The document discusses using mass spectrometry to analyze body fluids like urine, saliva, and exhaled breath condensate for medical diagnostics and biomarker discovery. It describes creating databases of accurate mass tags and retention times from mass spec analyses of peptides and proteins in body fluids to enable fast identification. Biomarkers found for diseases like COPD, pneumonia and changes after lung transplantation are mentioned.
Evgeny nikolaev proteomics of body liquids as a source for potential methods for medical diagnostics and mass spectrometry
1. Proteomics of body liquids as a source for potential methods for medical diagnostics and mass spectrometry Prof. Dr. Evgeny Nikolaev Institute for Energy Problems of Chemical Physics and Institute for biochemical physics Rus. Acad. Sci., Moscow, Russia.
2. Modern biological mass spectrometers are mainly ESI- TOF Measuring time of ion flights in vacuum MALDI-TOF Orbitraps Measuring frequencies of ion oscillations Ion traps Measuring ion motion stability parameters FT ICR Measuring frequencies of ion oscillations in magnetic field
5. API Ion source Linear Ion Trap C-Trap Orbitrap differential pumping differential pumping The Thermo Scientific* LTQ Orbitrap XL* hybrid FTMS Alexander Makarov Electrostatic axially harmonic orbital trapping: a high-performance technique of mass analysis. Anal. Chem. 2000; 72: 1156.
6. The main goal of our research is to connect the level of protein expression with diseases or to find disease biomarkers. Our Project:
7. Protein enzym Mass analyses fragmentation Isolated peptide Masses of peptide fragments Search in database scoring Protein and DND sequence database Mass analyses High throughput proteome analyses by tandem mass spectrometry methods Bottom-up method
8. Protein энзим анализ масс 1 Массы фрагментов пептидов Поиск в базе T ор- down method - direct mass spectrometry of proteins and peptides Ion transportation Mass analyses Masses of peptide fragments Search in database scoring Protein and DND sequence database fragmentation
9. KETAAAKFERQYL K ETAAAKFERQYL KE TAAAKFERQYL KET AAAKFERQYL KETA AAKFERQYL KETAA AKFERQYL KETAAA KFERQYL KETAAAK FERQYL KETAAAKF ERQYL KETAAAKFE RQYL KETAAAKFER QYL KETAAAKFERQ YL KETAAAKFERQY L Sequencing by MS/MS For unambiguous sequencing all peptide bonds should be broken
10. … -CHR – C(O) – NH – CHR’-… Polypeptide backbone fragmentation b y c z a x Collisionally Activated Dissociation (CAD) Electron Capture Dissociation (ECD) 1960s, 1990s 1998 Electron Detachment Dissociation (EDD) Electron Transfer Dissociation (ETD) 2004 2004 Infrared Multiphoton Dissociation (IRMPD) 1960s, 1995 157 nm UV Photodissociation Metastable-atom Induced Dissociation (MAID) 2004 2005
16. Ion cyclotron resonance mass spectrometer can measure masses with sub ppm accuracy Linear ion trap IR laser Electron gun Magnet
17. Other mass spectrometers with high accuracy of mass measurements are available now Orbitraps Q-TOFs …… . Mass accuracy 1-2 ppm ( intern. calib .), 5 ppm ( extern . calib. ) Resolution 2 0 000 -60 000 FWHM Rate of mass spectra measurements >20 Hz BRUKER micrOTOF-QII
18. At accuracy level of 1 ppm elementary composition of peptide with mass up to 600 Da and amino acid composition of peptide with mass up to 5 00 Da could be determined almost unambiguously It is not enough for peptide identification!
19. . If we are using liquid chromatography (LC) or Capillary electrophoreses (CE) we have another tag - LC retention time or CE retention time Accurate mass tag together with retention time Can identify peptide practically unambiguously! Accurate mass tag retention time Dick Smith group (PNNL)
21. Thus, there is a possibility in bottom-up approach to proteomics is to create using MS/MS a database for accurate mass tags and retention times as a reference base for fast quantitative measurements of proteins and peptides concentration in a sample
22. VGLQR YVQLR SLR Validated accurate mass tag ( SLTLGIEPVSPTSLR ) ... T GLYCESQTPR SLTLGIEPVSPTSLR VGLQRYVQLRSLR ... … T GLYCESQTPR SLTLGIEPVSPTSLR trypsinolyses Fragment (463-477) from Vasorin identification validation Vasorin (Homo Sapiens protein) 450 500 550 600 650 m/z 522.5 525.0 m/z LC- FTICR Accurate measured mass: 1568.8768 Putative mass tag from Homo Sapiens : SLTLGIEPVSPTSLR Calculated mass (1568.8773) And measured retention time 200 600 1,000 1,400 1,800 m/z y9 y8 b10 y7 b9 b8 y6 y12 y10 y11 b12 b6 y5 b7 b11 y13 b14 b13 y4 LC-MS/MS (e.g. with ion trap)
23. FT ICR I.Boldin, E.Nikolaev ASMS May 2010 Dynamicaly harmonized FT ICR cell Pressure limited (practically unlimited mass resolution)
25. BSA, 0.3mg/ml, 100scans accumulated, accumulation time in collision cell 50ms (7 Tesla) M Hn+
26. 22s R = 1.3*10 6 BSA (65 kD) high resolution mode on 7 Tesla magnet R = 0.9*10 6
27. Nb 3 Sn Coils NbTi Coils 21 Tesla FT-ICR Magnet Field Center to Flange 600 - 1100 mm 110 mm Bore Current Leads, Cryocooler, and Quench relief for Zero-Loss 2.2 ° K Cryostat D. Markiewicz, NHMFL T. Painter, NHMFL J. Miller, NHMFL Y. S. Choi, KBSI Slide from Alan Marshall
28. FT MS ESI Q-TOF ESI TOF Lab Lab Clinic Accurate mass tag retention time approach
29. The most attractive is human plasma, which contains practically all proteins (around 20000 non modified forms) Human Proteome Detection and Quantitation Project:hPDQ N. Leigh Anderson, Norman G. Anderson, Terry W. Pearson, Christoph H.Borchers, Amanda G. Paulovich, Scott D. Patterson, Michael Gillette, Ruedi Aebersold and Steven A. Carr Mol Cell Proteomics Jan.2009
30. Proteins in blood N. Leigh Anderson‡ and Norman G. Andersn Protein concentrations are different by 1 1 orders of magnitude!!! There is no method to solve this analytical problem !
31. The main task is searching for protein biomarker of early stages of diseases
32. Alzheimer’s disease is a progressive brain disorder of elderly people that gradually destroys a person’s memory and ability to learn, reason, make judgments, communicate and carry out daily activities. Alois Alzheimer (1864-1915) 1906 - 2006 Alzheimer disease
33. tangles Plaques A β – Amyloid A 1-42, Beta-amyloid peptide The main component of Alzheimer’s plaques (1984) Sequenced in 1987 1 DAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVVIA 42 Anomalous accumulation of Beta-amyloid in the form of polymeric aggregates (plaques, tangles) causes Alzheimer
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35. Pro 19 Substitution by proline Abolishes fibril formation Met 35 (O) Oxidation may be Important for toxicity and/or oligomerization Asp 7 Isomerized by 75% In plaques Essential residues for self-association Primary structure elements controlling A β oligomerization
36. The goal is to develop mass spectrometric methodology to distinguish peptides containing different isomeric forms of individual amino acids and to apply this methodology to fragments of Alzheimer disease Beta-amyloid
37. f ECD of 1-16 А β z10 z10 z9 z9 Z10 -57 (C α -C β bond destruction) c9 c9 y9 C A β 1-16 (isoAsp 7 ) A β 1-16 (Asp 7 ) Distinguishing aspartate/iso-aspartate in A β – Amyloid by ECD
49. Monitoring of exhaled protein composition after human lung transplantation Before surgery ( artificial lung ventilation ) 1 st month after surgery 15 months after surgery Pure protein spectrum because of disturbance of breath Dermcidin , Keratin 9 , Lysozyme , Ubiquitin Allograft adoptation and medical treatment Annexin 1 , Proteinases inhibitor, Bleomicine - hydrolase, keratin 8 Damaged epithelium removal Desmosomal proteins ( desmoglein , desmoplakin ) Epithelium healing Hornerin , filaggrin “ Normal” proteins Dermcidin , “normal” keratins , Cystatin A , Ubiquitin
50. Analyses of urine proteom Sick Healthy Urine is available in large quantities – ideal analyte for noninvasive diagnostic . Possibility of biomarker discovery is attracting big attention . 1500 proteins (from Mann’s group Adachi et al. Genome Biology 2006, V7, 9, R80) ; 2,362 proteins (Kentsis , A. et al. Proteomics Clinical Applications 2009, 3, (9), 1052-1061).
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52. Before use some proteins as biomarker we need to know its temporal variability and polymorphism (how different is its concentration in body liquids of different individuals) To clarify this we need to investigate proteomes of hundreds of healthy individuals
53. Two kinds of sample donors People “from street” (blood donation center) and people in “special conditions”.
54. For “people from street” Decision to include a person to the study group Current control for urogenital and other pathology including kidney pathology, prostatitis, arterial hypertension, diabetes Analysis of archival information from medical records General blood analysis Examination of internist Blood pressure measurement Control for treatment with diuretics and excessive consumption of fluids
55. For “healthy people data base” subset we need urine samples from persons under well controlled diet and having healthy lifestyle? In this case we can test urine temporal variability and polymorphism
56. Those are people p articipating In long term isolation experiments in the frame of space research programs. April- July 2009. March 2010 + 500 days. (The Institute for medical & biological problems RAS)
60. Sample concentration Amicon Ultra Ultracel-15 3 k Desalting and major protein removal Urine collection Centrifugation LC MS analyses Carboxymethylation and trypsinolyses
63. Our statistics of the collected AMT tags in the long term isolation experiment 447 LC-MS (liquid chromatography coupled with mass spectrometry) runs totally: among them 25 samples from each of 6 volunteers have been collected during105 days of isolation experiment. The number of peptides in the database 3468 The number of urine proteins in the database 1055 443 core proteins (all patients have them in their urine)
64. Current statistics of urinary proteome database for ordinary healthy people Smokers (41 sample) and non-smokers(46 samples) Peptides Proteins Total 2758 840
65. Current statistics of urinary proteome database 233 LC-MS (liquid chromatography coupled with mass spectrometry) runs totally: 102 with samples from smokers, 131 with samples from non-smokers. Using all peptides Peptides Proteins Non-smokers 2527 762 Smokers 1893 627 Total 2758 840
66. Influence of life stile on urine proteome Smokers vs. non-smokers urine proteome
67. 40% 35% Using all peptides Peptides Proteins Non-smokers 2527 762 Smokers 1893 627 Total 2758 840 Peptides Proteins 78 549 213 231 1662 865
68. 20% 21% Using all peptides Peptides Proteins Odd 2232 445 Even 2306 467 Non-smokers 2535 506 Peptides Proteins 61 406 49 303 2003 229
70. ! ! ! ! ! ! Differences in the numbers of observed proteins participating in particular biological process in urine of smokers and nonsmokers Transport, homophilic cell adhesion, lipid metabolic process, inflammatory response, innate immune response, epidermis development, defense response !
71. This type of proteome analyses should be personalized !!
72. Quantitative analyses by 18 O labeling 25 Individual non-labeled samples Pool of labeled Pool of non- labeled 25 Individual labeled samples MS 25 25
74. A List of Candidate Cancer Biomarkers for Targeted Proteomics Malu Polanski and N. Leigh Anderson Biomark Insights. 2006; 1: 1–48. The Plasma Proteome Institute list of 1261 proteins believed to be differentially expressed in human cancer As an initial approach, we have selected a subset of the candidates based on a set of criteria including number of total citations, number of recent citations, proportion of recent citations, known plasma concentration (implying existence of an assay) and clinical use in any context. This subset of 260 candidates 88 are detected in urine (Mann’s database) 75 (our database)
76. Molecular & Cellular Proteomics 9:2424–2437, 2010. Prof. Harald Mischak Mosaiques Diagnostics GmbH, Mellendorfer Strasse 7–9, 30625 Hannover, Germany.
77. ROC curves for classification of patient cohorts with “CKD pattern.” ROC analysis for CKD diagnosis of the training set and the test set after unblinding is shown. 85.5% sensitivity and 100% specificity Peptide (800 to 17,000 Da) patterns distinguishing patients with CKD from HC 230 patients 379 healthy
78. Samples from 3,600 individuals analyzed by capillary electrophoresis coupled to MS. All processed data were deposited in an Structured Query Language (SQL) database. This database currently contains 5,010 relevant unique urinary peptides that serve as a pool of potential classifiers for diagnosis and monitoring of various diseases.
79. HPLC-MS run duration is about 1.5-2 hours UPLC-MS duration is about 10-15 minutes We need faster technology!!
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81. Mobility Drift time Thermal diffusion-limited maximum resolution Temporal spread ION MOBILITY SPECTROMETRY (IMS) T k density N Ze K b av _ 2 16 3 K E L t drift 2 ln 16 T k LEZe R b d
82. ADDITIONAL ANALYTICAL PEAK CAPACITY DUE TO IMS Only 3 features discerned without drift time dimension ( * )
83.
Editor's Notes
Investigation of EBC of seventeen healthy non-smoking donors between 20 and 36 years of age revealed that the major proteins are cell keratins, whose spectrum, however, is polymorphous for different people. Pairs of cytoskeletal keratins 1/10 and 2/9 are invariant for mostly probes. No mutations in the sequences of these proteins in healthy donors have been detected. At the same time, other keratins are substantially different for individual sample.
Apart from keratins, dermcidin (known as a protein antibiotic originating in the sweat glands), prostaglandin H2 D-isomerase (PGDS2), alpha-1-microglobulin/bikunin precursor (AMBP), ubiquitin and cystatin A occurred also frequently ( 30 % of donors). In the same time, some proteins appeared only in single instance. There were immunoglobulin light chain region, human basement membrane heparan sulfate proteoglycan core protein (HSPG2), leukocyte-associated immunoglobulin-like receptor 1 isoform a precursor (LAIR1), lysosomal membrane glycoprotein-2 (LAMP2), cerebroside sulfate activator (CSA), kininogen 1, serum albumin.
We found keratins in most of the samples of patients. These keratins were identified as “normal”, because they were detected in EBC of healthy donors. Additionally, specific peptides of keratins 3, 4, 8 were identified in COPD samples. These keratins were not found in healthy samples; therefore, they were named “abnormal”. It is worth noting that the keratin set identified in samples from patients with acute pneumonia was more varied. Keratins 4/13, 7/19, 8/18 and 15 were also identified in those samples. Peptides of certain other proteins uncharacteristic of healthy EBC samples were discovered in COPD and pneumonia EBC samples: namely, Junction plakoglobin, Desmoplakin, Dermokine, alpha-2-glycoprotein 1, Alpha-1-acid glycoprotein 2, Filaggrin-2, Dynein, Lysozyme, Collagen alpha-1(XVIII), Hornerin.
Результаты анализа белкового состава конденсата выдыхаемого воздуха согласуются с результатами клинического наблюдения пациента, перенесшего трансплантацию легких, и, таким образом, характеризуют его состояние.
