title: Chemo-informatic strategy for imaging mass spectrometry-based hyperspectral profiling of lipid signatures in colorectal cancer.
abstract: Mass spectrometry imaging (MSI) provides the opportunity to investigate tumor biology from an entirely novel biochemical perspective and could lead to the identification of a new pool of cancer biomarkers. Effective clinical translation of histology-driven MSI in systems oncology requires precise colocalization of morphological and biochemical features as well as advanced methods for data treatment and interrogation. Currently proposed MSI workflows are subject to several limitations, including nonoptimized raw data preprocessing, imprecise image coregistration, and limited pattern recognition capabilities. Here we outline a comprehensive strategy for histology-driven MSI, using desorption electrospray ionization that covers (i) optimized data preprocessing for improved information recovery; (ii) precise image coregistration; and (iii) efficient extraction of tissue-specific molecular ion signatures for enhanced biochemical distinction of different tissue types. The proposed workflow has been used to investigate region-specific lipid signatures in colorectal cancer tissue. Unique lipid patterns were observed using this approach according to tissue type, and a tissue recognition system using multivariate molecular ion patterns allowed highly accurate (>98%) identification of pixels according to morphology (cancer, healthy mucosa, smooth muscle, and microvasculature). This strategy offers unique insights into tumor microenvironmental biochemistry and should facilitate compilation of a large-scale tissue morphology-specific MSI spectral database with which to pursue next-generation, fully automated histological approaches.
authors.affiliation: Computational and Systems Medicine, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London SW7 2AZ, United Kingdom.
authors.forename: Kirill A
authors.initials: KA
authors.lastname: Veselkov
authors.forename: Reza
authors.initials: R
authors.lastname: Mirnezami
authors.forename: Nicole
authors.initials: N
authors.lastname: Strittmatter
authors.forename: Robert D
authors.initials: RD
authors.lastname: Goldin
authors.forename: James
authors.initials: J
authors.lastname: Kinross
authors.forename: Abigail V M
authors.initials: AV
authors.lastname: Speller
authors.forename: Tigran
authors.initials: T
authors.lastname: Abramov
authors.forename: Emrys A
authors.initials: EA
authors.lastname: Jones
authors.forename: Ara
authors.initials: A
authors.lastname: Darzi
authors.forename: Elaine
authors.initials: E
authors.lastname: Holmes
authors.forename: Jeremy K
authors.initials: JK
authors.lastname: Nicholson
authors.forename: Zoltan
authors.initials: Z
authors.lastname: Takats
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000378:metabolism; D015179:Colorectal Neoplasms / Q000378:metabolism*; D019295:Computational Biology; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008055:Lipids / Q000737:chemistry*; D015999:Multivariate Analysis; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D012984:Software; D021241:Spectrometry, Mass, Electrospray Ionization
chemical_list: D015415:Biomarkers; D008055:Lipids