Molecular pathology with desorption electrospray ionization (DESI) - where we are and where we're going

Transcription

1 Molecular pathology with desorption electrospray ionization (DESI) - where we are and where we're going Dr. Emrys Jones Waters Users Meeting ASMS 2015 May 30th 2015 Waters Corporation 1

2 Definition: Seek to describe and understand the origins and mechanisms of disease at the molecular level, largely using patient samples 2015 Waters Corporation 2

3 Which is kidney, which is stomach? Gastric pits Glomeruli 2015 Waters Corporation 3

4 Normal The distinction between CC/UC is made on the basis of clinical, radiologic, endoscopic, and pathologic interpretations but cannot be differentiated in up to 15% of inflammatory bowel disease patients 2015 Waters Corporation 4

5 Imaging (or profiling) Mass Spectrometry Collect mass spectral data information directly from tissue section Raster across whole tissue Imaging Sample preselected regions Histology-directed profiling Many techniques currently in use including but not limited to: MALDI, DESI, SIMS, LAESI, PALDI, LDI, Laserspray 2015 Waters Corporation 5

6 Goals Develop a chemical information-based tissue identification system, which is functionally equivalent with the current, morphology-based tissue ID systems Use additional chemical information obtained from sections of patient tissue and models of disease to further understand the mechanisms of disease Work presented and described here is for research purposes, not for clinical diagnosis 2015 Waters Corporation 6

7 Desorption Electrospray Ionization (DESI) -Ambient analysis technique Solvent HV power supply V -Easily compatible with current histopathological workflows -Lipid and endogenous metabolites -Pharmaceuticals and their metabolites N 2 Spray capillary Nebulizer capillary Gas jet Sample Desorbed ions Inlet capillary -towards mass spectrometer Spray Surface Solid sample Takats et al. Science, Waters Corporation 7

8 Versatility of DESI-MS [M+H] + Nicotine lozenge 2015 Waters Corporation 8

9 Build upon DESI at Imperial College 2015 Waters Corporation 9

10 Association of metabolomic DESI MSI profiles of normal glandular and tumor tissue with hormone receptor status by American Association for Cancer Research Sabine Guenther et al. Cancer Res 2015;75: Waters Corporation 10

11 DESI on Waters instruments Prosolia 2D source Synapt G2Si Xevo G2 XS Emmanuelle Claude Presentation Tuesday DESI and ion mobility 2015 Waters Corporation 11

12 Requirements for Molecular Path. Robustness Automation- ease of use Depth of information Well populated database of high quality data for a large range of tissue types Data processing and visualization 2015 Waters Corporation 12

13 Requirements for Molecular Path. Robustness Automation- ease of use Depth of information Data processing and visualisation 2015 Waters Corporation 13

14 Continuing perceived issue with DESI-MS is sprayer to sprayer variability and the unique optimisation required in each case The new sprayer accommodates Tapertip emitters Standardised optimal parameters provide a good starting point 20μm 360μm 2015 Waters Corporation 14

15 Nebulisation Gas Inlet HV Connection Electrospray Solvent Typically operates at: 1.5µL/min solvent (MeOH 95%:5% H2O) ~11 hours running from 1mL syringe 5 bar N 2 (shares a supply with the instrument no need for stand alone bottle) 2015 Waters Corporation 15

16 Tip Positioning 0.5~1mm 0.4 mm Ф 2015 Waters Corporation 16

17 Stability, signal, spatial resolution 30µm pixel m/z E- coli colonies on filter paper Individual colony imaging Three ion overlay: m/z m/z m/z Single pixel spectrum 35mm x 25mm 150µm pixel Large area mapping 12 hours Data collected with: Cunyu Yan, University of Manchester see poster 547 Wednesday 2015 Waters Corporation 17

18 Robustness Automation- ease of use Depth of Information Data processing and visualisation 2015 Waters Corporation 18

19 An advantage of DESI is the minimal sample preparation Samples are taken directly from -80 C freezer and placed onto atmospheric sampling stage Low solvent flow rates ensure minimal damage to the tissue allowing subsequent staining for accurate co-registration of morphology and chemical images New version of MassLynx and HDI for experiment definition 2015 Waters Corporation 19

20 Take scan or photo of slide Acquire Place slide(s) onto DESI stage no sample preparation Select region(s) of interest & define experimental conditions Define slide corners on image in HDI software 2015 Waters Corporation 20

21 Automated single study Data Acquisition Slot A Treated samples Slot B Processing Untreated samples Ready to be viewed 2015 Waters Corporation 21

22 PC3 S1 S2 treated S3 S1 S2 untreated S3 PC2 3 sections untreated 3 sections treated Looking for lipidomic differences Regions of interest drawn on all six samples 2015 Waters Corporation 22

23 Robustness Automation- ease of use Depth of Information Data processing and visualisation 2015 Waters Corporation 23

24 DESI: repeat sampling Initial Under correct conditions, molecular signal from a single spot on a tissue section has a slow decay rate. 30 seconds Negative ion mode Because the surface has not been treated (e.g. with a matrix) it can be resampled DESI and MALDI Mark Towers Poster Wednesday 2015 Waters Corporation 24

25 DESI +ve then DESI -ve Positive ion mode analysis Negative ion mode analysis H&E staining an optical scanning for co-registration Concatenate positive and negative spectra for each co-registered pixel 2015 Waters Corporation 25

26 Strength of DESI imaging: Different spatial resolution DESI imaging Successive analysis at different pixel size: Survey scan then focus on region of interest at higher resolution 2015 Waters Corporation 26

27 Multiple MS/MS DESI imaging experiments MS DESI imaging m/z PC (32:1)Na + MS/MS DESI imaging m/z MS Positive mode 2. MS/MS m/z Negative mode 3. MS/MS m/z Positive mode 4. MS/MS m/z Positive mode 5. MS/MS m/z Negative mode MS DESI imaging m/z PC (36:4)Na + MS/MS DESI imaging Waters Corporation 27

28 Morphology and chemical- test case Select a relevant sample for testing tissue differentiation by DESI-MS Colorectal adenocarcinoma Two tissue types: tumour and connective tissue Tumour infiltrating connective tissue (CRC), less than 1cm² ~ 1cm Investigate speed of analysis and spatial resolution ~ 1cm Data can be compared with data collected independently on same tissue (open source data) 2015 Waters Corporation 28

29 Unsupervised tissue differentiation DESI imaging followed by Non negative matrix factorisation (5 factors requested, noise outputs not shown) ve ion mode ve ion mode Waters Corporation

30 15 10 Speed of analysis 5 Stage speed 100µm / second Mass spec scan 1 scan per second Pixel dimension 100 x 100µm Acquisition time 105 minutes Stage speed 500µm / second Mass spec scan 5 scan per second Pixel dimension 100 x 100µm Acquisition time 23 minutes x 150µm pixel size: move stage at 750µm/s, less y steps ~ 10 minutes 2015 Waters Corporation 30

31 Lipid signal variance how stable is the fingerprint? tumour 2015 Waters Corporation 31

32 Comparison between systems/ laboratories y x DESI analysis at Imperial for Gigascience 3D paper y x Section from same block obtained and analysed at Wilmslow imzml data taken from journal website Analyte processed data file Data binned, combined and normalised then subjected to unsupervised factorisation m/z Da Waters Corporation 32

33 x x x x Tumour tissue Background signal ICL 0 x x x 10-3 x Connective tissue Background signal Wilmslow 2015 Waters Corporation 33 x 10-4 x x 10-3 x x 10-4 x

34 Pixel size m/z m/z x 20 µm x 100µm 100 µm mm Waters Corporation 34

35 Spatial resolution Spatial resolution at this scale allows applications where previous system would not provide sufficient clarity of data No sample preparation therefore no concerns about migration of compounds e.g. during matrix deposition Experimental times become large at these pixel sizes Ideal situation for the multiple analysis approach 2015 Waters Corporation 35

36 dermis epidermis Mouse skin at 20 x 20µm pixel size TIC TIC image 2015 Waters Corporation 36

37 dermis epidermis m/z m/z Identities of lipid species to be confirmed by MSMS 2015 Waters Corporation 37

38 Tumour bearing zebrafish for PET tracer study WT T2 T Waters Corporation 38

39 Tumour specific lipid signals Low molecular weight signals 2015 Waters Corporation 39

40 40 micron imaging of tumour region only m/z m/z Waters Corporation 40

41 Moving forwards Data handling and visualization Co-registration and data extraction Cross platform conversion e.g. REIMS, MALDI Database building and architecture Experiments and collaborations Cover as many tissue types as possible Multi-site, multi system comparisons New application areas Source and technique development Increased sample capacity, automation Speed of analysis, processing reporting Histologist directed profiling saving time Analytical considerations Fixation and embedding Lockmass and normalization approaches QC and QA checks 2015 Waters Corporation 41

42 Anna Mroz Nicole Strittmatter Jocelyn Tillner Abigail Speller Nima Abbassi-Ghadi Zoltan Takats Mark Towers Emmanuelle Claude Steve Pringle Steve O Brian Cunyu Yan Perdita Barran Alex Kendall Anna Nicolaou Fiona Henderson Adam McMahon Kaye Williams 2015 Waters Corporation 42

43 Microbiology DESI Imaging 150 micron 12hours acquisition RGB overlay 2015 Waters Corporation 43

44 Top tip If you re going to mark where the tumour is in red ink, do it on the reverse of the slide 2015 Waters Corporation 44

45 PET tracer? 2015 Waters Corporation 45

46 Intact proteins Axel Walch 2015 Waters Corporation 46

47 Microbiology DESI Imaging No sample preparation Analyzed at atmospheric conditions Grown on agar on glass slide RGB overlay m/z m/z m/z m/z Waters Corporation 47

48 Unknown Histologically annotated datasets Inconsistent Poor Good data data Large cohorts of patient samples Database Misclassified Unclassified Classified 2015 Waters Corporation 48

49 2015 Waters Corporation 49

50 CRC tumour lipid profiling Cancerous regions Connective tissue 100 micron 20 micron 20 x 20µm pixel 2015 Waters Corporation 50

51 Fingerprint DESI imaging m/z Contact m/z From pores? m/z Overlay in HDI 2015 Waters Corporation 51

52 O HO S F CH Waters Corporation 52

53 Trypsin digested proteins from FFPE tissue Access to patient outcome data Very large sample numbers Extensive sample preparation 2015 Waters Corporation 53

54 Tissue Imaging Optimised DESI Probe 2015 Waters Corporation 54

55 0.5~1mm 0.4 mm Ф 2015 Waters Corporation 55

56 Typical Sprayer Problems Ideal Poorly cut or non central emitter Unstable capillary Due to Neb gas flow Poor Reproducibility Long Setup time for optimum performance 2015 Waters Corporation 56

57 2015 Waters Corporation 57

58 2015 Waters Corporation 58

59 Normalised Intensity Principal Component 2 (18.0 %) DESI and MALDI for tissue analysis DESI MALDI DESI Average Spectra MALDI m/z Principal Component 1 (47.7 %) Complimentary techniques for the greatest information depth Emmanuelle Claude Oral Presentation Monday Mark Towers Poster Wednesday 2015 Waters Corporation 59