MIDI Sherlock® Microbial Identification System with Traditional Methods

Sherlock Microbial Identification System with Traditional Methods

Importance of the Topic

Gas chromatographic analysis of fatty acid methyl esters provides a robust approach to rapid and reliable microbial identification. In diverse sectors such as clinical diagnostics, environmental monitoring and biodefense, fast and accurate detection of bacteria and yeast is vital for quality control, pathogen surveillance and research applications. The Sherlock platform addresses the need for low-cost, high-throughput and user-friendly identification methods without requiring extensive chromatographic expertise.

Objectives and Overview

The primary goal of the Sherlock Microbial Identification System is to automate the identification of microbial species by combining established GC-FAME protocols with pattern recognition software and comprehensive reference libraries. Key objectives include minimizing per-sample costs, standardizing instrument calibration, reducing labor intensity and improving biosafety by removing live organisms from the analytical instrument.

Methodology

Sample Preparation

• Harvest cells from culture plates, typically under one hour for 30 samples.
• Perform a four-step liquid-liquid extraction and esterification process, requiring about 1.5 hours per batch of 30 samples, with 35 minutes of automated incubation time for other tasks.
• No preliminary Gram stain or offline tests are needed before extraction and GC analysis.

GC-FAME Analysis and Data Processing

• Inject prepared fatty acid methyl ester extracts into an Agilent 6850 or 7890 GC equipped with an autosampler for unattended analysis.
• Use Agilent ChemStation software to control the GC and acquire chromatograms.
• Sherlock software applies pattern recognition algorithms to match sample profiles against species-specific libraries, eliminating manual calibration adjustments.

Optional Software Modules

• Library Generation for creating custom reference libraries from experimental data.
• Tracker/Cluster for contamination source tracing and strain grouping.
• DNA Software for 16S/28S rRNA sequence-based identification.

Used Instrumentation

• Agilent 6850 Series II GC (single-channel, 5%–95% humidity, 15–35 °C operation)
• Agilent 7890 Series GC (single-channel, similar specifications)
• Windows PC with MIDI Sherlock and Agilent ChemStation software

Main Results and Discussion

Throughput and Sensitivity

• Standard methods process 2 samples per hour per GC channel.
• Rapid aerobic methods deliver 6 samples per hour with twice the detection sensitivity of standard protocols.
• Sensitive methods for anaerobes and yeast achieve comparable sensitivity gains while maintaining a throughput of 2 samples per hour.

Calibration Stability and Reproducibility
Sherlock’s calibration mixture and algorithms stabilize instrument response over time, removing the need for manual tuning and reducing variability across analyses.

Biosafety Enhancement
Early extraction steps use sodium hydroxide at 100 °C to inactivate cells, ensuring no live microbes enter the GC system. High-risk pathogens can be processed in high-containment labs and transferred as decontaminated extracts.

Benefits and Practical Applications

• Cost-effective identification at under USD 3.00 per sample including all consumables.
• Minimal chromatography expertise required, enabling broader implementation in QA/QC and research labs.
• Flexible software modules support custom library creation, data export and compliance with electronic record regulations (21 CFR Part 11).
• Applicable to markets such as clinical microbiology, environmental testing, biodefense, food safety and pharmaceutical quality control.

Future Trends and Potential Applications

Integration of GC-FAME with advanced molecular techniques and cloud-based analytics is expected to further enhance identification speed and database expansion. Automated sample handling and enhanced pattern recognition models will broaden applicability to emerging pathogens, complex environmental matrices and real-time monitoring in bioprocessing.

Conclusion

The Sherlock Microbial Identification System offers a fully automated, cost-efficient and reproducible workflow for microbial identification across diverse applications. By leveraging gas chromatography, robust software and extensive libraries, the platform simplifies laboratory operations, enhances biosafety and delivers high throughput without sacrificing sensitivity or accuracy.