TNBC response to paclitaxel phenocopies interferon response which reveals cell cycle-associated resistance mechanisms.

Paclitaxel is a standard of care neoadjuvant therapy for patients with triple negative breast cancer (TNBC); however, it shows limited benefit for locally advanced or metastatic disease. Here we used a coordinated experimental-computational approach to explore the influence of paclitaxel on the cellular and molecular responses of TNBC cells. We found that escalating doses of paclitaxel resulted in multinucleation, promotion of senescence, and initiation of DNA damage induced apoptosis. Single-cell RNA sequencing (scRNA-seq) of TNBC cells after paclitaxel treatment revealed upregulation of innate immune programs canonically associated with interferon response and downregulation of cell cycle progression programs. Systematic exploration of transcriptional responses to paclitaxel and cancer-associated microenvironmental factors revealed common gene programs induced by paclitaxel, IFNB, and IFNG. Transcription factor (TF) enrichment analysis identified 13 TFs that were both enriched based on activity of downstream targets and also significantly upregulated after paclitaxel treatment. Functional assessment with siRNA knockdown confirmed that the TFs FOSL1, NFE2L2 and ELF3 mediate cellular proliferation and also regulate nuclear structure. We further explored the influence of these TFs on paclitaxel-induced cell cycle behavior via live cell imaging, which revealed altered progression rates through G1, S/G2 and M phases. We found that ELF3 knockdown synergized with paclitaxel treatment to lock cells in a G1 state and prevent cell cycle progression. Analysis of publicly available breast cancer patient data showed that high ELF3 expression was associated with poor prognosis and enrichment programs associated with cell cycle progression. Together these analyses disentangle the diverse aspects of paclitaxel response and identify ELF3 upregulation as a putative biomarker of paclitaxel resistance in TNBC.

Single-cell morphodynamical trajectories enable prediction of gene expression accompanying cell state change.

Extracellular signals induce changes to molecular programs that modulate multiple cellular phenotypes, including proliferation, motility, and differentiation status. The connection between dynamically adapting phenotypic states and the molecular programs that define them is not well understood. Here we develop data-driven models of single-cell phenotypic responses to extracellular stimuli by linking gene transcription levels to "morphodynamics" - changes in cell morphology and motility observable in time-lapse image data. We adopt a dynamics-first view of cell state by grouping single-cell trajectories into states with shared morphodynamic responses. The single-cell trajectories enable development of a first-of-its-kind computational approach to map live-cell dynamics to snapshot gene transcript levels, which we term MMIST, Molecular and Morphodynamics-Integrated Single-cell Trajectories. The key conceptual advance of MMIST is that cell behavior can be quantified based on dynamically defined states and that extracellular signals alter the overall distribution of cell states by altering rates of switching between states. We find a cell state landscape that is bound by epithelial and mesenchymal endpoints, with distinct sequences of epithelial to mesenchymal transition (EMT) and mesenchymal to epithelial transition (MET) intermediates. The analysis yields predictions for gene expression changes consistent with curated EMT gene sets and provides a prediction of thousands of RNA transcripts through extracellular signal-induced EMT and MET with near-continuous time resolution. The MMIST framework leverages true single-cell dynamical behavior to generate molecular-level omics inferences and is broadly applicable to other biological domains, time-lapse imaging approaches and molecular snapshot data.

MOBILE pipeline enables identification of context-specific networks and regulatory mechanisms.

Robust identification of context-specific network features that control cellular phenotypes remains a challenge. We here introduce MOBILE (Multi-Omics Binary Integration via Lasso Ensembles) to nominate molecular features associated with cellular phenotypes and pathways. First, we use MOBILE to nominate mechanisms of interferon-γ (IFNγ) regulated PD-L1 expression. Our analyses suggest that IFNγ-controlled PD-L1 expression involves BST2, CLIC2, FAM83D, ACSL5, and HIST2H2AA3 genes, which were supported by prior literature. We also compare networks activated by related family members transforming growth factor-beta 1 (TGFß1) and bone morphogenetic protein 2 (BMP2) and find that differences in ligand-induced changes in cell size and clustering properties are related to differences in laminin/collagen pathway activity. Finally, we demonstrate the broad applicability and adaptability of MOBILE by analyzing publicly available molecular datasets to investigate breast cancer subtype specific networks. Given the ever-growing availability of multi-omics datasets, we envision that MOBILE will be broadly useful for identification of context-specific molecular features and pathways.

Analysis and modeling of cancer drug responses using cell cycle phase-specific rate effects.

Identifying effective therapeutic treatment strategies is a major challenge to improving outcomes for patients with breast cancer. To gain a comprehensive understanding of how clinically relevant anti-cancer agents modulate cell cycle progression, here we use genetically engineered breast cancer cell lines to track drug-induced changes in cell number and cell cycle phase to reveal drug-specific cell cycle effects that vary across time. We use a linear chain trick (LCT) computational model, which faithfully captures drug-induced dynamic responses, correctly infers drug effects, and reproduces influences on specific cell cycle phases. We use the LCT model to predict the effects of unseen drug combinations and confirm these in independent validation experiments. Our integrated experimental and modeling approach opens avenues to assess drug responses, predict effective drug combinations, and identify optimal drug sequencing strategies.

Morphodynamical cell state description via live-cell imaging trajectory embedding.

Time-lapse imaging is a powerful approach to gain insight into the dynamic responses of cells, but the quantitative analysis of morphological changes over time remains challenging. Here, we exploit the concept of "trajectory embedding" to analyze cellular behavior using morphological feature trajectory histories-that is, multiple time points simultaneously, rather than the more common practice of examining morphological feature time courses in single timepoint (snapshot) morphological features. We apply this approach to analyze live-cell images of MCF10A mammary epithelial cells after treatment with a panel of microenvironmental perturbagens that strongly modulate cell motility, morphology, and cell cycle behavior. Our morphodynamical trajectory embedding analysis constructs a shared cell state landscape revealing ligand-specific regulation of cell state transitions and enables quantitative and descriptive models of single-cell trajectories. Additionally, we show that incorporation of trajectories into single-cell morphological analysis enables (i) systematic characterization of cell state trajectories, (ii) better separation of phenotypes, and (iii) more descriptive models of ligand-induced differences as compared to snapshot-based analysis. This morphodynamical trajectory embedding is broadly applicable to the quantitative analysis of cell responses via live-cell imaging across many biological and biomedical applications.

A lineage tree-based hidden Markov model quantifies cellular heterogeneity and plasticity.

Individual cells can assume a variety of molecular and phenotypic states and recent studies indicate that cells can rapidly adapt in response to therapeutic stress. Such phenotypic plasticity may confer resistance, but also presents opportunities to identify molecular programs that could be targeted for therapeutic benefit. Approaches to quantify tumor-drug responses typically focus on snapshot, population-level measurements. While informative, these methods lack lineage and temporal information, which are particularly critical for understanding dynamic processes such as cell state switching. As new technologies have become available to measure lineage relationships, modeling approaches will be needed to identify the forms of cell-to-cell heterogeneity present in these data. Here we apply a lineage tree-based adaptation of a hidden Markov model that employs single cell lineages as input to learn the characteristic patterns of phenotypic heterogeneity and state transitions. In benchmarking studies, we demonstrated that the model successfully classifies cells within experimentally-tractable dataset sizes. As an application, we analyzed experimental measurements in cancer and non-cancer cell populations under various treatments. We find evidence of multiple phenotypically distinct states, with considerable heterogeneity and unique drug responses. In total, this framework allows for the flexible modeling of single cell heterogeneity across lineages to quantify, understand, and control cell state switching.

A multi-omic analysis of MCF10A cells provides a resource for integrative assessment of ligand-mediated molecular and phenotypic responses.

The phenotype of a cell and its underlying molecular state is strongly influenced by extracellular signals, including growth factors, hormones, and extracellular matrix proteins. While these signals are normally tightly controlled, their dysregulation leads to phenotypic and molecular states associated with diverse diseases. To develop a detailed understanding of the linkage between molecular and phenotypic changes, we generated a comprehensive dataset that catalogs the transcriptional, proteomic, epigenomic and phenotypic responses of MCF10A mammary epithelial cells after exposure to the ligands EGF, HGF, OSM, IFNG, TGFB and BMP2. Systematic assessment of the molecular and cellular phenotypes induced by these ligands comprise the LINCS Microenvironment (ME) perturbation dataset, which has been curated and made publicly available for community-wide analysis and development of novel computational methods ( synapse.org/LINCS_MCF10A ). In illustrative analyses, we demonstrate how this dataset can be used to discover functionally related molecular features linked to specific cellular phenotypes. Beyond these analyses, this dataset will serve as a resource for the broader scientific community to mine for biological insights, to compare signals carried across distinct molecular modalities, and to develop new computational methods for integrative data analysis.

A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling.

Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. However, the construction and simulation of such models have proved challenging. Here, we developed a python-based model creation and simulation pipeline that converts a few structured text files into an SBML standard and is high-performance- and cloud-computing ready. We applied this pipeline to our large-scale, mechanistic pan-cancer signaling model (named SPARCED) and demonstrate it by adding an IFNγ pathway submodel. We then investigated whether a putative crosstalk mechanism could be consistent with experimental observations from the LINCS MCF10A Data Cube that IFNγ acts as an anti-proliferative factor. The analyses suggested this observation can be explained by IFNγ-induced SOCS1 sequestering activated EGF receptors. This work forms a foundational recipe for increased mechanistic model-based data integration on a single-cell level, an important building block for clinically-predictive mechanistic models.

Individual Cells Can Resolve Variations in Stimulus Intensity along the IGF-PI3K-AKT Signaling Axis.

Cells sense and respond to signals in their local environment by activating signaling cascades that lead to phenotypic changes. Differences in these signals can be discriminated at the population level; however, single cells have been thought to be limited in their capacity to distinguish ligand doses due to signaling noise. We describe here the rational development of a genetically encoded FoxO1 sensor, which serves as a down-stream readout of insulin growth factor-phosphatidylinositol 3-kinase IGF-PI3K-AKT signaling pathway activity. With this reporter, we tracked individual cell responses to multiple IGF-I doses, pathway inhibitors, and repeated treatments. We observed that individual cells can discriminate multiple IGF-I doses, and these responses are sustained over time, are reproducible at the single-cell level, and display cell-to-cell heterogeneity. These studies imply that cell-to-cell variation in signaling responses is biologically meaningful and support the endeavor to elucidate mechanisms of cell signaling at the level of the individual cell.

Quantification of growth factor signaling and pathway cross talk by live-cell imaging.

Peptide growth factors stimulate cellular responses through activation of their transmembrane receptors. Multiple intracellular signaling cascades are engaged following growth factor-receptor binding, leading to short- and long-term biological effects. Each receptor-activated signaling pathway does not act in isolation but rather interacts at different levels with other pathways to shape signaling networks that are distinctive for each growth factor. To gain insights into the specifics of growth factor-regulated interactions among different signaling cascades, we developed a HeLa cell line stably expressing fluorescent live-cell imaging reporters that are readouts for two major growth factor-stimulated pathways, Ras-Raf-Mek-ERK and phosphatidylinositol (PI) 3-kinase-Akt. Incubation of cells with epidermal growth factor (EGF) resulted in rapid, robust, and sustained ERK signaling but shorter-term activation of Akt. In contrast, hepatocyte growth factor induced sustained Akt signaling but weak and short-lived ERK activity, and insulin-like growth factor-I stimulated strong long-term Akt responses but negligible ERK signaling. To address potential interactions between signaling pathways, we employed specific small-molecule inhibitors. In cells incubated with EGF or platelet-derived growth factor-AA, Raf activation and the subsequent stimulation of ERK reduced Akt signaling, whereas Mek inhibition, which blocked ERK activation, enhanced Akt and turned transient effects into sustained responses. Our results reveal that individual growth factors initiate signaling cascades that vary markedly in strength and duration and demonstrate in living cells the dramatic effects of cross talk from Raf and Mek to PI 3-kinase and Akt. Our data further indicate how specific growth factors can encode distinct cellular behaviors by promoting complex interactions among signaling pathways.

Unraveling Growth Factor Signaling and Cell Cycle Progression in Individual Fibroblasts.

Cultured cells require the actions of growth factors to enter the cell cycle, but how individual members of a population respond to the same stimulus remains unknown. Here we have employed continuous monitoring by live cell imaging in a dual-reporter cell model to investigate the regulation of short-term growth factor signaling (protein kinase B (PKB/Akt) activity) and longer-term progression through the cell cycle (cyclin-dependent kinase 2 activity). In the total population, insulin-like growth factor-I (IGF-I)-enhanced cell cycle entry by >5-fold compared with serum-free medium (from 13.5 to 78%), but at the single cell level we observed a broad distribution in the timing of G1 exit (4-24 h, mean ∼12 h) that did not vary with either the amount or duration of IGF-I treatment. Cells that failed to re-enter the cell cycle exhibited similar responses to IGF-I in terms of integrated Akt activity and migration distance compared with those that did. We made similar observations with EGF, PDGF-AA, and PDGF-BB. As potential thresholds of growth factor-mediated cell cycle progression appeared to be heterogeneous within the population, the longer-term proliferative outcomes of individual cells to growth factor stimulation could not be predicted based solely on acute Akt signaling responses, no matter how robust these might be. Thus, although we could define a relationship at the population level between growth factor-induced Akt signaling dynamics and cell cycle progression, we could not predict the fate of individual cells.

Mapping growth-factor-modulated Akt signaling dynamics.

Growth factors alter cellular behavior through shared signaling cascades, raising the question of how specificity is achieved. Here, we have determined how growth factor actions are encoded into Akt signaling dynamics by real-time tracking of a fluorescent sensor. In individual cells, Akt activity was encoded in an analog pattern, with similar latencies (∼2 min) and half-maximal peak response times (range of 5-8 min). Yet, different growth factors promoted dose-dependent and heterogeneous changes in signaling dynamics. Insulin treatment caused sustained Akt activity, whereas EGF or PDGF-AA promoted transient signaling; PDGF-BB produced sustained responses at higher concentrations, but short-term effects at low doses, actions that were independent of the PDGF-α receptor. Transient responses to EGF were caused by negative feedback at the receptor level, as a second treatment yielded minimal responses, whereas parallel exposure to IGF-I caused full Akt activation. Small-molecule inhibitors reduced PDGF-BB signaling to transient responses, but only decreased the magnitude of IGF-I actions. Our observations reveal distinctions among growth factors that use shared components, and allow us to capture the consequences of receptor-specific regulatory mechanisms on Akt signaling.

Functional phosphorylation sites in cardiac myofilament proteins are evolutionarily conserved in skeletal myofilament proteins.

Protein phosphorylation plays an important role in regulating cardiac contractile function, but phosphorylation is not thought to play a regulatory role in skeletal muscle. To examine how myofilament phosphorylation arose in the human heart, we analyzed the amino acid sequences of 25 cardiac phosphorylation sites in animals ranging from fruit flies to humans. These analyses indicated that of the 25 human phosphorylation sites examined, 11 have been conserved across vertebrates and four have been sporadically present in vertebrates. Furthermore, all 11 of the cardiac sites found across vertebrates were present in skeletal muscle isoforms, along with three sites that were sporadically present. Based on the conservation of amino acid sequences between cardiac and skeletal contractile proteins, we tested for phosphorylation in mammalian skeletal muscle using several biochemical techniques and found evidence that multiple myofilament proteins were phosphorylated. Several of these phosphorylation sites were validated using mass spectrometry, including one site that is present in slow- and fast-twitch troponin I (TnI), but was lost in cardiac TnI. Thus, several myofilament phosphorylation sites present in the human heart likely arose in invertebrate muscle, have been evolutionarily conserved in skeletal muscle, and potentially have functional effects in both skeletal and cardiac muscle.

Separating myoblast differentiation from muscle cell fusion using IGF-I and the p38 MAP kinase inhibitor SB202190.

The p38 MAP kinases play critical roles in skeletal muscle biology, but the specific processes regulated by these kinases remain poorly defined. Here we find that activity of p38α/ß is important not only in early phases of myoblast differentiation, but also in later stages of myocyte fusion and myofibrillogenesis. By treatment of C2 myoblasts with the promyogenic growth factor insulin-like growth factor (IGF)-I, the early block in differentiation imposed by the p38 chemical inhibitor SB202190 could be overcome. Yet, under these conditions, IGF-I could not prevent the later impairment of muscle cell fusion, as marked by the nearly complete absence of multinucleated myofibers. Removal of SB202190 from the medium of differentiating myoblasts reversed the fusion block, as multinucleated myofibers were detected several hours later and reached ∼90% of the culture within 30 h. Analysis by quantitative mass spectroscopy of proteins that changed in abundance following removal of the inhibitor revealed a cohort of upregulated muscle-enriched molecules that may be important for both myofibrillogenesis and fusion. We have thus developed a model system that allows separation of myoblast differentiation from muscle cell fusion and should be useful in identifying specific steps regulated by p38 MAP kinase-mediated signaling in myogenesis.

Akt signaling dynamics in individual cells.

The protein kinase Akt (for which there are three isoforms) is a key intracellular mediator of many biological processes, yet knowledge of Akt signaling dynamics is limited. Here, we have constructed a fluorescent reporter molecule in a lentiviral delivery system to assess Akt kinase activity at the single cell level. The reporter, a fusion between a modified FoxO1 transcription factor and clover, a green fluorescent protein, rapidly translocates from the nucleus to the cytoplasm in response to Akt stimulation. Because of its long half-life and the intensity of clover fluorescence, the sensor provides a robust readout that can be tracked for days under a range of biological conditions. Using this reporter, we find that stimulation of Akt activity by IGF-I is encoded into stable and reproducible analog responses at the population level, but that single cell signaling outcomes are variable. This reporter, which provides a simple and dynamic measure of Akt activity, should be compatible with many cell types and experimental platforms, and thus opens the door to new insights into how Akt regulates its biological responses.

Accessibility of myofilament cysteines and effects on ATPase depend on the activation state during exposure to oxidants.

Signaling by reactive oxygen species has emerged as a major physiological process. Due to its high metabolic rate, striated muscle is especially subject to oxidative stress, and there are multiple examples in cardiac and skeletal muscle where oxidative stress modulates contractile function. Here we assessed the potential of cysteine oxidation as a mechanism for modulating contractile function in skeletal and cardiac muscle. Analyzing the cysteine content of the myofilament proteins in striated muscle, we found that cysteine residues are relatively rare, but are very similar between different muscle types and different vertebrate species. To refine this list of cysteines to those that may modulate function, we estimated the accessibility of oxidants to cysteine residues using protein crystal structures, and then sharpened these estimates using fluorescent labeling of cysteines in cardiac and skeletal myofibrils. We demonstrate that cysteine accessibility to oxidants and ATPase rates depend on the contractile state in which preparations are exposed. Oxidant exposure of skeletal and cardiac myofibrils in relaxing solution exposes myosin cysteines not accessible in rigor solution, and these modifications correspond to a decrease in maximum ATPase. Oxidant exposure under rigor conditions produces modifications that increase basal ATPase and calcium sensitivity in ventricular myofibrils, but these effects were muted in fast twitch muscle. These experiments reveal how structural and sequence variations can lead to divergent effects from oxidants in different muscle types.

Live cell imaging reveals marked variability in myoblast proliferation and fate.

BACKGROUND: During the process of muscle regeneration, activated stem cells termed satellite cells proliferate, and then differentiate to form new myofibers that restore the injured area. Yet not all satellite cells contribute to muscle repair. Some continue to proliferate, others die, and others become quiescent and are available for regeneration following subsequent injury. The mechanisms that regulate the adoption of different cell fates in a muscle cell precursor population remain unclear. METHODS: We have used live cell imaging and lineage tracing to study cell fate in the C2 myoblast line. RESULTS: Analyzing the behavior of individual myoblasts revealed marked variability in both cell cycle duration and viability, but similarities between cells derived from the same parental lineage. As a consequence, lineage sizes and outcomes differed dramatically, and individual lineages made uneven contributions toward the terminally differentiated population. Thus, the cohort of myoblasts undergoing differentiation at the end of an experiment differed dramatically from the lineages present at the beginning. Treatment with IGF-I increased myoblast number by maintaining viability and by stimulating a fraction of cells to complete one additional cell cycle in differentiation medium, and as a consequence reduced the variability of the terminal population compared with controls. CONCLUSION: Our results reveal that heterogeneity of responses to external cues is an intrinsic property of cultured myoblasts that may be explained in part by parental lineage, and demonstrate the power of live cell imaging for understanding how muscle differentiation is regulated.