Life Sciences Discovery and Technology Highlights

High-Throughput Biology

Consistent and Reproducible Cultures of Large-Scale 3D Mammary Epithelial Structures Using an Accessible Bioprinting Platform

Standard three-dimensional (3D) in vitro culture techniques, such as those used for mammary epithelial cells, rely on the random distribution of cells within hydrogels. Although these systems offer advantages over traditional two-dimensional (2D) models, limitations persist owing to the lack of control over cellular placement within the hydrogel. This results in experimental inconsistencies and random organoid morphology. Robust, high-throughput experimentation requires greater standardization of 3D epithelial culture techniques.

In this article, Reid et al. detail the use of a 3D bioprinting platform as an investigative tool to control the 3D formation of organoids through the self-assembly of human mammary epithelial cells. Experimental bioprinting procedures are optimized to enable the formation of controlled arrays of individual mammary organoids. The authors define the distance and cell number parameters necessary to print individual organoids that do not interact between print locations, as well as those required to generate large contiguous organoids connected through multiple print locations.

Reid et al. demonstrate that as few as 10 cells can be used to form 3D mammary structures in a single print and that prints up to 500 μm apart can fuse to form single large structures. Using these fusion parameters, the authors demonstrate that both linear and nonlinear (contiguous circles) can be generated with sizes of 3 mm in length/diameter. The authors confirm that cells from individual prints interact to form structures with a contiguous lumen. Finally, the authors demonstrate that organoids can be printed into human collagen hydrogels, allowing for all-human 3D culture systems.

This platform is adaptable to different culturing protocols and is superior to traditional random 3D culture techniques in efficiency, reproducibility, and scalability. Importantly, owing to the low-cost accessibility and computer numerical control-driven platform of the 3D bioprinter, the authors have the ability to disseminate their experiments with absolute precision to interested laboratories. (Reid, J. A.; et al. Breast Cancer Res. 2018, 20, 122.)

A Versatile Quantitative Microdroplet Elemental Imaging Method Optimized for Integration in Biochemical Workflows for Low-Volume Samples

Laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS) analysis of μ-droplets is becoming an attractive alternative for detecting and quantifying elements in biological samples. With minimal sample preparation required and detection limits comparable to those of solution nebulization ICP-MS, μ-droplets have substantial advantages over traditional elemental detection, particularly for low volumes, such as aliquots taken from samples required for multiple independent biochemical assays, or fluids and tissues where elements of interest exist at native concentrations not suited to the necessary dilution steps required for solution nebulization ICP-MS. However, the characteristics of μ-droplet residue deposition are heavily dependent on the matrix, and potential effects on signal suppression or enhancement have not been fully characterized.

Kysenius et al. present a validated and flexible high-throughput method for the quantification of elements in μ-droplets using LA-ICP-MS imaging and matrix-matched external calibrants. Imaging the entire μ-droplet area removes analytical uncertainty arising from the often heterogenous distribution when compared with radial or bisecting line scans that capture only a small portion of the droplet residue. The authors examine the effects of common matrices found in a standard biochemistry workflow, including native protein and salt contents, as well as reagents used in typical preparation steps for concurrent biochemical assays, such as total protein quantification and enzyme activity assays.

The authors find that matrix composition results in systemic, concentration-dependent signal enhancement and suppression for carbon, whereas high sodium content has a specific space-charge-like suppression effect on high masses. Kysenius et al. confirm the accuracy of their method using both a certified serum standard (Seronorm L1) and independent measurements of analyzed samples by solution nebulization ICP-MS, and then test the specificity and reproducibility by examining spinal cord tissue homogenates from SOD1-G93A transgenic mice with a known molecular phenotype of increased copper- and zinc-binding superoxide dismutase-1 expression and altered copper-to-zinc stoichiometry. The method is rapid and transferable to multiple other biological matrices and allows high-throughput analysis of low-volume samples with sensitivity comparable to that of standard solution nebulization ICP-MS protocols. (Kysenius, K.; et al. Anal. Bioanal. Chem. 2018, DOI: 10.1007/s00216-018-1362-6.)

High-Throughput Sequencing Revealed That MicroRNAs Are Involved in the Development of Superior and Inferior Grains in Bread Wheat

High-throughput sequencing is employed to investigate the expression of miRNAs and their target genes in superior and inferior seeds of Aikang 58. Small RNA sequencing reveals 620 conserved and 64 novel miRNAs in superior grains, and 623 conserved and 66 novel miRNAs in inferior grains. Among these, 97 known miRNAs and 8 novel miRNAs show differential expression between the superior and inferior seeds. Degradome sequencing reveals at least 140 candidate target genes associated with 35 miRNA families during the development of superior and inferior seeds. GO and KEGG pathway analysis shows that the differentially expressed miRNAs, both conserved and novel, are likely involved in hormone production, carbohydrate metabolic pathways, and cell division.

Wang et al. validate eight known and four novel grain development-related miRNAs and their target genes by quantitative real-time PCR to ensure the reliability of small RNA and degradome-seq results. Of these, miR160 and miR165/166 are knocked down in Arabidopsis using short-tandem target mimic (STTM160 and STTM165/166) technology, which confirms their roles in seed development. Specifically, STTM160 shows significantly smaller grain size, lower grain weight, shorter silique length, shorter plant height, and more serrated leaves, whereas STTM165/166 shows decreased seed number, disabled siliques, and curled upward leaves. (Wang, Y.; et al. Sci. Rep. 2018, 8, 13854.)

A Bayesian Framework for High-Throughput T-Cell Receptor Pairing

The study of T-cell receptor repertoires has generated new insights into immune system recognition. However, the ability to robustly characterize these populations has been limited by technical barriers and an inability to reliably infer heterodimeric chain pairings for T-cell receptors.

Holec et al. describe a novel analytical approach to an emerging immune repertoire sequencing method that improves the resolving power of this low-cost technology. This method relies on the distribution of a T-cell population across a 96-well plate, followed by barcoding and sequencing of relevant portions of each T-cell genome. Multicell analytical deconvolution for high-yield paired-chain evaluation (MAD-HYPE) uses Bayesian inference to more accurately extract T-cell receptor information, improving the ability to study and characterize T-cell populations for immunology and immunotherapy applications.

The MAD-HYPE algorithm is released as an open-source project under the Apache License and is available from https://github.com/birnbaumlab/MAD-HYPE. (Holec, P. V.; et al. Bioinformatics 2018, DOI: 10.1093/bioinformatics/bty801.)

Integrative Detection and Analysis of Structural Variation in Cancer Genomes

Structural variants (SVs) can contribute to oncogenesis through a variety of mechanisms. Despite their importance, the identification of SVs in cancer genomes remains challenging. Here, Dixon et al. present a framework that integrates optical mapping, high-throughput chromosome conformation capture (Hi-C), and whole-genome sequencing to systematically detect SVs in a variety of normal or cancer samples and cell lines.

Dixon et al. identify the unique strengths of each method and demonstrate that only integrative approaches can comprehensively identify SVs in the genome. By combining Hi-C and optical mapping, the authors resolve complex SVs and phase multiple SV events to a single haplotype. Furthermore, the authors observe widespread structural variation events affecting the functions of noncoding sequences, including the deletion of distal regulatory sequences, alteration of DNA replication timing, and creation of novel three-dimensional chromatin structural domains. The results indicate that noncoding SVs may be underappreciated mutational drivers in cancer genomes. (Dixon, J. R.; et al. Nat. Genet. 2018, DOI: 10.1038/s41588-018-0195-8.)

Independent and Joint GWAS for Growth Traits in Eucalyptus by Assembling Genome-Wide Data for 3373 Individuals across Four Breeding Populations

Genome-wide association studies (GWAS) in plants typically suffer from limited statistical power. An alternative to the logistical and cost challenge of increasing sample sizes is to gain power by meta-analysis using information from independent studies.

Müller et al. carry out GWAS for growth traits with six single-marker models and regional heritability mapping (RHM) in four Eucalyptus breeding populations independently and by joint GWAS, using gene and segment-based models, with data for 3373 individuals genotyped with a communal EUChip60KSNP platform. While single single-nucleotide polymorphism (SNP) GWAS hardly detects significant associations at high stringency in each population, gene-based joint GWAS reveals nine genes significantly associated with tree height. Associations detected using single-SNP GWAS, RHM, and joint GWAS set-based models explain, on average, 3%–20% of the phenotypic variance.

Whole-genome regression, conversely, captures 64%–89% of the pedigree-based heritability in all populations. Several associations independently detected for the same SNPs in different populations provide unprecedented GWAS validation results in forest trees. Rare and common associations are discovered in eight genes involved in cell wall biosynthesis and lignification.

With the increasing adoption of genomic prediction of complex phenotypes using shared SNPs and much larger tree breeding populations, joint GWAS approaches should provide increasing power to pinpoint discrete associations potentially useful toward tree breeding and molecular applications. (Müller, B. S. F.; et al. New Phytol. 2018, DOI: 10.1111/nph.15449.)

Microfluidics

Evaluating Nanomedicine in Microfluidics

Nanomedicines are engineered nanoscale structures that have extensive applications in the diagnostics and therapeutics of many diseases. Despite the rapid progress and tremendous potential of nanomedicines, their clinical translation process has still been slow, owing to the difficulty in understanding, evaluating, and predicting their behaviors in complex living organisms. Microfluidic techniques offer a promising way to resolve these challenges. Carefully designed microfluidic chips enable in vivo microenvironment simulation and high-throughput analysis, thus providing robust platforms for nanomedicine evaluation.

Here, He et al. summarize the recent developments and achievements of microfluidic methods in nanomedicine evaluation, which is categorized into four sections based on their target systems: microfluidic nanomedicine evaluation at single-cell, multicellular, organ, and organism levels. Finally, the authors provide perspectives on the challenges and future directions of microfluidic-based nanomedicine evaluation. (He, Z.; et al. Nanotechnology 2018, DOI: 10.1088/1361-6528/aae18a.)

Recent Developments of Microfluidics as a Tool for Biotechnology and Microbiology

Academic microfluidics has decisively shifted in recent years from research on phenomenology and proof-of-concept fluidic functionalities to developments oriented at applications with biology, medicine, and biotechnology in prime focus. Significant efforts have been made to demonstrate that microfluidics can be used in unspecialized laboratories to perform previously mundane tasks faster and easier, or to venture into new research areas that were unavailable or unattractive when only classical means of microbiology or biotechnology were employed.

In this article, Scheler et al. review a variety of biological experiments recently performed in microfluidic assays. The authors categorize the microfluidic systems by the key roles they play in the biological experiments as (1) controlled reaction chambers, (2) high-throughput arrays, or (3) micropositioning systems. Scheler et al. also discuss the outlook for further development and applications of microfluidics in biological sciences. (Scheler, O.; et al. Curr. Opin. Biotechnol. 2018, 55, 60–67.)

High-Throughput Single-Cell DNA Sequencing of Acute Myeloid Leukemia Tumors with Droplet Microfluidics

To enable the characterization of genetic heterogeneity in tumor cell populations, Pellegrino has developed a novel microfluidic approach that barcodes amplified genomic DNA from thousands of individual cancer cells confined to droplets. The barcodes are used to reassemble the genetic profiles of cells from next-generation sequencing data. By using this approach, the authors sequenced longitudinally collected acute myeloid leukemia (AML) tumor populations from two patients and genotyped up to 62 disease-relevant loci across more than 16,000 individual cells.

Targeted single-cell sequencing is able to sensitively identify cells harboring pathogenic mutations during complete remission and uncover complex clonal evolution within AML tumors that is not observable with bulk sequencing. The authors anticipate that this approach will make feasible the routine analysis of AML heterogeneity, leading to improved stratification and therapy selection for the disease. (Pellegrino, M.; et al. Genome Res. 2018, 9, 1345–1352.)

Circulating Tumor Cell Phenotyping via High-Throughput Acoustic Separation

The study of circulating tumor cells (CTCs) offers pathways to develop new diagnostic and prognostic biomarkers that benefit cancer treatments. To fully exploit and interpret the information provided by CTCs, a platform has been developed to integrate acoustics and microfluidics for the high-throughput isolation of rare CTCs from peripheral blood while preserving their structural, biological, and functional integrity.

Cancer cells are first isolated from leukocytes with a throughput of 7.5 mL h^–1, achieving a recovery rate of at least 86% while maintaining the cells’ ability to proliferate. High-throughput acoustic separation enables statistical analysis of isolated CTCs from prostate cancer patients to be performed to determine their size distribution and phenotypic heterogeneity for a range of biomarkers, including the visualization of CTCs with a loss of expression for the prostate-specific membrane antigen. The method also enables the isolation of even rarer, but clinically important, CTC clusters. (Wu, M.; et al. Small 2018, 32, e1801131.)

Advances in Immuno-Oncology

Checkpoint Inhibition in Ovarian Cancer: Rising Star or Just a Dream?

The introduction of checkpoint inhibitors revolutionized immuno-oncology. The efficacy of traditional immunotherapeutics, like vaccines and immunostimulants, is limited by persistent immune-escape strategies of cancer cells. Checkpoint inhibitors target these escape mechanisms and redirect the immune system to antitumor toxicity. Phenomenal results have been reported in entities like melanoma, where no other therapy is able to demonstrate survival benefit before the introduction of immunotherapeutics.

The first experience in ovarian cancer (OC) was reported for nivolumab, a fully human anti-programmed cell death protein 1 (PD-1) antibody, in 2015. While the data are extraordinary for a mono-immunotherapeutic agent and very promising, they do not match up to the revolutionary results in entities like melanoma. The key to exceptional treatment response in OC could be the identification of the most immunogenic patients. Pietzner et al. hypothesize that BRCA mutation could be a predictor of improved response in OC. The underlying DNA-repair deficiency should result in increased immunogenicity because of higher mutational load and more neoantigen presentation.

This hypothesis has not been tested to date and should be subject to future trials. This article provides an overview of the immunologic background of checkpoint inhibition (CI). It presents current data on nivolumab and other checkpoint inhibitors in solid tumors and OC specifically and depicts important topics in the management of this novel substance group, such as side effect control, diagnostic PD-1/programmed cell death-ligand 1 (PD-L1) expression assessment, and management of pseudoprogression. (Pietzner, K.; et al. J. Gynecol. Oncol. 2018, 29, e93. DOI: 10.3802/jgo.2018.29.e93.)

Immunooncology in Breast Cancer: Active and Passive Vaccination Strategies

Immunotherapies are set to become part of the therapeutic repertoire for breast cancer in the near future. Active vaccination is a promising strategy, especially in tumors that have a specific tumor-associated antigen. Although cellular immunotherapies have not yet shown efficacy, new technologies are on the way to improve this approach. Given the recent U.S. Food and Drug Administration approval of chimeric antigen receptor (CAR) T cells for leukemia, it is only a matter of time before solid tumors will follow. However, not all breast cancer patients will respond to cellular or other immunotherapy. As a result, subpopulations of breast cancer patients who benefit from this new approach must be defined. (Schutz, F.; et al. Breast Care (Basel) 2018, 13, 22–26.)

Artificial Intelligence

Dissecting Cancer Heterogeneity Based on Dimension Reduction of Transcriptomic Profiles Using Extreme Learning Machines

It is becoming increasingly clear that major malignancies, such as breast, colorectal, and gastric cancers, are not single disease entities, but are multiple cancer subtypes of distinct molecular properties. Molecular subtyping has been widely used to dissect intertumor biological heterogeneity in relation to clinical outcomes. A key step of this methodology is to perform unsupervised classification of gene expression profiles, which often suffers challenges of high-dimensionality, feature redundancy, and noise and irrelevant information.

To overcome these limitations, Wang et al. propose ELM-CC, which employs hidden observation features obtained from extreme learning machines (ELMs) for cancer classification (CC). To demonstrate its effectiveness and usefulness, the authors apply ELM-CC for gastric and ovarian cancer subtyping. Compared with the widely used consensus clustering method, their approach demonstrates much better clustering performance and identifies molecular subtypes that are much more clinically relevant. (Wang, K.; et al. PloS One 2018, 13, e0203824. Correction in Wang, K.; et al. PloS One 2018, 13, e0205548.)

Comparison between Support Vector Machine and Deep Learning: Machine Learning Technologies for Detecting Epiretinal Membrane Using 3D-OCT

In this study, Sonobe et al. compare deep learning (DL) with support vector machines (SVMs), both of which use three-dimensional optical coherence tomography (3D-OCT) images for detecting epiretinal membranes (ERMs).

In total, 529 3D-OCT images from the Tsukazaki Hospital ophthalmology database (184 non-ERM subjects and 205 ERM patients) are assessed; 80% of the images are divided for training (423 total: 245 non-ERM, 178 ERM) and 20% for testing (106 total: 59 non-ERM, 47 ERM). Using the 423 training images, a model is created with deep convolutional neural network and SVM, and the test data are evaluated.

The DL model’s sensitivity is 97.6% (95% confidence interval [CI] 87.7%–99.9%) and specificity is 98.0% (95% CI 89.7%–99.9%), and the area under the curve (AUC) is 0.993 (95% CI 0.993–0.994). In contrast, the SVM model’s sensitivity is 97.6% (95% CI 87.7%–99.9%), specificity is 94.2% (95% CI 84.0%–98.7%), and AUC is 0.988 (95% CI 0.987–0.988). In other words, the DL model is better than the SVM model in detecting ERM by using 3D-OCT images. (Sonobe, T.; et al. Int. Ophthalmol. 2018, DOI: 10.1007/s10792-018-1016-x.)

A Hybrid of Cuckoo Search and Minimization of Metabolic Adjustment to Optimize Metabolite Production in Genome-Scale Models

Metabolic engineering involves the modification and alteration of metabolic pathways to improve the production of desired substances. The modification can be made using in silico gene knockout simulation that can predict and analyze the disrupted genes that may enhance the metabolite production. Global optimization algorithms are widely used for identifying gene knockout strategies; however, their productions are less than theoretical maximum and the algorithms are easily trapped into local optima. These algorithms also require a very large computation time to obtain acceptable results. This is due to the complexity of the metabolic models, which are high-dimensional and contain thousands of reactions.

In this article, a hybrid algorithm of Cuckoo search and minimization of metabolic adjustment is proposed to overcome the aforementioned problems. The hybrid algorithm searches for the near-optimal set of gene knockouts that leads to the overproduction of metabolites. Computational experiments on two sets of genome-scale metabolic models demonstrate that the proposed algorithm is better than the previous works in terms of growth rate, biomass product couple yield, and computation time. (Arif, M. A.; et al. Comput. Biol. Med. 2018, 102, 112–119.)

Extending the Human Connectome Project across Ages: Imaging Protocols for the Life Span Development and Aging Projects

The Human Connectome Projects in Development (HCP-D) and Aging (HCP-A) are two large-scale brain imaging studies that will extend the recently completed HCP Young-Adult (HCP-YA) project to nearly the full life span, collecting structural, resting-state functional magnetic resonance imaging (fMRI), task fMRI, diffusion MRI, and perfusion MRI in participants from 5 to 100+ years of age. HCP-D is enrolling 1300+ healthy children, adolescents, and young adults (ages 5–21), and HCP-A is enrolling 1200+ healthy adults (ages 36–100+), with each study collecting longitudinal data in a subset of individuals at particular age ranges.

The imaging protocols of the HCP-D and HCP-A studies are very similar, differing primarily in the selection of different task-fMRI paradigms. Harms et al. strive to harmonize the imaging protocol to the greatest extent feasible with the completed HCP-YA (1200+ participants, ages 22–35), but some imaging-related changes are motivated or necessitated by hardware changes, the need to reduce the total amount of scanning per participant, and/or the additional challenges of working with young and elderly populations.

Here, the authors provide an overview of the common HCP-D/A imaging protocol, including data and rationales for protocol decisions and changes relative to HCP-YA. The result will be a large, rich, multimodal, and freely available set of consistently acquired data for use by the scientific community to investigate and define normative developmental and aging-related changes in the healthy human brain. (Harms, M. P.; et al. Neuroimage 2018, pii: S1053-8119(18)31865-2.)

Aberration Hubs in Protein Interaction Networks Highlight Actionable Targets in Cancer

Despite efforts for extensive molecular characterization of cancer patients, such as the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), the heterogeneous nature of cancer and limited knowledge of its contextual function of proteins complicate the identification of targetable genes.

In this report, the authors present aberration hub analysis for cancer (AbHAC) as a novel integrative approach to pinpoint aberration hubs, that is, individual proteins that interact extensively with genes that show aberrant mutation or expression. Analysis of breast cancer data of the TCGA and renal cancer data from the ICGC show that aberration hubs are involved in relevant cancer pathways, including factors promoting cell cycle and DNA replication in basal-like breast tumors, and Src kinase and vascular endothelial growth factor (VEGF) signaling in renal carcinoma.

Moreover, the analysis uncovers novel functionally relevant and actionable targets, among which the authors experimentally validate abnormal splicing of spleen tyrosine kinase as a key factor for cell proliferation in renal cancer. Thus, AbHAC provides an effective strategy to uncover novel disease factors that are only identifiable by examining mutational and expression data in the context of biological networks. (Karimzadeh, M.; et al. Oncotarget 2018, 9, 25166–25180.)