Table of Contents

• Coverage
• Dataset Description
  • Methods & Sampling
  • Data Processing Description
  • BCO-DMO Processing Description
• Data Files
• Supplemental Files
• Related Publications
• Related Datasets
• Parameters
• Instruments
• Deployments
• Project Information
• Funding

Related data table and dataset descriptions:

The primary data table for this dataset is provided under the "Data Files" section and contains total protein spectral counts while the table under "Supplemental Files" provides the exclusive protein spectral counts. 

Total spectral counts refer to the total number of spectra with peptide to spectrum matches (PSMs) that matches to each entry within the FASTA sequence database. This approach allows each peptide to map to multiple closely related sequences. In contrast, with exclusive spectral counts each peptide is only allowed to map to one sequence within the FASTA database, and when a peptide is found in multiple database sequences the one with the most peptides mapping (parsimony) to it is selected. There are pros and cons to each approach, where total spectral counts will double count peptides when two similar proteins are compared, and exclusive spectral counts will underrepresent less abundant proteins with shared peptides, favoring the most homolog with the most shared peptides. Considering protein groups with shared peptides or focusing on peptide-level analyses are alternative approaches that could be constructed from these results.  

See "Related Datasets" section for:
*  "AE1913 Peptide Spectral Counts" which includes the individual peptides associated with these proteins (includes total spectral counts for each peptide).
* "AE1913 Protein Identification FASTA"

CTD and other data from the same cruise are listed on deployment page AE1913: https://www.bco-dmo.org/deployment/916412

These data will become part of the Ocean Protein Portal (https://proteinportal.whoi.edu/; Saito et al., 2020).

The assembly, annotations, metatranscriptomic assembly products, the same exclusive protein spectral counts, and other useful information associated with this multi-omic analysis was published as a package at Zenodo (doi: 10.5281/zenodo.8287779). 

Methods are reported in Cohen et al. 2023 (biorxiv preprint doi: 10.1101/2023.11.20.567900) and are summarized below.
* This section describes how this and related datasets were generated (see "Related Datasets" section).

One half of the 142 mm filters (0.2-51 μm) collected by Clio were processed for metaproteomics. Proteins were extracted in an 1% SDS-based detergent in 50 mM HEPES at pH 8.5, reduced with dithiothreitol, alkylated with iodoacetamide, and purified using a polyacrylamide electrophoresis tube gel method. Protein quantification was performed using a BSA assay. Trypsin was added to the protein-bead mixture in a 1:20 trypsin:protein ratio. Peptides were purified using C18 tips and diluted to a concentration of 0.1 μg μL−1. 

Approximately 2-5 µg of purified peptides were injected onto a Dionex UltiMate 3000 RSLCnano LC system with an additional RSLCnano pump, run in online 2D active modulation mode interfaced with a Thermo Fusion mass spectrometer. The mass spectrometer acquired MS1 scans from 380 to 1,580 m/z at 240K resolution in the Orbitrap. MS2 were collected in data dependent mode in the ion trap with a cycle time of 2 seconds between scans and acquisition of charge states 2 to 10. MS2 scans had 1.6 m/z isolation window, 50 ms maximum injection time and 5 s dynamic exclusion time. 

Note: This dataset contains two different missing data identifiers "NA" and "-".   If there were partial matches to the functional annotation database, the missing ones were denoted with "-". If there were no matches at all, when the data frames were merged, the empty columns were denoted with "NA".

example lines in opp_TOTAL_spectralcounts.csv

"6","megahit_HN001_k141_101642.p1","-","-","-","SBP_bac_1,SBP_bac_8"...
vs
"4","megahit_HN001_k141_100671.p1",NA,NA,NA,NA,NA,NA,"X1_30_0.2"...

BCO-DMO Data Manager Processing Notes:
* Data from source file opp_TOTAL_spectralcounts.csv was imported into the BCO-DMO data system as the primary table for this dataset and appears under the Data Files section. First column was an un-named row of sequential numbers so was given the name "row_id".
* Data from source file opp_spectralcounts.csv was attached as as supplemental file (contains exclusive counts). First column was an un-named row of sequential numbers so was given the name "row_id".

NSF Award Abstract:
Microscopic communities in the ocean can be surprisingly diverse. This diversity makes it difficult to study the individual organisms and reactions that control specific reactions controlling nutrient cycles. Past studies confirm that iron and nitrogen are vital elements for biological growth. There is increasing evidence, however, that other chemicals such as silica, zinc, cobalt, and vitamin B12 may be just as important. This project will provide an unprecedented view of community distributions using new molecular methods to isolate and link active proteins to specific chemical cycles during the very first research deployment of a brand-new autonomous underwater vehicle (AUV). The AUV will collect samples in programed patterns by pumping water directly into its filtering mechanism and then return the samples to the ship for analysis. The Bermuda Atlantic Time-series Study (BATS) station, which provides abundant supporting data, is the site for this innovative investigation into the microbial ecology and chemistry of the open oceans. Additionally, data will be widely distributed to other scientists through the Ocean Protein Portal website being developed by the Woods Hole Oceanographic Institute (WHOI) and the Biological and Chemical Oceanography Data Management Office. Data will also contribute a new teaching module in the Marine Bioinorganic Chemistry course at WHOI.

This first scientific deployment of the newly engineered and constructed biogeochemical AUV, Clio, will generate a novel dataset to examine marine microbial biogeochemical cycles in the Northwestern Atlantic oligotrophic ocean in unprecedented detail and at high vertical resolution. First the project proposes to understand if the microbial community reflects the varying chemical composition and cyanobacterial species through nutrient response adaptations. Additionally, the research will determine if iron stress in the low light Prochlorococcus ecotyope found in the deep chlorophyll maximum is a persistent feature influenced by seasonal dust fluxes. The highly resolved vertical data from the in situ pumping capabilities of Clio are fundamental to a rigorous examination of these biogeochemical questions. This highly transformative dataset will greatly advance understanding of the nutrient and trace element cycling of this region and will be the first field validation of the potentially revolutionary capability these new approaches represent for the study of marine microbial biogeochemistry.