Mass Spectrometry analysis from immunoprecipitation using BATF2 antibody in IFNg-treated 32D cells

32D cells (ATCC, CRL-11346) were cultured with recombinant IFNγ (100ng/mL) for 17 hours. The cells were harvested and lysed in NETN buffer (50mM Tris pH 7.3, 170mM NaCl, 1nM EDTA, 0.5% NP-40) followed by sonication and ultracentrifugation. The immunoprecipitation (IP) was performed as described (Chen et al., 2019) including in-gel digestion. To form three peptide pools, each IP lane was cut into numerous bands and tryptic peptides were studied on nano-LC 1000 system (Thermo Fisher Scientific, San Jose, CA) coupled to Orbitrap Fusion (Thermo Fisher Scientific, San Jose, CA) mass spectrometer (MS). We used a two-column setup with precolumn (2cm x 100µmI.D) and analytical column (20cm x 75 µmI.D) filled with Reprosil-Pur Basic C18 (1.9 µm, Dr. Maish GmbH, Germany) to load our peptides which then were eluted using a 110min gradient of 2-30% B (90% acetonitrile) at a flow rate of 200nl/min. The eluted peptides were analyzed using Orbitrap Fusion mass spectrometer operated in the data-dependent acquisition mode acquiring fragmentation spectra of the top 35 strongest ions. The full MS scan was performed in Orbitrap in the range of 300-1400m/z at 120,000 resolutions (AGC 5e5, max IT 50ms) followed by rapid IonTrap HCD MS2 fragmentation (CE30%, AGC 5e4, max IT 30ms) at precursor isolation width of 3m/z. The dynamic exclusion was set to 5s. Proteome Discover 2.1 interface (Thermo Scientific) via Mascot algorithm was used to analyze MS raw files which were searched against target-decoy mouse NCBI refseq (downloaded on 2020-03-24). The following parameters were used for the search: variable modification of oxidation on methionine, destreak on cysteine and protein N-terminal acetylation; 20ppm precursor mass tolerance; 0.5Da fragment mass tolerance; 2 missed cleavages; enzyme Trysin. The peptide identification was done at a false discovery rate (FDR) < 0.05. Protein inference and quantitation were performed by gpGrouper (v1.0.040) using shared peptide iBAQ area distribution (Saltzman et al., 2018).