Abstract
The driving force behind recent developments in Life Sciences such as drug discovery, individual therapy planning or pathway detection in systems biology are the Omics-technologies (Proteomics, Lipidomics, Metabolomics, etc.). Over the last 15 years these technologies have been partially revolutionized due to the advance of a new bioanalytic methodology called MALDI imaging (matrix assisted laser desorption ionization).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
J. Behrmann, C. Etmann, T. Boskamp, R. Casadonte, J. Kriegsmann, and P. Maass. Deep learning for tumor classification in imaging mass spectrometry. Bioinformatics, 34(7):1215–1223, 2017.
T. Boskamp, D. Lachmund, J. Oetjen, Y. C. Hernandez, D. Trede, P. Maass, R. Casadonte, J. Kriegsmann, A. Warth, H. Dienemann, et al. A new classification method for maldi imaging mass spectrometry data acquired on formalin-fixed paraffin-embedded tissue samples. Biochimica et Biophysica Acta (BBA)- Proteins and Proteomics, 1865(7):916–926, 2017.
Y. Cordero Hernandez, T. Boskamp, R. Casadonte, L. Hauberg-Lotte, J. Oetjen, D. Lachmund, A. Peter, D. Trede, K. Kriegsmann, M. Kriegsmann, et al. Targeted feature extraction in maldi mass spectrometry imaging to discriminate proteomic profiles of breast and ovarian cancer. PROTEOMICS– Clinical Applications, page 1700168, 2018.
P. Fernsel and P. Maass. A survey on surrogate approaches to non-negative matrix factorization. Vietnam Journal of Mathematics, 46(4):987–1021, 2018.
M. R. Groseclose, P. P. Massion, P. Chaurand, and R. M. Caprioli. High-throughput proteomic analysis of formalin-fixed paraffin-embedded tissue microarrays using maldi imaging mass spectrometry. Proteomics, 8(18):3715–3724, 2008.
J. Kriegsmann, M. Kriegsmann, and R. Casadonte. Maldi tof imaging mass spectrometry in clinical pathology: a valuable tool for cancer diagnostics. International journal of oncology, 46(3):893–906, 2015.
J. Leuschner, M. Schmidt, P. Fernsel, D. Lachmund, T. Boskamp, and P. Maass. Supervised non-negative matrix factorization methods for maldi imaging applications. Bioinformatics, 2018.
R. Longuespée, R. Casadonte, M. Kriegsmann, C. Pottier, G. Picard de Muller, P. Delvenne, J. Kriegsmann, and E. De Pauw. Maldi mass spectrometry imaging: A cutting-edge tool for fundamental and clinical histopathology. PROTEOMICS–Clinical Applications, 10(7):701–719, 2016.
J. Oetjen, D. Lachmund, A. Palmer, T. Alexandrov, M. Becker, T. Boskamp, and P. Maass. An approach to optimize sample preparation for maldi imaging ms of ffpe sections using fractional factorial design of experiments. Analytical and bioanalytical chemistry, 408(24):6729–6740, 2016.
J. Oetjen, K. Veselkov, J. Watrous, J. McKenzie, M. Becker, L. Hauberg-Lotte, J. Kobarg, N. Strittmatter, A. Mróz, F. Hoffmann, et al. Benchmark datasets for 3d maldi-and desi-imaging mass spectrometry. gigascience 4: 20, 2015.
J. Quanico, L. Hauberg-Lotte, S. Devaux, Z. Laouby, C. Meriaux, A. Raffo-Romero, M. Rose, L. Westerheide, J. Vehmeyer, F. Rodet, et al. 3d maldi mass spectrometry imaging reveals specific localization of long-chain acylcarnitines within a 10-day time window of spinal cord injury. Scientific reports, 8(1):16083, 2018.
E. H. Seeley and R. M. Caprioli. Maldi imaging mass spectrometry of human tissue: method challenges and clinical perspectives. Trends in biotechnology, 29(3):136–143, 2011
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Maass, P., Hauberg-Lotte, L., Boskamp, T. (2021). MALDI Imaging: Exploring the molecular landscape. In: Bock, H.G., Küfer, KH., Maass, P., Milde, A., Schulz, V. (eds) German Success Stories in Industrial Mathematics. Mathematics in Industry, vol 35. Springer, Cham. https://doi.org/10.1007/978-3-030-81455-7_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-81455-7_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-81454-0
Online ISBN: 978-3-030-81455-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)