Abstract
Laboratory Automation and High-Throughput Chemistry
High-Throughput Automation in Chemical Process Development
High-throughput (HT) techniques built on laboratory automation technology and coupled to statistical experimental design and parallel experimentation have enabled the acceleration of chemical process development across multiple industries. HT technologies are often applied to interrogate wide, often multidimensional, experimental spaces to inform the design and optimization of any number of unit operations that chemical engineers use in process development. In this review, Selekman et al. outline the evolution of HT technology and provide a comprehensive overview of how HT automation is used throughout different industries, with a particular focus on chemical and pharmaceutical process development. In addition, the authors highlight the common strategies of how HT automation is incorporated into routine development activities to maximize its impact in various academic and industrial settings. (Selekman, J. A.; et al. Annu. Rev. Chem. Biomol. Eng.
High-Throughput Imaging for the Discovery of Cellular Mechanisms of Disease
High-throughput imaging (HTI) is a powerful tool in the discovery of cellular disease mechanisms. While traditional approaches to identify disease pathways often rely on knowledge of the causative genetic defect, HTI-based screens offer an unbiased discovery approach based on any morphological or functional defects of disease cells or tissues. In this review, the authors provide an overview of the use of HTI for the study of human disease mechanisms. The authors discuss key technical aspects of HTI and highlight representative examples of its practical applications for the discovery of molecular mechanisms of disease, focusing on infectious diseases and host–pathogen interactions, cancer, and rare genetic diseases. Pegoraro and Misteli also present some of the current challenges and possible solutions offered by novel cell culture systems and genome engineering approaches. (Pegoraro, G.; Misteli, T. Trends Genet.
High-Throughput Screening and Quantitation of Target Compounds in Biofluids by Coated Blade Spray-Mass Spectrometry
Most contemporary methods of screening and quantitating controlled substances and therapeutic drugs in biofluids typically require laborious, time-consuming, and expensive analytical workflows. In recent years, the authors of this new report have worked toward developing microextraction (μe)–mass spectrometry (MS) technologies that merge all the tedious steps of the classical methods into a simple, efficient, and low-cost methodology. Unquestionably, the automation of these technologies allows for faster sample throughput, greater reproducibility, and radically reduced analysis times.
Coated blade spray (CBS) is a μe technology engineered for extracting/enriching analytes of interest in complex matrices, and it can be directly coupled with MS instruments to achieve efficient screening and quantitative analysis. In this study, Tascon et al. introduce CBS as a technology that can be arranged to perform either rapid diagnostics (single vial) or high-throughput (HT, 96-well plate) analysis of biofluids. Furthermore, the authors demonstrate that performing 96-well CBS extractions at the same time allows the total analysis time to be reduced to less than 55 s per sample. Aiming to validate the versatility of CBS, substances comprising a broad range of molecular weights, moieties, protein binding, and polarities are selected. Thus, the HT CBS technology is used for the concomitant quantitation of 18 compounds (mixture of anabolics, β-2 agonists, diuretics, stimulants, narcotics, and β-blockers) spiked in human urine and plasma samples. Excellent precision (∼2.5%), accuracy (≥90%), and linearity (R2 ≥ 0.99) are attained for all the studied compounds, and the limits of quantitation (LOQs) are within the range of 0.1–10 ng mL–1 for plasma and 0.25–10 ng mL–1 for urine. The results reported in this paper confirm CBS’s great potential for achieving <60 s analyses of target compounds in a broad range of fields, such as those related to clinical diagnosis, food, the environment, and forensics. (Tascon, M.; et al. Anal. Chem.
HSP90 Inhibition Enhances Cancer Immunotherapy by Upregulating Interferon Response Genes
T-cell-based immunotherapies are promising treatments for cancer patients. Although durable responses can be achieved in some patients, many patients fail to respond to these therapies, underscoring the need for improvement with combination therapies. From a screen of 850 bioactive compounds, the authors identify HSP90 inhibitors as candidates for combination with immunotherapy.
Mbofung et al. show that inhibition of HSP90 with ganetespib enhances T-cell-mediated killing of patient-derived human melanoma cells by their autologous T cells in vitro and potentiates responses to anti-CTLA4 and anti-PD1 therapy in vivo. Mechanistic studies reveal that HSP90 inhibition results in upregulation of interferon response genes, which are essential for the enhanced killing of ganetespib-treated melanoma cells by T cells. Taken together, these findings provide evidence that HSP90 inhibition can potentiate T-cell-mediated antitumor immune responses, and rationale to explore the combination of immunotherapy and HSP90 inhibitors. Many patients fail to respond to T-cell-based immunotherapies. Here, the authors, through high-throughput screening, identify HSP90 inhibitors as a class of preferred drugs for treatment combination with immunotherapy. (Mbofung, R. M.; et al. Nat. Commun.
Novel High-Throughput Screening Assay for Binding Affinities of Perfluoroalkyl Iodide for Estrogen Receptor α and β Isoforms
Contaminants of emerging concern are continuously increasing, which makes it important to develop high-throughput screening techniques for the evaluation of their potential biological effects, especially endocrine-disrupting effects, which directly influence the population dynamics in environments. A novel competitive binding assay based on enzyme fragmentation complementation technology is established to screen the binding affinities of emerging chemicals for estrogen receptor (ER) α or β isoforms. Exogenous compounds can compete with the fragment (ED-ES) of genetically engineered β-galactosidase enzyme (β-gal) for binding to ERα or β, thus quantitatively altering the formation of enzymatically active β-gal and the hydrolysis of luminescent substrate. According to the monitoring of luminescence curves and the optimization of ERα or β concentrations, it is found that luminescent signals are sustainably emitted for 9 h, and 40 nM ERα or β in the system leads to the most sensitive luminescence response. Using 17β-estrodiol (E2) and genistein as the representative estrogenic hormones, their binding affinities for ERα and β are evaluated. The results are consistent with those determined by traditional methods, which confirm the reliability of this competitive binding assay based on β-gal. Four polyfluorinated iodine alkanes (PFIs) with specific structural characteristics in iodine substitution and carbon chain length are screened, and the results show diverse binding affinities and different preferences of these chemicals to ERα or β isoforms. The binding affinities of PFIs for ERα are consistent with the result from the MVLN transcriptional reporter assay. Overall, the competitive binding assay presented in this study provides a promising alternative to high-throughput screening of emerging chemicals with estrogenic effects, which is important in explaining their potential toxicological effects and human exposure risks. (Song, W.; et al. Talanta
Microfluidics Technologies
Microfluidics-Based Digital Quantitative PCR for Single-Cell Small RNA Quantification
Quantitative analyses of small RNAs at the single-cell level have been challenging because of the limited sensitivity and specificity of conventional real-time quantitative PCR methods. A digital quantitative PCR (dqPCR) method for miRNA quantification has been developed, but it requires the use of proprietary stem-loop primers and only applies to miRNA quantification. Here, Yu et al. report a microfluidics-based dqPCR (mdqPCR) method, which takes advantage of the Fluidigm BioMark HD system for both template partition and the subsequent high-throughput dqPCR. This mdqPCR method demonstrates excellent sensitivity and reproducibility suitable for quantitative analyses of not only miRNAs, but also all other small RNA species at the single-cell level. Using this method, the authors discover that each sperm has a unique miRNA profile. (Yu, T.; et al. Biol. Reprod.
Organ-on-Chip Models of Cancer Metastasis for Future Personalized Medicine: From Chip to the Patient
Most cancer patients do not die from the primary tumor but from its metastasis. Current in vitro and in vivo cancer models are incapable of satisfactorily predicting the outcome of various clinical treatments on patients. This is a serious limitation, and efforts are underway to develop a new generation of highly predictive cancer models with advanced capabilities. In this regard, organ-on-chip models of cancer metastasis emerge as powerful predictors of disease progression. They offer physiological-like conditions where the (hypothesized) mechanistic determinants of the disease can be assessed with ease. Combined with high-throughput characteristics, the employment of organ-on-chip technology would allow pharmaceutical companies and clinicians to test new therapeutic compounds and therapies. This permits the screening of a large battery of new drugs in a fast and economic manner, to accelerate the diagnosis of the disease in the near future, and to test personalized treatments using cells from patients. In this review, Caballero et al. describe the latest advances in the field of organ-on-chip models of cancer metastasis and their integration with advanced imaging, screening, and biosensing technologies for future precision medicine applications. The authors focus on their clinical applicability and market opportunities to drive research forward to the next generation of tumor models for improved cancer patient theranostics. (Caballero, D.; et al. Biomaterials
Applications of Microfluidics in Quantitative Biology
Quantitative biology is dedicated to taking advantage of quantitative reasoning and advanced engineering technologies to make biology more predictable. Microfluidics, as an emerging technique, provides new approaches to precisely control fluidic conditions on small scales and collect data in high-throughput and quantitative manners. In this review, the authors present the relevant applications of microfluidics to quantitative biology based on two major categories (channel-based microfluidics and droplet-based microfluidics), and their typical features. Bai et al. also envision some other microfluidic techniques that may not be employed in quantitative biology right now, but have great potential in the near future. (Bai, Y.; et al. Biotechnol. J.
Analytical Chemistry and Genome-Wide Emerging Technologies
Medicinal Chemistry of Therapeutic Peptides: Recent Developments in Synthesis and Design Optimizations
Peptide therapeutics have made tremendous progress in the past decade. Many of the inherent weaknesses of peptides that hampered their development as therapeutics are now more or less effectively tackled with recent scientific and technological advancements in integrated drug discovery settings. These include recent developments in synthetic organic chemistry, high-throughput recombinant production strategies, high-resolution analytical methods, high-throughput screening options, ingenious drug delivery strategies, and novel formulation preparations. In this review, Parthasarathy et al. briefly describe the key methodologies and strategies used in the therapeutic peptide development processes, with select examples of the most recent developments in the field. The aim of this review is to highlight the viable options a medicinal chemist may consider in order to improve a specific pharmacological property of interest in a peptide lead entity and thereby rationally assess the therapeutic potential this class of molecules possesses while they are traditionally (and incorrectly) considered “undruggable.” (Parthasarathy, A.; et al. Curr. Med. Chem.
Randomized CRISPR-Cas Transcriptional Perturbation Screening Reveals Protective Genes against Alpha-Synuclein Toxicity
The genome-wide perturbation of transcriptional networks with CRISPR-Cas technology has primarily involved systematic and targeted gene modulation. Here, Chen et al. develop PRISM (Perturbing Regulatory Interactions by Synthetic Modulators), a screening platform that uses randomized CRISPR-Cas transcription factors (crisprTFs) to globally perturb transcriptional networks. By applying PRISM to a yeast model of Parkinson’s disease (PD), the authors identify guide RNAs (gRNAs) that modulate transcriptional networks and protect cells from alpha-synuclein (αSyn) toxicity. One gRNA identified in this screen outperforms the most protective suppressors of αSyn toxicity reported previously, highlighting PRISM’s ability to identify modulators of important phenotypes. Gene expression profiling reveals genes differentially modulated by this strong protective gRNA that rescues yeast from αSyn toxicity when overexpressed. Human homologs of top-ranked hits protect against αSyn-induced cell death in a human neuronal PD model. Thus, high-throughput and unbiased perturbation of transcriptional networks via randomized crisprTFs can reveal complex biological phenotypes and effective disease modulators. (Chen, Y. C.; et al. Mol. Cell.
Automated Multiplex Genome-Scale Engineering in Yeast
Genome-scale engineering is indispensable in understanding and engineering microorganisms, but the current tools are mainly limited to bacterial systems. Here Si et al. report an automated platform for multiplex genome-scale engineering in Saccharomyces cerevisiae, an important eukaryotic model and widely used microbial cell factory. Standardized genetic parts encoding overexpression and knockdown mutations of >90% yeast genes are created in a single step from a full-length cDNA library. With the aid of CRISPR-Cas, these genetic parts are iteratively integrated into the repetitive genomic sequences in a modular manner using robotic automation. This system allows functional mapping and multiplex optimization on a genome scale for diverse phenotypes, including cellulase expression, isobutanol production, glycerol utilization, and acetic acid tolerance, and may greatly accelerate future genome-scale engineering endeavors in yeast. (Si, T.; et al. Nat. Commun.
Advances in Machine Learning
From Machine Learning to Deep Learning: Progress in Machine Intelligence for Rational Drug Discovery
Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine learning tools, such as quantitative structure–activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of “big” data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here Zhang et al. summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. Zhang et al. suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. (Zhang, L.; et al. Drug Discov. Today
Automatic Machine Learning–Based Identification of Jogging Periods from Accelerometer Measurements of Adolescents under Field Conditions
The assessment of health benefits associated with physical activity depends on the activity duration, intensity, and frequency; therefore, their correct identification is very valuable and important in epidemiological and clinical studies. The aims of this study are to develop an algorithm for automatic identification of intended jogging periods and to assess whether the identification performance is improved when using two accelerometers at the hip and ankle, compared with using only one at either position.
The study uses diarized jogging periods and the corresponding accelerometer data from 39 fifteen-year-old adolescents, collected under field conditions, as part of the GINIplus study. The data are obtained from two accelerometers placed at the hip and ankle. The automated feature engineering technique is performed to extract features from the raw accelerometer readings and to select a subset of the most significant features. Four machine learning algorithms are used for classification: logistic regression, support vector machines, random forest, and extremely randomized trees. Classification is performed using only data from the hip accelerometer, only data from the ankle accelerometer, and data from both accelerometers.
The reported jogging periods are verified by visual inspection and used as the gold standard. After the feature selection and tuning of the classification algorithms, all options provide a classification accuracy of at least 0.99, independent of the applied segmentation strategy with sliding windows of either 60 or 180 s. The best matching ratio, that is, the length of correctly identified jogging periods related to the total time, including the missed ones, is up to 0.875. It could be additionally improved up to 0.967 by application of postclassification rules, which considers the duration of breaks and jogging periods. There is no obvious benefit of using two accelerometers; rather, almost the same performance can be achieved from either accelerometer position.
Machine learning techniques can be used for automatic activity recognition, as they provide very accurate activity recognition—significantly more accurate than when keeping a diary. Identification of jogging periods in adolescents can be performed using only one accelerometer. Performance-wise, there is no significant benefit from using accelerometers on both locations. (Zdravevski, E.; et al. PLoS One
Combining Machine Learning and Nanofluidic Technology to Diagnose Pancreatic Cancer Using Exosomes
Circulating exosomes contain a wealth of proteomic and genetic information, presenting an enormous opportunity in cancer diagnostics. While microfluidic approaches have been used to successfully isolate cells from complex samples, scaling these approaches for exosome isolation has been limited by the low throughput and susceptibility to clogging of nanofluidics. Moreover, the analysis of exosomal biomarkers is confounded by substantial heterogeneity between patients and within a tumor itself. To address these challenges, Ko et al. present a multichannel nanofluidic system to analyze crude clinical samples. Using this platform, the authors isolate exosomes from healthy and diseased murine and clinical cohorts, profile the RNA cargo inside of these exosomes, and apply a machine learning algorithm to generate predictive panels that can identify samples derived from heterogeneous cancer-bearing individuals. Using this approach, Ko et al. classify cancer and precancer mice from healthy controls, as well as pancreatic cancer patients from healthy controls, in blinded studies. (Ko, J.; et al. ACS Nano