Peak learning of mass spectrometry imaging data using artificial neural networks

Walid M. Abdelmoula, Begona Gimenez-Cassina Lopez, Elizabeth C. Randall, Tina Kapur, Jann N. Sarkaria, Forest M. White, Jeffrey N. Agar, William M. Wells and Nathalie Y. R. Agar ()
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Walid M. Abdelmoula: Harvard Medical School
Begona Gimenez-Cassina Lopez: Harvard Medical School
Elizabeth C. Randall: Harvard Medical School
Tina Kapur: Harvard Medical School
Jann N. Sarkaria: Mayo Clinic, 200 First St SW
Forest M. White: Koch Institute for Integrative Cancer Research, MIT
Jeffrey N. Agar: Northeastern University, 412 TF (140 The Fenway)
William M. Wells: Harvard Medical School
Nathalie Y. R. Agar: Harvard Medical School

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract Mass spectrometry imaging (MSI) is an emerging technology that holds potential for improving, biomarker discovery, metabolomics research, pharmaceutical applications and clinical diagnosis. Despite many solutions being developed, the large data size and high dimensional nature of MSI, especially 3D datasets, still pose computational and memory complexities that hinder accurate identification of biologically relevant molecular patterns. Moreover, the subjectivity in the selection of parameters for conventional pre-processing approaches can lead to bias. Therefore, we assess if a probabilistic generative model based on a fully connected variational autoencoder can be used for unsupervised analysis and peak learning of MSI data to uncover hidden structures. The resulting msiPL method learns and visualizes the underlying non-linear spectral manifold, revealing biologically relevant clusters of tissue anatomy in a mouse kidney and tumor heterogeneity in human prostatectomy tissue, colorectal carcinoma, and glioblastoma mouse model, with identification of underlying m/z peaks. The method is applied for the analysis of MSI datasets ranging from 3.3 to 78.9 GB, without prior pre-processing and peak picking, and acquired using different mass spectrometers at different centers.

Date: 2021
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DOI: 10.1038/s41467-021-25744-8

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