Title | Peak learning of mass spectrometry imaging data using artificial neural networks. |
Publication Type | Journal Article |
Year of Publication | 2021 |
Authors | Abdelmoula WM, Lopez BGimenez-Ca, Randall EC, Kapur T, Sarkaria JN, White FM, Agar JN, Wells WM, Agar NYR |
Journal | Nat Commun |
Volume | 12 |
Issue | 1 |
Pagination | 5544 |
Date Published | 2021 09 20 |
ISSN | 2041-1723 |
Keywords | Algorithms, 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 |
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. |
DOI | 10.1038/s41467-021-25744-8 |
Alternate Journal | Nat Commun |
PubMed ID | 34545087 |
PubMed Central ID | PMC8452737 |
Grant List | T32 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 |