Life Sciences Discovery and Technology Highlights

Complex Reconstitution and Characterization by Combining Co-Expression Techniques in Escherichia coli with High-Throughput

Single protein expression technologies have strongly benefited from the structural genomics initiatives that have introduced parallelization at the laboratory level. Specifically, the developments made in the wake of these initiatives have revitalized the use of Escherichia coli as a major host for heterologous protein expression. In parallel to these improvements for single expression, technologies for complex reconstitution by coexpression in E. coli have been developed. Assessments of these coexpression technologies have highlighted the need for combinatorial experiments requiring automated protocols. These requirements can be fulfilled by adapting the high-throughput approaches that have been developed for single expression to the coexpression technologies. Yet, challenges are lying ahead that further need to be addressed and that are only starting to be taken into account in the case of single expression. These notably include the biophysical characterization of the samples at the small-scale level. Specifically, these approaches aim at discriminating the samples at an early stage of their production based on various biophysical criteria leading to cost-effectiveness and time saving. Vincentelli and Romier address these various issues to provide a broad and comprehensive overview of complex reconstitution and characterization by coexpression in E. coli (Vincentelli, R.; Romier, C. Adv. Exp. Med. Biol. 2016, 896, 43–58).

High-Throughput Sample Processing and Sample Management: The Functional Evolution of Classical Cytogenetic Assay towards Automation

High-throughput individual diagnostic dose assessment is essential for medical management of radiation-exposed subjects after a mass casualty. Cytogenetic assays, such as the dicentric chromosome assay (DCA), are recognized as the gold standard by international regulatory authorities. DCA is a multistep and multiday bioassay. DCA, as described in the International Atomic Energy Agency manual, can be used to assess doses up to 4 to 6 weeks postexposure quite accurately, but throughput is still a major issue and automation is very essential. The throughput is limited, both in terms of sample preparation as well as analysis of chromosome aberrations. Thus, there is a need to design and develop novel solutions that could use extensive laboratory automation for sample preparation and bioinformatics approaches for chromosome aberration analysis to overcome throughput issues.

Ramakumar et al. have transitioned the bench-based cytogenetic DCA to a coherent process, performing high-throughput automated biodosimetry for individual dose assessment and ensuring quality control and quality assurance aspects in accordance with international harmonized protocols. A laboratory information management system (LIMS) is designed, implemented, and adapted to manage increased sample processing capacity, develop and maintain standard operating procedures for robotic instruments, avoid data transcription errors during processing, and automate analysis of chromosome aberrations using an image analysis platform. The efforts described in this article intend to bridge the current technological gaps and enhance the potential application of DCA for a dose-based stratification of subjects following a mass casualty. This article describes one such potential integrated automated laboratory system and functional evolution of the classical DCA toward increasing critically needed throughput (Ramakumar, A.; et al. Mutat. Res. Genet. Toxicol. Environ. Mutagen. 2016, 793, 132–141).

Application of Imaging-Based Assays in Microplate Formats for High-Content Screening

The use of multiparametric microscopy-based screens with automated analysis has enabled the large-scale study of biological phenomena that are currently not measurable by any other method. Collectively referred to as high-content screening (HCS), or high-content analysis (HCA), these methods rely on an expanding array of imaging hardware and software automation. Coupled with an ever-growing amount of diverse chemical matter and functional genomic tools, HCS has helped open the door to a new frontier of understanding cell biology through phenotype-driven screening. With the ability to interrogate biology on a cell-by-cell basis in highly parallel microplate-based platforms, the utility of HCS continues to grow as advancements are made in acquisition speed, model system complexity, data management, and analysis systems. Fogel uses an example of screening for genetic factors regulating mitochondrial quality control to exemplify the practical considerations in developing and executing high-content campaigns (Fogel, A. L. Methods Mol. Biol. 2016, 1439, 273–304).

A Cell-Free Expression and Purification Process for Rapid Production of Protein Biologics

Cell-free protein synthesis has emerged as a powerful technology for rapid and efficient protein production. Cell-free methods are also amenable to automation, and such systems have been extensively used for high-throughput protein production and screening; however, current fluidic systems are not adequate for manufacturing protein biopharmaceuticals. In this work, the authors report on the initial development of a fluidic process for rapid end-to-end production of recombinant protein biologics. This process incorporates a bioreactor module that can be used with eukaryotic or prokaryotic lysates that are programmed for combined transcription/translation of an engineered DNA template encoding for specific protein targets. Purification of the cell-free expressed product occurs through a series of protein separation modules that are configurable for process-specific isolation of different proteins. Using this approach, Sullivan et al. demonstrate production of two bioactive human protein therapeutics, erythropoietin and granulocyte-macrophage colony-stimulating factor, in yeast and bacterial extracts, respectively, each within 24 h. This process is flexible, scalable, and amenable to automation for rapid production at the point of need of proteins with significant pharmaceutical, medical, or biotechnological value (Sullivan, C. J.; et al. Biotechnol. J. 2016, 11, 238–248).

High-Throughput Screening Approaches and Combinatorial Development of Biomaterials Using Microfluidics

From the first microfluidic devices used for analysis of single metabolic by-products to highly complex multicompartmental coculture organ-on-chip platforms, efforts of many multidisciplinary teams around the world have been invested in overcoming the limitations of conventional research methods in the biomedical field. Close spatial and temporal control over fluids and physical parameters, integration of sensors for direct readout, and the possibility to increase throughput of screening through parallelization, multiplexing, and automation are some of the advantages of microfluidic over conventional, 2D tissue culture in vitro systems. Moreover, small volumes and relatively small cell numbers used in experimental setups involving microfluidics can potentially decrease research cost.

On the other hand, these small volumes and numbers of cells also mean that many of the conventional molecular biology or biochemistry assays cannot be directly applied to experiments that are performed in microfluidic platforms. Development of different types of assays and evidence that such assays are indeed a suitable alternative to conventional ones is a step that needs to be taken to have microfluidics-based platforms fully adopted in biomedical research.

In this review, rather than providing a comprehensive overview of the literature on microfluidics, the authors aim to discuss developments in the field of microfluidics that can aid advancement of biomedical research, with emphasis on the field of biomaterials. Three important topics are discussed: screening, in particular high-throughput and combinatorial screening; mimicking of a natural microenvironment ranging from 3D hydrogel-based cellular niches to organ-on-chip devices; and production of biomaterials with closely controlled properties. While important technical aspects of various platforms are discussed, the focus is mainly on their applications, including the state-of-the-art, future perspectives and challenges.

Microfluidics, being a technology characterized by the engineered manipulation of fluids at the submillimeter scale, offers some interesting tools that can advance biomedical research and development. Screening platforms based on microfluidic technologies that allow high-throughput and combinatorial screening may lead to breakthrough discoveries not only in basic research but also relevant to clinical application. This is further strengthened by the fact that reliability of such screens may improve, since microfluidic systems allow close mimicking of physiological conditions. Finally, microfluidic systems are also very promising as microfactories of a new generation of natural or synthetic biomaterials and constructs, with finely controlled properties (Barata, D.; et al. Acta Biomater. 2016, 34, 1–20).

Microfluidic Techniques for High-Throughput Single Cell Analysis

The microfabrication of microfluidic control systems and the development of increasingly sensitive molecular amplification tools have enabled the miniaturization of single-cell analytical platforms. Only recently has the throughput of these platforms increased to a level at which populations can be screened at the single cell level. Techniques based on both active and passive manipulation are now capable of discriminating between single-cell phenotypes for sorting, diagnostic, or prognostic applications in a variety of clinical scenarios. The introduction of multiphase microfluidics enables the segmentation of single cells into biochemically discrete picoliter environments. The combination of these techniques is enabling a class of single-cell analytical platforms with great potential for data-driven biomedicine, genomics, and transcriptomics (Recee, A.; et al. Curr. Opin. Biotechnol. 2016, 40, 90–96).

Microfluidics for High-Throughput Quantitative Studies of Early Development

Developmental biology has traditionally relied on qualitative analyses. Recently, however, as in other fields of biology, researchers have become increasingly interested in acquiring quantitative knowledge about embryogenesis. Advances in fluorescence microscopy are enabling high-content imaging in live specimens. At the same time, microfluidics and automation technologies are increasing experimental throughput for studies of multicellular models of development. Furthermore, computer vision methods for processing and analyzing bioimage data are now leading the way toward quantitative biology.

In this report, Levario et al. review advances in the areas of fluorescence microscopy, microfluidics, and data analysis that are instrumental to performing high-content, high-throughput studies in biology and specifically in development. The authors discuss a case study of how these techniques have allowed quantitative analysis and modeling of pattern formation in the Drosophila embryo (Levario, T. J.; et al. Annu. Rev. Biomed. Eng. 2016, 18, 285–309. doi:10.1146/annurev-bioeng-100515-013926).

Integrated Droplet-Based Microextraction with ESI-MS for Removal of Matrix Interference in Single-Cell Analysis

Integrating droplet-based microfluidics with mass spectrometry is essential to high-throughput and multiple analysis of single cells. Nevertheless, matrix effects such as the interference of culture medium and intracellular components influence the sensitivity and the accuracy of results in single-cell analysis. To resolve this problem, Zhang et al. describe a method that integrates droplet-based microextraction with single-cell mass spectrometry. Specific extraction solvent is used to selectively obtain intracellular components of interest and remove interference of other components. Using this method, UDP-Glc-NAc, GSH, GSSG, AMP, adenosine diphosphate, and adenosine triphosphate are successfully detected in single MCF-7 cells. The authors also apply the method to study the change of unicellular metabolites in the biological process of dysfunctional oxidative phosphorylation. The method can realize matrix-free, selective, and sensitive detection of metabolites in single cells and has the capability for reliable and high-throughput single-cell analysis (Zhang, X. C.; et al. Sci. Rep. 2016, 6, 24730).

Automation of Technology for Cancer Research

Zebrafish embryos can be obtained for research purposes in large numbers at low cost, and embryos develop externally in limited space, making them highly suitable for high-throughput cancer studies and drug screens. Noninvasive live imaging of various processes within the larvae is possible due to their transparency during development and a multitude of available fluorescent transgenic reporter lines. To perform high-throughput studies, handling large amounts of embryos and larvae is required. With such high numbers of individuals, even minute tasks may become time-consuming and arduous.

Van der Ent et al. provide an overview of the developments in the automation of various steps of large-scale zebrafish cancer research for discovering important cancer pathways and drugs for the treatment of human disease. The focus lies on various tools developed for cancer cell implantation, embryo handling and sorting, microfluidic systems for imaging and drug treatment, and image acquisition and analysis. Examples are given of employment of these technologies within the fields of toxicology research and cancer research (van der Ent, W.; et al. Adv. Exp. Med. Biol. 2016, 916, 315–332).

Active Machine Learning-Driven Experimentation to Determine Compound Effects on Protein Patterns

High-throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g., similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments.

Previous publications by the authors describe an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. Naik et al. now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learns the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance (Naik, A. W. Elife 2016, 5, e10047).

Making Sense of Big Data in Health Research: Towards an EU Action Plan

Medicine and health care are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation, combined with automation and miniaturization, has triggered an explosion in data production that will soon reach exabyte proportions.

How are we going to deal with this exponential increase in data production? The potential of “big data” for improving health is enormous, but at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and health care for all Europeans (Auffray, C.; et al. Genome Med. 2016, 8, 71).

Deciphering Intratumor Heterogeneity Using Cancer Genome Analysis

Intratumor heterogeneity within individual cancer tissues underlies the numerous phenotypes of cancer. Tumor subclones ultimately affect therapeutic outcomes due to their distinct molecular features. Drug-resistant subclones are present at a low frequency in tissues at the time of biopsy but can also arise as a result of acquired somatic mutations. A number of different approaches have been used to understand the nature of intratumor heterogeneity. Clonal analysis using whole-exome or genome sequencing data can help monitor subclones in the context of tumor progression. Multiregional biopsies permit the molecular characterization of subclones within tumors. Deep sequencing has also provided researchers with the ability to measure the low allele fraction variant within a small number of cells. Ultimately, single-cell sequencing will enable the identification of every minor population within a tumor microenvironment. In the clinical context, the ability to identify and monitor the subclonal architecture of a tumor is valuable for the development of precise cancer therapeutic methods (Ryu, D.; et al. Hum. Genet. 2016, 135, 635–642).

Small Molecules Enhance CRISPR Genome Editing in Pluripotent Stem Cells

The bacterial CRISPR-Cas9 system has emerged as an effective tool for sequence-specific gene knockout through nonhomologous end joining (NHEJ), but it remains inefficient for precise editing of genome sequences. Yu et al. have developed a reporter-based screening approach for high-throughput identification of chemical compounds that can modulate precise genome editing through homology-directed repair (HDR). Using the screening method, Yu et al. identify small molecules that can enhance CRISPR-mediated HDR efficiency, 3-fold for large fragment insertions and 9-fold for point mutations. Interestingly, the authors also observe that a small molecule that inhibits HDR can enhance frame shift insertion and deletion (indel) mutations mediated by NHEJ. The identified small molecules function robustly in diverse cell types with minimal toxicity. The use of small molecules provides a simple and effective strategy to enhance precise genome engineering applications and facilitates the study of DNA repair mechanisms in mammalian cells (Yu, C.; et al. Cell Stem Cell 2015, 16, 142–147).

Precision Medicine in Cardiology

The cardiovascular research and clinical communities are ideally positioned to address the epidemic of noncommunicable causes of death, as well as advance understanding of human health and disease, through the development and implementation of precision medicine. New tools will be needed for describing the cardiovascular health status of individuals and populations, including “omic” data, exposome and social determinants of health, the microbiome, behaviors and motivations, patient-generated data, and the array of data in electronic medical records. Cardiovascular specialists can build on their experience and use precision medicine to facilitate discovery science and improve the efficiency of clinical research, with the goal of providing more precise information to improve the health of individuals and populations. Overcoming the barriers to implementing precision medicine will require addressing a range of technical and sociopolitical issues. Health care under precision medicine will become a more integrated, dynamic system, in which patients are no longer passive entities on whom measurements are made but instead are central stakeholders who contribute data and participate actively in shared decision making. Many traditionally defined diseases have common mechanisms; therefore, elimination of a siloed approach to medicine will ultimately pave the path to the creation of a universal precision medicine environment (Antman, E. M.; Loscalzo, J. Nat. Rev. Cardiol. 2016, doi: 10.1038/nrcardio).

Single-Cell Genomics for Virology

Single-cell sequencing technologies (i.e., single-cell analysis followed by deep sequencing) investigate cellular heterogeneity in many biological settings. It was only in the past year that single-cell sequencing analyses have been applied in the field of virology, providing new ways to explore viral diversity and cell response to viral infection, which are summarized in this review (Ciuffi, A.; et al. Viruses 2016, 8, E123).