RESUMO
The recent upswing in the integration of spatial multi-omics for conducting multidimensional information measurements is opening a new chapter in biological research. Mapping the landscape of various biomolecules including metabolites, proteins, nucleic acids, etc., and even deciphering their functional interactions and pathways is believed to provide a more holistic and nuanced exploration of the molecular intricacies within living systems. Mass spectrometry imaging (MSI) stands as a forefront technique for spatially mapping the metabolome, lipidome, and proteome within diverse tissue and cell samples. In this review, we offer a systematic survey delineating different MSI techniques for spatially resolved multi-omics analysis, elucidating their principles, capabilities, and limitations. Particularly, we focus on the advancements in methodologies aimed at augmenting the molecular sensitivity and specificity of MSI; and depict the burgeoning integration of MSI-based spatial metabolomics, lipidomics, and proteomics, encompassing the synergy with other imaging modalities. Furthermore, we offer speculative insights into the potential trajectory of MSI technology in the future.
RESUMO
Plasma pharmacokinetic (PK) data are required as an input function for graphical analysis of single positron emission computed tomography/computed tomography (SPECT/CT) and positron emission tomography/CT (PET/CT) data to evaluate tissue influx rate of radiotracers. Dynamic heart imaging data are often used as a surrogate of plasma PK. However, accumulation of radiolabel in the heart tissue may cause overprediction of plasma PK. Therefore, we developed a compartmental model, which involves forcing functions to describe intact and degraded radiolabeled proteins in plasma and their accumulation in heart tissue, to deconvolve plasma PK of 125I-amyloid beta 40 (125I-Aß 40) and 125I-insulin from their dynamic heart imaging data. The three-compartment model was shown to adequately describe the plasma concentration-time profile of intact/degraded proteins and the heart radioactivity time data obtained from SPECT/CT imaging for both tracers. The model was successfully applied to deconvolve the plasma PK of both tracers from their naïve datasets of dynamic heart imaging. In agreement with our previous observations made by conventional serial plasma sampling, the deconvolved plasma PK of 125I-Aß 40 and 125I-insulin in young mice exhibited lower area under the curve than aged mice. Further, Patlak plot parameters extracted using deconvolved plasma PK as input function successfully recapitulated age-dependent plasma-to-brain influx kinetics changes. Therefore, the compartment model developed in this study provides a novel approach to deconvolve plasma PK of radiotracers from their noninvasive dynamic heart imaging. This method facilitates the application of preclinical SPECT/PET imaging data to characterize distribution kinetics of tracers where simultaneous plasma sampling is not feasible. SIGNIFICANCE STATEMENT: Knowledge of plasma pharmacokinetics (PK) of a radiotracer is necessary to accurately estimate its plasma-to-brain influx. However, simultaneous plasma sampling during dynamic imaging procedures is not always feasible. In the current study, we developed approaches to deconvolve plasma PK from dynamic heart imaging data of two model radiotracers, 125I-amyloid beta 40 (125I-Aß 40) and 125I-insulin. This novel method is expected to minimize the need for conducting additional plasma PK studies and allow for accurate estimation of the brain influx rate.