Dataset Information

Rapid Cyanobacteria Species Identification with High Sensitivity Using Native Mass Spectrometry.

ABSTRACT: Cyanobacteria have evolved over billions of years to adapt and survive in diverse climates. Environmentally, this presents a huge challenge because cyanobacteria can now rapidly form algae blooms that are detrimental to aquatic life. In addition, many cyanobacteria produce toxins, making them hazardous to animals and humans that they encounter. Rapid identification of cyanobacteria is essential to monitor and prevent toxic algae blooms. Here, we show for the first time how native mass spectrometry can quickly and precisely identify cyanobacteria from diverse aquatic environments. By monitoring phycobiliproteins, abundant protein complexes within cyanobacteria, simple, easy-to-understand mass spectral "fingerprints" were created that were unique to each species. Moreover, our method is 10-fold more sensitive than the current MALDI-TOF mass spectrometric methods, meaning that cyanobacteria can be monitored using this technology prior to bloom formation. Together, the data show great promise for the simultaneous detection and identification of co-existing cyanobacteria in situ.

SUBMITTER: Sound JK

PROVIDER: S-EPMC8552214 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Rapid Cyanobacteria Species Identification with High Sensitivity Using Native Mass Spectrometry.

Sound Jaspreet K JK Peters Anna A Bellamy-Carter Jeddidiah J Rad-Menéndez Cecilia C MacKechnie Karen K Green David H DH Leney Aneika C AC

Analytical chemistry 20211017 42

Cyanobacteria have evolved over billions of years to adapt and survive in diverse climates. Environmentally, this presents a huge challenge because cyanobacteria can now rapidly form algae blooms that are detrimental to aquatic life. In addition, many cyanobacteria produce toxins, making them hazardous to animals and humans that they encounter. Rapid identification of cyanobacteria is essential to monitor and prevent toxic algae blooms. Here, we show for the first time how native mass spectromet ...[more]

PMID: 34657414

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Project description:Mass spectrometric sequencing of low abundance, integral membrane proteins, particularly the transmembrane domains, presents challenges that span the multiple phases of sample preparation including solubilization, purification, enzymatic digestion, peptide extraction, and chromatographic separation. We describe a method through which we have obtained high peptide coverage for 12 ?-aminobutyric acid type A receptor (GABAA receptor) subunits from 2 picomoles of affinity-purified GABAA receptors from rat brain neocortex. Focusing on the ?? subunit, we identified peptides covering 96% of the protein sequence from fragmentation spectra (MS2) using a database searching algorithm and deduced 80% of the amino acid residues in the protein from de novo sequencing of Orbitrap spectra. The workflow combined microscale membrane protein solubilization, protein delipidation, in-solution multi-enzyme digestion, multiple stationary phases for peptide extraction, and acquisition of high-resolution full scan and fragmentation spectra. For de novo sequencing of peptides containing the transmembrane domains, timed digestions with chymotrypsin were utilized to generate peptides with overlapping sequences that were then recovered by sequential solid phase extraction using a C4 followed by a porous graphitic carbon stationary phase. The specificity of peptide identifications and amino acid residue sequences was increased by high mass accuracy and charge state assignment to parent and fragment ions. Analysis of three separate brain samples demonstrated that 78% of the sequence of the ?? subunit was observed in all three replicates with an additional 13% covered in two of the three replicates, indicating a high degree of sequence coverage reproducibility. Label-free quantitative analysis was applied to the three replicates to determine the relative abundances of 11 ?-aminobutyric acid type A receptor subunits. The deep sequence MS data also revealed two N-glycosylation sites on the ?? subunit, confirmed two splice variants of the ?? subunit (??L and ??S) and resolved a database discrepancy in the sequence of the ?? subunit.

Dataset Information

Rapid Cyanobacteria Species Identification with High Sensitivity Using Native Mass Spectrometry.

Publications

Rapid Cyanobacteria Species Identification with High Sensitivity Using Native Mass Spectrometry.

Similar Datasets

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