Abstract
Laboratory Automation and High-Throughput Chemistry
Integrated Platform with Combination of On-Line Protein Digestion, Isotope Dimethyl Labeling, and Multidimensional Peptide Separation for High-Throughput Proteome Quantification
In recent years, various enzymatic microreactors and on-line enzyme digestion strategies have been widely applied in high-throughput proteome analysis. However, incomplete and irreproducible digestion has introduced some unexpected variations in comparative proteome quantification when the samples are digested and then chemically isotope labeled in different aliquots. To address these problems, Zhang et al. present an integrated platform for high-throughput proteome quantification with a combination of on-line low miss-cleavage protein digestion by an ultraperformance immobilized enzymatic reactor, on-line dimethyl labeling onto a C18 precolumn, peptide separation by two-dimensional nanoliquid chromatography, and mass spectrometry (MS) detection. Compared with traditional off-line methods, such a platform exhibits obvious advantages, such as high sensitivity, throughput, accuracy, precision, and ease of automation. All these results demonstrate that such a platform might become a promising technique for the quantitative proteome analysis (Zhang, S.; et al. Anal. Chim. Acta
Integration of Multiple Networks and Pathways Identifies Cancer Driver Genes in Pan-Cancer Analysis
Modern high-throughput genomic technologies represent a comprehensive hallmark of molecular changes in pan-cancer studies. Although different cancer gene signatures have been revealed, the mechanism of tumorigenesis has yet to be completely understood. Pathways and networks are important tools to explain the roles of genes in functional genomic studies. However, few methods consider the functional nonequal roles of genes in pathways and the complex gene–gene interactions in a network.
Cava et al. present a novel method in pan-cancer analysis that identifies deregulated genes with a functional role by integrating pathway and network data. A pan-cancer analysis of 7158 tumor/normal samples from 16 cancer types identifies 895 genes with a central role in pathways and deregulated in cancer. Comparing their approach with 15 current tools that identify cancer driver genes, the authors find that 35.6% of the 895 genes identified by their method are cancer driver genes with at least 2/15 tools. Finally, the authors apply a machine learning algorithm on 16 independent GEO cancer datasets to validate the diagnostic role of cancer driver genes for each cancer. The authors obtain a list of the top 10 cancer driver genes for each cancer considered in this study.
In conclusion, Cava et al.’s analysis confirms that there are several known cancer driver genes in common among different types of cancer, and highlights that cancer driver genes are able to regulate crucial pathways (Cava, C.; et al. BMC Genomics
High-Throughput Screening Raman Spectroscopy Platform for Label-Free Cellomics
Schie et al. present a high-throughput screening Raman spectroscopy (HTS-RS) platform for a rapid and label-free macromolecular fingerprinting of tens of thousands of eukaryotic cells. The newly proposed label-free HTS-RS platform combines automated imaging microscopy with Raman spectroscopy to enable rapid label-free screening of cells and can be applied to a large number of biomedical and clinical applications.
The potential of the new approach is illustrated by two applications. The first is an HTS-RS-based differential white blood cell (WBC) count. A classification model is trained using Raman spectra of 52,218 lymphocytes, 48,220 neutrophils, and 7294 monocytes from four volunteers. The model is applied to determine a WBC differential for two volunteers and three patients, producing comparable results between HTS-RS and machine counting. The second is HTS-RS-based identification of circulating tumor cells (CTCs) in 1:1, 1:9, and 1:99 mixtures of Panc1 cells, and leukocytes yielded ratios of 55:45, 10:90, and 3:97, respectively.
Because the newly developed HTS-RS platform can be transferred to many existing Raman devices in all laboratories, the proposed implementation will lead to a significant expansion of Raman spectroscopy as a standard tool in biomedical cell research and clinical diagnostics (Schie, I. W.; et al. Anal. Chem.
Microbial Culturomics Broadens Human Vaginal Flora Diversity: Genome Sequence and Description of Prevotella lascolaii sp. nov. Isolated from a Patient with Bacterial Vaginosis
Microbial culturomics is a new subfield of postgenomic medicine and omics biotechnology application that has broadened the authors’ awareness of bacterial diversity of the human microbiome, including the human vaginal flora bacterial diversity. Using culturomics, a new obligate anaerobic Gram stain–negative rod-shaped bacterium designated strain khD1T is isolated in the vagina of a patient with bacterial vaginosis and characterized using taxonogenomics. The most abundant cellular fatty acids are C15:0 anteiso (36%), C16:0 (19%), and C15:0 iso (10%).
Based on an analysis of the full-length 16S rRNA gene sequences, phylogenetic analysis shows that the strain khD1T exhibits 90% sequence similarity with Prevotella loescheii, the phylogenetically closest validated Prevotella species. With 3,763,057 bp length, the genome of strain khD1T contains (mol%) 48.7 G + C and 3248 predicted genes, including 3194 protein-coding and 54 RNA genes. Given the phenotypical and biochemical characteristic results, as well as genome sequencing, strain khD1T is considered to represent a novel species within the genus Prevotella, for which the name Prevotella lascolaii sp. nov. is proposed. The type strain is khD1T (= CSUR P0109, = DSM 101754).
These results show that microbial culturomics greatly improves the characterization of the human microbiome repertoire by isolating potential putative new species. Further studies will certainly clarify the microbial mechanisms of pathogenesis of these new microbes and their role in health and disease. Microbial culturomics is an important new addition to the diagnostic medicine toolbox and warrants attention in future medical, global health, and integrative biology postgraduate teaching curricula (Diop, K.; et al. OMICS
Building Trans-Omics Evidence: Using Imaging and “Omics” to Characterize Cancer Profiles
Utilization of single-modality data to build predictive models in cancer results in a rather narrow view of most patient profiles. Some clinical facets relate strongly to histology image features (e.g., tumor stages), whereas others are associated with genomic and proteomic variations (e.g., cancer subtypes and disease aggression biomarkers). Srivastava et al. hypothesize that there are coherent “trans-omics” features that characterize varied clinical cohorts across multiple sources of data, leading to more descriptive and robust disease characterization.
In this work, for 105 breast cancer patients from The Cancer Genome Atlas (TCGA), the authors consider four clinical attributes (American Joint Committee on Cancer [AJCC] stage, tumor stage, ER status, and PAM50 mRNA subtypes) and build predictive models using three different modalities of data (histopathological images, transcriptomics, and proteomics). Following this, the authors identify critical multilevel features that drive successful classification of patients for the various different cohorts.
To build predictors for each data type, Srivastava et al. employ widely used best practice techniques, including convolutional neural network (CNN)–based classifiers for histopathological images and regression models for proteogenomic data. As expected, histology images outperform molecular features while predicting cancer stages, and transcriptomics hold superior discriminatory power for ER status and PAM50 subtypes. In addition, there exist a few cases where all data modalities exhibit comparable performance. Further, the authors also identify sets of key genes and proteins whose expression and abundance correlate across each clinical cohort, including tumor severity and progression (e.g., GABARAP), ER status (e.g., ESRl), and disease subtypes (e.g., FOXCl). Thus, the authors quantitatively assess the efficacy of different data types to predict critical breast cancer patient attributes and improve disease characterization (Srivastava, A.; et al. Pac. Symp. Biocomput.
The Generation of Three-Dimensional Head and Neck Cancer Models for Drug Discovery in 384-Well Ultra-Low-Attachment Microplates
The poor success rate of cancer drug discovery has prompted efforts to develop more physiologically relevant cellular models for early preclinical cancer lead discovery assays. For solid tumors, this would dictate the implementation of three-dimensional (3D) tumor models that more accurately recapitulate human solid tumor architecture and biology. A number of anchorage-dependent and anchorage-independent in vitro 3D cancer models have been developed, together with homogeneous assay methods and high-content imaging approaches, to assess tumor spheroid morphology, growth, and viability.
Several significant technical challenges have restricted the implementation of some 3D models in high-throughput screening (HTS). Close et al. describe a method that uses 384-well U-bottomed ultra-low-attachment (ULA) microplates to produce head and neck tumor spheroids for cancer drug discovery assays. The production of multicellular head and neck cancer spheroids in 384-well ULA plates occurs in situ, does not impose an inordinate tissue culture burden for HTS, is readily compatible with automation and homogeneous assay detection methods, and produces high-quality uniform-sized spheroids that can be utilized in cancer drug cytotoxicity assays within days rather than weeks (Close, D. A.; et al. Methods Mol. Biol.
Microfluidics Technologies
Single-Cell Microscopy of Suspension Cultures Using a Microfluidics-Assisted Cell Screening Platform
Studies that rely on fluorescence imaging of nonadherent cells that are cultured in suspension, such as Escherichia coli, are often hampered by trade-offs that must be made between data throughput and imaging resolution. Okumus et al. describe a new platform for microfluidics-assisted cell screening (MACS) that overcomes this trade-off by temporarily immobilizing suspension cells within a microfluidics chip. This enables high-throughput and automated single-cell microscopy for a wide range of cell types and sizes. As cells can be rapidly sampled directly from a suspension culture, MACS bypasses the need for sample preparation and therefore allows measurements without perturbing the native cell physiology. The setup can also be integrated with complex growth chambers and can be used to enrich or sort the imaged cells. Furthermore, MACS facilitates the visualization of individual cytoplasmic fluorescent proteins (FPs) in E. coli, allowing low-abundance proteins to be counted using standard total internal reflection fluorescence (TIRF) microscopy. Finally, MACS can be used to impart mechanical pressure for assessing the structural integrity of individual cells and their response to mechanical perturbations, or to make cells take up chemicals that otherwise would not pass through the membrane.
This protocol describes the assembly of electronic control circuitry, the construction of liquid handling components, and the creation of the MACS microfluidics chip. The operation of MACS is described, and automation software is provided to integrate MACS control with image acquisition. Finally, the authors provide instructions for extending MACS using an external growth chamber (1 d) and for sorting rare cells of interest (Okumus, B.; et al. Nat. Protoc.
Dissecting Cell-Type Composition and Activity-Dependent Transcriptional State in Mammalian Brains by Massively Parallel Single-Nucleus RNA Sequencing
Massively parallel single-cell RNA sequencing can precisely resolve cellular diversity in a high-throughput manner at low cost, but unbiased isolation of intact single cells from complex tissues, such as adult mammalian brains, is challenging. Here, Hu et al. integrate sucrose gradient–assisted purification of nuclei with droplet microfluidics to develop a highly scalable single-nucleus RNA sequencing approach (sNucDrop-seq), which is free of enzymatic dissociation and nucleus sorting. By profiling ∼18,000 nuclei isolated from cortical tissues of adult mice, the authors demonstrate that sNucDrop-seq not only accurately reveals neuronal and nonneuronal subtype composition with high sensitivity but also enables in-depth analysis of transient transcriptional states driven by neuronal activity, at single-cell resolution, in vivo (Hu, P.; et al. Mol. Cell.
High-Throughput Microfluidics for the Screening of Yeast Libraries
Cell factory development is critically important for the efficient biological production of chemicals, biofuels, and pharmaceuticals. Many rounds of the design–build–test–learn cycles may be required before an engineered strain meets specific metrics required for industrial application. The bioindustry prefers products in secreted form (i.e., secreted products or extracellular metabolites), as they can lower the cost of downstream processing, reduce metabolic burden to cell hosts, and allow necessary modification of the final products, such as biopharmaceuticals. Yet, products in secreted form result in the disconnection of phenotype from genotype, which may have limited throughput in the test step for the identification of desired variants from large libraries of mutant strains. In droplet microfluidic screening, single cells are encapsulated in individual droplets to enable high-throughput processing and sorting of single cells or clones. Encapsulation in droplets allows this technology to overcome the throughput limitations present in traditional methods for screening by extracellular phenotypes. Huang et al. describe a protocol for high-throughput droplet microfluidics screening of yeast libraries for higher protein secretion. This protocol can be adapted for screening by a range of other extracellular products from yeast or other hosts (Huang, M.; et al. Methods Mol. Biol.
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 seen as 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 will permit 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 clinical applicability and market opportunities to advance the next generation of tumor models for improved cancer patient theranostics (Caballero, D.; et al. Biomaterials
Advances in Imaging
Automated Patterning and Probing with Multiple Nanoscale Tools for Single-Cell Analysis
The nanomanipulation approach that combines focused ion beam (FIB) milling and various imaging and probing techniques enables researchers to investigate the cellular structures in three dimensions. This fusion approach, however, requires extensive effort in locating and examining randomly distributed targets due to a limited field of view (FOV) when high magnification is desired.
In this study, Li et al. automate pattern and probe, particularly for single-cell analysis, achieved by computer-aided tools, including feature recognition and geometric planning algorithms. Scheduling serial FOVs for the imaging and probing of multiple cells is considered a rectangle covering problem, and optimal or near-optimal solutions are obtained with the heuristics developed. FIB milling is then employed automatically, followed by downstream analysis using atomic force microscopy (AFM) to probe the cellular interior. The authors’ strategy is applied to examine bacterial cells (Klebsiella pneumoniae) and achieves high efficiency with limited human interference. The developed algorithms can be easily adapted and integrated with different imaging platforms toward high-throughput imaging analysis of single cells (Li, J.; et al. Micron.
An Automated Epifluorescence Microscopy Imaging Assay for the Identification of Phospho-AKT-Level Modulators in Breast Cancer Cells
AKT is an enzyme of the PI3K/pAKT pathway, regulating proliferation and cell survival. High basal levels of active, phosphorylated AKT (pAKT) are associated with tumor progression and therapeutic resistance in some breast cancer subtypes, including HER2-positive breast cancers. Various stimuli can increase pAKT levels, and elevated basal pAKT levels are a feature of PTEN-deficient breast cancer cell lines.
This study develops an assay able to identify modulators of pAKT levels using an automated epifluorescence microscope and high-content analysis. To develop this assay, Kaemmerer et al. use HCC-1569, a PTEN-deficient, HER2-overexpressing breast cancer cell line with elevated basal pAKT levels. HCC-1569 cells are treated with a selective pharmacological inhibitor of AKT (MK-2206) to reduce basal pAKT levels or EGF to increase pAKT levels. Immunofluorescence images are acquired using an automated epifluorescence microscope, and the integrated intensity of cytoplasmic pAKT staining is calculated using high-content analysis software. The mean and median integrated cytoplasmic intensity are normalized using fold change and standard score to assess assay quality and identify the most robust data analysis. The highest z′ factor is achieved for median data normalization using the standard score method (z′ = 0.45).
Using Kaemmerer et al.’s assay allows identification of the calcium homeostasis–regulating proteins TPRV6, STIM1, and TRPC1 as modulators of pAKT levels in HCC-1569 cells. Calcium signaling controls a diverse array of cellular processes, and some calcium homeostasis–regulating proteins are involved in modulating pAKT levels in cancer cells. Thus, these identified hits present promising targets for further assessment (Kaemmerer, E.; et al. J. Pharmacol. Toxicol. Methods