Mass spectrometry imaging

Short description: Mass spectrometry technique that can visualize the spatial distribution of molecules

Mass spectrometry imaging (MSI) is a technique used in mass spectrometry to visualize the spatial distribution of molecules, as biomarkers, metabolites, peptides or proteins by their molecular masses. After collecting a mass spectrum at one spot, the sample is moved to reach another region, and so on, until the entire sample is scanned. By choosing a peak in the resulting spectra that corresponds to the compound of interest, the MS data is used to map its distribution across the sample. This results in pictures of the spatially resolved distribution of a compound pixel by pixel. Each data set contains a veritable gallery of pictures because any peak in each spectrum can be spatially mapped. Despite the fact that MSI has been generally considered a qualitative method, the signal generated by this technique is proportional to the relative abundance of the analyte. Therefore, quantification is possible, when its challenges are overcome. Although widely used traditional methodologies like radiochemistry and immunohistochemistry achieve the same goal as MSI, they are limited in their abilities to analyze multiple samples at once, and can prove to be lacking if researchers do not have prior knowledge of the samples being studied.[1] Most common ionization technologies in the field of MSI are DESI imaging, MALDI imaging, secondary ion mass spectrometry imaging (SIMS imaging) and Nanoscale SIMS (NanoSIMS).[2][3][4]

## Contents

* 1 History
* 2 Operation principle
* 2.1 Microprobe
* 2.2 Microscope
* 3 Ion source dependence
* 3.1 SIMS and NanoSIMS imaging
* 3.2 MALDI imaging
* 3.3 DESI imaging
* 3.4 SICRIT imaging
* 3.5 Comparative between the ionization techniques
* 4 Combination of various MSI techniques and other imaging techniques
* 5 Data processing
* 5.1 Standard data format for mass spectrometry imaging datasets
* 5.2 Software
* 6 Applications
* 7 Advantages, challenges and limitations
* 8 Notes
* 9 Further reading
* 10 References

## History

More than 50 years ago, MSI was introduced using secondary ion mass spectrometry (SIMS) to study semiconductor surfaces by Castaing and Slodzian.[5] However, it was the pioneering work of Richard Caprioli and colleagues in the late 1990s, demonstrating how matrix-assisted laser desorption/ionization (MALDI) could be applied to visualize large biomolecules (as proteins and lipids) in cells and tissue to reveal the function of these molecules and how function is changed by diseases like cancer, which led to the widespread use of MSI. Nowadays, different ionization techniques have been used, including SIMS, MALDI and desorption electrospray ionization (DESI), as well as other technologies. Still, MALDI is the current dominant technology with regard to clinical and biological applications of MSI.[6]

## Operation principle

The MSI is based on the spatial distribution of the sample. Therefore, the operation principle depends on the technique that is used to obtain the spatial information. The two techniques used in MSI are: microprobe and microscope.[7]

### Microprobe

This technique is performed using a focused ionization beam to analyze a specific region of the sample by generating a mass spectrum. The mass spectrum is stored along with the spatial coordination where the measurement took place. Then, a new region is selected and analyzed by moving the sample or the ionization beam. These steps are repeated until the entire sample has been scanned. By coupling all individual mass spectra, a distribution map of intensities as a function of × and y locations can be plotted. As a result, reconstructed molecular images of the sample are obtained.[7]

### Microscope

In this technique, a 2D position-sensitive detector is used to measure the spatial origin of the ions generated at the sample surface by the ion optics of the instruments. The resolution of the spatial information will depend on the magnification of the microscope, the quality of the ions optics and the sensitivity of the detector. A new region still needs to be scanned, but the number of positions drastically reduces. The limitation of this mode is the finite depth of vision present with all microscopes.[7]

## Ion source dependence

The ionization techniques available for MSI are suited to different applications. Some of the criteria for choosing the ionization method are the sample preparation requirement and the parameters of the measurement, as resolution, mass range and sensitivity. Based on that, the most common used ionization method are MALDI, SIMS AND DESI which are described below. Still, other minor techniques used are laser ablation electrospray ionization (LAESI), laser-ablation-inductively coupled plasma (LA-ICP) and nanospray desorption electrospray ionization (nano-DESI).

### SIMS and NanoSIMS imaging

Secondary ion mass spectrometry (SIMS) is used to analyze solid surfaces and thin films by sputtering the surface with a focused primary ion beam and collecting and analyzing ejected secondary ions. There are many different sources for a primary ion beam. However, the primary ion beam must contain ions that are at the higher end of the energy scale. Some common sources are: Cs+, O2+, O, Ar+ and Ga+.[8] SIMS imaging is performed in a manner similar to electron microscopy; the primary ion beam is emitted across the sample while secondary mass spectra are recorded.[9] SIMS proves to be advantageous in providing the highest image resolution but only over small area of samples.[10] More, this technique is widely regarded as one of the most sensitive forms of mass spectrometry as it can detect elements in concentrations as small as 1012-1016 atoms per cubic centimeter.[11][note 1][note 2]

Multiplexed ion beam imaging (MIBI) is a SIMS method that uses metal isotope labeled antibodies to label compounds in biological samples.[12]

Developments within SIMS: Some chemical modifications have been made within SIMS to increase the efficiency of the process. There are currently two separate techniques being used to help increase the overall efficiency by increasing the sensitivity of SIMS measurements: matrix-enhanced SIMS (ME-SIMS) - This has the same sample preparation as MALDI does as this simulates the chemical ionization properties of MALDI. ME-SIMS does not sample nearly as much material. However, if the analyte being tested has a low mass value then it can produce a similar looking spectra to that of a MALDI spectra. ME-SIMS has been so effective that it has been able to detect low mass chemicals at sub cellular levels that was not possible prior to the development of the ME-SIMS technique.[4] The second technique being used is called sample metallization (Meta-SIMS) - This is the process of gold or silver addition to the sample. This forms a layer of gold or silver around the sample and it is normally no more than 1-3 nm thick. Using this technique has resulted in an increase of sensitivity for larger mass samples. The addition of the metallic layer also allows for the conversion of insulating samples to conducting samples, thus charge compensation within SIMS experiments is no longer required.[13]

Subcellular (50 nm) resolution is enabled by NanoSIMS[2] allowing for absolute quantitative analysis at the organelle level.

### MALDI imaging

Mouse kidney: (a) MALDI spectra from the tissue. (b) H&E stained tissue. N-glycans at m/z = 1996.7 (c) is located in the cortex and medulla while m/z = 2158.7 (d) is in the cortex, (e) An overlay image of these two masses, (f) untreated control tissue.[14]

Main article: MALDI imaging

Matrix-assisted laser desorption ionization can be used as a mass spectrometry imaging technique for relatively large molecules.[4] It has recently been shown that the most effective type of matrix to use is an ionic matrix for MALDI imaging of tissue. In this version of the technique the sample, typically a thin tissue section, is moved in two dimensions while the mass spectrum is recorded.[15] Although MALDI has the benefit of being able to record the spatial distribution of larger molecules, it comes at the cost of lower resolution than the SIMS technique. The limit for the lateral resolution for most of the modern instruments using MALDI is 20

m. MALDI experiments commonly use either an Nd:YAG (355 nm) or N2 (337 nm) laser for ionization.[4]

Pharmacodynamics and toxicodynamics in tissue have been studied by MALDI imaging.[16]

### DESI imaging

Desorption electrospray Ionization is a less destructive technique, which couples simplicity and rapid analysis of the sample. The sample is sprayed with an electrically charged solvent mist at an angle that causes the ionization and desorption of various molecular species. Then, two-dimensional maps of the abundance of the selected ions in the surface of the sample in relation with the spatial distribution are generated.[17][10] This technique is applicable to solid, liquid, frozen and gaseous samples. Moreover, DESI allows analyzing a wide range of organic and biological compounds, as animal and plant tissues and cell culture samples, without complex sample preparation[6][10] Although, this technique has the poorest resolution among other, it can create high-quality image from a large area scan, as a whole body section scanning.[10]

Nano-DESI imaging

Nanospray Desoprtion Electrospray Ionization is a minimally destructive soft ionization technique based on liquid extraction.[18] The basic setup consists of two fused silica capillaries (primary and secondary). An extraction solvent is supplied through a primary capillary, forming a liquid bridge at the interface of the two capillaries and extracting molecules from the tissue surface. Afterwards, the extracted analytes are transferred and nebulized through the secondary capillary inside the inlet of a mass spectrometer. Nano-DESI offers great solvent versatility[19][20] and the possibility of analyte quantification by introducing of internal standards directly into the solvent.[21]

### SICRIT imaging

The Soft Ionization by Chemical Reaction in Transfer (SICRIT) source can be coupled with imaging techniques such as Atmospheric pressure (AP)-MALDI or laser ablation. In AP-MALDI + SICRIT configurations, SICRIT serves as an ambient-pressure post-ionization unit, increasing ion yield and extending molecular coverage, particularly for small metabolites and lipids. When combined with laser ablation, SICRIT enables soft ionization of ablated neutrals without matrix application, maintaining high spatial resolution down to ~1 μm. Recent studies[22] demonstrate that coupling laser desorption with plasma-based SICRIT ionization improves ionization efficiency and signal stability under atmospheric conditions. The technique offers compatibility with various mass spectrometers and minimal sample preparation. Limitations include dependence on ablation parameters and limited coverage of large biomolecules. SICRIT imaging is an emerging approach promising enhanced molecular coverage and flexible ambient-MSI workflows.

### Comparative between the ionization techniques

Comparison of typical parameters among MSI techniques[10] | Ionization Source  | Type of Ionization  | Analytes  | Spatial Resolution  | Mass Range

|
|
|
|
|

SIMS | Ion gun  | Hard  | Elemental ions, small molecules, lipids  | <10 μm  | 0-1000 Da
MALDI | UV laser beam  | Soft  | Lipids, peptide, proteins  | 20 μm  | 0-100 000 Da
DESI | Solvent Spray  | Soft  | Small molecules, lipids, peptides  | 50 μm  | 0-2000 Da
Nano-DESI | Solvent liquid bridge  | Soft  | Small molecules, lipids, peptides  | 10 μm  | 0-2000 Da
MALDI + SICRIT | Cold Plasma  | Soft  | Small molecules, lipids, peptides  | 0,25μm  | 0-2000 Da
LA + SICRIT | Cold Plasma  | Soft  | Small molecules, lipids, nucleotids  | < 5 μm  | 0-2000 Da

## Combination of various MSI techniques and other imaging techniques

Combining various MSI techniques can be beneficial, since each particular technique has its own advantage. For example, when information regards both proteins and lipids are necessary in the same tissue section, performing DESI to analyze the lipid, followed by MALDI to obtain information about the peptide, and finalize applying a stain (haematoxylin and eosin) for medical diagnosis of the structural characteristic of the tissue.[10] On the other side of MSI with other imaging techniques, fluorescence staining with MSI and magnetic resonance imaging (MRI) with MRI can be highlighted. Fluorescence staining can give information of the appearance of some proteins present in any process inside a tissue, while MSI may give information about the molecular changes presented in that process. Combining both techniques, multimodal picture or even 3D images of the distribution of different molecules can be generated.[10] Recent work has demonstrated that transmission-mode MALDI-2 combined with fluorescence microscopy on the same tissue section enables single-cell-resolved MSI and precise co-registration with the optical modality.[23] In contrast, MRI with MSI combines the continuous 3D representation of MRI image with detailed structural representation using molecular information from MSI. Even though, MSI itself can generate 3D images, the picture is just part of the reality due to the depth limitation in the analysis, while MRI provides, for example, detailed organ shape with additional anatomical information. This coupled technique can be beneficial for cancer precise diagnosis and neurosurgery.[10]

## Data processing

### Standard data format for mass spectrometry imaging datasets

The imzML was proposed to exchange data in a standardized XML file based on the mzML format.[24] Several imaging MS software tools support it. The advantage of this format is the flexibility to exchange data between different instruments and data analysis software.[25]

### Software

There are many free software packages available for visualization and mining of imaging mass spectrometry data. Converters from Thermo Fisher format, Analyze format, GRD format and Bruker format to imzML format were developed by the Computis project. Some software modules are also available for viewing mass spectrometry images in imzML format: Biomap (Novartis, free), Datacube Explorer (AMOLF, free),[26] EasyMSI (CEA), Mirion (JLU), MSiReader (NCSU, free)[27] and SpectralAnalysis.[28]

For processing .imzML files with the free statistical and graphics language R, a collection of R scripts is available, which permits parallel-processing of large files on a local computer, a remote cluster or on the Amazon cloud.[29]

Another free statistical package for processing imzML and Analyze 7.5 data in R exists, Cardinal.[30]

SPUTNIK [31] is an R package containing various filters to remove peaks characterized by an uncorrelated spatial distribution with the sample location or spatial randomness.

The Python ecosystem provides a range of specialized libraries for processing mass spectrometry data, serving distinct analytical needs. For comprehensive, end-to-end workflows, pyOpenMS offers extensive Python bindings for the C++ OpenMS library, facilitating complex operations such as signal processing, feature finding, and quantification. In contrast, pymzML is a highly efficient, lightweight parser optimized specifically for rapid data extraction and interaction with `mzML` files, making it ideal for prototyping and targeted data access. Pyteomics also functions as a versatile and lightweight toolkit, but supports a broader range of common proteomics formats (including `mgf` and `pepxml`) and is often utilized for its ease of use in general data handling and calculations. For more specific applications, matchms is a focused library dedicated to data processing, cleaning, and comparison, particularly for implementing various spectral similarity scoring algorithms essential in metabolomics.[32][33][34][35]

## Applications

A remarkable ability of MSI is to find out the localization of biomolecules in tissues, even though there are no previous information about them. This feature has made MSI a unique tool for clinical research and pharmacological research. It provides information about biomolecular changes related with diseases by tracking proteins, lipids, and cell metabolism. For example, identifying biomarkers by MSI can show detailed cancer diagnosis. In addition, low cost imaging for pharmaceuticals studies can be acquired, such as images of molecular signatures that would be indicative of treatment response for a specific drug or the effectiveness of a particular drug delivery method.[36][37][38]

Ion colocalization has been studied as a way to infer local interactions between biomolecules. Similarly to colocalization in microscopy imaging, correlation has been used to quantify the similarity between ion images and generate network models.[39]

## Advantages, challenges and limitations

The main advantage of MSI for studying the molecules location and distribution within the tissue is that this analysis can provide either greater selectivity, more information or more accuracy than others. Moreover, this tool requires less investment of time and resources for similar results.[17] The table below shows a comparison of advantages and disadvantages of some available techniques, including MSI, correlated with drug distribution analysis.[5]

Comparison of advantages and disadvantages of techniques assessing drug distribution[5] Methodology  | Question answered  | Advantages  | Disadvantages

|
|
|

Autoradiography  | Where and how much radioactivity  | Very high spatial resolution; reliable quantitation  | Ex vivo; requires radio-labelled drug; does not distinguish drug from metabolites.
Immunohistochemistry  | Where  | Short processing time; easy interpretation; inexpensive  | Ex vivo; requires antibodies, which vary in sensitivity and specificity; difficulties assigning; detection threshold; lack of standard scoring system
Fluorescence  | Where  | In vivo possible; reasonable cost  | Not quantitative; poor resolution; autofluorescent interference
Positron emission tomography (PET)  | Where, what and activity  | In vivo possible; good resolution; can be coupled to CT X-ray, gamma camera  | Expensive; short-lived isotopes; need cyclotron to produce isotopes
Coherent anti-Stokes

Raman scattering

microscopy (CARS)

| Where and what  | Label-free; sub-cellular spatial resolution  | Not quantitative; poor selectivity; high background noise
Electrochemical atomic

force microscopy (AFM)

| Where and what  | Label-free imaging; high resolution  | Not quantitative; poor reproducibility; high background
MSI | Where and what | Multiplex; label-free imaging; good spatial resolution | Semi-quantitative; ion-suppression effects; complex analysis

## Notes

1. ↑ by way of comparison, 1 cc of Carbon (diamond) contains about 1.8 × 1023 atoms. 1012 to 1016 corresponds to 6 parts per trillion (ppt) to 60 parts per billion (ppb).
2. ↑ the sensitivity varies by element (or molecule) as well as by nature of the surface being analyzed and conditions of the analysis.

## Further reading

"Imaging Trace Metals in Biological Systems" pp 81–134 in "Metals, Microbes and Minerals: The Biogeochemical Side of Life" (2021) pp xiv + 341. Authors Yu, Jyao; Harankhedkar, Shefali; Nabatilan, Arielle; Fahrni, Christopher; Walter de Gruyter, Berlin. Editors Kroneck, Peter M.H. and Sosa Torres, Martha. DOI 10.1515/9783110589771-004

## References

1. ↑ "SIMS and MADLI MS Imaging of the spinal cord". Proteomics 8 (18): 3746–3754. 2008. doi:10.1002/pmic.200800127. PMID 18712768.
2. ↑ 2.0 2.1 Siuzdak, Gary (September 2023). "Subcellular quantitative imaging of metabolites at the organelle level" (in en). Nature Metabolism 5 (9): 1446–1448. doi:10.1038/s42255-023-00882-z. ISSN 2522-5812. PMID 37679555. https://www.nature.com/articles/s42255-023-00882-z.
3. ↑ "MALDI mass spectrometric imaging of biological tissue sections". Mechanisms of Ageing and Development 126 (1): 177–185. 2005. doi:10.1016/j.mad.2004.09.032. PMID 15610777.
4. ↑ 4.0 4.1 4.2 4.3 "Imaging mass spectrometry". Mass Spectrometry Reviews 26 (4): 606–43. 2007. doi:10.1002/mas.20124. PMID 17471576. Bibcode: 2007MSRv...26..606M.
5. ↑ 5.0 5.1 5.2 Cobice, D F; Goodwin, R J A; Andren, P E; Nilsson, A; Mackay, C L; Andrew, R (2015-07-01). "Future technology insight: mass spectrometry imaging as a tool in drug research and development". British Journal of Pharmacology 172 (13): 3266–3283. doi:10.1111/bph.13135. ISSN 1476-5381. PMID 25766375.
6. ↑ 6.0 6.1 Addie, Ruben D.; Balluff, Benjamin; Bovée, Judith V. M. G.; Morreau, Hans; McDonnell, Liam A. (2015). "Current State and Future Challenges of Mass Spectrometry Imaging for Clinical Research". Analytical Chemistry 87 (13): 6426–6433. doi:10.1021/acs.analchem.5b00416. PMID 25803124. Bibcode: 2015AnaCh..87.6426A.
7. ↑ 7.0 7.1 7.2 McDonnell, Liam A.; Heeren, Ron M.A. (2007-07-01). "Imaging mass spectrometry". Mass Spectrometry Reviews 26 (4): 606–643. doi:10.1002/mas.20124. ISSN 1098-2787. PMID 17471576. Bibcode: 2007MSRv...26..606M.
8. ↑ "A concise review of mass spectrometry imaging". Journal of Chromatography A 1217 (25): 3946–3954. 2010. doi:10.1016/j.chroma.2010.01.033. PMID 20223463. Bibcode: 2010JChA.1217.3946A.
9. ↑ Penner-Hahn, James E. (2013). "Chapter 2. Technologies for Detecting Metals in Single Cells. Section 2.1, Secondary Ion Mass Specctrometry". in Banci, Lucia. Metallomics and the Cell. Metal Ions in Life Sciences. 12. Springer. pp. 15–40. doi:10.1007/978-94-007-5561-1_2. ISBN 978-94-007-5560-4. electronic-book ISBN 978-94-007-5561-1 ISSN 1559-0836electronic-ISSN 1868-0402
10. ↑ 10.0 10.1 10.2 10.3 10.4 10.5 10.6 10.7 Bodzon-Kulakowska, Anna; Suder, Piotr (2016-01-01). "Imaging mass spectrometry: Instrumentation, applications, and combination with other visualization techniques". Mass Spectrometry Reviews 35 (1): 147–169. doi:10.1002/mas.21468. ISSN 1098-2787. PMID 25962625. Bibcode: 2016MSRv...35..147B.
11. ↑ "High resolution chemical imaging with scanning ion probe SIMS". International Journal of Mass Spectrometry and Ion Processes 143: 191–212. 1995. doi:10.1016/0168-1176(94)04119-R. Bibcode: 1995IJMSI.143..191C.
12. ↑ Angelo, Michael; Bendall, Sean C; Finck, Rachel; Hale, Matthew B; Hitzman, Chuck; Borowsky, Alexander D; Levenson, Richard M; Lowe, John B et al. (2014). "Multiplexed ion beam imaging of human breast tumors". Nature Medicine 20 (4): 436–442. doi:10.1038/nm.3488. ISSN 1078-8956. PMID 24584119.
13. ↑ Improvements of metal adhesion to silicon films: a ToF-SIMS study.
14. ↑ Powers, Thomas W.; Neely, Benjamin A.; Shao, Yuan; Tang, Huiyuan; Troyer, Dean A.; Mehta, Anand S.; Haab, Brian B.; Drake, Richard R. (2014). "MALDI Imaging Mass Spectrometry Profiling of N-Glycans in Formalin-Fixed Paraffin Embedded Clinical Tissue Blocks and Tissue Microarrays". PLOS ONE 9 (9). doi:10.1371/journal.pone.0106255. ISSN 1932-6203. PMID 25184632. Bibcode: 2014PLoSO...9j6255P.
15. ↑ "New developments in profiling and imaging of proteins from tissue sections by MALDI mass spectrometry". J. Proteome Res. 5 (11): 2889–900. 2006. doi:10.1021/pr060346u. PMID 17081040.
16. ↑ Patel, Ekta (1 January 2015). "MALDI-MS imaging for the study of tissue pharmacodynamics and toxicodynamics". Bioanalysis 7 (1): 91–101. doi:10.4155/bio.14.280. PMID 25558938.
17. ↑ 17.0 17.1 Nilsson, Anna; Goodwin, Richard J. A.; Shariatgorji, Mohammadreza; Vallianatou, Theodosia; Webborn, Peter J. H.; Andrén, Per E. (2015-02-03). "Mass Spectrometry Imaging in Drug Development". Analytical Chemistry 87 (3): 1437–1455. doi:10.1021/ac504734s. ISSN 0003-2700. PMID 25526173. Bibcode: 2015AnaCh..87.1437N.
18. ↑ Roach, Patrick J.; Laskin, Julia; Laskin, Alexander (September 2010). "Nanospray desorption electrospray ionization: an ambient method for liquid-extraction surface sampling in mass spectrometry". The Analyst 135 (9): 2233–2236. doi:10.1039/c0an00312c. ISSN 1364-5528. PMID 20593081. Bibcode: 2010Ana...135.2233R.
19. ↑ Duncan, Kyle D.; Fang, Ru; Yuan, Jia; Chu, Rosalie K.; Dey, Sudhansu K.; Burnum-Johnson, Kristin E.; Lanekoff, Ingela (2018-06-19). "Quantitative Mass Spectrometry Imaging of Prostaglandins as Silver Ion Adducts with Nanospray Desorption Electrospray Ionization". Analytical Chemistry 90 (12): 7246–7252. doi:10.1021/acs.analchem.8b00350. ISSN 1520-6882. PMID 29676905. Bibcode: 2018AnaCh..90.7246D.
20. ↑ Hale, Oliver J.; Hughes, James W.; Sisley, Emma K.; Cooper, Helen J. (2022-04-12). "Native Ambient Mass Spectrometry Enables Analysis of Intact Endogenous Protein Assemblies up to 145 kDa Directly from Tissue". Analytical Chemistry 94 (14): 5608–5614. doi:10.1021/acs.analchem.1c05353. ISSN 1520-6882. PMID 35358391. Bibcode: 2022AnaCh..94.5608H.
21. ↑ Bergman, Hilde-Marléne; Lanekoff, Ingela (2017-09-25). "Profiling and quantifying endogenous molecules in single cells using nano-DESI MS" (in en). Analyst 142 (19): 3639–3647. doi:10.1039/C7AN00885F. ISSN 1364-5528. PMID 28835951. Bibcode: 2017Ana...142.3639B. https://pubs.rsc.org/en/content/articlelanding/2017/an/c7an00885f.
22. ↑ Young, Reuben S. E.; Piper, Ann-Katrin; McAlary, Luke; McKinnon, Jayden C.; Lum, Jeremy S.; Soltwisch, Jens; Niehaus, Marcel; Ellis, Shane R. (2025-10-15). "Subcellular mass spectrometry imaging of lipids and nucleotides using transmission geometry ambient laser desorption and plasma ionisation" (in en). Nature Communications 16 (1): 9130. doi:10.1038/s41467-025-64604-7. ISSN 2041-1723. PMID 41093837. Bibcode: 2025NatCo..16.9130Y.
23. ↑ Potthoff, Alexander; Schwenzfeier, Jan; Niehaus, Marcel; Bessler, Sebastian; Hoffmann, Emily; Soehnlein, Oliver; Höhndorf, Jens; Dreisewerd, Klaus et al. (2025-10-15). "Spatial biology using single-cell mass spectrometry imaging and integrated microscopy" (in en). Nature Communications 16 (1): 9129. doi:10.1038/s41467-025-64603-8. ISSN 2041-1723. PMID 41093848. Bibcode: 2025NatCo..16.9129P.
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27. ↑ Robichaud, G.; Garrard, K. P.; Barry, J. A.; Muddiman, D. C. (2013). "MSiReader: An Open-Source Interface to View and Analyze High Resolving Power MS Imaging Files on Matlab Platform". Journal of the American Society for Mass Spectrometry 24 (5): 718–721. doi:10.1007/s13361-013-0607-z. PMID 23536269. Bibcode: 2013JASMS..24..718R.
28. ↑ Race, A. M.; Palmer, A. D.; Dexter, A.; Steven, R. T.; Styles, I. B.; Bunch, J. (2016). "SpectralAnalysis: software for the masses". Analytical Chemistry 88 (19): 9451–9458. doi:10.1021/acs.analchem.6b01643. PMID 27558772. Bibcode: 2016AnaCh..88.9451R. http://pure-oai.bham.ac.uk/ws/files/31409283/spectralAnalysisArticle.pdf.
29. ↑ Gamboa-Becerra, Roberto; Ramírez-Chávez, Enrique; Molina-Torres, Jorge; Winkler, Robert (2015-07-01). "MSI.R scripts reveal volatile and semi-volatile features in low-temperature plasma mass spectrometry imaging (LTP-MSI) of chilli (Capsicum annuum)". Analytical and Bioanalytical Chemistry 407 (19): 5673–5684. doi:10.1007/s00216-015-8744-9. PMID 26007697.
30. ↑ Bemis, Kyle D.; Harry, April; Eberlin, Livia S.; Ferreira, Christina; van de Ven, Stephanie M.; Mallick, Parag; Stolowitz, Mark; Vitek, Olga (2015-03-15). "Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments". Bioinformatics 31 (14): 2418–2420. doi:10.1093/bioinformatics/btv146. PMID 25777525.
31. ↑ Inglese, Paolo; Correia, Gonçalo; Takats, Zoltan; Nicholson, Jeremy K.; Glen, Robert C. (2018). "SPUTNIK: An R package for filtering of spatially related peaks in mass spectrometry imaging data". Bioinformatics 35 (1): 178–180. doi:10.1093/bioinformatics/bty622. PMID 30010780.
32. ↑ Sing, Justin Cyril; Charkow, Joshua; Walter, Axel; Gao, Mingxuan; Müller, Tom David; Bittremieux, Wout; Sachsenberg, Timo; Röst, Hannes Luc (2025-04-04). "pyOpenMS-viz: Streamlining Mass Spectrometry Data Visualization with pandas". Journal of Proteome Research 24 (4): 2152–2158. doi:10.1021/acs.jproteome.4c00873. ISSN 1535-3893. PMID 40019346. https://doi.org/10.1021/acs.jproteome.4c00873.
33. ↑ Rosas-Román, Ignacio; Guillén-Alonso, Héctor; Moreno-Pedraza, Abigail; Winkler, Robert (2024-03-12). "Technical Note: mzML and imzML Libraries for Processing Mass Spectrometry Data with the High-Performance Programming Language Julia". Analytical Chemistry 96 (10): 3999–4004. doi:10.1021/acs.analchem.3c05853. ISSN 0003-2700. PMID 38427332. PMC 10938284. Bibcode: 2024AnaCh..96.3999R. https://doi.org/10.1021/acs.analchem.3c05853.
34. ↑ Monaghan, Joseph; Nguyen, Kiera; Woytowich, Nicholas; Keyowski, Alora; Duncan, Kyle (2024-11-29) (in en), imzML Writer: An Easy-to-Use Python Pipeline for Conversion of Continuously Acquired Raw Mass Spectrometry Imaging Files to imzML Format, ChemRxiv, doi:10.26434/chemrxiv-2024-0jjf6, https://chemrxiv.org/engage/chemrxiv/article-details/6744e0ee5a82cea2fadaa02c, retrieved 2025-11-06
35. ↑ Graeve, Marilyn De; Bittremieux, Wout; Naake, Thomas; Huber, Carolin; Anagho-Mattanovich, Matthias; Hoffmann, Nils; Marchal, Pierre; Chrone, Victor et al. (2025-05-19). "SpectriPy: Enhancing Cross-Language Mass Spectrometry Data Analysis with R and Python". Journal of Open Source Software 10 (109): 8070. doi:10.21105/joss.08070. ISSN 2475-9066.
36. ↑ Swales, John G.; Hamm, Gregory; Clench, Malcolm R.; Goodwin, Richard J.A. (March 2019). "Mass spectrometry imaging and its application in pharmaceutical research and development: A concise review". International Journal of Mass Spectrometry 437: 99–112. doi:10.1016/j.ijms.2018.02.007. ISSN 1387-3806. Bibcode: 2019IJMSp.437...99S.
37. ↑ Addie, Ruben D.; Balluff, Benjamin; Bovée, Judith V. M. G.; Morreau, Hans; McDonnell, Liam A. (2015-07-07). "Current State and Future Challenges of Mass Spectrometry Imaging for Clinical Research". Analytical Chemistry 87 (13): 6426–6433. doi:10.1021/acs.analchem.5b00416. ISSN 0003-2700. PMID 25803124. Bibcode: 2015AnaCh..87.6426A.
38. ↑ Aichler, Michaela; Walch, Axel (April 2015). "MALDI Imaging mass spectrometry: current frontiers and perspectives in pathology research and practice". Laboratory Investigation 95 (4): 422–431. doi:10.1038/labinvest.2014.156. ISSN 1530-0307. PMID 25621874.
39. ↑ Inglese, Paolo; Strittmatter, Nicole; Doria, Luisa; Mroz, Anna; Speller, Abigail; Poynter, Liam; Dannhorn, Andreas; Kudo, Hiromi; Mirnezami, Reza; Goldin, Robert D.; Nicholson, Jeremy K. (2018-01-09). "Network analysis of mass spectrometry imaging data from colorectal cancer identifies key metabolites common to metastatic development". bioRxiv 10.1101/230052.

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