Mesenchymal stem cells (MSCs) gain an increasing focus in the field of regenerative medicine due to their differentiation abilities into chondrocytes, adipocytes, and osteoblastic cells. However, it is apparent that the transformation processes are extremely complex and cause cellular heterogeneity. The study aimed to characterize differences between MSCs and cells after adipogenic (AD) or osteoblastic (OB) differentiation at the proteome level. Comparative proteomic profiling was performed using tandem mass spectrometry in data-independent acquisition mode. Proteins were quantified by deep neural networks in library-free mode and correlated to the Molecular Signature Database (MSigDB) hallmark gene set collections for functional annotation. We analyzed 4108 proteins across all samples, which revealed a distinct clustering between MSCs and cell differentiation states. Protein expression profiling identified activation of the Peroxisome proliferator-activated receptors (PPARs) signaling pathway after AD. In addition, two distinct protein marker panels could be defined for osteoblastic and adipocytic cell lineages. Hereby, overexpression of AEBP1 and MCM4 for OB as well as of FABP4 for AD was detected as the most promising molecular markers. Combination of deep neural network and machine-learning algorithms with data-independent mass spectrometry distinguish MSCs and cell lineages after adipogenic or osteoblastic differentiation. We identified specific proteins as the molecular basis for bone formation, which could be used for regenerative medicine in the future.

Chemotherapy resistance is one of the reasons for eye loss in patients with retinoblastoma (RB). RB chemotherapy resistance has been studied in different cell culture models, such as WERI-RB1. In addition, chemotherapy-resistant RB subclones, such as the etoposide-resistant WERI-ETOR cell line have been established to improve the understanding of chemotherapy resistance in RB. The objective of this study was to characterize cell line models of an etoposide-sensitive WERI-RB1 and its etoposide-resistant subclone, WERI-ETOR, by proteomic analysis. Subsequently, quantitative proteomics data served for correlation analysis with known drug perturbation profiles. Methodically, WERI-RB1 and WERI-ETOR were cultured, and prepared for quantitative mass spectrometry (MS). This was carried out in a data-independent acquisition (DIA) mode. The raw SWATH (sequential window acquisition of all theoretical mass spectra) files were processed using neural networks in a library-free mode along with machine-learning algorithms. Pathway-enrichment analysis was performed using the REACTOME-pathway resource, and correlated to the molecular signature database (MSigDB) hallmark gene set collections for functional annotation. Furthermore, a drug-connectivity analysis using the L1000 database was carried out to associate the mechanism of action (MOA) for different anticancer reagents to WERI-RB1/WERI-ETOR signatures. A total of 4756 proteins were identified across all samples, showing a distinct clustering between the groups. Of these proteins, 64 were significantly altered (q < 0.05 & log2FC |>2|, 22 higher in WERI-ETOR). Pathway analysis revealed the “retinoid metabolism and transport” pathway as an enriched metabolic pathway in WERI-ETOR cells, while the “sphingolipid de novo biosynthesis” pathway was identified in the WERI-RB1 cell line. In addition, this study revealed similar protein signatures of topoisomerase inhibitors in WERI-ETOR cells as well as ATPase inhibitors, acetylcholine receptor antagonists, and vascular endothelial growth factor receptor (VEGFR) inhibitors in the WERI-RB1 cell line. In this study, WERI-RB1 and WERI-ETOR were analyzed as a cell line model for chemotherapy resistance in RB using data-independent MS. Analysis of the global proteome identified activation of “sphingolipid de novo biosynthesis” in WERI-RB1, and revealed future potential treatment options for etoposide resistance in RB.

Background: Colorectal cancer (CRC) is frequently associated with metastasis, resulting in high mortality rates. Platelets are known to play a crucial role in the metastatic cascade influencing tumor microenvironment remodeling, promoting cell transformation, facilitating metastatic niche formation, and shielding circulating tumor cells from immune surveillance. However, platelet proteomic alterations during tumor cell-induced platelet aggregation (TCIPA) remain largely unexplored. This study aims to characterize the proteomic profile of TCIPA in CRC using an in vitro model that recapitulates key aspects of CRC metastasis. Methods: TCIPA was assessed via light transmission aggregometry using an in vitro model incorporating paired primary and metastatic cell cultures. Stable Isotope Labeling with Amino Acids in Cell culture (SILAC) allowed for the discrimination of healthy platelet and tumor cell proteomes prior to and following TCIPA. Data-independent acquisition mass spectrometry was employed to analyze intra- and extracellular tumor and platelet proteomes. Comparative proteomic profiling was performed using a range of bioinformatic analyses, including clustering, differential expression, and Gene Set Enrichment Analyses (GSEA). Results: Comparison of the baseline proteome profiles of the CRC cell lines SW480 and SW620 identified 263 significant differentially abundant proteins (FDR ≤ 0.05, log ₂ FC ≥ 1). The GSEA demonstrated enrichment of the ‘epithelial-mesenchymal transition’ (FDR: 5.617 × 10 ^− 5 ) gene set in SW480 cells. While SW480 exhibited rapid TCIPA, SW620 did not consistently interact with healthy platelets. Following TCIPA, 34 tumor proteins showed differential expression compared to their naïve status (without platelet-exposure). Notably, 17 of these proteins were significantly associated with CRC progression, particularly in the promotion of EMT, metastasis, tumor cell survival, proliferation, and metabolic reprogramming. Conclusions: This study successfully characterized the proteomic profiles of platelets, platelet secretomes, and colorectal tumor cells following TCIPA-induced activation. The findings highlight the significant role of several tumor proteins and their metabolic effects in colorectal cancer progression, particularly with regard to metastasis.

Validation of biomarkers for clinical diagnostics requires high-quality samples and standardized protocols. This study investigated the effect of a single freeze-and-thaw (FT) cycle on the quality of liquid nitrogen-stored clinical serum samples. Using mass spectrometry, serum protein abundance was analyzed in a human test cohort of 99 samples and a validation cohort 109 samples. After preprocessing, 213 proteins were analyzed in the test cohort and 248 in the validation cohort. Thirty proteins changed significantly in their measurability after freeze-thaw in the test cohort, and 11 were confirmed in the validation cohort. Among them, CP and IGHV6-1 showed potential for distinguishing malignant from benign pancreatic disease. No association was found between protein intensity and storage duration. Overall, the findings show that freeze-thaw cycles are an important pre-analytical factor that should be controlled when translating protein biomarkers into clinical practice.

Introduction: Cystic lesions of the pancreas have continued to present a clinical challenge for the past decades now. The increasing rate of detection, the lack of high-quality data on the natural biology of pancreatic cysts and the resulting difficulty to predict malignant transformation in different types of pancreatic cysts make patients with these diseases hard to manage. The German Pancreas Club Cyst Registry (GerPaCyst) (DRKS00025927) aims to establish a platform to discover and survey the natural and specific biology of pancreatic cysts such as IPMNs (main and branch ducts), SCNs, SPPT and MCNs, in a multicenter manner. Materials and methods: This manuscript is written according to the SPIRIT guidelines (See S2 and S3 Tables). Ethical approval was obtained from the University of Luebeck (2020-20-225) and all participating centers. In GerPaCyst patients aged ≥18 years with a pancreatic cyst under surveillance or scheduled for surgery should be enrolled. Participating centers will complete an electronic Case Report Form (eCRF) via REDCap which is designed as a longitudinal study minimizing the input of repeated measures. Changes in patient baseline data, cyst characteristics, both endoscopic and imaging data will be entered typically every 6–12 months during patient follow-up. Biobanking will be performed, when available. Duration of observation per patient is up to a maximum of 20 years or until end of follow-up or death. Results: The primary goal is to assess and calculate individual risk models for malignancy/high-grade dysplasia based on the collected clinical, molecular and imaging data (multi-omics prediction models) for each included cyst entity, therefore the primary outcome of this trial is the development of high-grade dysplasia/invasive cancer during follow-up or the absence of it. The secondary outcomes are death, quality of life measured by EQ-5D-5L questionnaire, end-of-follow up and perioperative characteristics, if applicable such as complications, length of hospital stay and clavien-dindo classification. Another goal will be to build a multicenter, interdisciplinary database to generate high-quality cyst biology data, which can then be used for further research questions. Additionally, this database will be utilized for registry- based interventional trials in the future. Discussion: GerPaCyst will provide a valuable platform for clinical outcomes research. Fundamental factors affecting the development of pancreatic cysts over time will be identified. New research questions might be answered during the study period and will be made available through continuous publications. Trial registration: The study was prospectively registered at the German Clinical Trial Register (DRKS) under DRKS00025927 on September 14 ^th , 2021, before inclusion of the first patient. The Universal Trial Number (UTN) is U1111-1302–9822.

Background Validation of biomarkers for clinical diagnostics and therapy necessitates the availability of a substantial number of high-quality samples, along with a complete set of clinical data. Establishing standards for sample collection, storage, and quality control is essential to reduce the variability of sample quality. Methods This study evaluated the impact of a single freeze-and-thaw (FT) cycle on the quality of liquid nitrogen-stored clinical serum aliquots. Data-independent acquisition mass spectrometry (DIA-MS) was performed to measure serum protein abundance in a test cohort comprising 25 patients and 99 samples, and a validation cohort comprising 109 patients/samples. Abundance differences of paired fresh and FT samples were assessed by employing biostatistics and bioinformatics approaches, including clustering analyses, linear mixed models, machine learning, and functional annotation. Results Following the library-free data analysis and implementation of strict data preprocessing procedures, data on the relative abundance of 213 (test cohort) and 248 (validation cohort) human serum proteins was available. Overall, the proteomic data demonstrated high quality and reproducibility. Significant changes in abundance between fresh and FT samples was observed for 30 proteins in the test cohort (q-value ≤0.05). Of these, 11 proteins (36.6%) were successfully validated in the validation cohort, including CP and IGHV6-1 which were identified as potential biomarker candidates for discriminating between patients with malignant or benign pancreatic disease. A correlation analysis of the measured protein intensity and storage duration revealed no significant association between the two variables. Conclusions Distinct protein abundance patterns can be discerned between fresh serum samples and samples stored in liquid nitrogen. The findings of this study suggest that FT cycles are an important pre-analytical factor and should be addressed during the translation of protein-based biomarkers into clinical use.