6 research outputs found
Limits and Prospects of Molecular Fingerprinting for Phenotyping Biological Systems Revealed through <i>In Silico</i> Modeling
Molecular fingerprinting via vibrational spectroscopy
characterizes
the chemical composition of molecularly complex media which enables
the classification of phenotypes associated with biological systems.
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. Considering
these factors, we developed an in silico model which
generates realistic, but configurable, molecular fingerprints. Using
experimental blood-based infrared spectra from two cancer-detection
applications, we validated the model and subsequently adjusted model
parameters to simulate diverse experimental settings, thereby yielding
insights into the framework of molecular fingerprinting. Intriguingly,
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. These findings quantitively demonstrate
the potential benefits of personalized molecular fingerprinting and
biochemical fractionation for applications in health diagnostics
Stability of person-specific blood-based infrared molecular fingerprints opens up prospects for health monitoring
Health state transitions are reflected in characteristic changes in the molecular composition of biofluids. Detecting these changes in parallel, across a broad spectrum of molecular species, could contribute to the detection of abnormal physiologies. Fingerprinting of biofluids by infrared vibrational spectroscopy offers that capacity. Whether its potential for health monitoring can indeed be exploited critically depends on how stable infrared molecular fingerprints (IMFs) of individuals prove to be over time. Here we report a proof-of-concept study that addresses this question. Using Fourier-transform infrared spectroscopy, we have fingerprinted blood serum and plasma samples from 31 healthy, non-symptomatic individuals, who were sampled up to 13 times over a period of 7 weeks and again after 6 months. The measurements were performed directly on liquid serum and plasma samples, yielding a time- and cost-effective workflow and a high degree of reproducibility. The resulting IMFs were found to be highly stable over clinically relevant time scales. Single measurements yielded a multiplicity of person-specific spectral markers, allowing individual molecular phenotypes to be detected and followed over time. This previously unknown temporal stability of individual biochemical fingerprints forms the basis for future applications of blood-based infrared spectral fingerprinting as a multiomics-based mode of health monitoring
Limits and Prospects of Molecular Fingerprinting for Phenotyping Biological Systems Revealed through <i>In Silico</i> Modeling
Molecular fingerprinting via vibrational spectroscopy
characterizes
the chemical composition of molecularly complex media which enables
the classification of phenotypes associated with biological systems.
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. Considering
these factors, we developed an in silico model which
generates realistic, but configurable, molecular fingerprints. Using
experimental blood-based infrared spectra from two cancer-detection
applications, we validated the model and subsequently adjusted model
parameters to simulate diverse experimental settings, thereby yielding
insights into the framework of molecular fingerprinting. Intriguingly,
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. These findings quantitively demonstrate
the potential benefits of personalized molecular fingerprinting and
biochemical fractionation for applications in health diagnostics