Biology subjects

Eberlin, L. S.

Publications and source records attributed to Eberlin, L. S..

3 recordsLinked to original sources

bioRxiv2024

Large-Scale Evaluation of Spatial Metabolomics Protocols and Technologies

Spatial metabolomics using imaging mass spectrometry (MS) enables untargeted and label-free metabolite mapping in biological samples. Despite the range of available imaging MS protocols and technologies, our understanding of metabolite detection under specific conditions is limited due to sparse empirical data and predictive theories. Consequently, challenges persist in designing new experiments, and accurately annotating and interpreting data. In this study, we systematically measured the detectability of 172 biologically-relevant metabolites across common imaging MS protocols using custom reference samples. We evaluated 24 MALDI-imaging MS protocols for untargeted metabolomics, and demonstrated the applicability of our findings to complex biological samples through comparison with animal tissue data. We showcased the potential for extending our results to further analytes by predicting metabolite detectability based on molecular properties. Additionally, our interlaboratory comparison of 10 imaging MS technologies, including MALDI, DESI, and IR-MALDESI, showed extensive metabolite coverage and comparable results, underscoring the broad applicability of our findings within the imaging MS community. We share our results and data through a new interactive web application integrated with METASPACE. This resource offers an extensive catalogue of detectable metabolite ions, facilitating protocol selection, supporting data annotation, and benefiting future untargeted spatial metabolomics studies.

systems biology↗

bioRxiv2023

Utilizing Aggregated Molecular Phenotype (AMP) Scores to Visualize Simultaneous Molecular Changes in Mass Spectrometry Imaging Data

Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often visualized using single ion images and further analyzed using machine learning and multivariate statistics to identify m/z features of interest and create predictive models for phenotypic classification. However, often only a single molecule or m/z feature is visualized per ion image, and mainly categorical classifications are provided from the predictive models. As an alternative approach, we developed an aggregated molecular phenotype (AMP) scoring system. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores therefore allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes, leading to high diagnostic accuracy and interpretability of predictive models. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization (DESI) MSI. Initial comparisons of cancerous human tissues to normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.

bioinformatics↗

bioRxiv2022

Adipose triglyceride lipase is regulated by CAMKK2-AMPK signaling and drives advanced prostate cancer

Lipid metabolism plays a central role in prostate cancer. To date, the major focus on prostate cancer lipid metabolism has centered on de novo lipogenesis and lipid uptake with little consideration for how cancer cells access these lipids once they are created or taken up and stored. Patient-derived phosphoproteomics identified adipose triglyceride lipase (ATGL), a previously suspected tumor suppressor, as a CAMKK2-AMPK signaling target that, conversely, promotes castration-resistant prostate cancer (CRPC) progression. Phosphorylation of ATGL increased its lipase activity, cancer cell proliferation, migration, and invasion. Shotgun lipidomics and mass spectrometry imaging demonstrated ATGLs profound regulation of lipid metabolism in vitro and in vivo, remodeling membrane composition. Inhibition of ATGL induced metabolic plasticity, causing a glycolytic shift that could be exploited therapeutically by co-targeting both metabolic pathways. Together, these data nominate ATGL and intracellular lipolysis as potential therapeutic targets for the treatment of CRPC and provide insights for future combination therapies.

cancer biology↗