High-throughput Quantification
High-throughput Quantification - Related Videos
07:47
ScanLag: High-throughput Quantification of Colony Growth and Lag Time
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Cited by 66 •
2014
ScanLag is a high-throughput method for measuring the delay in growth, namely lag time, as well as the growth rate of colonies for thousands of cells in parallel. Screening using ScanLag enables the discrimination between long lag-time and slow growth at the level of a single variant.
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.
12:29
Two Algorithms for High-throughput and Multi-parametric Quantification of Drosophila Neuromuscular Junction Morphology
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Cited by 11 •
2017
Two image analysis algorithms, "Drosophila NMJ Morphometrics" and "Drosophila NMJ Bouton Morphometrics" were created, to automatically quantify nine morphological features of the Drosophila neuromuscular junction (NMJ).
07:44
Quantification of dsDNA using the Hitachi F-7000 Fluorescence Spectrophotometer and PicoGreen Dye
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Cited by 6 •
2010
Demonstration of quantification of dsDNA using Molecular Probes PicoGreen dye and Hitachi F-7000 Fluorescence Spectrophotometer equipped with a microplate reader accessory.
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
Parallelization allows many biological samples to undergo comparable measurements within the same experimental framework. This increases efficiency and makes it possible to examine population-level patterns that may remain hidden in small-scale experiments. By processing samples in parallel, researchers can compare genetic, cellular, or drug-induced effects across a broader experimental landscape rather than relying on a limited number of observations.
Standardized sample handling reduces differences introduced before detection, helping ensure that measured variation reflects biology rather than inconsistent preparation. When samples are treated comparably, signals such as fluorescence, absorbance, or image-derived features can be converted into more comparable numerical values. This consistency strengthens downstream comparisons and helps researchers interpret changes across large sample sets.
Statistical normalization adjusts measurements so that systematic technical differences do not obscure biological patterns. Quality control then helps identify results that may be unreliable or affected by technical variation. Together, these steps support a clearer separation between meaningful changes and measurement artifacts, allowing researchers to recognize relationships across samples with greater confidence.
A typical workflow begins with parallelized sample handling, followed by automated detection of a measurable signal. The resulting fluorescence, absorbance, or image features are converted into numerical data and processed computationally. Researchers then apply normalization and quality control before comparing samples, identifying patterns, or evaluating genetic, cellular, or drug-induced effects.
The approach is especially useful when researchers need to compare large numbers of samples or search for patterns across a population. Its scale can reveal relationships that a small experiment may miss, while parallel processing improves efficiency. This makes it valuable for systematic screening of genetic, cellular, or drug-induced effects before focused mechanistic investigation.
By producing comparable measurements across many samples, the method can identify patterns associated with genetic changes, cellular states, or drug exposure. Those findings can guide later mechanistic studies by highlighting relationships that merit closer examination. In therapeutic development, the same screening-oriented capability can help researchers evaluate broad biological responses and prioritize effects for further investigation.