoma cells gene expression and drug-response

High-throughput phenotyping is becoming increasingly available thanks to analytical and bioinformatics approaches that enable the use of very high-dimensional data and to the availability of dynamic models that link phenomena across levels: from genes to cells, from cells to organs, and through the whole organism. The combination of phenomics, deep learning, and machine learning represents a strong potential for the phenotypical investigation, leading the way to a more embracing approach, called machine learning phenomics (MLP). In particular, in this work we present a novel MLP platform for phenomics investigation of cancer-cells response to therapy, exploiting and combining the potential of time-lapse microscopy for cell behavior data acquisition and robust deep learning software architectures for the latent phenotypes extraction. A two-step proof of concepts is designed. First, we demonstrate a strict correlation among gene expression and cell phenotype with the aim to identify new biomarkers and targets for tailored therapy in human colorectal cancer onset and progression. Experiments were conducted on human colorectal adenocarcinoma cells (DLD-1) and their profile was compared with an isogenic line in which the expression of LOX-1 transcript was knocked down. In addition, we also evaluate the phenotypic impact of the administration of different doses of an antineoplastic drug over DLD-1 cells. Under the omics paradigm, proteomics results are used to confirm the findings of the experiments.

Show your true color: Mammalian cell surface staining for tracking cellular identity in multiplexing and beyond

Fluorescence microscopy revolutionized cell biology and changed requirements for dyes towards higher brightness, novel capacities, and specific targets. With the need for multiplexing assays in high-throughput methodologies, surface staining gained particular interest because it allows rapid application of exogenous stains to track cellular identity in mixed populations. Indeed, the last decade has enriched the toolbox of general lipid stains, fluorescent lipid analogues, sugar-binding lectins, and protein-specific antibodies enabling the first rationally designed plasma membrane-specific dyes. Still, multiple challenges exist, and the unique properties of each dye must be considered when selecting a staining approach for a specific application. Recent advances are also promising that future dyes will provide ultimate brightness and photostability in diverse colors and reduced sizes for high-resolution imaging.

Facile One-Pot Nanoproteomics for Label-Free Proteome Profiling of 50-1000 Mammalian Cells

Recent advances in sample preparation enable label-free mass spectrometry (MS)-based proteome profiling of small numbers of mammalian cells. However, specific devices are often required to downscale sample processing volume from the standard 50-200 μL to sub-μL for effective nanoproteomics, which greatly impedes the implementation of current nanoproteomics methods by the proteomics research community. Herein, we report a facile one-pot nanoproteomics method termed SOPs-MS (surfactant-assisted one-pot sample processing at the standard volume coupled with MS) for convenient robust proteome profiling of 50-1000 mammalian cells. Building upon our recent development of SOPs-MS for label-free single-cell proteomics at a low μL volume, we have systematically evaluated its processing volume at 10-200 μL using 100 human cells. The processing volume of 50 μL that is in the range of volume for standard proteomics sample preparation has been selected for easy sample handling with a benchtop micropipette. SOPs-MS allows for reliable label-free quantification of ∼1200-2700 protein groups from 50 to 1000 MCF10A cells. When applied to small subpopulations of mouse colon crypt cells, SOPs-MS has revealed protein signatures between distinct subpopulation cells with identification of ∼1500-2500 protein groups for each subpopulation. SOPs-MS may pave the way for routine deep proteome profiling of small numbers of cells and low-input samples.

High-throughput injection molded microfluidic device for single-cell analysis of spatiotemporal dynamics

Single-cell level analysis of various cellular behaviors has been aided by recent developments in microfluidic technology. Polydimethylsiloxane (PDMS)-based microfluidic devices have been widely used to elucidate cell differentiation and migration under spatiotemporal stimulation. However, microfluidic devices fabricated with PDMS have inherent limitations due to material issues and non-scalable fabrication process. In this study, we designed and fabricated an injection molded microfluidic device that enables real-time chemical profile control. This device is made of polystyrene (PS), engineered with channel dimensions optimized for injection molding to achieve functionality and compatibility with single cell observation. We demonstrated the spatiotemporal dynamics in the device with computational simulation and experiments. In temporal dynamics, we observed extracellular signal-regulated kinase (ERK) activation of PC12 cells by stimulating the cells with growth factors (GFs). Also, we confirmed yes-associated protein (YAP) phase separation of HEK293 cells under stimulation using sorbitol. In spatial dynamics, we observed the migration of NIH 3T3 cells (transfected with Lifeact-GFP) under different spatiotemporal stimulations of PDGF. Using the injection molded plastic devices, we obtained comprehensive data more easily than before while using less time compared to previous PDMS models. This easy-to-use plastic microfluidic device promises to open a new approach for investigating the mechanisms of cell behavior at the single-cell level.

dNEMO: a tool for quantification of mRNA and punctate structures in time-lapse images of single cells

MOTIVATION

Many biological processes are regulated by single molecules and molecular assemblies within cells that are visible by microscopy as punctate features, often diffraction limited. Here, we present detecting-NEMO (dNEMO), a computational tool optimized for accurate and rapid measurement of fluorescent puncta in fixed-cell and time-lapse images.

RESULTS

The spot detection algorithm uses the à trous wavelet transform, a computationally inexpensive method that is robust to imaging noise. By combining automated with manual spot curation in the user interface, fluorescent puncta can be carefully selected and measured against their local background to extract high-quality single-cell data. Integrated into the workflow are segmentation and spot-inspection tools that enable almost real-time interaction with images without time consuming pre-processing steps. Although the software is agnostic to the type of puncta imaged, we demonstrate dNEMO using smFISH to measure transcript numbers in single cells in addition to the transient formation of IKK/NEMO puncta from time-lapse images of cells exposed to inflammatory stimuli. We conclude that dNEMO is an ideal user interface for rapid and accurate measurement of fluorescent molecular assemblies in biological imaging data.

AVAILABILITY AND IMPLEMENTATION

The data and software are freely available online at https://github.com/recleelab.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Pre-existing Cell States Control Heterogeneity of Both EGFR and CXCR4 Signaling

INTRODUCTION

CXCR4 and epidermal growth factor receptor (EGFR) represent two major families of receptors, G-protein coupled receptors and receptor tyrosine kinases, with central functions in cancer. While utilizing different upstream signaling molecules, both CXCR4 and EGFR activate kinases ERK and Akt, although single-cell activation of these kinases is markedly heterogeneous. One hypothesis regarding the origin of signaling heterogeneity proposes that intercellular variations arise from differences in pre-existing intracellular states set by extrinsic noise. While pre-existing cell states vary among cells, each pre-existing state defines deterministic signaling outputs to downstream effectors. Understanding causes of signaling heterogeneity will inform treatment of cancers with drugs targeting drivers of oncogenic signaling.

METHODS

We built a single-cell computational model to predict Akt and ERK responses to CXCR4- and EGFR-mediated stimulation. We investigated signaling heterogeneity through these receptors and tested model predictions using quantitative, live-cell time-lapse imaging.

RESULTS

We show that the pre-existing cell state predicts single-cell signaling through both CXCR4 and EGFR. Computational modeling reveals that the same set of pre-existing cell states explains signaling heterogeneity through both EGFR and CXCR4 at multiple doses of ligands and in two different breast cancer cell lines. The model also predicts how phosphatidylinositol-3-kinase (PI3K) targeted therapies potentiate ERK signaling in certain breast cancer cells and that low level, combined inhibition of MEK and PI3K ablates potentiated ERK signaling.

CONCLUSIONS

Our data demonstrate that a conserved motif exists for EGFR and CXCR4 signaling and suggest potential clinical utility of the computational model to optimize therapy.

Cell-Cycle-Dependent ERK Signaling Dynamics Direct Fate Specification in the Mammalian Preimplantation Embryo

Despite the noisy nature of single cells, multicellular organisms robustly generate different cell types from one zygote. This process involves dynamic cross regulation between signaling and gene expression that is difficult to capture with fixed-cell approaches. To study signaling dynamics and fate specification during preimplantation development, we generated a transgenic mouse expressing the ERK kinase translocation reporter and measured ERK activity in single cells of live embryos. Our results show primarily active ERK in both the inner cell mass and trophectoderm cells due to fibroblast growth factor (FGF) signaling. Strikingly, a subset of mitotic events results in a short pulse of ERK inactivity in both daughter cells that correlates with elevated endpoint NANOG levels. Moreover, endogenous tagging of Nanog in embryonic stem cells reveals that ERK inhibition promotes enhanced stabilization of NANOG protein after mitosis. Our data show that cell cycle, signaling, and differentiation are coordinated during preimplantation development.

Carrier-assisted One-pot Sample Preparation for Targeted Proteomics Analysis of Small Numbers of Human Cells

Protein analysis of small numbers of human cells is primarily achieved by targeted proteomics with antibody-based immunoassays, which have inherent limitations (e.g., low multiplex and unavailability of antibodies for new proteins). Mass spectrometry (MS)-based targeted proteomics has emerged as an alternative because it is antibody-free, high multiplex, and has high specificity and quantitation accuracy. Recent advances in MS instrumentation make MS-based targeted proteomics possible for multiplexed quantification of highly abundant proteins in single cells. However, there is a technical challenge for effective processing of single cells with minimal sample loss for MS analysis. To address this issue, we have recently developed a convenient protein carrier-assisted one-pot sample preparation coupled with liquid chromatography (LC) - selected reaction monitoring (SRM) termed cLC-SRM for targeted proteomics analysis of small numbers of human cells. This method capitalizes on using the combined excessive exogenous protein as a carrier and low-volume one-pot processing to greatly reduce surface adsorption losses as well as high-specificity LC-SRM to effectively address the increased dynamic concentration range due to the addition of exogeneous carrier protein. Its utility has been demonstrated by accurate quantification of most moderately abundant proteins in small numbers of cells (e.g., 10-100 cells) and highly abundant proteins in single cells. The easy-to-implement features and no need for specific devices make this method readily accessible to most proteomics laboratories. Herein we have provided a detailed protocol for cLC-SRM analysis of small numbers of human cells including cell sorting, cell lysis and digestion, LC-SRM analysis, and data analysis. Further improvements in detection sensitivity and sample throughput are needed towards targeted single-cell proteomics analysis. We anticipate that cLC-SRM will be broadly applied to biomedical research and systems biology with the potential of facilitating precision medicine.

Isolation of Highly Purified and Viable Retinal Endothelial Cells

The neonatal mouse retinal vascularization model has been widely used in the vascular biology field to investigate mechanisms of angiogenesis and arterial-venous fate specification during blood vessel formation and maturation. Recent advances in next-generation sequencing can further elucidate mechanisms of blood vessel formation and remodeling in this, as well as other, vascular development models. However, an optimized method for isolating retinal endothelial cells that limits tissue digestion-induced cell damage is required for next-generation sequencing applications. In this study, we established a method for isolating neonatal retinal endothelial cells that optimizes cell viability and purity. The CD31+/CD45- endothelial cell population was fluorescence-activated cell sorting (FACS)-isolated from digested postnatal retinas, found to be highly enriched for endothelial cell gene expression, and exhibited no change in viability for 60 min post-FACS. Thus, this method for retinal endothelial cell isolation is compatible with next-generation sequencing applications. Combining this isolation method with next-generation sequencing will enable further delineation of mechanisms underlying vascular development and maturation.

Single-Cell Analysis of Signaling Proteins Provides Insights into Proapoptotic Properties of Anticancer Drugs

Single-cell DNA analysis technology has provided unprecedented insights into many physiological and pathological processes. In contrast, technologies that allow protein analysis in single cells have lagged behind. Herein, a method called single-cell Plasmonic ImmunoSandwich Assay (scPISA) that is capable of measuring signaling proteins and protein complexes in single living cells is described. scPISA is straightforward, comprising specific in-cell extraction and ultrasensitive plasmonic detection. It is applied to evaluate the efficacy and kinetics of cytotoxic drugs. It reveals that different drugs exhibit distinct proapoptotic properties at the single-cell level. A set of new parameters is thus proposed for comprehensive and quantitative evaluation of the efficacy of anticancer drugs. It discloses that metformin can dramatically enhance the overall anticancer efficacy when combined with actinomycin D, although it itself is significantly less effective. Furthermore, scPISA reveals that survivin interacts with cytochrome C and caspase-3 in a dynamic fashion in single cells during continuous drug treatment. As compared with conventional assays, scPISA exhibits several significant advantages, such as ultrahigh sensitivity, single-cell resolution, fast speed, and so on. Therefore, this approach may provide a powerful tool for wide, important applications from basic research to clinical applications, particularly precision medicine.

Microfluidic platform enables live-cell imaging of signaling and transcription combined with multiplexed secretion measurements in the same single cells

Innate immune cells, including macrophages and dendritic cells, protect the host from pathogenic assaults in part through secretion of a program of cytokines and chemokines (C/Cs). Cell-to-cell variability in C/C secretion appears to contribute to the regulation of the immune response, but the sources of secretion variability are largely unknown. To begin to track the biological sources that control secretion variability, we developed and validated a microfluidic device to integrate live-cell imaging of fluorescent reporter proteins with a single-cell assay of protein secretion. We used this device to image NF-κB RelA nuclear translocation dynamics and Tnf transcription dynamics in macrophages in response to stimulation with the bacterial component lipopolysaccharide (LPS), followed by quantification of secretion of TNF, CCL2, CCL3, and CCL5. We found that the timing of the initial peak of RelA signaling in part determined the relative level of TNF and CCL3 secretion, but not CCL2 and CCL5 secretion. Our results support evidence that differences in timing across cell processes partly account for cell-to-cell variability in downstream responses, but that other factors introduce variability at each biological step.

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.

Nomenclature and heterogeneity: consequences for the use of mesenchymal stem cells in regenerative medicine

Mesenchymal stem cells (MSCs) are in development for many clinical indications, based both on ‘stem’ properties (tissue repair or regeneration) and on signaling repertoire (immunomodulatory and anti-inflammatory effects). Potential conflation of MSC properties with those of tissue-derived stromal cells presents difficulties in comparing study outcomes and represents a source of confusion in cell therapy development. Cultured MSCs demonstrate significant heterogeneity in clonogenicity and multi-lineage differentiation potential. However in vivo biology of MSCs includes native functions unrelated to regenerative medicine applications, so do nomenclature and heterogeneity matter? In this perspective we examine some consequences of the nomenclature debate and heterogeneity of MSCs. Regulatory expectations are considered, emphasizing that product development should prioritize detailed characterization of therapeutic cell populations for specific indications.

Biological signal generators: integrating synthetic biology tools and in silico control

Biological networks sense extracellular stimuli and generate appropriate outputs within the cell that determine cellular response. Biological signal generators are becoming an important tool for understanding how information is transmitted in these networks and controlling network behavior. Signal generators produce well-defined, dynamic, intracellular signals of important network components, such as kinase activity or the concentration of a specific transcription factor. Synthetic biology tools coupled with in silico control have enabled the construction of these sophisticated biological signal generators. Here we review recent advances in biological signal generator construction and their use in systems biology studies. Challenges for constructing signal generators for a wider range of biological networks and generalizing their use are discussed.

Fold-Change Detection of NF-κB at Target Genes with Different Transcript Outputs

The transcription factor nuclear factor (NF)-κB promotes inflammatory and stress-responsive gene transcription across a range of cell types in response to the cytokine tumor necrosis factor (TNF). Although NF-κB signaling exhibits significant variability across single cells, some target genes supporting high levels of TNF-inducible transcription exhibit fold-change detection of NF-κB, which may buffer against stochastic variation in signaling molecules. It is unknown whether fold-change detection is maintained at NF-κB target genes with low levels of TNF-inducible transcription, for which stochastic promoter events may be more pronounced. Here, we used a microfluidic cell-trapping device to measure how TNF-induced activation of NF-κB controls transcription in single Jurkat T cells at the promoters of integrated HIV and the endogenous cytokine gene IL6, which produce only a few transcripts per cell. We tracked TNF-stimulated NF-κB RelA nuclear translocation by live-cell imaging and then quantified transcript number by RNA FISH in the same cell. We found that TNF-induced transcript abundance at 2 h for low- and high-abundance target genes correlates with similar strength with the fold change in nuclear NF-κB. A computational model of TNF-NF-κB signaling, which implements fold-change detection from competition for binding to κB motifs, could reproduce fold-change detection across the experimentally measured range of transcript outputs. However, multiple model parameters affecting transcription had to be simultaneously varied across promoters to maintain fold-change detection while also matching other trends in the single-cell data for low-abundance transcripts. Our results suggest that cells use multiple biological mechanisms to tune transcriptional output while maintaining robustness of NF-κB fold-change detection.

Single-cell quantification of the concentrations and dissociation constants of endogenous proteins

Kinetic simulation is a useful approach for elucidating complex cell-signaling systems. The numerical simulations required for kinetic modeling in live cells critically require parameters such as protein concentrations and dissociation constants (K_d ). However, only a limited number of parameters have been measured experimentally in living cells. Here we describe an approach for quantifying the concentration and K_d of endogenous proteins at the single-cell level with CRISPR/Cas9-mediated knock-in and fluorescence cross-correlation spectroscopy. First, the mEGFP gene was knocked in at the end of the mitogen-activated protein kinase 1 (MAPK1) gene, encoding extracellular signal-regulated kinase 2 (ERK2), through homology-directed repair or microhomology-mediated end joining. Next, the HaloTag gene was knocked in at the end of the ribosomal S6 kinase 2 (RSK2) gene. We then used fluorescence correlation spectroscopy to measure the protein concentrations of endogenous ERK2-mEGFP and RSK2-HaloTag fusion constructs in living cells, revealing substantial heterogeneities. Moreover, fluorescence cross-correlation spectroscopy analyses revealed temporal changes in the apparent K_d values of the binding between ERK2-mEGFP and RSK2-HaloTag in response to epidermal growth factor stimulation. Our approach presented here provides a robust and efficient method for quantifying endogenous protein concentrations and dissociation constants in living cells.

Functional Programming of Innate Immune Cells in Response to Bordetella pertussis Infection and Vaccination

Despite widespread vaccination, B. pertussis remains one of the least controlled vaccine-preventable diseases. Although it is well known that acellular and whole cell pertussis vaccines induce distinct immune functionalities in memory cells, much less is known about the role of innate immunity in this process. In this review, we provide an overview of the known differences and similarities in innate receptors, innate immune cells and inflammatory signalling pathways induced by the pertussis vaccines either licensed or in development and compare this to primary infection with B. pertussis. Despite the crucial role of innate immunity in driving memory responses to B. pertussis, it is clear that a significant knowledge gap remains in our understanding of the early innate immune response to vaccination and infection. Such knowledge is essential to develop the next generation of pertussis vaccines with improved host defense against B. pertussis.

Single cell network analysis with a mixture of Nested Effects Models

Motivation

New technologies allow for the elaborate measurement of different traits of single cells under genetic perturbations. These interventional data promise to elucidate intra-cellular networks in unprecedented detail and further help to improve treatment of diseases like cancer. However, cell populations can be very heterogeneous.

Results

We developed a mixture of Nested Effects Models (M&NEM) for single-cell data to simultaneously identify different cellular subpopulations and their corresponding causal networks to explain the heterogeneity in a cell population. For inference, we assign each cell to a network with a certain probability and iteratively update the optimal networks and cell probabilities in an Expectation Maximization scheme. We validate our method in the controlled setting of a simulation study and apply it to three data sets of pooled CRISPR screens generated previously by two novel experimental techniques, namely Crop-Seq and Perturb-Seq.

Availability and implementation

The mixture Nested Effects Model (M&NEM) is available as the R-package mnem at https://github.com/cbg-ethz/mnem/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Engineering Approaches to Study Cellular Decision Making

In their native environment, cells are immersed in a complex milieu of biochemical and biophysical cues. These cues may include growth factors, the extracellular matrix, cell-cell contacts, stiffness, and topography, and they are responsible for regulating cellular behaviors such as adhesion, proliferation, migration, apoptosis, and differentiation. The decision-making process used to convert these extracellular inputs into actions is highly complex and sensitive to changes both in the type of individual cue (e.g., growth factor dose/level, timing) and in how these individual cues are combined (e.g., homotypic/heterotypic combinations). In this review, we highlight recent advances in the development of engineering-based approaches to study the cellular decision-making process. Specifically, we discuss the use of biomaterial platforms that enable controlled and tailored delivery of individual and combined cues, as well as the application of computational modeling to analyses of the complex cellular decision-making networks.

Quantitative profiling of innate immune activation by viral infection in single cells

Cells infected by viruses can exhibit diverse patterns of viral and cellular gene expression. The patterns arise in part from the stochastic or noisy reaction kinetics associated with the small number of genomes, enzymes, and other molecules that typically initiate virus replication and activate cellular anti-viral defenses. It is not known what features, if any, of the early viral or cellular gene expression correlate with later processes of viral replication or cell survival. Here we used two fluorescent reporters to visualize innate immune activation of human prostate cancer (PC3) cells against infection by vesicular stomatitis virus. The cells were engineered to express green-fluorescent protein under control of the promoter for IFIT2, an interferon-sensitive component of the anti-viral response, while red-fluorescent protein was expressed as a byproduct of virus infection. To isolate and quantitatively analyze single-cells, we used a unique microwell array device and open-source image processing software. Kinetic analysis of viral and cellular reporter profiles from hundreds of cells revealed novel relationships between gene expression and the outcome of infection. Specifically, the relative timing rather than the magnitude of the viral gene expression and innate immune activation correlated with the infection outcome. Earlier viral or anti-viral gene expression favored or hindered virus growth, respectively. Further, analysis of kinetic parameters estimated from these data suggests a trade-off between robust antiviral signaling and cell death, as indicated by a higher rate of detectable cell lysis in infected cells with a detectable immune response. In short, cells that activate an immune response lyse at a higher rate. More broadly, we demonstrate how the intrinsic heterogeneity of individual cell behaviors can be exploited to discover features of viral and host gene expression that correlate with single-cell outcomes, which will ultimately impact whether or not infections spread.

Accelerating Live Single-Cell Signalling Studies

The dynamics of signalling networks that couple environmental conditions with cellular behaviour can now be characterised in exquisite detail using live single-cell imaging experiments. Recent improvements in our abilities to introduce fluorescent sensors into cells, coupled with advances in pipelines for quantifying and extracting single-cell data, mean that high-throughput systematic analyses of signalling dynamics are becoming possible. In this review, we consider current technologies that are driving progress in the scale and range of such studies. Moreover, we discuss novel approaches that are allowing us to explore how pathways respond to changes in inputs and even predict the fate of a cell based upon its signalling history and state.

Determining the Limitations and Benefits of Noise in Gene Regulation and Signal Transduction through Single Cell, Microscopy-Based Analysis

Stochastic fluctuations, termed "noise," in the level of biological molecules can greatly impact cellular functions. While biological noise can sometimes be detrimental, recent studies have provided an increasing number of examples in which biological noise can be functionally beneficial. Rather than provide an exhaustive review of the growing literature in this field, in this review, we focus on single-cell studies based on quantitative microscopy that have generated a deeper understanding of the sources, characteristics, limitations, and benefits of biological noise. Specifically, we highlight studies showing how noise can help coordinate the expression of multiple downstream target genes, impact the channel capacity of signaling networks, and interact synergistically with oscillatory dynamics to enhance the sensitivity of signal processing. We conclude with a discussion of current challenges and future opportunities.

Cyclic Immunofluorescence (CycIF), A Highly Multiplexed Method for Single-cell Imaging

Cyclic Immunofluorescence (CycIF) is a public-domain method for performing highly multiplexed immunofluorescence imaging using a conventional epifluorescence microscope. It uses simple reagents and existing antibodies to construct images with up to 30 channels by sequential 4- to 6-channel imaging followed by fluorophore inactivation. Three variant methods are described, the most generally useful of which involves staining fixed cells with antibodies directly conjugated to Alexa Fluor dyes and imaging in four colors, inactivating fluorophores using a mild base in the presence of hydrogen peroxide and light, and then performing another round of staining and imaging. Cell morphology is preserved through multiple rounds of CycIF, and signal-to-noise ratios appear to increase. Unlike antibody-stripping methods, CycIF is gentle and optimized for monolayers of cultured cells. A second protocol involves indirect immunofluorescence and a third enables chemical inactivation of genetically encoded fluorescent proteins, allowing multiplex immunofluorescence to be combined with live-cell analysis of cells expressing fluorescent reporter proteins. © 2016 by John Wiley & Sons, Inc.

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.