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Hello everyone! The new issue of Space Metabolism Q&A is here again! In the last issue, we introduced resolution in spatial metabolomics (click to read)
.
In this issue, we will introduce the problem of ion peak number in spatial metabolomics
.
1.
What is the difference in the number of ion peaks between the spatial metabolome and the traditional LC-MS metabolome?
What is the difference in the number of ion peaks between the spatial metabolome and the traditional LC-MS metabolome?
In general, the number of spatial metabolomics ion peaks mentioned is the number of all detected ion peaks during the analysis of the entire section
.
Each pixel in the spatial metabolome image is independently detected by the mass spectrometry imaging system, so each pixel will have its own metabolomic information, and selecting different regions (pixel sets) will correspond to different numbers of ion peaks
.
In the traditional LC-MS metabolome, the number of ion peaks detected by a sample on the machine is fixed, and the two are all different
.
.
Each pixel in the spatial metabolome image is independently detected by the mass spectrometry imaging system, so each pixel will have its own metabolomic information, and selecting different regions (pixel sets) will correspond to different numbers of ion peaks
.
In the traditional LC-MS metabolome, the number of ion peaks detected by a sample on the machine is fixed, and the two are all different
.
2.
Is the number of output ion peaks as high as possible?
No, although the number of detected ion peaks is an important indicator for evaluating the results of spatial metabolism, a large part of ion peaks in spatial metabolomics detection originate from noise and ion fragments.
Such peaks have no practical significance and are invalid peaks, so simple It is meaningless to calculate the total number of ion peaks, which are redundant information and need to be filtered
.
Such peaks have no practical significance and are invalid peaks, so simple It is meaningless to calculate the total number of ion peaks, which are redundant information and need to be filtered
.
Fig.
Invalid ion peak corresponding to molecular imaging
3.
How to filter out invalid peaks?
for mass spectrometry imaging data .
The main processing judgment principle is that metabolites have differences in abundance in tissues and cells, but the distribution should be universal
.
Therefore, to determine whether an ion peak is valid, one can consider its distribution in the entire region, signal intensity and other information, and establish a threshold for screening
.
After data filtering, we can ensure that the results of the delivery project are all images of effective ion peaks, so as to prevent teachers from misleading data analysis
.
Figure shows the number of ion peaks of a measured item under different thresholds
4.
Does the number of ion peaks after deep spatial metabolome filtering meet the analysis requirements?
Does the number of ion peaks after deep spatial metabolome filtering meet the analysis requirements?
At present, deep space metabolism still shows excellent throughput after filtering out a large number of low-mass invalid peaks
.
According to different tissue types, the maximum number of filtered ion peaks is nearly 3000, and the average is about 1800.
It is your reliable spatial metabolomics analysis product
.
Summary: The data of spatial metabolomics is a high-dimensional data set composed of a large number of spatial coordinates and metabolome data, which is equivalent to tens of thousands of samples for metabolomics data analysis, which inevitably generates redundant ion peak information, so Before evaluating and using data, it is important not only to evaluate the number of ion peaks, but also to consider the ion peak quality
.
.
According to different tissue types, the maximum number of filtered ion peaks is nearly 3000, and the average is about 1800.
It is your reliable spatial metabolomics analysis product
.
Summary: The data of spatial metabolomics is a high-dimensional data set composed of a large number of spatial coordinates and metabolome data, which is equivalent to tens of thousands of samples for metabolomics data analysis, which inevitably generates redundant ion peak information, so Before evaluating and using data, it is important not only to evaluate the number of ion peaks, but also to consider the ion peak quality
.