Reproducible workflow for multiplexed deep-scale proteome and phosphoproteome analysis of tumor tissues by liquid chromatography–mass spectrometry

Abstract

Here we present an optimized workflow for global proteome and phosphoproteome analysis of tissues or cell lines that uses isobaric tags (TMT (tandem mass tags)-10) for multiplexed analysis and relative quantification, and provides 3× higher throughput than iTRAQ (isobaric tags for absolute and relative quantification)-4-based methods with high intra- and inter-laboratory reproducibility. The workflow was systematically characterized and benchmarked across three independent laboratories using two distinct breast cancer subtypes from patient-derived xenograft models to enable assessment of proteome and phosphoproteome depth and quantitative reproducibility. Each plex consisted of ten samples, each being 300 μg of peptide derived from <50 mg of wet-weight tissue. Of the 10,000 proteins quantified per sample, we could distinguish 7,700 human proteins derived from tumor cells and 3100 mouse proteins derived from the surrounding stroma and blood. The maximum deviation across replicates and laboratories was <7%, and the inter-laboratory correlation for TMT ratio-based comparison of the two breast cancer subtypes was r > 0.88. The maximum deviation for the phosphoproteome coverage was <24% across laboratories, with an average of >37,000 quantified phosphosites per sample and differential quantification correlations of r > 0.72. The full procedure, including sample processing and data generation, can be completed within 10 d for ten tissue samples, and 100 samples can be analyzed in −4 months using a single LC-MS/MS instrument. The high quality, depth, and reproducibility of the data obtained both within and across laboratories should enable new biological insights to be obtained from mass spectrometry-based proteomics analyses of cells and tissues together with proteogenomic data integration.

Genetic control of the human brain proteome

“…To our knowledge, the brain proteomic sequencing performed here are among the deepest thus far profiled with a total of 12,691 unique proteins (12,415 detected genes), which reflects about 79% of all expressed transcripts in the human brain. To achieve throughput and proteomic depth of sequencing, we used TMT isobaric labeling coupled with high-pH offline fractionation following well-established protocols [25]. A limitation of the proteomic data was the use of MS2 acquisition, which can suffer from the presence of co-isolated and co-fragmented interfering ions that can obscure quantification [41]; however, high-pH offline fractionation largely mitigates this issue [25].…”

“…To achieve throughput and proteomic depth of sequencing, we used TMT isobaric labeling coupled with high-pH offline fractionation following well-established protocols [25]. A limitation of the proteomic data was the use of MS2 acquisition, which can suffer from the presence of co-isolated and co-fragmented interfering ions that can obscure quantification [41]; however, high-pH offline fractionation largely mitigates this issue [25]. Another potential limitation is that the observed differences in the number of pQTLs and eQTLs may be in part due to technical differences in the proteomic and transcriptomic profiling methods.…”

Cerebral atherosclerosis contributes to Alzheimer’s dementia independently of its hallmark amyloid and tau pathologies

“…High pH fractionation was performed as previously described 33 with slight modification. Dried samples were re-suspended in high pH loading buffer (0.07% vol/vol NH4OH; 0.045% vol/vol formic acid, 2% vol/vol acetonitrile) and loaded onto an Agilent ZORBAX 300Extend-C18 column (2.1mm x 150 mm with 3.5 µm beads).…”

UbiFast, a rapid and deep-scale ubiquitylation profiling approach for biology and translational research

“…To demonstrate the utility of UbiFast approach to quantitatively profile the ubiquitylome of small amounts of tissue, we isolated tumors from two previously described breast cancer patient-derived xenograft (PDX) models, representing basal (WHIM2) and luminal (WHIM16) subtypes of breast cancer, respectively 29 ( Figure 3A). These models faithfully reproduce genomic features of the disease, exhibiting distinct expression of key basal and luminal genes, and proteome driven basal and luminal pathway signatures 30 . Ubiquitylated peptides were enriched from 5 replicates each of WHIM2 and WHIM16 using 0.5 mg of peptide per sample.…”