A method for eliminating imaging mass spectrometry flow sensitivity differences

By setting up a standard region in imaging mass cytometry and employing a linear regression model, the problem of signal instability caused by fluctuations in instrument sensitivity was solved, thus achieving accurate data calibration and reliable bioinformatics analysis.

CN116429870BActive Publication Date: 2026-06-12SHANGHAI LIDE BIOTECH CO LTD
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
SHANGHAI LIDE BIOTECH CO LTD
Filing Date
2022-09-30
Publication Date
2026-06-12

AI Technical Summary

Technical Problem

The sensitivity fluctuations of imaging mass cytometry affect the intensity of the detection signal, leading to inaccurate data consistency and bioinformatics analysis results.

Method used

By setting a standard area on one side of the sample and using the signal of the standard for calibration, the influence of instrument sensitivity fluctuations is eliminated by using a bivariate linear regression model. This includes selecting a lanthanide metal standard solution, performing a low-resolution scan of the standard area, and calibrating the signal with the sample area.

Benefits of technology

It effectively eliminates the influence of instrument sensitivity fluctuations on sample signals, improves data accuracy and consistency, and reduces errors in bioinformatics analysis results.

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Abstract

The application provides a signal calibration method for imaging mass spectrometry flow, which performs linear regression on sample signals through standard sample signals: first, a sample is selected as a standard sample, and a first standard curve and a first regression model are established according to standard samples on a slide of the sample; then, a second standard curve and a second regression model are established according to standard samples on a slide of a sample to be calibrated; then, the sample to be calibrated is scanned at a first resolution, log processing is performed on obtained original signal values, and the second regression model is inputted to obtain actual metal content of each pixel of the sample to be calibrated; finally, the actual metal content of each pixel is converted into a calibrated signal value through the first regression model, data calibration is completed, and the influence of instrument sensitivity fluctuation is eliminated.
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