High-throughput single cell analysis using multi-focus Raman spectroscopy under randomly interleaved scattering projection.

Raman microspectroscopy is a powerful tool for label-free monitoring of single-cell dynamics. However, the traditional single-point acquisition mode is extremely inefficient because it only analyzes one cell at a time. We propose a method that combines multi-focus Raman excitation with random interleaving of scattering projections. The approach uses a time-sharing multi-focus array to simultaneously excite multiple biological cells and synchronously projects their Raman spectra into a single spectral channel with varying shifts. The spectral shifts of the cells are randomly interleaved during data acquisition, resulting in a sequence of mixed spectra from which a compressive sensing method is utilized to reconstruct the time-lapse Raman spectra of the individual cells. The method's feasibility and performance are validated by numerical modeling and experimental investigations into biological spore germination. The results indicated that the throughput of single cell analysis can be increased by up to a factor of 15. The reconstructed spectra of the individual cells demonstrated exceptional fidelity, faithfully capturing the cellular changes in the individual cells. The developed technology paves the way towards high-throughput, label-free monitoring of living single cells, which has promising applications in cell biology.

Actin networks modulate heterogeneous NF-κB dynamics in response to TNFα.

The canonical NF-κB transcription factor RELA is a master regulator of immune and stress responses and is upregulated in pancreatic ductal adenocardinoma (PDAC) tumours. In this study, we characterised previously unexplored endogenous RELA-GFP dynamics in PDAC cell lines through live single-cell imaging. Our observations revealed that TNFα stimulation induces rapid, sustained, and non-oscillatory nuclear translocation of RELA. Through Bayesian analysis of single-cell datasets with variation in nuclear RELA, we predicted that RELA heterogeneity in PDAC cell lines is dependent on F-actin dynamics. RNA-seq analysis identified distinct clusters of RELA-regulated gene expression in PDAC cells, including TNFα-induced RELA upregulation of the actin regulators NUAK2 and ARHGAP31. Further, siRNA-mediated depletion of ARHGAP31 and NUAK2 altered TNFα-stimulated nuclear RELA dynamics in PDAC cells, establishing a novel negative feedback loop that regulates RELA activation by TNFα. Additionally, we characterised the NF-κB pathway in PDAC cells, identifying how NF-κB/IκB proteins genetically and physically interact with RELA in the absence or presence of TNFα. Taken together, we provide computational and experimental support for interdependence between the F-actin network and the NF-κB pathway with RELA translocation dynamics in PDAC.

The prognosis for the most common type of pancreatic cancer, pancreatic ductal adenocarcinoma, also known as PDAC, remains poor. Only around 4% of PDAC patients are likely to live 5 years after being diagnosed. The immune system plays a part in the progression of PDAC, as increased inflammation contributes to the growth of the tumour and its ability to resist treatment. The NF-κB proteins of the immune system are transcription factors that control when and how much certain other proteins are produced. The protein RELA is an important member of the NF-κB family. It is known to be overactivated in PDAC tumours and may be responsible for the high levels of inflammation found in this type of pancreatic cancer. A better understanding of how RELA is activated in PDAC cells will help develop medicines targeting this process. Butera et al. combined experimental and computational approaches to model a network of interactions between key molecules in lines of human PDAC cells grown in the laboratory to investigate how RELA is controlled. Usually, when the RELA protein is activated, it is located in the cell's nucleus, this means that tracking the location of RELA within the cell can reveal its activated form. To follow RELA, it was labelled with a fluorescent marker and visualised using live, high-throughput fluorescence microscopy. When RELA was activated with a pro-inflammatory molecule TNFα, it changed how it moved in and out of the cell's nucleus. Using computational modelling approaches, Butera et al. could build a statistical model that revealed that the location and activity of RELA is affected by actin, a key component of the cell's molecular scaffolding called the cytoskeleton. When actin was disrupted, RELA's activity was altered. Moreover, profiling targets of RELA using RNA sequencing revealed two genes that encode proteins related to the regulation of actin. This indicates that RELA activity ­ which is itself affected by actin organisation ­ can feed back to regulate actin. Butera et al. have identified genes regulated by RELA in PDAC cells that could be used as targets for anti-cancer drug development. Further research into the feedback between RELA and actin will help untangle the complex network that causes inflammation in pancreatic cancer.

Mesenchymal cell migration on one-dimensional micropatterns.

Quantitative studies of mesenchymal cell motion are important to elucidate cytoskeleton function and mechanisms of cell migration. To this end, confinement of cell motion to one dimension (1D) significantly simplifies the problem of cell shape in experimental and theoretical investigations. Here we review 1D migration assays employing micro-fabricated lanes and reflect on the advantages of such platforms. Data are analyzed using biophysical models of cell migration that reproduce the rich scenario of morphodynamic behavior found in 1D. We describe basic model assumptions and model behavior. It appears that mechanical models explain the occurrence of universal relations conserved across different cell lines such as the adhesion-velocity relation and the universal correlation between speed and persistence (UCSP). We highlight the unique opportunity of reproducible and standardized 1D assays to validate theory based on statistical measures from large data of trajectories and discuss the potential of experimental settings embedding controlled perturbations to probe response in migratory behavior.

Reversible, tunable epigenetic silencing of TCF1 generates flexibility in the T cell memory decision.

The immune system encodes information about the severity of a pathogenic threat in the quantity and type of memory cells it forms. This encoding emerges from lymphocyte decisions to maintain or lose self-renewal and memory potential during a challenge. By tracking CD8+ T cells at the single-cell and clonal lineage level using time-resolved transcriptomics, quantitative live imaging, and an acute infection model, we find that T cells will maintain or lose memory potential early after antigen recognition. However, following pathogen clearance, T cells may regain memory potential if initially lost. Mechanistically, this flexibility is implemented by a stochastic cis-epigenetic switch that tunably and reversibly silences the memory regulator, TCF1, in response to stimulation. Mathematical modeling shows how this flexibility allows memory T cell numbers to scale robustly with pathogen virulence and immune response magnitudes. We propose that flexibility and stochasticity in cellular decisions ensure optimal immune responses against diverse threats.

Minorities drive growth resumption in cross-feeding microbial communities.

Microbial communities are fundamental to life on Earth. Different strains within these communities are often connected by a highly connected metabolic network, where the growth of one strain depends on the metabolic activities of other community members. While distributed metabolic functions allow microbes to reduce costs and optimize metabolic pathways, they make them metabolically dependent. Here, we hypothesize that such dependencies can be detrimental in situations where the external conditions change rapidly, as they often do in natural environments. After a shift in external conditions, microbes need to remodel their metabolism, but they can only resume growth once partners on which they depend have also adapted to the new conditions. It is currently not well understood how microbial communities resolve this dilemma and how metabolic interactions are reestablished after an environmental shift. To address this question, we investigated the dynamical responses to environmental perturbation by microbial consortia with distributed anabolic functions. By measuring the regrowth times at the single-cell level in spatially structured communities, we found that metabolic dependencies lead to a growth delay after an environmental shift. However, a minority of cells-those in the immediate neighborhood of their metabolic partners-can regrow quickly and come to numerically dominate the community after the shift. The spatial arrangement of a microbial community is thus a key factor in determining the communities' ability to maintain metabolic interactions and growth in fluctuating conditions. Our results suggest that environmental fluctuations can limit the emergence of metabolic dependencies between microorganisms.

Cytotoxic and chemotactic dynamics of NK cells quantified by live-cell imaging.

Natural Killer (NK) cells detect and eliminate virus-infected cells and cancer cells, and are crucial players of the human immune defense system. Although the relevant molecular machineries involved in NK cell activation and NK-target cell interactions are largely known, how their collective signaling modulates the dynamic behaviors of NK cells, e.g., motility and cytotoxicity, and the rate-limiting kinetics involved are still in need of comprehensive investigations. In traditional bulk killing assays, heterogeneity and kinetic details of individual NK-target cell interactions are masked, seriously limiting analysis of the underlying dynamic mechanisms. Here we present detailed protocols of a number of live-cell imaging assays using fluorescent protein reporters and/or a live-cell dye that enable the acquisition of quantitative kinetic data at the single cell level for elucidating the mechanism underlying the interaction dynamics of primary human NK cells and epithelial cancer cells. Moreover, we discuss how the imaging data can be analyzed either alone or in combination to quantify and determine the key dynamic steps/intermediates involved in specific NK cell activity, e.g., NK cell cytotoxic modes and their associated kinetics, and NK cell motility toward different cancer targets. These live-cell imaging assays can be easily adapted to analyze the rate-limiting kinetics and heterogeneity of other cell-cell interaction dynamics, e.g., in T cell function.

Connecting single-cell ATP dynamics to overflow metabolism, cell growth, and the cell cycle in Escherichia coli.

Adenosine triphosphate (ATP) is an abundant and essential metabolite that cells consume and regenerate in large amounts to support growth. Although numerous studies have inferred the intracellular concentration of ATP in bacterial cultures, what happens in individual bacterial cells under stable growth conditions is less clear. Here, we use the QUEEN-2m biosensor to quantify ATP dynamics in single Escherichia coli cells in relation to their growth rate, metabolism, cell cycle, and cell lineage. We find that ATP dynamics are more complex than expected from population studies and are associated with growth-rate variability. Under stable nutrient-rich condition, cells can display large fluctuations in ATP level that are partially coordinated with the cell cycle. Abrogation of aerobic acetate fermentation (overflow metabolism) through genetic deletion considerably reduces both the amplitude of ATP level fluctuations and the cell-cycle trend. Similarly, growth in media in which acetate fermentation is lower or absent results in the reduction of ATP level fluctuation and cell-cycle trend. This suggests that overflow metabolism exhibits temporal dynamics, which contributes to fluctuating ATP levels during growth. Remarkably, at the single-cell level, growth rate negatively correlates with the amplitude of ATP fluctuation for each tested condition, linking ATP dynamics to growth-rate heterogeneity in clonal populations. Our work highlights the importance of single-cell analysis in studying metabolism and its implication to phenotypic diversity and cell growth.

Single-cell analysis of p53 transitional dynamics unravels stimulus- and cell type-dependent signaling output motifs.

BACKGROUND: To understand functional changes of complex biological networks, mathematical modeling of network topologies provides a quantitative measure of the way biological systems adapt to external stimuli. However, systemic network topology-based analysis often generates conflicting evidence depending on specific experimental conditions, leading to a limited mechanistic understanding of signaling networks and their differential dynamic outputs, an example of which is the regulation of p53 pathway responses to different stress stimuli and in variable mammalian cell types. Here, we employ a network motif approach to dissect key regulatory units of the p53 pathway and elucidate how network activities at the motif level generate context-specific dynamic responses. RESULTS: By combining single-cell imaging and mathematical modeling of dose-dependent p53 dynamics induced by three chemotherapeutics of distinct mechanism-of-actions, including Etoposide, Nutlin-3a and 5-fluorouracil, and in five cancer cell types, we uncovered novel and highly variable p53 dynamic responses, in particular p53 transitional dynamics induced at intermediate drug concentrations, and identified the functional roles of distinct positive and negative feedback motifs of the p53 pathway in modulating the central p53-Mdm2 negative feedback to generate stimulus- and cell type-specific signaling responses. The mechanistic understanding of p53 network dynamics also revealed previously unknown mediators of anticancer drug actions and phenotypic variations in cancer cells that impact drug sensitivity. CONCLUSIONS: Our results demonstrate that transitional dynamics of signaling proteins such as p53, activated at intermediate stimulus levels, vary the most between the dynamic outputs of different generic network motifs and can be employed as novel quantitative readouts to uncover and elucidate the key building blocks of large signaling networks. Our findings also provide new insight on drug mediators and phenotypic heterogeneity that underlie differential drug responses.

Live Imaging of the Dynamics of Mammalian Neural Crest Cell Migration.

Neural crest cells (NCC) are a multipotent cell population that play an important role in vertebrate development. Often touted as the fourth-germ layer, NCC are induced at the border of the neural and non-neural ectoderm during the neurulation phase of embryogenesis. NCC undergo an epithelial to mesenchymal transition (EMT) that facilitates their delamination and migration throughout the embryo. After reaching their final destination, NCC then differentiate into numerous distinct cell types including neurons and glia, melanocytes, and craniofacial chondrocytes and osteoblasts. Research into the signals and mechanisms regulating each step of NCC development has been instrumental to our understanding of vertebrate development, evolution, and disease. However, studying the single and collective cellular dynamics of mammalian NCC migration has proven difficult due to the challenges accessing, and limitations visualizing, NCC within an embryo that develops in utero. The following chapter describes methods for studying the dynamics of cranial NCC migration in whole mouse embryos and in two-dimensional (2D) and 3D explant cultures of the neural plate, but these methods can be adapted for NCC at any axial level of the embryo.

Monitoring single-cell dynamics of entry into quiescence during an unperturbed life cycle.

The life cycle of microorganisms is associated with dynamic metabolic transitions and complex cellular responses. In yeast, how metabolic signals control the progressive choreography of structural reorganizations observed in quiescent cells during a natural life cycle remains unclear. We have developed an integrated microfluidic device to address this question, enabling continuous single-cell tracking in a batch culture experiencing unperturbed nutrient exhaustion to unravel the coordination between metabolic and structural transitions within cells. Our technique reveals an abrupt fate divergence in the population, whereby a fraction of cells is unable to transition to respiratory metabolism and undergoes a reversible entry into a quiescence-like state leading to premature cell death. Further observations reveal that nonmonotonous internal pH fluctuations in respiration-competent cells orchestrate the successive waves of protein superassemblies formation that accompany the entry into a bona fide quiescent state. This ultimately leads to an abrupt cytosolic glass transition that occurs stochastically long after proliferation cessation. This new experimental framework provides a unique way to track single-cell fate dynamics over a long timescale in a population of cells that continuously modify their ecological niche.

The intracellular calcium dynamics in a single vascular endothelial cell being squeezed through a narrow microfluidic channel.

Revealing the mechanisms underlying the intracellular calcium responses in vascular endothelial cells (VECs) induced by mechanical stimuli contributes to a better understanding for vascular diseases, including hypertension, atherosclerosis, and aneurysm. Combining with experimental measurement and Computational Fluid Dynamics simulation, we developed a mechanobiological model to investigate the intracellular [Ca2+] response in a single VEC being squeezed through narrow microfluidic channel. The time-dependent cellular surface tension dynamics was quantified throughout the squeezing process. In our model, the various Ca2+ signaling pathways activated by mechanical stimulation is fully considered. The simulation results of our model exhibited well agreement with our experimental results. By using the model, we theoretically explored the mechanism of the two-peak intracellular [Ca2+] response in single VEC being squeezed through narrow channel and made some testable predictions for guiding experiment in the future.

Bacterial Growth Control Mechanisms Inferred from Multivariate Statistical Analysis of Single-Cell Measurements.

Analysis of single-cell measurements of bacterial growth and division often relied on testing preconceived models of cell size control mechanisms. Such an approach could limit the scope of data analysis and prevent us from uncovering new information. Here, we take an "agnostic" approach by applying regression methods to multiple simultaneously measured cellular variables, which allow us to infer dependencies among those variables from their apparent correlations. Besides previously observed correlations attributed to particular cell size control mechanisms, we identify dependencies that point to potentially new mechanisms. In particular, cells born smaller than their sisters tend to grow faster and make up for the size difference acquired during division. We also find that sister cells are correlated beyond what single-cell, size-control models predict. These trends are consistently found in repeat experiments, although the dependencies vary quantitatively. Such variation highlights the sensitivity of cell growth to environmental variations and the limitation of currently used experimental setups.

Computationally enhanced quantitative phase microscopy reveals autonomous oscillations in mammalian cell growth.

The fine balance of growth and division is a fundamental property of the physiology of cells, and one of the least understood. Its study has been thwarted by difficulties in the accurate measurement of cell size and the even greater challenges of measuring growth of a single cell over time. We address these limitations by demonstrating a computationally enhanced methodology for quantitative phase microscopy for adherent cells, using improved image processing algorithms and automated cell-tracking software. Accuracy has been improved more than twofold and this improvement is sufficient to establish the dynamics of cell growth and adherence to simple growth laws. It is also sufficient to reveal unknown features of cell growth, previously unmeasurable. With these methodological and analytical improvements, in several cell lines we document a remarkable oscillation in growth rate, occurring throughout the cell cycle, coupled to cell division or birth yet independent of cell cycle progression. We expect that further exploration with this advanced tool will provide a better understanding of growth rate regulation in mammalian cells.

EllipTrack: A Global-Local Cell-Tracking Pipeline for 2D Fluorescence Time-Lapse Microscopy.

Time-lapse microscopy provides an unprecedented opportunity to monitor single-cell dynamics. However, tracking cells for long periods remains a technical challenge, especially for multi-day, large-scale movies with rapid cell migration, high cell density, and drug treatments that alter cell morphology/behavior. Here, we present EllipTrack, a global-local cell-tracking pipeline optimized for tracking such movies. EllipTrack first implements a global track-linking algorithm to construct tracks that maximize the probability of cell lineages. Tracking mistakes are then corrected with a local track-correction module in which tracks generated by the global algorithm are systematically examined and amended if a more probable alternative can be found. Through benchmarking, we show that EllipTrack outperforms state-of-the-art cell trackers and generates nearly error-free cell lineages for multiple large-scale movies. In addition, EllipTrack can adapt to time- and cell-density-dependent changes in cell migration speeds and requires minimal training datasets. EllipTrack is available at https://github.com/tianchengzhe/EllipTrack.

Wide lag time distributions break a trade-off between reproduction and survival in bacteria.

Many microorganisms face a fundamental trade-off between reproduction and survival: Rapid growth boosts population size but makes microorganisms sensitive to external stressors. Here, we show that starved bacteria encountering new resources can break this trade-off by evolving phenotypic heterogeneity in lag time. We quantify the distribution of single-cell lag times of populations of starved Escherichia coli and show that population growth after starvation is primarily determined by the cells with shortest lag due to the exponential nature of bacterial population dynamics. As a consequence, cells with long lag times have no substantial effect on population growth resumption. However, we observe that these cells provide tolerance to stressors such as antibiotics. This allows an isogenic population to break the trade-off between reproduction and survival. We support this argument with an evolutionary model which shows that bacteria evolve wide lag time distributions when both rapid growth resumption and survival under stressful conditions are under selection. Our results can explain the prevalence of antibiotic tolerance by lag and demonstrate that the benefits of phenotypic heterogeneity in fluctuating environments are particularly high when minorities with extreme phenotypes dominate population dynamics.

A Live-Cell Screen for Altered Erk Dynamics Reveals Principles of Proliferative Control.

Complex, time-varying responses have been observed widely in cell signaling, but how specific dynamics are generated or regulated is largely unknown. One major obstacle has been that high-throughput screens are typically incompatible with the live-cell assays used to monitor dynamics. Here, we address this challenge by screening a library of 429 kinase inhibitors and monitoring extracellular-regulated kinase (Erk) activity over 5 h in more than 80,000 single primary mouse keratinocytes. Our screen reveals both known and uncharacterized modulators of Erk dynamics, including inhibitors of non-epidermal growth factor receptor (EGFR) receptor tyrosine kinases (RTKs) that increase Erk pulse frequency and overall activity. Using drug treatment and direct optogenetic control, we demonstrate that drug-induced changes to Erk dynamics alter the conditions under which cells proliferate. Our work opens the door to high-throughput screens using live-cell biosensors and reveals that cell proliferation integrates information from Erk dynamics as well as additional permissive cues.

Magnesium Flux Modulates Ribosomes to Increase Bacterial Survival.

Bacteria exhibit cell-to-cell variability in their resilience to stress, for example, following antibiotic exposure. Higher resilience is typically ascribed to "dormant" non-growing cellular states. Here, by measuring membrane potential dynamics of Bacillus subtilis cells, we show that actively growing bacteria can cope with ribosome-targeting antibiotics through an alternative mechanism based on ion flux modulation. Specifically, we observed two types of cellular behavior: growth-defective cells exhibited a mathematically predicted transient increase in membrane potential (hyperpolarization), followed by cell death, whereas growing cells lacked hyperpolarization events and showed elevated survival. Using structural perturbations of the ribosome and proteomic analysis, we uncovered that stress resilience arises from magnesium influx, which prevents hyperpolarization. Thus, ion flux modulation provides a distinct mechanism to cope with ribosomal stress. These results suggest new approaches to increase the effectiveness of ribosome-targeting antibiotics and reveal an intriguing connection between ribosomes and the membrane potential, two fundamental properties of cells.

Evidence that the human cell cycle is a series of uncoupled, memoryless phases.

The cell cycle is canonically described as a series of four consecutive phases: G1, S, G2, and M. In single cells, the duration of each phase varies, but the quantitative laws that govern phase durations are not well understood. Using time-lapse microscopy, we found that each phase duration follows an Erlang distribution and is statistically independent from other phases. We challenged this observation by perturbing phase durations through oncogene activation, inhibition of DNA synthesis, reduced temperature, and DNA damage. Despite large changes in durations in cell populations, phase durations remained uncoupled in individual cells. These results suggested that the independence of phase durations may arise from a large number of molecular factors that each exerts a minor influence on the rate of cell cycle progression. We tested this model by experimentally forcing phase coupling through inhibition of cyclin-dependent kinase 2 (CDK2) or overexpression of cyclin D. Our work provides an explanation for the historical observation that phase durations are both inherited and independent and suggests how cell cycle progression may be altered in disease states.

Stochastic Inheritance of Division and Death Times Determines the Size and Phenotype of CD8+ T Cell Families.

After antigen stimulation cognate naïve CD8+ T cells undergo rapid proliferation and ultimately their progeny differentiates into short-lived effectors and longer-lived memory T cells. Although the expansion of individual cells is very heterogeneous, the kinetics are reproducible at the level of the total population of cognate cells. After the expansion phase, the population contracts, and if antigen is cleared, a population of memory T cells remains behind. Different markers like CD62L, CD27, and KLRG1 have been used to define several T cell subsets (or cell fates) developing from individual naïve CD8+ T cells during the expansion phase. Growing evidence from high-throughput experiments, like single cell RNA sequencing, epigenetic profiling, and lineage tracing, highlights the need to model this differentiation process at the level of single cells. We model CD8+ T cell proliferation and differentiation as a competitive process between the division and death probabilities of individual cells (like in the Cyton model). We use an extended form of the Cyton model in which daughter cells inherit the division and death times from their mother cell in a stochastic manner (using lognormal distributions). We show that this stochastic model reproduces the dynamics of CD8+ T cells both at the population and at the single cell level. Modeling the expression of the CD62L, CD27, and KLRG1 markers of each individual cell, we find agreement with the changing phenotypic distributions of these markers in single cell RNA sequencing data. Retrospectively re-defining conventional T-cell subsets by "gating" on these markers, we find agreement with published population data, without having to assume that these subsets have different properties, i.e., correspond to different fates.