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WO2010142860A1 - Profil de surface cellulaire - Google Patents

Profil de surface cellulaire Download PDF

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Publication number
WO2010142860A1
WO2010142860A1 PCT/FI2010/050489 FI2010050489W WO2010142860A1 WO 2010142860 A1 WO2010142860 A1 WO 2010142860A1 FI 2010050489 W FI2010050489 W FI 2010050489W WO 2010142860 A1 WO2010142860 A1 WO 2010142860A1
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WO
WIPO (PCT)
Prior art keywords
cell surface
cell
proteins
protein
glycan
Prior art date
Application number
PCT/FI2010/050489
Other languages
English (en)
Inventor
Leena Valmu
Ilja Ritamo
Hannu Peltoniemi
Jarkko Räbinä
Original Assignee
Suomen Punainen Risti Veripalvelu
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Suomen Punainen Risti Veripalvelu filed Critical Suomen Punainen Risti Veripalvelu
Publication of WO2010142860A1 publication Critical patent/WO2010142860A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6842—Proteomic analysis of subsets of protein mixtures with reduced complexity, e.g. membrane proteins, phosphoproteins, organelle proteins
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2400/00—Assays, e.g. immunoassays or enzyme assays, involving carbohydrates
    • G01N2400/10—Polysaccharides, i.e. having more than five saccharide radicals attached to each other by glycosidic linkages; Derivatives thereof, e.g. ethers, esters

Definitions

  • the present invention relates to a method of characterizing and/or identifying molecular composition of cell surface and its use in analysis of the ceil status.
  • the present invention relates also to a method of forming a cell surface protein profile and its use in anaiysis of the ceil status.
  • the present invention relates to a platform for analyzing cell surface protein composition in a cell and its use both in analysis of the cell status and as giycoproteo- mic data handling tool.
  • stem cells are of enormous interest in regenerative medicine and huge expectations are placed on stem cell therapy.
  • stem cells such as mesenchymal, embryonic and hematopoietic stem ceils.
  • mesenchymal stem cells are fibroblast-like adult multipotent progenitor cells that can be isolated from bone marrow or cord blood. They are capable of differentiating into ail mesenchymal cell types like osteoblasts, chondroblasts and adipocytes.
  • Embryonic stem cells can differentiate into all ceils needed for a developed organism.
  • the approach on characterizing cells is based on enriched cell surface proteins, where peptides, glycans and glyco- peptides can be generated and further analyzed.
  • the present invention thus provides novel markers for cells, such as MCSs 1 to be used in the preparation of pure cell population.
  • the present invention also provides a novel use of cell surface protein, glycan or glycoprotein composition and/or a profile of a cell surface peptide, glycan or glycopeptide for the analysis of the cell status.
  • the term "status” here refers to, for example, differentiation stage, cell division or growth in culture, quality or purity of a cell preparation, or the origin of a ceil preparation.
  • the present invention relates also to a platform for identifying and/or characterizing the cell surface protein, glycan or glycoprotein composition on the cell and its use in analysis of the cell status.
  • An object of the present invention is to provide a method of characterizing, localizing and/or identifying cell surface protein, glycan and glycoprotein composition in a cell and its use in analysis of the cell status.
  • Another object of the present invention is to provide a method of forming a cell surface molecular profile based on relative distributions of these molecules as above mentioned analytes and the use of these analyte profiles in depiction of the cell status.
  • a further object of the present invention is to provide a method and/or platform for characterizing and/or identifying cell surface protein, glycan and glycoprotein composition in a cell and its use in analysis of the cell status.
  • a specific object of the present invention is to provide a cell surface proteomics platform for mesenchymal stem ceils. It is of note that basically the same platform can be applied for the analysis of celi surface gfycans and glycopeptides derived from cell surface enriched proteins.
  • Figure 1 shows an optimized cell type specific biotinylation for cell surface protein extraction.
  • Mesenchymal stem cells derived from two different origins, cord biood (A 1 B 1 C 1 D) and bone marrow (E 1 F 1 G 1 H) were biotinylated on six well plate with increased biotin amount as visualized in B and F.
  • the amount of proteins captured was visualized by Western blotting (B and F), from which the optical density was also calculated (C 1 D 1 G 1 H). Since the cell number was different in these two sample sets, due to different size of the cells, the extracted protein amount was visualized as the number of ⁇ g biotin per million cells both in logarithmic (C and G) and linear (D and H) scale.
  • Figure 2 shows an optimized magnetic bead capture for biotinylated cell surface proteins.
  • Bone marrow derived mesenchymal stem cells were biotinylated as optimized ( Figure 1) and captured with altered concentration of mBeads.
  • First lane represents total cell lysate in amount of 2/100 from the original sample.
  • Lanes with Bound proteins represent the protein amount from overall sample attached to the beads submitted in the amount depicted at the above of the iane.
  • Lanes with Unbound proteins visualizes the biotinylated proteins left unbound when the depicted magnetic bead amount was used.
  • Figure 3 shows a SDS-PAGE visualization of cell surface proteins extracted from biotinylated mesenchymal stem cells derived from cord blood.
  • Lane A represents total cell iysate proteins in approximated amount of 2/100 from the original sample.
  • Lane B represents mBead bound cell surface proteins from an aliquot of 98/100.
  • Lane C represents mBead unbound proteins from the iysate in approximated amount of 1/100.
  • BSA used in the binding assay is visualized by an arrow.
  • Figure 4 shows cellular locations of proteins identified in the DCi surface enriched protein fraction from mesenchymal stem cells using gel- based protein identification workflow.
  • the 147 identified proteins (shown in Table 2) from cord blood derived mesenchymal stem cells (A) as well as 222 identified proteins (shown in Table 3) from bone marrow derived mesenchymal stem cells (B) are visualized according to their cell surface, cytosolic, organelle of extracellular location. Also the amount of proteins without any known location is shown.
  • Figure 5 shows the cell surface proteomics data handling. An illustration of a workflow used for the data analysis of cell surface protein profiling.
  • Figure 6 shows a computing environment for the Cell Surface Protein Classifier (CSPC). An illustration of different data inputs and a software tool, which outputs classified identified proteins into Cell Surface and other locations.
  • Figure 7 shows two-dimensional peptide maps of proteomic profiles. Cell surface enriched proteins have been digested and the resulted peptides have been resolved as fingerprints in LC-MS profile, where x and y panels represent LC separation as retention time and m/z interval of 0-2000.
  • maps have been established either using Xcalibur software (A) or Progenesis LC-MS software (B).
  • Figure 8 shows cellular locations of proteins identified in the cell surface enriched protein fraction from cord blood derived mesenchymal stem cells as a result from protein profiling experiment.
  • FIG. 4 The 63 identified proteins (shown in Table 4) were visualized according to their cell surface, cytosolic, organelle of extracellular location. Also the amount of proteins without any known location is shown.
  • Figure 9 shows cell surface protein profiling differences in mesenchymal stem cells with different culturing conditions. Cell surface proteins were enriched from cord blood derived MSCs, which were either in non-confluent culturing conditions (A) or in an extreme confluency (B). Tryptic peptides digested directly from the enriched protein fractions were analysed in LC-MS/MS profiling technology. Relative comparison of mass spectrometric features in two sample sets was performed using Progenesis LC-MS expression analysis software (C).
  • FIG. 10 shows mass spectrometric fragmentation spectra of a glycopeptide.
  • Glycan portion fragmentation of the glycopeptide is annotated in pane! A, whereas identified peptide fragment ions (y- and b-series) are visualized in panel B for peptide CGLVPVLAENYNK with pyridylethyl modification in C1 and HexNAc modification in N 12.
  • Figure 11 shows protein identification pattern after a glycopeptide fragmentation search on the Mascot Server.
  • a glycopeptide fragmentation spectrum has been submitted into database search against an open SwissProt database with all protein entries from ail taxa (A) as well as against the tailored in-house database consisting cell surface enriched proteins from mesenchymal stem cells (B).
  • the x-axis of the histogram presenting the score distribution shows the Mascot score, which represents the reliability of the identification.
  • the y-axis shows the number of hits.
  • Staining pattern of green and yellow represent the significance of the identification - scores greater than 35 (the area outside green and yellow) indicate extensive homology when p ⁇ 0.05.
  • Figure 12 shows the overall workflow, which is used for the cell surface proteomics either in gel-based protein identification platform, which is a qualitative approach, or in protein profiling platform, where relative quantitation can be performed.
  • Figure 13 shows the overall ceil surface platform containing proteomic, glycomic and glycoproteomic perspective.
  • Figure 14 is a flow chart illustrating functionality of the CSPC according to an embodiment.
  • Figure 15 shows N-glycan profile of mononuclear celfs derived from umbilical cord blood.
  • the total cell lysate derived N-glycan profile is visualized in panel A whereas the N-glycan profile derived from cell surface enriched protein fraction is visualized in panel B.
  • the glycan compositions derived from mass spectrometric analysis are depicted in x-axis and grouped according to their glycan class. Overall relative intensity corresponding to each glycan composition is represented in the y-axis. Most likeiy glycan structures corresponding major glycan compositions are illustrated.
  • Figure 16 shows unsupervised Principle Component Analysis performed on LC-MS features detected from cell surface glycan sample from Jurkat T-cells grown in either high (square) or low (circle) glucose concentration. In the analysis features with ANOVA ⁇ 0.05 are taken into account.
  • Figure 17 shows unsupervised Principle Component Analysis performed on LC-IVIS features detected from cell surface glycan sample from mesenchymal stem cells grown either with fetal calf serum (circle) or human platelet lysate (square). In the analysis all detected features within the LC-MS run are taken into account.
  • Figure 18 shows the standardized expression profile of two features between samples derived from cell surface glycans from mesenchymal stem cells grown either with fetal celf serum (FCS) or human platelet lysate (PL) supplemented growth media.
  • Panel A visualizes the expression profile of the glycan composition S1G2H6N6 and the proposed glycan structure is also iilustrated below.
  • Panel B visualizes the expression profile of the glycan composition S3H7N6F1 and the proposed glycan structure is illustrated below.
  • x-axis represents the different samples and y-axis represents the standardised and normalized volume of the feature within different samples.
  • Figure 19 shows the mass spectrometric fragmentation spectra of a feature in the glycopeptide enriched fraction from mesenchymal stem cell samples grown either with animal or human material.
  • the feature has m/z of 1089.1207 and the charge state of three.
  • the typical mass differences or oxonium ions resulting from the giycan portion are annotated as follows: square is a hexosamine, circle is a hexose and triangle is a deoxyhexose.
  • Figure 20 shows unsupervised Principle Component Analysis performed on LC-MS features detected from enriched cell surface glycopeptide fraction from mesenchymal stem cells grown in either with fetal calf serum
  • Figure 21 shows the expression profiles of a glycopeptide feature between samples derived from mesenchymal stem cells grown either with fetal celf serum (FCS) or human platelet lysate (PL) supplemented growth media.
  • Panel A visualizes a zoomed view of 2D LC-MS map from three different analyses from cells with either FCS or platelet lysate. Here a glycopeptide with m/z of 10718042 and the charge state of three is zoomed.
  • panel B the standardized expression profile of the same giycopeptide is shown within the same sample set.
  • the x-axis represents the different samples and y-axis represents the standardised and normaiized volume of the feature within different samples.
  • the cell surface proteins enriched from isolated cells are used in qualitative analysis and/or quantitative analysis.
  • the cell sur- face proteins are identified using the gel based fractionation methodology prior proteomic identification.
  • the ceil surface proteins are digested into peptides directly when captured and used in cefl surface protein profiling. The obtained protein profile is then used to image ceil condition and/or state.
  • the compositions of cellular proteins, glycans or glycoproteins are analyzed from a total cell lysate, although the molecules involved in cellular communication reside on the cell surface.
  • the iack of technology competent enough to specifically unravel compositions of proteins, glycans and glycopeptides from cell surfaces is solved by the present invention. As shown by the present invention more accurate data of the cell surface molecular composition is provided compared to prior art analysis methods.
  • An object of the invention is thus a method of characterizing and/or identifying the cell surface molecules, such as protein, glycan and giycopeptide composition of a cell as depicted in Figure 13.
  • the cell surface proteins are isolated, digested into peptides, identified and their cellular location is iden- tified based on classification by CSPC method.
  • the cell surface proteins of an intact eel! are labelled, the cells are lysed and the labelled protein fractions are collected and digested into peptides that are identified and classified and localized.
  • Labelling, isolation, identification and classifi- cation and/or localization can be done with any method known in the art to be suitable for that action.
  • cell surface proteins are labelled with biotin.
  • identification of the proteins is made by mass spectrometry methods. Modern mass spectrometry methods provide the means to characterize the proteome of a cell even when the amount of sample available is very limited, which is often the case in research on therapeutic cells, such as stem cells.
  • cellular localization of the proteins is determined using a software tool and/or a computer aided tool that classifies the proteins based on information with regard to prior knowledge available in protein data- base and predetermined rules as exemplified in Figure 14.
  • the cell surface proteins of an intact cell are labelled with a label known in the art to be suitable for this action, the cells are fysed and the labelled protein fractions are collected and digested into peptides.
  • the cell surface proteins of an intact cell are labelled with biotin, the cells are lysed and the labelled protein fractions are collected and digested into peptides.
  • the proteins are identified and classified/localized using methods known in the art to be suitable for these actions.
  • the peptides are used in forming a peptide fingerprint or a peptide map that images and/or illustrates the status of the cell. Accordingly, the peptide platform and the map it produces can be used in illustrating the status of the cell.
  • the peptide level changes of the cell surface proteins can be identified from and/or based on the peptide platform/map.
  • the same principle can be used to systematically analyse giycans and giycopeptides derived from cell surface proteins and their profiles on cell surface.
  • tools such as mass spectrometry are used to determine the exact glycan structure linked to the particular peptide in question.
  • the mass spectrometry identification of the particular cell surface glycopeptide is assisted by the cell surface protein database, generated by the method/platform in the invention.
  • the "in-house” database of cell surface proteins is formed according to gel based proteomics methodology as depicted in Figure 12.
  • a computing device/apparatus, or its “comparison software tool” is configured, by means of, for example, an added or updates software routine and/or a circuitry, to receive the further mass spectrometry fragmentations, obtain the in- house database, compare the fragmentations with the information in the in- house database to obtain the giycan and/or peptide identifications.
  • human mesenchymal stem cells are isolated both from bone marrow and cord blood. Their cell surface proteins are labelled with biotin using know methods, such as described by G. Elia in Proteomics 8, 4012 (2008). The enriched cell surface protein fraction is analyzed by nano-LC coupled to LTQ Orbitrap XL mass spectrometer. In the qualitative approach with maximal protein identification coverage, 1 D-gel based protein identification is performed, whereas in the quantitative approach with differential expression analysis, direct in-solution digest is performed. Semi-automated cellular location algorithm is developed in order to facilitate the time consuming data analysis, as depicted in Figure 5.
  • Another object of the invention is a method of forming a cell surface protein (peptide as an analyte), glycan (glycan as an analyte) and glycoprotein (glycopeptide as an analyte) profile.
  • a further object of the invention is a method for analysing the cell status.
  • the cell surface protein composition and/or the peptide profile of the cell is compared with a reference composition and/or profile and similarities and/or differences between these compositions and/or profiles indicate the status of the cell. Similar profiling is performed on ceil surface enriched glycans and glycoproteins and/or glycopeptides. Status of a celi varies during its lifetime and depends on the stage of dif- ferentiation of the cell, the type of the cell, the quality of the cell, the condition of the cell and/or the growth of the cell, for example.
  • a further object of the invention is a use of a cell surface protein, glycan or glycoprotein profile to image cell condition and/or state.
  • the eel! surface protein or peptide or glycan or glycoprotein or glycopeptide profile and/or changes in the profile can be used in identifying the state or status of the ceil with regard to the cell type, quality, differentiation etc.
  • the term ceil refers to any vertebrate cell such as a blood cell or a stem ceil.
  • blood cells like monocytes and lymphocytes, such as T-cells.
  • stem cells like adult stem cells, such as mesenchymal and hematopoietic stem cells, and embryonic stem cells.
  • the cell is a human MSC.
  • the stem cell is a human hematopoietic stem cell.
  • a software tool classifies the identified proteins into the following groups according to their cellular location; cell surface proteins, cytosolic pro- teins, organelle proteins and secreted proteins.
  • the software tool classifies,, as illustrated below, the proteins based on the UniProt DB data as depicted in Example 3.
  • Figure 6 is a simplified block chart only showing some inputs, an output, and some functional entities and elements that are all logical units whose implementation may differ from what is shown. It is apparent to a person skilled in the art that the computing environment may also comprise other entities and elements, such as the "comparison software tool" element.
  • the CSPC is part of a computing device/apparatus (not shown in
  • Figure 6 such as a laptop computer or a desktop computer.
  • the computing device is configured to perform one or more of user CSPS functionalities described below with an embodiment.
  • the computing device may comprise other software tools and/or units, and the computing device comprises different interfaces, such as a receiving unit for inputs, a sending (transmitting) unit for outputs, a network interface to connect to a communication network via which the UniProt database, for example, can be accessed, and a user interface.
  • the rules and the contaminants illustrated in Figure 6 may be in separate files in the computing devices memory or in a memory of another computing device and/or in an external memory, and the file containing the rules may stored to different memory than the file containing the contaminants.
  • the file containing the rules may be the one depicted in Table 4.
  • Te file containing the contaminants may be any file containing information and/or rules with which it is easy to define what in the input to be processed (such as the protein search results) should also be in the output, what should be filtered away.
  • the computing device/apparatus may generally include a processor, controifer, control unit, micro-controller, or the like connected to a memory and to various interfaces of the apparatus.
  • the processor is a central processing unit, but the processor may be an additional operation processor.
  • the CSPC may be configured as a computer or a processor, or a microprocessor, such as a single-chip computer element, or as a chipset, including at least a memory for providing storage area used for arithmetic operation and an operation processor for executing the arithmetic operation.
  • the CSPC, or the "comparing software tool” may comprise one or more computer processors, application-specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field-programmable gate arrays (FPGA) 1 and/or other hardware components that have been programmed in such a way to carry out one or more functions of one or more em- bodiments.
  • ASIC application-specific integrated circuits
  • DSP digital signal processors
  • DSPD digital signal processing devices
  • PLD programmable logic devices
  • FPGA field-programmable gate arrays
  • the computing device/apparatus may generally include volatile and/or non- volatile memory and typically store content, data, or the like.
  • the memory may store computer program (software) code, such as software applications (for example, for the CSPC) or operating systems, information, data, content, or the like for the processor to perform steps associated with operation of the computing device/apparatus in accordance with embodiments.
  • the memory may be, for example, random access memory, a hard drive, or other fixed data memory or storage device. Further, the memory, or part of it, may be removable memory detachably connected to the apparatus.
  • the CSPC, the "comparison software tool" and the interfaces may be software and/or software-hardware and/or firmware components (recorded indelibly on a medium such as read-only-memory or embodied in hardwired computer circuitry).
  • the software tool receives, in step 1401 , the Mascot, or any other search engine, result file, i.e. the protein search result file.
  • the software tool obtains, in step 1402, using protein identifiers, such as UniRef protein Ids, in the Mascot, or any other search engine, result file as search keys, for each protein identifier a corresponding UniRef data entry from the EBI database services, or corresponding data entries from a corresponding data service to obtain for each identifier a protein name, such as a UniProt name.
  • the software tool also creates, in step 1403, an output file, and inserts, in step 1404, the obtained protein names to the output file.
  • the software tool obtains, in step 1405, using the obtained protein names as search keys, for each protein name (i.e. for each protein) a cor- responding UniProt data entry from the EBI database service.
  • the software tool classifies, in step 1406, each protein by means of predefined classification rules and information in the obtained UniProt data entries.
  • the classification rules may be part of the software tool, i.e. part of the software instructions, or the software tool may be configured to obtain, via its interface, the classification rules from a memory as described with Figure 6.
  • An example of a set of classification rules is illustrated in Table 4.
  • One protein name may be classified to several categories. For each protein, when a protein name is classified, the protein name is associated, in step 1407, with the corresponding classification result(s) in the output file.
  • the software tool checks, in step 1408, for each protein name, information on protein giycosyiation sites to have for each protein name localization information with visualization information so that it is possible to visualize whether the corresponding site location is indicated as potential or as known in the corre- sponding data entry.
  • the localization information and visualization information may be determined to one or more protein giycosyiation, or any other protein site for which UniProt entries contain information on site and site accuracy.
  • the software toof then associates, in step 1409, each protein name with the corresponding localization information visualized according to the visualization information protein site-specifically in the output file.
  • the localization information is visualized by means of using brackets: possible locations are within brackets, accurate locations are without brackets.
  • Other visualization such as using different colour or font or font size, may be used as well; there is no limitations how the visualization is performed, and in some embodiments the visualization may be not performed.
  • the location information and visualization information are added and associated separately but protein site-specifically.
  • the visualization information may be also called as accuracy information.
  • the software too! obtains, in step 141O 1 via its interface a file in the computing environment, the file containing proteins to be filtered, i.e. in the example a file containing contaminants, in order to filter proteins that are known to be on air, for example, and therefore contaminating the cell surface protein samples.
  • a filtering rule is to filter from the output file proteins whose name is in the obtained file. It should be appreciated that other filtering criteria may be used, and the file may contain names of proteins that the user wants to be classified (if found in the results received in step 1401), proteins not appearing in the filtering list wifl be filtered.
  • Using a separate file instead of coding corresponding information to the software tool itself provides the advantage that updating the information on contaminants, or any other protein, is much more easier, and each user may define his/her own contaminants, or corresponding information.
  • the software tooi knows the contaminants, it deletes, in step 1411 , corresponding proteins and the information associated from the output file.
  • the output file is ready, and the CSPC outputs, in step 1412, the outcome, i.e. the output file.
  • the output file may then be manually updated and/or used as base of the "in-house" database, for example.
  • steps 1410 and 1411 may be performed after the protein names are determined, i.e. directly after step 1404 before step 1405.
  • the output file is obtained by concatenating different intermediate results only after step 1411.
  • a further example is performing the steps 1404-1411 protein by protein.
  • Other functions can also be executed before, between, after or within the steps. For example it is possible to show a user a dialog box and then receive from the user an instruction to fetch a specific Mascot result file before step 1401. Another example is that the CSPC stores the output file.
  • an embodiment may comprise only steps 1401-1405 and 1412, or steps 1401-1407 and 1412, or steps 1401-1405, 1408, 1409 and 1412, or steps 1401-1405 and 1410-1412, or steps 1401-1407 and 1410-1412, or steps 1401-1405 and 1408-1412. It is even possible to skip obtaining the protein name and to use only the protein identifiers. However, using the names is much more informative. Further, if the received results al- ready contain the names, there is no need to perform step 1402. However, it may be performed and an additional step in which it is compared whether or not the obtained name corresponds to the name in the received result file.
  • Cell surface protein or glycan or glycoprotein profiling mainly as peptides and glycans and glycopeptides as analytes, provides an excellent tool to depict cellular phenomena, as reported in multiple studies for various cell types. Nevertheless, cell surface proteins are among the most difficult to study due to their low solubility and heavy modification degree, mainly in the form of glycosy!ation.
  • an optimized platform for molecular analysis of ceil surface proteins and/or glycoproteins, on optionally therapeuti- cally important cell class is provided.
  • Bone marrow-derived mesenchymal stem cells Bone marrow-derived MSCs were obtained as described earlier
  • bone marrow from femoral collum and trochanteric region obtained during orthopedic surgery was cultured in minimum essential medium ⁇ ( ⁇ -MEM), supplemented with 20 mM HEPES, 10% fetal calf serum, 1 ⁇ penicillin-strepto- mycin, and 2 mM L-glutamine (all from Gibco, Grand Island, NY, USA).
  • ⁇ -MEM minimum essential medium
  • the cells were washed with Ca 2+ and Mg 2+ - free phosphate buffered saline (PBS) (Gibco) and subcultured further by plating the cells at a density of 2000 to 3000 cells/cm 2 in the same medium. Half of the medium was replaced by fresh medium twice a week until confluence was almost reached.
  • PBS Ca 2+ and Mg 2+ - free phosphate buffered saline
  • the MSC lines used were analyzed for MSC phenotype as described (Heiskanen et al., 2007, Stem Cells 25, 197).
  • the cell lines were analyzed by flow cytometry for the expression of the MSC markers (CD73, CD90 and CD105) and the absence of differentiation markers (CD14, CD19, CD34, CD45 and HLA-DR).
  • Fluorescein isothiocyanate (FITC)- or phycoerythrin- conjugated antibodies against CD14, CD34, CD45 and CD73 were from BD Biosciences (San Jose, CA) and against CD105 from Abeam Ltd. (Cambridge, U.K.).
  • FITC- and phycoerythhnconjugated isotypic controls were from BD Biosciences. Unconjugated antibodies against CD90 and human leukocyte anti- gens HLA-DR were from BD Biosciences.
  • FITC-conjugated goat anti-mouse IgG antibody was from Sigma-Ald
  • Cord blood was collected in a multiple bag system containing 17ml of citrate phosphate dextrose buffer (Cord Blood Collection System; Eltest, Bonn, Germany). Collections were performed at the Helsinki University Central Hospital, Department of Obstetrics and Gynaecology, and Helsinki Maternity Hospital. All donors gave informed consent and the study protocol was approved by ethical review board of Helsinki University Centra! Hospital and the Finnish Red Cross Blood Service. Prior to the isolation of mononuclear cells, the anti-coagulated cord blood was diluted 1 :2 with 2 mM EDTA-PBS. Mononuclear cells were isolated using Ficoil-Hypaque (Amersham Biosciences, Pis- caway, NJ 1 USA) gradient centrifugation.
  • fibronectin Sigma coated tissue culture plates (Nunc) in prolifera- tion medium consisting of minimum essential medium ⁇ ( ⁇ MEM) with GIu- tamax (Gibco) and 10% fetal calf serum (FCS) (Gibco) supplemented with 10 ng/mL epidermal growth factor (EGF, Sigma), 1Qng/mL recombinant human platelet-derived growth factor (mPDGF-BB; R&D Systems, Minneapolis, MN, USA), 50 nM Dexamethasone (Sigma), 100 U/ml penicillin + 100 ⁇ g/ml strep- tomycin (Invitrogen).
  • ⁇ MEM minimum essential medium ⁇
  • FCS fetal calf serum
  • the initial MSC iine establishment was performed in a humidified incubator with hypoxic conditions (5% CO 2 , 3% O 2 and 37 0 C). Cells were allowed to adhere overnight and non-adherent cells were washed out with medium changes. Proliferation media was renewed twice a week. Established lines were passaged when almost confluent and re-plated at 1000-3000 cells/cm 2 in proliferation media in normoxic conditions (5% CO 2 , 20% O 2 and 37°C).
  • FITC 1 PE-and APC-conjugated isotypic controls were used. Label- ling was carried out in 100 ⁇ l of 0.3% ultra pure bovine serum albumin (BSA) in phosphate buffered saline (PBS) on ice for 30 minutes. Flow cytometric analysis was performed on FACSAria (Becton Dickinson Biosciences) with a 488 nm blue laser for (PE and FITC) and a 633 nm red laser for (APC). Fluorescence was measured using 530/30 nm (FITC), 585/42 nm (PE) and 660/20 nm (APC) bandpass filters. Data were analysed using FACSDiva software (BD Bio- ciences).
  • BSA bovine serum albumin
  • PBS phosphate buffered saline
  • Multipotent differentiation capacity was characterized by inducing differentiation. 4-5 th passage cells were treated with osteogenesis, chondro- genesis or adipogenesis inducing media up to 3 weeks. Differentiation capacity was evaluated with standard staining methods. Cord blood derived mononuclear cells
  • Cord blood derived mononuclear cells were isolated essentially according to the protocol for cord blood-derived mesenchymal stem cells. After isolation of mononuclear cells by using Ficoil-Hypaque (Amersham Biosciences, Piscaway, NJ, USA) the cells were further washed in PBS several times.
  • Jurkat T cells were cultured in RPMI 1640 supplemented with 10% fetal bovine serum and penicillin-streptomycin (all from Gibco, Grand Island, NY, USA). After two weeks the cells were transferred to glucose-free RPMI 1640 medium supplemented with either 5 mM or 25 mM glucose. After culturing 3 days in either low or high glucose the cells from both conditions were divided into three technical replicates and pelleted by centrifugation.
  • Biotin label (EZ-Link NHS-LC-biotin or EZ-Link NHS-SS-biotin, Thermo Fisher Scientific Inc.) was resolved in D-PBS buffer (Dulbecco's phosphate buffered saline, Dulbecco). The former label was used for western blotting experiments and the latter otherwise. Amount of label used was optimized for each cell type to be analyzed, as depicted in Example 1. Prior labelling adherent cells were washed thrice with ice cold D-PBS. Labelling solution was added on the cells and they were incubated on ice for 30 minutes.
  • the cells were washed twice with ice cold D-PBS and unreactive label was blocked by incubating with 20 mM glycine in D-PBS for 15 minutes. The cells were washed thrice with ice cold D-PBS.
  • the cells were lysed in lysis buffer containing 2% NP-40, 1% Triton- X 100, 10% glycerol, 350 mM sodium chloride, protease inhibitors (EDTA free protease inhibitor tablet, Roche) in PBS for 5 minutes.
  • the cells were scraped off the plate, moved to a microcentrifuge tube and incubated on ice for 30 minutes. 2 ⁇ l of 10 U/ ⁇ l DNAase (DNase I recombinant, RNase-free, Roche) was added per 50 ⁇ l of lysate and the mixture was incubated in room temperature for 50 minutes. Lysate was centrifuged 15000 rpm at +4°C for 20 minutes. Supernatant was saved.
  • Magnetic streptavidin beads (Dynabeads MyOne Streptavidin T1 , Invitrogen) were washed with lysis buffer. Amount of beads was 400 ⁇ l per mg of biotin label. The beads were blocked by using 1% ultra pure bovine serum albumin (BSA, Sigma-Aldrich) in lysis buffer and incubating 30 minutes in room temperature in rotation. 0.1% of ultra pure BSA and the washed beads were added to the cell lysate and the mixture was incubated for 40 minutes in room temperature. The beads with captured proteins were separated from the solution with magnetic stand (DynaMag-Spin, Invitrogen). The solution containing the unbound proteins was saved.
  • BSA bovine serum albumin
  • the beads were washed thrice with lysis buffer, twice with lysis buffer, which had only half of the detergents and didn't contain any glycerol, thrice with D-PBS and once with water.
  • the bound proteins were eluted from the beads by incubating 30 minutes in elution buffer (50 mM DTT, 25 mM Tris, pH 7.5). The eluate was saved and the elution step was repeated. The first and the second eluate were combined and vacuum dried.
  • SDS-PAGE was performed essentially as described ⁇ Laemmii, 1970, Nature 227, 680) using 12% gel.
  • the gel was silver stained prior to proteolytic digestion.
  • the gel was fixed with 30% ethanol in 0.5% acetic acid soiu- tion for 1h.
  • the gel was rinsed with 20% ethanol for 10 minutes followed by rinse with water for another 10 minutes.
  • the gel was sensitized using 0.02% sodium thiosulfate solution for 1 minute and rinsed twice with water for 20 seconds.
  • the gel was incubated in 0.2% silver nitrate solution for 30 minutes and rinsed with water for 10 seconds.
  • Fresh development solution (37% formalde- hyde, 3% potassium carbonate, 0.001 % sodium thiosulfate) was made and the gel was developed until desired intensity was achieved. The development reaction was stopped by discarding development solution and adding 5% Tris in 2.5% acetic acid solution. The developed gel was rinsed with water and stored in water until the bands were cut for in-gel digestion.
  • SDS-PAGE gel lane which contained the proteins eluted from streptavidin beads, was cut into 2mm slices for in-gel digestion. Each slice was cut into pieces with diameter of 0.5 mm, which were shrunk by adding twice 200 ⁇ l of acetonitrile. Gel pieces were rehydrated with 100 ⁇ l of 20 mM DTT in 0.1 M ammonium bicarbonate for 30 min at 56 0 C. Excess liquid was removed and the gel pieces were dehydrated as above. 100 ⁇ l of 55 mM iodoacetamide in 0.1 M ammonium bicarbonate was added and the gel pieces were incubated 15 min in dark at room temperature.
  • the mixture was diluted with water prior to digestion to decrease urea concentration. 15 ⁇ l of immobilized trypsin was washed with 10O mM Tris (pH 7.8) and finally suspended in 10 ⁇ i of digestion buffer (10O mM Tris pH 7.8) and transferred to the protein sample. The reaction mixture was incubated at 37°C overnight with rapid shaking. The reaction was stopped by adding the reaction mixture on the top of PD MiniTrap G-10 column (GE Healthcare, Uppsala, Sweden) equilibrated with 20 mM NH 4 HCO 3 . Equilibration buffer was added so that the total volume of sample and added buffer was 800 ⁇ l. The peptide fraction was then eluted and collected according to the manufacture's instructions. The sample was vacuum dried and dissolved twice in small volume of water and vacuum dried again.
  • N-glycans were released enzymaticaily from cell surface proteins by using PNGase F (Sigma). Peptides digested with immobilized trypsin were dissolved in 20 ⁇ l of 25 mM NH 4 HCO 3 and 5 ⁇ l (2.5 U) of PNGase F was added. The reaction mixture was incubated overnight at +37°C. Peptides were removed from the reaction mixture by using C18 ZipTip (Millipore) as follows. The reaction mixture was dried under vacuum and dissolved with 5% acetic acid after which it was aspirated and dispensed 10 cycles with ZipTip for the binding of peptide material. Finally, the remaining solution containing the N- glycans was dried under vacuum.
  • N-glycans were reduced by using NaBH 4 to eliminate anomer peaks in LC-MS.
  • 1% NaBH 4 in 10 mM NaOH was added to dried sample and the reaction mixture was incubated at room temperature for 2 hours. The reaction was terminated by adding slowly 5% acetic acid until the fizzing stopped.
  • the reduced glycans were purified and desalted by using graphitized carbon containing TopTip 10 ⁇ l pipette tips (Glygen, Columbia, ND). Tips were conditioned by adding three times 20 ⁇ l of 80 % (v/v) acetonitrile containing 0.1 % TFA followed by four times 20 ⁇ l of H 2 O. The sample solution was applied to tip and pushed through the sorbent bed.
  • Glycopeptides were enriched from cell surface peptide fraction using reversed- phase (RP) based solid-phase extraction (SPE). Peptides digested with immobilized trypsin were subjected to C18 ZipTip column (ZipTipci ⁇ , P10, Millipore) equilibrated with 0.1 % trifluoroacetic acid (TFA). Glycopeptides were eluted from the sorbent with 3 ⁇ l of 18% acetonitrile in 0.1% TFA.
  • RP reversed- phase
  • SPE solid-phase extraction
  • the mass spectrometer was calibrated with Thermo Fisher Scientific standard LTQ calibration solution consisting of caffeine, MRFA tetrapep- tide and Ultramark 1621.
  • the instrument was tuned with glu-fibrinopeptide B (Sigma-Aldrich).
  • Full scan for eluting peptides was acquired in mass range of 300-2000 m/z on Orbitrap-detector with 60 000 resolution at 400 m/z, AGC target set to 200 000 and maximum inject time set to 800 ms.
  • AGC target set to 200 000
  • maximum inject time set to 800 ms Based on full MS scan, six MS/MS data-dependent scans were acquired on LTQ with AGC target set to 10 000 and maximum inject time set to 100 ms. Isolation width of 2 m/z was used for precursor selection.
  • Glycans were analysed with liquid chromatography (LC) - mass spectrometry (MS). Glycans were loaded to reversed-phase precoiumn (NanoEase Atlantis dC18, 180 ⁇ m x 23.5 mm, Waters) with 10% acetonitrile in 0.1 % formic acid and separated in RP analytical column (PepMap 100, 75 ⁇ m x 150mm, Dionex Corporation) with linear gradient (40 - 90%) of 90% acetonitrile and 10% isopropanol in 0.1% formic acid in 30 minutes. Ultimate 3000 LC instrument (Dionex Corporation) was operated in nano scale with flow rate of 0.3 ⁇ l/min.
  • LC liquid chromatography
  • MS mass spectrometry
  • the mass spectrometer was calibrated with Thermo Fisher Scientific standard LTQ calibration solution consisting of caffeine, MRFA tetrapeptide and Ultramark 1621.
  • the instrument was tuned with permethylated monosialylated biantennary glycan standard (ProZyme, Inc.).
  • Full scan for eluting glycans was acquired in mass range of 300 - 2000 m/z on Orbitrap-detector with 60 000 resolution at 400 m/z, AGC target set to 200 000 and maximum inject time set to 800 ms.
  • MS/MS scan needed to have at least ten peaks and maximum charge of +2 was allowed for fragment ions.
  • MS/MS scans were aggregated if precursor m/z values matched with +/- 0.02 m/z tolerance and elution time difference was less than 30 s. Following parameters were used for peak picking from MS/MS-scan: minimum signal-to-noise ratio 1 ; minimum peak width 0.01 Da; expected peak width 0.4 Da and maximum peak width 1 Da.
  • MS/MS data files from mass spectrometer were processed with Mascot Distiller (Matrix Science Ltd., version 2.3.1) to extract MS/MS data.
  • Full scan was considered valid, if it contained at least one peak.
  • Precursor charge state and m/z value were re-determined from parent scan.
  • Maximum precursor charge of +7 was allowed to have corresponding MS/MS scan being included in the analysis.
  • MS/MS scan needed to have at least ten peaks and precursor charge was allowed to be maximum charge for fragment ions.
  • MS/MS scans were aggregated if precursor m/z values matched with +/- 0.02 m/z tolerance and elution time difference was less than 30 s. Following parameters were used for peak picking from MS/MS-scan: minimum signal-to-noise ratio 4; minimum peak width 0.001 Da; expected peak width 0.2 Da and maximum peak width 1 Da.
  • Progenesis LC-MS software (Version 2.6, Nonlinear Dynamics Ltd.) was used for LC-MS differential expression analysis and to extract MS level data (features).
  • GlycanlD In-house developed R-library called GlycanlD was used to derive glycan compositions from MS and MS/MS data.
  • GlycanlD comprises functions for spectrum matching, statistical scoring and visualization.
  • glycan compositions that match feature masses are either calculated based on a given set of rules or searched in database (mass calculation).
  • Giycan compositions that match precursor masses and fragments are searched as well (MSMS).
  • Precursor masses are matched as feature masses above. Fragments are matched against theoretical fragments that a given composition could produce. The latter matches are ranked with statistical score, which takes into account number of matched fragments and their intensity in relation to random set of fragments.
  • EXAMPLE 1 Cell type specific surface protein enrichment
  • cell surface protein enrichment was performed.
  • the cell surface proteins were biotinylated, when cells were stiil intact, and captured from cell lysates using streptavidin magnetic beads.
  • CeI! surface protein biotinylation needs to be carefully optimized for each cell type to be analyzed. This is mainly due for two reasons: First, biotinylation has to occur in good enough stoichiometry in order to label each cell surface protein to confirm extensive cell surface capture. Second, on the other hand, excess biotin labelling will interfere with tryptic digestion of cell surface proteins due to biotin binding mainly into lysine residues of proteins.
  • CB MSC cord blood
  • BM MSC bone marrow
  • the cell surface proteins were labelled with different amounts of EZ-link sulfo-NHS-LC-biotin as mentioned in Materials and methods.
  • Biotinylated cell surface proteins were purified by streptavidin magnetic beads, analyzed by SDS-PAGE and visualized using HRP-conjugated streptavidin complex after Western blotting. The optical density of the lanes were determined and further visualized ( Figure 1).
  • Optimal biotin amount for cord blood derived mesenchymal stem cells was determined to be 0.75 mg biotin per 1 x 10 6 cells, whereas for bone marrow derived mesenchymal stem celis it was determined to be 1.5 mg biotin per 1 x 10 6 cells.
  • Optimal streptavidin magnetic bead amount to be used for capturing process was determined as 400 ⁇ l beads per mg biotin used ( Figure 2).
  • cell surface proteins of mesenchymal stem cells derived from either cord blood or bone marrow were maximally identified using cell surface biotinylation and 1 D gel-based proteomic identification.
  • One-dimensional SDS-PAGE has been documented to be one of the most efficient protein fractionation methodology prior mass spectrometric protein identification, for example in the thorough HUPO Plasma Proteome project (Omenn et a/., 2005, Proteomics 5, 3226). This proteomic technology is, however, highly qualitative and only minimal, if any, quantitative conclusions can be drawn.
  • cytoskeletal proteins which have come most likely along with membrane proteins in the entrapping procedure. These include for example catenins and clathrins, which are commonly known to associate into plasma membrane on its cytoplasmic side.
  • cytoskeletal proteins which have come most likely along with membrane proteins in the entrapping procedure. These include for example catenins and clathrins, which are commonly known to associate into plasma membrane on its cytoplasmic side.
  • secretory proteins which have been identified in the study, some are of bovine origin and thus likely originate from fetal calf serum, which is an important component in the common cell culturing condition.
  • Some secretory proteins are of human origin and are likely to represent proteins that these stems cells do secrete and thereafter do bind to outer cell surface structures. The existence of identified proteins from different organelles is most difficult to interpret. It is possible, and even likely, that these proteins do appear also on cell surface in addition to their organelle location, if only occasionally and still not proven.
  • CSPC Cell surface protein classifier
  • CSPS Cell surface protein classifier
  • EMBL-EBI European Molecular Biology Laboratory - European Bioinformatics Institute
  • UniProt protein entries for the identified proteins.
  • the identified proteins were classified into five categories based on prior knowledge of their location in eel!. The classification was done using subcellular location and keyword subsections of each database entry and classification rules as shown in Table 4. TABLE 4.
  • CSPC classification rules Classification is based on string matching with the UniProt protein database subcellular location and keyword subsections
  • the first category contained proteins that were known to be located on the surface of ceil.
  • the second category (unknown membrane) contained proteins that were reported to have some link to any membrane of celt but could't be unambiguously placed into the first category. Proteins in this category needed to be checked manually.
  • the third category cytosolic proteins and the fourth category (organelle) contained proteins that were related to an organelle of cell.
  • the fifth category (secreted) contained proteins that were secreted from cell.
  • CSPC extracted information also on the protein glycosylation sites. Here both N- and O-giycosyiation sites were visualized differently according to the database knowledge on these glycosylates.
  • CSPC also filtered out majority of known contaminating protein identifications (e.g. bovine serum albumin and keratins) amongst identified proteins.
  • CSPC gave result as an XML-file, which could be opened with e.g. Microsoft Excel. Multiple isomers, variants, precursors and homotogues appear with the same hit number in Mascot search. Some proteins are also classified into second CSPC location category of "unknown membrane”. Due to these reasons the CSPC output file needed still to be manually refined. In refinement step each protein hit was validated based on prior knowledge and the result was summarized by removing irrelevant columns. Structure and contents of CSPC output file and manually refined data file are described in Table 5. TABLE 5. Description of the structure and contents of CSPC output file and refined data file. "CSPC column” refers to the column header of the CSPC output file. “Raw data” refers to the unmodified CSPC output file and "Refined data” to the file, which has been manually sorted for more sophisticated output. Entry in these two columns denotes the existence of this information in these data formats.
  • Example 2 In addition to qualitative cell surface proteomic technology, which utilizes gel-based approach and ieads to maximal protein identification (Example 2), a cell surface protein profiling procedure has been performed in this example.
  • Cadherin-2 and Cadherin-13 whose functions in cellular junctions are likely to be relevant factors in cellular confluency. Also increased cell surface expression is seen for integrin alpha-10, poliovirus receptor, Tyrosine-protein kinase-like 7 and Trophoblast glycoprotein.
  • the cell surface glycan profiling procedure according to the present invention was performed with cord blood derived mononuclear cells, where glycan sample from the cell surface was compared to a giycan sample from total cell lysate derived from the same cell type (see Figure 15 and Table 8).
  • the observed N-glycan compositions fit well to the published glycan structure data of cord blood derived mononuclear cells (Hemmoranta et a!., 2007 Exp. Hematol. 35, 1279), but the analysis showed clear differences between the total cell lysate N-glycan profile (Figure 15, panel A) and the cell surface N-glycan profile ( Figure 15, panel B).
  • the analysis of cell surface protein glycosylation instead of the total cell proteins, provides more accurate picture of the cell surface glycan composition.
  • the present invention thus provides a method for characterizing specifically cell surface glycan composition and use of the said composition in analysis of status of a cell.
  • the novel glycomic profiling platform according to the invention was used for comparing the surface giycan profiles acquired from Jurkat T-cells that were cultured in different glucose concentrations. It has been shown by nucleotide sugar analysis and lectin FACS that culturing of Jurkat cells in high glucose resulted in an increased de novo biosynthesis of UDP-GIcNAc by the hexosamine pathway utilizing glucose and increase in ⁇ 1 ,6GIcNAc branched N-glycans respectively (Grigorian et al., J. Biol. Chem. 282, 20027).
  • Jurkat T-ce!ls were cultured in RPMI 1640 that contained either 5 or 25 mM glucose after which cell surface N-glycans were isolated and analysed according to the methods of the present invention.
  • RPMI 1640 contained either 5 or 25 mM glucose
  • three cellular samples with similar condition in question were identically processed.
  • LC-MS differential expression analysis was performed using the Progenesis LC-MS software (Version 2.0, Nonlinear Dynamics Ltd.), where also technical replicates were compared.
  • Giycan compositions were derived from MS data as described in the Materials and Methods. Multiple cell surface glycans were identified to have different prevalence in cells grown in either low or high glucose. Features with ANOVA ⁇ 0.05 and identified as glycan compositions resulted in 26 glycan structures that were more prevalent in low glucose culturing (Table 10). On the basis of monosaccharide compositions these included several different giycan structures with different amount of branches. When Jurkat cells were cultured in high glucose concentration, 23 more prevalent giycan structures were detected compared to low glucose cufturing (Table 11). These included, for example, the disiafylated glycan S2H5N4.
  • N-glycan branching was not seen but some specific large glycan structures such as G1 H11 N8F2, S1H7N6F3, G1 H7N7F1 and S1H6N8F2 were higher in the high glucose experiment. These can be interpreted as rnultibranched N-glycans on the basis of known N-glycan structures and biosynthetic routes. Further, enriched in high glucose cell surface there were more glycans that contained several sialic acid residues.
  • the monosiaiylated diantennary glycan S1 H5N4 existed as two feature numbers in glycans enriched in low glucose cells and the disialylated diantennary glycan S2H5N4 existed as three feature numbers in glycans enriched in high glucose cells.
  • Glucose and UDP- GIcNAc are intermediates in the biosynthesis route of CMP-sialic acid (Essentials of Glycobiology, Second Edition, 2009, 204, edited by Varki et al.) and therefore increased metabolic synthesis of sialic acid in high glucose conditions is possible.
  • H5-H8 A high number of hexose polymer structures (H5-H8) were observed in the samples where cells had been cultured with high glucose concentration. These structures were however removed from the glycan composition lists as they were likely contaminations.
  • the high number of non- human monosaccharide N-glycolylneuraminic acid detected in both sample groups was due to culturing conditions; fetal calf serum was added to the cell culture medium.
  • N-glycolylneuraminic acid is incorporated onto ceil surface when cell culturing is performed in the presence of animal derived material as seen in more detail in the following example.
  • Glycan compositions which were statistically more prevalent (ANOVA ⁇ 0.05) in Jurkat T-cells cultured with low glucose concentration.
  • the method used for derivation of the giycan composition is shown in the middle column and the number of features representing each glycan composition is shown in the right hand column.
  • Glycans are listed in descending order by the relative abundance of the glycan structure. Abbreviations used are as follows; S is N-acetylneuraminic acid, H is hexose, N is N-acetylhexosamine, F is deoxyhexose and G is N-glycolylneuraminic acid.
  • Glycan compositions which were statistically more prevalent (ANOVA ⁇ 0.05) in Jurkat T-ceils cultured with high glucose concentration. The method used to derive glycan composition is shown in the middle column and the amount of features representing each glycan composition is shown in the right hand column. Glycans are listed in descending order by the relative abundance of the glycan structure. Abbreviations used are as follows; S is N- acetylneuraminic acid, H is hexose, N is N-acetylhexosamine, F is deoxyhexose and G is N-glycolylneuraminic acid.
  • PCA Principal component analysis
  • the novel glycomic profiling platform according to the invention was used for comparing the surface giycan profiles acquired from mesenchymal stem cells that were cultured in either fetal caif serum or human platelet lysate supplemented medium. It has previously been shown that stem cell culturing with animal derived material induces changes in cellular glycome, mainly in the incorporation of N- glycolylneuraminic acid (Heiskanen et al., Stem Cells 197, 2007). In the present invention we demonstrate that this can be visualized using cell surface glycomic platform.
  • mesenchymal stem cells derived from bone marrow were cultured in minimum essential medium ⁇ ( ⁇ -MEM) supplemented either with 10% fetal calf serum (FCS) or 0.5% human platelet lysate together with 2.5% human plasma, as described (Schallmoser K et af., Transfusion. 1436, 2007).
  • ceil surface N-glycans were isolated and analysed according to the methods of the present invention.
  • LC-MS differential expression analysis was performed with Progenesis LC-MS software (Version 2.0, Nonlinear Dynamics Ltd.), where three mass spectrometric replicates were compared.
  • Glycan compositions were derived from MS data as described in Materials and Methods. Multiple cell surface glycans were identified to have different prevalence in cells grown in either FCS or human platelet lysate. Features with ANOVA ⁇ 0.05 and identified as glycan compositions resulted in 46 glycan structures that were more prevalent in FCS supplemented c ⁇ lturing (Table 13). When mesenchymal stem cells were cultured in a medium supplemented with human platelet lysate, twenty-three more prevalent glycan structures were detected as compared to cufturing with FCS (Table 14). Majority of the differences in glycans between two samples resulted from differential sialylation.
  • Neu5Gc N- glycolylneuraminic acid
  • the disialylated biantennary N-glycan S2H5N4 predominated in a cell sample cultured with human material, whereas the same structure with N-glycolylneuraminic acid (S1G1H5N4) predominated in the sample cultured with the animal-derived material.
  • Glycan compositions which were statistically more prevalent (ANOVA ⁇ 0.05) in mesenchymal stem cells cultured with fetal calf serum. The method used to derive glycan composition is shown in the middle column and the number of features representing each glycan composition is shown in the right hand column. Glycans are listed in descending order by the feature number representing each glycan structure. Abbreviations used are as follows; S is N-acetylneuraminic acid, H is hexose, N is N-acetylhexosamine, F is deoxyhexose and G is N-glycolylneuraminic acid.
  • Glycan compositions which were statistically more prevalent (ANOVA ⁇ 0.05) in mesenchymal stem ceils cultured with pfatelet iysate.
  • the method used to derive giycan composition is shown in the middle column and the amount of features representing each glycan composition is shown in the right hand column.
  • Glycans are listed in descending order by the feature number representing each glycan structure. Abbreviations used are as follows; S is N-acetylneuraminic acid, H is hexose, N is N-acetylhexosamine, F is deoxyhexose and G is N-glycolylneuramintc acid.
  • PCA Principal component analysis
  • panel B the standardized expression profile of the glycan feature with m/z of 1077.7894 and the charge state of four is shown.
  • the giycan composition of S3H7N6F1 can be derived, when the glycan is fully permethyiated and ionized as an adduct with four sodiums.
  • This glycan feature showed slightly increased expression profile in cellular samples that were cultured with human material with the fold change of 1.31 and ANOVA of 0.00728.
  • the proposed glycan structure is illustrated in Figure 18 below panel B. These exemplary phenomena are easily explained by differential incorporation of N-g!yco!ylneuraminic acids onto cell surface of stem cells when they are cultured in the two above mentioned conditions.
  • EXAMPLE 9 DIFFERENTIAL CELL SURFACE GLYCOPEPTIDOME AS A MARKER OF MESENCHYMAL STEM CELL CULTURING WITH FETAL CALF SERUM VERSUS HUMAN PLATELET LYSATE
  • the novel glycopeptidome profiling platform according to the invention was used for comparing the surface glycopeptide profiles acquired from mesenchymal stem cells that were cultured in either fetal calf serum or human platelet lysate supplemented medium (as described in Example 8).
  • cell surface enriched protein fraction was digested into peptides and subjected further into glycopeptides enrichment using solid-phase extraction. Enriched glycopeptide fraction was analysed according to the methods of the present invention.
  • LC-MS differential expression analysis was performed with Progenesis LC-MS software (Version 2.0, Nonlinear Dynamics Ltd.), where three technical replicates were compared.
  • glycopeptide enrichment in the sample was proven by MSMS fragmentation of the detected features.
  • the typical fragmentation pattern of a glycopeptide could be observed, as visualized in Figure 19 for a feature with m/z of 1089.1207 and the charge state of three resulting deconvoluted mass of 3267.3621.
  • the glycan portion of this glycopeptide most likely was the doubie- fucosyiated nonsialylated biantennary N-glycan H5N4F2.
  • PCA Principal component analysis

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Abstract

La présente invention concerne un procédé de caractérisation et/ou d'identification de la composition moléculaire d'une surface cellulaire dans une cellule et son utilisation pour l'analyse de l'état de la cellule. La présente invention concerne également un procédé de formation d'un profil de protéines, peptides, glycanes ou glycopeptides de surface cellulaire et son utilisation pour l'analyse de l'état de la cellule. La présente invention concerne également une plateforme pour l'analyse d'une composition de protéines, peptides, glycanes ou glycopeptides de surface cellulaire dans une cellule et son utilisation pour l'analyse de l'état de la cellule. L'invention concerne également l'utilisation d'une composition de protéines de surface cellulaire en tant qu'outil de manipulation de données glycoprotéomiques.
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