Multimodal Deep Learning Approach for Dynamic Sampling with Automatic Feature Selection in Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging

Abstract

Acquisitions of mass-per-charge (m/z) spectrometry data from tissue samples, at high spatial resolutions, using Mass Spectrometry Imaging (MSI), require hours to days of time. The Deep Learning Approach for Dynamic Sampling (DLADS) and Supervised Learning Approach for Dynamic Sampling with Least-Squares (SLADS-LS) algorithms follow compressed sensing principles to minimize the number of physical measurements performed, generating low-error reconstructions from spatially sparse data. Measurement locations are actively determined during scanning, according to which are estimated, by a machine learning model, to provide the most relevant information to an intended reconstruction process. Preliminary results for DLADS and SLADS-LS simulations with Matrix-Assisted Laser Desorption/Ionization (MALDI) MSI match prior 70% throughput improvements, achieved in nanoscale Desorption Electro-Spray Ionization (nano-DESI) MSI. A new multimodal DLADS variant incorporates optical imaging for a 5% improvement to final reconstruction quality, with DLADS holding a 4% advantage over SLADS-LS regression performance. Further, a Forward Feature Selection (FFS) algorithm replaces expert-based determination of m/z channels targeted during scans, with negligible impact to location selection and reconstruction quality.