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Hood / Galas Labs retreat Technology and Quantitative Blood Signatures

Hamilton, Montana June 2009. Hood / Galas Labs retreat Technology and Quantitative Blood Signatures. “There's Plenty of Room at the Bottom”. Fifty Years Ago. Lunik 1 passes the moon Passenger jets fly trans-Atlantic First integrated circuit FDA hearings on The Pill

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Hood / Galas Labs retreat Technology and Quantitative Blood Signatures

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  1. Hamilton, Montana June 2009 Hood / Galas Labs retreatTechnology and Quantitative Blood Signatures

  2. “There's Plenty of Room at the Bottom” Fifty Years Ago • Lunik 1 passes the moon • Passenger jets fly trans-Atlantic • First integrated circuit • FDA hearings on The Pill • Miles Davis' “Kind of Blue” • François Truffaut's “400 Blows” • Richard Feynman's“There's Plenty of Room at the Bottom”

  3. Plenty of Room at the Bottom • Great opportunities in science and technology at nanoscale • Can we write Encyclopedia Britannica on the head of a pin? • Biology demonstrates packaging of data at extremely high density(DNA uses 50 atoms/bit)‏ Source: Technion institute (Haifa)‏

  4. The Great Questions of Biology (ca. 1959) • What is the sequence of the genome? • How is DNA sequence connected to protein sequence? • What is the structure of RNA? Single or double chain? • How are proteins synthesized? • Where does the RNA go?

  5. Put the atoms where the chemist says • Scanning Tunneling Microscopy (STM) • Binnig & Rohrer, 1981 • Atomic resolution on conductive surfaces(XY 0.1nm, Z 0.01nm)‏ • Atomic Force Microscopy (AFM)‏ • Binnig, Quate, & Gerber, 1986 • Molecular resolution of biological materials Xe atoms on Ni crystal, imaged, and rearranged.

  6. True 3D imaging at the nanoscale Credit: Degen C. L. et.al. PNAS 2009;106:1313-1317

  7. Beyond Feynmann • Challenges manipulating whole systems at nanoscale • Molecular Self Assembly • Monolayers • “DNA origami” Atomic force microscope image of a smiley face nanostructure created from DNA using the "scaffolded DNA origami" technique. Credit: Paul Rothemund.

  8. Integrated Blood Barcode Chips • Use DNA encoded antibodies to self assemble microarrays in a microchip • Use microfluidics to automate blood preparation and ELISA analysis • Keep volumes tiny Fan et al., 2008

  9. Single-molecule protein counting with Nanostring • Another use of DNA encoding • Single-molecule detection eliminates need for amplification • 900-plex measurement (e.g. 30 tissues * 30 tissue-specific proteins)‏

  10. Small-scale technology and quantitative blood signatures • Issues • Limited sample size(and limited potential for amplification)‏ • High accuracy required • Risk of data overload • Implementation • Sample collection • Sample analysis • Data storage and interpretation

  11. Sample Collection & Storage

  12. 30.00 25.00 20.00 RNA Level (Log 2) 15.00 10.00 120 Hs_miR-499_1 Hs_miR-493*_1 Hs_miR-141_1 5.00 Hs_miR-802_2 Hs_miR-125b-1*_1 Hs_miR-221*_1 100 0.00 80 RNU 1A RNA 6B 60 Troponin level 40 20 0 sample 1 sample 2 sample 3 sample 4 sample 5 sample 6 Troponin level Biomarker Discovery • Biomarker candidates • Transcriptomic based list • microRNA • Protein • Disease associated pathways/networks • miRNA-mRNA interaction • miRNA based biomarker • A combination of miRNA and protein

  13. Variations Caused by Technology • microRNA • Inter and intra platform correlation • Array – low • qPCR • Inter platform – low • Intra platform – moderate • Direct sequencing

  14. Variations Caused by Sample Preparation and Handling Collection methods and time may affect the composition of biomolecules in the sample Frozen vs. chemical preservations Plasma vs. serum First urine vs. regular urine Biological samples are not stable Shipping and storage Different extraction methods contribute significant variations Amount of RNA microRNA levels Amount of protein

  15. Variations Caused by Sample Preparation and Handling Amount and quality of RNA

  16. Variations Caused by Sample Preparation and Handling Affect of different anti-coagulation agents Human plasma sample using Heparin as anticoagulant

  17. Variations Caused by Sample Preparation and Handling Serum preparation

  18. Variations Caused by Sample Preparation and Handling • Urine – freezer-induced urinary sediments Uromodulin Albumin Alpha-1-microglobulin

  19. Sample Fractionation & Quantification

  20. MSTIQ (MS2-based ion quantification) • ITA (index-ion triggered analysis) MRM, iMSTIQ, SPR, Nanostring, … Benchtop mass spec, ELISA, Western blot, … Validate proteins of interest (targeted proteomics) Develop diagnostic tools Technology platforms for protein biomarker discovery Screen for biomarkers (Shotgun proteomics) SILAC, iTRAQ, ICAT, label-free, …

  21. 114 115 116 117 430.2232 50 45 40 35 390.2767 30 25 117.1119 Relativ Intensity 291.2103 20 529.2993 15 230.0952 258.0769 10 602.3571 145.1110 505.2930 711.3664 173.1279 5 0 200 300 400 500 600 700 800 900 1000 1100 m/z MS-based quantitative proteomics using stable isotopes MS1 CID MS2 114.0 116.0 118.0 SILAC, ICAT, 16O/18O, … iTRAQ, TMT, … Can we use these MS2 fragment ions for quantification?

  22. MSTIQ tag R K (0) ( 0 ) +4 +4 or +4 (+4) ( +8) 0 0 0 4 Da 4 Da MSTIQ: MS2-based ion quantification Peptides Incorporated via SILAC, 16O/18O, or peptide synthesis, ... NHS H Light Heavy NHS L MS2 b-ion y-ion

  23. A strategy to reproducibly detect low abundant peptides Index-ion triggered analysis (ITA) (3) Detection of an index ion during MS1 triggers CID on ions that are  m/z away from the index ion, independent of their own MS1 intensities. Intensity (2) Spike in an index peptide at a level that can be reproducibly detected during MS1 (i.e. 100 fmol)  m/z m/z (1) Targeted peptides ions may be of low abundance Only isotopically different

  24. Index peptide: H L Isobaric MSTIQ peptides: “HH” “HL” “LH” Intensity  m/z m/z ITA + MSTIQ = iMSTIQ (indexed MS2-based ion quantification) H CID

  25. CID mass range “contaminant” peptides targeted peptides MS1 Spectra of peptide “FAISYQEK” (m/z=568.2973) Index peptide: (m/z = 572.3044)

  26. MS2 spectra of the peptide “FAISYQEK” y5 y6 y7 y3 b1 b2 b3 y4

  27. Log [ratio (HL/LH)] Log [fmol of injected HL] ≥1 fmol peptides in human glyco-serum were quantified by iMSTIQ

  28. Specific features of the iMSTIQ • iMSTIQ quantifies at the MS2 level, and therefore benefits from the MS1 mass filter providing high selectivity and S/N. (in comparison with SILAC, ICAT, 16O/18O...) • Full MS2 spectra are acquired and multiple fragments are used for quantification, allowing confident identification and quantification with statistical validation. (in comparison with iTRAQ, TMT…) • Application of the index ions allows reproducible detection of low abundant peptides. • Unlike MRM/SRM, no pre-determination and optimization of transitions (m/z and retention time) are needed. • Taking advantage of the high throughput of the existing tandem mass spectrometry platforms (i.e. LTQ-Orbitrap), up to 2000 peptides can be monitored per run.

  29. Goal: to develop medical instruments to measure protein levels in blood, based on microfluidics and mass spectrometry technologies Customized MS focusing on targeted proteins Microfluid-based processing: depletion, enrichment, fractionation, digestion…

  30. Wide Dynamic Range 10 9 8 7 6 5 4 3 2 Key: to find your target at the right scale 1

  31. Wide dynamic range of protein concentrations in serum MW=20 kD  mg/mL 50 nmol/ml MS1  ug/mL 50 pmol/ml iMSTIQ MRM  ng/mL 50 fmol/ml Nanostring?  pg/mL 50 amol/ml N.L. Anderson et al.; The human plasma proteome; MCP; 2002

  32. Data Processing& Biology Larger Data Sets & Greater Computing Power: What can we Expect?

  33. Computational Biology:Scaling Up • Larger data sets  more computing • Experiments continue to yield more data and generate it faster • How will we manage the following? • Data Storage • Parallel Computation at Scale • Analytically Complex Computation • (all increasingly important)

  34. Questions of Scale • Storage: • Sequencers, Microarray, Transcriptomes, Personalized Genome, Mass Spec • Storage getting cheaper, denser • How much denser could things get? • Are we satisfied for now? • Sharing data • Are we satisfied with how we move large amounts of data?

  35. Questions of Scale • Parallel Computation at Scale • Relatively simple computations applied independently to lots of data points • Image processing, Base calling, Mass spectrum analysis, Database operations • Data and computing power need to be co-located • Complex Analysis • Expensive computations, harder to parallelize • Sequence alignments, network inference, interaction studies (multiple genes, proteins)

  36. Old Picture: Failure of Moore’s Law

  37. Old Picture: Parallelism • Parallelism seen as difficult • Still is; languages poor. • Mostly vector parallelism • Long in use for large-scale simulations • Weather, earthquakes, ocean currents, bombs, structures, oil & gas • Cray, CUDA • Mostly shared-memory machines

  38. Problems with Old Questions • Increasing transistor density is not the same as increasing speed • Lately, we’re seeing multiple cores • Per-transistor price rises with density • Initial cost and power • Some simple but interesting problems already swamped one machine. • Servers handling many thousands of clients.

  39. Parallelism Happens • Forget Moore’s Law: Even as it has remained in effect, many have ignored it and moved to parallel computation “early”. • Forget vector & shared-memory parallelism: Lots of interesting problems are mostly embarrassingly parallel. • Real leaps of progress here • Still, some tasks not easily parallelized • Complexity remains with combinatorial explosion: • network inference, gene/protein interactions

  40. Parallelism Happens • People have stopped caring that there’s a faster processor out there • Better to be concerned with price per computation performed rather than processor speed • Provided the computation can be done in reasonable time in parallel. • Embarrassingly parallel does not mean trivial

  41. Problems of Scale • Component failures • Great achievement to expect and tolerate component failures • Disks • Whole machines • Much cheaper to build failure-management than to buy expensive “reliable” hardware • Reliable hardware still fails. With large-enough systems, these are noticeable.

  42. Consequences • Think about bigger problems, provided expensive parts are parallelizable. • Consider rentable tools and platforms • e.g. Amazon, cloud computing

  43. Conclusions • Plan for parallelism going forward • What are the right platforms for us? • But with caution: • Computational complexity still critical • Parallelism won’t help combinatorial explosion • Network inference, Gene/protein interaction problems, clustering problems, WGAS • Algorithms are critical here

  44. More Discussion…

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