Metabolic Labeling of Human Bone Marrow Mesenchymal Stem Cells for the Quantitative Analysis of their Chondrogenic Differentiation

Human mesenchymal stem cells (hMSCs), residing in bone marrow as well as in the synovial lining of joints, can be triggered to differentiate toward chondrocytes. Thus, hMSCs harbor great therapeutic potential for the repair of cartilage defects in osteoarthritis (OA) and other articular diseases. However, the molecular mechanisms underlying the chondrogenesis process are still in part unknown. In this work, we applied for the first time the stable isotope labeling by amino acids in cell culture (SILAC) technique for the quantitative analysis of protein modulation during the chondrogenic differentiation process of hMSCs. First, we have standardized the metabolic labeling procedure on MSCs isolated from bone marrow (hBMSCs), and we have assessed the quality of chondrogenesis taking place in these conditions. Then, chondrogenic differentiation was induced on these labeled cells, and a quantitative proteomics approach has been followed to evaluate protein changes between two differentiation days. With this strategy, we could identify 622 different proteins by LC–MALDI-TOF/TOF analysis and find 65 proteins whose abundance was significantly modulated between day 2 and day 14 of chondrogenesis. Immunohistochemistry analyses were performed to verify the changes on a panel of six proteins that play different biological roles in the cell: fibronectin, gelsolin, vimentin, alpha-ATPase, mitochondrial superoxide dismutase, and cyclophilin A. All of these proteins were increased at day 14 compared to day 2 of chondrogenic induction, thus being markers of the enhanced extracellular matrix synthesis, cell adhesion, metabolism, and response to stress processes that take place in the early steps of chondrogenesis. Our strategy has allowed an additional insight into both specific protein function and the mechanisms of chondrogenesis and has provided a panel of protein markers of this differentiation process in hBMSCs

Pierre Marie Auguste Broussonet, Paris, [France], to James Edward Smith

Has edited works of [Pierre Richer de] Belleval

Quantitative Proteomic Profiling of Human Articular Cartilage Degradation in Osteoarthritis

Osteoarthritis (OA) is the most common rheumatic pathology and is characterized primarily by articular cartilage degradation. Despite its high prevalence, there is no effective therapy to slow disease progression or regenerate the damaged tissue. Therefore, new diagnostic and monitoring tests for OA are urgently needed, which would also promote the development of alternative therapeutic strategies. In the present study, we have performed an iTRAQ-based quantitative proteomic analysis of secretomes from healthy human articular cartilage explants, comparing their protein profile to those from unwounded (early disease) and wounded (advanced disease) zones of osteoarthritic tissue. This strategy allowed us to identify a panel of 76 proteins that are distinctively released by the diseased tissue. Clustering analysis allowed the classification of proteins according to their different profile of release from cartilage. Among these proteins, the altered release of osteoprotegerin (decreased in OA) and periostin (increased in OA), both involved in bone remodelling processes, was verified in further analyses. Moreover, periostin was also increased in the synovial fluid of OA patients. Altogether, the present work provides a novel insight into the mechanisms of human cartilage degradation and a number of new cartilage-characteristic proteins with possible biomarker value for early diagnosis and prognosis of OA

Cryoconservation of Peptide Extracts from Trypsin Digestion of Proteins for Proteomic Analysis in a Hospital Biobank Facility

We tested a semiautomated protocol for the proper storage and conservation in a hospital biobank of tryptic peptide extracts coming from samples with low and high protein complexity for subsequent mass spectrometry analysis. Low-complexity samples (serum albumin, serotransferrin. and alpha-S1-casein) were loaded in replicates in SDS-PAGE and subjected to standard in-gel trypsin digestion. For LC–MALDI–TOF/TOF analysis, purified β-galactosidase and human serum samples were in-solution digested following standard procedures and desalted with C18 stage-tips. In both cases, peptides extracts were aliquoted in individually 2D coded tubes, vacuum-dried, barcode-read, and stored in an automated −20 °C freezer in the Biobank facility. Samples were kept dried at −20 °C until the corresponding time-point of analysis, then reconstituted in the proper buffer and analyzed by either MALDI-TOF/TOF (peptide fingerprinting and MS/MS) or LC–MALDI-TOF/TOF following a highly reproducible pattern to ensure the reproducibility of the results. Protein identification was done with either Mascot or Protein Pilot as search engines using constant parameters. Over a period of 1 year we checked six different time points at days 0, 7, 30, 90, 180, and 365. We compared MS and MS/MS protein score, number of identified peptides, and coverage of the identified proteins. In the low complexity samples, the number of peptides detected gradually decreased over time, especially affecting the MS score. However, two of the three proteins – serum albumin and serotransferrin – were identified by both PMF and MS/MS at day 90. By day 180, only MS/MS identification in some replicates was possible. By LC–MS/MS, β-galactosidase and the most abundant serum proteins were identified with good scores at all time points even by day 365, with no detectable peptide loss or decrease in the fragmentation efficiency, although a progressive decrease in peptide intensity indicates that detection of low abundant proteins could not be optimal after very long periods of time. Our results encourage us to use the biobank facility in the future for long-term storage – up to 3 months – of dried peptide extracts

Cryoconservation of Peptide Extracts from Trypsin Digestion of Proteins for Proteomic Analysis in a Hospital Biobank Facility

We tested a semiautomated protocol for the proper storage and conservation in a hospital biobank of tryptic peptide extracts coming from samples with low and high protein complexity for subsequent mass spectrometry analysis. Low-complexity samples (serum albumin, serotransferrin. and alpha-S1-casein) were loaded in replicates in SDS-PAGE and subjected to standard in-gel trypsin digestion. For LC–MALDI–TOF/TOF analysis, purified β-galactosidase and human serum samples were in-solution digested following standard procedures and desalted with C18 stage-tips. In both cases, peptides extracts were aliquoted in individually 2D coded tubes, vacuum-dried, barcode-read, and stored in an automated −20 °C freezer in the Biobank facility. Samples were kept dried at −20 °C until the corresponding time-point of analysis, then reconstituted in the proper buffer and analyzed by either MALDI-TOF/TOF (peptide fingerprinting and MS/MS) or LC–MALDI-TOF/TOF following a highly reproducible pattern to ensure the reproducibility of the results. Protein identification was done with either Mascot or Protein Pilot as search engines using constant parameters. Over a period of 1 year we checked six different time points at days 0, 7, 30, 90, 180, and 365. We compared MS and MS/MS protein score, number of identified peptides, and coverage of the identified proteins. In the low complexity samples, the number of peptides detected gradually decreased over time, especially affecting the MS score. However, two of the three proteins – serum albumin and serotransferrin – were identified by both PMF and MS/MS at day 90. By day 180, only MS/MS identification in some replicates was possible. By LC–MS/MS, β-galactosidase and the most abundant serum proteins were identified with good scores at all time points even by day 365, with no detectable peptide loss or decrease in the fragmentation efficiency, although a progressive decrease in peptide intensity indicates that detection of low abundant proteins could not be optimal after very long periods of time. Our results encourage us to use the biobank facility in the future for long-term storage – up to 3 months – of dried peptide extracts

Analysis of Autoantibody Profiles in Osteoarthritis Using Comprehensive Protein Array Concepts

Osteoarthritis (OA) is the most common rheumatic disease and one of the most disabling pathologies worldwide. To date, the diagnostic methods of OA are very limited, and there are no available medications capable of halting its characteristic cartilage degeneration. Therefore, there is a significant interest in new biomarkers useful for the early diagnosis, prognosis, and therapeutic monitoring. In the recent years, protein microarrays have emerged as a powerful proteomic tool to search for new biomarkers. In this study, we have used two concepts for generating protein arrays, antigen microarrays, and NAPPA (nucleic acid programmable protein arrays), to characterize differential autoantibody profiles in a set of 62 samples from OA, rheumatoid arthritis (RA), and healthy controls. An untargeted screen was performed on 3840 protein fragments spotted on planar antigen arrays, and 373 antigens were selected for validation on bead-based arrays. In the NAPPA approach, a targeted screening was performed on 80 preselected proteins. The autoantibody targeting CHST14 was validated by ELISA in the same set of patients. Altogether, nine and seven disease related autoantibody target candidates were identified, and this work demonstrates a combination of these two array concepts for biomarker discovery and their usefulness for characterizing disease-specific autoantibody profiles

Surfing Transcriptomic Landscapes. A Step beyond the Annotation of Chromosome 16 Proteome

The Spanish team of the Human Proteome Project (SpHPP) marked the annotation of Chr16 and data analysis as one of its priorities. Precise annotation of Chromosome 16 proteins according to C-HPP criteria is presented. Moreover, Human Body Map 2.0 RNA-Seq and Encyclopedia of DNA Elements (ENCODE) data sets were used to obtain further information relative to cell/tissue specific chromosome 16 coding gene expression patterns and to infer the presence of missing proteins. Twenty-four shotgun 2D-LC–MS/MS and gel/LC–MS/MS MIAPE compliant experiments, representing 41% coverage of chromosome 16 proteins, were performed. Furthermore, mapping of large-scale multicenter mass spectrometry data sets from CCD18, MCF7, Jurkat, and Ramos cell lines into RNA-Seq data allowed further insights relative to correlation of chromosome 16 transcripts and proteins. Detection and quantification of chromosome 16 proteins in biological matrices by SRM procedures are also primary goals of the SpHPP. Two strategies were undertaken: one focused on known proteins, taking advantage of MS data already available, and the second, aimed at the detection of the missing proteins, is based on the expression of recombinant proteins to gather MS information and optimize SRM methods that will be used in real biological samples. SRM methods for 49 known proteins and for recombinant forms of 24 missing proteins are reported in this study

Surfing Transcriptomic Landscapes. A Step beyond the Annotation of Chromosome 16 Proteome

The Spanish team of the Human Proteome Project (SpHPP) marked the annotation of Chr16 and data analysis as one of its priorities. Precise annotation of Chromosome 16 proteins according to C-HPP criteria is presented. Moreover, Human Body Map 2.0 RNA-Seq and Encyclopedia of DNA Elements (ENCODE) data sets were used to obtain further information relative to cell/tissue specific chromosome 16 coding gene expression patterns and to infer the presence of missing proteins. Twenty-four shotgun 2D-LC–MS/MS and gel/LC–MS/MS MIAPE compliant experiments, representing 41% coverage of chromosome 16 proteins, were performed. Furthermore, mapping of large-scale multicenter mass spectrometry data sets from CCD18, MCF7, Jurkat, and Ramos cell lines into RNA-Seq data allowed further insights relative to correlation of chromosome 16 transcripts and proteins. Detection and quantification of chromosome 16 proteins in biological matrices by SRM procedures are also primary goals of the SpHPP. Two strategies were undertaken: one focused on known proteins, taking advantage of MS data already available, and the second, aimed at the detection of the missing proteins, is based on the expression of recombinant proteins to gather MS information and optimize SRM methods that will be used in real biological samples. SRM methods for 49 known proteins and for recombinant forms of 24 missing proteins are reported in this study

Surfing Transcriptomic Landscapes. A Step beyond the Annotation of Chromosome 16 Proteome

The Spanish team of the Human Proteome Project (SpHPP) marked the annotation of Chr16 and data analysis as one of its priorities. Precise annotation of Chromosome 16 proteins according to C-HPP criteria is presented. Moreover, Human Body Map 2.0 RNA-Seq and Encyclopedia of DNA Elements (ENCODE) data sets were used to obtain further information relative to cell/tissue specific chromosome 16 coding gene expression patterns and to infer the presence of missing proteins. Twenty-four shotgun 2D-LC–MS/MS and gel/LC–MS/MS MIAPE compliant experiments, representing 41% coverage of chromosome 16 proteins, were performed. Furthermore, mapping of large-scale multicenter mass spectrometry data sets from CCD18, MCF7, Jurkat, and Ramos cell lines into RNA-Seq data allowed further insights relative to correlation of chromosome 16 transcripts and proteins. Detection and quantification of chromosome 16 proteins in biological matrices by SRM procedures are also primary goals of the SpHPP. Two strategies were undertaken: one focused on known proteins, taking advantage of MS data already available, and the second, aimed at the detection of the missing proteins, is based on the expression of recombinant proteins to gather MS information and optimize SRM methods that will be used in real biological samples. SRM methods for 49 known proteins and for recombinant forms of 24 missing proteins are reported in this study

Surfing Transcriptomic Landscapes. A Step beyond the Annotation of Chromosome 16 Proteome

The Spanish team of the Human Proteome Project (SpHPP) marked the annotation of Chr16 and data analysis as one of its priorities. Precise annotation of Chromosome 16 proteins according to C-HPP criteria is presented. Moreover, Human Body Map 2.0 RNA-Seq and Encyclopedia of DNA Elements (ENCODE) data sets were used to obtain further information relative to cell/tissue specific chromosome 16 coding gene expression patterns and to infer the presence of missing proteins. Twenty-four shotgun 2D-LC–MS/MS and gel/LC–MS/MS MIAPE compliant experiments, representing 41% coverage of chromosome 16 proteins, were performed. Furthermore, mapping of large-scale multicenter mass spectrometry data sets from CCD18, MCF7, Jurkat, and Ramos cell lines into RNA-Seq data allowed further insights relative to correlation of chromosome 16 transcripts and proteins. Detection and quantification of chromosome 16 proteins in biological matrices by SRM procedures are also primary goals of the SpHPP. Two strategies were undertaken: one focused on known proteins, taking advantage of MS data already available, and the second, aimed at the detection of the missing proteins, is based on the expression of recombinant proteins to gather MS information and optimize SRM methods that will be used in real biological samples. SRM methods for 49 known proteins and for recombinant forms of 24 missing proteins are reported in this study