Current methods for diagnosing human disease are still incapable of rapidly
and accurately screening for multiple diseases simultaneously on a large scale, and at an
affordable price. MALDI-ToF mass spectrometry is an ultra-sensitive, ultra-fast, lowcost,
high-throughput technology that has the potential to achieve this goal, allowing human
phenotype characterization and thus phenomic screening for multiple disease states.
In this review, we will discuss the main advances achieved so far, putting forward targeted
applications of MALDI-ToF mass spectrometry in the service of human disease detection.
This review focuses on the methodological workflow as MALDI-ToF data processing
for phenomic analysis, using state-of-the-art bioinformatic pipelines and software
tools. The role of mathematical modelling, machine learning, and artificial intelligence algorithms
for disease screening are considered. Moreover, we present some previously developed
tools for disease diagnostics and screening based on MALDI-ToF analysis. We
discuss the remaining challenges that are ahead when implementing MALDI-ToF into
clinical laboratories. Differentiating a standard profile from a single disease phenotype is
challenging, but the potential to simultaneously run multiple algorithm screens for different
disease phenotypes may only be limited by computing power once this initial hurdle
is overcome. The ability to explore the full potential of human clinical phenomics may
be closer than imagined; this review gives an insight into the benefits this technology
may reap for the future of clinical diagnostics.