Well Artifact Detection
Well Artifact Detection - Related Videos
06:42
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10:23
Equipment Setup and Artifact Removal for Simultaneous Electroencephalogram and Functional Magnetic Resonance Imaging for Clinical Review in Epilepsy
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10:07
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06:58
Synthesis of an In vivo MRI-detectable Apoptosis Probe
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Early detection of apoptosis may identify at-risk cell populations in a variety of diseases. Here we demonstrate a method to link an early apoptosis-detection protein (Annexin V) to a MRI-detectable iron oxide nanoparticle (SPIO). This method may be extended to other proteins of interest to generate MRI-detectable molecular imaging probes.
10:48
How to Detect Amygdala Activity with Magnetoencephalography using Source Imaging
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Cited by 28 •
2013
This article describes how to record amygdala activity with magnetoencephalography (MEG). In addition this article will describe how to conduct trace fear conditioning without awareness, a task that activates the amygdala. It will cover 3 topics: 1) Designing a trace conditioning paradigm using backward masking to manipulate awareness. 2) Recording brain activity during the task using magnetoencephalography. 3) Using source imaging to recover signal from subcortical structures.
FAQs
The method compares each well with expected spatial and statistical patterns across the plate or assay. A measurement becomes more concerning when it deviates from neighboring wells, replicate behavior, or the overall distribution rather than simply being unusually high or low. This comparison helps separate technical irregularities from genuine biological differences in bioengineered experiments.
Bubbles, uneven liquid handling, evaporation, contamination, edge effects, and imaging problems can all produce abnormal measurements or visible features. These causes may alter well intensity, appearance, or consistency without reflecting the biology under study. Recognizing their distinct physical or spatial signatures helps researchers interpret flagged wells and identify weaknesses in the experimental setup.
Spatial patterns reveal whether irregularities cluster at plate edges, follow liquid-handling locations, or affect particular regions. Plate-layout checks add experimental context by showing whether flagged wells share a treatment, control, or replicate arrangement. Together, these analyses help determine whether an apparent result reflects a technical pattern or a biologically meaningful response.
A practical workflow begins by collecting well measurements or images, then comparing them with expected spatial and statistical patterns. Researchers flag deviations, examine their appearance and distribution, and compare affected wells with replicates and the plate layout. The resulting characterization can guide quality control decisions and protocol adjustments before a larger experiment proceeds.
Researchers can apply it during quality control of microplates, culture plates, and other bioengineered assay platforms, especially before committing resources to larger experiments. Early screening identifies wells that may distort results and reveals recurring technical problems. This supports more reliable cell-based assays by allowing protocols or handling practices to be refined in advance.
By identifying wells affected by bubbles, evaporation, contamination, handling variation, edge effects, or imaging problems, the analysis prevents technical noise from being interpreted as biological variation. Automated image analysis, intensity thresholds, replicate comparisons, and plate-layout checks provide complementary evidence. The outcome is stronger data quality and a clearer basis for refining cell-based assay protocols.