1. Micro-Raman Studies of Eye and Lung Tissue Infrared & Raman Discussion Group 20 th December, 2007 Centre for Clinical Raman Microscopy Queen’s University of Belfast C R M C Rene Beattie

2. Centre for Clinical Raman Microscopy C R M C Interdisciplinary group at Queen’s University of Belfast Chemistry Pharmacy Vision Sciences Respiratory Pathology Funding: BBSRC, DHSS R&D office (NI), MRC, ESF Prof Madeleine Ennis and Prof John McGarvey + 6 academic staff, 2 postdocs, 5 PhD, 1 technician Jobin Yvon Horiba LabRamHR 633 + 785 nm (10 +100 mW) Confocal: nominal 1.5  m axial resolution 0.8  m XY resolution

3. Raman Microscopy and Clinical Biochemistry – Why? high information level: chemical physical spatial Water is weak scatterer non contact, focused radiation (UV, Vis, NIR) Raman scattering is non-destructive Raman microscopy is sensitive: <1 pg Resonance enhances specificity and sensitivity

4. Lung Tissue

5. Bronchus Trachea Bronchiole Alveolus Schematic of diagram of the human lung Alveolar Type 2 Fibroblast Alveolar Type 1 Connective tissue Surfactant Capillaries

6. Its biological role: Antioxidant, mops up free radicals Lung’s purpose is oxygen absorption! Homologous family: Chromanol ring Branched acyl tail (‘phytyl’) Vitamin E in the Alveolus R1R1R1R1 R2R2R2R2 R3R3R3R3 O H O CH 3 CH 3 CH 3 CH 3 CH 3

7. Raman Intensity / Arbitrary 750500100012501750300027501500 Raman Shift / cm -1  -tocopherol 3 C H 3 C HOHO CH 3 CH 3 CH 3 CH 3 CH 3 CH 3 CH 3 CH 3CH3  -tocopherol CH3 CH 3 CH 3 OH O CH 3 CH 3 CH 3 CH 3 CH 3CH3 Raman spectroscopy can distinguish tocopherol homologues CH 3CH3 CH 3 CH 3

8. CH 3CH3 CH 3 CH 3 Raman Intensity / Arbitrary 750500100012501750300027501500 Raman Shift / cm -1 Raman spectroscopy can distinguish tocopherol oxidation products OO CH 3 CH 3 CH 3 OH OO CH3 CH 3 CH 3 OH O CH 3 CH 3 CH 3 CH 3 CH 3 CH 3CH3 CH 3 CH 3  - tocopherolquinone  -tocopherol CH 3CH3 CH 3 CH 3

9. CH3 CH 3 CH 3 OH O CH 3 CH 3 OH O CH 3 COOH COOH Raman Intensity / Arbitrary 750500100012501750300027501500 Raman Shift / cm -1 CH3 CH 3 CH 3 OH O CH 3 CH 3 CH 3 CH 3 CH 3 CH 3CH3 CH 3 CH 3 CH3  - carboxyethyl hydrochroman  -tocopherol Raman spectroscopy can distinguish tocopherol metabolic products CH3

10. Quantifying tocopherol in biological matrices Cultured A549 cells Immortalised lung cells Linear  -tocopherol uptake (aT) Supplemented with 0-50 uM aT Physiological range HPLC aT within cells Raman signal of homogenate PLS Regression

11. Intensity of tocopherol signal vs protein or fat is proportional to its relative concentration. R2 = 0.99 = 0.99 0 20 40 60 80 100 020406080100 measured wt % aT predicted wt % aT weight % of aT in PAME 0 5 051015202530 Predicted aT [nmol/mg] R 2 = 0.95 R 2 0 5 10 15 20 2530051015202530 Measured aT [nmol/mg] HPLC measured aT / A549 cells 10 25 50 aT/prot[nmol/mg] 0 10 20 30 0 Supplemented aT /  M A549 cells supplemented with aT

12.   most abundant,  10%  Alveolar Type 2 (AT2) cells  <5% total cells  secrete surfactant  facilitates oxygen absorption  80% dipalmitoylated phosphatidylcholine (16 C, 0 olefin)  Tocopherols secreted in this surfactant mixture Tocopherol distribution in lung tissues

13. Raman spectroscopy can map tocopherol distribution in biological tissues x20 x100 5  m aT, gT aT / PAME aT, Porphyrin, Nuclear Protein Absolute Signal intensity Relative Signal Intensity Mouse Lung, 10  m section

14. Raman microscopy is able to: Distinguish multiple tocopherolic compounds Quantify their concentration Map their distribution in biological tissues Beattie et al. FASEBJ, 2007 (21) 766- 76

15. most common cancer-related death globally. 213,380 new cases of lung cancer in US 2007 Very poor prognosis 5 year survival is only 15% most important factor for survival is the extent of disease. Early diagnosis and treatment is the key aim Lung Cancer

16. Raman spectra of normal and malignant bronchial tissue Normal 6008001000120014001600 Wavenumber (cm-1) Malignant Relative Raman Intensity

17. PCA of Raman spectra of normal and malignant bronchial tissue NormalMalignant sensitivity 85% specificity 60% Principal component 1 scores Principal component 2 scores

18. Random Forests Multiple Decision Tree Models created Each model given 1 vote Sample assigned according to majority vote Independent test set critical Raman Data Training n=70 Test n=40 sensitivity of 90% and specificity of 75%

19. Raman spectra can predict short term recurrence of bronchial cancers. RecurredNon-recurred sensitivity 73% specificity 74% Principal component 1 scores Principal Component 3 scores

20. Raman can predict: Malignant tumours in the Bronchus Recurrence within 1 year of treatment Potential Benefit? Guide treatment regime

21. Ocular Tissue

22. Schematic Human Eye Cornea Lens Iris Retina Choroid Sclera Bruch’s membrane

23. Photo Transduction Photoreceptor layers outer Retinal Pigment Epithelium Waste Removal + xs Light Absorption Schematic retina section Cell Control Photoreceptor cell bodies Signal Amplification Outer Plexiform Layer Cell Control Inner Nuclear Layer Signal Amplification Inner Plexiform Layer Cell Control Ganglion Cell Layer Signal Routing Nerve Fibre Layer Light Layered Structure defined by regions within cells Outer Layers Inner Layers Energy Productioninner 1 Cell deep 1 cell deep

24. Comparison of schematic retina section with a 1  m spacing Raman map of a section of retina Retinal Pigment Epithelium Photoreceptor layers Photoreceptor cell bodies Outer Plexiform Layer Inner Nuclear Layer Inner Plexiform Layer Ganglion Cell Layer Nerve Fibre Layer Protein DNA Fatty Acid Cytochrome c Heme Light

25. Retinitis Pigmentosa (RP) Heterogeneous group of hereditary eye diseases Progressive vision loss Death of the light-sensitive cells in the retina. night blindness narrowing of the visual field narrowed retinal blood vessels retinal pigmentation

26. Transgenic model of RP Porcine knock-in model Overproduction of mutated rhodopsin Recorded Raman from outer layers Analysed with PCA Variation in Raman signal

27. -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0.6 -0.4-0.3-0.2-0.100.10.20.3 0 45  m Key POSONLPIS Principal Component Analysis of the Raman spectra of photoreceptor outer segment of normal and Transgenic pig retina OuterInner Normal Transgenic Normal tissue: progressive change with depth RP tissue: no systematic variation with depth Distinct separation

28. Detailed histochemical images Probe layered structure of normal retina Probe abnormalities due to RP Raman microscopy of retina Beattie et al. Mol Vis 2007 (13) 1106- 13 2005 (11) 825- 32 2005 (11) 825- 32

29. Advanced Macular Degeneration leading cause of blindness: 500,000 people in the UK Age-related process usually observable from 50 years Age-related process usually observable from 50 years of age 2 types: dry AMD (90%) and wet AMD (10%). Dry AMD means visual cells simply stop functioning Wet AMD is caused by blood leaking into the photoreceptors

30. Bruch’s Membrane, AGEs and Dry AMD BM: Pentalaminar collagen struture 2-6  m thick Separates photoreceptors and choroid Support + Filter AGEs: reaction of metabolic intermediates BM not turned over: AGEs builds up over time Cross links cause stiffening, degrade function Compromised conditions: photoreceptor death

31. 8001 0001 2001 4001 600 Raman Intensity / arbitrary Raman Shift / cm -1 Collagen Heme AGEs Some spectra encountered in the Bruch’s Membrane

32. MDA PENTOSIDINE FRUCTOSYL- LYSINE Raman shift (cm -1 ) 500100015002000250030003500 MG-H2 GOLD HNE G-H1 METHIONINE SULFOXIDE Relative Raman Intensity / Arbitrary Raman spectra of selected AGEs

33. Raman study of human bruch’s membrane Obtained human donor eyecups ½ BM/choroid used for chromatography HPLC: Pentosidine GCMS: CML/CEL Raman microscopy Confocal (BM 4  m thick) PLS regression

34. Measured concentration Predicted concentration 0 1 2 3 4 5 0 123456 CML R 2 val = 0.91 CEL R 2 val = 0.75 Raman shift / cm -1 8001000120014001600 Raman Intensity / arbitrary pentosidine CML Ln (measured pentosidine) Ln (predicted pentosidine) -4 -2 0 2 4 -4-202 4 Pentosdine R 2 val = 0.91 Raman signal from donor eyecups correlates with HPLC measurements of selected AGEs

35. Summary of BM / AGE Currently AGEs analysis: Destructive Highly specific Multiple adducts difficult Raman spectroscopy: Non-destructive Broad range of AGE detected Multiple adducts possible Collagen, fat, heme and amino acids Glenn et al. FASEBJ, 2007 (21) 3542- 52

36. Raman microscopy is an effective probe of biochemical composition in biological tissues. DetectingIdentifyingDistinguishingQuantifyingMapping Vitamin E and related compounds Lung cancer Retinal pathologies Advance Glycation Endproducts It can provide information on many constituents at once within a given system (in our studies >30 is not uncommon). In addition to analytical applications, Raman spectroscopy has a bright future in basic biochemical investigations Summary

37. Acknowledgements Prof John McGarvey Prof Madeleine Ennis Prof Stuart Elborn Prof Alan Stitt Prof Peter Hamilton Dr Vicky Kett Dr Bettina Schock Dr Jim Curry Dr Ania Pawlak Dr Josie Glenn Dr Simon Brockbank Dr Joe Quinn Dr Lindsay Barrett Mr Ciaran Maguire Dr Nick Magee Dr Sorcha Finnegan Mr Ahcene Taleb Dr Liam McAuley Collaborators Dr Fransesco Galli (tocopherol metabolites) Prof Vincent Munier (AGEs) Prof Jenny Ames (AGEs) Prof Mike Boulton (Bruch’s Membranes) Prof Bob Petters (Porcine RP model retinas) Dr Steven Bell (Raman substrates) Centre for Clinical Raman Microscopy

38. 5  m Porphyrin, aTQ 5  m aT, aTQ aT, aCEHCQ Raman spectroscopy can map tocopherol oxidation and metabolic product distribution in biological tissues x20 x100 Absolute Signal intensity Mouse Lung, 10  m section

39. p< (Gender)High Group p< (Age group) High Group 0.001Male0.05Over 60 0.05Male0.05Over 60 0.0001Female0.05Over 60 n.a. 0.05Over 60 0.00001Female0.00001Over 60 Comparing Raman-predicted and Chromatography- measured AGE accumulation Pentosidine (Raman) CML (Raman) Overall Raman Signal Pentosidine (HPLC) CML (GCMS)