Browsing by Author "Eberlin, Livia S."

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Distinctive Glycerophospholipid Profiles of Human Seminoma and Adjacent Normal Tissues by Desorption Electrospray Ionization Imaging Mass Spectrometry
    (American Chemical Society, 2011) Masterson, Timothy A.; Dill, Allison L.; Eberlin, Livia S.; Mattarozzi, Monica; Cheng, Liang; Beck, Stephen D. W.; Bianchi, Federica; Cooks, R. Graham; Urology, School of Medicine
    Desorption electrospray ionization mass spectrometry (DESI-MS) has been successfully used to discriminate between normal and cancerous human tissue from different anatomical sites. On the basis of this, DESI-MS imaging was used to characterize human seminoma and adjacent normal tissue. Seminoma and adjacent normal paired human tissue sections (40 tissues) from 15 patients undergoing radical orchiectomy were flash frozen in liquid nitrogen and sectioned to 15 μm thickness and thaw mounted to glass slides. The entire sample was two-dimensionally analyzed by the charged solvent spray to form a molecular image of the biological tissue. DESI-MS images were compared with formalin-fixed, hematoxylin and eosin (H&E) stained slides of the same material. Increased signal intensity was detected for two seminolipids [seminolipid (16:0/16:0) and seminolipid (30:0)] in the normal tubule testis tissue; these compounds were undetectable in seminoma tissue, as well as from the surrounding fat, muscle, and blood vessels. A glycerophosphoinositol [PI(18:0/20:4)] was also found at increased intensity in the normal testes tubule tissue when compared with seminoma tissue. Ascorbic acid (i.e., vitamin C) was found at increased amounts in seminoma tissue when compared with normal tissue. DESI-MS analysis was successfully used to visualize the location of several types of molecules across human seminoma and normal tissues. Discrimination between seminoma and adjacent normal testes tubules was achieved on the basis of the spatial distributions and varying intensities of particular lipid species as well as ascorbic acid. The increased presence of ascorbic acid within seminoma compared with normal seminiferous tubules was previously unknown.
  • Thumbnail Image
    Item
    Identifying Clinically Relevant Bacteria Directly from Culture and Clinical Samples with a Handheld Mass Spectrometry Probe
    (Oxford University Press, 2022) Povilaitis, Sydney C.; Chakraborty, Ashish; Kirkpatrick, Lindsey M.; Downey, Rachel D.; Hauger, Sarmistha B.; Eberlin, Livia S.; Pediatrics, School of Medicine
    Background: Rapid identification of bacteria is critical to prevent antimicrobial resistance and ensure positive patient outcomes. We have developed the MasSpec Pen, a handheld mass spectrometry-based device that enables rapid analysis of biological samples. Here, we evaluated the MasSpec Pen for identification of bacteria from culture and clinical samples. Methods: A total of 247 molecular profiles were obtained from 43 well-characterized strains of 8 bacteria species that are clinically relevant to osteoarticular infections, including Staphylococcus aureus, Group A and B Streptococcus, and Kingella kingae, using the MasSpec Pen coupled to a high-resolution mass spectrometer. The molecular profiles were used to generate statistical classifiers based on metabolites that were predictive of Gram stain category, genus, and species. Then, we directly analyzed samples from 4 patients, including surgical specimens and clinical isolates, and used the classifiers to predict the etiologic agent. Results: High accuracies were achieved for all levels of classification with a mean accuracy of 93.3% considering training and validation sets. Several biomolecules were detected at varied abundances between classes, many of which were selected as predictive features in the classifiers including glycerophospholipids and quorum-sensing molecules. The classifiers also enabled correct identification of Gram stain type and genus of the etiologic agent from 3 surgical specimens and all classification levels for clinical specimen isolates. Conclusions: The MasSpec Pen enables identification of several bacteria at different taxonomic levels in seconds from cultured samples and has potential for culture-independent identification of bacteria directly from clinical samples based on the detection of metabolic species.
  • Thumbnail Image
    Item
    Multivariate statistical differentiation of renal cell carcinomas based on lipidomic analysis by ambient ionization imaging mass spectrometry
    (Springer, 2010) Dill, Allison L.; Eberlin, Livia S.; Zheng, Cheng; Costa, Anthony B.; Ifa, Demian R.; Cheng, Liang; Masterson, Timothy A.; Koch, Michael O.; Vitek, Olga; Cooks, R. Graham; Pathology and Laboratory Medicine, School of Medicine
    Desorption electrospray ionization (DESI) mass spectrometry (MS) was used in an imaging mode to interrogate the lipid profiles of thin tissue sections of 11 sample pairs of human papillary renal cell carcinoma (RCC) and adjacent normal tissue and nine sample pairs of clear cell RCC and adjacent normal tissue. DESI-MS images showing the spatial distributions of particular glycerophospholipids (GPs) and free fatty acids in the negative ion mode were compared to serial tissue sections stained with hematoxylin and eosin (H&E). Increased absolute intensities as well as changes in relative abundance were seen for particular compounds in the tumor regions of the samples. Multivariate statistical analysis using orthogonal projection to latent structures treated partial least square discriminate analysis (PLS-DA) was used for visualization and classification of the tissue pairs using the full mass spectra as predictors. PLS-DA successfully distinguished tumor from normal tissue for both papillary and clear cell RCC with misclassification rates obtained from the validation set of 14.3% and 7.8%, respectively. It was also used to distinguish papillary and clear cell RCC from each other and from the combined normal tissues with a reasonable misclassification rate of 23%, as determined from the validation set. Overall DESI-MS imaging combined with multivariate statistical analysis shows promise as a molecular pathology technique for diagnosing cancerous and normal tissue on the basis of GP profiles.