4.7 Article

MassImager: A software for interactive and in-depth analysis of mass spectrometry imaging data

Journal

ANALYTICA CHIMICA ACTA
Volume 1015, Issue -, Pages 50-57

Publisher

ELSEVIER SCIENCE BV
DOI: 10.1016/j.aca.2018.02.030

Keywords

Mass spectrometry imaging; Data processing software; Interactive visualization; In-situ biomarker discovery; Artificial intelligent pathological diagnosis

Funding

  1. National Natural Science Foundation of China [81373370, 81773678]
  2. Fundamental Research Funds for the Central Universities [3332015177]
  3. PUMC Youth Fund
  4. National Instrumentation Program [2016YFF0100304]
  5. CAMS Innovation Fund for Medical Sciences of Peking Union Medical College [2016-I2M-1-009]

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Mass spectrometry imaging (MSI) has become a powerful tool to probe molecule events in biological tissue. However, it is a widely held viewpoint that one of the biggest challenges is an easy-to-use data processing software for discovering the underlying biological information from complicated and huge MSI dataset. Here, a user-friendly and full-featured MSI software including three subsystems, Solution, Visualization and Intelligence, named MassImager, is developed focusing on interactive visualization, in-situ biomarker discovery and artificial intelligent pathological diagnosis. Simplified data preprocessing and high-throughput MSI data exchange, serialization jointly guarantee the quick reconstruction of ion image and rapid analysis of dozens of gigabytes datasets. It also offers diverse self-defined operations for visual processing, including multiple ion visualization, multiple channel superposition, image normalization, visual resolution enhancement and image filter. Regions-of-interest analysis can be performed precisely through the interactive visualization between the ion images and mass spectra, also the overlaid optical image guide, to directly find out the region-specific biomarkers. Moreover, automatic pattern recognition can be achieved immediately upon the supervised or unsupervised multivariate statistical modeling. Clear discrimination between cancer tissue and adjacent tissue within a MSI dataset can be seen in the generated pattern image, which shows great potential in visually in-situ biomarker discovery and artificial intelligent pathological diagnosis of cancer. All the features are integrated together in MassImager to provide a deep MSI processing solution at the in-situ metabolomics level for biomarker discovery and future clinical pathological diagnosis. (C) 2018 The Authors. Published by Elsevier B.V.

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