High-throughput System
High-throughput System - Related Videos
09:44
High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes
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2015
Here we describe a cell-based reporter gene assay as a valuable tool to screen chemical libraries for compounds modulating post-transcriptional control mechanisms exerted through 3’ UTR.
06:19
High-Throughput Screening to Obtain Crystal Hits for Protein Crystallography
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Cited by 16 •
2023
This protocol details high-throughput crystallization screening, ranging from the 1,536 microassay plate preparation to the end of a 6 week experimental time window. Details are included about the sample setup, the imaging obtained, and how users can perform analyses using an artificial intelligence-enabled graphical user interface to quickly and efficiently identify macromolecular crystallization conditions.
08:12
High-throughput Screening for Protein-based Inheritance in S. cerevisiae
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Cited by 2 •
2017
This protocol describes a high-throughput methodology to functionally screen for protein-based inheritance in S. cerevisiae.
07:00
qPCRTag Analysis - A High Throughput, Real Time PCR Assay for Sc2.0 Genotyping
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Cited by 18 •
2015
Designer chromosomes of the Synthetic Yeast Genome project, Sc2.0, can be distinguished from their native counterparts using a PCR-based genotyping assay called PCRTagging, which has a presence/absence endpoint. Here we describe a high-throughput real time PCR detection method for PCRTag genotyping.
06:50
A High-Throughput Electrochemiluminescence 7-Plex Assay Simultaneously Screening for Type 1 Diabetes and Multiple Autoimmune Diseases
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Cited by 3 •
2020
We model a simple multiplexed ECL assay that combines 7 autoantibody assays together. The assay is capable of screening for T1D and multiple other autoimmune diseases, simultaneously, including celiac disease, autoimmune thyroid disease, and autoimmune polyglandular syndrome 1.
FAQs
Standardized workflows keep sample handling, measurements, and experimental conditions consistent across many parallel tests. This consistency reduces variation introduced by manual differences and makes results easier to compare across samples or conditions. In neuroscience, standardized processing supports more reliable comparisons of neuronal activity, gene expression, synaptic function, and drug responses.
Miniaturized assays allow many measurements or experimental conditions to be handled within a compact, parallelized format. Their value lies in increasing experimental capacity while maintaining controlled conditions and reducing manual effort. This design helps researchers examine broad response patterns across extensive sample sets rather than relying on a small number of individual tests.
Computational analysis converts the large volume of measurements produced by parallel experiments into structured datasets that can be examined systematically. Researchers can use these datasets to identify patterns and compare biological responses across samples or conditions. The resulting organization supports more focused interpretation of neuronal, genetic, synaptic, or drug-response measurements.
Robotic handling reduces the amount of manual effort required to process many samples or conditions and helps apply the same workflow repeatedly. When combined with standardized procedures, it supports consistent experimental conditions across a large dataset. This repeatability is especially useful when neuroscience studies compare numerous neuronal responses or treatment conditions.
A typical workflow combines miniaturized assays, robotic handling, standardized experimental steps, and computational analysis. Samples or conditions are processed in parallel, measurements are collected under consistent conditions, and the resulting data are organized for comparison. This sequence increases experimental capacity while preserving a structured basis for interpreting large-scale results.
Researchers would choose this approach when they need to examine many samples, measurements, or experimental conditions efficiently. In neuroscience, it can support large-scale screening of neuronal activity, gene expression, synaptic function, or drug responses. The approach is useful when broad comparisons are needed to identify response patterns across extensive experimental datasets.
These datasets can reveal patterns among neuronal or treatment-related responses and allow researchers to compare biological behavior across many conditions. Such comparisons help identify potentially important mechanisms or responses and prioritize them for further study. The system therefore supports an initial large-scale investigation before researchers focus more closely on selected findings.