A Convolutional Neural Network and Transfer Learning Approach for Accelerated Quantitative Mass Spectrometry Imaging.

Area of Science:

• Analytical Chemistry
• Computational Chemistry
• Biomedical Imaging

Background:

• Quantitative mass spectrometry imaging (qMSI) is crucial for analyzing molecular distributions in biological tissues.
• Current qMSI methods are often time-consuming, labor-intensive, and prone to errors during data acquisition and analysis.
• There is a need for accelerated and more robust qMSI techniques to improve experimental throughput and reproducibility.

Purpose of the Study:

• To develop and validate a novel approach for accelerating quantitative mass spectrometry imaging (qMSI) measurements.
• To leverage machine learning, specifically convolutional neural networks (CNNs) with transfer learning, to enhance qMSI analysis speed and accuracy.
• To reduce variability and potential errors associated with traditional qMSI data processing.

Main Methods:

• A convolutional neural network (CNN) model was developed using a transfer learning strategy.
• The CNN was trained on a dataset of ion images with known analyte concentrations.
• The trained model was applied to new tissue samples for quantitative analysis of specific molecules.

Main Results:

• The developed CNN model successfully accelerated qMSI measurements.
• Accurate analyte concentrations were determined in new tissue samples using the transfer learning approach.
• The method demonstrated a significant reduction in analysis time compared to conventional qMSI techniques.

Conclusions:

• The integration of CNNs with transfer learning offers a powerful and efficient method for accelerating qMSI.
• This approach enhances the speed and decreases the variability of quantitative analysis in mass spectrometry imaging.
• The validated model holds promise for routine application in future qMSI experiments, improving data acquisition and analysis efficiency.