Use of an Agitation Measure to Screen for Suicide and Self-Harm Risk Among Emergency Department Patients.

BACKGROUND: Suicidality alone is insensitive to suicide risk among emergency department (ED) patients. OBJECTIVE: We describe the performance of adding an objective assessment of agitation to a suicide screening instrument for predicting suicide and self-harm after an ED encounter. METHODS: We tested the performance of a novel screener combining the presence of suicidality or agitation for predicting suicide within 90 days or a repeat ED visit for self-harm within 30 days using retrospective data from all patients seen in an urban safety net ED over 27 months. Patients were assessed for suicidality using the Columbia-Suicide Severity Rating Scale-Clinical Practice Screener and for agitation using either the Behavioral Activity Rating Scale or Richmond Agitation Sedation Scale. We hypothesized that a screener based on the presence of either suicidality or agitation would be more sensitive to suicide risk than the Columbia-Suicide Severity Rating Scale-Clinical Practice Screener alone. The screener's performance is described, and multivariable regression evaluates the correlations between screening and outcomes. RESULTS: The sample comprised 16,467 patients seen in the ED who had available suicide screening and agitation data. Thirteen patients (0.08%) died by suicide within 90 days after ED discharge. The sensitivity and specificity of the screener combining suicidality and agitation for predicting suicide was 0.69 (95% confidence interval, 0.44-0.94) and 0.74 (0.44-0.94), respectively. The sensitivity and specificity for agitation combined with positive suicide screening for self-harm within 30 days were 0.95 (0.89-1.00) and 0.73 (0.73-0.74). For both outcomes, augmenting the Columbia-Suicide Severity Rating Scale-Clinical Practice Screener with a measure of agitation improved both sensitivity and overall performance compared to historical performance of the Columbia-Suicide Severity Rating Scale-Clinical Practice Screener alone. CONCLUSIONS: Combining a brief objective measure of agitation with a common suicide screening instrument improved sensitivity and predictive performance for suicide and self-harm risk after ED discharge. These findings speak to the importance of assessing agitation not only for imminent safety risk during the patient encounter but also for reducing the likelihood of future adverse events. This work can improve the detection and management of suicide risk in emergency settings.

Simulated driving performance among daily and occasional cannabis users.

OBJECTIVE: Daily cannabis users develop tolerance to some drug effects, but the extent to which this diminishes driving impairment is uncertain. This study compared the impact of acute cannabis use on driving performance in occasional and daily cannabis users using a driving simulator. METHODS: We used a within-subjects design to observe driving performance in adults age 25 to 45 years with different cannabis use histories. Eighty-five participants (43 males, 42 females) were included in the final analysis: 24 occasional users (1 to 2 times per week), 31 daily users and 30 non-users. A car-based driving simulator (MiniSim™, National Advanced Driving Simulator) was used to obtain two measures of driving performance, standard deviation of lateral placement (SDLP) and speed relative to posted speed limit, in simulated urban driving scenarios at baseline and 30 min after a 15 min ad libitum cannabis smoking period. Participants smoked self-supplied cannabis flower product (15% to 30% tetrahydrocannabinol (THC). Blood samples were collected before and after smoking (30 min after the start of smoking). Non-users performed the same driving scenarios before and after an equivalent rest interval. Changes in driving performance were analyzed by repeated measures general linear models. RESULTS: Mean whole blood THC cannabinoids concentrations post smoking were use THC = 6.4 ± 5.6 ng/ml, THC-COOH = 10.9 ± 8.79 ng/mL for occasional users and THC = 36.4 ± 37.4 ng/mL, THC-COOH = 98.1 ± 90.6 ng/mL for daily users. On a scale of 0 to 100, the mean post-use score of subjective high was similar in occasional users and daily users (52.4 and 47.2, respectively). In covariate-adjusted analysis, occasional users had a significant increase in SDLP in the straight road segment from pre to post compared to non-users; non-users decreased by a mean of 1.1 cm (25.5 cm to 24.4 cm) while occasional users increased by a mean of 1.9 cm (21.7 cm to 23.6 cm; p = 0.02). Daily users also increased adjusted SDLP in straight road segments from baseline to post-use (23.2 cm to 25.0 cm), but the change relative to non-users was not statistically significant (p = 0.08). The standardized mean difference in unadjusted SDLP from baseline to post-use in the straight road segments comparing occasional users to non-users was 0.64 (95% CI 0.09 - 1.19), a statistically significant moderate increase. When occasional users were contrasted with daily users, the baseline to post changes in SDLP were not statistically significant. Daily users exhibited a mean decrease in baseline to post-use adjusted speed in straight road segments of 1.16 mph; a significant change compared to slight speed increases in the non-users and occasional users (p = 0.02 and p = 0.01, respectively). CONCLUSION: We observed a decrement in driving performance assessed by SDLP after acute cannabis smoking that was statistically significant only in the occasional users in comparison to the nonusers. Direct contrasts between the occasional users and daily users in SDLP were not statistically significant. Daily users drove slower after cannabis use as compared to the occasional use group and non-users. The study results do not conclusively establish that occasional users exhibit more driving impairment than daily users when both smoke cannabis ad libitum.

Atg2, Atg9 and Atg18 in mitochondrial integrity, cardiac function and healthspan in Drosophila.

In yeast, the Atg2-Atg18 complex regulates Atg9 recycling from phagophore assembly site during autophagy; their function in higher eukaryotes remains largely unknown. In a targeted screening in Drosophila melanogaster, we show that Mef2-GAL4-RNAi-mediated knockdown of Atg2, Atg9 or Atg18 in the heart and indirect flight muscles led to shortened healthspan (declined locomotive function) and lifespan. These flies displayed an accelerated age-dependent loss of cardiac function along with cardiac hypertrophy (increased heart tube wall thickness) and structural abnormality (distortion of the lumen surface). Using the Mef2-GAL4-MitoTimer mitochondrial reporter system and transmission electron microscopy, we observed significant elongation of mitochondria and reduced number of lysosome-targeted autophagosomes containing mitochondria in the heart tube but exaggerated mitochondrial fragmentation and reduced mitochondrial density in indirect flight muscles. These findings provide the first direct evidence of the importance of Atg2-Atg18/Atg9 autophagy complex in the maintenance of mitochondrial integrity and, regulation of heart and muscle functions in Drosophila, raising the possibility of augmenting Atg2-Atg18/Atg9 activity in promoting mitochondrial health and, muscle and heart function.

PD-L1 immunostaining scoring for non-small cell lung cancer based on immunosurveillance parameters.

Non-Small Cell Lung Cancer (NSCLC) is the leading cause of cancer death globally, and new immunotherapies developed and under development targeting PD-1/PD-L1 checkpoint inhibition require accurate patient selection to assure good clinical outcome. PD-L1 immunohistochemistry is the current biomarker assay used for patient selection, but still imprecise in predicting therapy response. Exploring this issue, we performed computational tissue analysis of PD-L1 immunostaining in procured NSCLC tissues (n = 50) using the Merck KGaA anti-PD-L1 clone MKP1A07310. Staining patterns and PD-L1 cut-off points were interrogated using relevant cancer immune-surveillance biomarkers. Groups with high PD-L1 expression levels (above 25/50% staining cut-off points) were enriched for a biomarker profile in the tumor-nest and microenvironment indicating escape from host-immunity, as represented by increased numbers of cells positive for CD8 and Granzyme B (immune-effectors), FOXP3 (immune-suppressive), and CD68 (P < 0.05). Manual analysis of PD-L1 staining patterns identified tumors with an immune-induced reactive pattern relevant for immunotherapy that would ordinarily be excluded by the arbitrary 25% staining threshold (P < 0.05). Conversely, some cases with completely or predominantly immune-independent constitutive PD-L1 staining patterns that indicate insensitivity to immunotherapy may have been incorrectly selected using this staining cut-off point criterion. Therefore, we propose differentiation of reactive vs constitutive PD-L1 staining patterns to improve the accuracy of this biomarker assay in selecting NSCLC patients for PD-1/PD-L1 immunotherapy.

Ampk phosphorylation of Ulk1 is required for targeting of mitochondria to lysosomes in exercise-induced mitophagy.

Mitochondrial health is critical for skeletal muscle function and is improved by exercise training through both mitochondrial biogenesis and removal of damaged/dysfunctional mitochondria via mitophagy. The mechanisms underlying exercise-induced mitophagy have not been fully elucidated. Here, we show that acute treadmill running in mice causes mitochondrial oxidative stress at 3-12 h and mitophagy at 6 h post-exercise in skeletal muscle. These changes were monitored using a novel fluorescent reporter gene, pMitoTimer, that allows assessment of mitochondrial oxidative stress and mitophagy in vivo, and were preceded by increased phosphorylation of AMP activated protein kinase (Ampk) at tyrosine 172 and of unc-51 like autophagy activating kinase 1 (Ulk1) at serine 555. Using mice expressing dominant negative and constitutively active Ampk in skeletal muscle, we demonstrate that Ulk1 activation is dependent on Ampk. Furthermore, exercise-induced metabolic adaptation requires Ulk1. These findings provide direct evidence of exercise-induced mitophagy and demonstrate the importance of Ampk-Ulk1 signaling in skeletal muscle.Exercise is associated with biogenesis and removal of dysfunctional mitochondria. Here the authors use a mitochondrial reporter gene to demonstrate the occurrence of mitophagy following exercise in mice, and show this is dependent on AMPK and ULK1 signaling.

Efficacy and Molecular Mechanisms of Differentiated Response to the Aurora and Angiogenic Kinase Inhibitor ENMD-2076 in Preclinical Models of p53-Mutated Triple-Negative Breast Cancer.

PURPOSE: Triple-negative breast cancer (TNBC) is a subtype associated with poor prognosis and for which there are limited therapeutic options. The purpose of this study was to evaluate the efficacy of ENMD-2076 in p53-mutated TNBC patient-derived xenograft (PDX) models and describe patterns of terminal cell fate in models demonstrating sensitivity, intrinsic resistance, and acquired resistance to ENMD-2076. EXPERIMENTAL DESIGN: p53-mutated, TNBC PDX models were treated with ENMD-2076 and evaluated for mechanisms of sensitivity or resistance to treatment. Correlative tissue testing was performed on tumor tissue to assess for markers of proliferation, apoptosis, senescence, and pathways of resistance after treatment and at the time of acquired resistance. RESULTS: Sensitivity to ENMD-2076 200 mg/kg daily was associated with induction of apoptosis while models exhibiting intrinsic or acquired resistance to treatment presented with a senescent phenotype. Response to ENMD-2076 was accompanied by an increase in p53 and p73 levels, even within the background of mutant p53. Treatment with ENMD-2076 resulted in a decrease in pAurA and an increase in pHH3. We observed a TNBC subtype switch from the luminal androgen receptor to the basal-like subtype at acquired resistance. CONCLUSION: ENMD-2076 has antitumor activity in preclinical models of p53-mutated TNBC. Increased levels of p53 and p73 correlated with sensitivity whereas senescence was associated with resistance to ENMD-2076. The novel finding of a TNBC subtype switch at time of acquired resistance may provide mechanistic insights into the biologic effects of selective pressure of anticancer treatments on TNBC. ENMD-2076 is currently being evaluated in a Phase 2 clinical trial in patients with metastatic, previously treated TNBC where these biologic correlates can be further explored.

Dual Targeting of PDGFRα and FGFR1 Displays Synergistic Efficacy in Malignant Rhabdoid Tumors.

Subunits of the SWI/SNF chromatin remodeling complex are mutated in a significant proportion of human cancers. Malignant rhabdoid tumors (MRTs) are lethal pediatric cancers characterized by a deficiency in the SWI/SNF subunit SMARCB1. Here, we employ an integrated molecular profiling and chemical biology approach to demonstrate that the receptor tyrosine kinases (RTKs) PDGFRα and FGFR1 are coactivated in MRT cells and that dual blockade of these receptors has synergistic efficacy. Inhibitor combinations targeting both receptors and the dual inhibitor ponatinib suppress the AKT and ERK1/2 pathways leading to apoptosis. MRT cells that have acquired resistance to the PDGFRα inhibitor pazopanib are susceptible to FGFR inhibitors. We show that PDGFRα levels are regulated by SMARCB1 expression, and assessment of clinical specimens documents the expression of both PDGFRα and FGFR1 in rhabdoid tumor patients. Our findings support a therapeutic approach in cancers with SWI/SNF deficiencies by exploiting RTK coactivation dependencies.

Systematic analysis of tumour cell-extracellular matrix adhesion identifies independent prognostic factors in breast cancer.

Tumour cell-extracellular matrix (ECM) interactions are fundamental for discrete steps in breast cancer progression. In particular, cancer cell adhesion to ECM proteins present in the microenvironment is critical for accelerating tumour growth and facilitating metastatic spread. To assess the utility of tumour cell-ECM adhesion as a means for discovering prognostic factors in breast cancer survival, here we perform a systematic phenotypic screen and characterise the adhesion properties of a panel of human HER2 amplified breast cancer cell lines across six ECM proteins commonly deregulated in breast cancer. We determine a gene expression signature that defines a subset of cell lines displaying impaired adhesion to laminin. Cells with impaired laminin adhesion showed an enrichment in genes associated with cell motility and molecular pathways linked to cytokine signalling and inflammation. Evaluation of this gene set in the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort of 1,964 patients identifies the F12 and STC2 genes as independent prognostic factors for overall survival in breast cancer. Our study demonstrates the potential of in vitro cell adhesion screens as a novel approach for identifying prognostic factors for disease outcome.

An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer.

BACKGROUND: Triple-Negative Breast Cancer (TNBC) is an aggressive disease with a poor prognosis. Clinically, TNBC patients have limited treatment options besides chemotherapy. The goal of this study was to determine the kinase dependency in TNBC cell lines and to predict compounds that could inhibit these kinases using integrative bioinformatics analysis. RESULTS: We integrated publicly available gene expression data, high-throughput pharmacological profiling data, and quantitative in vitro kinase binding data to determine the kinase dependency in 12 TNBC cell lines. We employed Kinase Addiction Ranker (KAR), a novel bioinformatics approach, which integrated these data sources to dissect kinase dependency in TNBC cell lines. We then used the kinase dependency predicted by KAR for each TNBC cell line to query K-Map for compounds targeting these kinases. We validated our predictions using published and new experimental data. CONCLUSIONS: In summary, we implemented an integrative bioinformatics analysis that determines kinase dependency in TNBC. Our analysis revealed candidate kinases as potential targets in TNBC for further pharmacological and biological studies.

Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data.

MOTIVATION: Targeted kinase inhibitors have dramatically improved cancer treatment, but kinase dependency for an individual patient or cancer cell can be challenging to predict. Kinase dependency does not always correspond with gene expression and mutation status. High-throughput drug screens are powerful tools for determining kinase dependency, but drug polypharmacology can make results difficult to interpret. RESULTS: We developed Kinase Addiction Ranker (KAR), an algorithm that integrates high-throughput drug screening data, comprehensive kinase inhibition data and gene expression profiles to identify kinase dependency in cancer cells. We applied KAR to predict kinase dependency of 21 lung cancer cell lines and 151 leukemia patient samples using published datasets. We experimentally validated KAR predictions of FGFR and MTOR dependence in lung cancer cell line H1581, showing synergistic reduction in proliferation after combining ponatinib and AZD8055. AVAILABILITY AND IMPLEMENTATION: KAR can be downloaded as a Python function or a MATLAB script along with example inputs and outputs at: http://tanlab.ucdenver.edu/KAR/. CONTACT: aikchoon.tan@ucdenver.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DSigDB: drug signatures database for gene set analysis.

UNLABELLED: We report the creation of Drug Signatures Database (DSigDB), a new gene set resource that relates drugs/compounds and their target genes, for gene set enrichment analysis (GSEA). DSigDB currently holds 22 527 gene sets, consists of 17 389 unique compounds covering 19 531 genes. We also developed an online DSigDB resource that allows users to search, view and download drugs/compounds and gene sets. DSigDB gene sets provide seamless integration to GSEA software for linking gene expressions with drugs/compounds for drug repurposing and translational research. AVAILABILITY AND IMPLEMENTATION: DSigDB is freely available for non-commercial use at http://tanlab.ucdenver.edu/DSigDB. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. CONTACT: aikchoon.tan@ucdenver.edu.

Systems biology approaches for advancing the discovery of effective drug combinations.

Complex diseases like cancer are regulated by large, interconnected networks with many pathways affecting cell proliferation, invasion, and drug resistance. However, current cancer therapy predominantly relies on the reductionist approach of one gene-one disease. Combinations of drugs may overcome drug resistance by limiting mutations and induction of escape pathways, but given the enormous number of possible drug combinations, strategies to reduce the search space and prioritize experiments are needed. In this review, we focus on the use of computational modeling, bioinformatics and high-throughput experimental methods for discovery of drug combinations. We highlight cutting-edge systems approaches, including large-scale modeling of cell signaling networks, network motif analysis, statistical association-based models, identifying correlations in gene signatures, functional genomics, and high-throughput combination screens. We also present a list of publicly available data and resources to aid in discovery of drug combinations. Integration of these systems approaches will enable faster discovery and translation of clinically relevant drug combinations. Graphical abstractSpectrum of Systems Biology Approaches for Drug Combinations.

Automated microscopy of cardiac myocyte hypertrophy: a case study on the role of intracellular α-adrenergic receptors.

Traditional approaches for measuring cardiac myocyte hypertrophy have been of low throughput and subjective, limiting the scope of experimental studies designed to understand it. Here, we describe an automated image acquisition and analysis platform for studying the dynamics of cardiac myocyte hypertrophy in vitro. Image acquisition scripts record 5 × 5 mosaic images of fluorescent protein-labeled neonatal rat ventricular myocytes from each well of a 96-well plate using the microscope's automated stage and focus. Image analysis algorithms automatically segment myocyte boundaries, track myocytes, and quantify changes in shape. We describe each step of the image acquisition and analysis algorithms and provide specific examples of how to implement them using Metamorph and CellProfiler software. With this system, shape dynamics of thousands of individual cardiac myocytes can be tracked for up to a week. This imaging platform was recently applied to study reversal of cardiac myocyte hypertrophy following withdrawal of the α-adrenergic agonist phenylephrine. Hypertrophy readily reversed at low but not high levels of α-adrenergic signaling, leading to identification of an intracellular population of α-adrenergic receptors responsible for this reversibility delay.

A novel MitoTimer reporter gene for mitochondrial content, structure, stress, and damage in vivo.

Mitochondrial dysfunction plays important roles in many diseases, but there is no satisfactory method to assess mitochondrial health in vivo. Here, we engineered a MitoTimer reporter gene from the existing Timer reporter gene. MitoTimer encodes a mitochondria-targeted green fluorescent protein when newly synthesized, which shifts irreversibly to red fluorescence when oxidized. Confocal microscopy confirmed targeting of the MitoTimer protein to mitochondria in cultured cells, Caenorhabditis elegans touch receptor neurons, Drosophila melanogaster heart and indirect flight muscle, and mouse skeletal muscle. A ratiometric algorithm revealed that conditions that cause mitochondrial stress led to a significant shift toward red fluorescence as well as accumulation of pure red fluorescent puncta of damaged mitochondria targeted for mitophagy. Long term voluntary exercise resulted in a significant fluorescence shift toward green, in mice and D. melanogaster, as well as significantly improved structure and increased content in mouse FDB muscle. In contrast, high-fat feeding in mice resulted in a significant shift toward red fluorescence and accumulation of pure red puncta in skeletal muscle, which were completely ameliorated by voluntary wheel running. Hence, MitoTimer allows for robust analysis of multiple parameters of mitochondrial health under both physiological and pathological conditions and will be highly useful for future research of mitochondrial health in multiple disciplines in vivo.

Phenotypic screen quantifying differential regulation of cardiac myocyte hypertrophy identifies CITED4 regulation of myocyte elongation.

Cardiac hypertrophy is controlled by a highly connected signaling network with many effectors of cardiac myocyte size. Quantification of the contribution of individual pathways to specific changes in shape and transcript abundance is needed to better understand hypertrophy signaling and to improve heart failure therapies. We stimulated cardiac myocytes with 15 hypertrophic agonists and quantitatively characterized differential regulation of 5 shape features using high-throughput microscopy and transcript levels of 12 genes using qPCR. Transcripts measured were associated with phenotypes including fibrosis, cell death, contractility, proliferation, angiogenesis, inflammation, and the fetal cardiac gene program. While hypertrophy pathways are highly connected, the agonist screen revealed distinct hypertrophy phenotypic signatures for the 15 receptor agonists. We then used k-means clustering of inputs and outputs to identify a network map linking input modules to output modules. Five modules were identified within inputs and outputs with many maladaptive outputs grouping together in one module: Bax, C/EBPß, Serca2a, TNFα, and CTGF. Subsequently, we identified mechanisms underlying two correlations revealed in the agonist screen: correlation between regulators of fibrosis and cell death signaling (CTGF and Bax mRNA) caused by AngII; and myocyte proliferation (CITED4 mRNA) and elongation caused by Nrg1. Follow-up experiments revealed positive regulation of Bax mRNA level by CTGF and an incoherent feedforward loop linking Nrg1, CITED4 and elongation. With this agonist screen, we identified the most influential inputs in the cardiac hypertrophy signaling network for a variety of features related to pathological and protective hypertrophy signaling and shared regulation among cardiac myocyte phenotypes.

Network reconstruction and systems analysis of cardiac myocyte hypertrophy signaling.

Cardiac hypertrophy is managed by a dense web of signaling pathways with many pathways influencing myocyte growth. A quantitative understanding of the contributions of individual pathways and their interactions is needed to better understand hypertrophy signaling and to develop more effective therapies for heart failure. We developed a computational model of the cardiac myocyte hypertrophy signaling network to determine how the components and network topology lead to differential regulation of transcription factors, gene expression, and myocyte size. Our computational model of the hypertrophy signaling network contains 106 species and 193 reactions, integrating 14 established pathways regulating cardiac myocyte growth. 109 of 114 model predictions were validated using published experimental data testing the effects of receptor activation on transcription factors and myocyte phenotypic outputs. Network motif analysis revealed an enrichment of bifan and biparallel cross-talk motifs. Sensitivity analysis was used to inform clustering of the network into modules and to identify species with the greatest effects on cell growth. Many species influenced hypertrophy, but only a few nodes had large positive or negative influences. Ras, a network hub, had the greatest effect on cell area and influenced more species than any other protein in the network. We validated this model prediction in cultured cardiac myocytes. With this integrative computational model, we identified the most influential species in the cardiac hypertrophy signaling network and demonstrate how different levels of network organization affect myocyte size, transcription factors, and gene expression.

Automated imaging reveals a concentration dependent delay in reversibility of cardiac myocyte hypertrophy.

Cardiac hypertrophy is controlled by a dense signaling network with many pathways associated with cardiac myocyte growth. New large scale methodology is required to quantitatively characterize the pathways that distinguish reversible forms of hypertrophy from irreversible forms that lead to heart failure. Our automated image acquisition method records 5×5 mosaic images of fluorescent protein-labeled cardiac myocytes within each well of a 96-well plate using an automated stage and focus. Post-processing algorithms automatically identify cell edges, quantify cell phenotypes, and track cells. We uniquely applied our imaging platform to study hypertrophy reversibility in a scalable cell model. Cell area changes after washout of a dose response to the α-adrenergic receptor (αAR) agonist phenylephrine (PE) showed that hypertrophy reverses at low but not high levels of α-adrenergic signaling: a reversibility delay. Perturbations with specialized αAR antagonists, a mathematical model, and live imaging of αAR localization identify the mechanism for this reversibility delay: ligand trapping with internalized PE acting on intracellular αAR's.

Automated image analysis identifies signaling pathways regulating distinct signatures of cardiac myocyte hypertrophy.

Cardiac hypertrophy is controlled by a complex signal transduction and gene regulatory network, containing multiple layers of crosstalk and feedback. While numerous individual components of this network have been identified, understanding how these elements are coordinated to regulate heart growth remains a challenge. Past approaches to measure cardiac myocyte hypertrophy have been manual and often qualitative, hindering the ability to systematically characterize the network's higher-order control structure and identify therapeutic targets. Here, we develop and validate an automated image analysis approach for objectively quantifying multiple hypertrophic phenotypes from immunofluorescence images. This approach incorporates cardiac myocyte-specific optimizations and provides quantitative measures of myocyte size, elongation, circularity, sarcomeric organization, and cell-cell contact. As a proof-of-concept, we examined the hypertrophic response to α-adrenergic, ß-adrenergic, tumor necrosis factor (TNFα), insulin-like growth factor-1 (IGF-1), and fetal bovine serum pathways. While all five hypertrophic pathways increased myocyte size, other hypertrophic metrics were differentially regulated, forming a distinct phenotype signature for each pathway. Sarcomeric organization was uniquely enhanced by α-adrenergic signaling. TNFα and α-adrenergic pathways markedly decreased cell circularity due to increased myocyte protrusion. Surprisingly, adrenergic and IGF-1 pathways differentially regulated myocyte-myocyte contact, potentially forming a feed-forward loop that regulates hypertrophy. Automated image analysis unlocks a range of new quantitative phenotypic data, aiding dissection of the complex hypertrophic signaling network and enabling myocyte-based high-content drug screening.