Peak learning of mass spectrometry imaging data using artificial neural networks.

TitlePeak learning of mass spectrometry imaging data using artificial neural networks.
Publication TypeJournal Article
Year of Publication2021
AuthorsAbdelmoula WM, Lopez BGimenez-Ca, Randall EC, Kapur T, Sarkaria JN, White FM, Agar JN, Wells WM, Agar NYR
JournalNat Commun
Date Published2021 09 20
KeywordsAlgorithms, alpha-Defensins, Animals, Connective Tissue, Deep Learning, Disease Models, Animal, Humans, Imaging, Three-Dimensional, Kidney, Metabolomics, Mice, Neoplasms, Neural Networks, Computer, Nonlinear Dynamics, Reproducibility of Results, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization

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.

Alternate JournalNat Commun
PubMed ID34545087
PubMed Central IDPMC8452737
Grant ListT32 EB025823 / EB / NIBIB NIH HHS / United States
R01 CA201469 / CA / NCI NIH HHS / United States
P41 EB015898 / EB / NIBIB NIH HHS / United States
R25 CA089017 / CA / NCI NIH HHS / United States
P41 EB028741 / EB / NIBIB NIH HHS / United States
U54 CA210180 / CA / NCI NIH HHS / United States