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.

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 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.

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.

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.