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. 2015 Jun 24;4:328–331. doi: 10.1016/j.dib.2015.06.005

Lipid and protein maps defining arterial layers in atherosclerotic aorta

Marta Martin-Lorenzo a, Benjamin Balluff b, Aroa S Maroto a, Ricardo J Carreira b, Rene JM van Zeijl b, Laura Gonzalez-Calero a, Fernando de la Cuesta c, Maria G Barderas c, Luis F Lopez-Almodovar d, Luis R Padial e, Liam A McDonnell b, Fernando Vivanco a,f, Gloria Alvarez-Llamas a,⁎
PMCID: PMC4510571  PMID: 26217810

Abstract

Subclinical atherosclerosis cannot be predicted and novel therapeutic targets are needed. The molecular anatomy of healthy and atherosclerotic tissue is pursued to identify ongoing molecular changes in atherosclerosis development. Mass Spectrometry Imaging (MSI) accounts with the unique advantage of analyzing proteins and metabolites (lipids) while preserving their original localization; thus two dimensional maps can be obtained. Main molecular alterations were investigated in a rabbit model in response to early development of atherosclerosis. Aortic arterial layers (intima and media) and calcified regions were investigated in detail by MALDI-MSI and proteins and lipids specifically defining those areas of interest were identified. These data further complement main findings previously published in J Proteomics (M. Martin-Lorenzo et al., J. Proteomics. (In press); M. Martin-Lorenzo et al., J. Proteomics 108 (2014) 465–468.) [1,2].

Specifications table

Subject area Biology
More specific subject area Cardiovascular disease, MSI development and application to arterial tissue
Type of data Table and figure
How data was acquired MALDI-MSI, FTICR
Data format Analyzed
Experimental factors Specific and careful tissue treatment was applied as previously published [1]
Experimental features
Data source location LUMC (Leiden, The Netherlands), IIS-Fundación Jiménez Díaz (Madrid, Spain)
Data accessibility
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Value of the data

  • •

    A novel unexplored ex vivo imaging approach in cardiovascular disease;

  • •

    30 µm high spatial resolution is applied to investigate atherosclerosis tissue layers;

  • •

    This is the first time specific protein localization and alteration in response to atherosclerosis is shown by MALDI-MSI;

  • •

    TMSB4X up-regulation in atherosclerosis is firstly identified at its original location.

1. Data, experimental design, materials and methods

1.1. Data

Specific molecular features (m/z values) were identified by MALDI-MSI, corresponding to proteins and lipids specifically defining intima, media or calcified regions in atherosclerotic rabbit aorta (Fig. 1). m/z values with specific location, and fold change in response to atherosclerosis early development are compiled in Table 1. Tentative identification was performed and is also shown.

Fig. 1.

Fig. 1

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Representative MALDI-MSI images for proteins (A) and lipids (B, C) in rabbit aorta. Intima (I) and media (M) layers and calcified regions (P) in the intima are defined by specific m/z values. Characterization of samples is made according to histology: H&E, Oil-Red (OR) and Red Alizarin (RA).

Table 1.

MALDI-MSI m/z values with specific localization in the intima or media layer are shown (left column): xp means specifically located in the calcified region of the intima layer. Comparison between healthy and atherosclerotic tissues is also included (right column):↑increased in atherosclerosis;↓decreased in atherosclerosis; P: pathologic (atherosclerotic) tissue; C: control (healthy) tissue. Bold numbers show statistical significance (p Value <0.05, Mann–Whitney test). Identification was performed by FT-ICR measurements, MaTisse database, MSiMass list database and literature [12,13].

Arterial localization
 Atherosclerosis
 Molecule
m/z Media Intima p-Value Trend Fold change (P/C) p-Value
Proteins
3011 x 0.0108 ↑ 1.67 0.0022 SEL1L, IQGAP1, GANAB, NCSTN, UGDH, CYBA, YWHAG, MIF, EIF2S3, SYNM, ITGA5, NDUFS7, COL12A1, VASN, EEF1A1, MYBPC1, HBA1-2, ENO1, UBA1, CA3, MUC5B
3553 x 0.0022 ↓ 0.64 0.0152 NSF, PSMC4, ACTB, MYL2, PKM2, HSPD1
3569 x 0.0022 ↓ 0.67 0.0303 DHRS7, ACTB, MYL2, PKM2, ERP44, S100A6
4597 x 0.0022 ↓ 0.92 0.4589 –
4614 x 0.0022 ↓ 0.93 0.6494 HBB
4762 x 0.0303 ↑ 3.00 0.0022 TMSB4X
4778 x 0.0303 ↑ 2.07 0.0022 –
5620 x 0.0022 ↓ 0.58 0.0087 –
6182 x 0.0022 ↓ 0.49 0.0022 –
6199 x 0.0022 ↓ 0.57 0.0152 –



Lipids
255 x 0.0152 ↑ 4.98 0.0022 SFA
518 x 0.0022 ↑ 8.74 0.0022 Lysolipids
520 x 0.0260 ↑ 4.58 0.0260 Lysolipids
522 x 0.0022 ↑ 5.64 0.0022 LPC (0:0/18:1), lysolipids
535 xP 0.0381 ↑ 4.21 0.0381 –
536 xP 0.3524 ↑ 1.42 0.1714 –
568 xP 0.1714 ↑ 3.57 0.0667 –
675 xP 0.0667 ↑ 6.84 0.0190 PA
676 xP 0.1143 ↑ 4.61 0.0381 PA+PG
691 xP 0.0667 ↑ 4.43 0.0381 SM+PA+PE−Cer
722 xP 0.1143 ↑ 4.76 0.1143 PC+PE
800 x 0.0022 ↑ 3.74 0.0022 SM
864 x 0.0087 ↑ 9.77 0.0022 PG
865 x 0.0087 ↑ 6.54 0.0022 PI
866 x 0.0260 ↑ 1.03 0.0022 PC
891 x 0.0931 ↑ 6.52 0.0022 Glc−GP+PI
893 x 0.0433 ↑ 6.18 0.0411 PS
895 xP 0.3874 ↑ 1.51 0.1320 TG
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1.2. Experimental design

A rabbit model of atherosclerosis was developed as previously published [3] to investigate molecular alterations in arterial tissue in response to atherosclerosis. High-spatial-resolution MALDI-MSI was applied to comparatively analyze histologically-based arterial regions of interest from control and atherosclerotic aortas.

1.3. Materials and methods

The ascending aortic section of each animal was dissected, snap frozen in liquid nitrogen without any fixation and stored at −80 °C [4,5].Three different MALDI-MSI protocols were applied for the detection of proteins [2], lipids [6] and metabolites [7,8]. Public libraries of MALDI-MSI data, MSiMass list database [9] and MaTisse [10] were used to assign identity of the most significantly altered protein molecular feature using a mass tolerance of ±3 Da [11]. Lipid molecular identification was performed by using exact mass measurements, peak peaking and spatial filtering combined with Lipidsmap database using a tolerance of ≤0.005 Da, as previously published [12,13]. For comparison between control and atherosclerotic tissue, a random selection of the whole spectra sets from these regions were then imported into ClinProTools 3.0 (Bruker Daltonik) where they underwent smoothing, baseline subtraction, mass spectral alignment and normalization. Mann–Whitney non-parametric tests were performed using GraphPad Prism software.

Acknowledgments

This work is financially supported by the Cyttron II project “Imaging Mass Spectrometry”, ISCIII (PI11/01401, PI13/01873, CP09/00229) and IDCSalud (Grant number 3371/002). MML is funded by Fundación Conchita Rabago and gratefully acknowledges the travel funding supplied by SePROT and the COST Action BM1104 for the Short Term Scientific Missions to LUMC. BB and RC are funded by the Marie Curie Actions of the European Union (BB no. 331866, SITH FP7-PEOPLE-2012-IEF, RC no. 303344, ENIGMAS FP7-PEOPLE-2011-IEF).

These results are lined up with the Spanish initiative on the Human Proteome Project (SpHPP).

References

  • 1.Martin-Lorenzo M., Balluff B., Maroto A.S., Carreira R.J., van Zeijl R.J.M., Gonzalez-Calero L., de la Cuesta F., Barderas M.G., Lopez-Almodovar L.F., Padial L.R., McDonnell L.A., Vivanco F., Alvarez-Llamas G. J. Proteomics. 2015 doi: 10.1016/j.jprot.2015.06.005. (In press) [DOI] [PubMed] [Google Scholar]
  • 2.Martin-Lorenzo M., Balluff B., Sanz-Maroto A., van Zeijl R.J., Vivanco F., Alvarez-Llamas G. J. Proteomics. 2014;108:465–468. doi: 10.1016/j.jprot.2014.06.013. [DOI] [PubMed] [Google Scholar]
  • 3.Martin-Lorenzo M., Zubiri I., Maroto A., Gonzalez-Calero L., Posada-Ayala M., de la Cuesta F. KLK1 and ZG16B proteins and arginine-proline metabolism identified as novel targets to monitor atherosclerosis, acute coronary syndrome and recovery. Metabolomics. 2014 doi: 10.1007/s11306-014-0761-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Chughtai K., Heeren R.M. Mass spectrometric imaging for biomedical tissue analysis. Chem. Rev. 2010;110(5):3237–3277. doi: 10.1021/cr100012c. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Meding S., Walch A. MALDI imaging mass spectrometry for direct tissue analysis. Methods Mol. Biol. 2013;931:537–546. doi: 10.1007/978-1-62703-056-4_29. [DOI] [PubMed] [Google Scholar]
  • 6.Willems S.M., van R.A., van Z.R., Deelder A.M., McDonnell L.A., Hogendoorn P.C. Imaging mass spectrometry of myxoid sarcomas identifies proteins and lipids specific to tumour type and grade, and reveals biochemical intratumour heterogeneity. J. Pathol. 2010;222(4):400–409. doi: 10.1002/path.2771. [DOI] [PubMed] [Google Scholar]
  • 7.Jones E.A., Shyti R., van Zeijl R.J., van Heiningen S.H., Ferrari M.D., Deelder A.M. Imaging mass spectrometry to visualize biomolecule distributions in mouse brain tissue following hemispheric cortical spreading depression. J Proteomics. 2012;75(16):5027–5035. doi: 10.1016/j.jprot.2012.06.025. [DOI] [PubMed] [Google Scholar]
  • 8.Dekker T.J., Jones E.A., Corver W.E., van Zeijl R.J., Deelder A.M., Tollenaar R.A. Towards imaging metabolic pathways in tissues. Anal. Bioanal. Chem. 2014 doi: 10.1007/s00216-014-8305-7. [DOI] [PubMed] [Google Scholar]
  • 9.McDonnell L.A., Walch A., Stoeckli M., Corthals G.L. MSiMass list: a public database of identifications for protein MALDI-MS imaging. J. Proteome Res. 2013 doi: 10.1021/pr400620y. [DOI] [PubMed] [Google Scholar]
  • 10.Maier S.K., Hahne H., Gholami A.M., Balluff B., Meding S., Schoene C. Comprehensive identification of proteins from MALDI imaging. Mol. Cell Proteomics. 2013;12(10):2901–2910. doi: 10.1074/mcp.M113.027599. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Balluff B., Rauser S., Meding S., Elsner M., Schone C., Feuchtinger A. MALDI imaging identifies prognostic seven-protein signature of novel tissue markers in intestinal-type gastric cancer. Am. J. Pathol. 2011;179(6):2720–2729. doi: 10.1016/j.ajpath.2011.08.032. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Chughtai K., Jiang L., Greenwood T.R., Glunde K., Heeren R.M. Mass spectrometry images acylcarnitines, phosphatidylcholines, and sphingomyelin in MDA-MB-231 breast tumor models. J. Lipid Res. 2013;54(2):333–344. doi: 10.1194/jlr.M027961. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Schiller J., Zschornig O., Petkovic M., Muller M., Arnhold J., Arnold K. Lipid analysis of human HDL and LDL by MALDI-TOF mass spectrometry and (31)P NMR. J. Lipid Res. 2001;42(9):1501–1508. [PubMed] [Google Scholar]

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