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Short wave infrared multi-colored nanoprobe based diagnostics for distal site metastatic surveillance and molecular phenotyping

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TitleInfo
Title
Short wave infrared multi-colored nanoprobe based diagnostics for distal site metastatic surveillance and molecular phenotyping
Name (type = personal)
NamePart (type = family)
Kantamneni
NamePart (type = given)
Harini
NamePart (type = date)
1991-
DisplayForm
Harini Kantamneni
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Roth
NamePart (type = given)
Charles M
DisplayForm
Charles M Roth
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
chair
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
School of Graduate Studies
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (encoding = w3cdtf); (keyDate = yes); (qualifier = exact)
2019
DateOther (encoding = w3cdtf); (qualifier = exact); (type = degree)
2019-05
Language
LanguageTerm (authority = ISO 639-3:2007); (type = text)
English
Abstract (type = abstract)
Current clinical imaging modalities are lacking in their ability to quantify parameters that are critical for the early detection of cancer metastases and heterogeneity. Early identification of micro lesions and tracking the growth of cancerous lesions in real time are vital to realize the complete potential of precision medicine. In addition to uncontrolled growth of cells, the dynamic nature of cancer leads to the presence of different sub-populations of cell types among patients or within a single tumor. These variable molecular phenotypes may have different growth and metastases patterns along with variations in response to therapy. It is therefore imperative to evaluate the presence of biomarkers specific to cancer phenotypes, stage of progression and metastatic potential for development of more effective personalized treatment courses. Current clinical diagnostic imaging modalities like MRI, CT and X-ray possess the ability to determine anatomical details; however, they lack the sensitivity and specificity for molecular phenotyping of lesions. Optical imaging, on the other hand, uses non-ionizing radiation to potentially visualize and characterize both anatomical and molecular details of the disease state. Although the ability to develop optical contrast agents targeted to a diverse range of biological targets is attractive for preclinical and clinical applications, imaging in the visible wavelength region yields poor depth of penetration caused by tissue absorbance and scattering. The near-infrared (NIR) and short-wave infrared (SWIR) regions are more amenable for biological imaging due to minimal attenuation and low tissue auto-fluorescence in these regions.
Rare earth nanoparticles (RENPs) when excited with NIR light (980 nm) emit in the SWIR region (1000 - 1700 nm), allowing for deeper tissue illumination and detection depth. Additionally, RENPs can produce upconversion emissions in the visible region (450-800 nm), when excited with NIR radiation. The ability to emit in the SWIR region (for deep-tissue imaging-preclinical studies) and visible region (for validation of in-vitro and ex-vivo samples) is attractive for the use of RENPs as optical contrast agents. Furthermore, RENPs possess tunable optical properties with varying emission wavelengths based on dopant chemistry, allowing for generation of a library of multi-colored nanoparticles. RENPs can be rendered biocompatible for in vivo imaging by encapsulation in human serum albumin to form rare earth albumin nanocomposites (ReANCs). Additionally, the albumin shell facilitates biofunctionalization and fabrication of cancer-biomarker targeted probes for precision targeting of lesions.
The focus of this dissertation is to use the multi-colored and biomarker-specific targeting properties of ReANCs to create an in vivo imaging platform capable of discerning early stage multi-organ metastases with the goal of molecular mapping of tumors at distinct metastatic sites. In this thesis dissertation, it is shown that ReANCs modified with a targeting moiety can detect lung micro lesions in an in vivo metastatic breast cancer model. Furthermore, the potential of ReANCs to differentially localize to distinct metastatic sites, including the adrenal glands, lungs and bone marrow, was evaluated in in vivo models using orthotopic tumors and spontaneous metastases. Notably, passively accumulated ReANCs could be imaged to the bone space prior to detection with conventional imaging modalities and preferential localization of actively targeted ReANCs to organs including the lungs and adrenals. Owing to the proficiency of the ReANC imaging system to detect micro lesions, in vivo, multi-colored nanoparticles were then used to discern specific molecular phenotypes in a bilateral tumor model. Additionally, the biocompatibility and in vivo localization of RENPs modified with an alternate polymer coating, polyethylenimine (PEI), was evaluated as an imaging contrast agent and gene delivery vehicle. The resulting nanophotonic formulation demonstrated superior resolution with increased signal-to-noise ratio in a lung tumor model. Furthermore, the surface charge was modified to reduce toxicity even at concentrations as high as 625 μg/mL. The RENPs@PEI formulation was also used to deliver genetic cargo (FILIP1L oligo) to FILIP1L overexpressing cells more efficiently than FILIP1L oligo alone. In summary, a precision-targeted, SWIR-emitting ReANCs has been advanced to detect ~ 1 cm deep tissue micro-lesions in multi-organ metastatic in vivo mouse models.
Subject (authority = RUETD)
Topic
Chemical and Biochemical Engineering
Subject (authority = LCSH)
Topic
Infrared imaging
Subject (authority = LCSH)
Topic
Metastasis -- Imaging
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_9607
PhysicalDescription
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application/pdf
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text/xml
Extent
1 online resource (xviii, 215 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
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School of Graduate Studies Electronic Theses and Dissertations
Identifier (type = local)
rucore10001600001
Location
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NjNbRU
Identifier (type = doi)
doi:10.7282/t3-3ecp-p753
Genre (authority = ExL-Esploro)
ETD doctoral
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The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Kantamneni
GivenName
Harini
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2019-03-25 17:36:26
AssociatedEntity
Name
Harini Kantamneni
Role
Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
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Copyright protected
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Status
Open
Reason
Permission or license
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2019-03-31T10:28:34
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2019-03-31T10:28:34
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