Browsing by Author "Baird, Zane"

Now showing 1 - 4 of 4
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    Enumeration of Rare Cells in Whole Blood by Signal Ion Emission Reactive Release Amplification with Same-Sample RNA Analysis
    (ACS, 2019) Baird, Zane; Cao, Zehui; Barron, M. Regina; Vorsilak, Anna; Deiss, Frédérique; Pugia, Michael; Chemistry and Chemical Biology, School of Science
    Herein is presented a platform capable of detecting less than 30 cells from a whole blood sample by size-exclusion filtration, microfluidic sample handling, and mass spectrometric detection through signal ion emission reactive release amplification (SIERRA). This represents an approximate 10-fold improvement in detection limits from previous work. Detection by SIERRA is accomplished through the use of novel nanoparticle reagents coupled with custom fluidic fixtures for precise sample transfer. Sample processing is performed in standardized 96-well microtiter plates with commonly available laboratory instrumentation to facilitate assay automation. The detection system is easily amenable to multiplex detection, and compatibility with PCR-based gene assays is demonstrated.
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    Lipid and metabolite profiles of human brain tumors by desorption electrospray ionization-MS
    (PNAS Online, 2016-02-09) Jarmusch, Alan K.; Pirro, Valentina; Baird, Zane; Hattab, Eyas M.; Cohen-Gadol, Aaron A.; Cooks, R. Graham; Department of Pathology and Laboratory Medicine, IU School of Medicine
    Examination of tissue sections using desorption electrospray ionization (DESI)-MS revealed phospholipid-derived signals that differ between gray matter, white matter, gliomas, meningiomas, and pituitary tumors, allowing their ready discrimination by multivariate statistics. A set of lower mass signals, some corresponding to oncometabolites, including 2-hydroxyglutaric acid and N-acetyl-aspartic acid, was also observed in the DESI mass spectra, and these data further assisted in discrimination between brain parenchyma and gliomas. The combined information from the lipid and metabolite MS profiles recorded by DESI-MS and explored using multivariate statistics allowed successful differentiation of gray matter (n = 223), white matter (n = 66), gliomas (n = 158), meningiomas (n = 111), and pituitary tumors (n = 154) from 58 patients. A linear discriminant model used to distinguish brain parenchyma and gliomas yielded an overall sensitivity of 97.4% and a specificity of 98.5%. Furthermore, a discriminant model was created for tumor types (i.e., glioma, meningioma, and pituitary), which were discriminated with an overall sensitivity of 99.4% and a specificity of 99.7%. Unsupervised multivariate statistics were used to explore the chemical differences between anatomical regions of brain parenchyma and secondary infiltration. Infiltration of gliomas into normal tissue can be detected by DESI-MS. One hurdle to implementation of DESI-MS intraoperatively is the need for tissue freezing and sectioning, which we address by analyzing smeared biopsy tissue. Tissue smears are shown to give the same chemical information as tissue sections, eliminating the need for sectioning before MS analysis. These results lay the foundation for implementation of intraoperative DESI-MS evaluation of tissue smears for rapid diagnosis.
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    Multiplexed Signal Ion Emission Reactive Release Amplification (SIERRA) Assay for the Culture-Free Detection of Gram-Negative and Gram-Positive Bacteria and Antimicrobial Resistance Genes
    (American Chemical Society, 2021) Pugia, Michael; Bose, Tiyash; Tjioe, Marco; Frabutt, Dylan; Baird, Zane; Cao, Zehui; Vorsilak, Anna; McLuckey, Ian; Barron, M. Regina; Barron, Monica; Denys, Gerald; Carpenter, Jessica; Das, Amitava; Kaur, Karamjeet; Roy, Sashwati; Sen, Chandan K.; Deiss, Frédérique; Chemistry and Chemical Biology, School of Science
    The global prevalence of antibiotic-resistant bacteria has increased the risk of dangerous infections, requiring rapid diagnosis and treatment. The standard method for diagnosis of bacterial infections remains dependent on slow culture-based methods, carried out in central laboratories, not easily extensible to rapid identification of organisms, and thus not optimal for timely treatments at the point-of-care (POC). Here, we demonstrate rapid detection of bacteria by combining electrochemical immunoassays (EC-IA) for pathogen identification with confirmatory quantitative mass spectral immunoassays (MS-IA) based on signal ion emission reactive release amplification (SIERRA) nanoparticles with unique mass labels. This diagnostic method uses compatible reagents for all involved assays and standard fluidics for automatic sample preparation at POC. EC-IA, based on alkaline phosphatase-conjugated pathogen-specific antibodies, quantified down to 104 bacteria per sample when testing Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa lysates. EC-IA quantitation was also obtained for wound samples. The MS-IA using nanoparticles against S. aureus, E. coli, Klebsiella pneumoniae, and P. aeruginosa allowed selective quantitation of ∼105 bacteria per sample. This method preserves bacterial cells allowing extraction and amplification of 16S ribosomal RNA genes and antibiotic resistance genes, as was demonstrated through identification and quantitation of two strains of E. coli, resistant and nonresistant due to β-lactamase cefotaximase genes. Finally, the combined immunoassays were compared against culture using remnant deidentified patient urine samples. The sensitivities for these immunoassays were 83, 95, and 92% for the prediction of S. aureus, P. aeruginosa, and E. coli or K. pneumoniae positive culture, respectively, while specificities were 85, 92, and 97%. The diagnostic platform presented here with fluidics and combined immunoassays allows for pathogen isolation within 5 min and identification in as little as 15 min to 1 h, to help guide the decision for additional testing, optimally only on positive samples, such as multiplexed or resistance gene assays (6 h).
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    Utilization of electronic health records for the assessment of adiponectin receptor autoantibodies during the progression of cardio-metabolic comorbidities
    (Probiologists, 2020) Pugia, Michael J.; Pradhan, Meeta; Qi, Rong; Eastes, Doreen L.; Vorsilak, Anna; Mills, Bradley J.; Baird, Zane; Wijeratne, Aruna; McAhren, Scott M.; Mosley, Amber; Shekhar, Anantha; Robertson, Daniel H.; Biochemistry and Molecular Biology, School of Medicine
    Background: Diabetes is a complex, multi-symptomatic disease whose complications drives increases in healthcare costs as the diabetes prevalence grows rapidly world-wide. Real-world electronic health records (EHRs) coupled with patient biospecimens, biological understanding, and technologies can characterize emerging diagnostic autoimmune markers resulting from proteomic discoveries. Methods: Circulating autoantibodies for C‑terminal fragments of adiponectin receptor 1 (IgG-CTF) were measured by immunoassay to establish the reference range using midpoint samples from 1862 participants in a 20-year observational study of type 2 diabetes and cardiovascular arterial disease (CVAD) conducted by the Fairbanks Institute. The White Blood Cell elastase activity in these patients was assessed using immunoassays for Bikunin and Uristatin. Participants were assigned to four cohorts (healthy, T2D, CV, CV+T2D) based on analysis of their EHRs and the diagnostic biomarkers values and patient status were assessed ten-years post-sample. Results: The IgG-CTF reference range was determined to be 75–821 ng/mL and IgG-CTF out-ofrange values did not predict cohort or comorbidity as determined from the EHRs at 10 years after sample collection nor did IgG-CTF demonstrate a significant risk for comorbidity or death. Many patients at sample collection time had other conditions (hypertension, hyperlipidemia, or other risk factors) of which only hypertension, Uristatin and Bikunin values correlated with increased risk of developing additional comorbidities (odds ratio 2.58–13.11, P<0.05). Conclusions: This study confirms that retrospective analysis of biorepositories coupled with EHRs can establish reference ranges for novel autoimmune diagnostic markers and provide insights into prediction of specific health outcomes and correlations to other markers.