In a recent study in Nature Sustainability, researchers describe a diagnostic approach that combines dried sera spots (DSS) with nanoparticle-enhanced laser desorption and ionization mass spectrometric methods (NPELDI-MS) for accurate and cost-effective cancer detection.
Study: A sustainable approach to universal metabolic cancer diagnosis. Image Credit: PeopleImages.com - Yuri A/Shutterstock.com
Background
More than a billion individuals globally have missed illness diagnoses, emphasizing the need for more reliable and inexpensive diagnostic methods. Metabolic diagnosis has potential but confronts limitations due to biospecimen application and analytical robustness.
Population-based diagnostics increase survival rates, minimize treatment morbidities, and save healthcare expenses, especially for severe illnesses and malignancies.
The scarcity of diagnostic facilities in developing nations adds to undetected cases. Mass spectrometry is the most often used technology for metabolic diagnostics with dried spots, although it requires time-consuming separation.
About the study
In the present study, researchers created a standardized metabolism-informed tailored therapeutic approach to reduce missed diagnoses of gastric cancers (GC), colorectal cancers (CC), and pancreatic cancers (PC) in impoverished countries.
The researchers used organic matrices like DHB and built multiplexed metabolic microarrays using ferric nanoparticles (NPs) to boost detection performance.
For sensitivity, they acquired unambiguous mass spectra applying a conventional metabolite combination and confirmed the size-exclusive influence of ferric NPs on direct metabolic extraction from complicated biospecimens for specificity.
The team investigated whether metabolites extracted from dried blood spots (DSSs) can be accurately quantified and profiled using NPELDI MS. They adjusted serum quantities and measured the DSS extract's usual mass spectrum to quantify the targeted metabolites. They also tested the NPELDI MS platform's linearity factor and dynamic range with phenylalanine.
To demonstrate the robustness of NPELDI MS, the researchers carried out targeted quantification of additional indicator molecules and compared the spectrum consistency obtained from the matched DSS and serum samples.
After demonstrating the viability of using DSS in metabolic diagnostics, the researchers examined its application to different blood samples for untargeted profiling and targeted quantification based on storage conditions and punching sites.
Researchers used NPELDI MS to differentiate cancer cases from healthy donors by collecting untargeted metabolic profiles from 180 DSSs. They developed chemometric models and classifiers to assess diagnostic performance.
They also created an estimating model for large-scale population-based screening in a hypothetical 100,000-person community, with optical colonoscopy as a baseline. The researchers obtained 245 serum samples from diverse cancer groups.
They assigned cosine similarity scores to each group and developed a theoretical model based on 100,000 populations to calculate missed diagnosis rates.
Results
The NPELDI MS technique provides for the rapid detection of numerous malignancies in minutes at a minimal cost while being environmentally friendly, user-friendly, and serum-equivalently precise.
It can lower the projected proportion of undetected CC cases from 84% to 29%, GC from 78% to 57%, and PC from 35% to 9.3%, for a total reduction of 20% to 55%. NPELDI MS readings revealed linear correlations with analyte levels, with a detection limit as low as 0.1 μM.
Introducing ferric nanoparticles enabled the effective adsorption of metabolites with a wide surface area of 79 m2/g, promoting photo-thermal desorptions through strong ultraviolet absorption of 200 to 500 nm and a high thermal capacity of 653 J/kg/K.
The carbon distribution (in glucose) within the nanoparticle-nanoparticle metabolic hybrids demonstrated metabolites trapped preferentially on particulate surfaces, unlike biomacromolecules. Contrastingly, organic matrices exhibited no preference for ionizing or desorbing metabolite molecules except proteins.
Even using the best-practice sample-preparing approach, NPELDI MS data outperformed MS data from accessible organic matrices. The researchers discovered that utilizing gold or silver nanoparticles to identify five predictor metabolites resulted in lower MS signal intensities than ferric nanoparticles (≤11-fold higher).
Ferric NPs have a reduced thermal conductivity of 3.50 W/m/K compared to metal ones (317 W/m/K for gold and 429 W/m/K for silver), enabling photo-thermal metabolite desorption.
The study demonstrated cancer detection using DSS metabolic profiles is highly repeatable, with 84% of all peaks exhibiting intensity c.v.s of less than 15% for intra-chip detection.
The researchers discovered significant metabolic variations between HDs and different cancer groups, with two upregulated and two downregulated metabolites seen in DSS and serum-derived models.
However, the new models' diagnostic efficacy in distinguishing cancer from HDs was inadequate. Calibration curves were generated by estimating the intensity ratios of analytes and spiked internal standards. Isotopic quantification resulted in an average recovery of 96% for glucose and 104% for lactate, demonstrating that researchers can quantify the targeted metabolites consistently.
Based on the study findings, the NPELDI MS technique, which uses a consistent workflow and paper-based DSS, can enhance long-term metabolic diagnosis in colorectal, gastric, and pancreatic malignancies.
This strategy minimizes the number of undetected instances while also contributing to healthcare sustainability. The platform provides rapid, cost-effective, and reliable cancer detection in minutes, making it suitable for large-scale clinic applications.
DSS-derived models outperform clinically validated biomarkers in identifying cancer patients with serum-equivalent precision using metabolic diagnosis. Additional research could confirm this strategy for various illnesses and create less-priced MS platforms for point-of-care testing.
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