Rapid Screening and Dereplication of Microbial Natural Products Using Data Independent Acquisition (DIA) UPLC-QTOF-MS Coupled with Streamlined Informatics

Significance of the Topic

The rapid and accurate identification of microbial natural products is essential for drug discovery, chemical biology, and industrial biotechnology. Integrating high-resolution UPLC–QToF mass spectrometry with data-independent acquisition (DIA) and automated informatics accelerates dereplication, reduces redundancy, and enhances the discovery of novel bioactive compounds from complex microbial extracts.

Objectives and Study Overview

This work aims to establish a high-throughput workflow for screening marine microbial metabolites. Key goals include:

• Automated dereplication of known compounds using precursor and fragmentation data.
• Structural elucidation of unknown components through streamlined informatics.
• Integration of phenotypic profiling with untargeted metabolomics to pinpoint bioactive molecules.

Methodology

Marine sediment-derived microbes were cultivated with XAD-16 resin, extracted in methanol/dichloromethane, and fractionated on C18 using a stepped methanol gradient. Final dilutions yielded samples at approximately 5 µg/mL for analysis.

Instrumentation Used

Chromatography and mass spectrometry were configured as follows:

• UPLC: ACQUITY UPLC I-Class system; BEH C18 column (2.1×50 mm, 1.8 µm) at 50 °C; flow rate 0.8 mL/min; mobile phases water and acetonitrile with 0.1% formic acid.
• Mass spectrometry: Xevo G2-XS QTof; MSE acquisition in ESI+ and ESI–; scan range 50–1800 Da; low collision energy 6 eV, high energy 25–45 eV; capillary voltage 3 kV/2.5 kV; source 120 °C; desolvation 500 °C.

Results and Discussion

Using the Natural Product Application Solution in UNIFI with a custom marine microbial database and online ChemSpider search, the workflow delivered:

• Simultaneous collection of exact mass precursors and corresponding fragment spectra for each analyte.
• Automated dereplication of known metabolites based on exact mass and isotopic distribution.
• Structural elucidation of unknown peaks via in-silico fragment matching (MassFragment™) and batch searches.
• Integration of cytological profiling data with metabolomics to generate an annotated compound–activity network and identify priority leads.

Benefits and Practical Applications

This integrated platform offers:

• Streamlined identification and confirmation of known microbial natural products, reducing analysis time.
• Enhanced discovery of novel compounds through targeted structural elucidation workflows.
• Data-driven prioritization of extracts based on biological phenotype–chemical chemotype correlations.
• Creation of a Compound Activity Map to guide follow-up isolation and bioassays.

Future Trends and Opportunities

Advancements and potential developments include:

• Direct integration of the Natural Product Atlas with UNIFI for real-time dereplication.
• Expansion of spectral libraries and machine learning models to improve fragment prediction accuracy.
• Coupling with high-content imaging platforms for broader phenotypic screening campaigns.
• Application to other microbial sources and environmental matrices for diverse natural product discovery.

Conclusion

The described DIA UPLC-QToF MS workflow, paired with UNIFI informatics, establishes a robust, high-throughput pipeline for the rapid dereplication and discovery of microbial natural products. By combining exact mass, fragmentation data, and phenotypic profiling, researchers can efficiently navigate complex extract libraries and accelerate the identification of bioactive leads.

References

1. Kurita L. K.; Linington R. G. Connecting Phenotype and Chemotype: High-Content Discovery Strategies for Natural Product Research. J. Nat. Prod. 2015, 78, 587–596.
2. Waters Corporation. Using Natural Products Application Solution with UNIFI for the Identification of Chemical Ingredients of Green Tea Extract. 2013; 720004837en.
3. Waters Corporation. Microbial Databases for Rapid Screening and Dereplication of Microbial Natural Products Using UPLC-QTof-MS Coupled to Novel Informatics Platform. 2018; 720006412EN.