Cardinal is an R package for statistical analysis of mass spectrometry-based imaging (MSI) experiments of biological samples such as tissues. Cardinal supports both Matrix-Assisted Laser Desorption/Ionization (MALDI) and Desorption Electrospray Ionization-based MSI workflows, and experiments with multiple tissues and complex designs. The main analytical functionalities include (1) image segmentation, which partitions a tissue into regions of homogeneous chemical composition, selects the number of segments and the subset of informative ions, and characterizes the associated uncertainty and (2) image classification, which assigns locations on the tissue to pre-defined classes, selects the subset of informative ions, and estimates the resulting classification error by (cross-) validation. The statistical methods are based on mixture modeling and regularization.Contact: o.vitek@neu.eduAvailability and implementation: The code, the documentation, and examples are available open-source at www.cardinalmsi.org under the Artistic-2.0 license. The package is available at www.bioconductor.org.

A chemical affinity system exhibiting antibody-like properties is described. The system exploits bioconjugates with appended phenylboronic acid (PBA) moieties and a support-bound phenylboronic acid complexing reagent derived from salicylhydroxamic acid (SHA) for protein immobilization on a chromatographic support. The structure of the PBA.SHA complex was characterized by 11B NMR and mass spectrometry and compared with complexes derived from model compounds. Protein modification reagents were synthesized from 3-aminophenylboronic acid and utilized to prepare bioconjugates from alkaline phosphatase (AP) and horseradish peroxidase (HRP). AP obtained from one source afforded PBA bioconjugates exhibiting significant loss of enzymatic activity, whereas AP obtained from a second source afforded PBA bioconjugates exhibiting only a modest loss of enzymatic activity. Conversely, HRP afforded PBA bioconjugates exhibiting no loss of enzymatic activity. SHA-modified Sepharose was prepared by reaction of methyl 4-[(6-aminohexanoylamino)methyl]salicylate with CNBr-activated Sepharose 4B, followed by treatment with aqueous alkaline hydroxylamine. PBA-AP and PBA-HRP conjugates were efficiently immobilized on SHA-Sepharose at pH 8.3. PBA-AP conjugates were retained after washing with acidic buffers at pH 6.7, 4.2, and 2.5, whereas PBA-HRP conjugates were retained after washing with buffer at pH 6.7, but were eluted to some extent at and below pH 4.2. The results are interpreted in terms of multivalent interactions involving boronic acid complex formation between the enzyme bioconjugates and immobilized complexing reagent.

Advances in precision molecular imaging promise to transform our ability to detect, diagnose and treat disease. Here, we describe the engineering and validation of a new cystine knot peptide (knottin) that selectively recognizes human integrin αvβ6 with single-digit nanomolar affinity. We solve its 3D structure by NMR and x-ray crystallography and validate leads with 3 different radiolabels in pre-clinical models of cancer. We evaluate the lead tracer’s safety, biodistribution and pharmacokinetics in healthy human volunteers, and show its ability to detect multiple cancers (pancreatic, cervical and lung) in patients at two study locations. Additionally, we demonstrate that the knottin PET tracers can also detect fibrotic lung disease in idiopathic pulmonary fibrosis patients. Our results indicate that these cystine knot PET tracers may have potential utility in multiple disease states that are associated with upregulation of integrin αvβ6.

Mass spectrometry imaging is a powerful tool for investigating the spatial distribution of chemical compounds in a biological sample such as tissue. Two common goals of these experiments are unsupervised segmentation of images into newly discovered homogeneous segments and supervised classification of images into predefined classes. In both cases, the important secondary goals are to characterize the uncertainty associated with the segmentation and with the classification and to characterize the spectral features that define each segment or class. Recent analysis methods have focused on the spatial structure of the data to improve results. However, they either do not address these secondary goals or do this with separate post hoc procedures.We introduce spatial shrunken centroids, a statistical model-based framework for both supervised classification and unsupervised segmentation. It takes as input sets of previously detected, aligned, quantified, and normalized spectral features and expresses both spatial and multivariate nature of the data using probabilistic modeling. It selects informative subsets of spectral features that define each unsupervised segment or supervised class and quantifies and visualizes the uncertainty in spatial segmentations and in tissue classification. In the unsupervised setting, it also guides the choice of an appropriate number of segments. We demonstrate the usefulness of this framework in a supervised human renal cell carcinoma experimental dataset and several unsupervised experimental datasets, including a pig fetus cross-section, three rodent brains, and a controlled image with known ground truth. This framework is available for use within the open-source R package Cardinal as part of a full pipeline for the processing, visualization, and statistical analysis of mass spectrometry imaging experiments.