The consistent and accurate quantification of proteins by mass spectrometry (MS)-based proteomics depends on the performance of instruments, acquisition methods and data analysis software. In collaboration with the software developers, we evaluated OpenSWATH, SWATH2.0, Skyline, Spectronaut and DIA-Umpire, five of the most widely used software methods for processing data from SWATH-MS (sequential window acquisition of all theoretical fragment ion spectra), a method that uses data-independent acquisition (DIA) for label-free protein quantification. We analyzed high-complexity test datasets from hybrid proteome samples of defined quantitative composition acquired on two different MS instruments using different SWATH isolation windows setups. For consistent evaluation we developed LFQbench, an R-package to calculate metrics of precision and accuracy in label-free quantitative MS, and report the identification performance, robustness and specificity of each software tool. Our reference datasets enabled developers to improve their software tools. After optimization, all tools provided highly convergent identification and reliable quantification performance, underscoring their robustness for label-free quantitative proteomics.

We present a data-independent acquisition mass spectrometry method, ultradefinition (UD) MS(E). This approach utilizes ion mobility drift time-specific collision-energy profiles to enhance precursor fragmentation efficiency over current MS(E) and high-definition (HD) MS(E) data-independent acquisition techniques. UDMS(E) provided high reproducibility and substantially improved proteome coverage of the HeLa cell proteome compared to previous implementations of MS(E), and it also outperformed a state-of-the-art data-dependent acquisition workflow. Additionally, we report a software tool, ISOQuant, for processing label-free quantitative UDMS(E) data.

Luxembourg Key Points• CLL-derived exosomes are internalized by stromal cells, deliver functional microRNA and proteins, and activate key signaling pathways.• Stromal cells exposed to CLLderived exosomes demonstrate a CAF-like phenotype and secrete factors promoting CLL cell survival.Exosomes derived from solid tumor cells are involved in immune suppression, angiogenesis, and metastasis, but the role of leukemia-derived exosomes has been less investigated. The pathogenesis of chronic lymphocytic leukemia (CLL) is stringently associated with a tumorsupportive microenvironment and a dysfunctional immune system. Here, we explore the role of CLL-derived exosomes in the cellular and molecular mechanisms by which malignant cells create this favorable surrounding. We show that CLL-derived exosomes are actively incorporated by endothelial and mesenchymal stem cells ex vivo and in vivo and that the transfer of exosomal protein and microRNA induces an inflammatory phenotype in the target cells, which resembles the phenotype of cancer-associated fibroblasts (CAFs). As a result, stromal cells show enhanced proliferation, migration, and secretion of inflammatory cytokines, contributing to a tumor-supportive microenvironment. Exosome uptake by endothelial cells increased angiogenesis ex vivo and in vivo, and coinjection of CLL-derived exosomes and CLL cells promoted tumor growth in immunodeficient mice. Finally, we detected a-smooth actin-positive stromal cells in lymph nodes of CLL patients. These findings demonstrate that CLL-derived exosomes actively promote disease progression by modulating several functions of surrounding stromal cells that acquire features of cancer-associated fibroblasts. (Blood. 2015;126(9):1106-1117

Efficient and reproducible sample preparation is a prerequisite for any robust and sensitive quantitative bottom-up proteomics workflow. Here, we performed an independent comparison between single-pot solid-phase-enhanced sample preparation (SP3), filter-aided sample preparation (FASP), and a commercial kit based on the in-StageTip (iST) method. We assessed their performance for the processing of proteomic samples in the low μg range using varying amounts of HeLa cell lysate (1-20 μg of total protein). All three workflows showed similar performances for 20 μg of starting material. When handling sample sizes below 10 μg, the number of identified proteins and peptides as well as the quantitative reproducibility and precision drastically dropped in case of FASP. In contrast, SP3 and iST provided high proteome coverage even in the low μg range. Even when digesting 1 μg of starting material, both methods still enabled the identification of over 3000 proteins and between 25 000 and 30 000 peptides. On average, the quantitative reproducibility between experimental replicates was slightly higher in case of SP3 (R = 0.97 (SP3); R = 0.93 (iST)). Applying SP3 toward the characterization of the proteome of FACS-sorted tumor-associated macrophages in the B16 tumor model enabled the quantification of 2965 proteins and revealed a "mixed" M1/M2 phenotype.