Evaluation of Fatty Acids Profling in a Blood Drop Spotted on DBS Card by using a Robot-assisted GC Method

Significance of the Topic

Understanding the detailed profile of fatty acids in human blood is critical for monitoring metabolic health and supporting disease prevention efforts. Traditional venipuncture requires larger volumes and complex logistics, whereas analysis of dried blood spots (DBS) minimizes patient discomfort, simplifies sample handling, and enables broad population screening.

Objectives and Study Overview

This work presents a fully automated analytical workflow to quantify fatty acid methyl esters (FAMEs) in a single DBS. The goal was to integrate sample collection, direct derivatization, and chromatographic analysis into a robot-assisted platform, delivering reproducible and rapid results suitable for large-scale clinical research.

Methodology

A fingertip blood drop was applied to DBS cards and air-dried. A robotic preparative station carried out two-step derivatization directly in the sampling vial: first with sodium methoxide in methanol, then boron trifluoride in methanol. Reaction conditions were optimized (vortex mixing, heated incubation) to convert complex lipid species into FAMEs. After methylation, n-heptane extraction was automated to isolate the target analytes.

Instrumentation

• Shimadzu AOC-6000 robotic preparative station for automated sample processing
• Shimadzu Nexis GC-2030 gas chromatograph with SLB-IL60 ionic liquid capillary column (30 m × 0.25 mm, 0.20 µm)
• Shimadzu GCMS-TQ8050 NX triple quadrupole mass spectrometer (full-scan mode, 40–550 amu)
• Flame ionization detector (FID) for quantification

Main Results and Discussion

The ionic liquid column achieved baseline separation of 31 FAMEs within a single run. The automated derivatization delivered consistent precision by eliminating manual handling variability. Identification combined MS spectral matching (>85% similarity) and linear retention index (±10 tolerance) to resolve structural isomers. The blood drop exhibited a fatty acid distribution of 37.1% saturated (SFA), 27.5% monounsaturated (MUFA), and 35.4% polyunsaturated (PUFA), with an ω-6/ω-3 ratio of 14.5. Key biomarkers such as palmitic, oleic, linoleic, arachidonic, and docosahexaenoic acids were quantified reliably.

Practical Implications

The streamlined DBS-based protocol supports high-throughput lipid profiling in clinical and research laboratories. Its minimal sample requirement and automated workflow facilitate large cohort studies, nutritional monitoring, and quality assurance in pharmaceutical and dietary research.

Future Trends and Applications

Emerging directions include establishing quantitative fatty acid reference databases correlated with disease states, extending automation to other lipid classes, and implementing point-of-care devices leveraging microfluidics and AI-driven data analysis for personalized health assessment.

Conclusion

The robot-assisted GC-MS/FID method for DBS enables rapid, reliable, and high-throughput fatty acid profiling from minimal blood volumes. Its integration of direct derivatization and advanced chromatography makes it a powerful tool for clinical lipidomics and large-scale nutritional studies.

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