Analysis of protein phosphorylation by monolithic extraction columns based on poly(divinylbenzene) containing embedded titanium dioxide and zirconium dioxide nano-powders

The potential of an organic monolith with incorporated titanium dioxide (TiO(2)) and zirconium dioxide (ZrO(2)) nanoparticles was evaluated for the selective enrichment of phosphorylated peptides from tryptic digests. A pipette tip was fitted with a monolith based on divinylbenzene (DVB) of highly porous structure, which allows sample to pass through the monolithic bed. The enrichment of phosphopeptides was enhanced by increasing the pipetting cycles during the sample preparation and a higher recovery could be achieved with adequate buffer systems. A complete automated process was developed for enrichment of phosphopeptides leading to high reproducibility and resulting in a robust method designed to minimize analytical variance while providing high sensitivity at high sample throughput. The effect of particle size on the selectivity of phosphopeptides was investigated by comparative studies with nano- and microscale TiO(2) and ZrO(2) powders. Eleven phosphopeptides from alpha-casein digest could be recovered by an optimized mixture of microscale TiO(2)/ZrO(2) particles, whereas nine additional phosphopeptides could be retained by the same mixture of nano-structured material. When compared to conventional immobilized metal-ion affinity chromatography and commercial phosphorylation-enrichment kits, higher selectivity was observed in case of self fabricated tips. About 20 phosphopeptides could be retained from alpha-casein and five from beta-casein digests by using TiO(2) and ZrO(2) based extraction tips. Further selectivity for phosphopeptides was demonstrated by enriching a digest of in vitro phosphorylated extracellular signal regulated kinase 1 (ERK1). Two phosphorylated peptides of ERK1 could be identified by MALDI-MS/MS measurements and a following MASCOT database search.     

2-D DIGE analyses of enriched secretory lysosomes reveal heterogeneous profiles of functionally relevant proteins in leukemic and activated human NK cells

As part of the innate immune system, natural killer (NK) cells detect and lyse tumor and virus-infected cells without prior antigen-dependent recognition and expansion. To this end, they utilize dual-function organelles that combine properties of conventional lysosomes and exocytotic vesicles. Upon stimulation, these secretory lysosomes (SLs) release their cytotoxic molecules into the immunological synapse. In addition, several molecules associated with secretory vesicles become exposed on the plasma membrane. Recent studies often took advantage of the few established NK cell lines, for instance to analyze the exocytotic machinery associated with NK cell vesicles. NK cell lines and primary NK cells differ, however, substantially in the expression of "typical" surface receptors and their requirements to induce target cell lysis. Here, we directly compared the lysosomal compartments of different NK cell populations. We enriched SLs of two leukemic cell lines (YTS and NKL) and IL-2-expanded NK cells by subcellular fractionation and characterized their proteome by 2-D difference gel electrophoresis and MS. Although the overall protein composition of the lysosomal preparations was very similar and more than 90% of the proteins were present at comparable levels, we define a cell line-specific setup of functionally relevant proteins involved in antigen presentation and cytotoxic effector function.     

A high confidence, manually validated human blood plasma protein reference set

Background

The immense diagnostic potential of human plasma has prompted great interest and effort in cataloging its contents, exemplified by the Human Proteome Organization (HUPO) Plasma Proteome Project (PPP) pilot project. Due to challenges in obtaining a reliable blood plasma protein list, HUPO later re-analysed their own original dataset with a more stringent statistical treatment that resulted in a much reduced list of high confidence (at least 95%) proteins compared with their original findings. In order to facilitate the discovery of novel biomarkers in the future and to realize the full diagnostic potential of blood plasma, we feel that there is still a need for an ultra-high confidence reference list (at least 99% confidence) of blood plasma proteins.

Methods

To address the complexity and dynamic protein concentration range of the plasma proteome, we employed a linear ion-trap-Fourier transform (LTQ-FT) and a linear ion trap-Orbitrap (LTQ-Orbitrap) for mass spectrometry (MS) analysis. Both instruments allow the measurement of peptide masses in the low ppm range. Furthermore, we employed a statistical score that allows database peptide identification searching using the products of two consecutive stages of tandem mass spectrometry (MS3). The combination of MS3 with very high mass accuracy in the parent peptide allows peptide identification with orders of magnitude more confidence than that typically achieved.

Results

Herein we established a high confidence set of 697 blood plasma proteins and achieved a high 'average sequence coverage' of more than 14 peptides per protein and a median of 6 peptides per protein. All proteins annotated as belonging to the immunoglobulin family as well as all hypothetical proteins whose peptides completely matched immunoglobulin sequences were excluded from this protein list. We also compared the results of using two high-end MS instruments as well as the use of various peptide and protein separation approaches. Furthermore, we characterized the plasma proteins using cellular localization information, as well as comparing our list of proteins to data from other sources, including the HUPO PPP dataset.

Conclusion

Superior instrumentation combined with rigorous validation criteria gave rise to a set of 697 plasma proteins in which we have very high confidence, demonstrated by an exceptionally low false peptide identification rate of 0.29%.     

A rapid, sensitive and economical assessment of monoclonal antibody conformational stability by intrinsic tryptophan fluorescence spectroscopy

Steady-state intrinsic tryptophan fluorescence spectroscopy is used as a rapid, robust and economic way for screening the thermal protein conformational stability in various formulations used during the early biotechnology development phase. The most important parameters affecting protein stability in a liquid formulation, e. g. during the initial purification steps or preformulation development, are the pH of the solution, ionic strength, presence of excipients and combinations thereof. A well-defined protocol is presented for the investigation of the thermal conformational stability of proteins. This allows the determination of the denaturation temperature as a function of solution conditions. Using intrinsic tryptophan fluorescence spectroscopy for monitoring the denaturation and folding of proteins, it is crucial to understand the influence of different formulation parameters on the intrinsic fluorescence probes of proteins. Therefore, we have re-evaluated and re-assessed the influence of temperature, pH, ionic strength, buffer composition on the emission spectra of tryptophan, phenylalanine and tyrosine to correctly analyse and evaluate the data obtained from thermal-induced protein denaturation as a function of the solution parameters mentioned above. The results of this study are a prerequisite for using this method as a screening assay for analysing the conformational stability of proteins in solution. The data obtained from intrinsic protein fluorescence spectroscopy are compared to data derived from calorimetry. The advantage, challenges and applicability using intrinsic tryptophan fluorescence spectroscopy as a routine development method in pharmaceutical biotechnology are discussed.     

Towards zero training for brain-computer interfacing

Electroencephalogram (EEG) signals are highly subject-specific and vary considerably even between recording sessions of the same user within the same experimental paradigm. This challenges a stable operation of Brain-Computer Interface (BCI) systems. The classical approach is to train users by neurofeedback to produce fixed stereotypical patterns of brain activity. In the machine learning approach, a widely adapted method for dealing with those variances is to record a so called calibration measurement on the beginning of each session in order to optimize spatial filters and classifiers specifically for each subject and each day. This adaptation of the system to the individual brain signature of each user relieves from the need of extensive user training. In this paper we suggest a new method that overcomes the requirement of these time-consuming calibration recordings for long-term BCI users. The method takes advantage of knowledge collected in previous sessions: By a novel technique, prototypical spatial filters are determined which have better generalization properties compared to single-session filters. In particular, they can be used in follow-up sessions without the need to recalibrate the system. This way the calibration periods can be dramatically shortened or even completely omitted for these 'experienced' BCI users. The feasibility of our novel approach is demonstrated with a series of online BCI experiments. Although performed without any calibration measurement at all, no loss of classification performance was observed.     

Preferred spatial frequencies for human face processing are associated with optimal class discrimination in the machine

Psychophysical studies suggest that humans preferentially use a narrow band of low spatial frequencies for face recognition. Here we asked whether artificial face recognition systems have an improved recognition performance at the same spatial frequencies as humans. To this end, we estimated recognition performance over a large database of face images by computing three discriminability measures: Fisher Linear Discriminant Analysis, Non-Parametric Discriminant Analysis, and Mutual Information. In order to address frequency dependence, discriminabilities were measured as a function of (filtered) image size. All three measures revealed a maximum at the same image sizes, where the spatial frequency content corresponds to the psychophysical found frequencies. Our results therefore support the notion that the critical band of spatial frequencies for face recognition in humans and machines follows from inherent properties of face images, and that the use of these frequencies is associated with optimal face recognition performance.     

Signaling signatures and functional properties of anti-human CD28 superagonistic antibodies

Superagonistic CD28 antibodies (CD28SAs) activate T lymphocytes without concomitant perturbation of the TCR/CD3-complex. In rodents these reagents induce the preferential expansion of regulatory T cells and can be used for the treatment of autoimmune diseases. Unexpectedly, the humanized CD28 superagonist TGN1412 caused severe and life threatening adverse effects during a recently conducted phase I clinical trail. The underlying molecular mechanisms are as yet unclear. We show that TGN1412 as well as the commercially available CD28 superagonist ANC28.1 induce a delayed but extremely sustained calcium response in human naïve and memory CD4⁺ T cells but not in cynomolgus T lymphocytes. The sustained Ca⁺⁺-signal was associated with the activation of multiple intracellular signaling pathways and together these events culminated in the rapid de novo synthesis of high amounts of pro-inflammatory cytokines, most notably IFN-γ and TNF-α. Importantly, sustained transmembranous calcium flux, activation of Src-kinases as well as activation of PI3K were found to be absolutely required for CD28SA-mediated production of IFN-γ and IL-2. Collectively, our data suggest a molecular basis for the severe side effects caused by TGN1412 and impinge upon the relevance of non-human primates as preclinical models for reagents that are supposed to modify the function of human T cells.