In Silico Modeling Reveals Potential Benefits of Personalized Molecular Fingerprinting for Health Diagnostics

Article

This study demonstrates the potential benefits of personalized molecular fingerprinting and biochemical fractionation for applications in health diagnostics

Researchers at the University of Munich (LMU), the Max Planck Institute of Quantum Optics (MPQ), and the Ludwig Maximilian University of Munich (LMU) have published a study in Analytical Chemistry that details the limits and prospects of molecular fingerprinting for phenotyping biological systems through in silico modeling (1).

Health and medical science healthcare on virtual interface, Innovation and digital technology in future on hospital background. | Image Credit: © ipopba - stock.adobe.com

Health and medical science healthcare on virtual interface, Innovation and digital technology in future on hospital background. | Image Credit: © ipopba - stock.adobe.com

Molecular fingerprinting via vibrational spectroscopy characterizes the chemical composition of molecularly complex media, which enables the classification of phenotypes associated with biological systems. The researchers developed an in silico model which generates realistic, but configurable, molecular fingerprints and validated the model using experimental blood-based infrared (IR) spectra from two cancer-detection applications. The model revealed substantial improvements in classifying clinically relevant phenotypes when the biological variability was reduced from a between-person to a within-person level and when the chemical complexity of the spectra was reduced.

Vibrational spectroscopy by means of Raman or IR techniques is a powerful analytical platform capable of characterizing molecular samples at any state of matter in a label-free manner. Because every molecule exhibits a unique vibrational spectrum, spectroscopic approaches are able to quantify individual molecular contributions in complex matrices. Although determining changes in the nature and quantity of individual molecular species is challenged by overlapping spectral bands, the vibrational spectrum still reflects the overall molecular composition of a given sample and is therefore referred to as its “molecular fingerprint." Statistical or machine learning methods can identify spectral patterns specific to molecular phenotypes and consequently classify samples.

Molecular fingerprinting by vibrational spectroscopy has been increasingly applied to biomedical problems. The approach has been used to classify bacteria and cell (sub)types, distinguish between benign and malignant tissues, and identify diseases based on fingerprint spectra of biofluids. However, the interplay between factors such as biological variability, measurement noise, chemical complexity, and cohort size makes it challenging to investigate their impact on how the classification performs.

The in silico model developed by the researchers generates realistic, but configurable, molecular fingerprints, thereby yielding insights into the framework of molecular fingerprinting. The researchers validated the model using experimental blood-based IR spectra from two cancer-detection applications and subsequently adjusted model parameters to simulate diverse experimental settings. The model revealed substantial improvements in classifying clinically relevant phenotypes when the biological variability was reduced from a between-person to a within-person level and when the chemical complexity of the spectra was reduced.

Overall, the findings of this study demonstrate the potential benefits of personalized molecular fingerprinting and biochemical fractionation for applications in health diagnostics. The researchers believe that the results of their study could pave the way for the development of more accurate and personalized diagnostic tools for various diseases.

Reference

(1) Eissa, T.; Kepesidis, K. V.; Zigman, M.; Huber, M. Limits and Prospects of Molecular Fingerprinting for Phenotyping Biological Systems Revealed through In Silico Modeling. Anal. Chem. 2023, ASAP. DOI: 10.1021/acs.analchem.2c04711

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