The Edge Effect in High-Throughput Proteomics: A Cautionary Tale.
Colleen B MaxwellJatinderpal K SandhuThong H CaoGerry P McCannLeong L NgDonald J L JonesPublished in: Journal of the American Society for Mass Spectrometry (2023)
In order for mass spectrometry to continue to grow as a platform for high-throughput clinical and translational research, careful consideration must be given to quality control by ensuring that the assay performs reproducibly and accurately and precisely. In particular, the throughput required for large cohort clinical validation in biomarker discovery and diagnostic screening has driven the growth of multiplexed targeted liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) assays paired with sample preparation and analysis in multiwell plates. However, large scale MS-based proteomics studies are often plagued by batch effects: sources of technical variation in the data, which can arise from a diverse array of sources such as sample preparation batches, different reagent lots, or indeed MS signal drift. These batch effects can confound the detection of true signal differences, resulting in incorrect conclusions being drawn about significant biological effects or lack thereof. Here, we present an intraplate batch effect termed the edge effect arising from temperature gradients in multiwell plates, commonly reported in preclinical cell culture studies but not yet reported in a clinical proteomics setting. We present methods herein to ameliorate the phenomenon including proper assessment of heating techniques for multiwell plates and incorporation of surrogate standards, which can normalize for intraplate variation.
Keyphrases
- mass spectrometry
- high throughput
- liquid chromatography
- tandem mass spectrometry
- high performance liquid chromatography
- gas chromatography
- high resolution mass spectrometry
- ultra high performance liquid chromatography
- high resolution
- single cell
- capillary electrophoresis
- simultaneous determination
- quality control
- solid phase extraction
- multiple sclerosis
- small molecule
- drinking water
- electronic health record
- machine learning
- big data
- molecularly imprinted
- deep learning
- high density
- ms ms