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Label-Free Intrinsic Imaging- LFII™ Particle Physics and the Future of Biomedicine

LFII™ technology in High Performance Capillary Electrophoresisin life sciences, diagnostics and analytical chemistry

John Hassard, Department of Physics, Imperial CollegeFounder and CTO deltaDOT www.deltadot.com j.hassard@deltadot.com

May 11th 2007

Particle Physics vs Biomedicine

•Funding difficult and getting more so

•Highly evolved scientific methodology – cutting edge technology

•Really interesting – seek simplicity

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Analysecorrelate

Separation and analysis of proteins

• Conventional techniques (CE, MS, HPLC, 2D SDS PAGE…) separate in one or two usually orthogonal dimensions

• Key performance indicators are sensitivity, resolution, quantification accuracy, throughput, dynamic ranges in more than one parameter (eg Mw, concentration)

• Typically, there is a play-off between key parameters. Eg increase in sensitivity can result in decrease in resolving power (more label needed); increase in throughput can lead to less quantification

• LFII is a new approach which rewrites these trade-offs by using proprietary multipixel approach based on HEP algorithms.

Resolution and spectral analyses:

LFII draws on techniques from other disciplines Use spectral information, excellent instrumentation and time-elapse algorithms

Data

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astrophysics

Particle physics

Multipixel Detection and Vertexing

5 positions(214, 254, 280nm)

1:1 image

Standard capillary

512 pixel PDA

UV Light Source

Optics

UV filter

Optics

Detector

Signal

DataProcessing

(GST & EVA)

Separation

512 electropherograms

Generalised Separation Transform

R 1/PTIn a 4 tesla field:

In z-t space in a separation, a similar transform exists in v-space.This allows us to aggregate millions of images from multiple pixels in an unbiased way – the GST

RPair-wise summation of x-y points in 2D space allows a peak in R-space to be developed in an unbiased way hence to find the track in 3D.

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GST

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• Based on CMSTR – track finder for CMS and work by David Colling and JH : Signal bands move with known velocity function.

• GST takes all pixels and for each time frame and each pixel calculates a velocity which a biomolecule would have to have to reach that pixel at that time.

• This is then histogrammed according to a weight determined by the Beer-Lambert absorption

• This results in an unbiased determination of velocity while retaining all peak shape information

• It requires a ‘virtual vertex’ to be assumed. Equiphase vertexing is a more specific application of GST

• Increases S/N & Retains Peak Shape

Generalised Separation Transform (GST)

Early use of vertexing to identify small signals

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Peak finding

ConstructEquiphase map

• Based on GST, TASSO/Aleph/Babar/D0 vertexing work and work by D. Sideris. The first stage of EVA processing is to perform peak finding on each of the 512 Electropherograms

• The time and amplitude of each peak is determined

• The Equiphase Map is constructed using every identified peak for each pixel

Equiphase Vertexing Algorithm (EVA)

Sample-run profile(Bacterial cell analysis)

The Vertex

Finding the vertex allows us to identify the unlabelled protein or DNA bands effectively increasing the signal to noise

Single vertexes are generally used but multiple vertexing can also be achieved

Detecting multiple vertices allows higher throughput, improved systematics and allows sample injected at different times to be identified e.g Virtual colour in DNA sequencingWork by Gary Taylor, Phil Lewis, John Hassard and others

Powerful Multipixel Detection Algorithms

Raw Data GST

EVA

Multiple Analyses

Overlay of the EVA processed data of nine consecutive E. coli lysate runs all separated under the same conditions.

Conventional PAGE

Conventional CE

Relative Standard DeviationPeak Time 0.98%Peak Height 4.56%

deltaDOT Peregrine

E.coli Analysis

Glycoproteins

Ribonuclease B Glycoforms

Relative Standard DeviationPeak Time 0.23%Peak Height 3.03%

Analysis of Antibody Standard

An overlay of EVA-processed data of 8 consecutive runs of Beckman Control Standard IgG in denaturing condition.

Peptide Standards Molecular weights (Da):

1.Leucin enkephalin: 555 2.Bradykinin fragment: 572 3.Methionine enkephalin: 573 4.Bradykinin: 1059 5.Oxytocin: 1009 6.[Arg8]- Vasopressin: 10877.Luteinising hormone releasing hormone: 1207 8.Substance P: 13479.Bombesin: 1638

deltaDOT data

Relative Standard DeviationPeak Time 0.44%Peak Height 2.98%

Peptide analysis

Bacteria & Viruses

Baculovirus.

Dilution series on stock 2.9E7 pfu/mL.1:10 1:20 1:30 1:40

In this initial experiment the peaks show a small variation, the peaks correspond to the dilution colour.

y = 0.0011x + 1E-05

R2 = 0.956

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Panel A (single pixel) Panel B (512 pixels EVA data)

Escherichia coli.

Good linearity is shown in the initial data set, further experiments are in progress

Advantages of LFIITM in chips

• High sensitivity, quantification, dynamic range

• High Resolution

• Possibility for multiple simultaneous injections

• Reduced Cost

• S/N L, compared to S/N L3 for labelled systems

Latest chip DNA data

Particle physics inputs

• Algorithms• Detectors• Grid• Materials• Techniques (eg Babar ADC approach)• Problem solving approach

• I’d be happy to discuss any ideas you have (john@deltadot.com)

Conclusions

• We have established LFII as the most promising new approach in biotechnology. At the heart of the world’s biggest rapid vaccine development program

• LFII rewrites and reduces the compromises inherent in separation technologies, using a multisensor approach derived from other fields. Analysis power is a combination of several parameters, and LFII optimises this.

• LFII is agnostic as to target, and allows powerful relatively bias-free approaches to analysis

• LFII is based entirely on things particle physicists take for granted

• Particle physics cannot take for granted the continued goodwill and funding from governments without a redoubling of effort to show wealth creation.