Automatic Generic Registration of Mass Spectrometry Imaging Data to Histology Using Nonlinear Stochastic Embedding

Walid M. Abdelmoula; Karolina Skraskova; Benjamin Balluff; Ricardo J. Carreira; Else A. Tolner; Boudewijn P. F. Lelieveldt; Laurens van der Maaten; Hans Morreau; Arn M. J. M. van den Maagdenberg; Ron M. A. Heeren; Liam A. McDonnell; Jouke Dijkstra

doi:10.1021/ac502170f

Automatic Generic Registration of Mass Spectrometry Imaging Data to Histology Using Nonlinear Stochastic Embedding

Walid M. Abdelmoula, Karolina Skraskova, Benjamin Balluff, Ricardo J. Carreira, Else A. Tolner, Boudewijn P. F. Lelieveldt, Laurens van der Maaten, Hans Morreau, Arn M. J. M. van den Maagdenberg, Ron M. A. Heeren, Liam A. McDonnell^*, Jouke Dijkstra

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

Abstract

The combination of mass spectrometry imaging and histology has proven a powerful approach for obtaining molecular signatures from specific cells/tissues of interest, whether to identify biomolecular changes associated with specific histopathological entities or to determine the amount of a drug in specific organs/compartments. Currently there is no software that is able to explicitly register mass spectrometry imaging data spanning different ionization techniques or mass analyzers. Accordingly, the full capabilities of mass spectrometry imaging are at present underexploited. Here we present a fully automated generic approach for registering mass spectrometry imaging data to histology and demonstrate its capabilities for multiple mass analyzers, multiple ionization sources, and multiple tissue types.

Original language	English
Pages (from-to)	9204-9211
Journal	Analytical Chemistry
Volume	86
Issue number	18
DOIs	https://doi.org/10.1021/ac502170f
Publication status	Published - 16 Sept 2014

Access to Document

10.1021/ac502170f

Cite this

Abdelmoula, W. M., Skraskova, K., Balluff, B., Carreira, R. J., Tolner, E. A., Lelieveldt, B. P. F., van der Maaten, L., Morreau, H., van den Maagdenberg, A. M. J. M., Heeren, R. M. A., McDonnell, L. A., & Dijkstra, J. (2014). Automatic Generic Registration of Mass Spectrometry Imaging Data to Histology Using Nonlinear Stochastic Embedding. Analytical Chemistry, 86(18), 9204-9211. https://doi.org/10.1021/ac502170f

@article{a00825e5d99f4fb29fefd8d7c68cb3af,

title = "Automatic Generic Registration of Mass Spectrometry Imaging Data to Histology Using Nonlinear Stochastic Embedding",

abstract = "The combination of mass spectrometry imaging and histology has proven a powerful approach for obtaining molecular signatures from specific cells/tissues of interest, whether to identify biomolecular changes associated with specific histopathological entities or to determine the amount of a drug in specific organs/compartments. Currently there is no software that is able to explicitly register mass spectrometry imaging data spanning different ionization techniques or mass analyzers. Accordingly, the full capabilities of mass spectrometry imaging are at present underexploited. Here we present a fully automated generic approach for registering mass spectrometry imaging data to histology and demonstrate its capabilities for multiple mass analyzers, multiple ionization sources, and multiple tissue types.",

author = "Abdelmoula, {Walid M.} and Karolina Skraskova and Benjamin Balluff and Carreira, {Ricardo J.} and Tolner, {Else A.} and Lelieveldt, {Boudewijn P. F.} and {van der Maaten}, Laurens and Hans Morreau and {van den Maagdenberg}, {Arn M. J. M.} and Heeren, {Ron M. A.} and McDonnell, {Liam A.} and Jouke Dijkstra",

year = "2014",

month = sep,

day = "16",

doi = "10.1021/ac502170f",

language = "English",

volume = "86",

pages = "9204--9211",

journal = "Analytical Chemistry",

issn = "0003-2700",

publisher = "American Chemical Society",

number = "18",

}

Abdelmoula, WM, Skraskova, K, Balluff, B, Carreira, RJ, Tolner, EA, Lelieveldt, BPF, van der Maaten, L, Morreau, H, van den Maagdenberg, AMJM, Heeren, RMA, McDonnell, LA & Dijkstra, J 2014, 'Automatic Generic Registration of Mass Spectrometry Imaging Data to Histology Using Nonlinear Stochastic Embedding', Analytical Chemistry, vol. 86, no. 18, pp. 9204-9211. https://doi.org/10.1021/ac502170f

TY - JOUR

T1 - Automatic Generic Registration of Mass Spectrometry Imaging Data to Histology Using Nonlinear Stochastic Embedding

AU - Abdelmoula, Walid M.

AU - Skraskova, Karolina

AU - Balluff, Benjamin

AU - Carreira, Ricardo J.

AU - Tolner, Else A.

AU - Lelieveldt, Boudewijn P. F.

AU - van der Maaten, Laurens

AU - Morreau, Hans

AU - van den Maagdenberg, Arn M. J. M.

AU - Heeren, Ron M. A.

AU - McDonnell, Liam A.

AU - Dijkstra, Jouke

PY - 2014/9/16

Y1 - 2014/9/16

N2 - The combination of mass spectrometry imaging and histology has proven a powerful approach for obtaining molecular signatures from specific cells/tissues of interest, whether to identify biomolecular changes associated with specific histopathological entities or to determine the amount of a drug in specific organs/compartments. Currently there is no software that is able to explicitly register mass spectrometry imaging data spanning different ionization techniques or mass analyzers. Accordingly, the full capabilities of mass spectrometry imaging are at present underexploited. Here we present a fully automated generic approach for registering mass spectrometry imaging data to histology and demonstrate its capabilities for multiple mass analyzers, multiple ionization sources, and multiple tissue types.

AB - The combination of mass spectrometry imaging and histology has proven a powerful approach for obtaining molecular signatures from specific cells/tissues of interest, whether to identify biomolecular changes associated with specific histopathological entities or to determine the amount of a drug in specific organs/compartments. Currently there is no software that is able to explicitly register mass spectrometry imaging data spanning different ionization techniques or mass analyzers. Accordingly, the full capabilities of mass spectrometry imaging are at present underexploited. Here we present a fully automated generic approach for registering mass spectrometry imaging data to histology and demonstrate its capabilities for multiple mass analyzers, multiple ionization sources, and multiple tissue types.

U2 - 10.1021/ac502170f

DO - 10.1021/ac502170f

M3 - Article

C2 - 25133861

SN - 0003-2700

VL - 86

SP - 9204

EP - 9211

JO - Analytical Chemistry

JF - Analytical Chemistry

IS - 18

ER -

Automatic Generic Registration of Mass Spectrometry Imaging Data to Histology Using Nonlinear Stochastic Embedding

Abstract

Access to Document

Fingerprint

Cite this