Raman-Activated Droplet Sorting (RADS) for Label-Free High-Throughput Screening of Microalgal Single-Cells

Abstract Backgroundα-Amylases is one of the most important starch degrading enzymes and has the widest range of industrial applications. Bacillus licheniformis has been widely used as a cell factory for industrial production of amylase. However, difficulties in genetic modification of B. licheniformis have limited its widespread use. Directed evolution, based on the combination of random mutagenesis and high throughput screening (HTS), has been proven an effective strategy in strain improvement for increasing the productivity, but it requires a suitable HTS system to screen the desired mutants. Droplet-based microfluidics has emerged as a powerful tool for single-cell screening with ultra-high throughput, however, the accessibility of a droplet microfluidic HTS platform to users having no background in microfluidics is still an issue. ResultsHere, we first developed a microfluidic HTS platform based on fluorescence-activated droplet sorting (FADS) technology. This platform allowed (i) encapsulation of single cells in monodisperse water-in-oil droplets; (ii) cell growth and protein production in droplets; (iii) sorting of droplets based on their fluorescence intensities. To validate the platform, a model selection experiment of a binary mixture of Bacillus strains was performed and a 45.6-fold enrichment was achieved at a sorting rate of 300 droplets per second. Furthermore, we used the platform for the selection of higher α-amylase-producing strains from a library of B. licheniformis strains (a strain already used at industrial-scale). The B. licheniformis mutant library was generated by atmospheric and room temperature plasma (ARTP) mutagenesis. The clones displaying over 50% improvement in α-amylases productivity compared to the wild-type were isolated.ConclusionsWe established an efficient droplet microfluidic platform which consisted of droplet generation, droplet incubation, and sorting of droplets with a throughput of up to 1 × 106 droplets per h. The screening platform was demonstrated by successfully identifying B. licheniformis clones with improved α-amylase production. We believe that the droplet platform could be extended to the development of other industrially valuable strains.

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

International Journal of Molecular Sciences ◽

10.3390/ijms22094417 ◽

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.

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

APOPTOSIS ◽

10.1007/s10495-014-1009-9 ◽

2014 ◽

Vol 19 (9) ◽

pp. 1411-1418 ◽

Cited By ~ 10

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

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

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/2472555217727701 ◽

2017 ◽

Vol 22 (10) ◽

pp. 1203-1210 ◽

Cited By ~ 5

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.

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

SLAS DISCOVERY Advancing Life Sciences ◽

10.1177/2472555217727517 ◽

2017 ◽

Vol 22 (10) ◽

pp. 1246-1252 ◽

Cited By ~ 1

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.

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High Throughput Screening and Selection of Single Cells in Suspension Using Fluorescence Parameters

ESACT Proceedings - Animal Cell Technology Meets Genomics ◽

10.1007/1-4020-3103-3_101 ◽

2005 ◽

pp. 517-520

Author(s):

C. Giese ◽

C.D. Demmler ◽

G. Gradl ◽

U. Marx

Keyword(s):

High Throughput ◽

High Throughput Screening ◽

Single Cells ◽

Fluorescence Parameters ◽

Selection Of

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

SLAS TECHNOLOGY Translating Life Sciences Innovation ◽

10.1177/24726303211047839 ◽

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.

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