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In biological environments, the surface of nanoparticles (NPs) are modified by protein corona (PC) that determines their biological behavior. Unfortunately, in vitro tests still give different PC than in vivo tests causing in vitro-in vivo discrepancy; hence, in vitro studies are not indicative for the NPs' behavior in vivo. Here is demonstrated that PC in vitro is strongly influenced by the type of extracellular fluid (ECF), blood or lymph, by their high and low flow conditions and transitions between ECFs, and a combination of these parameters. As a result, this in vitro study approaches fluidic and dynamic variations to which NPs are exposed in vivo: different ECF that NPs encounter first in different injection routes, different transitions in-between ECFs during circulation, and simultaneous change in the exposed flow in these transitions. The most-abundant proteins in PCs are found to be not the most abundant in ECFs, but those having high affinity for binding to the surface of NPs. Moreover, some proteins are differently abundant in PCs at different flows, which indicate force-promoted binding, catch bonds. These results suggest that future in vitro studies should consider more complex incubation conditions to improve the in vitro-in vivo consistency necessary for translational research., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Site-specific transcription factors (TFs) play an essential role in mammalian development and function as they are vital for the majority of cellular processes. Despite their biological importance, TF proteomic data is scarce in the literature, likely due to difficulties in detecting peptides as the abundance of TFs in cells tends to be low. In recent years, significant improvements in MS-based technologies in terms of sensitivity and specificity have increased the interest in developing quantitative methodologies specifically targeting relatively lowly abundant proteins such as TFs in mammalian models. Such efforts would be greatly aided by the availability of TF peptide-specific information as such data would not only enable improvements in speed and accuracy of protein identifications, but also ameliorate cross-comparisons of quantitative proteomics data and allow for a more efficient development of targeted proteomics assays. However, to date, no comprehensive TF proteotypic peptide database has been developed. To address this evident lack of TF peptide data in public repositories, we are generating a comprehensive, experimentally derived TF proteotypic peptide spectral library dataset based on in vitro protein expression. Our library currently contains peptide information for 89 TFs and this number is set to increase in the near future. All MS data have been deposited in the ProteomeXchange with identifier PXD001212 (http://proteomecentral.proteomexchange.org/dataset/PXD001212)., (© 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

We present an integrated proteomics platform designed for performing differential analyses. Since reproducible results are essential for comparative studies, we explain how we improved reproducibility at every step of our laboratory processes, e.g. by taking advantage of the powerful laboratory information management system we developed. The differential capacity of our platform is validated by detecting known markers in a real sample and by a spiking experiment. We introduce an innovative two-dimensional (2-D) plot for displaying identification results combined with chromatographic data. This 2-D plot is very convenient for detecting differential proteins. We also adapt standard multivariate statistical techniques to show that peptide identification scores can be used for reliable and sensitive differential studies. The interest of the protein separation approach we generally apply is justified by numerous statistics, complemented by a comparison with a simple shotgun analysis performed on a small volume sample. By introducing an automatic integration step after mass spectrometry data identification, we are able to search numerous databases systematically, including the human genome and expressed sequence tags. Finally, we explain how rigorous data processing can be combined with the work of human experts to set high quality standards, and hence obtain reliable (false positive < 0.35%) and nonredundant protein identifications.

Human tuberculosis is caused by the intracellular pathogen Mycobacterium tuberculosis. Sequencing of the genome of M. tuberculosis strain H37Rv has predicted 3924 open reading frames, and enabled identification of proteins from this bacterium by peptide mass fingerprinting. Extracellular proteins from the culture medium and proteins in cellular extracts were examined by two-dimensional gel electrophoresis using immobilized pH gradient technology. By mass spectrometry and immunodetection, 49 culture filtrate proteins and 118 lysate proteins were identified, 83 of which were novel. To date, 288 proteins have been identified in M. tuberculosis proteome studies, and a list is presented which includes all identified proteins (available at http://www.ssi.dk/publichealth/tbimmun). The information obtained from the M. tuberculosis proteome so far is discussed in relation to the information obtained from the complete genome sequence.