Evaluation of FASP, SP3, and iST Protocols for Proteomic Sample Preparation in the Low Microgram Range.

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 (R2 = 0.97 (SP3); R2 = 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.

Drift time-specific collision energies enable deep-coverage data-independent acquisition 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.

A systems level analysis reveals transcriptomic and proteomic complexity in Ixodes ricinus midgut and salivary glands during early attachment and feeding.

Although pathogens are usually transmitted within the first 24-48 h of attachment of the castor bean tick Ixodes ricinus, little is known about the tick's biological responses at these earliest phases of attachment. Tick midgut and salivary glands are the main tissues involved in tick blood feeding and pathogen transmission but the limited genomic information for I. ricinus delays the application of high-throughput methods to study their physiology. We took advantage of the latest advances in the fields of Next Generation RNA-Sequencing and Label-free Quantitative Proteomics to deliver an unprecedented, quantitative description of the gene expression dynamics in the midgut and salivary glands of this disease vector upon attachment to the vertebrate host. A total of 373 of 1510 identified proteins had higher expression in the salivary glands, but only 110 had correspondingly high transcript levels in the same tissue. Furthermore, there was midgut-specific expression of 217 genes at both the transcriptome and proteome level. Tissue-dependent transcript, but not protein, accumulation was revealed for 552 of 885 genes. Moreover, we discovered the enrichment of tick salivary glands in proteins involved in gene transcription and translation, which agrees with the secretory role of this tissue; this finding also agrees with our finding of lower tick t-RNA representation in the salivary glands when compared with the midgut. The midgut, in turn, is enriched in metabolic components and proteins that support its mechanical integrity in order to accommodate and metabolize the ingested blood. Beyond understanding the physiological events that support hematophagy by arthropod ectoparasites, we discovered more than 1500 proteins located at the interface between ticks, the vertebrate host, and the tick-borne pathogens. Thus, our work significantly improves the knowledge of the genetics underlying the transmission lifecycle of this tick species, which is an essential step for developing alternative methods to better control tick-borne diseases.

In-depth evaluation of software tools for data-independent acquisition based label-free quantification.

Label-free quantification (LFQ) based on data-independent acquisition workflows currently experiences increasing popularity. Several software tools have been recently published or are commercially available. The present study focuses on the evaluation of three different software packages (Progenesis, synapter, and ISOQuant) supporting ion mobility enhanced data-independent acquisition data. In order to benchmark the LFQ performance of the different tools, we generated two hybrid proteome samples of defined quantitative composition containing tryptically digested proteomes of three different species (mouse, yeast, Escherichia coli). This model dataset simulates complex biological samples containing large numbers of both unregulated (background) proteins as well as up- and downregulated proteins with exactly known ratios between samples. We determined the number and dynamic range of quantifiable proteins and analyzed the influence of applied algorithms (retention time alignment, clustering, normalization, etc.) on quantification results. Analysis of technical reproducibility revealed median coefficients of variation of reported protein abundances below 5% for MS(E) data for Progenesis and ISOQuant. Regarding accuracy of LFQ, evaluation with synapter and ISOQuant yielded superior results compared to Progenesis. In addition, we discuss reporting formats and user friendliness of the software packages. The data generated in this study have been deposited to the ProteomeXchange Consortium with identifier PXD001240 (http://proteomecentral.proteomexchange.org/dataset/PXD001240).

Protein corona of nanoparticles: distinct proteins regulate the cellular uptake.

Understanding nanoparticle-protein interactions is a crucial issue in the development of targeted nanomaterial delivery. Besides unraveling the composition of the nanoparticle's protein coronas, distinct proteins thereof could control nanoparticle uptake into specific cell types. Here we differentially analyzed the protein corona composition on four polymeric differently functionalized nanoparticles by label-free quantitative mass spectrometry. Next, we correlated the relative abundance of identified proteins in the corona with enhanced or decreased cellular uptake of nanoparticles into human cancer and bone marrow stem cells to identify key candidates. Finally, we verified these candidate proteins by artificially decorating nanoparticles with individual proteins showing that nanoparticles precoated with the apolipoproteins ApoA4 or ApoC3 significantly decreased the cellular uptake, whereas precoating with ApoH increased the cellular uptake.

Mast cell-deficient Kit(W-sh) "Sash" mutant mice display aberrant myelopoiesis leading to the accumulation of splenocytes that act as myeloid-derived suppressor cells.

Mast cell-deficient Kit(W-sh) "sash" mice are widely used to investigate mast cell functions. However, mutations of c-Kit also affect additional cells of hematopoietic and nonimmune origin. In this study, we demonstrate that Kit(W-sh) causes aberrant extramedullary myelopoiesis characterized by the expansion of immature lineage-negative cells, common myeloid progenitors, and granulocyte/macrophage progenitors in the spleen. A consistent feature shared by these cell types is the reduced expression of c-Kit. Populations expressing intermediate and high levels of Ly6G, a component of the myeloid differentiation Ag Gr-1, are also highly expanded in the spleen of sash mice. These cells are able to suppress T cell responses in vitro and phenotypically and functionally resemble myeloid-derived suppressor cells (MDSC). MDSC typically accumulate in tumor-bearing hosts and are able to dampen immune responses. Consequently, transfer of MDSC from naive sash mice into line 1 alveolar cell carcinoma tumor-bearing wild-type littermates leads to enhanced tumor progression. However, although it can also be observed in sash mice, accelerated growth of transplanted line 1 alveolar cell carcinoma tumors is a mast cell-independent phenomenon. Thus, the Kit(W-sh) mutation broadly affects key steps in myelopoiesis that may have an impact on mast cell research.

In-depth protein profiling of the postsynaptic density from mouse hippocampus using data-independent acquisition proteomics.

Located at neuronal terminals, the postsynaptic density (PSD) is a highly complex network of cytoskeletal scaffolding and signaling proteins responsible for the transduction and modulation of glutamatergic signaling between neurons. Using ion-mobility enhanced data-independent label-free LC-MS/MS, we established a reference proteome of crude synaptosomes, synaptic junctions, and PSD derived from mouse hippocampus including TOP3-based absolute quantification values for identified proteins. The final dataset across all fractions comprised 49 491 peptides corresponding to 4558 protein groups. Of these, 2102 protein groups were identified in highly purified PSD in at least two biological replicates. Identified proteins play pivotal roles in neurological and synaptic processes providing a rich resource for studies on hippocampal PSD function as well as on the pathogenesis of neuropsychiatric disorders. All MS data have been deposited in the ProteomeXchange with identifier PXD000590 (http://proteomecentral.proteomexchange.org/dataset/PXD000590).

Biomedical applications of ion mobility-enhanced data-independent acquisition-based label-free quantitative proteomics.

Mass spectrometry-based proteomics greatly benefited from recent improvements in instrument performance and the development of bioinformatics solutions facilitating the high-throughput quantification of proteins in complex biological samples. In addition to quantification approaches using stable isotope labeling, label-free quantification has emerged as the method of choice for many laboratories. Over the last years, data-independent acquisition approaches have gained increasing popularity. The integration of ion mobility separation into commercial instruments enabled researchers to achieve deep proteome coverage from limiting sample amounts. Additionally, ion mobility provides a new dimension of separation for the quantitative assessment of complex proteomes, facilitating precise label-free quantification even of highly complex samples. The present work provides a thorough overview of the combination of ion mobility and data-independent acquisition-based label-free quantification LC-MS and its applications in biomedical research.

Proteome-wide characterization of the RNA-binding protein RALY-interactome using the in vivo-biotinylation-pulldown-quant (iBioPQ) approach.

RALY is a member of the heterogeneous nuclear ribonucleoproteins, a family of RNA-binding proteins generally involved in many processes of mRNA metabolism. No quantitative proteomic analysis of RALY-containing ribonucleoparticles (RNPs) has been performed so far, and the biological role of RALY remains elusive. Here, we present a workflow for the characterization of RALY's interaction partners, termed iBioPQ, that involves in vivo biotinylation of biotin acceptor peptide (BAP)-fused protein in the presence of the prokaryotic biotin holoenzyme synthetase of BirA so that it can be purified using streptavidin-coated magnetic beads, circumventing the need for specific antibodies and providing efficient pulldowns. Protein eluates were subjected to tryptic digestion and identified using data-independent acquisition on an ion-mobility enabled high-resolution nanoUPLC-QTOF system. Using label-free quantification, we identified 143 proteins displaying at least 2-fold difference in pulldown compared to controls. Gene Ontology overrepresentation analysis revealed an enrichment of proteins involved in mRNA metabolism and translational control. Among the most abundant interacting proteins, we confirmed RNA-dependent interactions of RALY with MATR3, PABP1 and ELAVL1. Comparative analysis of pulldowns after RNase treatment revealed a protein-protein interaction of RALY with eIF4AIII, FMRP, and hnRNP-C. Our data show that RALY-containing RNPs are much more heterogeneous than previously hypothesized.

Quantitative and integrative proteome analysis of peripheral nerve myelin identifies novel myelin proteins and candidate neuropathy loci.

Peripheral nerve myelin facilitates rapid impulse conduction and normal motor and sensory functions. Many aspects of myelin biogenesis, glia-axonal interactions, and nerve homeostasis are poorly understood at the molecular level. We therefore hypothesized that only a fraction of all relevant myelin proteins has been identified so far. Combining gel-based and gel-free proteomic approaches, we identified 545 proteins in purified mouse sciatic nerve myelin, including 36 previously known myelin constituents. By mass spectrometric quantification, the predominant P0, periaxin, and myelin basic protein constitute 21, 16, and 8% of the total myelin protein, respectively, suggesting that their relative abundance was previously misestimated due to technical limitations regarding protein separation and visualization. Focusing on tetraspan-transmembrane proteins, we validated novel myelin constituents using immuno-based methods. Bioinformatic comparison with mRNA-abundance profiles allowed the categorization in functional groups coregulated during myelin biogenesis and maturation. By differential myelin proteome analysis, we found that the abundance of septin 9, the protein affected in hereditary neuralgic amyotrophy, is strongly increased in a novel mouse model of demyelinating neuropathy caused by the loss of prion protein. Finally, the systematic comparison of our compendium with the positions of human disease loci allowed us to identify several candidate genes for hereditary demyelinating neuropathies. These results illustrate how the integration of unbiased proteome, transcriptome, and genome data can contribute to a molecular dissection of the biogenesis, cell biology, metabolism, and pathology of myelin.

Label-free quantification in ion mobility-enhanced data-independent acquisition proteomics.

Unbiased data-independent acquisition (DIA) strategies have gained increased popularity in the field of quantitative proteomics. The integration of ion mobility separation (IMS) into DIA workflows provides an additional dimension of separation to liquid chromatography-mass spectrometry (LC-MS), and it increases the achievable analytical depth of DIA approaches. Here we provide a detailed protocol for a label-free quantitative proteomics workflow based on ion mobility-enhanced DIA, which synchronizes precursor ion drift times with collision energies to improve precursor fragmentation efficiency. The protocol comprises a detailed description of all major steps including instrument setup, filter-aided sample preparation, LC-IMS-MS analysis and data processing. Our protocol can handle proteome samples of any complexity, and it enables a highly reproducible and accurate precursor intensity-based label-free quantification of up to 5,600 proteins across multiple runs in complete cellular lysates. Depending on the number of samples to be analyzed, the protocol takes a minimum of 3 d to complete from proteolytic digestion to data evaluation.

A multicenter study benchmarks software tools for label-free proteome quantification.

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, SWATH 2.0, Skyline, Spectronaut and DIA-Umpire, five of the most widely used software methods for processing data from sequential window acquisition of all theoretical fragment-ion spectra (SWATH)-MS, which uses data-independent acquisition (DIA) for label-free protein quantification. We analyzed high-complexity test data sets from hybrid proteome samples of defined quantitative composition acquired on two different MS instruments using different SWATH isolation-window 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 data sets 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.

Quantitative profiling of the protein coronas that form around nanoparticles.

Nanoparticle applications in biotechnology and biomedicine are steadily increasing. In biological fluids, proteins bind to nanoparticles that form the protein corona, crucially affecting the nanoparticles' biological identity. As the corona affects in vitro and/or in vivo nanoparticle applications, we developed a method to obtain time-resolved protein corona profiles formed on various nanoparticles. After incubation in plasma or a similar biofluid, or after injection into a mouse, the first analytical step is sedimentation of the nanoparticle-protein complexes through a sucrose cushion, thereby allowing analysis of early corona formation time points. Next, corona profiles are visualized by gel electrophoresis and quantitatively analyzed after tryptic digestion using label-free liquid chromatography-high-resolution mass spectrometry. In contrast to other approaches, our established methodology allows the researcher to obtain qualitative and quantitative high-resolution corona signatures. The protocol can be readily extended to the investigation of protein coronas from various nanomaterials (as an example, we applied this protocol to different silica nanoparticles (SiNPs) and polystyrene nanoparticles (PSNPs)). Depending on the number of samples, the protocol from nanoparticle-protein complex recovery to data evaluation takes ~8-12 d to complete.

Rapid formation of plasma protein corona critically affects nanoparticle pathophysiology.

In biological fluids, proteins bind to the surface of nanoparticles to form a coating known as the protein corona, which can critically affect the interaction of the nanoparticles with living systems. As physiological systems are highly dynamic, it is important to obtain a time-resolved knowledge of protein-corona formation, development and biological relevancy. Here we show that label-free snapshot proteomics can be used to obtain quantitative time-resolved profiles of human plasma coronas formed on silica and polystyrene nanoparticles of various size and surface functionalization. Complex time- and nanoparticle-specific coronas, which comprise almost 300 different proteins, were found to form rapidly (<0.5 minutes) and, over time, to change significantly in terms of the amount of bound protein, but not in composition. Rapid corona formation is found to affect haemolysis, thrombocyte activation, nanoparticle uptake and endothelial cell death at an early exposure time.

Nanoparticle size is a critical physicochemical determinant of the human blood plasma corona: a comprehensive quantitative proteomic analysis.

In biological fluids, proteins associate with nanoparticles, leading to a protein "corona" defining the biological identity of the particle. However, a comprehensive knowledge of particle-guided protein fingerprints and their dependence on nanomaterial properties is incomplete. We studied the long-lived ("hard") blood plasma derived corona on monodispersed amorphous silica nanoparticles differing in size (20, 30, and 100 nm). Employing label-free liquid chromatography mass spectrometry, one- and two-dimensional gel electrophoresis, and immunoblotting the composition of the protein corona was analyzed not only qualitatively but also quantitatively. Detected proteins were bioinformatically classified according to their physicochemical and biological properties. Binding of the 125 identified proteins did not simply reflect their relative abundance in the plasma but revealed an enrichment of specific lipoproteins as well as proteins involved in coagulation and the complement pathway. In contrast, immunoglobulins and acute phase response proteins displayed a lower affinity for the particles. Protein decoration of the negatively charged particles did not correlate with protein size or charge, demonstrating that electrostatic effects alone are not the major driving force regulating the nanoparticle-protein interaction. Remarkably, even differences in particle size of only 10 nm significantly determined the nanoparticle corona, although no clear correlation with particle surface volume, protein size, or charge was evident. Particle size quantitatively influenced the particle's decoration with 37% of all identified proteins, including (patho)biologically relevant candidates. We demonstrate the complexity of the plasma corona and its still unresolved physicochemical regulation, which need to be considered in nanobioscience in the future.