Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping

High-throughput phenotyping is emerging as an important technology to dissect phenotypic components in plants. Efficient image processing and feature extraction are prerequisites to quantify plant growth and performance based on phenotypic traits. Issues include data management, image analysis, and result visualization of large-scale phenotypic data sets. Here, we present Integrated Analysis Platform (IAP), an open-source framework for high-throughput plant phenotyping. IAP provides user-friendly interfaces, and its core functions are highly adaptable. Our system supports image data transfer from different acquisition environments and large-scale image analysis for different plant species based on real-time imaging data obtained from different spectra. Due to the huge amount of data to manage, we utilized a common data structure for efficient storage and organization of data for both input data and result data. We implemented a block-based method for automated image processing to extract a representative list of plant phenotypic traits. We also provide tools for build-in data plotting and result export. For validation of IAP, we performed an example experiment that contains 33 maize (Zea mays ‘Fernandez’) plants, which were grown for 9 weeks in an automated greenhouse with nondestructive imaging. Subsequently, the image data were subjected to automated analysis with the maize pipeline implemented in our system. We found that the computed digital volume and number of leaves correlate with our manually measured data in high accuracy up to 0.98 and 0.95, respectively. In summary, IAP provides a multiple set of functionalities for import/export, management, and automated analysis of high-throughput plant phenotyping data, and its analysis results are highly reliable.Plant bioinformatics faces the challenge of integrating information from the related “omics” fields to elucidate the functional relationship between genotype and observed phenotype (Edwards and Batley, 2004), known as the genotype-phenotype map (Houle et al., 2010). One of the main obstacles is our currently limited ability of systemic depiction and quantification of plant phenotypes, representing the so-called phenotyping bottleneck phenomenon (Furbank and Tester, 2011). To get a comprehensive genotype-phenotype map, more accurate and precise phenotyping strategies are required to empower high-resolution linkage mapping and genome-wide association studies in order to uncover underlying genetic variants associated with complex phenotypic traits, which aim to improve the efficiency, effectiveness, and economy of cultivars in plant breeding (Cobb et al., 2013). In the era of phenomics, automatic high-throughput phenotyping in a noninvasive manner is applied to identify and quantify plant phenotypic traits. Plants are bred in fully automated greenhouses under predefined environmental conditions with controlled temperature, watering, and humidity. To meet the demand of data access, exchange, and sharing, several phenomics-related projects in the context of several consortia have been launched, such as the International Plant Phenotyping Network (http://www.plantphenomics.com/), the European Plant Phenotyping Network (http://www.plant-phenotyping-network.eu/), and the German Plant Phenotyping Network (http://www.dppn.de/).Thanks to the development of new imaging and transport systems, various automated or semiautomated high-throughput plant phenotyping systems are being developed and used to examine plant function and performance under controlled conditions. PHENOPSIS (Granier et al., 2006) is one of the pioneering platforms that was developed to dissect genotype-environment effects on plant growth in Arabidopsis (Arabidopsis thaliana). GROWSCREEN (Walter et al., 2007; Biskup et al., 2009; Jansen et al., 2009; Nagel et al., 2012) was designed for rapid optical phenotyping of different plant species with respect to different biological aspects. Other systems in the context of high-throughput phenotyping include Phenodyn/Phenoarch (Sadok et al., 2007), TraitMill (Reuzeau et al., 2005; Reuzeau, 2007), Phenoscope (Tisné et al., 2013), RootReader3D (Clark et al., 2011), GROW Map (http://www.fz-juelich.de/ibg/ibg-2/EN/methods_jppc/methods_node.html), and LemnaTec Scanalyzer 3D. These developments enable the phenotyping of specific organs (e.g. leaf, root, and shoot) or of whole plants. Some of them are even used for three-dimensional plant analysis (Clark et al., 2011). Consequently, several specific software applications (a comprehensive list can be found at http://www.phenomics.cn/links.php), such as HYPOTrace (Wang et al., 2009), HTPheno (Hartmann et al., 2011), LAMINA (Bylesjö et al., 2008), PhenoPhyte (Green et al., 2012), Rosette Tracker (De Vylder et al., 2012), LeafAnalyser (Weight et al., 2008), RootNav (Pound et al., 2013), SmartGrain (Tanabata et al., 2012), and LemnaGrid, were designed to extract a wide range of measurements, such as height/length, width, shape, projected area, digital volume, compactness, relative growth rate, and colorimetric analysis.The huge amount of generated image data from various phenotyping systems requires appropriate data management as well as an appropriate analytical framework for data interpretation (Fiorani and Schurr, 2013). However, most of the developed image-analysis tools are designed for a specific task, for specific plant species, or are not freely available to the research community. They lack flexibility in terms of needed adaptations to meet new analysis requirements. For example, it would be desirable that a system could handle imaging data from different sources (either from fully automated high-throughput phenotyping systems or from setups where images are acquired manually), different imaging modalities (fluorescence, near-infrared, and thermal imaging), and/or different species (wheat [Triticum aestivum], barley [Hordeum vulgare], maize [Zea mays], and Arabidopsis).In this work, we present Integrated Analysis Platform (IAP), a scalable open-source framework, for high-throughput plant phenotyping data processing. IAP handles different image sources and helps to organize phenotypic data by retaining the metadata from the input in the result data set. In order to measure phenotypic traits in new or modified setups, users can easily create new analysis pipelines or modify the predefined ones. IAP provides various user-friendly interfaces at different system levels to meet the demands of users (e.g. software developers, bioinformaticians, and biologists) with different experiences in software programming.     

GUItars: A GUI Tool for Analysis of High-Throughput RNA Interference Screening Data

Background

High-throughput RNA interference (RNAi) screening has become a widely used approach to elucidating gene functions. However, analysis and annotation of large data sets generated from these screens has been a challenge for researchers without a programming background. Over the years, numerous data analysis methods were produced for plate quality control and hit selection and implemented by a few open-access software packages. Recently, strictly standardized mean difference (SSMD) has become a widely used method for RNAi screening analysis mainly due to its better control of false negative and false positive rates and its ability to quantify RNAi effects with a statistical basis. We have developed GUItars to enable researchers without a programming background to use SSMD as both a plate quality and a hit selection metric to analyze large data sets.

Results

The software is accompanied by an intuitive graphical user interface for easy and rapid analysis workflow. SSMD analysis methods have been provided to the users along with traditionally-used z-score, normalized percent activity, and t-test methods for hit selection. GUItars is capable of analyzing large-scale data sets from screens with or without replicates. The software is designed to automatically generate and save numerous graphical outputs known to be among the most informative high-throughput data visualization tools capturing plate-wise and screen-wise performances. Graphical outputs are also written in HTML format for easy access, and a comprehensive summary of screening results is written into tab-delimited output files.

Conclusion

With GUItars, we demonstrated robust SSMD-based analysis workflow on a 3840-gene small interfering RNA (siRNA) library and identified 200 siRNAs that increased and 150 siRNAs that decreased the assay activities with moderate to stronger effects. GUItars enables rapid analysis and illustration of data from large- or small-scale RNAi screens using SSMD and other traditional analysis methods. The software is freely available at http://sourceforge.net/projects/guitars/.     

miRDOA:集数据存储与在线分析于一体的综合型microRNA数据库

miRNA(microRNA)是一类小的非编码RNA,普遍存在于动物、植物等多个物种中,研究表明在原生生物以及病毒中也有miRNA。miRNA在生物的成长、发育、细胞凋亡、神经紊乱、癌症等多个方面都起到至关重要的作用,miRNA还可作为Biomarker应用于癌症等疾病的诊断和治疗。miRDOA (miRNA Database and Online Analysis)数据库存储了二百多个物种的miRNA相关信息,提供了基本的浏览和搜索功能。用户可以通过物种来浏览miRNA相关数据,也可以通过miRNA、靶基因或者序列进行检索。除此之外,miRDOA还提供了在线分析模块,主要对高通量测序数据进行初步分析,在此基础上,添加了靶基因预测、表达谱数据差异分析,以及在线BLAST功能。miRDOA是一个完全免费的开源的数据库网站,并实时更新。     

An Enzymatic Assay for High-Throughput Screening of Cytidine-Producing Microbial Strains

Cytidine is an industrially useful precursor for the production of antiviral compounds and a variety of industrial compounds. Interest in the microbial production of cytidine has grown recently and high-throughput screening of cytidine over-producers is an important approach in large-scale industrial production using microorganisms. An enzymatic assay for cytidine was developed combining cytidine deaminase (CDA) and indophenol method. CDA catalyzes the cleavage of cytidine to uridine and NH₃, the latter of which can be accurately determined using the indophenol method. The assay was performed in 96-well plates and had a linear detection range of cytidine of 0.058 - 10 mM. This assay was used to determine the amount of cytidine in fermentation flasks and the results were compared with that of High Perfomance Liquid Chromatography (HPLC) method. The detection range of the CDA method is not as wide as that of the HPLC, furthermore the correlation factor of CDA method is not as high as that of HPLC. However, it was suitable for the detection of large numbers of crude samples and was applied to high-throughput screening for high cytidine-producing strains using 96-well deep-hole culture plates. This assay was proved to be simple, accurate, specific and suitable for cytidine detection and high-throughput screening of cytidine-producing strains in large numbers of samples (96 well or more).     

A High-Throughput Fluorescence-Based Assay System for Appetite-Regulating Gene and Drug Screening

The increasing number of people suffering from metabolic syndrome and obesity is becoming a serious problem not only in developed countries, but also in developing countries. However, there are few agents currently approved for the treatment of obesity. Those that are available are mainly appetite suppressants and gastrointestinal fat blockers. We have developed a simple and rapid method for the measurement of the feeding volume of Danio rerio (zebrafish). This assay can be used to screen appetite suppressants and enhancers. In this study, zebrafish were fed viable paramecia that were fluorescently-labeled, and feeding volume was measured using a 96-well microplate reader. Gene expression analysis of brain-derived neurotrophic factor (bdnf), knockdown of appetite-regulating genes (neuropeptide Y, preproinsulin, melanocortin 4 receptor, agouti related protein, and cannabinoid receptor 1), and the administration of clinical appetite suppressants (fluoxetine, sibutramine, mazindol, phentermine, and rimonabant) revealed the similarity among mechanisms regulating appetite in zebrafish and mammals. In combination with behavioral analysis, we were able to evaluate adverse effects on locomotor activities from gene knockdown and chemical treatments. In conclusion, we have developed an assay that uses zebrafish, which can be applied to high-throughput screening and target gene discovery for appetite suppressants and enhancers.     

Gene Context Analysis in the Integrated Microbial Genomes (IMG) Data Management System

Computational methods for determining the function of genes in newly sequenced genomes have been traditionally based on sequence similarity to genes whose function has been identified experimentally. Function prediction methods can be extended using gene context analysis approaches such as examining the conservation of chromosomal gene clusters, gene fusion events and co-occurrence profiles across genomes. Context analysis is based on the observation that functionally related genes are often having similar gene context and relies on the identification of such events across phylogenetically diverse collection of genomes. We have used the data management system of the Integrated Microbial Genomes (IMG) as the framework to implement and explore the power of gene context analysis methods because it provides one of the largest available genome integrations. Visualization and search tools to facilitate gene context analysis have been developed and applied across all publicly available archaeal and bacterial genomes in IMG. These computations are now maintained as part of IMG''s regular genome content update cycle. IMG is available at: http://img.jgi.doe.gov.     

An High-Throughput In Vivo Screening System to Select H3K4-Specific Histone Demethylase Inhibitors

Background

Histone demethylases (HDMs) have a prominent role in epigenetic regulation and are emerging as potential therapeutic cancer targets. The search for small molecules able to inhibit HDMs in vivo is very active but at the present few compounds were found to be specific for defined classes of these enzymes.

Methodology/Principal Findings

In order to discover inhibitors specific for H3K4 histone demethylation we set up a screening system which tests the effects of candidate small molecule inhibitors on a S.cerevisiae strain which requires Jhd2 demethylase activity to efficiently grow in the presence of rapamycin. In order to validate the system we screened a library of 45 structurally different compounds designed as competitive inhibitors of α -ketoglutarate (α-KG) cofactor of the enzyme, and found that one of them inhibited Jhd2 activity in vitro and in vivo. The same compound effectively inhibits human Jumonji AT-Rich Interactive Domain (JARID) 1B and 1D in vitro and increases H3K4 tri-methylation in HeLa cell nuclear extracts (NEs). When added in vivo to HeLa cells, the compound leads to an increase of tri-methyl-H3K4 (H3K4me3) but does not affect H3K9 tri-methylation. We describe the cytostatic and toxic effects of the compound on HeLa cells at concentrations compatible with its inhibitory activity.

Conclusions/Significance

Our screening system is proved to be very useful in testing putative H3K4-specific HDM inhibitors for the capacity of acting in vivo without significantly altering the activity of other important 2-oxoglutarate oxygenases.     

HTSstation: A Web Application and Open-Access Libraries for High-Throughput Sequencing Data Analysis

The HTSstation analysis portal is a suite of simple web forms coupled to modular analysis pipelines for various applications of High-Throughput Sequencing including ChIP-seq, RNA-seq, 4C-seq and re-sequencing. HTSstation offers biologists the possibility to rapidly investigate their HTS data using an intuitive web application with heuristically pre-defined parameters. A number of open-source software components have been implemented and can be used to build, configure and run HTS analysis pipelines reactively. Besides, our programming framework empowers developers with the possibility to design their own workflows and integrate additional third-party software. The HTSstation web application is accessible at http://htsstation.epfl.ch.     

Discovery of Bile Salt Hydrolase Inhibitors Using an Efficient High-Throughput Screening System

The global trend of restricting the use of antibiotic growth promoters (AGP) in animal production necessitates the need to develop valid alternatives to maintain productivity and sustainability of food animals. Previous studies suggest inhibition of bile salt hydrolase (BSH), an intestinal bacteria-produced enzyme that exerts negative impact on host fat digestion and utilization, is a promising approach to promote animal growth performance. To achieve the long term goal of developing novel alternatives to AGPs, in this study, a rapid and convenient high-throughput screening (HTS) system was developed and successfully used for identification of BSH inhibitors. With the aid of a high-purity BSH from a chicken Lactobacillus salivarius strain, we optimized various screening conditions (e.g. BSH concentration, reaction buffer pH, incubation temperature and length, substrate type and concentration) and establish a precipitation-based screening approach to identify BSH inhibitors using 96-well or 384-well microplates. A pilot HTS was performed using a small compound library comprised of 2,240 biologically active and structurally diverse compounds. Among the 107 hits, several promising and potent BSH inhibitors (e.g. riboflavin and phenethyl caffeate) were selected and validated by standard BSH activity assay. Interestingly, the HTS also identified a panel of antibiotics as BSH inhibitor; in particular, various tetracycline antibiotics and roxarsone, the widely used AGP, have been demonstrated to display potent inhibitory effect on BSH. Together, this study developed an efficient HTS system and identified several BSH inhibitors with potential as alternatives to AGP. In addition, the findings from this study also suggest a new mode of action of AGP for promoting animal growth.     

Development and Implementation of a High-Throughput Compound Screening Assay for Targeting Disrupted ER Calcium Homeostasis in Alzheimer's Disease

Disrupted intracellular calcium homeostasis is believed to occur early in the cascade of events leading to Alzheimer''s disease (AD) pathology. Particularly familial AD mutations linked to Presenilins result in exaggerated agonist-evoked calcium release from endoplasmic reticulum (ER). Here we report the development of a fully automated high-throughput calcium imaging assay utilizing a genetically-encoded FRET-based calcium indicator at single cell resolution for compound screening. The established high-throughput screening assay offers several advantages over conventional high-throughput calcium imaging technologies. We employed this assay for drug discovery in AD by screening compound libraries consisting of over 20,000 small molecules followed by structure-activity-relationship analysis. This led to the identification of Bepridil, a calcium channel antagonist drug in addition to four further lead structures capable of normalizing the potentiated FAD-PS1-induced calcium release from ER. Interestingly, it has recently been reported that Bepridil can reduce Aβ production by lowering BACE1 activity. Indeed, we also detected lowered Aβ, increased sAPPα and decreased sAPPβ fragment levels upon Bepridil treatment. The latter findings suggest that Bepridil may provide a multifactorial therapeutic modality for AD by simultaneously addressing multiple aspects of the disease.     

Analysis of Compound Synergy in High-Throughput Cellular Screens by Population-Based Lifetime Modeling

Despite the successful introduction of potent anti-cancer therapeutics, most of these drugs lead to only modest tumor-shrinkage or transient responses, followed by re-growth of tumors. Combining different compounds has resulted in enhanced tumor control and prolonged survival. However, methods querying the efficacy of such combinations have been hampered by limited scalability, analytical resolution, statistical feasibility, or a combination thereof. We have developed a theoretical framework modeling cellular viability as a stochastic lifetime process to determine synergistic compound combinations from high-throughput cellular screens. We apply our method to data derived from chemical perturbations of 65 cancer cell lines with two inhibitors. Our analysis revealed synergy for the combination of both compounds in subsets of cell lines. By contrast, in cell lines in which inhibition of one of both targets was sufficient to induce cell death, no synergy was detected, compatible with the topology of the oncogenically activated signaling network. In summary, we provide a tool for the measurement of synergy strength for combination perturbation experiments that might help define pathway topologies and direct clinical trials.     

Improving Hierarchical Models Using Historical Data with Applications in High-Throughput Genomics Data Analysis

Modern high-throughput biotechnologies such as microarray and next-generation sequencing produce a massive amount of information for each sample assayed. However, in a typical high-throughput experiment, only limited amount of data are observed for each individual feature, thus the classical “large p, small n” problem. Bayesian hierarchical model, capable of borrowing strength across features within the same dataset, has been recognized as an effective tool in analyzing such data. However, the shrinkage effect, the most prominent feature of hierarchical features, can lead to undesirable over-correction for some features. In this work, we discuss possible causes of the over-correction problem and propose several alternative solutions. Our strategy is rooted in the fact that in the Big Data era, large amount of historical data are available which should be taken advantage of. Our strategy presents a new framework to enhance the Bayesian hierarchical model. Through simulation and real data analysis, we demonstrated superior performance of the proposed strategy. Our new strategy also enables borrowing information across different platforms which could be extremely useful with emergence of new technologies and accumulation of data from different platforms in the Big Data era. Our method has been implemented in R package “adaptiveHM,” which is freely available from https://github.com/benliemory/adaptiveHM.     

Unified Materials Information System (UMIS): An Integrated Material Stocks and Flows Data Structure

Modern society depends on the use of many diverse materials. Effectively managing these materials is becoming increasingly important and complex, from the analysis of supply chains, to quantifying their environmental impacts, to understanding future resource availability. Material stocks and flows data enable such analyses, but currently exist mainly as discrete packages, with highly varied type, scope, and structure. These factors constitute a powerful barrier to holistic integration and thus universal analysis of existing and yet to be published material stocks and flows data. We present the Unified Materials Information System (UMIS) to overcome this barrier by enabling material stocks and flows data to be comprehensively integrated across space, time, materials, and data type independent of their disaggregation, without loss of information, and avoiding double counting. UMIS can therefore be applied to structure diverse material stocks and flows data and their metadata across material systems analysis methods such as material flow analysis (MFA), input‐output analysis, and life cycle assessment. UMIS uniquely labels and visualizes processes and flows in UMIS diagrams; therefore, material stocks and flows data visualized in UMIS diagrams can be individually referenced in databases and computational models. Applications of UMIS to restructure existing material stocks and flows data represented by block flow diagrams, system dynamics diagrams, Sankey diagrams, matrices, and derived using the economy‐wide MFA classification system are presented to exemplify use. UMIS advances the capabilities with which complex quantitative material systems analysis, archiving, and computation of material stocks and flows data can be performed.     

The Logic of EGFR/ErbB Signaling: Theoretical Properties and Analysis of High-Throughput Data

The epidermal growth factor receptor (EGFR) signaling pathway is probably the best-studied receptor system in mammalian cells, and it also has become a popular example for employing mathematical modeling to cellular signaling networks. Dynamic models have the highest explanatory and predictive potential; however, the lack of kinetic information restricts current models of EGFR signaling to smaller sub-networks. This work aims to provide a large-scale qualitative model that comprises the main and also the side routes of EGFR/ErbB signaling and that still enables one to derive important functional properties and predictions. Using a recently introduced logical modeling framework, we first examined general topological properties and the qualitative stimulus-response behavior of the network. With species equivalence classes, we introduce a new technique for logical networks that reveals sets of nodes strongly coupled in their behavior. We also analyzed a model variant which explicitly accounts for uncertainties regarding the logical combination of signals in the model. The predictive power of this model is still high, indicating highly redundant sub-structures in the network. Finally, one key advance of this work is the introduction of new techniques for assessing high-throughput data with logical models (and their underlying interaction graph). By employing these techniques for phospho-proteomic data from primary hepatocytes and the HepG2 cell line, we demonstrate that our approach enables one to uncover inconsistencies between experimental results and our current qualitative knowledge and to generate new hypotheses and conclusions. Our results strongly suggest that the Rac/Cdc42 induced p38 and JNK cascades are independent of PI3K in both primary hepatocytes and HepG2. Furthermore, we detected that the activation of JNK in response to neuregulin follows a PI3K-dependent signaling pathway.     

Integrated Analysis of Microarray Data of Atherosclerotic Plaques: Modulation of the Ubiquitin-Proteasome System

Atherosclerosis is a typical complex multi-factorial disease and many molecules at different levels and pathways were involved in its development. Some studies have investigated the dysregulation in atherosclerosis at mRNA, miRNA or DNA methylation level, respectively. However, to our knowledge, the studies that integrated these data and revealed the abnormal networks of atherosclerosis have not been reported. Using microarray technology, we analyzed the omics data in atherosclerosis at mRNA, miRNA and DNA methylation levels. Our results demonstrated that the global DNA methylation and expression of miRNA/mRNA were significantly decreased in atherosclerotic plaque than in normal vascular tissue. The interaction network constructed using the integrative data revealed many genes, cellular processes and signaling pathways which were widely considered to play crucial roles in atherosclerosis and also revealed some genes, miRNAs or signaling pathways which have not been investigated in atherosclerosis until now (e.g. miR-519d and SNTB2). Moreover, the overall protein ubiquitination in atherosclerotic plaque was significantly increased. The proteasome activity was increased early but decreased in advanced atherosclerosis. Our study revealed many classic and novel genes and miRNAs involved in atherosclerosis and indicated the effects of ubiquitin-proteasome system on atherosclerosis might be closely related to the course of atherosclerosis. However, the efficacy of proteasome inhibitors in the treatment of atherosclerosis still needs more research.     

Tn-Seq Explorer: A Tool for Analysis of High-Throughput Sequencing Data of Transposon Mutant Libraries

Tn-seq is a high throughput technique for analysis of transposon mutant libraries. Tn-seq Explorer was developed as a convenient and easy-to-use package of tools for exploration of the Tn-seq data. In a typical application, the user will have obtained a collection of sequence reads adjacent to transposon insertions in a reference genome. The reads are first aligned to the reference genome using one of the tools available for this task. Tn-seq Explorer reads the alignment and the gene annotation, and provides the user with a set of tools to investigate the data and identify possibly essential or advantageous genes as those that contain significantly low counts of transposon insertions. Emphasis is placed on providing flexibility in selecting parameters and methodology most appropriate for each particular dataset. Tn-seq Explorer is written in Java as a menu-driven, stand-alone application. It was tested on Windows, Mac OS, and Linux operating systems. The source code is distributed under the terms of GNU General Public License. The program and the source code are available for download at http://www.cmbl.uga.edu/downloads/programs/Tn_seq_Explorer/ and https://github.com/sina-cb/Tn-seqExplorer.     

Fluorescence Imaging-Based High-Throughput Screening of Fast- and Slow-Cycling LOV Proteins

Light-oxygen-voltage (LOV) domains function as blue light-inducible molecular switches. The photosensory LOV domains derived from plants and fungi have provided an indispensable tool for optogenetics. Here we develop a high-throughput screening system to efficiently improve switch-off kinetics of LOV domains. The present system is based on fluorescence imaging of thermal reversion of a flavin cofactor bound to LOV domains. We conducted multi site-directed random mutagenesis of seven amino acid residues surrounding the flavin cofactor of the second LOV domain derived from Avena sativa phototropin 1 (AsLOV2). The gene library was introduced into Escherichia coli cells. Then thermal reversion of AsLOV2 variants, respectively expressed in different bacterial colonies on agar plate, was imaged with a stereoscopic fluorescence microscope. Based on the mutagenesis and imaging-based screening, we isolated 12 different variants showing substantially faster thermal reversion kinetics than wild-type AsLOV2. Among them, AsLOV2-V416T exhibited thermal reversion with a time constant of 2.6 s, 21-fold faster than wild-type AsLOV2. With a slight modification of the present approach, we also have efficiently isolated 8 different decelerated variants, represented by AsLOV2-V416L that exhibited thermal reversion with a time constant of 4.3×10³ s (78-fold slower than wild-type AsLOV2). The present approach based on fluorescence imaging of the thermal reversion of the flavin cofactor is generally applicable to a variety of blue light-inducible molecular switches and may provide a new opportunity for the development of molecular tools for emerging optogenetics.