Phenotypic and Metabolic Profiling of Biological Samples in Near Real-Time Using Raman Spectroscopy
| dc.contributor.author | Zu, Theresah Nom Korbieh | en |
| dc.contributor.committeechair | Senger, Ryan S. | en |
| dc.contributor.committeemember | Collakova, Eva | en |
| dc.contributor.committeemember | Barone, Justin R. | en |
| dc.contributor.committeemember | Ogejo, Jactone Arogo | en |
| dc.contributor.committeemember | Robertson, John L. | en |
| dc.contributor.department | Biological Systems Engineering | en |
| dc.date.accessioned | 2016-04-15T06:00:43Z | en |
| dc.date.available | 2016-04-15T06:00:43Z | en |
| dc.date.issued | 2014-10-22 | en |
| dc.description.abstract | Raman spectroscopy, together with multivariate statistical analyses, has proven to be a near real-time analytical technique capable of phenotyping cells, tissues and organs. This dissertation will show exclusively the application of the Raman spectroscopy phenotypic profiling method to; (i) microbial toxicity, (ii) ex-vivo organ perfusion, and (iii) subcellular location targeting. Real-time analytical methods for monitoring living biological systems will enable study of the physiological changes associated with growth, genetic manipulations, and adverse environmental conditions. Most existing analytical methods (NMR exempt), though highly accurate, must be performed off-line and most require destruction of the studied sample. These attributes make these methodologies less desirable to the study of physiological changes of cells, tissues, and organs. In this work, Raman spectroscopy has been identified and shown to be a good candidate for real-time analysis mainly because it can be performed: (i) in near real-time, (ii) non-destructively and with minimal sample preparation, (iii) through a glass barrier (i.e., can be performed in situ), and (iv) with minimal spectral interference from water. Here, Raman spectroscopy was used in combination with multivariate statistics to analyze the differing toxic effects of 4-C chain alcohols on E. coli. Good correlations were established between Raman spectra and off-line analytical techniques used to measure: (i) saturated, unsaturated, and cyclopropane fatty acids; (ii) amino acid composition of total protein; and (iii) cell membrane fluidity. Also, Raman 'fingerprint' analysis was used to discriminate among different phenotypic responses of cells. In addition, this methodology was applied to analyze perfusates of organs maintained by the VasoWave® organ perfusion system. Raman fingerprints can be used to assess organ health, and it is believed this data can be used to inform decisions such as whether or not to transplant an organ. Finally, molecular biology techniques were used to design and produce specific protein targets harboring a silver binding domain fusion, which upon release migrate to specific subcellular locations. By employing the related technique of surface-enhanced Raman scattering (SERS), which produces a highly amplified Raman signal in the presence of metallic nanoparticle substrates (e.g., silver nanoparticles), different regions of the E. coli cell structure were studied. The target regions studied by the technique included: (i) outer cell membrane, (ii) periplasm, and the (iii) cytoplasm. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:3833 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/65153 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Raman spectroscopy | en |
| dc.subject | surface enhanced Raman scattering | en |
| dc.subject | microbial phenotyping | en |
| dc.subject | alcohol toxicity | en |
| dc.subject | organ transplantation | en |
| dc.subject | ex vivo perfusion | en |
| dc.subject | near real-time analysis | en |
| dc.subject | silver nanoparticles | en |
| dc.title | Phenotypic and Metabolic Profiling of Biological Samples in Near Real-Time Using Raman Spectroscopy | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Biological Systems Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |