MultiplexMS: A mass spectrometry-based multiplexing strategy for ultra-high-throughput analysis of complex mixtures
DOI10.5281/zenodo.7968494Zenodo7968494MaRDI QIDQ6692727
Dataset published at Zenodo repository.
Author name not available (Why is that?)
Publication date: 20 December 2022
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Abstract: Throughput for chemical analysis of natural products mixtures has not kept pace with recent developments in genome sequencing technologies and laboratory automation for high-throughput screening, leading to a disconnect between chemical and biological profiling at the library scale that limits new molecule discovery. Here we report a new strategy for sample multiplexing that can increase mass spectrometry-based profiling up to 30-fold over traditional methods. This strategy involves the analysis of pooled samples and subsequent computational deconvolution to reconstruct peak lists for each sample in the set. We validated this approach using in silico experiments and demonstrated that the method has a high precision (97%) for large, pooled samples (n = 30), particularly for infrequently occurring metabolites (n 10) of relevance in drug discovery applications. Finally, we repeated a recently reported biological activity profiling study on 925 natural products extracts, leading to the rediscovery of all previously reported bioactive metabolites using just 5% of the previously required MS acquisition time. This new method is compatible with mass spectrometry data from any instrument vendor and is supported by an open-source software package available at https://github.com/liningtonlab/MultiplexMS.Throughput for chemical analysis of natural products mixtures has not kept pace with recent developments in genome sequencing technologies and laboratory automation for high-throughput screening, leading to a disconnect between chemical and biological profiling at the library scale that limits new molecule discovery. Here we report a new strategy for sample multiplexing that can increase mass spectrometry-based profiling up to 30-fold over traditional methods. This strategy involves the analysis of pooled samples and subsequent computational deconvolution to reconstruct peak lists for each sample in the set. We validated this approach using in silico experiments and demonstrated that the method has a high precision (97%) for large, pooled samples (n = 30), particularly for infrequently occurring metabolites (n 10) of relevance in drug discovery applications. Finally, we repeated a recently reported biological activity profiling study on 925 natural products extracts, leading to the rediscovery of all previously reported bioactive metabolites using just 5% of the previously required MS acquisition time. This new method is compatible with mass spectrometry data from any instrument vendor and is supported by an open-source software package available at https://github.com/liningtonlab/MultiplexMS.
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