It is not too uncommon, that certain components cannot be detected with certain mass spectrometry methods. That is a huge problem for complete *omics studies. Some substances are simply hard to fragment or Ionize at all, depending on the mechanism how this is accomplished. You should thus clarify, which ionization method etc. you are using so it becomes clear, if it is "just" such a general problem or if there may be some other issue.

Hi Faruk,

I'm sorry I can't answer your question about ms. However, I'm also working with NK exosomes and wanted to find out what antibody manufacturer and/or which amino acid sequence you are using for detection of CD9. Thanks,

Allyson

Dear Thomas,

I dont know much about the ms methods. I did it from someone else. so here is protocol they send me. I hope it will help you to help me understand my problem.

In-gel tryptic digestion was performed after reduction with dithioerythritol and S-carbamidomethylation with iodoacetamide. Gel pieces were washed two times with aqueous 50% (v:v) acetonitrile containing 25 mM ammonium bicarbonate, then once with acetonitrile and dried in a vacuum concentrator for 20 min. Sequencing-grade, modified porcine trypsin (Promega) was dissolved in 50 mM acetic acid, then diluted 5-fold with 25 mM ammonium bicarbonate to give a final trypsin concentration of 0.02 mg/mL. Gel pieces were rehydrated by adding 25 mL of trypsin solution, and after 10 min enough 25 mM ammonium bicarbonate solution was added to cover the gel pieces. Digests were incubated overnight at 37oC. Peptides were extracted by washing three times with aqueous 50% (v:v) acetonitrile containing 0.1% (v:v) trifluoroacetic acid, before drying in a vacuum concentrator and reconstituting in aqueous 0.1% (v:v) trifluoroacetic acid. A common sample pool was created by taking equal aliquots of all samples.

LC-MS/MS

Samples were loaded onto an UltiMate 3000 RSLCnano HPLC system (Thermo) equipped with a PepMap 100 Å C18, 5 µm trap column (300 µm x 5 mm Thermo) and a PepMap, 2 µm, 100 Å, C18 EasyNano nanocapillary column (75 mm x 150 mm, Thermo). The trap wash solvent was aqueous 0.05% (v:v) trifluoroacetic acid and the trapping flow rate was 15 µL/min. The trap was washed for 3 min before switching flow to the capillary column. Separation used gradient elution of two solvents: solvent A, aqueous 1% (v:v) formic acid; solvent B, aqueous 80% (v:v) acetonitrile containing 1% (v:v) formic acid. The flow rate for the capillary column was 300 nL/min and the column temperature was 40°C. The linear multi-step gradient profile was: 3-10% B over 7 mins, 10-35% B over 30 mins, 35-99% B over 5 mins and then proceeded to wash with 99% solvent B for 4 min. The column was returned to initial conditions and re-equilibrated for 15 min before subsequent injections.

The nanoLC system was interfaced with an Orbitrap Fusion hybrid mass spectrometer (Thermo) with an EasyNano ionisation source (Thermo). Positive ESI-MS and MS2 spectra were acquired using Xcalibur software (version 4.0, Thermo). Instrument source settings were: ion spray voltage, 1,900 V; sweep gas, 0 Arb; ion transfer tube temperature; 275°C. MS1 spectra were acquired in the Orbitrap with: 120,000 resolution, scan range: m/z 375-1,500; AGC target, 4e5; max fill time, 100 ms. Data dependant acquisition was performed in top speed mode using a 1 s cycle, selecting the most intense precursors with charge states >1. Easy-IC was used for internal calibration. Dynamic exclusion was performed for 50 s post precursor selection and a minimum threshold for fragmentation was set at 5e3. MS2 spectra were acquired in the linear ion trap with: scan rate, turbo; quadrupole isolation, 1.6 m/z; activation type, HCD; activation energy: 32%; AGC target, 5e3; first mass, 110 m/z; max fill time, 100 ms. Acquisitions were arranged by Xcalibur to inject ions for all available parallelizable time.

Data Analysis

Peak lists were converted from centroided .raw to .mgf format using MSConvert (ProteoWizard 3.0.9967). Mascot Daemon (version 2.5.1, Matrix Science) was used to search against the human subsets of the UniProt database (20,261 sequences; 11,330,198 residues) using a locally-running copy of the Mascot program (Matrix Science Ltd., version 2.5.1). Search criteria specified: Enzyme, trypsin; Fixed modifications, Carbamidomethyl (C); Variable modifications, Oxidation (M), Deamidation (N,Q), Gln->pyroGlu (N-term Q), Glu->pyro-Glu (N-term E); Peptide tolerance, 3 ppm; MS/MS tolerance, 0.5 Da; Instrument, ESI-TRAP. The Mascot .dat result file was imported into Scaffold (version 4.7.5, Proteome Software) and a second search run against the same databases using X!Tandem.

Peptide identifications were accepted if they could be established at greater than 5.0% probability to achieve an FDR less than 1.0% by the Scaffold Local FDR algorithm. Protein identifications were accepted if they could be established at greater than 96.0% probability to achieve an FDR less than 1.0% and contained at least 2 identified peptides. Protein probabilities were assigned by the Protein Prophet algorithm (Nesvizhskii, Al et al Anal. Chem. 2003;75(17):4646-58). Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony. Proteins sharing significant peptide evidence were grouped into clusters.

Spectral counting-based relative quantification and statistical testing were performed in Scaffold. Pair-wise comparisons were made using total spectral counts with the probability of difference calculated using Fisher’s exact test. A multi-way comparison of all sample groups was performed by ANOVA of normalised spectral counts. In both cases a Hochberg and Benjamini multiple-test corrected p-value threshold of