High-throughput Technologies
High-throughput Technologies - 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.
13:27
High-Throughput DNA Plasmid Multiplexing and Transfection Using Acoustic Nanodispensing Technology
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Cited by 3 •
2019
This protocol describes high-throughput plasmid transfection of mammalian cells in a 384-well plate using acoustic droplet ejection technology. The time-consuming, error-prone DNA dispensing and multiplexing, but also the transfection reagent dispensing, are software-driven and performed by a nanodispenser device. The cells are then seeded in these prefilled wells.
09:45
Microfluidics-based High-throughput Circulating Tumor Cell Sorting and Single-cell Sequencing Technology
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2025
Circulating tumor cells (CTCs) are critical for the study of cancer progression and metastasis. This article presents a high-throughput, integrated protocol for CTC enrichment and single-CTC sequencing, improving capture efficiency and CTC purity while reducing contamination and sequencing costs, thereby advancing precision oncology research and clinical applications.
10:47
High-throughput, Automated Extraction of DNA and RNA from Clinical Samples using TruTip Technology on Common Liquid Handling Robots
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Cited by 13 •
2013
TruTip is a simple nucleic acid extraction technology whereby a porous, monolithic binding matrix is inserted into a pipette tip. Consequently, the sample preparation format is compatible with most liquid handling instruments, and can be used for many medium to high-throughput clinical applications and sample types.
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.
FAQs
Miniaturization allows many assays or experimental conditions to occupy a smaller format, while robotics performs repeated handling consistently across samples. Multiplexed detection collects several measurements within the same analytical workflow, and computational processing organizes the resulting data. Together, these components increase the number of observations generated per experiment while supporting standardized comparison across tumor samples, cell models, or treatment conditions.
Standardized processing reduces variation caused by differences in how samples are handled, measured, or analyzed. When the same workflow is applied across many samples or experimental conditions, observed differences are more readily interpreted as biological or treatment-related patterns. This consistency is particularly useful for comparing molecular profiles and drug responses across tumor samples or cancer cell models.
The approach can support broad measurements at several molecular levels, including genomic, transcriptomic, and proteomic patterns. It can also be applied to drug-response analysis in tumor samples or cell models. Examining these complementary data types helps researchers characterize molecular differences, relate them to cancer biology, and assess how experimental treatments affect different biological systems.
Computational data processing converts the large set of measurements produced by parallel assays into organized, interpretable results. It enables researchers to examine patterns across samples, molecular data types, or treatment conditions rather than evaluating each result in isolation. This analysis supports comparisons of tumor biology and treatment response, helping identify patterns that may warrant further investigation as biomarkers or therapeutic leads.
A typical workflow begins by arranging numerous biological samples or experimental conditions in a miniaturized assay format. Robotics then supports repeated processing, multiplexed detection records the selected measurements, and computational methods organize and analyze the results. In cancer studies, the workflow may compare tumor samples or cell models across genomic, transcriptomic, proteomic, or drug-response measurements.
Researchers may use them when they need to compare therapeutic candidates or examine treatment responses across many tumor samples or cell models. Parallel testing can reveal whether responses differ among biological systems and can expose molecular patterns associated with those differences. The resulting comparisons support treatment-focused studies and may contribute to more precise strategies for investigating cancer biology and therapy.