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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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Hamilton montana june 2009

Hamilton, Montana

June 2009

Hood / Galas Labs retreatTechnology and Quantitative Blood Signatures


Fifty years ago

“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”


Plenty of room at the bottom
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)‏


The great questions of biology ca 1959
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?


Put the atoms where the chemist says
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.


True 3d imaging at the nanoscale
True 3D imaging at the nanoscale

Credit: Degen C. L. et.al. PNAS 2009;106:1313-1317


Beyond feynmann
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.


Integrated blood barcode chips
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


Single molecule protein counting with nanostring
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)‏


Small scale technology and quantitative blood signatures
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



Biomarker discovery

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


Variations caused by technology
Variations Caused by Technology

  • microRNA

    • Inter and intra platform correlation

    • Array – low

    • qPCR

      • Inter platform – low

      • Intra platform – moderate

    • Direct sequencing


Variations caused by sample preparation and handling
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



Variations caused by sample preparation and handling2
Variations Caused by Sample Preparation and Handling

Affect of different anti-coagulation agents

Human plasma sample using

Heparin as anticoagulant



Variations caused by sample preparation and handling4
Variations Caused by Sample Preparation and Handling

  • Urine – freezer-induced urinary sediments

Uromodulin

Albumin

Alpha-1-microglobulin


Sample fractionation quantification
Sample Fractionation & Quantification


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, …


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?


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


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


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


CID mass range

“contaminant” peptides

targeted peptides

MS1 Spectra of peptide “FAISYQEK” (m/z=568.2973)

Index peptide: (m/z = 572.3044)



Log [ratio (HL/LH)]

Log [fmol of injected HL]

≥1 fmol peptides in human glyco-serum were quantified by iMSTIQ


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.


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…


Wide Dynamic Range levels in blood, based on microfluidics and mass spectrometry technologies

10

9

8

7

6

5

4

3

2

Key: to find your target at the right scale

1


Wide dynamic range of protein concentrations in serum levels in blood, based on microfluidics and mass spectrometry technologies

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


Data processing biology

Data Processing levels in blood, based on microfluidics and mass spectrometry technologies& Biology

Larger Data Sets &

Greater Computing Power:

What can we Expect?


Computational biology scaling up
Computational Biology: levels in blood, based on microfluidics and mass spectrometry technologiesScaling 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)


Questions of scale
Questions of Scale levels in blood, based on microfluidics and mass spectrometry technologies

  • 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?


Questions of scale1
Questions of Scale levels in blood, based on microfluidics and mass spectrometry technologies

  • 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)


Old picture failure of moore s law
Old Picture: Failure of Moore’s Law levels in blood, based on microfluidics and mass spectrometry technologies


Old picture parallelism
Old Picture: Parallelism levels in blood, based on microfluidics and mass spectrometry technologies

  • 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


Problems with old questions
Problems with Old Questions levels in blood, based on microfluidics and mass spectrometry technologies

  • 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.


Parallelism happens
Parallelism Happens levels in blood, based on microfluidics and mass spectrometry technologies

  • 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


Parallelism happens1
Parallelism Happens levels in blood, based on microfluidics and mass spectrometry technologies

  • 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


Problems of scale
Problems of Scale levels in blood, based on microfluidics and mass spectrometry technologies

  • 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.


Consequences
Consequences levels in blood, based on microfluidics and mass spectrometry technologies

  • Think about bigger problems, provided expensive parts are parallelizable.

  • Consider rentable tools and platforms

    • e.g. Amazon, cloud computing


Conclusions
Conclusions levels in blood, based on microfluidics and mass spectrometry technologies

  • 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


More discussion
More Discussion… levels in blood, based on microfluidics and mass spectrometry technologies


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