The new era of quantitative cell imaging-challenges and opportunities.

Quantitative optical microscopy-an emerging, transformative approach to single-cell biology-has seen dramatic methodological advancements over the past few years. However, its impact has been hampered by challenges in the areas of data generation, management, and analysis. Here we outline these technical and cultural challenges and provide our perspective on the trajectory of this field, ushering in a new era of quantitative, data-driven microscopy. We also contrast it to the three decades of enormous advances in the field of genomics that have significantly enhanced the reproducibility and wider adoption of a plethora of genomic approaches.

Sample Preparation and Diagnostic Methods for a Variety of Settings: A Comprehensive Review.

Sample preparation is an essential step for nearly every type of biochemical analysis in use today. Among the most important of these analyses is the diagnosis of diseases, since their treatment may rely greatly on time and, in the case of infectious diseases, containing their spread within a population to prevent outbreaks. To address this, many different methods have been developed for use in the wide variety of settings for which they are needed. In this work, we have reviewed the literature and report on a broad range of methods that have been developed in recent years and their applications to point-of-care (POC), high-throughput screening, and low-resource and traditional clinical settings for diagnosis, including some of those that were developed in response to the coronavirus disease 2019 (COVID-19) pandemic. In addition to covering alternative approaches and improvements to traditional sample preparation techniques such as extractions and separations, techniques that have been developed with focuses on integration with smart devices, laboratory automation, and biosensors are also discussed.

Therapeutic approaches for SARS-CoV-2 infection.

Therapeutic approaches to COVID-19 treatment require appropriate inhibitors to target crucial proteins of SARS-CoV-2 replication machinery. It's been approximately 12 months since the pandemic started, yet no known specific drugs are available. However, research progresses with time in terms of high throughput virtual screening (HTVS) and rational design of repurposed, novel synthetic and natural products discovery by understanding the viral life cycle, immuno-pathological and clinical outcomes in patients based on host's nutritional, metabolic, and lifestyle status. Further, complementary and alternative medicine (CAM) approaches have also improved resiliency and immune responses. In this article, we summarize all the therapeutic antiviral strategies for COVID-19 drug discovery including computer aided virtual screening, repurposed drugs, immunomodulators, vaccines, plasma therapy, various adjunct therapies, and phage technology to unravel insightful mechanistic pathways of targeting SARS-CoV-2 and host's intrinsic, innate immunity at multiple checkpoints that aid in the containment of the disease.

High-throughput screening assays for SARS-CoV-2 drug development: Current status and future directions.

In response to the ongoing coronavirus disease 2019 (COVID-19) pandemic, a panel of assays has been developed and applied to screen collections of approved and investigational drugs for anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) activity in a quantitative high-throughput screening (qHTS) format. In this review, we applied data-driven approaches to evaluate the ability of each assay to identify potential anti-SARS-CoV-2 leads. Multitarget assays were found to show advantages in terms of accuracy and efficiency over single-target assays, whereas target-specific assays were more suitable for investigating compound mechanisms of action. Moreover, strict filtering with counter screens might be more detrimental than beneficial in identifying true positives. Thus, developing novel HTS assays acting simultaneously against multiple targets in the SARS-CoV-2 life cycle will benefit anti-COVID-19 drug discovery.

Evaluating and evolving a screening library in academia: the St Jude approach.

The quality of lead compounds is a key factor for determining the success of chemical probe and drug discovery programs. Given that high-throughput screening (HTS) continues to be a dominant lead generation paradigm, access to high-quality screening libraries is crucial for such efforts in both industry and academia. Here, we discuss the strategy implemented a decade ago to build from scratch one of the largest compound collections in academia, containing ∼575 000 carefully annotated small molecules, and a recent multidisciplinary effort designed to further enhance the collection to meet our research demands for the next decade.

High-Throughput Mass Spectrometry for Hit Identification: Current Landscape and Future Perspectives.

For nearly two decades mass spectrometry has been used as a label-free, direct-detection method for both functional and affinity-based screening of a wide range of therapeutically relevant target classes. Here, we present an overview of several established and emerging mass spectrometry platforms and summarize the unique strengths and performance characteristics of each as they apply to high-throughput screening. Multiple examples from the recent literature are highlighted in order to illustrate the power of each individual technique, with special emphasis given to cases where the use of mass spectrometry was found to be differentiating when compared with other detection formats. Indeed, as many of these examples will demonstrate, the inherent strengths of mass spectrometry-sensitivity, specificity, wide dynamic range, and amenability to complex matrices-can be leveraged to enhance the discriminating power and physiological relevance of assays included in screening cascades. It is our hope that this review will serve as a useful guide to readers of all backgrounds and experience levels on the applicability and benefits of mass spectrometry in the search for hits, leads, and, ultimately, drugs.

Prominent and emerging anti-diabetic molecular targets.

Diabetes is on the rise across the globe affecting more than 463 million people and crucially increasing morbidities of diabetes-associated diseases. Urgent and immense actions are needed to improve diabetes prevention and treatment. Regarding the correlation of diabetes with many associated diseases, inhibition of the disease progression is more crucial than controlling symptoms. Currently, anti-diabetic drugs are accompanied by undesirable side-effects and target confined types of biomolecules. Thus, extensive research is demanding to identify novel disease mechanisms and molecular targets as probable candidates for effective treatment of diabetes. This review discusses the conventional molecule targets that have been applied for their therapeutic rationale in treatment of diabetes. Further, the emerging and prospective molecular targets for the future focus of library screenings are presented.

Image-based profiling for drug discovery: due for a machine-learning upgrade?

Image-based profiling is a maturing strategy by which the rich information present in biological images is reduced to a multidimensional profile, a collection of extracted image-based features. These profiles can be mined for relevant patterns, revealing unexpected biological activity that is useful for many steps in the drug discovery process. Such applications include identifying disease-associated screenable phenotypes, understanding disease mechanisms and predicting a drug's activity, toxicity or mechanism of action. Several of these applications have been recently validated and have moved into production mode within academia and the pharmaceutical industry. Some of these have yielded disappointing results in practice but are now of renewed interest due to improved machine-learning strategies that better leverage image-based information. Although challenges remain, novel computational technologies such as deep learning and single-cell methods that better capture the biological information in images hold promise for accelerating drug discovery.

Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy.

Advances in molecular biology, microfluidics and bioinformatics have empowered the study of thousands or even millions of individual cells from malignant tumours at the single-cell level of resolution. This high-dimensional, multi-faceted characterization of the genomic, transcriptomic, epigenomic and proteomic features of the tumour and/or the associated immune and stromal cells enables the dissection of tumour heterogeneity, the complex interactions between tumour cells and their microenvironment, and the details of the evolutionary trajectory of each tumour. Single-cell transcriptomics, the ability to track individual T cell clones through paired sequencing of the T cell receptor genes and high-dimensional single-cell spatial analysis are all areas of particular relevance to immuno-oncology. Multidimensional biomarker signatures will increasingly be crucial to guiding clinical decision-making in each patient with cancer. High-dimensional single-cell technologies are likely to provide the resolution and richness of data required to generate such clinically relevant signatures in immuno-oncology. In this Perspective, we describe advances made using transformative single-cell analysis technologies, especially in relation to clinical response and resistance to immunotherapy, and discuss the growing utility of single-cell approaches for answering important research questions.

CRISPR Meets Zebrafish: Accelerating the Discovery of New Therapeutic Targets.

Bringing a new drug to the market costs an average of US$2.6 billion and takes more than 10 years from discovery to regulatory approval. Despite the need to reduce cost and time to increase productivity, pharma companies tend to crowd their efforts in the same indications and drug targets. This results in the commercialization of drugs that share the same mechanism of action (MoA) and, in many cases, equivalent efficacies among them-an outcome that helps neither patients nor the balance sheet of the companies trying to bring therapeutics to the same patient population. Indeed, the discovery of new therapeutic targets, based on a deeper understanding of the disease biology, would likely provide more innovative MoAs and potentially greater drug efficacies. It would also bring better chances for identifying appropriate treatments according to the patient's genetic stratification. Nowadays, we count with an enormous amount of unprocessed information on potential disease targets that could be extracted from omics data obtained from patient samples. In addition, hundreds of pharmacological and genetic screenings have been performed to identify innovative drug targets. Traditionally, rodents have been the animal models of choice to perform functional genomic studies. The high experimental cost, combined with the low throughput provided by those models, however, is a bottleneck for discovering and validating novel genetic disease associations. To overcome these limitations, we propose that zebrafish, in conjunction with the use of CRISPR/Cas9 genome-editing tools, could streamline functional genomic processes to bring biologically relevant knowledge on innovative disease targets in a shorter time frame.

Delivering Robust Candidates to the Drug Pipeline through Computational Analysis of Arrayed CRISPR Screens.

Genome-wide arrayed CRISPR screening is a powerful method for drug target identification as it enables exploration of the effect of individual gene perturbations using diverse highly multiplexed functional and phenotypic assays. Using high-content imaging, we can measure changes in biomarker expression, intracellular localization, and cell morphology. Here we present the computational pipeline we have developed to support the analysis and interpretation of arrayed CRISPR screens. This includes evaluating the quality of guide RNA libraries, performing image analysis, evaluating assay results quality, data processing, hit identification, ranking, visualization, and biological interpretation.

Molecular subtyping in pancreatic neuroendocrine neoplasms: New insights into clinical, pathological unmet needs and challenges.

Pancreatic neuroendocrine neoplasms (PanNENs) contain two primary subtypes with distinct molecular features and associated clinical outcomes: well-differentiated neuroendocrine tumors (NETs) and poorly differentiated neuroendocrine carcinomas (NECs). PanNENs are a group of clinically heterogeneous tumors, whose diagnosis is based on tumor morphologic features and proliferation indices. However, these standards incompletely meet clinical needs by failing to adequately assess the likelihood of tumor recurrence and the potential for therapeutic response. We therefore focused on discussing molecular advances that facilitate the understanding of heterogeneity and exploration of reliable recurrence/treatment predictors. Taking advantage of high-throughput technologies, emerging methods of molecular subtyping in PanNETs include classifications based on co-existing multi-gene mutations, a large-scale loss of heterozygosity or copy number variation, and islet cell type-specific signatures. PanNEC molecular updates were discussed as well. This review aims to help the field classify PanNEN molecular subtypes, gain insights to aid in the solving of clinical, pathological unmet needs, and detect challenges and concerns of genetically-driven trials.

Innovative and rapid antimicrobial susceptibility testing systems.

Antimicrobial resistance (AMR) is a major threat to human health worldwide, and the rapid detection and quantification of resistance, combined with antimicrobial stewardship, are key interventions to combat the spread and emergence of AMR. Antimicrobial susceptibility testing (AST) systems are the collective set of diagnostic processes that facilitate the phenotypic and genotypic assessment of AMR and antibiotic susceptibility. Over the past 30 years, only a few high-throughput AST methods have been developed and widely implemented. By contrast, several studies have established proof of principle for various innovative AST methods, including both molecular-based and genome-based methods, which await clinical trials and regulatory review. In this Review, we discuss the current state of AST systems in the broadest technical, translational and implementation-related scope.

Enteric Virome and Carcinogenesis in the Gut.

Colorectal cancer (CRC) is a leading cause of cancer-related deaths in both the USA and the world. Recent research has demonstrated the involvement of the gut microbiota in CRC development and progression. Microbial biomarkers of disease have focused primarily on the bacterial component of the microbiome; however, the viral portion of the microbiome, consisting of both bacteriophages and eukaryotic viruses, together known as the virome, has been lesser studied. Here we review the recent advancements in high-throughput sequencing (HTS) technologies and bioinformatics, which have enabled scientists to better understand how viruses might influence the development of colorectal cancer. We discuss the contemporary findings revealing modulations in the virome and their correlation with CRC development and progression. While a variety of challenges still face viral HTS detection in clinical specimens, we consider herein numerous next steps for future basic and clinical research. Clinicians need to move away from a single infectious agent model for disease etiology by grasping new, more encompassing etiological paradigms, in which communities of various microbial components interact with each other and the host. The reporting and indexing of patient health information, socioeconomic data, and other relevant metadata will enable identification of predictive variables and covariates of viral presence and CRC development. Altogether, the virome has a more profound role in carcinogenesis and cancer progression than once thought, and viruses, specific for either human cells or bacteria, are clinically relevant in understanding CRC pathology, patient prognosis, and treatment development.

Fragments: where are we now?

Fragment-based drug discovery (FBDD) has become a mainstream technology for the identification of chemical hit matter in drug discovery programs. To date, the food and drug administration has approved four drugs, and over forty compounds are in clinical studies that can trace their origins to a fragment-based screen. The challenges associated with implementing an FBDD approach are many and diverse, ranging from the library design to developing methods for identifying weak affinity compounds. In this article, we give an overview of current progress in fragment library design, fragment to lead optimisation and on the advancement in techniques used for screening. Finally, we will comment on the future opportunities and challenges in this field.

High throughput approaches to study RNA-protein interactions in vitro.

To understand the regulation of gene expression it is critical to determine how proteins interact with and discriminate between different RNAs. In this review, we discuss experimental techniques that utilize high throughput approaches to characterize the interactions of proteins with large numbers of RNAs in vitro. We describe the underlying principles for the main methods, briefly discuss their scope and limitations, and outline how insight from the techniques contributes to our understanding of specificity for RNA-protein interactions.

Opportunities and Challenges in Phenotypic Screening for Neurodegenerative Disease Research.

Toxic misfolded proteins potentially underly many neurodegenerative diseases, but individual targets which regulate these proteins and their downstream detrimental effects are often unknown. Phenotypic screening is an unbiased method to screen for novel targets and therapeutic molecules and span the range from primitive model organisms such as Sacchaomyces cerevisiae, which allow for high-throughput screening to patient-derived cell-lines that have a close connection to the disease biology but are limited in screening capacity. This perspective will review current phenotypic models, as well as the chemical screening strategies most often employed. Advances in in 3D cell cultures, high-content screens, robotic microscopy, CRISPR screening, and use of machine learning methods to process the enormous amount of data generated by these screens are certain to change the paradigm for phenotypic screening and will be discussed.