MIDI Sherlock® Microbial Identification System with Traditional Methods & PLFA

Significance of the topic

The ability to identify microorganisms rapidly and accurately is critical in fields ranging from clinical diagnostics to environmental monitoring. Fatty acid methyl ester (FAME) profiling and phospholipid fatty acid (PLFA) analysis provide robust biochemical fingerprints of microbial cells. By coupling these approaches with gas chromatography and advanced pattern‐recognition algorithms, laboratories can achieve high‐throughput, cost‐effective identification without extensive chromatographic expertise.

Objectives and overview of the system

The Sherlock Microbial Identification System aims to streamline microbial identification by converting phospholipid fatty acids into FAMEs and analyzing them via gas chromatography. The system integrates automated sample preparation, chromatographic analysis on Agilent 6850 or 7890 instruments, and pattern‐matching against comprehensive libraries. Key objectives include reducing per‐sample cost, minimizing manual calibration, and delivering reproducible results for a wide range of microbes.

Methodology and instrumentation

Sample preparation relies on a four‐step liquid‐liquid extraction of PLFAs, followed by derivatization to FAMEs. A technician can prepare 30 samples in under two hours using generic reagents. After extraction, samples are loaded into the autosampler of an Agilent GC:

• Agilent 6850 Series II GC or 7890 GC with single‐channel capability
• Windows PC running Agilent ChemStation and MIDI Sherlock software
• Optional Sherlock DNA module for sequence‐based analysis

Chromatographic methods include standard (2 samples/hour), rapid (6 samples/hour for aerobes), and sensitive protocols (2 samples/hour with doubled sensitivity). Data analysis employs dendrograms, PCA, and clustering tools for pattern recognition and contamination tracking.

Main results and discussion

The system achieves under USD 3.00 consumable cost per sample while maintaining high throughput. Automated calibration through a proprietary mixture eliminates manual tuning and ensures consistency across instruments. Through integration of software modules, users can perform quality control, build custom libraries, track contaminants, and generate publication‐quality graphics. The absence of live cultures in the GC instrument enhances biosafety and allows external servicing of hardware.

Benefits and practical applications

The Sherlock MIS offers:

• Low operational cost and minimal sample preparation time
• Automated pattern recognition without specialized chromatography training
• Comprehensive libraries covering over 1,000 bacterial, anaerobic, and yeast species
• Applications in clinical microbiology, biodefense, bioremediation, oil analysis, soil science, water quality, and more

Its modular software architecture supports trend analysis, custom reporting, and data export for integration with external databases.

Future trends and possibilities

Advances may include tighter integration of lipid profiling with DNA‐based identification, expansion of species libraries, enhanced automation of sample preparation, and deployment of cloud‐based analytics for remote collaboration. Real‐time monitoring and machine‐learning algorithms could further improve identification accuracy and reduce turnaround time.

Conclusion

The Sherlock Microbial Identification System represents a versatile platform for lipid‐based microbial characterization. By combining efficient sample processing, reliable gas chromatography, and sophisticated software tools, it delivers reproducible, cost‐effective identification suited for diverse laboratory settings.

Reference

• Sherlock Microbial Identification System Specification Sheet, MIDI Inc., October 2014