Neoantigen-based immunotherapies promise to improve patient outcomes over the current standard of care. However, detecting these cancer-specific antigens is one of the significant challenges in the field of mass spectrometry. Even though the first sequencing of the immunopeptides was done decades ago, today there is still a diversity of the protocols used for neoantigen isolation from the cell surface. This heterogeneity makes it difficult to compare results between the laboratories and the studies. Isolation of the neoantigens from the cell surface is usually done by mild acid elution (MAE) or immunoprecipitation (IP) protocol. However, limited amounts of the neoantigens present on the cell surface impose a challenge and require instrumentation with enough sensitivity and accuracy for their detection. Detecting these neopeptides from small amounts of available patient tissue limits the scope of most of the studies to cell cultures. Here, we summarize protocols for the extraction and identification of the major histocompatibility complex (MHC) class I and II peptides. We aimed to evaluate existing methods in terms of the appropriateness of the isolation procedure, as well as instrumental parameters used for neoantigen detection. We also focus on the amount of the material used in the protocols as the critical factor to consider when analyzing neoantigens. Beyond experimental aspects, there are numerous readily available proteomics suits/tools applicable for neoantigen discovery; however, experimental validation is still necessary for neoantigen characterization.

Developments in quantitative proteomics and data-independent acquisition (DIA) methodology is enabling quantification of proteins in biological samples. Currently, there are a few reports on DIA mass spectrometry (MS) approaches for proteome analysis of formalin-fixed paraffin-embedded (FFPE) tissues. Therefore, to facilitate detection and quantification of immune- and glioblastoma (GBM)-relevant proteins from FFPE patient materials, we established a simple and precise DIA-MS workflow. We first evaluated different lysis buffers for their efficiency in protein extractions from FFPE GBM tissues. Our results showed that more than 1700 proteins were detected and over 1400 proteins were quantified from GBM FFPE tissue microdissections. GBM-relevant proteins (e.g., GFAP, FN1, VIM, and MBP) were quantified with high precision (median coefficient of variation <12%). In addition, immune-related proteins (e.g., ILF2, MIF, and CD38) were consistently detected and quantified. The strategy holds great potential for routinizing protein quantification in FFPE tissue samples.

Human tissues are an important link between organ-specific spatial molecular information, patient pathology, and patient treatment options. However, patient tissues are uniquely obtained by time and location, and limited in their availability and size. Currently, little knowledge exists about appropriate and simplified protocols for routine MS-based analysis of the various types and sizes of tissues. Following standard procedures used in pathology, we selected small fresh frozen uterine tissue samples to investigate how the tissue preparation protocol affected the subsequent proteomics analysis. First, we observed that protein extraction with 0.1% SDS followed by extraction with a 30% ACN/urea resulted in a decrease in the number of identified proteins, when compared to extraction with 30% ACN/urea only. The decrease in the number of proteins was approximately 55% and 20%, for 10 and 16 μm thick tissue, respectively. Interestingly, the relative abundance of the proteins shared between the two methods was higher when SDS/ACN/urea was used, compared to the 30% ACN/urea extraction, indicating the role of SDS to be beneficial for protein solubility. Second, the influence of tissue thickness was investigated by comparing the results obtained for 10, 16, and 20 μm thick (1 mm²) tissue using urea/30% ACN. We observed an increase in the number of identified proteins and corresponding quantity with an increase in the tissue thickness. Finally, by analyzing very small amounts of tissues (∼0.2 mm²) of 10, 16, and 20 μm thickness, we observed that the increase in tissue thickness resulted in a higher number of protein identifications and corresponding quantitative values.

Proteomics of human tissues and isolated cellular subpopulations create new opportunities for therapy and monitoring of a patients' treatment in the clinic. Important considerations in such analysis include recovery of adequate amounts of protein for analysis and reproducibility in sample collection. In this study we compared several protocols for proteomic sample preparation: i) filter-aided sample preparation (FASP), ii) in-solution digestion (ISD) and iii) a pressure-assisted digestion (PCT) method. PCT method is known for already a decade [1], however it is not widely used in proteomic research. We assessed protocols for proteome profiling of isolated immune cell subsets and formalin-fixed paraffin embedded (FFPE) tissue samples. Our results show that the ISD method has very good efficiency of protein and peptide identification from the whole proteome, while the FASP method is particularly effective in identification of membrane proteins. Pressure-assisted digestion methods generally provide lower numbers of protein/peptide identifications, but have gained in popularity due to their shorter digestion time making them considerably faster than for ISD or FASP. Furthermore, PCT does not result in substantial sample loss when applied to samples of 50 000 cells. Analysis of FFPE tissues shows comparable results. ISD method similarly yields the highest number of identifications. Furthermore, proteins isolated from FFPE samples show a significant reduction of cleavages at lysine sites due to chemical modifications with formaldehyde-such as methylation (+14 Da) being among the most common. The data we present will be helpful for making decisions about the robust preparation of clinical samples for biomarker discovery and studies on pathomechanisms of various diseases.