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Sparse autoencoder–based feature extraction from TOF–SIMS image data of human skin structures

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Abstract

Time-of-flight secondary ion mass spectrometry (TOF–SIMS) is a useful and versatile tool for surface analysis, enabling detailed compositional information to be obtained for the surfaces of diverse samples. Furthermore, in the case of two- or three-dimensional imaging, the measurement sensitivity in the higher molecular weight range can be improved by using a cluster ion source, thus further enriching the TOF–SIMS information. Therefore, appropriate analytical methods are required to interpret this TOF–SIMS data. This study explored the capabilities of a sparse autoencoder, a feature extraction method based on artificial neural networks, to process TOF–SIMS image data. The sparse autoencoder was applied to TOF–SIMS images of human skin keratinocytes to extract the distribution of endogenous intercellular lipids and externally penetrated drugs. The results were compared with those obtained using principal component analysis (PCA) and multivariate curve resolution (MCR), which are conventionally used for extracting features from TOF–SIMS data. This confirmed that the sparse autoencoder matches, and often betters, the feature extraction performance of conventional methods, while also offering greater flexibility.

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Data availability

The raw data of this study are available on request with proper reason from the corresponding author, Kazuhiro Matsuda.

Code availability

Not applicable.

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Authors and Affiliations

  1. Surface Science Laboratories, Toray Research Center, Inc, 3-3-7, Sonoyama, Otsu, Shiga, 520-8567, Japan

    Kazuhiro Matsuda

  2. Faculty of Science and Technology, Seikei University, 3-3-1 Kichijoji-kitamachi, Musashino, Tokyo, 180-8633, Japan

    Kazuhiro Matsuda & Satoka Aoyagi

Authors
  1. Kazuhiro Matsuda
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  2. Satoka Aoyagi
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Contributions

Kazuhiro Matsuda was responsible for writing (original draft), investigation, data acquisition (sample preparation and TOF–SIMS measurements), data processing, and data visualization.

Satoka Aoyagi was responsible for supervision, methodology, and writing (review and editing).

Corresponding author

Correspondence to Kazuhiro Matsuda.

Ethics declarations

Ethics approval

The evaluation of this study was based on the Declaration of Helsinki and conducted under approval of the ethics committee of Toray research center Inc.

Consent to participate

Written informed consent was obtained from the provider of skin corneocytes for publication of this study.

Consent for publication

Consent for publication was obtained from the provider of skin corneocytes by written form.

Conflict of interest

The authors declare no competing interests.

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Matsuda, K., Aoyagi, S. Sparse autoencoder–based feature extraction from TOF–SIMS image data of human skin structures. Anal Bioanal Chem 414, 1177–1186 (2022). https://doi.org/10.1007/s00216-021-03744-3

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  • DOI: https://doi.org/10.1007/s00216-021-03744-3

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