Unmasking Airborne Threats: Guided-Transformers for Portable Aerosol Mass Spectrometry

TL;DR

This paper introduces MS-DGFormer, a transformer-based framework that enables real-time, portable aerosol pathogen detection using minimally processed MALDI-MS data, overcoming traditional laboratory limitations.

Contribution

The study presents a novel transformer architecture with a dictionary encoder and SVD-based denoising, allowing single-shot spectral analysis for portable environmental pathogen detection.

Findings

Achieves high accuracy in pathogen identification from raw spectra

Enables real-time analysis with minimal sample preparation

Facilitates portable MALDI-MS deployment for environmental monitoring

Abstract

Matrix Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI-MS) is a cornerstone in biomolecular analysis, offering precise identification of pathogens through unique mass spectral signatures. Yet, its reliance on labor-intensive sample preparation and multi-shot spectral averaging restricts its use to laboratory settings, rendering it impractical for real-time environmental monitoring. These limitations are especially pronounced in emerging aerosol MALDI-MS systems, where autonomous sampling generates noisy spectra for unknown aerosol analytes, requiring single-shot detection for effective analysis. Addressing these challenges, we propose the Mass Spectral Dictionary-Guided Transformer (MS-DGFormer): a data-driven framework that redefines spectral analysis by directly processing raw, minimally prepared mass spectral data. MS-DGFormer leverages a transformer architecture, designed to capture the long-range dependencies inherent in these time-series spectra. To enhance feature extraction, we introduce a novel dictionary encoder that integrates denoised spectral information derived from Singular Value Decomposition (SVD), enabling the model to discern critical biomolecular patterns from single-shot spectra with robust performance. This innovation provides a system to achieve superior pathogen identification from aerosol samples, facilitating autonomous, real-time analysis in field conditions. By eliminating the need for extensive preprocessing, our method unlocks the potential for portable, deployable MALDI-MS platforms, revolutionizing environmental pathogen detection and rapid response to biological threats.