Exosomes are small membrane vesicles that are secreted by a multitude of cell types. The exosomes derived from dendritic cells (Dex), tumor cells (Tex), and malignant effusions demonstrate immunomodulatory functions, and are even under clinical trial for cancer treatments. In this study we report the phase I clinical trial of the ascites-derived exosomes (Aex) in combination with the granulocyte-macrophage colony-stimulating factor (GM-CSF) in the immunotherapy of colorectal cancer (CRC). The Aex isolated by sucrose/D(2)O density gradient ultracentrifugation are 60-90-nm vesicles that contain the diverse immunomodulatory markers of exosomes and tumor-associated carcinoembryonic antigen (CEA). Totally 40 patients (HLA-A0201(+)CEA(+)) with advanced CRC were enrolled in the study, and randomly assigned to treatments with Aex alone or Aex plus GM-CSF. Patients in both groups received a total of four subcutaneous immunizations at weekly intervals. We found that both therapies were safe and well tolerated, and that Aex plus GM-CSF but not Aex alone can induce beneficial tumor-specific antitumor cytotoxic T lymphocyte (CTL) response. Therefore, our study suggests that the immunotherapy of CRC with Aex in combination with GM-CSF is feasible and safe, and thus can serve as an alternative choice in the immunotherapy of advanced CRC.

Deposition of insoluble protein aggregates is a hallmark of neurodegenerative diseases. The universal presence of β-amyloid and tau in Alzheimer's disease (AD) has facilitated advancement of the amyloid cascade and tau hypotheses that have dominated AD pathogenesis research and therapeutic development. However, the underlying etiology of the disease remains to be fully elucidated. Here we report a comprehensive study of the human brain-insoluble proteome in AD by mass spectrometry. We identify 4,216 proteins, among which 36 proteins accumulate in the disease, including U1-70K and other U1 small nuclear ribonucleoprotein (U1 snRNP) spliceosome components. Similar accumulations in mild cognitive impairment cases indicate that spliceosome changes occur in early stages of AD. Multiple U1 snRNP subunits form cytoplasmic tangle-like structures in AD but not in other examined neurodegenerative disorders, including Parkinson disease and frontotemporal lobar degeneration. Comparison of RNA from AD and control brains reveals dysregulated RNA processing with accumulation of unspliced RNA species in AD, including myc boxdependent-interacting protein 1, clusterin, and presenilin-1. U1-70K knockdown or antisense oligonucleotide inhibition of U1 snRNP increases the protein level of amyloid precursor protein.Thus, our results demonstrate unique U1 snRNP pathology and implicate abnormal RNA splicing in AD pathogenesis.proteomics | liquid chromatography-tandem mass spectrometry | U1A | RNA-seq | premature cleavage and polyadenylation

Highlights d Deep profiling of proteome and phosphoproteome in AD progression d Validation of protein alterations in two independent AD cohorts d Identification of Ab-induced protein changes in AD and the 5xFAD mouse model d Prioritization of proteins and pathways in AD by multi-omics

Summary Isobaric labeling quantification by mass spectrometry (MS) has emerged as a powerful technology for multiplexed large-scale protein profiling, but measurement accuracy in complex mixtures is confounded by the interference from co-isolated ions, resulting in ratio compression. Here we report that the ratio compression can be essentially resolved by the combination of pre-MS peptide fractionation, MS2-based interference detection and post-MS computational interference correction. To recapitulate the complexity of biological samples, we pooled tandem mass tag (TMT) labeled E. coli peptides at 1 : 3 : 10 ratios, and added in ∼20-fold more rat peptides as background, followed by the analysis of two dimensional liquid chromatography (LC)-MS/MS. Systematic investigation show that quantitative interference was impacted by LC fractionation depth, MS isolation window and peptide loading amount. Exhaustive fractionation (320 × 4 h) can nearly eliminate the interference and achieve results comparable to the MS3-based method. Importantly, the interference in MS2 scans can be estimated by the intensity of contaminated y1 product ions, and we thus developed an algorithm to correct reporter ion ratios of tryptic peptides. Our data indicate that intermediate fractionation (40 × 2 h) and y1 ion-based correction allow accurate and deep TMT profiling of more than 10,000 proteins, which represents a straightforward and affordable strategy in isobaric labeling proteomics.

Database search programs are essential tools for identifying peptides via mass spectrometry (MS) in shotgun proteomics. Simultaneously achieving high sensitivity and high specificity during a database search is crucial for improving proteome coverage. Here we present JUMP, a new hybrid database search program that generates amino acid tags and ranks peptide spectrum matches (PSMs) by an integrated score from the tags and pattern matching. In a typical run of liquid chromatography coupled with high-resolution tandem MS, more than 95% of MS/MS spectra can generate at least one tag, whereas the remaining spectra are usually too poor to derive genuine PSMs. To enhance search sensitivity, the JUMP program enables the use of tags as short as one amino acid. Using a target-decoy strategy, we compared JUMP with other programs (e.g. SEQUEST, Mascot, PEAKS DB, and InsPecT) in the analysis of multiple datasets and found that JUMP outperformed these preexisting programs. JUMP also permitted the analysis of multiple co-fragmented peptides from "mixture spectra" to further increase PSMs. In addition, JUMP-derived tags allowed partial de novo sequencing and facilitated the unambiguous assignment of modified residues. In summary, JUMP is an effective database search algorithm complementary to current search programs. Molecular & Cellular Proteomics 13: 10.1074/mcp.O114.039586, 3663-3673, 2014.Peptide identification by tandem mass spectra is a critical step in mass spectrometry (MS)-based 1 proteomics (1). Numerous computational algorithms and software tools have been developed for this purpose (2-6). These algorithms can be classified into three categories: (i) pattern-based database search, (ii) de novo sequencing, and (iii) hybrid search that combines database search and de novo sequencing. With the continuous development of high-performance liquid chromatography and high-resolution mass spectrometers, it is now possible to analyze almost all protein components in mammalian cells (7). In contrast to rapid data collection, it remains a challenge to extract accurate information from the raw data to identify peptides with low false positive rates (specificity) and minimal false negatives (sensitivity) (8).Database search methods usually assign peptide sequences by comparing MS/MS spectra to theoretical peptide spectra predicted from a protein database, as exemplified in SEQUEST (9), Mascot (10), OMSSA (11), X!Tandem (12), Spectrum Mill (13), ProteinProspector (14), MyriMatch (15), Crux (16), MS-GFDB (17), Andromeda (18), BaMS 2 (19), and Morpheus (20). Some other programs, such as SpectraST (21) and Pepitome (22), utilize a spectral library composed of experimentally identified and validated MS/MS spectra. These methods use a variety of scoring algorithms to rank potential peptide spectrum matches (PSMs) and select the top hit as a putative PSM. However, not all PSMs are correctly assigned. For example, false peptides may be assigned to MS/MS spectra with numerous noisy peaks and poor fragmentation patterns. If the samples conta...