scholarly journals Deep mutational scanning by FACS-sorting of encapsulated E. coli micro-colonies

2018 ◽  
Author(s):  
Lars Behrendt ◽  
Amelie Stein ◽  
Shiraz Ali Shah ◽  
Karsten Zengler ◽  
Søren J. Sørensen ◽  
...  
Keyword(s):  
Amino Acid ◽  
High Throughput ◽  
High Throughput Screening ◽  
Fluorescent Protein ◽  
Single Cells ◽  
Fluorescence Signal ◽  
Amino Acid Substitutions ◽  
Nucleotide Exchange ◽  
High Throughput Analysis ◽  
Wide Range

AbstractWe present a method for high-throughput screening of protein variants where the signal is enhanced by micro-encapsulation of single cells into 20-30 μm agarose beads. Cells inside beads are propagated using standard agitation in liquid media and grow clonally into micro-colonies harboring several hundred bacteria. We have, as a proof-of-concept, analyzed random amino acid substitutions in the five C-terminal β-strands of the Green Fluorescent Protein (GFP). Starting from libraries of variants, each bead represents a clonal line of cells that can be separated by Fluorescence Activated Cell Sorting (FACS). Pools representing collections of individual variants with desired properties are subsequently analyzed by deep sequencing. Notably, the encapsulation approach described holds the potential for high-throughput analysis of systems where the fluorescence signal from a single cell is insufficient for detection. Fusion to GFP, or use of fluorogenic substrates, allows coupling protein levels or activity to sequence for a wide range of proteins. Here we analyzed more than 10,000 individual variants to gauge the effect of mutations on GFP-fluorescence. In the mutated region, we observed virtually all amino acid substitutions that are accessible by single nucleotide exchange. Lastly, we assessed the performance of biophysical protein stability predictors, FoldX and Rosetta, in predicting the outcome of the experiment. Both tools display good performance on average, suggesting that loss of thermodynamic stability is a key mechanism for the observed variation of the mutants. This, in turn, suggests that deep mutational scanning datasets may be used to more efficiently fine-tune such predictors, especially for mutations poorly covered by current biophysical data.

scholarly journals ZF-AutoML: An Easy Machine-Learning-Based Method to Detect Anomalies in Fluorescent-Labelled Zebrafish

Inventions ◽  
2019 ◽  
Vol 4 (4) ◽  
pp. 72
Author(s):  
Ryota Sawaki ◽  
Daisuke Sato ◽  
Hiroko Nakayama ◽  
Yuki Nakagawa ◽  
Yasuhito Shimada
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
High Throughput ◽  
High Throughput Screening ◽  
Large Scale ◽  
Fluorescent Protein ◽  
Learning Program ◽  
High Throughput Analysis ◽  
Easy Method ◽  
High Fecundity

Background: Zebrafish are efficient animal models for conducting whole organism drug testing and toxicological evaluation of chemicals. They are frequently used for high-throughput screening owing to their high fecundity. Peripheral experimental equipment and analytical software are required for zebrafish screening, which need to be further developed. Machine learning has emerged as a powerful tool for large-scale image analysis and has been applied in zebrafish research as well. However, its use by individual researchers is restricted due to the cost and the procedure of machine learning for specific research purposes. Methods: We developed a simple and easy method for zebrafish image analysis, particularly fluorescent labelled ones, using the free machine learning program Google AutoML. We performed machine learning using vascular- and macrophage-Enhanced Green Fluorescent Protein (EGFP) fishes under normal and abnormal conditions (treated with anti-angiogenesis drugs or by wounding the caudal fin). Then, we tested the system using a new set of zebrafish images. Results: While machine learning can detect abnormalities in the fish in both strains with more than 95% accuracy, the learning procedure needs image pre-processing for the images of the macrophage-EGFP fishes. In addition, we developed a batch uploading software, ZF-ImageR, for Windows (.exe) and MacOS (.app) to enable high-throughput analysis using AutoML. Conclusions: We established a protocol to utilize conventional machine learning platforms for analyzing zebrafish phenotypes, which enables fluorescence-based, phenotype-driven zebrafish screening.

scholarly journals High-Throughput Analysis of Selected Urinary Hydroxy Polycyclic Aromatic Hydrocarbons by an Innovative Automated Solid-Phase Microextraction

Molecules ◽  
2018 ◽  
Vol 23 (8) ◽  
pp. 1869 ◽  
Author(s):  
Stefano Dugheri ◽  
Alessandro Bonari ◽  
Matteo Gentili ◽  
Giovanni Cappelli ◽  
Ilenia Pompilio ◽  
...  
Keyword(s):  
Polycyclic Aromatic Hydrocarbons ◽  
High Throughput ◽  
Aromatic Hydrocarbons ◽  
High Throughput Screening ◽  
Solid Phase Microextraction ◽  
Solid Phase ◽  
User Friendliness ◽  
High Throughput Analysis ◽  
Polycyclic Aromatic ◽  
User Friendly

High-throughput screening of samples is the strategy of choice to detect occupational exposure biomarkers, yet it requires a user-friendly apparatus that gives relatively prompt results while ensuring high degrees of selectivity, precision, accuracy and automation, particularly in the preparation process. Miniaturization has attracted much attention in analytical chemistry and has driven solvent and sample savings as easier automation, the latter thanks to the introduction on the market of the three axis autosampler. In light of the above, this contribution describes a novel user-friendly solid-phase microextraction (SPME) off- and on-line platform coupled with gas chromatography and triple quadrupole-mass spectrometry to determine urinary metabolites of polycyclic aromatic hydrocarbons 1- and 2-hydroxy-naphthalene, 9-hydroxy-phenanthrene, 1-hydroxy-pyrene, 3- and 9-hydroxy-benzoantracene, and 3-hydroxy-benzo[a]pyrene. In this new procedure, chromatography’s sensitivity is combined with the user-friendliness of N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide on-fiber SPME derivatization using direct immersion sampling; moreover, specific isotope-labelled internal standards provide quantitative accuracy. The detection limits for the seven OH-PAHs ranged from 0.25 to 4.52 ng/L. Intra-(from 2.5 to 3.0%) and inter-session (from 2.4 to 3.9%) repeatability was also evaluated. This method serves to identify suitable risk-control strategies for occupational hygiene conservation programs.

scholarly journals Beeporter: a high-throughput tool for non-invasive analysis of honey bee virus infection

2021 ◽  
Author(s):  
Jay D. Evans ◽  
Olubukola Banmeke ◽  
Evan C. Palmer-Young ◽  
Yanping Chen ◽  
Eugene V. Ryabov
Keyword(s):  
Virus Infection ◽  
Honey Bee ◽  
High Throughput ◽  
Honey Bees ◽  
High Throughput Screening ◽  
Fluorescent Protein ◽  
Imaging System ◽  
Uv Light ◽  
Light Sources ◽  
Deformed Wing Virus

ABSTRACTHoney bees face numerous pests and pathogens but arguably none are as devastating as Deformed wing virus (DWV). Development of antiviral therapeutics and virus-resistant honey bee lines to control DWV in honey bees is slowed by the lack of a cost-effective high-throughput screening of DWV infection. Currently, analysis of virus infection and screening for antiviral treatments in bees and their colonies is tedious, requiring a well-equipped molecular biology laboratory and the use of hazardous chemicals. Here we utilize a cDNA clone of DWV tagged with green fluorescent protein (GFP) to develop the Beeporter assay, a method for detection and quantification of DWV infection in live honey bees. The assay involves infection of honey bee pupae by injecting a standardized DWV-GFP inoculum, followed by incubation for up to 44 hours. GFP fluorescence is recorded at intervals via commonly available long-wave UV light sources and a smartphone camera or a standard ultraviolet transilluminator gel imaging system. Nonlethal DWV monitoring allows high-throughput screening of antiviral candidates and a direct breeding tool for identifying honey bee parents with increased antivirus resistance. For even more rapid drug screening, we also describe a method for screening bees using 96-well trays and a spectrophotometer.

scholarly journals DeepTetrad: high-throughput analysis of meiotic tetrads by deep learning in plants

2019 ◽  
Author(s):  
Eun-Cheon Lim ◽  
Jaeil Kim ◽  
Jihye Park ◽  
Eun-Jung Kim ◽  
Juhyun Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Chromosome Segregation ◽  
High Throughput ◽  
Fluorescent Protein ◽  
Genetic Dissection ◽  
Crossover Frequency ◽  
Tetrad Analysis ◽  
High Throughput Analysis ◽  
Crossover Interference ◽  
Pollen Tetrad

AbstractMeiotic crossovers facilitate chromosome segregation and create new combinations of alleles in gametes. Crossover frequency varies along chromosomes and crossover interference limits the coincidence of closely spaced crossovers. Crossovers can be measured by observing the inheritance of linked transgenes expressing different colors of fluorescent protein in Arabidopsis pollen tetrads. Here we establish DeepTetrad, a deep learning-based image recognition package for pollen tetrad analysis that enables high-throughput measurements of crossover frequency and interference in individual plants. DeepTetrad will accelerate genetic dissection of mechanisms that control meiotic recombination.

scholarly journals Directed Evolution of Aptamer Discovery Technologies

2021 ◽  
Author(s):  
Diana Wu ◽  
Chelsea Gordon ◽  
John Shin ◽  
Michael Eisenstein ◽  
Hyongsok Tom Soh
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Click Reaction ◽  
Clinical Diagnostics ◽  
High Quality ◽  
Affinity Reagents ◽  
Wide Range ◽  
Target Binding ◽  
Modified Nucleobases ◽  
Parallel Fashion

Although antibodies are a powerful tool for molecular biology and clinical diagnostics, there are many emerging applications for which nucleic acid-based aptamers can be advantageous. However, generating high-quality aptamers with sufficient affinity and specificity for biomedical applications is a challenging feat for most research laboratories. In this Account, we describe four techniques developed in our lab to accelerate the discovery of high quality aptamer reagents that can achieve robust binding even for challenging molecular targets. The first method is particle display, in which we convert solution-phase aptamers into aptamer particles that can be screened via fluorescence-activated cell sorting (FACS) to quantitatively isolate individual aptamer particles based on their affinity. This enables the efficient isolation of high-affinity aptamers in fewer selection rounds than conventional methods, thereby minimizing selection biases and reducing the emergence of artifacts in the final aptamer pool. We subsequently developed the multi-parametric particle display (MPPD) method, which employs two-color FACS to isolate aptamer particles based on both affinity and specificity, yielding aptamers that exhibit excellent target binding even in complex matrices like serum. The third method is a click chemistry-based particle display (click-PD) that enables the generation and high-throughput screening of non-nattural aptamers with a wide range of base modifications. We have shown that these base-modified aptamers can achieve robust affinity and specificity for targets that have proven challenging or inaccessible with natural nucleotide-based aptamer libraries. Lastly, we describe the non-natural aptamer array (N2A2) platform, in which a modified benchtop sequencing instrument is used to characterize base-modified aptamers in a massively parallel fashion, enabling the efficient identification of molecules with excellent affinity and specificity for their targets. This system first generates aptamer clusters on the flow-cell surface that incorporate alkyne-modified nucleobases, and then performs a click reaction to couple those nucleobases to an azide-modified chemical moiety. This yields a sequence-defined array of tens of millions of base-modified sequences, which can then be characterized in a high-throughput fashion. Collectively, we believe that these advancements are helping to make aptamer technology more accessible, efficient, and robust, thereby enabling the use of these affinity reagents for a wider range of molecular recognition and detection-based applications.

scholarly journals High throughput method development and optimised production of leaf protein concentrates with potential to support the agri-industry

2021 ◽  
Author(s):  
Ajay Iyer ◽  
Lisa Guerrier ◽  
Salomé Leveque ◽  
Charles S. Bestwick ◽  
Sylvia H. Duncan ◽  
...  
Keyword(s):  
Amino Acid ◽  
High Throughput ◽  
High Throughput Screening ◽  
Protein Extraction ◽  
Leaf Protein ◽  
Protein Recovery ◽  
Screening Methods ◽  
Extraction Technology ◽  
Protein Concentrates ◽  
Ethanol Precipitation

AbstractInvasive plants offer an interesting and unconventional source of protein and the considerable investment made towards their eradication can potentially be salvaged through their revalorisation. To identify viable sources, effective and high-throughput screening methods are required, as well as efficient procedures to isolate these components. Rigorous assessment of low-cost, high-throughput screening assays for total sugar, phenolics and protein was performed, and ninhydrin, Lever and Fast Blue assays were found to be most suitable owing to high reliability scores and false positive errors less than 1%. These assays were used to characterise invasive Scottish plants such as Gorse (Ulex europeans), Broom (Cystisus scoparius) and Fireweed (Chamaenerion angustifolium). Protein extraction (alkali-, heat- and enzyme assisted) were tested on these plants, and further purification (acid and ethanol precipitation, as well as ultrafiltration) procedures were tested on Gorse, based on protein recovery values. Cellulase treatment and ethanol precipitation gave the highest protein recovery (64.0 ± 0.5%) and purity (96.8 ± 0.1%) with Gorse. The amino acid profile of the purified protein revealed high levels of essential amino acids (34.8 ± 0.0%). Comparison of results with preceding literature revealed a strong association between amino acid profiles and overall protein recovery with the extraction method employed. The final purity of the protein concentrates was closely associated to the protein content of the initial plant mass. Leaf protein extraction technology can effectively raise crop harvest indices, revalorise underutilised plants and waste streams.

Rapid generation of Hybrid Biochemical/Mechanical Cues in Heterogeneous Droplets for High-Throughput Screening of Cellular Response

Lab on a Chip ◽  
2021 ◽  
Author(s):  
Xing Zhao ◽  
Gaozhi Ou ◽  
Mengcheng Lei ◽  
Yang Zhang ◽  
Lina Li ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
High Throughput ◽  
High Throughput Screening ◽  
Cellular Response ◽  
Wide Range ◽  
Rapid Generation ◽  
P Cells

Cells in native microenvironment are subjected to varying combinations of biochemical cues and mechanical cues in a wide range. Despite many signaling pathways have been found to be responsive for...

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