Interference: A Much-Neglected Aspect in High-Throughput Screening of Nanoparticles

2020 ◽  
Vol 39 (5) ◽  
pp. 397-421
Author(s):  
Charlene Andraos ◽  
Il Je Yu ◽  
Mary Gulumian
Keyword(s):  
Big Data ◽  
High Throughput ◽  
High Throughput Screening ◽  
Fluorescent Dyes ◽  
Membrane Integrity ◽  
Data Generation ◽  
Label Free ◽  
Predictive Toxicology ◽  
Toxicity Assay ◽  
Novel Concept

Despite several studies addressing nanoparticle (NP) interference with conventional toxicity assay systems, it appears that researchers still rely heavily on these assays, particularly for high-throughput screening (HTS) applications in order to generate “big” data for predictive toxicity approaches. Moreover, researchers often overlook investigating the different types of interference mechanisms as the type is evidently dependent on the type of assay system implemented. The approaches implemented in the literature appear to be not adequate as it often addresses only one type of interference mechanism with the exclusion of others. For example, interference of NPs that have entered cells would require intracellular assessment of their interference with fluorescent dyes, which has so far been neglected. The present study investigated the mechanisms of interference of gold NPs and silver NPs in assay systems implemented in HTS including optical interference as well as adsorption or catalysis. The conventional assays selected cover all optical read-out systems, that is, absorbance (XTT toxicity assay), fluorescence (CytoTox-ONE Homogeneous membrane integrity assay), and luminescence (CellTiter Glo luminescent assay). Furthermore, this study demonstrated NP quenching of fluorescent dyes also used in HTS (2′,7′-dichlorofluorescein, propidium iodide, and 5,5′,6,6′-tetrachloro-1,1′,3,3′-tetraethyl-benzamidazolocarbocyanin iodide). To conclude, NP interference is, as such, not a novel concept, however, ignoring this aspect in HTS may jeopardize attempts in predictive toxicology. It should be mandatory to report the assessment of all mechanisms of interference within HTS, as well as to confirm results with label-free methodologies to ensure reliable big data generation for predictive toxicology.

scholarly journals Use of High-Throughput Mass Spectrometry to Reduce False Positives in Protease uHTS Screens

2014 ◽  
Vol 20 (2) ◽  
pp. 212-222 ◽  
Author(s):  
Gregory C. Adam ◽  
Juncai Meng ◽  
Joseph M. Rizzo ◽  
Adam Amoss ◽  
Jeffrey W. Lusen ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
High Throughput ◽  
High Throughput Screening ◽  
Fluorescent Dyes ◽  
False Positives ◽  
Indirect Detection ◽  
Detection Methods ◽  
Label Free ◽  
Fluorescent Compound ◽  
Time Resolved

As a label-free technology, mass spectrometry (MS) enables assays to be generated that monitor the conversion of substrates with native sequences to products without the requirement for substrate modifications or indirect detection methods. Although traditional liquid chromatography (LC)–MS methods are relatively slow for a high-throughput screening (HTS) paradigm, with cycle times typically ≥60 s per sample, the Agilent RapidFire High-Throughput Mass Spectrometry (HTMS) System, with a cycle time of 5–7 s per sample, enables rapid analysis of compound numbers compatible with HTS. By monitoring changes in mass directly, HTMS assays can be used as a triaging tool by eliminating large numbers of false positives resulting from fluorescent compound interference or from compounds interacting with hydrophobic fluorescent dyes appended to substrates. Herein, HTMS assays were developed for multiple protease programs, including cysteine, serine, and aspartyl proteases, and applied as a confirmatory assay. The confirmation rate for each protease assay averaged <30%, independent of the primary assay technology used (i.e., luminescent, fluorescent, and time-resolved fluorescent technologies). Importantly, >99% of compounds designed to inhibit the enzymes were confirmed by the corresponding HTMS assay. Hence, HTMS is an effective tool for removing detection-based false positives from ultrahigh-throughput screening, resulting in hit lists enriched in true actives for downstream dose response titrations and hit-to-lead efforts.

scholarly journals Accelerating organic solar cell material's discovery: high-throughput screening and big data

2021 ◽  
Author(s):  
Xabier Rodríguez-Martínez ◽  
Enrique Pascual-San-José ◽  
Mariano Campoy-Quiles
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
High Throughput ◽  
Organic Solar Cells ◽  
High Throughput Screening ◽  
Organic Solar Cell ◽  
State Of The Art ◽  
Review Article ◽  
Machine Learning Algorithms ◽  
Device Optimization

This review article presents the state-of-the-art in high-throughput computational and experimental screening routines with application in organic solar cells, including materials discovery, device optimization and machine-learning algorithms.

scholarly journals Development of a Robust High-Throughput Screening Platform for Inhibitors of the Striatal-Enriched Tyrosine Phosphatase (STEP)

2021 ◽  
Vol 22 (9) ◽  
pp. 4417
Author(s):  
Lester J Lambert ◽  
Stefan Grotegut ◽  
Maria Celeridad ◽  
Palak Gosalia ◽  
Laurent JS De Backer ◽  
...  
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
Tyrosine Kinases ◽  
High Throughput Screening ◽  
Kinase Inhibitors ◽  
Tyrosine Phosphatase ◽  
Protein Tyrosine Phosphatases ◽  
Synaptic Function ◽  
Label Free ◽  
Protein Tyrosine

Many human diseases are the result of abnormal expression or activation of protein tyrosine kinases (PTKs) and protein tyrosine phosphatases (PTPs). Not surprisingly, more than 30 tyrosine kinase inhibitors (TKIs) are currently in clinical use and provide unique treatment options for many patients. PTPs on the other hand have long been regarded as “undruggable” and only recently have gained increased attention in drug discovery. Striatal-enriched tyrosine phosphatase (STEP) is a neuron-specific PTP that is overactive in Alzheimer’s disease (AD) and other neurodegenerative and neuropsychiatric disorders, including Parkinson’s disease, schizophrenia, and fragile X syndrome. An emergent model suggests that the increase in STEP activity interferes with synaptic function and contributes to the characteristic cognitive and behavioral deficits present in these diseases. Prior efforts to generate STEP inhibitors with properties that warrant clinical development have largely failed. To identify novel STEP inhibitor scaffolds, we developed a biophysical, label-free high-throughput screening (HTS) platform based on the protein thermal shift (PTS) technology. In contrast to conventional HTS using STEP enzymatic assays, we found the PTS platform highly robust and capable of identifying true hits with confirmed STEP inhibitory activity and selectivity. This new platform promises to greatly advance STEP drug discovery and should be applicable to other PTP targets.

Label free high throughput screening for apoptosis inducing chemicals using time-lapse microscopy signal processing

APOPTOSIS ◽  
2014 ◽  
Vol 19 (9) ◽  
pp. 1411-1418 ◽  
Author(s):  
Obaid Aftab ◽  
Madiha Nazir ◽  
Mårten Fryknäs ◽  
Ulf Hammerling ◽  
Rolf Larsson ◽  
...  
Keyword(s):  
Signal Processing ◽  
High Throughput ◽  
High Throughput Screening ◽  
Time Lapse ◽  
Label Free ◽  
Time Lapse Microscopy

scholarly journals Integration of an In Situ MALDI-Based High-Throughput Screening Process: A Case Study with Receptor Tyrosine Kinase c-MET

2017 ◽  
Vol 22 (10) ◽  
pp. 1203-1210 ◽  
Author(s):  
Katrin Beeman ◽  
Jens Baumgärtner ◽  
Manuel Laubenheimer ◽  
Karlheinz Hergesell ◽  
Martin Hoffmann ◽  
...  
Keyword(s):  
Drug Discovery ◽  
High Throughput ◽  
High Throughput Screening ◽  
Maldi Ms ◽  
Kinase Assay ◽  
Label Free ◽  
Screening Process ◽  
Label Free Detection ◽  
Ic50 Values

Mass spectrometry (MS) is known for its label-free detection of substrates and products from a variety of enzyme reactions. Recent hardware improvements have increased interest in the use of matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS for high-throughput drug discovery. Despite interest in this technology, several challenges remain and must be overcome before MALDI-MS can be integrated as an automated “in-line reader” for high-throughput drug discovery. Two such hurdles include in situ sample processing and deposition, as well as integration of MALDI-MS for enzymatic screening assays that usually contain high levels of MS-incompatible components. Here we adapt our c-MET kinase assay to optimize for MALDI-MS compatibility and test its feasibility for compound screening. The pros and cons of the Echo (Labcyte) as a transfer system for in situ MALDI-MS sample preparation are discussed. We demonstrate that this method generates robust data in a 1536-grid format. We use the MALDI-MS to directly measure the ratio of c-MET substrate and phosphorylated product to acquire IC50 curves and demonstrate that the pharmacology is unaffected. The resulting IC50 values correlate well between the common label-based capillary electrophoresis and the label-free MALDI-MS detection method. We predict that label-free MALDI-MS-based high-throughput screening will become increasingly important and more widely used for drug discovery.

scholarly journals MALDIViz: A Comprehensive Informatics Tool for MALDI-MS Data Visualization and Analysis

2017 ◽  
Vol 22 (10) ◽  
pp. 1246-1252 ◽  
Author(s):  
Kishore Kumar Jagadeesan ◽  
Simon Ekström
Keyword(s):  
Mass Spectrometry ◽  
High Throughput ◽  
High Throughput Screening ◽  
Web Application ◽  
Low Cost ◽  
Maldi Ms ◽  
Ionization Mass ◽  
Label Free ◽  
Label Free Detection ◽  
R Shiny

Recently, mass spectrometry (MS) has emerged as an important tool for high-throughput screening (HTS) providing a direct and label-free detection method, complementing traditional fluorescent and colorimetric methodologies. Among the various MS techniques used for HTS, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) provides many of the characteristics required for high-throughput analyses, such as low cost, speed, and automation. However, visualization and analysis of the large datasets generated by HTS MALDI-MS can pose significant challenges, especially for multiparametric experiments. The datasets can be generated fast, and the complexity of the experimental data (e.g., screening many different sorbent phases, the sorbent mass, and the load, wash, and elution conditions) makes manual data analysis difficult. To address these challenges, a comprehensive informatics tool called MALDIViz was developed. This tool is an R-Shiny-based web application, accessible independently of the operating system and without the need to install any program locally. It has been designed to facilitate easy analysis and visualization of MALDI-MS datasets, comparison of multiplex experiments, and export of the analysis results to high-quality images.

Automated High-Throughput System Combining Small-Scale Synthesis with Bioassays and Reaction Screening

2021 ◽  
pp. 247263032110478
Author(s):  
Nicolás M. Morato ◽  
MyPhuong T. Le ◽  
Dylan T. Holden ◽  
R. Graham Cooks
Keyword(s):  
High Throughput ◽  
High Throughput Screening ◽  
Desorption Electrospray Ionization ◽  
Ambient Ionization ◽  
Machine Learning Algorithms ◽  
Small Scale ◽  
Label Free ◽  
Minimal Sample ◽  
Secondary Droplets ◽  
Automated Platform

The Purdue Make It system is a unique automated platform capable of small-scale in situ synthesis, screening small-molecule reactions, and performing direct label-free bioassays. The platform is based on desorption electrospray ionization (DESI), an ambient ionization method that allows for minimal sample workup and is capable of accelerating reactions in secondary droplets, thus conferring unique advantages compared with other high-throughput screening technologies. By combining DESI with liquid handling robotics, the system achieves throughputs of more than 1 sample/s, handling up to 6144 samples in a single run. As little as 100 fmol/spot of analyte is required to perform both initial analysis by mass spectrometry (MS) and further MSn structural characterization. The data obtained are processed using custom software so that results are easily visualized as interactive heatmaps of reaction plates based on the peak intensities of m/ z values of interest. In this paper, we review the system’s capabilities as described in previous publications and demonstrate its utilization in two new high-throughput campaigns: (1) the screening of 188 unique combinatorial reactions (24 reaction types, 188 unique reaction mixtures) to determine reactivity trends and (2) label-free studies of the nicotinamide N-methyltransferase enzyme directly from the bioassay buffer. The system’s versatility holds promise for several future directions, including the collection of secondary droplets containing the products from successful reaction screening measurements, the development of machine learning algorithms using data collected from compound library screening, and the adaption of a variety of relevant bioassays to high-throughput MS.

scholarly journals Intuitive Web-Based Experimental Design for High-Throughput Biomedical Data

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Andreas Friedrich ◽  
Erhan Kenar ◽  
Oliver Kohlbacher ◽  
Sven Nahnsen
Keyword(s):  
Big Data ◽  
Experimental Design ◽  
High Throughput ◽  
Large Scale ◽  
Integrated Design ◽  
Added Value ◽  
Biomedical Data ◽  
Data Generation ◽  
Web Based ◽  
Data Annotation

Big data bioinformatics aims at drawing biological conclusions from huge and complex biological datasets. Added value from the analysis of big data, however, is only possible if the data is accompanied by accurate metadata annotation. Particularly in high-throughput experiments intelligent approaches are needed to keep track of the experimental design, including the conditions that are studied as well as information that might be interesting for failure analysis or further experiments in the future. In addition to the management of this information, means for an integrated design and interfaces for structured data annotation are urgently needed by researchers. Here, we propose a factor-based experimental design approach that enables scientists to easily create large-scale experiments with the help of a web-based system. We present a novel implementation of a web-based interface allowing the collection of arbitrary metadata. To exchange and edit information we provide a spreadsheet-based, humanly readable format. Subsequently, sample sheets with identifiers and metainformation for data generation facilities can be created. Data files created after measurement of the samples can be uploaded to a datastore, where they are automatically linked to the previously created experimental design model.

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