Cell-based fuzzy metrics enhance high-content screening (HCS) assay robustness

T-Probe: An Integrated Microscale Device for Online In Situ Single Cell Analysis and Metabolic Profiling Using Mass Spectrometry

The exploration of single cells reveals cell heterogeneity and biological principle of cellular metabolism. Although a number of mass spectrometry (MS) based single cell MS (SCMS) techniques have been dedicatedly developed with high efficiency and sensitivity, limitations still exist. In this work, we introduced a microscale multifunctional device, the T-probe, which integrates cellular contents extraction and immediate ionization, to implement online in situ SCMS analysis at ambient conditions with minimal sample preparation. With high sensitivity and reproducibility, the T-probe was employed for MS analysis of single HeLa cells under control and anticancer drug treatment conditions. Intracellular species and xenobiotic metabolites were detected, and changes of cellular metabolic profiles induced by drug treatment were measured. Combining SCMS experiments with statistical data analyses, including Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) and two-sample t-test, we provided biological insights into cellular metabolic response to drug treatment. Online MS/MS analysis was conducted at single cell level to identify species of interest, including endogenous metabolites and the drug compound. Using the T-probe SCMS technique combined with comprehensive data analyses, we provide an approach to understanding cellular metabolism and evaluate chemotherapies at the single cell level.

Tissue Phenomics for prognostic biomarker discovery in low- and intermediate-risk prostate cancer

Tissue Phenomics is the discipline of mining tissue images to identify patterns that are related to clinical outcome providing potential prognostic and predictive value. This involves the discovery process from assay development, image analysis, and data mining to the final interpretation and validation of the findings. Importantly, this process is not linear but allows backward steps and optimization loops over multiple sub-processes. We provide a detailed description of the Tissue Phenomics methodology while exemplifying each step on the application of prostate cancer recurrence prediction. In particular, we automatically identified tissue-based biomarkers having significant prognostic value for low- and intermediate-risk prostate cancer patients (Gleason scores 6–7b) after radical prostatectomy. We found that promising phenes were related to CD8(+) and CD68(+) cells in the microenvironment of cancerous glands in combination with the local micro-vascularization. Recurrence prediction based on the selected phenes yielded accuracies up to 83% thereby clearly outperforming prediction based on the Gleason score. Moreover, we compared different machine learning algorithms to combine the most relevant phenes resulting in increased accuracies of 88% for tumor progression prediction. These findings will be of potential use for future prognostic tests for prostate cancer patients and provide a proof-of-principle of the Tissue Phenomics approach.

ITGB1-dependent upregulation of Caveolin-1 switches TGFβ signalling from tumour-suppressive to oncogenic in prostate cancer

Caveolin-1 (CAV1) is over-expressed in prostate cancer (PCa) and is associated with adverse prognosis, but the molecular mechanisms linking CAV1 expression to disease progression are poorly understood. Extensive gene expression correlation analysis, quantitative multiplex imaging of clinical samples, and analysis of the CAV1-dependent transcriptome, supported that CAV1 re-programmes TGFβ signalling from tumour suppressive to oncogenic (i.e. induction of SLUG, PAI-1 and suppression of CDH1, DSP, CDKN1A). Supporting such a role, CAV1 knockdown led to growth arrest and inhibition of cell invasion in prostate cancer cell lines. Rationalized RNAi screening and high-content microscopy in search for CAV1 upstream regulators revealed integrin beta1 (ITGB1) and integrin associated proteins as CAV1 regulators. Our work suggests TGFβ signalling and beta1 integrins as potential therapeutic targets in PCa over-expressing CAV1, and contributes to better understand the paradoxical dual role of TGFβ in tumour biology.

A novel high content analysis tool reveals Rab8-driven actin and FA reorganization through Rho GTPases and calpain/MT1

Rab8 is a small Ras-related GTPase that regulates polarized membrane transport to the plasma membrane. A high content analysis (HCA) tool developed to dissect Rab8-mediated actin and focal adhesion (FA) reorganization revealed that Rab8 activation significantly induced Rac1/Tiam1 to mediate cortical actin (CA) formation and RhoA-dependent stress fibre (SF) disassembly. Rab8 activation increased Rac1 activity, while its depletion activated RhoA, which led to reorganization of the actin cytoskeleton. Rab8 was also associated with FA, promoting their disassembly in a microtubule dependent manner. This Rab8 effect involved Calpain, MT1-MMP and Rho GTPases. Moreover, we demonstrate the role of Rab8 in the cell migration process. Indeed, Rab8 is required for EGF-induced cell polarization and chemotaxis as well as for the directional persistency of intrinsic cell motility. These data reveal that Rab8 drives cell motility by mechanisms both dependent and independent of Rho GTPases, thereby regulating the establishment of cell polarity, turnover of FA, and actin cytoskeleton rearrangements, thus determining the directionality of cell migration.

Flow Cytometry: Impact on Early Drug Discovery

Modern flow cytometers can make optical measurements of 10 or more parameters per cell at tens of thousands of cells per second and more than five orders of magnitude dynamic range. Although flow cytometry is used in most drug discovery stages, "sip-and-spit" sampling technology has restricted it to low-sample-throughput applications. The advent of HyperCyt sampling technology has recently made possible primary screening applications in which tens of thousands of compounds are analyzed per day. Target-multiplexing methodologies in combination with extended multiparameter analyses enable profiling of lead candidates early in the discovery process, when the greatest numbers of candidates are available for evaluation. The ability to sample small volumes with negligible waste reduces reagent costs, compound usage, and consumption of cells. Improved compound library formatting strategies can further extend primary screening opportunities when samples are scarce. Dozens of targets have been screened in 384- and 1536-well assay formats, predominantly in academic screening lab settings. In concert with commercial platform evolution and trending drug discovery strategies, HyperCyt-based systems are now finding their way into mainstream screening labs. Recent advances in flow-based imaging, mass spectrometry, and parallel sample processing promise dramatically expanded single-cell profiling capabilities to bolster systems-level approaches to drug discovery.

Designing drugs that overcome antibacterial resistance: where do we stand and what should we do?

INTRODUCTION

In recent years, infections caused by multidrug-resistant bacterial pathogens have become a huge issue to public healthcare systems. Indeed, the misuse of antibiotics has led to, over the past 30 years, the emergence of a number of resistant bacterial strains including Staphylococcus aureus, Neisseria gonorrhoeae, Escherichia coli and Mycobacterium tuberculosis. Unfortunately, efforts to produce new antibiotics have not been sufficient to cope with the emergence of these new antibiotic-resistant (AR) strains.

AREAS COVERED

There is an urgent need to invent and employ unconventional strategies for antimicrobial drug development to tackle the rising global threats imposed by the spread of antimicrobial resistance. Herein, the authors discuss these novel design strategies and provide their expert perspective on the subject.

EXPERT OPINION

To deal with the growing threat of AR, it is important to cut down the use of antibiotics to the very minimum to diminish the risk of unknown drug-resistant bacteria and increase antibacterial vaccination programs. Furthermore, it is important to develop new classes of antibiotics that can deal with multidrug-resistant bacterial pathogens.

Identifying and quantifying heterogeneity in high content analysis: application of heterogeneity indices to drug discovery

One of the greatest challenges in biomedical research, drug discovery and diagnostics is understanding how seemingly identical cells can respond differently to perturbagens including drugs for disease treatment. Although heterogeneity has become an accepted characteristic of a population of cells, in drug discovery it is not routinely evaluated or reported. The standard practice for cell-based, high content assays has been to assume a normal distribution and to report a well-to-well average value with a standard deviation. To address this important issue we sought to define a method that could be readily implemented to identify, quantify and characterize heterogeneity in cellular and small organism assays to guide decisions during drug discovery and experimental cell/tissue profiling. Our study revealed that heterogeneity can be effectively identified and quantified with three indices that indicate diversity, non-normality and percent outliers. The indices were evaluated using the induction and inhibition of STAT3 activation in five cell lines where the systems response including sample preparation and instrument performance were well characterized and controlled. These heterogeneity indices provide a standardized method that can easily be integrated into small and large scale screening or profiling projects to guide interpretation of the biology, as well as the development of therapeutics and diagnostics. Understanding the heterogeneity in the response to perturbagens will become a critical factor in designing strategies for the development of therapeutics including targeted polypharmacology.

Neoclassic drug discovery: the case for lead generation using phenotypic and functional approaches

Innovation and new molecular entity production by the pharmaceutical industry has been below expectations. Surprisingly, more first-in-class small-molecule drugs approved by the U.S. Food and Drug Administration (FDA) between 1999 and 2008 were identified by functional phenotypic lead generation strategies reminiscent of pre-genomics pharmacology than contemporary molecular targeted strategies that encompass the vast majority of lead generation efforts. This observation, in conjunction with the difficulty in validating molecular targets for drug discovery, has diminished the impact of the "genomics revolution" and has led to a growing grassroots movement and now broader trend in pharma to reconsider the use of modern physiology-based or phenotypic drug discovery (PDD) strategies. This "From the Guest Editors" column provides an introduction and overview of the two-part special issues of Journal of Biomolecular Screening on PDD. Terminology and the business case for use of PDD are defined. Key issues such as assay performance, chemical optimization, target identification, and challenges to the organization and implementation of PDD are discussed. Possible solutions for these challenges and a new neoclassic vision for PDD that combines phenotypic and functional approaches with technology innovations resulting from the genomics-driven era of target-based drug discovery (TDD) are also described. Finally, an overview of the manuscripts in this special edition is provided.