Simple deprotection of acetal type protecting groups under neutral conditions

The hallmarks of Alzheimer's disease (AD) are characterized by cognitive decline and behavioral changes. The most prominent brain region affected by the progression of AD is the hippocampal formation. The pathogenesis involves a successive loss of hippocampal neurons accompanied by a decline in learning and memory consolidation mainly attributed to an accumulation of senile plaques. The amyloid precursor protein (APP) has been identified as precursor of Aβ-peptides, the main constituents of senile plaques. Until now, little is known about the physiological function of APP within the central nervous system. The allocation of APP to the proteome of the highly dynamic presynaptic active zone (PAZ) highlights APP as a yet unknown player in neuronal communication and signaling. In this study, we analyze the impact of APP deletion on the hippocampal PAZ proteome. The native hippocampal PAZ derived from APP mouse mutants (APP-KOs and NexCreAPP/APLP2-cDKOs) was isolated by subcellular fractionation and immunopurification. Subsequently, an isobaric labeling was performed using TMT6 for protein identification and quantification by high-resolution mass spectrometry. We combine bioinformatics tools and biochemical approaches to address the proteomics dataset and to understand the role of individual proteins. The impact of APP deletion on the hippocampal PAZ proteome was visualized by creating protein-protein interaction (PPI) networks that incorporated APP into the synaptic vesicle cycle, cytoskeletal organization, and calcium-homeostasis. The combination of subcellular fractionation, immunopurification, proteomic analysis, and bioinformatics allowed us to identify APP as structural and functional regulator in a context-sensitive manner within the hippocampal active zone network

Online-2D NanoLC-MS for Crude Serum Proteome Profiling : Assessing Sample Preparation Impact on Proteome Composition

Although current LC-MS technology permits scientists to efficiently screen clinical samples in translational research, e.g., steroids, biogenic amines, and even plasma or serum proteomes, in a daily routine, maintaining the balance between throughput and analytical depth is still a limiting factor. A typical approach to enhance the proteome depth is employing offline two-dimensional (2D) fractionation techniques before reversed-phase nanoLC-MS/MS analysis (1D-nanoLC-MS). These additional sample preparation steps usually require extensive sample manipulation, which could result in sample alteration and sample loss. Here, we present and compare 1D-nanoLC-MS with an automated online-2D high-pH RP × low pH RP separation method for deep proteome profiling using a nanoLC system coupled to a high-resolution accurate-mass mass spectrometer. The proof-of-principle study permitted the identification of ca. 500 proteins with ∼10,000 peptides in 15 enzymatically digested crude serum samples collected from healthy donors in 3 laboratories across Europe. The developed method identified 60% more peptides in comparison with conventional 1D nanoLC-MS/MS analysis with ca. 4 times lower throughput while retaining the quantitative information. Serum sample preparation related changes were revealed by applying unsupervised classification techniques and, therefore, must be taken into account while planning multicentric biomarker discovery and validation studies. Overall, this novel method reduces sample complexity and boosts the number of peptide and protein identifications without the need for extra sample handling procedures for samples equivalent to less than 1 μL of blood, which expands the space for potential biomarker discovery by looking deeper into the composition of biofluids

Performance Evaluation of the Q Exactive HF‑X for Shotgun Proteomics

Progress in proteomics is mainly driven by advances in mass spectrometric (MS) technologies. Here we benchmarked the performance of the latest MS instrument in the benchtop Orbitrap series, the Q Exactive HF-X, against its predecessor for proteomics applications. A new peak-picking algorithm, a brighter ion source, and optimized ion transfers enable productive MS/MS acquisition above 40 Hz at 7500 resolution. The hardware and software improvements collectively resulted in improved peptide and protein identifications across all comparable conditions, with an increase of up to 50 percent at short LC–MS gradients, yielding identification rates of more than 1000 unique peptides per minute. Alternatively, the Q Exactive HF-X is capable of achieving the same proteome coverage as its predecessor in approximately half the gradient time or at 10-fold lower sample loads. The Q Exactive HF-X also enables rapid phosphoproteomics with routine analysis of more than 5000 phosphopeptides with short single-shot 15 min LC–MS/MS measurements, or 16 700 phosphopeptides quantified across ten conditions in six gradient hours using TMT10-plex and offline peptide fractionation. Finally, exciting perspectives for data-independent acquisition are highlighted with reproducible identification of 55 000 unique peptides covering 5900 proteins in half an hour of MS analysis

Rapid and Deep Proteomes by Faster Sequencing on a Benchtop Quadrupole Ultra-High-Field Orbitrap Mass Spectrometer

Shotgun proteomics is a powerful technology for global analysis of proteins and their post-translational modifications. Here, we investigate the faster sequencing speed of the latest Q Exactive HF mass spectrometer, which features an ultra-high-field Orbitrap mass analyzer. Proteome coverage is evaluated by four different acquisition methods and benchmarked across three generations of Q Exactive instruments (ProteomeXchange data set PXD001305). We find the ultra-high-field Orbitrap mass analyzer to be capable of attaining a sequencing speed above 20 Hz, and it routinely exceeds 10 peptide spectrum matches per second or up to 600 new peptides sequenced per gradient minute. We identify 4400 proteins from 1 μg of HeLa digest using a 1 h gradient, which is an approximately 30% improvement compared to that with previous instrumentation. In addition, we show that very deep proteome coverage can be achieved in less than 24 h of analysis time by offline high-pH reversed-phase peptide fractionation, from which we identify more than 140 000 unique peptide sequences. This is comparable to state-of-the-art multiday, multienzyme efforts. Finally, the acquisition methods are evaluated for single-shot phosphoproteomics, where we identify 7600 unique HeLa phosphopeptides in one gradient hour and find the quality of fragmentation spectra to be more important than quantity for accurate site assignment

Overview of the experimental design.

<p>A Workflow of subcellular fractionation and immunopurification of the native hippocampal PAZ. B Experimental outline of isobaric labeling of peptides with TMT⁶ and MS analysis by nano-high-pressure liquid chromatography (nHPLC-ESI). C Example of peptide signals (m/z) for the six reporter groups. D Differences in protein abundance of hippocampal PAZ constituents between APP-mutant and control. E Pie chart diagram of proteins attributed to the PAZ. F Scheme of a PPI network illustrating proteins (exemplarily designated as A-K) as nodes and edge betweeness. The thickness of the connections represents the importance of the respective edges for information flow within the network (edge betweenness). Change in abundance of more than ±10% is reflected by increasing sizes of nodes. The color code corresponds to the degree of up- (magenta) and downregulation (green). Nodes in yellow represent proteins with changes in abundance of less than ±10%. UF, upper fractions; LF, lower fractions; IP, immunopurification, MB, magnetic bead; PM, plasma membrane; SV, synaptic vesicle, SC, signaling cascade; CS, cytoskeleton; ME, metabolic enzymes; MI, mitochondria; O, others.</p

Relative abundance of proteins mapped to the subcommunity structure of the network.

<p>The size of the rings corresponds to the respective number of proteins. Communities were subdivided into functional clusters according to Fig 5. A Impact of APP deletion. B Impact of the NexCre-cDKO. Change in abundance of more than ±10% is reflected by increasing sizes of nodes. The color code corresponds to the degree of up- (magenta) and downregulation (green). Nodes in yellow represent proteins with changes in abundance of less than ±10%.</p