The authors would like to acknowledge Marcos Di Falco for his expertise and advice with the preparation of the samples for nano-LC MS/MS as well as Dr. Zoran Minic from the University of Regina for the LC MS/MS analysis. This work was supported by NSERC (DG402214-2011) and CRC (950-221184) funding. D.C. was supported by Concordia Institute for Water, Energy, and Sustainable Systems and FQRNT.

1. Prepare Reagents
<ol>
	<li>Prepare SDS-extraction solution: 0.1 M Tris-HCl pH 7.5, 5 % glycerol, 10 mM EDTA and 1 % SDS. Filter-sterilize using a 0.22 &#181;m filter and store at 4 &#176;C.</li>
	<li>Prepare stock reagents needed for the polyacrylamide gel.
	<ol>
		<li>Prepare 1.5 M Tris-HCL pH 8.8. Filter-sterilize using a 0.22 &#181;m filter and store at room temperature.</li>
		<li>0.5 M Tris-HCL pH 6.8. Filter-sterilize using a 0.22 &#181;m filter and store at room temperature.</li>
		<li>Prepare 10 % SDS. Filter sterilize using a 0.22 &#181;m filter and store at room temperature.</li>
	</ol>
	</li>
	<li>Prepare solutions needed for in-gel trypsin digest and peptide extraction.
	<ol>
		<li>Prepare 100 mM ammonium bicarbonate. Filter-sterilize using a 0.22 &#181;m filter and store at 4 &#176;C.</li>
		<li>Prepare 1 M DTT stocks. Suspended in 100 mM ammonium bicarbonate and store at -80 &#176;C.</li>
		<li>Prepare 550 mM iodoacetamide stocks. Suspended in 100 mM ammonium bicarbonate and store at -80 &#176;C.</li>
		<li>Prepare 100 ng/&#181;l trypsin stocks. Aliquot 60 &#181;l into 1.5 ml microcentrifuge tubes and store at -80 &#176;C.</li>
		<li>Prepare extraction solution: 1% formic acid, 2% acetonitrile.</li>
		<li>Prepare resuspension solution: 5% acetonitrile and 0.1% formic acid.</li>
	</ol>
	</li>
</ol>
2. Perform Cell Lysis in Cartridge Filter Unit with Cells Preserved in RNA Stabilization Solution
<ol>
	<li>Expel the RNA stabilization solution from the cartridge filter unit (1.5 ml) and into a 2 ml microcentrifuge tube using a 60 ml syringe attached at the luer-lock end of the filter.</li>
	<li>Centrifuge the 2 ml microcentrifuge tube for 10 min&#160;at 17,000 x g to pellet any cellular debris. This is done so that the filter in step 2.3 does not clog.</li>
	<li>Transfer supernatant to a 10 K ultracentrifugal filter unit to capture any proteins originating from cells that have lysed from the freeze/thaw of the sample. Do not discard the pellet. It will be used in step 2.5. Perform centrifugation of the ultracentrifugal filter unit at 3,270 x g for 30 min&#160;or until the volume has reduced to about 600 &#181;l.</li>
	<li>Melt the tip of a p10 tip and block the non luer-lock side of the cartridge filter unit with the open end of the tip. This will ensure that no extraction buffer and biomass escapes during steps 2.5-2.7.</li>
	<li>Suspend the pellet in 1 ml SDS-extraction solution and pipette it into the original cartridge filter unit. The easiest way to pipette into the cartridge filter unit is to stack a p200 tip onto a p1000 tip and use this double tip for pipetting.</li>
	<li>Add 1 ml of SDS extraction solution to the cartridge filter unit so the total volume is about 2 ml and incubate for 10 min&#160;at room temperature while rotating in a hybridization oven. Place 3 crumpled lab wipes at the bottom of a 50 ml conical tube. Parafilm both ends of the cartridge filter unit closed and put the cartridge filter unit into the 50 ml tube. Close the lid and put the tube into the hybridization oven.</li>
	<li>After 10 min&#160;place the Sterivex filter on a foam floater and secure it in place with a 5 ml syringe on the luer-lock end. Float and incubate in a 95 &#176;C water-bath for 15 min.</li>
	<li>Let the cartridge filter unit cool and rotate at room temperature for 1 hour (as in step 2.6).</li>
	<li>Expel the SDS extraction solution/cell lysate out of the filter with a 60 ml syringe into the same ultracentrifugal filter unit as before (step 2.3). Add 1 ml of fresh SDS extraction solution to the cartridge filter unit and mix for 30 sec&#160;by hand, then expel into the ultracentrifugal filter unit using the 60 ml syringe. This is to rinse the cartridge filter unit to ensure all proteins have been removed.</li>
	<li>Perform centrifugation on the ultracentrifugal filter unit for 45 min&#160;at 3,270 x g or until the volume in the filter unit is less than 600 &#181;l.</li>
	<li>Discard flow-through and top up the ultracentrifugal filter unit with fresh SDS-extraction solution.</li>
	<li>Centrifuge the filter unit for another 45 min&#160;at 3,270 x g.</li>
	<li>Repeat steps 2.11 and 2.12 twice more. Ensure the final volume in the ultracentrifugal filter unit is at most 600 &#181;l at the end of the final spin.</li>
	<li>At this point, split the concentrate in two. One fraction will be used for DNA precipitation and the other for protein precipitation. 
	Note: The fractionation amount depends on the amount of biomass that was filtered and the intended uses of the products. In our case, we split the concentrate with 10% towards DNA precipitation and 90% towards protein precipitation.</li>
</ol>
3. Protein Precipitation
<ol>
	<li>Add 4 volumes methanol:acetone (50:50) to one volume of concentrate and vortex for 10 sec. Incubate overnight at -20 &#176;C.</li>
	<li>Spin down at 17,000 x g for 30 min. Decant the supernatant and let the pellet (may be invisible) dry in a speedvac for 1 hr (or until dry). 
	Note: Do not over dry the pellet as this may make it difficult to resuspend.</li>
	<li>Suspend the pellet in 25 &#181;l of SDS-extraction solution. Let sit for one hour then resuspend by pipetting up and down.</li>
	<li>Quantify protein using a protein assay kit and the manufacturers instructions.</li>
</ol>
4. DNA Precipitation
<ol>
	<li>Add SDS-extraction solution to the concentrate fraction to be used for DNA precipitation until the 500 &#181;l mark. This step is simply to increase the volume of the solution, making it easier to work with.</li>
	<li>Add 0.583 volumes of a protein precipitation reagent (such as the MPC protein precipitation reagent) and vortex for 10 sec. You should see a white precipitate form. 
	Note: We have also used phenol:chloroform DNA extraction methods, but it is more difficult due to the low volumes. We obtained better DNA yields using the MPC Protein Precipitation Reagent.</li>
	<li>Centrifuge at 17,000 x g and 4 &#176;C for 10 min.</li>
	<li>Transfer supernatant to another 1.5 ml microcentrifuge tube and add 0.95 volumes of isopropanol. Invert 30-40 times.</li>
	<li>Centrifuge for 10 min at 4 &#176;C at maximum speed.</li>
	<li>Carefully decant and discard the supernatant.</li>
	<li>Rinse twice with 750 &#181;l of 70% ethanol.</li>
	<li>Remove as much ethanol as possible by pipetting, then let air-dry. 
	Note: Do not over dry the DNA as this may make it difficult to resuspend.</li>
	<li>Resuspend in 25 &#181;l of low TE buffer,&#160;pH 8&#160;(10 mM Tris-HCl, 0.1 mM EDTA).</li>
	<li>Quantify the DNA using a dsDNA assay kit and the manufacturers instructions. Perform agarose gel (1%) electrophoresis on 3 &#181;l of the DNA to check its quality.</li>
</ol>
5. SDS-PAGE Gel of Proteins
<ol>
	<li>Prepare sample buffer (950 &#181;l Laemmli sample buffer and 50 &#181;l &#946;-mercaptoethanol).</li>
	<li>Add the corresponding volume for 15 &#181;g of protein, or a max of 20 &#181;l to equal volume of sample buffer and boil for 4 min. Samples can now be stored at -80 &#176;C until SDS-PAGE can be performed.</li>
	<li>Prepare 10% acrylamide resolving gel with a 5% acrylamide stacking gel.</li>
	<li>Load the gel with samples and 4 &#181;l of a protein ladder. Run the gel at constant 120 V until the 250 kDa ladder marker has just reached the resolving gel.</li>
	<li>Stain the gel using a coomassie based stain according to the manufacturers instructions.</li>
</ol>
6. In-gel Trypsin Digest and Peptide Extraction
Note: All steps from here until 6.10&#160;are performed in a biological safety cabinet to minimize contamination.
<ol>
	<li>Cut off excess gel under the 10 kDa ladder mark for each lane. Each lane will be analyzed on the MS/MS separately. Cut each lane into 1 mm x 1 mm squares to increase surface area and place all squares from the lane into a low-binding micro-centrifuge tube. Repeat this for all lanes. (1% acetic acid can be used to prevent the gel from drying out and becoming difficult to cut). 
	Note: Minimizing contamination is critical at this stage, so gel slicing is performed in a biological safety cabinet on the glass SDS-PAGE gel mold used to make the gel.</li>
	<li>De-stain
	<ol>
		<li>Dispense 50 &#181;l of 100 mM ammonium bicarbonate into each low-binding tube, close cap and incubate at 37 &#176;C for 10 min.</li>
		<li>Dispense 50 &#181;l of acetonitrile, close cap and incubate at 37 &#176;C for 5 min.</li>
		<li>Aspirate and discard 150 &#181;l.</li>
		<li>Repeat steps 6.2.1 and 6.2.2.</li>
		<li>Aspirate and discard 95 &#181;l.</li>
	</ol>
	</li>
	<li>Dehydration
	<ol>
		<li>Dispense 50 &#181;l acetonitrile, close cap and wait 5 min. Aspirate and discard 45 &#181;l and wait 10 min.</li>
	</ol>
	</li>
	<li>Reduction
	<ol>
		<li>Dispense 50 &#181;l DTT (10 mM, diluted in 100 mM ammonium bicarbonate), close cap and wait 30 min&#160;at 37 &#176;C.</li>
	</ol>
	</li>
	<li>Alkylation
	<ol>
		<li>Dispense 50 &#181;l iodoacetamide (55 mM, diluted in 100 mM ammonium bicarbonate), close cap and wait 20 min&#160;at 37 &#176;C.</li>
		<li>Dispense 100 &#181;l of acetonitrile, close cap and incubate for 5 min&#160;at 37 &#176;C. Aspirate and discard 195 &#181;l.</li>
	</ol>
	</li>
	<li>Wash
	<ol>
		<li>Dispense 50 &#181;l ammonium bicarbonate, close cap and incubate for 10 min&#160;at 37 &#176;C.</li>
		<li>Dispense 50 &#181;l of acetonitrile, close cap and incubate at 37 &#176;C for 5 min. Aspirate and discard 120 &#181;l.</li>
	</ol>
	</li>
	<li>Dehydration
	<ol>
		<li>Dispense 50 &#181;l acetonitrile, close cap and wait 5 min. Aspirate and discard 45 &#181;l.</li>
		<li>Dispense 50 &#181;l of acetonitrile, wait 5 min.</li>
		<li>Aspirate and discard 75 &#181;l and wait 5 min.</li>
	</ol>
	</li>
	<li>Digestion
	<ol>
		<li>Dispense 25-30 &#181;l of 6 ng/&#181;l Trypsin (until all gel fragments are covered). Close cap and wait 30 min&#160;at room temperature.</li>
		<li>Incubate overnight (4.5 hr&#160;minimum) at 37 &#176;C.</li>
		<li>Let sit at room temperature for 30 min.</li>
	</ol>
	</li>
	<li>Extraction and Peptide Transfer
	<ol>
		<li>Dispense 30 &#181;l of extraction solution and cover with lid for 30 min&#160;on ice.</li>
		<li>Aspirate 30 &#181;l and dispense aspirated volume in a new labeled low-binding tube.</li>
		<li>Dispense 12 &#181;l of extraction solution and 12 &#181;l of acetonitrile into the original tube (with gel). Close cap and incubate for 30 min&#160;on ice.</li>
		<li>Aspirate 15 &#181;l and deposit into the new tube.</li>
		<li>Repeat steps (6.9.3 and 6.9.4).</li>
	</ol>
	</li>
	<li>Place new tubes in a SpeedVac until dry.</li>
	<li>Resuspend in 50 &#181;l of resuspension solution. Vortex on medium for 10 min&#160;to resuspend.</li>
	<li>Pipette peptide solution into either nano/LC vials or 96-well plates suitable for nano/LC MS/MS work.</li>
</ol>

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C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Integrative+analysis+of+environmental+sequences+using+MEGAN4.">Integrative analysis of environmental sequences using MEGAN4.</a> Genome research. 21 (9), 1552-1560 (2011).</li><li>Ea Ottesen, ., Marin, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Metatranscriptomic+analysis+of+autonomously+collected+and+preserved+marine+bacterioplankton.">Metatranscriptomic analysis of autonomously collected and preserved marine bacterioplankton.</a> The ISME journal. 5 (12), 1881-1895 (2011).</li><li>Feike, J., J&#252;rgens, K., Hollibaugh, J. T., Kr&#252;ger, S., Jost, G., Labrenz, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+unbiased+metatranscriptomics+in+suboxic+waters+of+the+central+Baltic+Sea+using+a+new+in+situ+fixation+system.">Measuring unbiased metatranscriptomics in suboxic waters of the central Baltic Sea using a new in situ fixation system.</a> The ISME journal. 6 (2), 461-470 (2012).</li></ol>

The authors have nothing to disclose.

Sample preservation is key to metaproteomic studies and previous work demonstrated that an RNA stabilization solution is a useful storage buffer for storing cells prior to protein extraction 28. Ideally, samples would be preserved in situ to negate shifts in protein expression during handling 33,34. In fact, in situ sampling and fixation technologies have been developed, which allow for the autonomous collection and preservation of samples by ship-deployed instruments. However, access to these technologies is not always feasible. In the common case that it is not, samples should be preserved as soon as possible after collection.
Here we present a protocol for extracting protein from RNA stabilization solution stored cells collected on a cartridge filter unit, which is commonly used in aquatic microbiology. The protocol includes cell lysis using an SDS-lysis solution and heating, followed by a protein concentration step using ultracentrifugal filter units that doubled as a necessary desalting step. It must be noted that the concentrating and desalting steps cannot be overlooked. We found that a minimum of three buffer exchange steps was required to desalt our concentrate. Due to the high salt concentration of the RNA stabilization solution, if proper desalting does not occur, too much salt will be precipitated during the overnight protein precipitation step and the desalting and precipitation step will have to be repeated. Additionally, if desalination is not properly performed the 1D-PAGE will not work and the samples will be lost.
Next, the concentrated lysate was divided so that both protein precipitation and DNA precipitation could be performed. This is useful as it is often desirable that metagenomic and metaproteomic data be generated from the same samples. If a protein is not represented in the protein sequence database then the peptide will not be identified. Including the genomic data from the same sample as the proteomic data reduces the risk of not being able to identify a protein due to its absence from the database.
Although this protocol was optimized for use with cartridge filter units and validated to work on coastal ocean microbial communities, it can be adapted for use with other types of environmental samples and filters. However, it should be clearly stated that the success of this protocol is dependent on an adequate amount of starting biomass. Therefore in aquatic ecosystems where biomass may be very low, we recommend increasing the volume of water filtered accordingly.

Microorganisms are ubiquitous and play essential roles in Earth&#8217;s biogeochemical cycles 1. Currently, there are numerous molecular approaches available for characterizing microbial community structure and function. Most common is the analysis of 16S rRNA gene sequences PCR-amplified from environmental DNA 2&#8211;4. A disadvantage of 16S rRNA gene analysis is that it only provides information on phylogenetic identity and community structure, with little information on metabolic function. In contrast, approaches such as metagenomics, metatranscriptomics and metaproteomics provide information on community structure and metabolism. Metagenomics, or the analysis of the gene content of an assemblage of organisms, provides information about the structure and functional potential of the community 5&#8211;8. Although powerful, this functional potential may not correspond to the metabolic activities of the organisms. An organism&#8217;s genotype is represented by its genes, each of which can be transcribed to RNA and further translated to protein, resulting in a phenotype. Thus, to aid in the understanding of microbial functional activity in an environment, post-genomic analysis should be performed 9. Metatranscriptomics, or the analysis of RNA transcripts is useful because it reveals which genes are transcribed in any given environment. However, mRNA levels do not always match their corresponding protein levels due to translational regulation, RNA half-life, and the fact that multiple protein copies can be generated for every mRNA 10.
For these reasons metaproteomics is now recognized as an important tool for environmental microbiology. Common metaproteomic analyses use a shotgun proteomic approach where the near full complement of proteins in a complex sample are purified and analyzed simultaneously, usually through enzymatic digestion into peptides and analysis on a mass spectrometer. Subsequent tandem mass spectrometry (MS/MS) &#8220;peptide fingerprinting&#8221; is used to determine the peptide sequence and potential protein of origin by protein database searching (for a review see 11). Proteomic work has come a long way in the past 25 years thanks to the increase in genomic data availability and the increase in the sensitivity and accuracy of mass spectrometers allowing for high-throughput protein identification and quantification 11,12. Since proteins are the final product of gene expression, metaproteomic data can help determine which organisms are active in any given environment and what proteins they are expressing. This is advantageous when trying to determine how a particular set of environmental variables will affect the phenotype of an organism or community. Early on, MS/MS-based metaproteomic studies in the ocean were used to identify specific proteins in targeted microbial lineages, with the first study focusing on the light driven proton pump proteorhodopsin in SAR11 marine bacteria 13. More recently, comparative metaproteomic analyses have elucidated differential protein expression patterns between complex communities. Examples include the identification of temporal shifts in metabolism in the coastal Northwest Atlantic Ocean 14 or the Antarctic Peninsula 5. Other studies have described variations in protein expression patterns across spatial scales, for instance, along a geographical transect from a low-nutrient ocean gyre to a highly productive coastal upwelling system 15. For further reviews of metaproteomics we recommend Schneider et al. (2010) 9 and Williams et al. (2014) 16. Targeted proteomics has also been employed in recent years to quantify expression of specific metabolic pathways in the environment 17,18.
There are three main phases in metaproteomic analysis. The first phase is sample preparation, which includes sample collection, cell lysis and concentration of protein. Sample collection in marine microbiology often entails the filtration of seawater through a pre-filter to remove larger eukaryotic cells, particles and particle-associated bacteria, followed by filtration for the capture of free living microbial cells, commonly with the use of a 0.22 &#181;m cartridge filter unit 19,20. These filters are incased in a plastic cylinder and a cell lysis and protein extraction protocol that can be performed within the filter unit would be a valuable tool. Once biomass is obtained, the cells must be lysed to allow for protein extraction. Several methods can be employed, including guanidine-HCl lysis 21 and sodium dodecyl sulfate (SDS)-based lysis methods. Although detergents like SDS are very efficient at disrupting membranes and solubilizing many protein types, concentrations as low as 0.1% can interfere with downstream protein digestion and MS analysis 22. Of major concern is the negative effects of SDS on trypsin digestion efficiency, resolving power of reverse phase liquid chromatography and ion suppression or accumulation inside the ion source 23.
The second phase is fractionation and analysis, where proteins are subjected to enzymatic digestion followed by LC MS/MS analysis, resulting in a m/z fragmentation pattern that can be used to ascertain the primary amino acid sequence of the initial tryptic peptide. Various digestion methods can be performed depending on the types of detergents used, as well as the downstream mass spectrometry workflow. In our protocol, 1-D PAGE electrophoresis followed by removal of SDS from the gel is utilized in order to remove any detergent contamination. The analysis of proteins that are difficult to solubilize, such as membrane proteins, requires the use of high concentrations of SDS or other detergents. This leads to compatibility issues with SDS-gel electrophoresis. If the objective of a study requires the solubilization of these hard to solubilize proteins, the tube-gel system can be used 22,24. The tube-gel method incorporates proteins within the gel matrix without the use of electrophoresis. Subsequently any detergents used for solubilization are removed before protein digestion.
The third phase is the bioinformatic analysis. In this phase the MS/MS peptide data are searched against a database of translated nucleotide sequences to determine which peptides and proteins are present in the sample. The identification of peptides is dependent on the database it is searched against. Marine metaproteomic data are commonly searched against databases comprised of reference genomes, metagenomic data such as the Global Ocean Sampling dataset 25, as well as single cell amplified genomes from uncultivated lineages 26,27. Protein identification can also be increased by the inclusion of metagenomic sequences from the same sample as the metaproteomic data was derived 5.
Here we provide a protocol for the generation of peptides suitable for MS/MS-based analysis from microbial biomass collected by filtration and stored in an RNA stabilization solution. The protocol described here allows for DNA and protein to be isolated from the same sample so that all steps leading up to the protein and DNA precipitations are identical. From a practical perspective, less filtration is required since only one filter is required for both protein and DNA extraction. We would also like to acknowledge that this protocol was created through the combination, adaptation and modification of two previously published protocols. The cell lysis steps are adapted from Saito et al. (2011) 28 and the in-gel trypsin digest component is adapted from Shevchenko et al. (2007) 29.

<table><tbody><tr><td>Sterivex -GP 0.22 &amp;mu;m filter unit</td><td>Millipore</td><td>SVGP01050</td><td>Sampling</td></tr><tr><td>RNAlater Stabilization Solution</td><td>Ambion</td><td>AM7021</td><td>Sampling</td></tr><tr><td>Tris&amp;nbsp;</td><td>Bio Basic</td><td>77-86-1 or TB0196-500G</td><td>Protein Extraction/ SDS PAGE gel</td></tr><tr><td>DTT</td><td>Sigma-Aldrich</td><td>D0632-1G</td><td>Protein Extraction</td></tr><tr><td>SDS</td><td>Bio Basic</td><td>15-21-3</td><td>Protein Extraction/ SDS PAGE gel</td></tr><tr><td>EDTA</td><td>Bio Basic</td><td>6381-92-6</td><td>Protein Extraction</td></tr><tr><td>Glycerol</td><td>Fisher Scientific</td><td>56-81-5</td><td>Protein Extraction</td></tr><tr><td>10K Amicon Filter</td><td>Millipore</td><td>UFC801024</td><td>Protein Extraction</td></tr><tr><td>Methanol</td><td>Sigma-Aldrich</td><td>179337-4L</td><td>Protein Precipitation</td></tr><tr><td>Acetone</td><td>Fisher Scientific</td><td>67-64-1</td><td>Protein Precipitation</td></tr><tr><td>MPC Protein Precipitation reagent</td><td>Epicenter</td><td>mmP03750</td><td>DNA Precipitation</td></tr><tr><td>2-Propanol</td><td>Fisher Scientific</td><td>67-63-0</td><td>DNA Precipitation</td></tr><tr><td>Qubit dsDNA BR Assay kit</td><td>Life Technologies</td><td>Q32850</td><td>DNA Quantification</td></tr><tr><td>Qubit Protein Assay kit</td><td>Life Technologies</td><td>Q33211</td><td>Protein Quantification</td></tr><tr><td>Sucrose</td><td>Bio Basic</td><td>57-50-1</td><td>SDS PAGE gel</td></tr><tr><td>TEMED</td><td>Bio Rad</td><td>161-0800</td><td>SDS PAGE gel</td></tr><tr><td>APS</td><td>Bio Rad</td><td>161-0700</td><td>SDS PAGE gel</td></tr><tr><td>30% Acrylamide</td><td>Bio Rad</td><td>161-0158</td><td>SDS PAGE gel</td></tr><tr><td>SimplyBlue SafeStain</td><td>Invitrogen</td><td>LC6060</td><td>SDS PAGE gel</td></tr><tr><td>Glycine</td><td>Bio Rad</td><td>161-0718</td><td>SDS PAGE gel</td></tr><tr><td>B-mercaptoethanol</td><td>Bio Basic</td><td>60-24-2</td><td>SDS PAGE gel</td></tr><tr><td>Laemmli Sample Buffer</td><td>Bio Rad</td><td>161-0737</td><td>SDS PAGE gel</td></tr><tr><td>Precision Plus Protein Kaleidoscope Ladder</td><td>Bio Rad</td><td>161-0375EDU</td><td>SDS PAGE gel</td></tr><tr><td>Acetonitrile</td><td>VWR</td><td>CABDH6044-4</td><td>In-gel Trypsin digest</td></tr><tr><td>NH4HCO3</td><td>Bio Basic</td><td>1066-33-7</td><td>In-gel Trypsin digest</td></tr><tr><td>DTT</td><td>Sigma-Aldrich</td><td>D0632-1G</td><td>In-gel Trypsin digest</td></tr><tr><td>Formic Acid</td><td>Sigma-Aldrich</td><td>F0507-500ML</td><td>In-gel Trypsin digest</td></tr><tr><td>HPLC grade H2O</td><td>Sigma-Aldrich</td><td>270733-4L</td><td>In-gel Trypsin digest</td></tr><tr><td>Iodoacetamide</td><td>Bio Basic</td><td>144-48-9</td><td>In-gel Trypsin digest</td></tr><tr><td>Trypsin</td><td>Promega</td><td>V5111</td><td>In-gel Trypsin digest</td></tr><tr><td>Protein LoBind Tube 1.5 ml</td><td>Eppendorf</td><td>22431081</td><td>In-gel Trypsin digest</td></tr><tr><td>2 ml ROBO vial 9 mm</td><td>Candian Life Science</td><td>VT009/C395SB</td><td>In-gel Trypsin digest</td></tr><tr><td>PP BM insert, No spring</td><td>Candian Life Science</td><td>4025P-631</td><td>In-gel Trypsin digest</td></tr></tbody></table>

an aquatic microbial metaproteomics workflow: from cells to tryptic peptides suitable for tandem mass spectrometry-based analysis

Meta-omic technologies such as metagenomics, metatranscriptomics and metaproteomics can aid in the understanding of microbial community structure and metabolism. Although powerful, metagenomics alone can only elucidate functional potential. On the other hand, metaproteomics enables the description of the expressed in situ metabolism and function of a community. Here we describe a protocol for cell lysis, protein and DNA isolation, as well as peptide digestion and extraction from marine microbial cells collected on a cartridge filter unit (such as the Sterivex filter unit) and preserved in an RNA stabilization solution (like RNAlater). In mass spectrometry-based proteomics studies, the identification of peptides and proteins is performed by comparing peptide tandem mass spectra to a database of translated nucleotide sequences. Including the metagenome of a sample in the search database increases the number of peptides and proteins that can be identified from the mass spectra. Hence, in this protocol DNA is isolated from the same filter, which can be used subsequently for metagenomic analysis.

As a demonstration, we performed the protocol on two seawater samples collected from the surface and the chlorophyll maximum of the coastal ocean in Northern Canada. While at sea, 6-7 L of seawater was passed through a 3 &#181;m GF/D prefilter, then microbial cells were collected onto a 0.22 &#181;m cartridge filter unit following the protocol of Walsh et al. 20. Cells were immediately stored in an RNA stabilization solution until further processing. Upon returning to the lab, we performed the protocol as it is presented here. The concentrated cell lysate was divided; protein was precipitated from 90% of the volume, while DNA was precipitated from the remaining 10% of the volume. We recovered 24-26 &#181;g of protein and 250-308 ng of high quality DNA from these samples (Figure 1). After the in-gel trypsin digestion and peptide extraction, we subjected the peptides to MS/MS analysis using a nano-LC coupled to the Orbitrap Elite mass spectrometer (Thermo Fisher Scientific, Waltham, MA, USA). From the peptides, we generated over 23,000 MS/MS spectra per sample. Peptides and proteins were then identified by searching these spectra against a custom in-house sequence database using the PEAKS bioinformatics tool (BSI, Waterloo, ON, Canada). The database was comprised of predicted proteins from marine reference genomes and metagenomes. The search resulted in the identification of around 1,000 peptides and 700-800 proteins for each sample. Naturally, these results are dependent on microbial cell abundance, MS instrumentation, and protein search database and algorithms. Nonetheless, these results demonstrate that this protocol has the potential to produce adequate tryptic peptides suitable for identify hundreds of proteins in the environment. Moreover, since metagenomic libraries can be constructed from as little as 100 ng of DNA 30, this protocol also has potential to provide adequate quantities of DNA to generate matched metagenomic-metaproteomic datasets.
Taxonomic and functional composition of the metaproteomes was analyzed using a combination of BLASTp and the MEGAN (Metagenome analyzer) software package 31,32 (Figure 2). Proteins assigned to Alpha-proteobacteria were the most highly represented in the dataset, the vast majority of which were assigned to the SAR11 clade. The Rhododobacterales clade of Alpha-proteobacteria was also highly represented and identified most often in surface waters. Proteins assigned to Bacteroidetes were evenly distributed between the surface and chlorophyll maximum, but Flavobacteria proteins were identified to a greater degree at the chlorophyll maximum. Gamma-proteobacterial proteins were evenly distributed throughout the water column while Beta-proteobacterial proteins were found predominately in the surface. From a functional perspective, a wide range of metabolic pathways were identified. Vertical structuring of these metabolic pathways was apparent. For example, proteins associated with amino acid metabolism, carbohydrate metabolism and prokaryotic carbon fixation pathways were identified primarily at the surface, and nitrogen metabolism was found exclusively at the surface. Photosynthetic carbon fixation proteins were observed primarily at the chlorophyll maximum while proteins involved in photosynthesis were identified evenly between the surface and chlorophyll maximum. These results demonstrate that a wide variety of proteins from a diversity of microbial taxa can be detected using the protocol presented here.
<img alt="Figure 1" src="/files/ftp_upload/52827/52827fig1.jpg" /> 
Figure 1: Genomic DNA from 2 depths at Arctic station S633. The first lane contains 4 &#181;l of a 1 kb DNA ladder, lane 2 contains 3 &#181;l of genomic DNA extraction from S633_2 m, lane 3 contains 3 &#181;l of genomic DNA extraction from S633_20 m and lanes 4-6 contain 0.5 &#181;l (85 ng), 2 &#181;l (333 ng) and 4 &#181;l (667 ng) of HindIII digested lambda DNA.
<img alt="Figure 2" src="/files/ftp_upload/52827/52827fig2.jpg" /> 
Figure 2: Taxonomic and functional analysis of 2 depths at Arctic station S633. Taxonomic diversity comparison of the Arctic station 633 surface and chlorophyll maximum waters (A) created using MEGAN. Functional diversity comparison of the Arctic station 633 surface and chlorophyll maximum waters (B) created using MEGAN to query against the KEGG database. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/52827/52827fig2highres.jpg" target="_blank">Please click here to view a larger version of this figure.</a>

Watch this Scientific Journal Video about An Aquatic Microbial Metaproteomics Workflow: From Cells to Tryptic Peptides Suitable for Tandem Mass Spectrometry-based Analysis at JoVE.com

An Aquatic Microbial Metaproteomics Workflow: From Cells to Tryptic Peptides Suitable for Tandem Mass Spectrometry-based Analysis

This protocol is for the extraction and concentration of protein and DNA from microbial biomass collected from seawater, followed by the generation of tryptic peptides suitable for tandem mass spectrometry-based proteomic analysis.

Meta-omic technologies such as metagenomics, metatranscriptomics and metaproteomics can aid in the understanding of microbial community structure and ...

an-aquatic-microbial-metaproteomics-workflow-from-cells-to-tryptic

Nanyang Technological University

Research

JoVE Journal

Environment

8.7K Views.  Concordia University. This protocol is for the extraction and concentration of protein and DNA from microbial biomass collected from seawater, followed by the generation of tryptic peptides suitable for tandem mass spectrometry-based proteomic analysis.

Video: An Aquatic Microbial Metaproteomics Workflow: From Cells to Tryptic Peptides Suitable for Tandem Mass Spectrometry-based Analysis

Resolving Affinity Purified Protein Complexes by Blue Native PAGE and Protein Correlation Profiling

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological processes. Affinity purification coupled to mass spectrometry (AP-MS) has become the method of choice for identifying protein-protein interactions. However, conventional AP-MS studies provide information on protein interactions, but the organizational information is lost. To address this issue, we developed a strategy to unravel the distinct functional assemblies a protein might be involved in, by resolving affinity-purified protein complexes prior to their characterization by mass spectrometry. Protein complexes isolated through affinity purification of a bait protein using an epitope tag and competitive elution are separated through blue native electrophoresis. Comparison of protein migration profiles through correlation profiling using quantitative mass spectrometry allows assignment of interacting proteins to distinct molecular entities. This method is able to resolve protein complexes of close molecular weights that might not be resolved by traditional chromatographic techniques such as gel filtration. With little more work than conventional AP-geLC-MS/MS, we demonstrate this strategy may in many cases be adequate for obtaining protein complex topological information concomitantly to identifying protein interactions.

Here we present protocols for affinity purification of protein complexes and their separation by blue native PAGE, followed by protein correlation profiling using label free quantitative mass spectrometry. This method is useful to resolve interactomes into distinct protein complexes.

Most proteins act in association with others; hence, it is crucial to characterize these functional units in order to fully understand biological ...

Biochemistry

Deep Proteome Profiling by Isobaric Labeling, Extensive Liquid Chromatography, Mass Spectrometry, and Software-assisted Quantification

Many exceptional advances have been made in mass spectrometry (MS)-based proteomics, with particular technical progress in liquid chromatography (LC) coupled to tandem mass spectrometry (LC-MS/MS) and isobaric labeling multiplexing capacity. Here, we introduce a deep-proteomics profiling protocol that combines 10-plex tandem mass tag (TMT) labeling with an extensive LC/LC-MS/MS platform, and post-MS computational interference correction to accurately quantitate whole proteomes. This protocol includes the following main steps: protein extraction and digestion, TMT labeling, 2-dimensional (2D) LC, high-resolution mass spectrometry, and computational data processing. Quality control steps are included for troubleshooting and evaluating experimental variation. More than 10,000 proteins in mammalian samples can be confidently quantitated with this protocol. This protocol can also be applied to the quantitation of post translational modifications with minor changes. This multiplexed, robust method provides a powerful tool for proteomic analysis in a variety of complex samples, including cell culture, animal tissues, and human clinical specimens.

We present a protocol to accurately quantitate proteins with isobaric labelling, extensive fractionation, bioinformatics tools, and quality control steps in combination with liquid chromatography interfaced to a high-resolution mass spectrometer.

Many exceptional advances have been made in mass spectrometry (MS)-based proteomics, with particular technical progress in liquid chromatography (LC) ...

Single-throughput Complementary High-resolution Analytical Techniques for Characterizing Complex Natural Organic Matter Mixtures

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to characterize. However, to understand the thermodynamic and kinetic controls on greenhouse gas (carbon dioxide [CO2] and methane [CH4]) production resulting from the decomposition of NOM, a molecular-level characterization coupled with microbial proteome analyses is necessary. Further, climate and environmental changes are expected to perturb natural ecosystems, potentially upsetting complex interactions that influence both the supply of organic matter substrates and the microorganisms performing the transformations. A detailed molecular characterization of the organic matter, microbial proteomics, and the pathways and transformations by which organic matter is decomposed will be necessary to predict the direction and magnitude of the effects of environmental changes. This article describes a methodological throughput for comprehensive metabolite characterization in a single sample by direct injection Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS), gas chromatography mass spectrometry (GC-MS), nuclear magnetic resonance (NMR) spectroscopy, liquid chromatography mass spectrometry (LC-MS), and proteomics analysis. This approach results in a fully-paired dataset which improves statistical confidence for inferring pathways of organic matter decomposition, the resulting CO2 and CH4 production rates, and their responses to environmental perturbation. Herein we present results of applying this method to NOM samples collected from peatlands; however, the protocol is applicable to any NOM sample (e.g., peat, forested soils, marine sediments, etc.).

This protocol describes a single throughput for complementary analytical and omics techniques culminating in a fully-paired characterization of natural organic matter and microbial proteomics in different ecosystems. This approach permits robust comparisons for identifying metabolic pathways and transformations important for describing greenhouse gas production and predicting responses to environmental change.

Natural organic matter (NOM) is composed of a highly complex mixture of thousands of organic compounds which, historically, proved difficult to ...

Fast Enzymatic Processing of Proteins for MS Detection with a Flow-through Microreactor

The vast majority of mass spectrometry (MS)-based protein analysis methods involve an enzymatic digestion step prior to detection, typically with trypsin. This step is necessary for the generation of small molecular weight peptides, generally with MW &#60; 3,000-4,000 Da, that fall within the effective scan range of mass spectrometry instrumentation. Conventional protocols involve O/N enzymatic digestion at 37 &#186;C. Recent advances have led to the development of a variety of strategies, typically involving the use of a microreactor with immobilized enzymes or of a range of complementary physical processes that reduce the time necessary for proteolytic digestion to a few minutes (e.g., microwave or high-pressure). In this work, we describe a simple and cost-effective approach that can be implemented in any laboratory for achieving fast enzymatic digestion of a protein. The protein (or protein mixture) is adsorbed on C18-bonded reversed-phase high performance liquid chromatography (HPLC) silica particles preloaded in a capillary column, and trypsin in aqueous buffer is infused over the particles for a short period of time. To enable on-line MS detection, the tryptic peptides are eluted with a solvent system with increased organic content directly in the MS ion source. This approach avoids the use of high-priced immobilized enzyme particles and does not necessitate any aid for completing the process. Protein digestion and complete sample analysis can be accomplished in less than ~3 min and ~30 min, respectively.

A quick protocol for proteolytic digestion with an in-house built flow-through tryptic microreactor coupled to an electrospray ionization (ESI) mass spectrometer is presented. The fabrication of the microreactor, the experimental setup and the data acquisition process are described.

The vast majority of mass spectrometry (MS)-based protein analysis methods involve an enzymatic digestion step prior to detection, typically with trypsin. ...

Bioengineering

A Mass Spectrometry-Based Proteomics Approach for Global and High-Confidence Protein R-Methylation Analysis

Protein Arginine (R)-methylation is a widespread protein post-translational modification (PTM) involved in the regulation of several cellular pathways, including RNA processing, signal transduction, DNA damage response, miRNA biogenesis, and translation.
In recent years, thanks to biochemical and analytical developments, mass spectrometry (MS)-based proteomics has emerged as the most effective strategy to characterize the cellular methyl-proteome with single-site resolution. However, identifying and profiling in vivo protein R-methylation by MS remains challenging and error-prone, mainly due to the substoichiometric nature of this modification and the presence of various amino acid substitutions and chemical methyl-esterification of acidic residues that are isobaric to methylation. Thus, enrichment methods to enhance the identification of R-methyl-peptides and orthogonal validation strategies to reduce False Discovery Rates (FDR) in methyl-proteomics studies are required.
Here, a protocol specifically designed for high-confidence R-methyl-peptides identification and quantitation from cellular samples is described, which couples metabolic labeling of cells with heavy isotope-encoded Methionine (hmSILAC) and dual protease in-solution digestion of whole cell extract, followed by off-line High-pH Reversed Phase (HpH-RP) chromatography fractionation and affinity enrichment of R-methyl-peptides using anti-pan-R-methyl antibodies. Upon high-resolution MS analysis, raw data are first processed with the MaxQuant software package and the results are then analyzed by hmSEEKER, a software designed for the in-depth search of MS peak pairs corresponding to light and heavy methyl-peptide within the MaxQuant output files.

Protein Arginine (R)-methylation is a wide-spread post-translational modification regulating multiple biological pathways. Mass spectrometry is the best technology to globally profile the R-methyl-proteome, when coupled to biochemical approaches for modified peptide enrichment. The workflow designed for the high confidence identification of global R-methylation in human cells is described here.

Protein Arginine (R)-methylation is a widespread protein post-translational modification (PTM) involved in the regulation of several cellular pathways, ...

Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry (CCMS)

There is a variety of approaches to reduce the complexity of the proteome on the basis of functional small molecule-protein interactions such as affinity chromatography 1 or Activity Based Protein Profiling 2. Trifunctional Capture Compounds (CCs, Figure 1A) 3 are the basis for a generic approach, in which the initial equilibrium-driven interaction between a small molecule probe (the selectivity function, here S-adenosyl-L-homocysteine, SAH, Figure 1A) and target proteins is irreversibly fixed upon photo-crosslinking between an independent photo-activable reactivity function (here a phenylazide) of the CC and the surface of the target proteins. The sorting function (here biotin) serves to isolate the CC - protein conjugates from complex biological mixtures with the help of a solid phase (here streptavidin magnetic beads). Two configurations of the experiments are possible: "off-bead" 4 or the presently described "on-bead" configuration (Figure 1B). The selectivity function may be virtually any small molecule of interest (substrates, inhibitors, drug molecules).
S-Adenosyl-L-methionine (SAM, Figure 1A) is probably, second to ATP, the most widely used cofactor in nature 5, 6. It is used as the major methyl group donor in all living organisms with the chemical reaction being catalyzed by SAM-dependent methyltransferases (MTases), which methylate DNA 7, RNA 8, proteins 9, or small molecules 10. Given the crucial role of methylation reactions in diverse physiological scenarios (gene regulation, epigenetics, metabolism), the profiling of MTases can be expected to become of similar importance in functional proteomics as the profiling of kinases. Analytical tools for their profiling, however, have not been available. We recently introduced a CC with SAH as selectivity group to fill this technological gap (Figure 1A).
SAH, the product of SAM after methyl transfer, is a known general MTase product inhibitor 11. For this reason and because the natural cofactor SAM is used by further enzymes transferring other parts of the cofactor or initiating radical reactions as well as because of its chemical instability 12, SAH is an ideal selectivity function for a CC to target MTases. Here, we report the utility of the SAH-CC and CCMS by profiling MTases and other SAH-binding proteins from the strain DH5&alpha; of Escherichia coli (E. coli), one of the best-characterized prokaryotes, which has served as the preferred model organism in countless biochemical, biological, and biotechnological studies. Photo-activated crosslinking enhances yield and sensitivity of the experiment, and the specificity can be readily tested for in competition experiments using an excess of free SAH.

Profiling of Methyltransferases and Other S-adenosyl-L-homocysteine-binding Proteins by Capture Compound Mass Spectrometry CCMS

Capture Compounds are trifunctional small molecules to reduce the complexity of the proteome by functional reversible small molecule-protein interaction followed by photo-crosslinking and purification. Here we use a Capture Compound with S-adenosyl-L-homocysteine-binding as selectivity function to isolate methyltransferases from an Escherichia coli whole cell lysate and identify them by MS.

There is a variety of approaches to reduce the complexity of the proteome on the basis of functional small molecule-protein interactions such as affinity ...

Biology

Profiling Thiol Redox Proteome Using Isotope Tagging Mass Spectrometry

Pseudomonas syringae pv. tomato strain DC3000 not only causes bacterial speck disease in Solanum lycopersicum but also on Brassica species, as well as on Arabidopsis thaliana, a genetically tractable host plant1,2. The accumulation of reactive oxygen species (ROS) in cotyledons inoculated with DC3000 indicates a role of ROS in modulating necrotic cell death during bacterial speck disease of tomato3. Hydrogen peroxide, a component of ROS, is produced after inoculation of tomato plants with Pseudomonas3. Hydrogen peroxide can be detected using a histochemical stain 3'-3' diaminobenzidine (DAB)4. DAB staining reacts with hydrogen peroxide to produce a brown stain on the leaf tissue4. ROS has a regulatory role of the cellular redox environment, which can change the redox status of certain proteins5. Cysteine is an important amino acid sensitive to redox changes. Under mild oxidation, reversible oxidation of cysteine sulfhydryl groups serves as redox sensors and signal transducers that regulate a variety of physiological processes6,7. Tandem mass tag (TMT) reagents enable concurrent identification and multiplexed quantitation of proteins in different samples using tandem mass spectrometry8,9. The cysteine-reactive TMT (cysTMT) reagents enable selective labeling and relative quantitation of cysteine-containing peptides from up to six biological samples. Each isobaric cysTMT tag has the same nominal parent mass and is composed of a sulfhydryl-reactive group, a MS-neutral spacer arm and an MS/MS reporter10. After labeling, the samples were subject to protease digestion. The cysteine-labeled peptides were enriched using a resin containing anti-TMT antibody. During MS/MS analysis, a series of reporter ions (i.e., 126-131 Da) emerge in the low mass region, providing information on relative quantitation. The workflow is effective for reducing sample complexity, improving dynamic range and studying cysteine modifications. Here we present redox proteomic analysis of the Pst DC3000 treated tomato (Rio Grande) leaves using cysTMT technology. This high-throughput method has the potential to be applied to studying other redox-regulated physiological processes.

Reactive oxygen species level is elevated when cells encounter stress conditions. Here we show the example of 3'-3' diaminobenzidine staining as well as cysTMT labeling and mass spectrometry to profile the redox proteome in Pseudomonas syringae treated tomato leaves.

Pseudomonas syringae pv. tomato strain DC3000 not only causes bacterial speck disease in Solanum lycopersicum but also on Brassica species, as well as on ...

Metabolic Labeling and Membrane Fractionation for Comparative Proteomic Analysis of Arabidopsis thaliana Suspension Cell Cultures

Plasma membrane microdomains are features based on the physical properties of the lipid and sterol environment and have particular roles in signaling processes. Extracting sterol-enriched membrane microdomains from plant cells for proteomic analysis is a difficult task mainly due to multiple preparation steps and sources for contaminations from other cellular compartments. The plasma membrane constitutes only about 5-20% of all the membranes in a plant cell, and therefore isolation of highly purified plasma membrane fraction is challenging. A frequently used method involves aqueous two-phase partitioning in polyethylene glycol and dextran, which yields plasma membrane vesicles with a purity of 95% 1. Sterol-rich membrane microdomains within the plasma membrane are insoluble upon treatment with cold nonionic detergents at alkaline pH. This detergent-resistant membrane fraction can be separated from the bulk plasma membrane by ultracentrifugation in a sucrose gradient 2. Subsequently, proteins can be extracted from the low density band of the sucrose gradient by methanol/chloroform precipitation. Extracted protein will then be trypsin digested, desalted and finally analyzed by LC-MS/MS. Our extraction protocol for sterol-rich microdomains is optimized for the preparation of clean detergent-resistant membrane fractions from Arabidopsis thaliana cell cultures.
We use full metabolic labeling of Arabidopsis thaliana suspension cell cultures with K15NO3 as the only nitrogen source for quantitative comparative proteomic studies following biological treatment of interest 3. By mixing equal ratios of labeled and unlabeled cell cultures for joint protein extraction the influence of preparation steps on final quantitative result is kept at a minimum. Also loss of material during extraction will affect both control and treatment samples in the same way, and therefore the ratio of light and heave peptide will remain constant. In the proposed method either labeled or unlabeled cell culture undergoes a biological treatment, while the other serves as control 4.

Metabolic Labeling and Membrane Fractionation for Comparative Proteomic Analysis of Arabidopsis thaliana Suspension Cell Cultures

Here we describe a robust method for the fractionation of plant plasma membranes into detergent resistant and detergent soluble membranes based on a mixture of unlabeled and in vivo fully 15N labeled Arabidopsis thaliana cell cultures. The procedure is applied for comparative proteomic studies to understand signaling processes.

Plasma membrane microdomains are features based on the physical properties of the lipid and sterol environment and have particular roles in signaling ...

Galaxy-Enabled Metaproteomics: Insights into Host-Microbiome Interaction

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Clinical metaproteomics reveals host-microbiome interactions underlying diseases. However, challenges to this approach exist. In particular, the characterization of microbial proteins present in low abundance relative to host proteins is difficult. Other significant challenges are attributed to using very large protein sequence databases, which impedes sensitivity and accuracy during peptide and protein identification from mass spectrometry data in addition to retrieving taxonomy and functional annotations and performing statistical analysis. To address these problems, we present an integrated bioinformatics workflow for mass spectrometry-based metaproteomics that combines custom protein sequence database generation, peptide-spectrum match generation and verification, quantification, taxonomic and functional annotations, and statistical analysis. This workflow also offers characterization of human proteins (while prioritizing microbial proteins), thus offering insights into host-microbe dynamics in disease. The tools and workflow are deployed in the Galaxy ecosystem, enabling the development, optimization, and dissemination of these computational resources. We have applied this workflow for metaproteomic analysis of numerous clinical sample types, such as nasopharyngeal swabs and bronchoalveolar lavage fluid. Here, we demonstrate its utility via the analysis of residual fluid from cervical swabs. The complete workflow and accompanying training resources are accessible on the Galaxy Training Network to equip non-experts and experienced researchers with the necessary knowledge and tools to analyze their data.

Clinical metaproteomics offers insights into the human microbiome and its contributions to disease. We harnessed the computational power of the Galaxy platform to develop a modular bioinformatics workflow that facilitates complex mass spectrometry-based metaproteomic analysis and characterization of diverse clinical sample types relevant to studies of disease.

Clinical metaproteomics reveals host-microbiome interactions underlying diseases. However, challenges to this approach exist. In particular, the ...

Immunology and Infection

High-throughput and Deep-proteome Profiling by 16-plex Tandem Mass Tag Labeling Coupled with Two-dimensional Chromatography and Mass Spectrometry

Isobaric tandem mass tag (TMT) labeling is widely used in proteomics because of its high multiplexing capacity and deep proteome coverage. Recently, an expanded 16-plex TMT method has been introduced, which further increases the throughput of proteomic studies. In this manuscript, we present an optimized protocol for 16-plex TMT-based deep-proteome profiling, including protein sample preparation, enzymatic digestion, TMT labeling reaction, two-dimensional reverse-phase liquid chromatography (LC/LC) fractionation, tandem mass spectrometry (MS/MS), and computational data processing. The crucial quality control steps and improvements in the process specific for the 16-plex TMT analysis are highlighted. This multiplexed process offers a powerful tool for profiling a variety of complex samples such as cells, tissues, and clinical specimens. More than 10,000 proteins and posttranslational modifications such as phosphorylation, methylation, acetylation, and ubiquitination in highly complex biological samples from up to 16 different samples can be quantified in a single experiment, providing a potent tool for basic and clinical research.

Presented here is an optimized high-throughput protocol developed with 16-plex tandem mass tag reagents, enabling quantitative proteome profiling of biological samples. Extensive basic pH fractionation and high-resolution LC-MS/MS mitigate ratio compression and provide deep proteome coverage.

Isobaric tandem mass tag (TMT) labeling is widely used in proteomics because of its high multiplexing capacity and deep proteome coverage. Recently, an ...

An Aquatic Microbial Metaproteomics Workflow: From Cells to Tryptic Peptides Suitable for Tandem Mass Spectrometry-based Analysis

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