Introduction to Spatial Mapping of Biomolecules by Imaging Mass Spectrometry

By Bindesh Shrestha

Imaging mass spectrometry (MS) techniques are often utilized without an understanding of their underlying principles, making it difficult for scientists to determine when and how they can exploit MS to visualize their biomolecules of interest. Introduction to Spatial Mapping of Biomolecules by Imaging Mass Spectrometry is an essential reference to help scientists determine the status and strategies of biomolecule analysis, describing its many applications for diverse classes of biomolecules.

The book builds a foundation of imaging MS knowledge by introducing ionization sources, sample preparation, visualization guidelines, molecule identification, quantification, data analysis, etc. The second section contains chapters focused on case studies on analyzing a biomolecule class of molecules. Case studies include an introduction/background, and a summary of successful imaging MS studies with illustrative figures and future directions.

• Provides the introductory foundations of imaging mass spectrometry for those new to the technique
• Organized by topic to facilitate a quick deep dive, allowing researchers to immediately apply the imaging MS techniques to their work
• Includes case studies summarizing the imaging MS techniques developed for the class of molecules

• Language: English
• Publisher: Elsevier Science
• Release date: Apr 23, 2021
• ISBN: 9780128189993

Bindesh Shrestha

Dr. Bindesh Shrestha is a senior scientist at the Waters Corporation focusing on imaging mass spectrometry. He has co-authored more than two dozen peer-reviewed articles in the field of mass spectrometry spanning over the last decade. His publications have been featured multiple times on the covers of leading journals, including Angewandte Chemie, Analytical Chemistry, Analyst, and more.

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MA

Chapter 1

Fundamentals of imaging mass spectrometry

Chapter Outline

Principles of imaging mass spectrometry 1

Advantages and limitation of imaging mass spectrometry 4

References 8

Abstract

Imaging mass spectrometry (MS) is an analytical technique that can visualize the distribution of molecules using a tool called a mass spectrometer. In the case of biological samples, the visualized molecules include any species that can be detected by the mass spectrometer, such as metabolites, drugs, lipids, peptides, and proteins. A brief introduction of the fundamentals of imaging MS is presented in this chapter.

Keywords

Imaging mass spectrometry; Mass spectrometery imaging; Metabolite imaging; Drug imaging; Lipid imaging; MALDI imaging; MS imaging; imaging MS

Imaging mass spectrometry (MS) is an analytical technique that can visualize the distribution of molecules using a tool called a mass spectrometer. In the case of biological samples, the visualized molecules include any species that can be detected by the mass spectrometer, such as metabolites, drugs, lipids, peptides, and proteins.

A mass spectrometer is an analytical tool that can measure the mass-to-charge ratio (m/z) of ions of molecules detected in a sample. The molecular analysis of bulk biological samples is done after the sample is homogenized, extracted, or processed according to the molecule-of-interest. The processed aliquot is injected into liquid chromatography for separation. The separated molecules are ionized using an electrospray mechanism and detected by a mass spectrometer. The detected m/z ion intensities are assigned to molecules and examined for the presence of one or multiple molecules or their up/downregulation. This general bulk analysis workflow template is widely used in all MS-based omics analyses, such as metabolomics, proteomics, as well as in quantitative and qualitative molecular analyses. Such bulk tissue analysis can provide information on the identity and the concentration of molecule-of-interest in a bulk sample but does not provide insight into the location of a molecule within the sample. In the imaging MS workflow, a thin slice of tissue is sectioned instead of homogenization of the chunk of tissue. The sectioning is followed by sample preparation steps such as matrix application, and finally, the sample is analyzed by desorption/sampling and ionization using a mass spectrometer. A comparison of workflow between bulk analysis of tissue and imaging MS is illustrated in Fig. 1.1.

Fig. 1.1 A comparison between bulk tissue analysis workflow using electrospray liquid chromatography–mass spectrometer and tissue imaging using matrix-assisted laser desorption/ionization mass spectrometer.

Principles of imaging mass spectrometry

In all imaging MS techniques, molecules are extracted from the sample surface, such as tissue sections. The extracted material is ionized by an ionization source as intact molecular species, or as their fragments, or tagged surrogates, and then detected by a mass analyzer component. The visual distributions of detected molecules are created by aligning the intensity of ions with the location of their ionization. Spatial imaging by mass spectrometers dates back to the 1960s and 1970s using secondary ions or laser as a desorption and ionization source.³⁵,³⁶ There are several reviews on imaging MS, some of the selected reviews from the last 10 years are included in the references herein.¹–²⁷

In an overwhelming majority of the imaging MS techniques, spatial distribution is constructed by analyzing one pixel at a time by an ionization source capable of regional extraction or desorption, and often, ionization of molecules from a sample. A pictorial representation of the imaging MS is given in Fig. 1.2. Pixel-by-pixel analysis is usually achieved by moving samples placed on a two-dimensional translational stage, as depicted in Fig. 1.3. The pixel-by-pixel movement can either be in a typewriter mode, where acquisition is made in one lateral direction, such as left-to-right or top-to-bottom or vice versa. Alternatively, acquisition can be made in a serpentine manner where acquisition switches between both lateral directions.

Fig. 1.2 General representation of imaging MS workflow, where a tissue section such as mouse brain section is interrogated pixel-by-pixel producing mass spectrum for each pixel, and the ion intensity distribution for each ion in each pixel is plotted as a false-color image.

Fig. 1.3 During the imaging MS acquisition, the molecules on the tissue sections are sampled by a focused desorption mechanism. By moving the sample stage in a prescribed manner, such as in typewriter or serpentine modes, spatial information of molecules on each pixel can be obtained.

Each pixel corresponds to an x–y coordinate location on the sample and has a unique mass spectrum. A mass spectrum consists of values of detected ions, represented by a mass-to-charge ratio (m/z), and their corresponding intensities. As a simple illustration, the top portion of Fig. 1.4 has four mass spectra consisting of three same ions with m/z ratios of 123, 456, and 789. The heights of each ion in mass spectra plots denote their intensities. This plot can also be represented as an intensity table. MS images are created by assigning a false-color intensity for each ion at each pixel, as shown in the figure. The pixel-by-pixel imaging MS workflow is most commonly used and also sometimes referred to as microprobe mode. In an alternative instrumental setup, called microscope mode, the ions are imaged using a position-sensitive detector. In theory, microprobe imaging instruments should have higher spatial because the resolution is not dictated by the optical properties of the laser beam but the ion optics of the mass spectrometer with the ability to image below the diffraction limit.²⁸ Microscope mode can potentially have higher throughput but often lower mass resolving power.²⁹–³⁰ Almost all of the imaging MS studies discussed in this book and the literature is done pixel-by-pixel or in microprobe mode.

Fig. 1.4 A conceptual framework of imaging MS data is illustrated by a four-pixel image consisting of three ions. Mass spectra are generated for each pixel can be converted to ion intensities of all the ions at each pixel. Finally, MS images for all ions are obtained by correlating their ion intensities on each pixel with a colormap definition.

In principle, each pixel of imaging MS data can be considered as an independent MS experiment. Like any MS experiments, we can either aim to detect all the ions in the acquired mass range, called here as profiling MS imaging, or aim a selected mass or fragment, named here as targeted MS imaging. In profiling MS imaging experiments, all the ions above a threshold detection limit in that biological microenvironment are detected. In this manner, the spatial distribution of hundreds to thousands of molecules can be obtained without a strict selection criterion, labeling, or a priori knowledge. It should be noted that there is a degree of inherent selection due to the choice of sample preparation workflow, experimental parameters or conditions, or type of instrumentation.

Advantages and limitation of imaging mass spectrometry

There are many advantages of imaging MS over other molecular imaging techniques. Most of the imaging MS workflow does not require any label, probe, or tracer. Unlabeled imaging allows for visualization of molecules that cannot be labeled, as well as makes it possible for the a priori discovery experiments where we do not know what molecules are present. Multiplex molecular imaging, i.e., imaging of multiple molecules in a single experiment, is not only possible, but it is a norm in the majority of imaging MS experiments. Fig. 1.5 shows a panel of desorption electrospray ionization (DESI) MS images of sagittal brain section of three ions simultaneously imaged in the same acquisition without any labeling.

Fig. 1.5 Visualization of three molecular lipid ions using desorption electrospray ionization (DESI) imaging mass sagittal rodent brain section without any labeling is shown on top. The bottom shows an ion overlay image of the same three ions showing contextual distribution.

Imaging MS is capable of providing a molecular visualization of a specific molecule instead of an indication for the presence of a class of molecules. For example, Fig. 1.6 shows the optical image of the whole-body mouse section (a), corresponding DESI MS/MS image of propranolol after 1 hour of dosing (b), optical image after 1 hour of dosing with [3H] propranolol (c), and corresponding whole-body autoradiography image (d). DESI showed the drug was detected in the brain, lung, and stomach regions. The autoradiogram also showed the detection but cannot differentiate between the drug or its radiolabeled metabolites.³¹ Another example is an autoradiogram map of the sagittal kidney section of mice after subcutaneous administration of carbon-14 isotope of a drug shows the distribution of the drug and two metabolites of drug that retained the radiolabel unchanged between two timepoints.³² In contrast, matrix-assisted laser desorption/ionization (MALDI) images of the similar tissue show the drug was predominantly detected in the outer medulla and cortex after 30 minutes of dose. However, after 2 hours, the drug was detected only in the inner medulla, and one metabolite was predominantly detected in the cortex in both time points, but with higher abundance at the later timepoint. All MALDI MS images were acquired without any labeling. Imaging MS also has a high dynamic range spanning over several decades of concentration and is quantitative. Quantitative analysis of imaging MS is discussed more in detail in another chapter.

Fig. 1.6 A comparison of desorption electrospray ionization (DESI) mass spectrometry image (B) with whole-body autoradiography (WBA) image is shown with their respective optical image at (A) and (C). Adapted with permission from Kertesz V, Van Berkel GJ, Vavrek M, Koeplinger KA, Schneider BB, Covey TR. Anal Chem . 2008;80:5168–5177. Copyright 2007 American Chemical Society.

Some of the most common utility of imaging MS applications, until early 2020, were found by examining the keyword of imaging MS literature in the National Institutes of Health PubMed database.³³ Aside from the general imaging terms, the most popular author keywords in the order of the highest frequency are proteomics, metabolomics, lipids, biomarkers, lipidomics, cancer, phospholipids, brain, pharmacokinetics, Alzheimer's disease, atherosclerosis, cell imaging, and metabolism. If we count the most frequently appearing words in the abstract as well, they are humans, animals, male, female, mice, proteomics, rats, middle-aged, proteome, brain, adult, biomarkers. A pictorial cloud representation of this is given in Fig. 1.7. From this simple analysis, we can gauge that imaging MS has been mostly focused on biomolecular or pharmacokinetics analysis of tissue in diseases such as cancer.

Fig. 1.7 Bibliometric map of the author's keyword of imaging mass spectrometry in National Institutes of Health PubMed data, created using VOSviewer software. ³³–³⁴

Most of the advantages and limitations of imaging MS stems from being a mass spectrometric technique. The major limitation is that the molecule needs to be ionized and detected at the physiological quantity present in the ex vivo biological samples. Depending on the goal of the imaging experiment, the requirement for MS imaging applications may differ a lot. Higher requirements usually mean more time or resources. Fig. 1.8 depicts an example of subjective requirements for two hypothetical-related applications, whole-body imaging of cancer drug and diagnosis of a cancerous tumor by imaging MS. Drug imaging would require a confident molecular identification of the drug and its metabolites, as well as, higher quantitative assay. In contrast, cancer diagnosis would require higher throughput and must be very easy to use for nonexperts. Imaging MS has a wide variety of applications than these two hypothetical examples. However, with each set of unique applications, the required toolsets for imaging MS will change. Luckily, the research and development in imaging MS are proliferating and improving the availability of better protocols, software, and hardware. Imaging MS, as an analytical tool, has a unique ability to analyze thousands of molecules in biological systems without any a priori labeling. Some of those molecules cannot be directly mapped using any other tools. With broader adoption of imaging MS, we can anticipate improvements in reproducibility from the sample preparation to data acquisition to data analysis leading to the more widespread use of imaging MS in biomedical research followed by translational research and ultimately in routine clinical usage.

Fig. 1.8 Subjective requirements for two hypothetical biomedical imaging MS applications, whole-body cancer drug imaging, and diagnosis of a cancerous tumor by MS imaging are presented as examples. The requirements may drastically change depending on the specific application on hand.

References

1. van Hove ERA, Smith DF, Heeren RM. A concise review of mass spectrometry imaging. J Chromatogr A. 2010;1217:3946–3954.

2. Schwamborn K, Caprioli RM. MALDI imaging mass spectrometry–painting molecular pictures. Mol Oncol. 2010;4:529–538.

3. Castellino S, Groseclose MR, Wagner D. MALDI imaging mass spectrometry: bridging biology and chemistry in drug development. Bioanalysis. 2011;3:2427–2441.

4. Seeley EH, Caprioli RM. MALDI imaging mass spectrometry of human tissue: method challenges and clinical perspectives. Trends Biotechnol. 2011;29:136–143.

5. Vickerman JC. Molecular imaging and depth profiling by mass spectrometry—SIMS, MALDI or DESI?. Analyst. 2011;136:2199–2217.

6. Goto-Inoue N, Hayasaka T, Zaima N, Setou M. Imaging mass spectrometry for lipidomics. Biochimica et Biophysica Acta (BBA) - Molecular and Cell Biology of Lipids. 2011;1811:961–969.

7. Norris JL, Caprioli RM. Analysis of tissue specimens by matrix-assisted laser desorption/ionization imaging mass spectrometry in biological and clinical research. Chem Rev. 2013;113:2309–2342.

8. Wu C, Dill AL, Eberlin LS, Cooks RG, Ifa DR. Mass spectrometry imaging under ambient conditions. Mass Spectrom Rev. 2013;32:218–243.

9. Römpp A, Spengler B. Mass spectrometry imaging with high resolution in mass and space. Histochem Cell Biol. 2013;139:759–783.

10. Ferguson CN, Fowler JW, Waxer JF, Gatti RA, Loo JA. Mass spectrometry-based tissue imaging of small moleculesAdvancements of Mass Spectrometry in Biomedical Research: Springer; 2014:283–299.

11. Bodzon-Kulakowska A, Suder P. Imaging mass spectrometry: instrumentation, applications, and combination with other visualization techniques. Mass Spectrom Rev. 2016;35:147–169.

12. Schwamborn K, Kriegsmann M, Weichert W. MALDI imaging mass spectrometry—from bench to bedside. Biochim Biophy Acta Proteins Proteom. 2017;1865:776–783.

13. Baker TC, Han J, Borchers CH. Recent advancements in matrix-assisted laser desorption/ionization mass spectrometry imaging. Curr Opin Biotechnol. 2017;43:62–69.

14. Siegel TP, Hamm G, Bunch J, Cappell J, Fletcher JS, Schwamborn K. Mass spectrometry imaging and integration with other imaging modalities for greater molecular understanding of biological tissues. Mol Imaging Biol. 2018;20:888–901.

15. Buchberger AR, DeLaney K, Johnson J, Li L. Mass spectrometry imaging: a review of emerging advancements and future insights. Anal Chem. 2018;90:240–265.

16. Xue J, Bai Y, Liu H. Recent advances in ambient mass spectrometry imaging. TrAC Trends Anal Chem. 2019:115659.

17. Gilmore IS, Heiles S, Pieterse CL. Metabolic imaging at the single-cell scale: recent advances in mass spectrometry imaging. Annu Rev Anal Chem. 2019;12:201–224.

18. Xiao Y, Deng J, Yao Y, Fang L, Yang Y, Luan T. Recent advances of ambient mass spectrometry imaging for biological tissues: a review. Anal Chim Acta. 2020;1117:74–88.

19. Alexandrov T. Spatial metabolomics and imaging mass spectrometry in the age of artificial intelligence. Annu Rev Biomed Data Sci. 2020;3.

20. Aichler M, Walch A. MALDI Imaging mass spectrometry: current frontiers and perspectives in pathology research and practice. Lab Investig. 2015;95:422–431.

21. Norris JL, Caprioli RM. Imaging mass spectrometry: a new tool for pathology in a molecular age. Proteomics Clin Appl. 2013;7:733–738.

22. Spengler B. Mass spectrometry imaging of biomolecular information. Anal Chem. 2015;87:64–82.

23. Shariatgorji M, Svenningsson P, Andrén PE. Mass spectrometry imaging, an emerging technology in neuropsychopharmacology. Neuropsychopharmacology. 2014;39:34–49.

24. Johnson Jr RW, Talaty N. Tissue imaging by mass spectrometry: a practical guide for the medicinal chemist. ACS Med Chem Lett. 2019;10:161–167.

25. Samarah LZ, Vertes A. Mass spectrometry imaging based on laser desorption ionization from inorganic and nanophotonic platformsView; (n/a):20200063 n/a.

26. Spraker JE, Luu GT, Sanchez LM. Imaging mass spectrometry for natural products discovery: a review of ionization methods. Nat Prod Rep. 2020;37:150–162.

27. Goodwin RJA, Takats Z, Bunch J. A critical and concise review of mass spectrometry applied to imaging in drug discovery. Slas Discov Adv Sci Drug Discov. 2020, doi:2472555220941843

28. Luxembourg SL, McDonnell LA, Mize TH, Heeren RM. Infrared mass spectrometric imaging below the diffraction limit. J Proteome Res. 2005;4:671–673.

29. Luxembourg SL, Mize TH, McDonnell LA, Heeren RM. High-spatial resolution mass spectrometric imaging of peptide and protein distributions on a surface. Anal Chem. 2004;76:5339–5344.

30. Spengler B. MALDI-mass spectrometry imaging. In: Peter-Katalinic FHJ, ed. MALDI MS: A Practical Guide to Instrumentation, Methods, and Applications. 2nd ed.: Wiley-VCH Verlag GmbH & Co. KGaA; 2014:133–167.

31. Kertesz V, Van Berkel GJ, Vavrek M, Koeplinger KA, Schneider BB, Covey TR. Comparison of drug distribution images from whole-body thin tissue sections obtained using desorption electrospray ionization tandem mass spectrometry and autoradiography. Anal Chem. 2008;80:5168–5177.

32. Goodwin RJA, Nilsson A, Mackay CL, Swales JG, Johansson MK, Billger M, Andrén PE, Iverson SL. Exemplifying the screening power of mass spectrometry imaging over label-based technologies for simultaneous monitoring of drug and metabolite distributions in tissue sections. J Biomol Screen. 2015;21:187–193.

33. Data from PubMed. Accessed on July 2020.

34. Van Eck N, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics.