High‐throughput cloning and expression library creation for functional proteomics

The study of protein function usually requires the use of a cloned version of the gene for protein expression and functional assays. This strategy is particularly important when the information available regarding function is limited. The functional characterization of the thousands of newly identified proteins revealed by genomics requires faster methods than traditional single‐gene experiments, creating the need for fast, flexible, and reliable cloning systems. These collections of ORF clones can be coupled with high‐throughput proteomics platforms, such as protein microarrays and cell‐based assays, to answer biological questions. In this tutorial, we provide the background for DNA cloning, discuss the major high‐throughput cloning systems (Gateway® Technology, Flexi® Vector Systems, and Creator^TM DNA Cloning System) and compare them side‐by‐side. We also report an example of high‐throughput cloning study and its application in functional proteomics. This tutorial is part of the International Proteomics Tutorial Programme (IPTP12).     

Utilizing systems biology to unravel stomatal function and the hierarchies underpinning its control

Stomata control the concomitant exchange of CO₂ and transpiration in land plants. While a constant supply of CO₂ is need to maintain the rate of photosynthesis, the accompanying water losses must be tightly regulated to prevent dehydration and undesired metabolic changes. The factors affecting stomatal movement are directly coupled with the cellular networks of guard cells. Although the guard cell has been used as a model for characterization of signaling pathways, several important questions about its functioning remain elusive. Current modeling approaches describe the stomatal conductance in terms of relatively few easy‐to‐measure variables being unsuitable for in silico design of genetic manipulation strategies. Here, we argue that a system biology approach, combining modeling and high‐throughput experiments, may be used to elucidate the mechanisms underlying stomata control and to determine targets for modulation of stomatal responses to environment. In support of our opinion, we review studies demonstrating how high‐throughput approaches have provided a systems‐view of guard cells. Finally, we emphasize the opportunities and challenges of genome‐scale modeling and large‐scale data integration for in silico manipulation of guard cell functions to improve crop yields, particularly under stress conditions which are of pertinence both to climate change and water use efficiency.     

Promises and pitfalls of using high‐throughput sequencing for diet analysis

The application of high‐throughput sequencing‐based approaches to DNA extracted from environmental samples such as gut contents and faeces has become a popular tool for studying dietary habits of animals. Due to the high resolution and prey detection capacity they provide, both metabarcoding and shotgun sequencing are increasingly used to address ecological questions grounded in dietary relationships. Despite their great promise in this context, recent research has unveiled how a wealth of biological (related to the study system) and technical (related to the methodology) factors can distort the signal of taxonomic composition and diversity. Here, we review these studies in the light of high‐throughput sequencing‐based assessment of trophic interactions. We address how the study design can account for distortion factors, and how acknowledging limitations and biases inherent to sequencing‐based diet analyses are essential for obtaining reliable results, thus drawing appropriate conclusions. Furthermore, we suggest strategies to minimize the effect of distortion factors, measures to increase reproducibility, replicability and comparability of studies, and options to scale up DNA sequencing‐based diet analyses. In doing so, we aim to aid end‐users in designing reliable diet studies by informing them about the complexity and limitations of DNA sequencing‐based diet analyses, and encourage researchers to create and improve tools that will eventually drive this field to its maturity.     

A highly sensitive near‐infrared fluorescent detection method to analyze signalling pathways by reverse‐phase protein array

The comprehensive and quantitative analysis of the protein phosphorylation patterns in different cellular context is of considerable and general interest. The ability to quantify phosphorylation of discrete signalling proteins in large collections of biological samples would greatly favour the development of systems biology in the field of cell signalling. Reverse‐phase protein array (RPPA) potentially represents a very attractive approach to map signal transduction networks with high throughput. In the present report, we describe an improved detection method for RPPA combining near‐infrared with one or two rounds of tyramide‐based signal amplification. The LOQ was lowered from 6.84 attomoles with a direct detection protocol to 0.21 attomole with two amplification steps. We validated this method in the context of intracellular signal transduction triggered by follicle‐stimulating hormone in HEK293 cells. We consistently detected phosphorylated proteins in the sub‐attomole range from less than 1 ng of total cell extracts. Importantly, the method correlated with Western blot analysis of the same samples while displaying excellent intra‐ and inter‐slide reproducibility. We conclude that RPPA combined with amplified near‐infrared detection can be used to capture the subtle regulations intrinsic to signalling network dynamics at an unprecedented throughput, from minute amounts of biological samples.     

Plant cell packs: a scalable platform for recombinant protein production and metabolic engineering

Industrial plant biotechnology applications include the production of sustainable fuels, complex metabolites and recombinant proteins, but process development can be impaired by a lack of reliable and scalable screening methods. Here, we describe a rapid and versatile expression system which involves the infusion of Agrobacterium tumefaciens into three‐dimensional, porous plant cell aggregates deprived of cultivation medium, which we have termed plant cell packs (PCPs). This approach is compatible with different plant species such as Nicotiana tabacum BY2, Nicotiana benthamiana or Daucus carota and 10‐times more effective than transient expression in liquid plant cell culture. We found that the expression of several proteins was similar in PCPs and intact plants, for example, 47 and 55 mg/kg for antibody 2G12 expressed in BY2 PCPs and N. tabacum plants respectively. Additionally, the expression of specific enzymes can either increase the content of natural plant metabolites or be used to synthesize novel small molecules in the PCPs. The PCP method is currently scalable from a microtiter plate format suitable for high‐throughput screening to 150‐mL columns suitable for initial product preparation. It therefore combined the speed of transient expression in plants with the throughput of microbial screening systems. Plant cell packs therefore provide a convenient new platform for synthetic biology approaches, metabolic engineering and conventional recombinant protein expression techniques that require the multiplex analysis of several dozen up to hundreds of constructs for efficient product and process development.     

Systematic prediction of human membrane receptor interactions

Membrane receptor‐activated signal transduction pathways are integral to cellular functions and disease mechanisms in humans. Identification of the full set of proteins interacting with membrane receptors by high‐throughput experimental means is difficult because methods to directly identify protein interactions are largely not applicable to membrane proteins. Unlike prior approaches that attempted to predict the global human interactome, we used a computational strategy that only focused on discovering the interacting partners of human membrane receptors leading to improved results for these proteins. We predict specific interactions based on statistical integration of biological data containing highly informative direct and indirect evidences together with feedback from experts. The predicted membrane receptor interactome provides a system‐wide view, and generates new biological hypotheses regarding interactions between membrane receptors and other proteins. We have experimentally validated a number of these interactions. The results suggest that a framework of systematically integrating computational predictions, global analyses, biological experimentation and expert feedback is a feasible strategy to study the human membrane receptor interactome.     

A Robust and Adaptable High Throughput Screening Method to Study Host-Microbiota Interactions in the Human Intestine

The intestinal microbiota has many beneficial roles for its host. However, the precise mechanisms developed by the microbiota to influence the host intestinal cell responses are only partially known. The complexity of the ecosystem and our inability to culture most of these micro-organisms have led to the development of molecular approaches such as functional metagenomics, i.e. the heterologous expression of a metagenome in order to identify functions. This elegant strategy coupled to high throughput screening allowed to identify novel enzymes from different ecosystems where culture methods have not yet been adapted to isolate the candidate microorganisms. We have proposed to use this functional metagenomic approach in order to model the microbiota’s interaction with the host by combining this heterologous expression with intestinal reporter cell lines. The addition of the cellular component to this functional metagenomic approach introduced a second important source of variability resulting in a novel challenge for high throughput screening. First attempts of high throughput screening with various reporter cell-lines showed a high distribution of the response and consequent difficulties to reproduce the response, impairing an easy and clear identification of confirmed hits. In this study, we developed a robust and reproducible methodology to combine these two biological systems for high throughput application. We optimized experimental setups and completed them by appropriate statistical analysis tools allowing the use this innovative approach in a high throughput manner and on a broad range of reporter assays. We herewith present a methodology allowing a high throughput screening combining two biological systems. Therefore ideal conditions for homogeneity, sensitivity and reproducibility of both metagenomic clones as well as reporter cell lines have been identified and validated. We believe that this innovative method will allow the identification of new bioactive microbial molecules and, subsequently, will promote understanding of host-microbiota interactions.     

A high‐throughput transient expression system for rice

Rice is an important global crop and represents a vital source of calories for many food insecure regions. Efforts to improve this crop by improving yield, nutritional content, stress tolerance, or resilience to climate change are certain to include biotechnological approaches, which rely on the expression of transgenes in planta. The throughput and cost of currently available transgenic expression systems is frequently incompatible with modern, high‐throughput molecular cloning methods. Here, we present a protocol for isolating high yields of green rice protoplasts and for PEG‐mediated transformation of isolated protoplasts. Factors affecting transformation efficiency were investigated, and the resulting protocol is fast, cheap, robust, high‐throughput, and does not require specialist equipment. When coupled to a high‐throughput modular cloning system such as Golden Gate, this transient expression system provides a valuable resource to help break the “design‐build‐test” bottleneck by permitting the rapid screening of large numbers of transgenic expression cassettes prior to stable plant transformation. We used this system to rapidly assess the expression level, subcellular localisation, and protein aggregation pattern of nine single‐gene expression cassettes, which represent the essential component parts of the β‐cyanobacterial carboxysome.     

Strategies for selecting Recombinant CHO cell lines for cGMP manufacturing: Realizing the potential in bioreactors

Manufacture of recombinant proteins from mammalian cell lines requires the use of bioreactor systems at scales of up to 20,000 L. The cost and complexity of such systems can prohibit their extensive use during the process to construct and select the manufacturing cell line. It is therefore common practice to develop a model of the production process in a small scale vessel, such as a shake‐flask, where lower costs, ease of handling, and higher throughput are possible. This model can then be used to select a small number of cell lines for further evaluation in bioreactor culture. Here, we extend our previous work investigating cell line construction strategies to assess how well the behavior of cell lines in such a shake‐flask assessment predicts behavior in the associated bioreactor production process. A panel of 29 GS‐CHO cell lines, all producing the same antibody, were selected to include a mixture of high and low producers from a pool of 175 transfectants. Assessment of this panel in 10 L bioreactor culture revealed wide variation in parameters including growth, productivity, and metabolite utilization. In general, those cell lines which were high producing in the bioreactor cultures had also been higher producing in an earlier shake‐flask assessment. However, some changes in rank position of the evaluated cell lines were seen between the two systems. A potential explanation of these observations is discussed and approaches to improve the predictability of assessments used for cell line selection are considered. © 2010 American Institute of Chemical Engineers Biotechnol. Prog., 2010     

Evaluation of different biological data and computational classification methods for use in protein interaction prediction

Protein–protein interactions play a key role in many biological systems. High‐throughput methods can directly detect the set of interacting proteins in yeast, but the results are often incomplete and exhibit high false‐positive and false‐negative rates. Recently, many different research groups independently suggested using supervised learning methods to integrate direct and indirect biological data sources for the protein interaction prediction task. However, the data sources, approaches, and implementations varied. Furthermore, the protein interaction prediction task itself can be subdivided into prediction of (1) physical interaction, (2) co‐complex relationship, and (3) pathway co‐membership. To investigate systematically the utility of different data sources and the way the data is encoded as features for predicting each of these types of protein interactions, we assembled a large set of biological features and varied their encoding for use in each of the three prediction tasks. Six different classifiers were used to assess the accuracy in predicting interactions, Random Forest (RF), RF similarity‐based k‐Nearest‐Neighbor, Naïve Bayes, Decision Tree, Logistic Regression, and Support Vector Machine. For all classifiers, the three prediction tasks had different success rates, and co‐complex prediction appears to be an easier task than the other two. Independently of prediction task, however, the RF classifier consistently ranked as one of the top two classifiers for all combinations of feature sets. Therefore, we used this classifier to study the importance of different biological datasets. First, we used the splitting function of the RF tree structure, the Gini index, to estimate feature importance. Second, we determined classification accuracy when only the top‐ranking features were used as an input in the classifier. We find that the importance of different features depends on the specific prediction task and the way they are encoded. Strikingly, gene expression is consistently the most important feature for all three prediction tasks, while the protein interactions identified using the yeast‐2‐hybrid system were not among the top‐ranking features under any condition. Proteins 2006. © 2006 Wiley‐Liss, Inc.     

Using single‐cell genomics to understand developmental processes and cell fate decisions

High‐throughput ‐omics techniques have revolutionised biology, allowing for thorough and unbiased characterisation of the molecular states of biological systems. However, cellular decision‐making is inherently a unicellular process to which “bulk” ‐omics techniques are poorly suited, as they capture ensemble averages of cell states. Recently developed single‐cell methods bridge this gap, allowing high‐throughput molecular surveys of individual cells. In this review, we cover core concepts of analysis of single‐cell gene expression data and highlight areas of developmental biology where single‐cell techniques have made important contributions. These include understanding of cell‐to‐cell heterogeneity, the tracing of differentiation pathways, quantification of gene expression from specific alleles, and the future directions of cell lineage tracing and spatial gene expression analysis.     

Spectroscopic methods and their applicability for high‐throughput characterization of mammalian cell cultures in automated cell culture systems

The number and use of automated cell culture systems for mammalian cell culture are steadily increasing. Automated cell culture systems require miniaturized analytics with a high throughput to obtain as much information as possible from single experiments. Standard analytics commonly used for conventional bioreactor samples cannot handle the high throughput and the low sample volumes. Spectroscopic methods provide a means of meeting this analytical requirement and afford fast and direct access to process information. In the first part of this review, UV/VIS, fluorescence, Raman, near‐infrared, and mid‐infrared spectroscopy are presented. In the second part of the review, these spectroscopic methods are evaluated in terms of their applicability in the new field of mammalian cell culture processes in automated cell culture systems. Unlike standard bioreactors, these automated systems have special requirements that apply to the use of spectroscopic methods. Therefore, they are compared with regard to cell culture automation, throughput, and required sample volume.     

High throughput automated microbial bioreactor system used for clone selection and rapid scale‐down process optimization

High throughput automated fermentation systems have become a useful tool in early bioprocess development. In this study, we investigated a 24 x 15 mL single use microbioreactor system, ambr 15f, designed for microbial culture. We compared the fed‐batch growth and production capabilities of this system for two Escherichia coli strains, BL21 (DE3) and MC4100, and two industrially relevant molecules, hGH and scFv. In addition, different carbon sources were tested using bolus, linear or exponential feeding strategies, showing the capacity of the ambr 15f system to handle automated feeding. We used power per unit volume (P/V) as a scale criterion to compare the ambr 15f with 1 L stirred bioreactors which were previously scaled‐up to 20 L with a different biological system, thus showing a potential 1,300 fold scale comparability in terms of both growth and product yield. By exposing the cells grown in the ambr 15f system to a level of shear expected in an industrial centrifuge, we determined that the cells are as robust as those from a bench scale bioreactor. These results provide evidence that the ambr 15f system is an efficient high throughput microbial system that can be used for strain and molecule selection as well as rapid scale‐up. © 2017 The Authors Biotechnology Progress published by Wiley Periodicals, Inc. on behalf of American Institute of Chemical Engineers Biotechnol. Prog., 34:58–68, 2018     

Three‐dimensional cell culture microarray for high‐throughput studies of stem cell fate

We have developed a novel three‐dimensional (3D) cellular microarray platform to enable the rapid and efficient tracking of stem cell fate and quantification of specific stem cell markers. This platform consists of a miniaturized 3D cell culture array on a functionalized glass slide for spatially addressable high‐throughput screening. A microarray spotter was used to deposit cells onto a modified glass surface to yield an array consisting of cells encapsulated in alginate gel spots with volumes as low as 60 nL. A method based on an immunofluorescence technique scaled down to function on a cellular microarray was also used to quantify specific cell marker protein levels in situ. Our results revealed that this platform is suitable for studying the expansion of mouse embryonic stem (ES) cells as they retain their pluripotent and undifferentiated state. We also examined neural commitment of mouse ES cells on the microarray and observed the generation of neuroectodermal precursor cells characterized by expression of the neural marker Sox‐1, whose levels were also measured in situ using a GFP reporter system. In addition, the high‐throughput capacity of the platform was tested using a dual‐slide system that allowed rapid screening of the effects of tretinoin and fibroblast growth factor‐4 (FGF‐4) on the pluripotency of mouse ES cells. This high‐throughput platform is a powerful new tool for investigating cellular mechanisms involved in stem cell expansion and differentiation and provides the basis for rapid identification of signals and conditions that can be used to direct cellular responses. Biotechnol. Bioeng. 2010; 106: 106–118. © 2010 Wiley Periodicals, Inc.     

Plasmonic laser treatment for Morpholino oligomer delivery in antisense applications

Several cell transfection techniques have been developed in the last decades for specific applications and for various types of molecules. In this context, laser based approaches are of great interest due to their minimal invasiveness and spatial selectivity. In particular, laser induced plasmon based delivery of exogenous molecules into cells can have great impact on future applications. This approach allows high‐throughput laser transfection by excitation of plasmon resonances at gold nanoparticles non‐specifically attached to the cell membrane. In this study, we demonstrate specific gene‐knockdown by transfection of Morpholino oligos using this technique with optimized particle size. Furthermore, we evaluated the cytotoxicity of plasmonic laser treatment by various assays, including LDH activity and ROS formation. In summary, this study gives important insights into this new approach and clearly demonstrates its relevance for possible biological applications. (© 2014 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Novel approach for the prediction of cell densities and viability in standardized translucent cell culture biochips with near infrared spectroscopy

Near infrared spectroscopy is a rapid and nondestructive method for compositional analysis of biological material. The technology is widely used within bioreactors and possesses potential as a standardized method for quality control in miniaturized microfluidic cell culture systems. Here, we established a method for quantification of cell density and viability of adherent HepaRG cells cultured in a translucent, miniaturized cell culture biochip. The newly developed statistical models for interpretation of near infrared spectroscopy from biochips are the basis for a novel method of fast, continuous, and contact‐free analysis of cell viability and real‐time monitoring of cell growth. The technique thus paves the way for a robust and reliable high‐throughput analysis of biochip‐embedded cell cultures.