Molecular pathology with desorption electrospray ...

©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


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


Which is kidney, which is stomach?

Gastric pits

Glomeruli


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


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


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


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


Versatility of DESI-MS

[M+H]+

Nicotine lozenge


Build upon DESI at Imperial College


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


Synapt G2Si Xevo G2 XS

Prosolia 2D source

DESI on Waters instruments

Emmanuelle Claude Presentation Tuesday DESI and ion mobility


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


Requirements for Molecular Path.

Robustness


Depth of information

Data processing and visualisation


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


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)


Tip Positioning

0.4 mm Ф

0.5~1mm


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


Robustness


Depth of Information



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


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


Slot A

Slot B

Treated samples

Untreated samples

1

2

3 5

4 6

Data Acquisition

Processing

Ready to be viewed

Automated single study


3 sections untreated 3 sections treated Looking for lipidomic differences Regions of interest drawn on all six samples

treated

untreated

PC2

PC3

S1

S2

S3

S1

S2

S3


Robustness


Depth of Information



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


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


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


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


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


10 20 30 40 50 60

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70

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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+ve ion mode


5 10 15 20 25 30 35 40 45 50

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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

200 300 400 500 600 700 800 900 1000 1100 12000

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10

15

200 300 400 500 600 700 800 900 1000 1100 12000

20

40

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150 x 150µm pixel size: move stage at 750µm/s, less y steps ~ 10 minutes


tumour

Lipid signal variance – how stable is the fingerprint?


Comparison between systems/ laboratories

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x

y

x

y

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


0

2

4x 10

-3

2

4

6

8

10x 10

-4

0

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4

x 10-3

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x 10-4

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-3

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-4

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Connective tissue Tumour tissue

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-3

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-4

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-3

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-4

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Background signal ICL Background signal Wilmslow


Pixel size

100 µm

20 x 20 µm m/z 844.5 m/z 744.5

5mm

10 20 30 40 50 60

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100 x 100µm

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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


TIC derm

is

epid

erm

is

TIC image

Mouse skin at 20 x 20µm pixel size


m/z 876.68 m/z 786.53

derm

is

epid

erm

is

Identities of lipid species to be confirmed by MSMS


WT

T2

T1

Tumour bearing zebrafish for PET tracer study


Tumour specific lipid signals

Low molecular weight signals


m/z 252.1

m/z 834.6

40 micron imaging of tumour region only


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


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


12hours acquisition

Microbiology DESI Imaging

RGB overlay

150 micron


Top tip

If you’re going to mark where the tumour is in red ink, do it on the reverse of the slide…


PET tracer?


Intact proteins

Axel Walch


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


Good data

Unknown

Classified

Database

Large cohorts of patient samples

Histologically annotated datasets

Unclassified Misclassified

Inconsistent data Poor data


20 x 20µm pixel

100 micron

20 micron

CRC tumour lipid profiling

Connective tissue

Cancerous regions


Fingerprint DESI imaging

Contact m/z 405.2

From pores? m/z 358.2

m/z 430.2

Overlay in HDI


S

CH3

O

OHF


Trypsin digested proteins from FFPE tissue

Access to patient outcome data Very large sample numbers Extensive sample preparation


Tissue Imaging Optimised DESI Probe


0.4 mm Ф

0.5~1mm


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


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

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