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@ARTICLE{AbuSammour:275219,
      author       = {D. Abu Sammour and J. L. Cairns and T. Boskamp and C.
                      Marsching and T. Kessler$^*$ and C. Ramallo Guevara and V.
                      Panitz$^*$ and A. Sadik$^*$ and J. Cordes and S. Schmidt and
                      S. A. Mohammed and M. F. Rittel and M. Friedrich$^*$ and M.
                      Platten$^*$ and I. Wolf and A. von Deimling$^*$ and C.
                      Opitz$^*$ and W. Wick$^*$ and C. Hopf},
      title        = {{S}patial probabilistic mapping of metabolite ensembles in
                      mass spectrometry imaging.},
      journal      = {Nature Communications},
      volume       = {14},
      number       = {1},
      issn         = {2041-1723},
      address      = {[London]},
      publisher    = {Nature Publishing Group UK},
      reportid     = {DKFZ-2023-00677},
      pages        = {1823},
      year         = {2023},
      abstract     = {Mass spectrometry imaging vows to enable simultaneous
                      spatially resolved investigation of hundreds of metabolites
                      in tissues, but it primarily relies on traditional ion
                      images for non-data-driven metabolite visualization and
                      analysis. The rendering and interpretation of ion images
                      neither considers nonlinearities in the resolving power of
                      mass spectrometers nor does it yet evaluate the statistical
                      significance of differential spatial metabolite abundance.
                      Here, we outline the computational framework moleculaR (
                      https://github.com/CeMOS-Mannheim/moleculaR ) that is
                      expected to improve signal reliability by data-dependent
                      Gaussian-weighting of ion intensities and that introduces
                      probabilistic molecular mapping of statistically significant
                      nonrandom patterns of relative spatial abundance of
                      metabolites-of-interest in tissue. moleculaR also enables
                      cross-tissue statistical comparisons and collective
                      molecular projections of entire biomolecular ensembles
                      followed by their spatial statistical significance
                      evaluation on a single tissue plane. It thereby fosters the
                      spatially resolved investigation of ion milieus, lipid
                      remodeling pathways, or complex scores like the adenylate
                      energy charge within the same image.},
      cin          = {B320 / HD01 / B350 / D170 / B300},
      ddc          = {500},
      cid          = {I:(DE-He78)B320-20160331 / I:(DE-He78)HD01-20160331 /
                      I:(DE-He78)B350-20160331 / I:(DE-He78)D170-20160331 /
                      I:(DE-He78)B300-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37005414},
      doi          = {10.1038/s41467-023-37394-z},
      url          = {https://inrepo02.dkfz.de/record/275219},
}