High-content Datasets Research Articles

LiveCellMiner: A new tool to analyze mitotic progression.

Live-cell imaging has become state of the art to accurately identify the nature of mitotic and cell cycle defects. Low- and high-throughput microscopy setups have yield huge data amounts of cells recorded in different experimental and pathological conditions. Tailored semi-automated and automated image analysis approaches allow the analysis of high-content screening data sets, saving time and avoiding bias. However, they were mostly designed for very specific experimental setups, which restricts their flexibility and usability. The general need for dedicated experiment-specific user-annotated training sets and experiment-specific user-defined segmentation parameters remains a major bottleneck for fully automating the analysis process. In this work we present LiveCellMiner, a highly flexible open-source software tool to automatically extract, analyze and visualize both aggregated and time-resolved image features with potential biological relevance. The software tool allows analysis across high-content data sets obtained in different platforms, in a quantitative and unbiased manner. As proof of principle application, we analyze here the dynamic chromatin and tubulin cytoskeleton features in human cells passing through mitosis highlighting the versatile and flexible potential of this tool set.

A Data-Driven Lens to Understand Human Biology: An Interview with Daphne Koller

A Data-Driven Lens to Understand Human Biology: An Interview with Daphne Koller

Druggable genome and precision medicine in cancer: current challenges.

The past decades have seen tremendous developments with respect to "specific" therapeutics that target key signaling molecules to conquer cancer. The key advancements with multiomics technologies, especially genomics, have allowed physicians and molecular oncologists to design "tailor-made" solutions to the specific oncogenes that are deregulated in individual patients, a strategy which has turned out to be successful though the patients quickly develop resistance. The swift integration of multidisciplinary approaches has led to the development of "next generation" therapeutics and, with synergistic therapeutic regimes combined with immune checkpoint inhibitors to reactivate the dampened immune response, has provided the much-needed promise for cancer patients. Despite these advances, a large portion of the druggable genome remains understudied, and the role of druggable genome in the immune system needs further attention. Establishment of patient-derived organoid models has fastened the preclinical validation of novel therapeutics for swift clinical translation. We summarized the current advances and challenges and also stress the importance of biobanking and collection of longitudinal data sets with structured clinical information, as well as the critical role these "high content data sets" will play in designing new therapeutic regimes in a tailor-made fashion.

Systems analysis of benign bladder disorders: insights from omics analysis.

The signaling pathways and effectors that drive the response of the bladder to nonmalignant insults or injury are incompletely defined. Interrogation of biological systems has been revolutionized by the ability to generate high-content data sets that capture information on a variety of biomolecules in cells and tissues, from DNA to RNA to proteins. In oncology, such an approach has led to the identification of cancer subtypes, improved prognostic capability, and has provided a basis for precision treatment of patients. In contrast, systematic molecular characterization of benign bladder disorders has lagged behind, such that our ability to uncover novel therapeutic interventions or increase our mechanistic understanding of such conditions is limited. Here, we discuss existing literature on the application of omics approaches, including transcriptomics and proteomics, to urinary tract conditions characterized by pathological tissue remodeling. We discuss molecular pathways implicated in remodeling, challenges in the field, and aspirations for omics-based research in the future.

CytoNorm: A Normalization Algorithm for Cytometry Data.

High‐dimensional flow cytometry has matured to a level that enables deep phenotyping of cellular systems at a clinical scale. The resulting high‐content data sets allow characterizing the human immune system at unprecedented single cell resolution. However, the results are highly dependent on sample preparation and measurements might drift over time. While various controls exist for assessment and improvement of data quality in a single sample, the challenges of cross‐sample normalization attempts have been limited to aligning marker distributions across subjects. These approaches, inspired by bulk genomics and proteomics assays, ignore the single‐cell nature of the data and risk the removal of biologically relevant signals. This work proposes CytoNorm, a normalization algorithm to ensure internal consistency between clinical samples based on shared controls across various study batches. Data from the shared controls is used to learn the appropriate transformations for each batch (e.g., each analysis day). Importantly, some sources of technical variation are strongly influenced by the amount of protein expressed on specific cell types, requiring several population‐specific transformations to normalize cells from a heterogeneous sample. To address this, our approach first identifies the overall cellular distribution using a clustering step, and calculates subset‐specific transformations on the control samples by computing their quantile distributions and aligning them with splines. These transformations are then applied to all other clinical samples in the batch to remove the batch‐specific variations. We evaluated the algorithm on a customized data set with two shared controls across batches. One control sample was used for calculation of the normalization transformations and the second control was used as a blinded test set and evaluated with Earth Mover's distance. Additional results are provided using two real‐world clinical data sets. Overall, our method compared favorably to standard normalization procedures. The algorithm is implemented in the R package “CytoNorm” and available via the following link: http://www.github.com/saeyslab/CytoNorm © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Transcriptomic profiles of tissues from rats treated with anticancer drug combinations

To achieve therapeutic goals, many cancer chemotherapeutics are used at doses close to their maximally tolerated doses. Thus, it may be expected that when therapies are combined at therapeutic doses, toxicity profiles may change. In many ways, prediction of synergistic toxicities for drug combinations is similar to predicting synergistic efficacy, and is dependent upon building hypotheses from molecular mechanisms of drug toxicity. The key objective of this initiative was to generate and make publicly available key high-content data sets for mechanistic hypothesis generation as it pertains to a unique toxicity profile of a drug pair for several anticancer drug combinations. The expectation is that tissue-based genomic information that are derived from target tissues will also facilitate the generation and testing of mechanistic hypotheses. The view is that availability of these data sets for bioinformaticians and other scientists will contribute to analysis of these data and evaluation of the approach.

Informatics-Based Discovery of Disease-Associated Immune Profiles.

Advances in flow and mass cytometry are enabling ultra-high resolution immune profiling in mice and humans on an unprecedented scale. However, the resulting high-content datasets challenge traditional views of cytometry data, which are both limited in scope and biased by pre-existing hypotheses. Computational solutions are now emerging (e.g., Citrus, AutoGate, SPADE) that automate cell gating or enable visualization of relative subset abundance within healthy versus diseased mice or humans. Yet these tools require significant computational fluency and fail to show quantitative relationships between discrete immune phenotypes and continuous disease variables. Here we describe a simple informatics platform that uses hierarchical clustering and nearest neighbor algorithms to associate manually gated immune phenotypes with clinical or pre-clinical disease endpoints of interest in a rapid and unbiased manner. Using this approach, we identify discrete immune profiles that correspond with either weight loss or histologic colitis in a T cell transfer model of inflammatory bowel disease (IBD), and show distinct nodes of immune dysregulation in the IBDs, Crohn’s disease and ulcerative colitis. This streamlined informatics approach for cytometry data analysis leverages publicly available software, can be applied to manually or computationally gated cytometry data, is suitable for any clinical or pre-clinical setting, and embraces ultra-high content flow and mass cytometry as a discovery engine.

From "N of 1" to N of more.

The current era of biomedical research finds us surrounded by massive amounts of molecular data—particularly genomic data—both from populations and from individual patients. High-throughput methods to characterize the genome, transcriptome, proteome, and metabolome, combined with innovative approaches that define protein–DNA interactions, epigenetic modifications of DNA and histones, allele specificity, and noncoding RNA expression are producing data at a dizzying pace. The ability to generate genomic data quickly and affordably dominates the motivation toward personalized therapies—taking what we have learned from discovery-based efforts and applying it to improve individual patient care and outcomes. Precision medicine initiatives now endorsed by the White House seek to develop cohorts and deploy the associated “big data” to improve understanding of complex diseases and stimulate new diagnostic tests and treatments. Along with these large-scale discovery efforts, researchers and clinicians already are using high-content data sets to diagnose and treat individual patients. In this translational paradigm, conventional medical evidence is combined with high-content data from a single patient, leading to actionable, even therapeutic, insights within days. Although each anecdote is initially patient-specific, it is increasingly common to observe situations where data from one patient could inform the care of other individuals with molecular and clinical similarities. It is now a major challenge and an opportunity to develop effective ways to disseminate new data and conclusions about potentially informative patients to clinicians and geneticists worldwide. Indeed, this type of communication is one of the goals of Cold Spring Harbor Molecular Case Studies. If our communications are successful, desperate parents of children with undiagnosed conditions will no longer need to embark on diagnostic odysseys like Matt and Cristina Might did (“Hunting down my son's killer,” http://matt.might.net/articles/my-sons-killer/), physicians will be alerted to extraordinary clinical presentations and responses, and basic scientists will be given leads for translational research. In doing so, “N of 1” becomes N of many. As individuals aggregate into cohorts, pathophysiology and therapeutic opportunities should come into sharper focus. Effectively, the journal aims to profile precision medicine in action. We project that our content will include not only descriptive case studies but also a forum to add information pursuant to the initial description to report the success or failure of therapeutic interventions. In this regard, we encourage the reporting of information even if the result is “negative” relative to expectation—this is still critically important to share. We consider the manuscripts from David Goldstein's laboratory, both of which are published in this inaugural issue, to be exemplary of the Research Report and Follow-Up Report formats. Our desired breadth spans many medical disciplines—not just oncology and medical genetics but the total spectrum of precision medicine and its associated measurements. Our rapid publication of accepted manuscripts will ensure timely communication of case reports and full-length research articles. Position Statements and Commentaries will highlight emerging applications, challenges, and paradigm shifts in the precision medicine space. We invite you to explore this inaugural issue, which we feel highlights our goals for the journal and the potential impact of rapidly reporting disease-relevant high-content data sets. We encourage the submission of manuscripts featuring high-content data sets from individual patients, families, and larger cohorts to further the dialog and discourse on these topics and to ensure that pertinent information about precision medicine efforts is widely disseminated in the biomedical research and clinical practice communities. Competing Interest Statement The authors have declared no competing interest.

Abstract 4710: Evaluation of the glutaminase inhibitor CB-839 in non-small cell lung cancer

Abstract Glutamine is an abundant nutrient used by cancer cells to support energetic and biosynthetic pathways. Given that many cancer cells exhibit glutamine dependence, there has been interest in developing inhibitors targeting glutaminase, which catalyzes conversion of glutamine to glutamate. CB-839 is a glutaminase inhibitor with preclinical efficacy in suppressing cancer cell growth in culture and in vivo. It is now in Phase I clinical trials for solid and hematological malignancies, including non-small cell lung cancer (NSCLC). Here we set out to identify biomarkers predicting sensitivity of NSCLC cells to CB-839, using a highly annotated panel of 81 cell lines. These lines had heterogeneous oncogenotypes and displayed about a 5-fold range in their ability to tolerate glutamine deprivation. Assessing CB-839 sensitivity in ∼40 lines revealed a 5-fold difference in growth suppression among cell lines. There was a strong correlation between CB-839 sensitivity and both glutamine dependence and glutamate secretion. In a subset of 4 representative cell lines, CB-839 suppressed [U-13C]glutamine metabolism in both sensitive and insensitive cells suggesting other mechanisms contribute to CB-839 sensitivity. To identify potential biomarkers predicting CB-839 sensitivity, the glutamine dependence and drug sensitivity phenotypes were correlated with several orthogonal high-content data sets including mutational analysis of commonly-mutated oncogenes and tumor suppressors; sensitivity to chemotherapeutic agents; gene expression arrays; and reverse phase protein arrays (RPPA). Although we did not identify a strong correlation between mutational status and CB-839 sensitivity, expression of phospho-Rb (S807/811) and Smad3 did correlate with drug sensitivity. In addition, sensitivity to glutamine withdrawal correlated with expression of c-Myc and MEK2 protein. While further work is necessary to examine the importance of these proteins in glutamine metabolism, we hypothesize that one or more of these may represent novel biomarkers predicting sensitivity to glutaminase inhibition. Citation Format: Lindsey K. Boroughs, Pei-Hsuan Chen, Ling Cai, Mirna Rodriguez, Winter Zhang, Kenneth E. Huffman, Lauren A. Byers, Luc Girard, Adi F. Gazdar, John V. Heymach, Michael A. White, John D. Minna, Ethan Emberley, Alison Pan, Frank Parlati, Ralph J. DeBerardinis. Evaluation of the glutaminase inhibitor CB-839 in non-small cell lung cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4710. doi:10.1158/1538-7445.AM2015-4710

A Provisional Gene Regulatory Atlas for Mouse Heart Development

Congenital Heart Disease (CHD) is one of the most common birth defects. Elucidating the molecular mechanisms underlying normal cardiac development is an important step towards early identification of abnormalities during the developmental program and towards the creation of early intervention strategies. We developed a novel computational strategy for leveraging high-content data sets, including a large selection of microarray data associated with mouse cardiac development, mouse genome sequence, ChIP-seq data of selected mouse transcription factors and Y2H data of mouse protein-protein interactions, to infer the active transcriptional regulatory network of mouse cardiac development. We identified phase-specific expression activity for 765 overlapping gene co-expression modules that were defined for obtained cardiac lineage microarray data. For each co-expression module, we identified the phase of cardiac development where gene expression for that module was higher than other phases. Co-expression modules were found to be consistent with biological pathway knowledge in Wikipathways, and met expectations for enrichment of pathways involved in heart lineage development. Over 359,000 transcription factor-target relationships were inferred by analyzing the promoter sequences within each gene module for overrepresentation against the JASPAR database of Transcription Factor Binding Site (TFBS) motifs. The provisional regulatory network will provide a framework of studying the genetic basis of CHD.

Utilization of translational bioinformatics to identify novel biomarkers of bortezomib resistance in multiple myeloma.

Multiple myeloma (MM) is an incurable malignant neoplasm hallmarked by a clonal expansion of plasma cells, the presence of a monoclonal protein in the serum and/or urine (M-spike), lytic bone lesions, and end organ damage. Clinical outcomes for patients with MM have improved greatly over the last decade as a result of the re-purposing of compounds such as thalidomide derivatives, as well as the development of novel chemotherapeutic agents including first and second generation proteasome inhibitors, bortezomib (Bz) and carfilzomib. Unfortunately, despite these improvements, the majority of patients relapse following treatment. While Bz, one of the most commonly used proteasome inhibitors, has been successfully incorporated into clinical practice, some MM patients have de novo resistance to Bz, and the majority of the remainder subsequently develop drug resistance following treatment. A significant gap in clinical care is the lack of a reliable clinical test that would predict which MM patients have or will subsequently develop Bz resistance. Thus, as Bz resistance remains a significant challenge, research efforts are needed to identify novel biomarkers of early Bz resistance, particularly when an early therapeutic intervention can be initiated. Recent advances in MM research indicate that genomic data can be extracted to identify novel biomarkers that can be utilized to select more effective, personalized treatment protocols for individual patients. Computationally integrating large patient databases with data from whole transcriptome profiling and laboratory-based models can potentially revolutionize our understanding of MM disease mechanisms. This systems-wide approach can provide rational therapeutic targets and novel biomarkers of risk and treatment response. In this review, we discuss the use of high-content datasets (predominantly gene expression profiling) to identify novel biomarkers of treatment response and resistance to Bz in MM.

Analysis of Lipid Experiments (ALEX): A Software Framework for Analysis of High-Resolution Shotgun Lipidomics Data

Global lipidomics analysis across large sample sizes produces high-content datasets that require dedicated software tools supporting lipid identification and quantification, efficient data management and lipidome visualization. Here we present a novel software-based platform for streamlined data processing, management and visualization of shotgun lipidomics data acquired using high-resolution Orbitrap mass spectrometry. The platform features the ALEX framework designed for automated identification and export of lipid species intensity directly from proprietary mass spectral data files, and an auxiliary workflow using database exploration tools for integration of sample information, computation of lipid abundance and lipidome visualization. A key feature of the platform is the organization of lipidomics data in ”database table format” which provides the user with an unsurpassed flexibility for rapid lipidome navigation using selected features within the dataset. To demonstrate the efficacy of the platform, we present a comparative neurolipidomics study of cerebellum, hippocampus and somatosensory barrel cortex (S1BF) from wild-type and knockout mice devoid of the putative lipid phosphate phosphatase PRG-1 (plasticity related gene-1). The presented framework is generic, extendable to processing and integration of other lipidomic data structures, can be interfaced with post-processing protocols supporting statistical testing and multivariate analysis, and can serve as an avenue for disseminating lipidomics data within the scientific community. The ALEX software is available at www.msLipidomics.info.

ASSA13-01-10 Value of BNP, WBC, Creatinine, TNT and CK-MB to Predict Acute Myocardial Infarction Mortality During Hospitalisation in Patients Aged 80 and Over

<h3>Abstract</h3> Live-cell imaging has become state of the art to accurately identify the nature of mitotic and cell cycle defects. Low- and high-throughput microscopy setups have yield huge data amounts of cells recorded in different experimental and pathological conditions. Tailored semi-automated and automated image analysis approaches allow the analysis of high-content screening data sets, saving time and avoiding bias. However, they were mostly designed for very specific experimental setups, which restricts their flexibility and usability. The general need for dedicated experiment-specific user-annotated training sets and experiment-specific user-defined segmentation parameters remains a major bottleneck for fully automating the analysis process. In this work we present LiveCellMiner, a highly flexible open-source software tool to automatically extract, analyze and visualize both aggregated and time-resolved image features with potential biological relevance. The software tool allows analysis across high-content data sets obtained in different platforms, in a quantitative and unbiased manner. As proof of principle application, we analyze here the dynamic chromatin and tubulin cytoskeleton features in human cells passing through mitosis highlighting the versatile and flexible potential of this tool set.

Research Resource: dkCOIN, the National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK) Consortium Interconnectivity Network: A Pilot Program to Aggregate Research Resources Generated by Multiple Research Consortia

The National Institute of Diabetes, Digestive and Kidney Diseases (NIDDK) supports multiple basic science consortia that generate high-content datasets, reagent resources, and methodologies, in the fields of kidney, urology, hematology, digestive, and endocrine diseases, as well as metabolic diseases such as diabetes and obesity. These currently include the Beta Cell Biology Consortium, the Nuclear Receptor Signaling Atlas, the Diabetic Complications Consortium, and the Mouse Metabolic Phenotyping Centers. Recognizing the synergy that would accrue from aggregating information generated and curated by these initiatives in a contiguous informatics network, we created the NIDDK Consortium Interconnectivity Network (dkCOIN; www.dkcoin.org). The goal of this pilot project, organized by the NIDDK, was to establish a single point of access to a toolkit of interconnected resources (datasets, reagents, and protocols) generated from individual consortia that could be readily accessed by biologists of diverse backgrounds and research interests. During the pilot phase of this activity dkCOIN collected nearly 2000 consortium-curated resources, including datasets (functional genomics) and reagents (mouse strains, antibodies, and adenoviral constructs) and built nearly 3000 resource-to-resource connections, thereby demonstrating the feasibility of further extending this database in the future. Thus, dkCOIN promises to be a useful informatics solution for rapidly identifying useful resources generated by participating research consortia.

Spectronaut A fast and efficient algorithm for MRM-like processing of data independent acquisition (SWATH-MS) data

recently been shown that targeted analysis of data independently acquired (DIA) peptide fragment ion spectra (SWATH-MS) has the potential to combine the scale of shotgun proteomics with the analytical accuracy of MRM [1]. We have developed the software Spectronaut for analysis of high content datasets which are typically produced in DIA mode. Spectronaut adapts algorithms of mProphet [2] providing fast and efficient processing of DIA data and accurate error statistics.