Rapid Evaporative Ionization Mass Spectrometry Imaging Platform for Direct Mapping from Bulk Tissue and Bacterial Growth Media

Transcription

1 Supporting Information for Rapid Evaporative Ionization Mass Spectrometry Imaging Platform for Direct Mapping from Bulk Tissue and Bacterial Growth Media Ottmar Golf, 1 Nicole Strittmatter, 1 Tamas Karancsi, 2 Steven D. Pringle, 3 Abigail V. M. Speller, 1 Anna Mroz, 1 James M. Kinross, 1 Nima Abbassi-Ghadi 1, Emrys A. Jones, 1,3,* and Zoltan Takats 1,* 1 Section of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, SW7 2AZ London, United Kingdom 2 Waters Research Centre, HU 1033 Budapest, Hungary 3 Waters Corporation, SK9 4AX Wilmslow, United Kingdom * Corresponding authors Zoltan Takats: z.takats@imperial.ac.uk, Tel: +44 (0) Emrys A. Jones: emrys.jones@imperial.ac.uk, Tel: +44 (0) Contents 1 Table of Figures...S-2 2 Experimental Section...S DESI Analysis...S Data Processing...S-3 3 Supporting Graphical Information...S-5 4 References...S-16 S-1

2 1 Table of Figures Figure S-1. Workflow of combined DESI and REIMS imaging platform analysis for co-registration of histological features between optical image, DESI and REIMS data....s-5 Figure S-2. Heated coil interface used on the Waters Xevo G2-S instrument for improved sensitivity and robustness towards contamination....s-6 Figure S-3. Impact on carbonization, burning-valley and crater size for various cutting speeds (A) and time the electrosurgical tip remains inside the sample (B). REIMS imaging in cutting mode at low spatial resolution evaporates the top surface layer of the sample (C)....S-7 Figure S-4. Total ion counts at different frequencies and voltages in cutting mode. Red: 1 standard deviation, purple: 1.96 standard deviations (95% confidence interval)....s-9 Figure S-5. Total ion count (TIC) and concordance correlation coefficient (CCC) dependencies in pointing mode. Red: 1 standard deviation, purple: 1.96 standard deviations (95% confidence interval)..s-9 Figure S-6. Changes in mass spectral patterns of porcine liver obtained in cutting mode for low and high voltages compared to an iknife reference spectrum. TIC = total ion count....s-10 Figure S-7. Principal component analysis plots of healthy and cancerous liver tissues for REIMS imaging cutting and pointing mode as well as for DESI data. PC: Principal component, percentage values are explained variance....s-11 Figure S-8. Univariate intensity comparison of single phospholipid ion species. Depicted images of samples are RGB ion-images of the respective ions. DESI and REIMS show similar relative intensity values for the same ions. PE: phosphatidyl-ethanolamine....s-12 Figure S-9. Example mass spectrum from P. aeruginosa bacterium together with most prominent metabolite classes for distinct mass ranges....s-13 Figure S-10. Principal component analysis plot of three different bacterial strains together with agar medium. PC: Principal component, percentage values are explained variance....s-14 Figure S-11. Mean mass spectra and mean phospholipid-class intensity levels for each lipid species. Mean intensities of phospholipid classes are stable across the species, with highest level for PA class and lowest level for PG class. PA: phosphatidic acid, PE: phosphatidyl-ethanolamine, PG: phosphatidyl-glycerol. n(p. aeruginosa) = 48, n(b. subtilis) = 45, n(s. aureus) = S-15 S-2

3 2 Experimental Section 2.1 DESI Analysis DESI analysis of tissues was carried out on a Thermo Exactive mass spectrometer (Thermo Fisher Scientific Inc., Germany) in negative ion mode. DESI imaging pixel size was set to 100 m, electrospray solvent was methanol:water (95:5 vol/vol) at a solvent flow rate of 1.5 L/min and zero-grade nitrogen nebulizing gas pressure of 4 bar. Following DESI analysis, tissue sections were stained with H&E and digitally scanned (Nano-Zoomer 2.0-HT, Hamamatsu, Japan) to create optical images for comparison with the MS images. Line scan mode (cutting mode) REIMS analysis of one liver metastasis sample was performed on a Thermo Orbitrap Discovery (Thermo Fisher Scientific Inc., Germany) and spot sampling (pointing mode) analysis of another liver metastasis sample and a microorganism culture was performed on a Waters Xevo G2-S Q-TOF instrument (Waters Micromass, UK) in negative ion mode. The Waters Xevo G2-S was equipped with a modified atmospheric interface combining an orthogonal Venturi-pump for aerosol transfer and a heated capillary inlet (Supporting Information Figure S-2). 2.2 Data Processing Raw spectral profiles were loaded into Matlab environment (Version R2014a, Mathworks, USA) for preprocessing, MS-image visualization and pattern recognition analysis. All mass spectra were linearly interpolated to a common interval of 0.1 Da and individually normalized to the total ion count (TIC) of each mass spectrum. These data were used for univariate comparison of intensity levels across liver tissue types and ionization techniques and for bacterial MS-image visualization of single ions. Peak annotation for liver metastasis samples is based on m/z accuracy obtained from the unprocessed raw files, while bacterial peak annotation is based on mass accuracy and on tandem-ms spectra obtained by the bipolar forceps approach. Multivariate MS-image visualization was performed on mass spectra additionally binned to 1 Da intervals in the mass range of m/z Da for biological tissue and m/z for bacteria. For multivariate image visualization, MS-images and optical images were coregistered to define regions of interest (ROIs) for building a supervised training model. Defined ROIs (classes) were healthy and cancerous tissue for the liver samples and one region for each bacterium plus agar, resulting in 2 classes for liver samples and 4 classes for bacterial samples. The training model was used to classify each pixel of the same sample and colour code the obtained score-values into red-green-blue colour scale. This S-3

4 supervised strategy for image visualization is based on an algorithm that combines recursive maximum margin criterion (RMMC) with linear discriminant analysis (LDA) and is described in detail elsewhere. 1 For unsupervised analysis, principal component analysis (PCA) was performed on the mass spectra defined by the ROIs. Concordance correlation coefficients were used to measure the agreement between REIMS imaging platform (RIP) mass spectra and iknife mass spectra. This quantitative measure is defined as ρ c = 2ρσ RIP σ iknife 2 2 σ RIP + σ iknife + (μ RIP μ iknife ) 2 where ρ c is the concordance correlation coefficient 2 and ρ is Pearson s correlation coefficient. σ RIP/iKnife is the standard deviation of the mean intensity values of μ RIP/iKnife. A low concordance correlation coefficient close to the value of zero indicates low agreement, while a value close to the value of one suggests high similarity between spectral profiles. Boxplots show the median at the central mark within the box with 25 th and 75 th percentiles at the edges of the box. The upper and lower whiskers account for approximately 2.7 standard deviations (99.3% data coverage). Mass spectra were standardized to 100% intensity scale before their data was visualized with boxplots. S-4

5 3 Supporting Graphical Information Figure S-1. Workflow of combined DESI and REIMS imaging platform analysis for coregistration of histological features between optical image, DESI and REIMS data. S-5

6 Figure S-2. Heated coil interface used on the Waters Xevo G2-S instrument for improved sensitivity and robustness towards contamination. S-6

7 Figure S-3. Impact on carbonization, burning-valley and crater size for various cutting speeds (A) and time the electrosurgical tip remains inside the sample (B). REIMS imaging in cutting mode at low spatial resolution evaporates the top surface layer of the sample (C). S-7

8 Table S-1. Theoretical sampling time and resolution for 2x2 cm sample. Cutting mode sampling at 1 mm/s cutting speed and 25 s per row, which includes return time to a new row. Pointing mode sampling at 3 s per pixel. Pointing Mode Cutting Mode Pixel Size No. of Pixels Time / min No. of Rows MS scan time / s Time / min 2 mm mm m m S-8

9 Parameter Dependance Figure S-4. Total ion counts at different frequencies and voltages in cutting mode. Red: 1 standard deviation, purple: 1.96 standard deviations (95% confidence interval). Figure S-5. Total ion count (TIC) and concordance correlation coefficient (CCC) dependencies in pointing mode. Red: 1 standard deviation, purple: 1.96 standard deviations (95% confidence interval). S-9

10 Figure S-6. Changes in mass spectral patterns of porcine liver obtained in cutting mode for low and high voltages compared to an iknife reference spectrum. TIC = total ion count. S-10

11 Figure S-7. Principal component analysis plots of healthy and cancerous liver tissues for REIMS imaging cutting and pointing mode as well as for DESI data. PC: Principal component, percentage values are explained variance. S-11

12 Figure S-8. Univariate intensity comparison of single phospholipid ion species. Depicted images of samples are RGB ion-images of the respective ions. DESI and REIMS show similar relative intensity values for the same ions. PE: phosphatidylethanolamine. S-12

13 Figure S-9. Example mass spectrum from P. aeruginosa bacterium together with most prominent metabolite classes for distinct mass ranges. S-13

14 Figure S-10. Principal component analysis plot of three different bacterial strains together with agar medium. PC: Principal component, percentage values are explained variance. S-14

15 Figure S-11. Mean mass spectra and mean phospholipid-class intensity levels for each lipid species. Mean intensities of phospholipid classes are stable across the species, with highest level for PA class and lowest level for PG class. PA: phosphatidic acid, PE: phosphatidyl-ethanolamine, PG: phosphatidyl-glycerol. n(p. aeruginosa) = 48, n(b. subtilis) = 45, n(s. aureus) = 52. S-15

16 4 References (1) Veselkov, K. A.; Mirnezami, R.; Strittmatter, N.; Goldin, R. D.; Kinross, J.; Speller, A. V. M.; Abramov, T.; Jones, E. A.; Darzi, A.; Holmes, E.; Nicholson, J. K.; Takats, Z. Proc. Natl. Acad. Sci. U. S. A. 2014, 111, (2) Lin, L. I. Biometrics 1989, 45, S-16