John Hassard, Department of Physics, Imperial College Founder and CTO deltaDOT deltadot

1. RECFALabel-Free Intrinsic Imaging- LFII™ Particle Physics and the Future of BiomedicineLFII™ technology in High Performance Capillary Electrophoresisin life sciences, diagnostics and analytical chemistry John Hassard, Department of Physics, Imperial College Founder and CTO deltaDOT www.deltadot.com j.hassard@deltadot.com May 11th 2007

2. Analyse correlate Particle Physics vs Biomedicine • Funding difficult and • getting more so • Highly evolved • scientific methodology – • cutting edge technology • Really interesting – • seek simplicity

3. 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. Separation and analysis of proteins

4. Resolution and spectral analyses:LFII draws on techniques from other disciplines Use spectral information, excellent instrumentation and time-elapse algorithms Data Information Knowledge astrophysics Particle physics

5. UV Light Source 512 electropherograms Signal UV filter Optics Separation Optics Data Processing (GST & EVA) Detector Multipixel Detection and Vertexing 5 positions (214, 254, 280nm) 1:1 image Standard capillary 512 pixel PDA

6. R Generalised Separation Transform In a 4 tesla field: R a 1/PT Pair-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. 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

7. Generalised Separation Transform (GST) • 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

8. Early use of vertexing to identify small signals

9. Peak finding Equiphase Vertexing Algorithm (EVA) • 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 Construct Equiphase map

10. Sample-run profile(Bacterial cell analysis)

11. 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 sequencing Work by Gary Taylor, Phil Lewis, John Hassard and others

12. Powerful Multipixel Detection Algorithms Raw Data GST EVA

13. Multiple Analyses

14. E.coli Analysis Conventional PAGE Conventional CE deltaDOT Peregrine Relative Standard Deviation Peak Time 0.98% Peak Height 4.56% Overlay of the EVA processed data of nine consecutive E. coli lysate runs all separated under the same conditions.

15. Glycoproteins Relative Standard Deviation Peak Time 0.23% Peak Height 3.03% Ribonuclease B Glycoforms

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

17. Peptide analysis Relative Standard Deviation Peak Time 0.44% Peak Height 2.98% deltaDOT data Peptide Standards Molecular weights (Da): 1.Leucin enkephalin: 5552.Bradykinin fragment: 572 3.Methionine enkephalin: 5734.Bradykinin: 1059 5.Oxytocin: 1009 6.[Arg8]- Vasopressin: 1087 7.Luteinising hormone releasing hormone: 1207 8.Substance P: 1347 9.Bombesin: 1638

18. Bacteria & Viruses Escherichia coli. Baculovirus. Panel A (single pixel) Panel B (512 pixels EVA data) Dilution series on stock 2.9E7 pfu/mL. 1:10 1:20 1:30 1:40 In this initial experiment the peaks show a smallvariation, the peaks correspond to the dilution colour. Good linearity is shown in the initial data set, further experiments are in progress

19. High sensitivity, quantification, dynamic range High Resolution Possibility for multiple simultaneous injections Reduced Cost S/N a L, compared to S/N a L3 for labelled systems Advantages of LFIITM in chips

20. Latest chip DNA data

21. 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) Particle physics inputs

22. 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. Conclusions