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  • Brief Communication
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Cardinal v.3: a versatile open-source software for mass spectrometry imaging analysis

Nature Methods volume 20, pages 1883–1886 (2023)Cite this article

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Abstract

Cardinal v.3 is an open-source software for reproducible analysis of mass spectrometry imaging experiments. A major update from its previous versions, Cardinal v.3 supports most mass spectrometry imaging workflows. Its analytical capabilities include advanced data processing such as mass recalibration, advanced statistical analyses such as single-ion segmentation and rough annotation-based classification, and memory-efficient analyses of large-scale multitissue experiments.

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Fig. 1: Common MSI data analysis steps and the corresponding functionalities in Cardinal.
Fig. 2: Reanalysis of three public datasets with Cardinal.

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Data availability

The three publicly available datasets that were reanalyzed in this study are available via the PRIDE repository (https://www.ebi.ac.uk/pride/) with the identifiers PXD001283, PXD011104 and PXD026459. The raw, reanalysis and R code are available in the MassIVE.quant repository with the identifier MSV000086099 and RMSV000000684 (case study 1), MSV000086102 and RMSV000000664 (case study 2) and MSV000089594 and RMSV000000686 (case studies 3 and 4). Reanalysis and R code are also available in GitHub (https://github.com/Vitek-Lab/Cardinal3-vignettes).

Code availability

The following R (v.4.2.1 and 4.2.2) packages were used in this study: Cardinal v.3.1.0, CardinalNN v.0.0.1, reticulate v.1.28 and ggplot2 v.3.4.1. The following python (v.3.10.8) libraries were used in this study: classification (installed from DeepMSI) v.0.0.1, matplotlib v.3.6.2, numpy v.1.23.5, pandas v.1.5.2 and torch v.1.12.1. Code for the reanalysis of public datasets including generation of the figures is available on GitHub: https://github.com/Vitek-Lab/Cardinal3-vignettes. The Cardinal software can be downloaded via: Bioconductor, https://www.bioconductor.org/packages/release/bioc/html/Cardinal.html; Bioconda, https://anaconda.org/bioconda/bioconductor-cardinal; Biocontainers, https://biocontainers.pro/tools/bioconductor-cardinal; GitHub (Source code), https://github.com/kuwisdelu/Cardinal (Artistic 2.0) and https://github.com/DanGuo1223/CardinalNN (MIT license).

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Acknowledgements

We thank A. Weber for valuable feedback. This work was supported by NSF-BIO/DBI 1759736, NSF-BIO/DBI 1950412, NIH-NLM-R01 1R01LM013115 and the Chan-Zuckerberg foundation awarded to O.V., and the Hans A. Krebs Medical Scientist Programme, Faculty of Medicine, University of Freiburg, Germany awarded to M.C.F. The funders had no role in the study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Author notes

  1. These authors contributed equally: Kylie Ariel Bemis, Melanie Christine Föll.

Authors and Affiliations

  1. Khoury College of Computer Sciences, Northeastern University, Boston, MA, USA

    Kylie Ariel Bemis, Melanie Christine Föll, Dan Guo, Sai Srikanth Lakkimsetty & Olga Vitek

  2. Institute of Surgical Pathology, Medical Center, University of Freiburg, Faculty of Medicine, Freiburg, Germany

    Melanie Christine Föll

  3. German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany

    Melanie Christine Föll

Authors
  1. Kylie Ariel Bemis
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Contributions

K.A.B. developed the methods and wrote the code for the Cardinal and Matter software. D.G. developed the methods and wrote the code for the CardinalNN software. M.C.F. and S.S.L. tested all software. K.A.B., M.C.F. and D.G. developed and analyzed the data for the case studies and generated the vignettes. S.S.L. revised the vignettes. M.C.F. wrote the manuscript and generated the figures. M.C.F. and O.V. revised and edited the manuscript. O.V. supervised the work and provided funding.

Corresponding author

Correspondence to Olga Vitek.

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The authors declare no competing interests.

Peer review

Peer review information

Nature Methods thanks Nathan Patterson and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editor: Arunima Singh, in collaboration with the Nature Methods team.

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Extended data

Extended Data Fig. 1 Cardinal v.3 supports visualization and preprocessing of high mass resolution data.

a, Reproducing the overlay ion image of three m/z features with different spatial abundances (Fig. 1a in original publication). b, Zoomed in average mass spectra show two peaks that are only 0.05 m/z apart (Fig. 2c in original publication) and could be detected as separate peaks. The red dotted lines indicate that both peaks will be picked separately during peak detection. c, Visualizing the spatial distribution of both peaks shows that they are present in different tissue regions (Fig. 2b in original publication). The peaks in the original publication were likely mislabeled as suggested by Fig. S5 of the same publication and the inverse image we obtained here.

Extended Data Fig. 2 Cardinal v.3 enables more accurate m/z values in the multiple replicates classification dataset.

a, Zoomed in mass spectra for six random spectra of the first dataset show m/z shifts between the spectra before m/z alignment. Applying Cardinal’s new mass alignment step increases the alignment of the peaks substantially. b, Zoomed in mass spectra for six random spectra of the first dataset before and after mass re-calibration show how Cardinal’s new mass re-calibration method shifts the monoisotopic angiotensin peak towards its theoretical m/z position (m/z 1296.7, dashed vertical line).

Extended Data Table 1 Cardinal is available via different resources
Full size table

Supplementary information

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Bemis, K.A., Föll, M.C., Guo, D. et al. Cardinal v.3: a versatile open-source software for mass spectrometry imaging analysis. Nat Methods 20, 1883–1886 (2023). https://doi.org/10.1038/s41592-023-02070-z

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