Evaluation of integrin αvβ cystine knot PET tracers to detect cancer and idiopathic pulmonary fibrosis.

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 αvβ 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.

Improving Proteome Coverage by Reducing Sample Complexity via Chromatography.

High performance liquid chromatography (HPLC) is currently one of the most powerful analytical tools that has revolutionized the field of proteomics. Formerly known as high pressure liquid chromatography, this technique was introduced in the early 1960s to improve the efficiency of liquid chromatography separations using small stationary phase particles packed in columns. Since its introduction, continued advancements in column technology, development of different stationary phase materials and improved instrumentation has allowed the full potential of this technique to be realized.

Protein biomarkers on tissue as imaged via MALDI mass spectrometry: A systematic approach to study the limits of detection.

MALDI mass spectrometry imaging (MSI) is emerging as a tool for protein and peptide imaging across tissue sections. Despite extensive study, there does not yet exist a baseline study evaluating the potential capabilities for this technique to detect diverse proteins in tissue sections. In this study, we developed a systematic approach for characterizing MALDI-MSI workflows in terms of limits of detection, coefficients of variation, spatial resolution, and the identification of endogenous tissue proteins.

Probabilistic Segmentation of Mass Spectrometry (MS) Images Helps Select Important Ions and Characterize Confidence in the Resulting Segments.

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.

Cardinal: an R package for statistical analysis of mass spectrometry-based imaging experiments.

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.