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.

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.

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.

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.

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.

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.

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.

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.