Secondary Ion Mass Spectrometry

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Advanced processing of 1D, 2D and 3D imaging Secondary Ion Mass Spectrometry data.

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Secondary Ion Mass Spectrometry

  1. 1. SECONDARY IONMASS SPECTROMETRYDATA ANALYSIS IN 1D, 2D AND 3DALEX HENDERSON Surface Analysis Research Centre University of Manchester, UK
  2. 2. SIMS technique Fire energetic ions at a solid – primary ions Solid emits (sputters) positive ions negative ions electrons and neutrals These secondary ions produce a mass spectrum Secondary Ion Mass Spectrometry
  3. 3. Comparison with other MS No (chromatographic) separation step so spectra are overlapping Limited amount of sample available Relatively low throughput so no large databases of spectra Highly surface sensitive so detects contamination easily (pro and con) Need to transport analyte to gas phase; proteins decompose, polymers generally OK MS/MS being developed (here) Negative ion spectra available
  4. 4. 1D analysisCollections of spectra
  5. 5. O H 2C O C C 15H 31 O Typical SIMS spectrum HC O C C 15H 31 O + H 2C O P O CH2 CH2 N(CH 3) 3 OH 1,2-Dipalm itoyl-sn-glycero-3-phosphatidylcholine (DPPC) (positive ion) 50,000 45,000 CH3 40,000 CH2 CH N + CH3 O CH3 + 35,000 CH3 HO P O CH2 CH2 N CH3 OH CH3 30,000Intensity 25,000 20,000 15,000 10,000 5,000 0 0 20 40 60 80 100 120 140 160 180 200 m/z The Static SIMS Library, SurfaceSpectra Ltd  100 – 250k data points  1 – 3000 u mass range www.surfacespectra.com
  6. 6. Spectra of bacteria Enterococcus spp. Proteus mirabilis 4 4 x 10 En x 10 Pm 6 5 4.5 5 4 3.5 4 3 3 2.5 2 2 1.5 1 1 0.5 0 0 0 100 200 300 400 500 600 700 800 0 100 200 300 400 500 600 700 800 m/z m/z Applied Surface Science 252 (2006) 6719-6722
  7. 7. Spectra of bacteria Enterococcus spp. Proteus mirabilis En Pm 8000 2500 7000 2000 6000 5000 1500 4000 1000 3000 2000 500 1000 0 0 100 150 200 250 300 350 400 450 500 100 150 200 250 300 350 400 450 500 m/z m/z Applied Surface Science 252 (2006) 6719-6722
  8. 8. PCA of bacteria5 species of bacteriaNo a prioriknowledge usedin PCAPositive ion spectra1-800 u, rebinned to1 u stepsSquare root ofintensitySum-normalised Applied Surface Science 252 (2006) 6719-6722
  9. 9. Canonical Variates Analysis(Discriminant Function Analysis) Principal components are pure (orthogonal) aspects of the data Canonical variates are combinations of PCs that best describe an a priori class structure Based on Fisher’s Linear Discriminant, subtly different from Linear Discriminant Analysis (LDA): no assumption of normally distributed classes CVA is also referred to as DFA
  10. 10. Canonical Variates Analysis(Discriminant Function Analysis) Maximise ratio of between-group variance to within- group variance
  11. 11. PC-CVA bacteria classification5 species of bacteriaClass structure used9 PCs selected usingPRESS testCross-validationindicates percentcorrectly classified: Cf 75% CC Ec 92% CC En 100% CC Kp 25% CC Pm 50% CC Applied Surface Science 252 (2006) 6869-6874
  12. 12. 2D analysisSpatially organised collections of spectraHyperspectral images
  13. 13. Imaging SIMS Focussed ion beam stepped from point to point 2D data generated Full mass spectrum at each point/pixel Beam diameter down to ~1-2 µm (50 nm possible, but can damage sample)
  14. 14. SIMS image - ‘chemical photograph’ 4500 4000 3500 3000intensity 2500 2000 1500 1000 500 0 0 100 200 300 400 500 600 m/z Total ion spectrum Total ion image Bead diameter 50 μm Data courtesy of Penn State University, USA
  15. 15. Selected ion images 4500 4000 3500 3000intensity 2500 2000 1500 1000 500 0 0 100 200 300 400 500 600 m/z Data courtesy of Penn State University, USA
  16. 16. Maximum Autocorrelation Factor Analysis(MAF) Incorporates spatial information with spectral information Determine the correlation with the same image offset diagonally by 1 pixel Autocorrelation means scaling invariant Computation time ~4× PCA Possible issue when image feature size ≈ pixel size
  17. 17. MAF versus PCA of HeLa cells a b ca) Total ion image, scaled and smoothedb) Score on PC 6, scaled and smoothedc) Score on MAF 4, scaled and smoothedMAF captures the distinction between inner and outer cellular regionsmore clearly than PCA. Comparison with the total ion image shows theadditional information available following multivariate analysis. Surface and Interface Analysis 41 (2009) 666-674
  18. 18. MAF versus PCA of HeLa cells Loading on PC 6 Loading on MAF 4 MAF loadings more ‘informative’ Surface and Interface Analysis 41 (2009) 666-674
  19. 19. High mass resolution requiredNominally all at m/z 86 Lipid (DPPC) m/z = 86.0969692 Unknown Silicon substrate [Si3H2]+ m/z = 85.9464332Separation = 0.15 u m/z 86 Analytical chemistry 80 (2008) 9058-9064
  20. 20. Rat kidney tissue  Cryo-microtomed slices  Sequential layers  Unwashed  Washed in methanol  Data is  10x10 tiles  32x32 pixels per tile  24000 channels per pixel  2.4 billion datapoints  19.2 GB (as doubles in MATLAB) Almost all zeros, so use sparse matricies to handle the data
  21. 21. Summation of all tissue pixels
  22. 22. Clear changes in chemical distribution over small mass range Individual images from nominal mass m/z 197 Images generated at 0.05 u increments from 196.75 to 197.30 Images are 9 x 9 mm2 comprising 10 x 10 tiles of 32 x 32 pixelsJohn S Fletcher Ionoptika J105 April 2009 Analytical and Bioanalytical Chemistry 396 (2010) 85-104
  23. 23. Rat kidney section - overlay320 x 320 pixels9 x 9 mm Full mass spectrum available at each pixel Lipid (DPPC) DAG Salt related Analytical and Bioanalytical Chemistry 396 (2010) 85-104
  24. 24. SIMS and AFM AFM SIMS (total ion) Identify points of reference between (Human cheek cells) images Register images and overlay AFM following‘projective’ transform and cropping in MATLAB Applied Surface Science 255 (2008) 1264–1270
  25. 25. SIMS and AFM nm µm µm Use AFM z-height to offset SIMS data Use AFM x- and y-position to accurately scale SIMS x- and y-positions Applied Surface Science 255 (2008) 1264–1270
  26. 26. 3D analysisThree-dimensional spatially organised collectionsof spectra‘Hyper-’hyperspectral images
  27. 27. HeLa-M cells Immortalised cervical cancer cell line commonly used as a model system for biology Cultured on poly-L-lysine coated substrates AFM after freeze drying: 30 – 50 µm diameter 0.6 – 1µm thick
  28. 28. Formalin fixed, freeze-dried HeLa cells Lipid (DPPC) increasing ion dose Around the nucleus 184 152 Inside the nucleus  1st layer: evidence of chemistry having spread outside cell area  3rd layer: spectra show chemistry still mixed up between nucleus and membrane  4th layer: one cell exhibits bimodal nucleusRapid Commun. Mass Spectrom. 25 (2011) 925-932 indicating the dividing stage of cell cycle – mitosis
  29. 29. 3D visualisation – issues Problem with point of view of ‘observer’ Mass analyser is normal to sample Different sample heights appear to be at same level Data size:  256 x 256 pixels x 10 layers x 11400 channels  7.5 billion datapoints = 58 GB  >99% of data is zero so only ~5 GB in RAM
  30. 30. 3D visualisation – our approach PCA of whole dataset (sparse, MATLAB) Identify PC that separates substrate from organic overlayer Assume substrate is flat Slide columns of pixels to align with crossover in score of that PC Discard substrate-rich pixels Repeat PCA to identify peaks or components Render results (AVS Express) Rapid Commun. Mass Spectrom. 25 (2011) 925-932
  31. 31. Two sample preparation methodsAim is to maintain the biological integrity of cells in vacuum system Formalin fixed, freeze-dried cells Frozen hydrated cells  Hela-M cells cultured on poly-  Hela-M cells cultured on one L-lysine coated silicon wafer plate of hinged two-plate steel  Formalin fixed (4%) substrate coated in poly-L-lysine  Cells are washed with  Cells are washed with ammonium ammonium formate (0.15 M) formate (0.15M) to remove salts prior to analysis to remove  Cells trapped between plates salts and rapidly frozen in liquid  Freeze-dried in vacuum propane  Sandwich fractured in vacuum at 160 K to reveal cells Rapid Commun. Mass Spectrom. 25 (2011) 925-932
  32. 32. Frozen hydrated HeLa cells Lipid (DPPC headgroup) @ m/z 184.1 Nucleic acid (adenine) @ m/z 136.1 Rapid Commun. Mass Spectrom. 25 (2011) 925-932
  33. 33. Formalin fixed, freeze-dried HeLa cells Lipid (DPPC) @ m/z 184.1 Principal component indicatingRapid Commun. Mass Spectrom. 25 (2011) 925-932 guanine & tryptophan
  34. 34. Movies available online256 x 256 pix  Frozen hydrated HeLa cellsx 10 layers  http://www.youtube.com/watch?v=sPuMYUQPvHQx 11400 chans  Visualisation of the membrane (green, m/z 184.1, phosphocholine) and nucleus (red, m/z 136.1, adenine)7.5 billion pts  Formalin fixed, freeze-dried HeLa cells= 58 GB  http://www.youtube.com/watch?v=2phMkYo7yhg  Visualisation of the membrane (green, m/z 184.1,>99% of data phosphocholine) and nucleus (purple, negative scoresis zero so only on principal component 5)~5 GB in RAM  Possible mitosis visible at lower right
  35. 35. Summary What SIMS lacks in ultimate species identification it makes up for in spatial location New ionisation sources produce data nearer to ‘traditional’ MS, opening up resources Tandem MS approaches are breaking through
  36. 36.  SARC SARC  Validation algorithms  John Vickerman John Vickerman  Roy Goodacre Nick Lockyer  Nick Lockyer John Fletcher  Yun Xu  John Fletcher Sadia Sheraz (nee Rabbani)  Elon Correa ... Sadia Rabbani (now Sheraz)  and the rest!  Visualisation group in  ... and the rest! Research Computer ServicesAcknowledgements www.sarc.manchester.ac.uk
  37. 37. www.sarc.manchester.ac.ukalex.henderson@manchester.ac.uk

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