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INTRODUCTION

Through advances in DESI sprayer design, acquisition software, data directed methods and the coupling of an automated slide loader, time slots previously reserved for a single large MS imaging acquisition, such as overnight, can now potentially be used to collect a full 3D dataset or a large cohort of clinical or DMPK samples.

Here we introduce developments towards the combination of a DESI-MS system, an automated slide loader (fig. 1) and acquisition and processing software for high throughput mass spectrometry imaging.

Automating high throughput 3D imaging using desorption electrospray ionisation (DESI) mass spectrometry

Emrys Jones1; Lukasz Migas2, Emmy Hoyes1, Richard Chapman1, Emmanuelle Claude1; James Langridge1, Steve Pringle1, Mike Morris1, Zoltan Takats3

1. Waters Corporation, Wilmslow, UK; 2. Manchester Institute of Biotechnology, University of Manchester, Manchester, UK; 3. Imperial College London South Kensington Campus, London, UK.

METHODS

All experiments were carried out on a Xevo G2-XS Q-TOF

(Waters, UK) equipped with a Prosolia (Indianapolis) 2D DESI

stage and Waters DESI sprayer. All data collected in negative ion mode. Tissues were sectioned onto glass slides at 20µm

thickness; no further sample preparation was required. For the 3D imaging, sections were taken at 100 µm intervals.

DESI parameters: 1.5µL/min 95:5 methanol: water; 6 bar N2;

7mm sprayer to capillary distance; 2 mm sprayer to surface;

75° sprayer angle. This set up has been shown to provide high intensity signal at fast scan rates [1]. The examples in figure 2

were collected with an MS scan time of 0.186 s and stage movement of 200µm per second allowing five 40µm pixels to

be collected per second. Faster rates will be described. Regions of interest were defined on scanned images of the

slides in the HDI v1.4 software, creating a MassLynx experiment file to be imported into a sample list; a fully

automated approach based on a rapid initial scan using Waters Research Enabled Software (WREnS) is also introduced. 3D

reconstruction, processing and viewing was carried out using custom written software and MATLAB (Mathworks).

CONCLUSION

No requirement for sample preparation makes DESI ideal for high

throughput, automated imaging.

Single section tissue imaging at 10 minute time scale with a pixel size of 100 x 100 µm and mass range 50-1200 Da.

Automated slide loading and region of interest detection shown as proof of principle.

Next step is full integration of all components into single system.

Full 3D image in <20 hours acquisition, suitable for overnight

data collection and processing.

Workflow for automated 3D reconstruction in place.

Techniques developed here applicable to many novel imaging approaches and applications.

Figure 4. Manual experiment set up with automated data

collection. All slides are scanned and experiments defined by user before running a multi slide experiment.

Survey scan approach

The gentle sampling of DESI makes repeat analysis possible, thus

multiple experiments can be conducted in series on the same sample. One application of this is the user independent survey

scan, where a rapid coarse scan is carried out to differentiate between tissue and substrate, which is then used to automatically

define the analysis region within the same software environment. This is demonstrated in figure 5 where a 90 second full slide scan

is used to define the higher spatial resolution image. The ion signal used to define the region and mode of analysis scan can be

tailored to the application.

Control and processing software

For automated processes, minimal user interaction is desired,

however with imaging MS input is required in defining the regions to be imaged. Ideally slides would be loaded onto the system, the

experiment defined and run with no further input from the analyst.

Optical approaches could be taken to detecting the tissue sections on the slide (fig 3), however the unique properties of DESI-MS

allow for a chemical survey scan to be carried out.

RESULTS

Speed of acquisition and tissue stability

In order to maximise the benefit of automatic slide loading the acquisition time per sample should be in the region of minutes, not hours, and

developments with hardware and software have made this a realistic proposition. Figure 6 demonstrates the results of imaging at different MS

scan rates. Although spectral intensity is lower, the data and image quality are preserved, at 30 scan/s the 14 x 8mm area is covered in 12 minutes.

Figure 8: Acquisition time of 40 sections used to create 3D map

of mouse kidney. R: m/z 764.52, G:772.53; B 762.50.

Figure 2. DESI imaging of Mouse sagittal brain section

demonstrating excellent data quality at higher scan rates than previous studies. Data shown collected at 5 pixels per second.

Total acquisition time

The 40 kidney sections (fig 8.) taken at 100µm intervals were imaged at

100 x 100µm and 10 scans per second. The largest section was 14x 10mm.

Total acquisition time was 16 hours including the post processing steps.

Figure 12: 3D visualisation of four lipid species within a mouse kidney.

Colours and transparencies were manually defined prior to rendering.

Figure 1. Steps required for a fully automated MS imaging

system and an illustration of the modified DESI plate.

To view the above 3D dataset

go to Skfb.ly/NXnK or scan

the QR code to the right.

m/z 762.53

m/z 772.53

m/z 764.52

m/z 890.63

Figure 5. Use of MS survey scan to automatically define the

region to be imaged at the desired analytical conditions.

Figure 11. Isosurface output from auto-aligned data. Once the x-y

offset and rotation is calculated for the first mass (typically the TIC) all others are processed using these transformation

constants.

Semi automatic approach

For the minimal deviation from a standard work flow, each slide

in the proposed slide loader run is defined by the user creating an experiment worksheet which can be added to a sequence list. A

modification to the DESI source control software will control the loading/unloading of slides in between each acquisition (fig 4).

Figure 6. Comparison of imaging a section of mouse kidney at varying

mass spectrometry scan rates. DESI stage speed was adjusted to ensure the same pixel dimensions-100µm x 100µm .

Figure 7. The upper portion of a mouse kidney section was imaged and left

on the DESI stage for 14 hours. The lower portion was then imaged, and even without normalisation the results are highly comparable.

Data processing

It is not only the acquisition of 3D imaging MS data that

requires automation and simplification if it is to become routine, the reconstruction and visualisation has also in the past been

very labour intensive. For the data presented here, a number of automated steps

have been developed to go from a large amount of imaging MS data to easily visualised three dimensional representations.

Firstly, as shown in figure 9, a program runs through all raw data extracting co-ordinates and selected m/z integration

ranges for each sample in a given set.

These can then be imported into a software package such as MATLAB to be combined into a data cube for each m/z range.

Previous 3D imaging experiments have used fiducial markers incorporated into an embedding material to allow for the

alignment of subsequent sections [2]. This requires a carefully designed experiment and has proven problematic.

By using the data harvesting routine to create a total ion (or total lipid) map for each section, object definition approaches

can be used to find the centre of a section, this is followed by iterative rotations around this centre to maximise the

correlation with the previous (or a selected) section. The need for this correction step, and an outline of the processes involved

are shown in figure 10.

Figure 9. Data harvesting method for

multiple raw files in the same experiment. A C# program is utilised to extract the co-

ordinates and intensities integrated between selected mass windows.

Figure 10. A) The requirement for correction of data position and rotation. Error in sectioning and region of

interest selection results in images from subsequent sections not aligning when stacked. B) A MATLAB algorithm that automatically positions, aligns and rotates each z plane based on correlation with previous

section during a rotation around the object centre.

While future systems may incorporate a cold cabinet for the queued slides,

the stability of samples at ambient conditions over 10+ hours needs to be demonstrated. Results of an investigation into this are presented in figure

7, where the two halves of a section were imaged 14 hours apart.

The effect of the alignment and rotational

correction can be seen in figure 11. The top two isosurfaces have had the z-axis

stretched to emphasise the extent of

misalignment of the raw data and the improvement made by this quick

optimisation process. Once aligned, a number of processes can

be carried out to enhance the clarity of the surfaces created, such as smoothing

and thresholding. These can then be exported as a common 3D model object

to be visualised by a variety of programs. The examples in figure 12 are

combinations of isosurfaces that have been imported into Blender (open source,

Blender Foundation) to be viewed with different material properties such as

colour and transparency. These can then

be shared by a number of viewing platforms.

Multi-section multivariate approaches have already been reported [3] and could

be applied here.

References

1. Tillner et al.; Analytical Chemistry (2016), 88, 4808-4816

2. Jang et al.; Analytical Chemistry (2015), 87, 5947-5956 3. Jones et al,; Journal of Proteome Research (2013), 12, 1847-1855

The authors would like to thank our collaborators at the WMIC, Manchester,

for provide samples and access to microtome facilities.

Figure 3: Different methods of multiple slide analysis, including

optical region definition where edge and feature analysis algorithms are employed.

A. B.