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Burdette, J. E.

Publications and source records attributed to Burdette, J. E..

4 recordsLinked to original sources

Tensile Expansion Mass Spectrometry for single cell metabolomics imaging

Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) enables the spatial mapping of endogenous biomolecules within native biological specimens; however, it remains limited in achieving single-cell resolution. While advances in instrument modifications, computational processing methods, and tissue-based sample preparation have facilitated high lateral resolutions and cellular level imaging, resolving metabolic heterogeneity at the single-cell level remains challenging for users without specific expertise or custom instrumentation. Here, we present tensile expansion mass spectrometry (TExMS), a cost-effective approach for single-cell MALDI-MSI that is compatible with commercial MSI instrumentation. TExMS utilizes highly stretchable hydrogels as a substrate for live-cell seeding, attachment, and desiccation, avoiding the need for chemical fixation and enabling the retention of both intracellular and extracellular metabolites, including media-derived components that are lost during fixation and washing. We used TExMS to expand individual cells of a human high-grade serous ovarian cancer (HGSOC) cell line and spatially map their small molecule (<800 Da) production. TExMS enabled [~]4-fold linear expansion of the hydrogel, translating to a [~]1.7-fold increase in average cell area and [~]1.3-fold increase in nuclear area and resulting in improved lateral resolution of metabolite distributions. Benchmarking against other platforms for high resolution MALDI-MSI, TExMS offered comparable spatial resolution to microgrid-enabled MALDI-MSI with 15 to 20-fold shorter acquisition times. We then used TExMS to map numerous intermediates from glycolysis, the tricarboxylic acid (TCA) cycle, and amino acid biosynthesis and probe the effects of serum starvation conditions on metabolic flux through these pathways, demonstrating a powerful use case for single-cell MALDI-MSI through TExMS. Table of Contents (TOC) O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=85 SRC="FIGDIR/small/745024v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@18f8c09org.highwire.dtl.DTLVardef@132bc4aorg.highwire.dtl.DTLVardef@1e7c2caorg.highwire.dtl.DTLVardef@a586cf_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

Branched-chain amino acid catabolism promotes ovarian cancer cell proliferation via phosphorylation of mTOR

Ovarian cancer is the sixth leading cause of cancer-related mortality among individuals with ovaries, and high-grade serous ovarian cancer (HGSOC) is the most common and lethal subtype. Characterized by a distinct and aggressive metastatic pattern, HGSOC can originate in the fallopian tube with the transformation of fallopian tube epithelial (FTE) cells, which metastasize to the ovary and subsequently to the omentum and peritoneal cavity. The omentum is a privileged metastatic site, and the metabolic exchange underlying omental metastasis could provide enzyme or receptor targets to block spread. In this study, we adapted a mass spectrometry imaging (MSI) protocol to investigate spatial location of 3D cocultures of tumorigenic FTE cells when grown in proximity to murine omental explants as a model of early metastatic colonization. Our analysis revealed several altered metabolites in tumorigenic FTE/omentum cocultures, namely changes in branched-chain amino acids (BCAA), including valine. We quantified the heightened consumption of valine, other BCAAs, and other amino acid-derived metabolites in omental cocultures using LC-MS assays. Our analysis revealed that metabolite concentrations when monitored with MSI from cell culture media in living culture systems have notable considerations for how MSI data may produce signatures that induce ionization suppression. Supplementation with valine enhanced proliferation and mTOR signaling in tumorigenic FTE cells, suggesting the potential of BCAAs as a nutrient utilized by tumor cells during omental colonization and a possible target for metastasis. SIGNIFICANCEThis study uncovers altered amino acid metabolism, specifically increased BCAA catabolism, at the interface of ovarian cancer cells and omental tissue in a coculture model of HGSOC secondary metastasis. Enhanced BCAA catabolism may promote cancer cell proliferation through mTOR signaling, presenting potential therapeutic value. These findings deepen our understanding of HGSOC pathogenesis and the metastatic tumor microenvironment, offering insights for developing new treatment strategies.

cancer biology↗

An integrated approach to protein discovery and detection from complex biofluids

Ovarian cancer, a leading cause of cancer related deaths among women, has been notoriously difficult to routinely screen for and diagnose early. Researchers and clinicians continue to seek routinely usable, non-invasive, screening methods as early detection significantly improves survival. Biomarker screening is ideal; however, currently available ovarian cancer biomarkers lack desirable sensitivity and specificity. Furthermore, the most fatal forms, high grade serous cancers often originate in the fallopian tube; therefore, sampling from the vaginal environment provides more proximal sources for tumor detection. To address these shortcomings and leverage proximal sampling, we developed an untargeted mass spectrometry microprotein profiling method and identified a signature of cystatin A, validated this protein in an animal model, and sought to overcome the limits of detection inherent to mass spectrometry by demonstrating that cystatin A is present at 100 pM concentrations using a label-free microtoroid resonator. The findings highlight the potential utility for early-stage detection where cystatin A levels would be low. Significance StatementIt is now clear that high-grade serous ovarian cancer can originate in the fallopian tube epithelium. These tumors colonize the ovary and then metastasize throughout the peritoneum. This discovery has raised important, and yet unaddressed, questions how we might be able to detect and screen for this deadly disease for which there is no routine screening. We have leveraged vaginal lavages from a murine model of the disease as a complex biological fluid for untargeted discovery of microproteins using mass. We improved our limits of detection by conjugating a cystatin A antibody to the surface of a microtoroid resonator to allow us to specifically detect cystatin A from vaginal lavages at early time points across biological replicates.

cancer biology↗

Early Detection of Ovarian Cancer Using Cells Sourced from a Local Microenvironment

Mass spectrometry (MS) offers high levels of specificity and sensitivity in clinical applications, and we have previously been able to demonstrate that matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS is capable of distinguishing two-component biological mixtures at low limits of detection. Ovarian cancer is notoriously difficult to detect due to the lack of any screening methods for early detection. By sampling a local microenvironment, such as the vaginal fluids, a MS based method is presented that was capable of monitoring disease progression from vaginally collected, local samples from tumor bearing mice. A murine xenograft model of high grade serous ovarian carcinoma (HGSOC) was used for this study and vaginal lavages were obtained from mice on a weekly basis throughout disease progression and subjected to our MALDI-TOF MS workflow followed by statistical analyses. Proteins in the 4-20 kDa region of the mass spectrum could consistently be measured to yield a fingerprint that correlated with disease progression over time. These fingerprints were found to be statistically stable across all mice with the protein fingerprint converging towards the end point of the study. MALDI-TOF MS serves as a unique analytical technique for measuring a sampled vaginal microenvironment in a specific and sensitive manner for the detection of HGSOC in a murine model.

cancer biology↗