molecular pathology with desorption electrospray ... · molecular pathology with desorption...
TRANSCRIPT
©2015 Waters Corporation 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 2
Definition: Seek to describe and understand the origins and mechanisms of disease at the molecular level, largely using patient samples
©2015 Waters Corporation 4
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
Normal
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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
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Goals
Use additional chemical information obtained from sections of
patient tissue and models of disease to further understand the
mechanisms of disease
Develop a chemical information-based tissue identification
system, which is functionally equivalent with the current,
morphology-based tissue ID systems
Work presented and described here is for research purposes,
not for clinical diagnosis
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Solvent
N2
V
Nebulizer capillary
Spray capillary
Gas jet
Spray
Surface
Sample Desorbed ions
Inlet capillary -towards mass spectrometer
HV power supply
Solid sample
Takats et al. Science, 2004
Desorption Electrospray Ionization (DESI)
-Ambient analysis technique -Easily compatible with current histopathological workflows -Lipid and endogenous metabolites -Pharmaceuticals and their metabolites
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Association of metabolomic DESI MSI profiles of normal
glandular and tumor tissue with hormone receptor status.
Sabine Guenther et al. Cancer Res 2015;75:1828-1837
©2015 by American Association for Cancer Research
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Synapt G2Si Xevo G2 XS
Prosolia 2D source
DESI on Waters instruments
Emmanuelle Claude Presentation Tuesday DESI and ion mobility
©2015 Waters Corporation 12
Requirements for Molecular Path.
Robustness
Automation- ease of use
Depth of information
Data processing and visualization
Well populated database of high quality data for a large range of tissue types
©2015 Waters Corporation 13
Requirements for Molecular Path.
Robustness
Automation- ease of use
Depth of information
Data processing and visualisation
©2015 Waters Corporation 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
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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 N2 (shares a supply with the instrument no need for stand alone bottle)
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35mm x 25mm 150µm pixel
30µm pixel
m/z 719.5
Single pixel spectrum
Large area mapping – 12 hours
Individual colony imaging
Three ion overlay: m/z 464.3 m/z 450.3 m/z 144.0
Stability, signal, spatial resolution
Data collected with: Cunyu Yan, University of Manchester – see poster 547 Wednesday
E- coli colonies on filter paper
©2015 Waters Corporation 18
Robustness
Automation- ease of use
Depth of Information
Data processing and visualisation
©2015 Waters Corporation 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 1.3.5 for experiment
definition
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Take scan or photo of slide
Place slide(s) onto DESI stage – no sample preparation
Define slide corners on image in HDI software
Select region(s) of interest & define experimental
conditions
Acquire
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Slot A
Slot B
Treated samples
Untreated samples
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Data Acquisition
Processing
Ready to be viewed
Automated single study
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3 sections untreated 3 sections treated Looking for lipidomic differences Regions of interest drawn on all six samples
treated
untreated
PC2
PC3
S1
S2
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S1
S2
S3
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Robustness
Automation- ease of use
Depth of Information
Data processing and visualisation
©2015 Waters Corporation 24
DESI: repeat sampling
Negative ion mode
Initial
30 seconds
DESI and MALDI Mark Towers Poster Wednesday
Under correct conditions, molecular signal from a single spot on a tissue section has a slow decay rate.
Because the surface has not been treated (e.g. with a matrix) it can be resampled
©2015 Waters Corporation 25
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
DESI +ve then DESI -ve
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Successive analysis at different pixel size: ‘Survey’ scan then focus on region of interest at higher resolution
Strength of DESI imaging: Different spatial resolution DESI imaging
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Multiple MS/MS DESI imaging experiments
MS/MS DESI imaging m/z 147.1 551.6
MS/MS DESI imaging 621.5
MS DESI imaging m/z 804.6 PC (36:4)Na+
MS DESI imaging m/z 754.53 PC (32:1)Na+
1. MS Positive mode 2. MS/MS m/z 890.5 Negative mode 3. MS/MS m/z 754.5 Positive mode 4. MS/MS m/z 804.6 Positive mode 5. MS/MS m/z 890.5 Negative mode
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Morphology and chemical- test case
Select a relevant sample for
testing tissue differentiation by
DESI-MS
Tumour infiltrating connective
tissue (CRC), less than 1cm²
Investigate speed of analysis
and spatial resolution
Data can be compared with data
collected independently on same
tissue (open source data)
Two tissue types: tumour and connective tissue
Colorectal adenocarcinoma
~ 1cm
~ 1cm
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DESI imaging followed by Non negative matrix factorisation (5 factors requested, noise outputs not shown)
Unsupervised tissue differentiation
-ve ion mode
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Speed of analysis
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
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150 x 150µm pixel size: move stage at 750µm/s, less y steps ~ 10 minutes
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Comparison between systems/ laboratories
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DESI analysis at Imperial for Gigascience 3D paper
imzML data taken from journal website
Section from same block obtained and analysed at Wilmslow
‘Analyte’ processed data file
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Data binned, combined and normalised then subjected to unsupervised factorisation m/z 600-1000 Da
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Background signal ICL Background signal Wilmslow
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Pixel size
100 µm
20 x 20 µm m/z 844.5 m/z 744.5
5mm
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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 37
m/z 876.68 m/z 786.53
derm
is
epid
erm
is
Identities of lipid species to be confirmed by MSMS
©2015 Waters Corporation 41
Moving forwards
Data handling and visualization
Co-registration and data extraction
Database building and architecture
Cross platform conversion e.g. REIMS, MALDI
New application areas
Multi-site, multi system comparisons
Experiments and collaborations
Cover as many tissue types as possible
Increased sample capacity, automation
Source and technique development
Histologist directed profiling saving time
Speed of analysis, processing reporting
QC and QA checks
Lockmass and normalization approaches
Fixation and embedding
Analytical considerations
©2015 Waters Corporation 42
Anna Mroz Nicole Strittmatter Jocelyn Tillner Abigail Speller Nima Abbassi-Ghadi Zoltan Takats
Cunyu Yan Perdita Barran Alex Kendall Anna Nicolaou Fiona Henderson Adam McMahon Kaye Williams
Mark Towers Emmanuelle Claude Steve Pringle Steve O’Brian
©2015 Waters Corporation 44
Top tip
If you’re going to mark where the tumour is in red ink, do it on the reverse of the slide…
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m/z 140.1 m/z 254.3 m/z 768.6 m/z 421.5
Grown on agar on glass slide
No sample preparation Analyzed at atmospheric conditions
Microbiology DESI Imaging
RGB overlay
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Good data
Unknown
Classified
Database
Large cohorts of patient samples
Histologically annotated datasets
Unclassified Misclassified
Inconsistent data Poor data
©2015 Waters Corporation 50
20 x 20µm pixel
100 micron
20 micron
CRC tumour lipid profiling
Connective tissue
Cancerous regions
©2015 Waters Corporation 51
Fingerprint DESI imaging
Contact m/z 405.2
From pores? m/z 358.2
m/z 430.2
Overlay in HDI
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Trypsin digested proteins from FFPE tissue
Access to patient outcome data Very large sample numbers Extensive sample preparation
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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 59
DESI and MALDI for tissue analysis
Principal Component 1 (47.7 %) Pri
ncip
al Com
ponent
2 (
18.0
%)
m/z
DESI
MALDI
Average Spectra
Norm
alised I
nte
nsity
600 700 800 900
DESI
MALDI
Complimentary techniques for the greatest
information depth
Emmanuelle Claude – Oral Presentation Monday Mark Towers – Poster – Wednesday