Browsing by Subject "Fluorescence"

Resonance energy transfer (RET) logic provides a method for building integrated molecular circuits using self-assembled networks of fluorescent molecules to perform computation. The unique operating principles and materials of these circuits make them suitable candidates for introducing computation to domains that are incompatible with conventional silicon-based systems. To realize the full potential of this technology, however, a variety of technical challenges currently preventing the design of larger, more complex systems must be overcome. Two of these primary challenges are energy loss and exciton loss. Energy loss forces the outputs from RET devices to be red-shifted from their inputs. This prevents most independently designed RET components from being cascaded with one another. Exciton loss weakens the output signals from RET devices, making it difficult to observe the computational results. Together, these forms of signal degradation constrain both the size and topology of RET systems.

This work explores new RET devices and circuit design strategies that address the above limitations. The primary contributions of this dissertation are threefold. First, a RET device capable of restoring energy in these systems is designed and experimentally demonstrated. This device enables cascading and feedback in RET logic, two circuit design concepts commonly used in large-scale digital systems. Second, a new style of RET logic design, called Pre-Charge Logic (PCL), is introduced. PCL addresses both forms of signal loss while simultaneously providing a library of cascadable RET logic gates, many of which cannot be implemented using previous methods of RET logic design. Third, the design methods of PCL are explored and validated by simulation and experimental demonstration. Continuous-time Markov chain modeling confirms that the proposed PCL devices perform their intended Boolean operations, while an experimental demonstration of a PCL PASS gate substantiates the underlying operating principles of these devices. Collectively, these contributions pave the way for developing larger, more complex RET systems in the future.

According to the World Health Organization, there were over 2 million new breast cancer cases in 2018. This number is projected to steadily increase year after year. American Cancer Society projections for 2020 list the breast as the leading cancer site for new cancer cases in females, estimating breast cancer to represent 30% of all new cases and 15% of cancer-related deaths.

A leading cause of breast cancer deaths is due to tumor recurrence following therapy. These tumors can recur years, sometimes decades, after treatment from reservoirs of residual cells that persist in a dormant state. Conversely, the absence of residual invasive disease following adjuvant therapy constitutes pathological complete response (pCR) and is positively associated with long-term relapse-free survival. This risk for recurrence is higher for women with human epidermal growth factor receptor 2 (Her2+) breast cancer or triple-negative breast cancer (TNBC). Approximately 50-70% of Her2+ patients and 40-55% of TNBC patients who undergo standard therapy achieve pCR; however, in the remaining patients, only a partial response occurs, leaving residual disease and an increased risk of relapse.

To mitigate the cancer burden, years of research have focused on several common biological capabilities of cancer, deemed the Hallmarks of Cancer, including sustained proliferation, genome mutations, replicative immortality, resistance to cell death, and a deregulated metabolism. Several recent studies have further reported that this last hallmark, metabolism, may be vital to understanding the underlying behavior of dormant and recurrent tumors. Once understood, these changes in metabolic pathways, referred to as metabolic reprogramming, can be leveraged as vulnerabilities and allow for the development of strategies to eliminate residual disease or prevent residual tumor cells’ subsequent reactivation into full recurrence.

For nearly 100 years, increased aerobic glycolysis has been considered a feature of rapidly proliferating primary tumors. This occurrence, where cells continue to use the metabolic pathway where glucose is converted to lactic acid to release its stored energy and produce adenosine triphosphate (ATP) despite the presence of oxygen, has been termed the Warburg Effect. Because of this, physicians frequently use nuclear medicine directly imaging glucose uptake, fluorodeoxyglucose (FDG) Positron Emission Tomography (PET) imaging, for the diagnosis and staging of cancer. In addition to glycolysis, mitochondrial metabolism through oxidative phosphorylation has grown in recognition as an additional energy source for cancer cells. In mitochondrial metabolism, the tricarboxylic acid (TCA) cycle generates energy carriers to be used in the electron transport chain. Here, the mitochondrial membrane potential provides a gradient to produce large amounts of ATP. Additionally, the TCA cycle can rely on sources of carbon besides glucose alone. A steadily growing consensus points to other energetic sources, such as glutamine, amino acids, and lipids, that are key to survival, especially following environmental stress, treatment, or before migration and metastasis.

Though metabolic reprogramming underpins aspects of tumor dormancy and recurrence, currently, there are no techniques available to provide a systems-level approach to investigate the major axes of metabolism. Several techniques that offer insights into cellular metabolism exist, such as the Seahorse assay, metabolomics, and FDG-PET imaging. They, however, are limited to in vitro model systems, single-time point analyses of in vivo model systems, or single-endpoint analysis of in vivo model systems, respectively. Further, neither the Seahorse assay nor metabolomics can capture information about both the tumor and its native microenvironment. Therefore, there is an unmet need for a method to study metabolism at a spatial resolution that can elucidate the metabolic modulation of residual cell populations longitudinally and across in vitro and in vivo models.

Optical imaging is well-suited to address this gap in technologies owing to its ability to measure multiple metabolic endpoints non-destructively and repeatedly. The Center for Global Women’s Health Technologies has developed protocols for the use of two optical probes 2-[N-(7-nitrobenz-2-oxa-1, 3-diaxol-4-yl) amino]-2-deoxyglucose (2-NBDG) and tetramethylrhodamine, ethyl ester (TMRE), to image glucose uptake and mitochondrial membrane potential, respectively, in preclinical cancer models. These endpoints are superior to imaging of the endogenous fluorescence of NADH and FAD (referred to as the redox ratio) by providing a direct measure of a substrate (glucose uptake) and metabolic output (mitochondrial metabolism). This optical, metabolic imaging approach fills a critical gap that exists between in vitro studies on single cells (Seahorse Extracellular Flux Assay) and whole-body imaging (FDG-PET imaging) and is complementary to metabolomics and immunohistochemistry (IHC) with endpoints measuring the major axes of metabolism.

The work described here details an innovative platform to image changes in the metabolism of primary tumors, residual disease, and recurrent tumors using a Her2+ genetically engineered mouse model. This model exhibits key features of dormancy and mimics sustained use of targeted therapy to facilitate understanding of tumor biology and function, assess recurrence risk, and design therapies to mitigate residual disease and recurrence altogether. Imaging at a cellular level resolution will not only document acute metabolic changes following Her2 downregulation but also allow for metabolic imaging of dormant cell populations that are typically too small to study in human patients, typically referred to as no evidence of disease (NED) in humans. This platform will push metabolic studies of tumor dormancy further.

Three specific aims were proposed towards this ultimate goal to develop a multiparametric platform to characterize the metabolic reprogramming of preclinical cancer models.

Aim 1 establishes the functional flexibility of the fluorescent glucose analog 2-NBDG to measure glycolytic demand and the fluorescent cation TMRE to measure mitochondrial membrane potential to report on the metabolic changes that occur throughout tumor progression, dormancy, and recurrence. Using a genetically engineered mouse-derived three-dimensional in vitro mammosphere model allowed for metabolic endpoints to be captured across key time points. Doxycycline (dox) addition and withdrawal modulates expression of Her2, which is overexpressed in primary and re-activated mammospheres, and downregulated in regressing and dormant mammospheres. The mammospheres were characterized using immunofluorescence to confirm phenotype. Ki67 expression was high in primary and re-activated mammospheres, confirming a proliferative phenotype typical of both primary and recurrent disease presented in the clinic. On the other hand, short-term dox withdrawal resulted in increased cleaved caspase 3 (CC3) expression, confirming apoptosis due to Her2 downregulation. Finally, both Ki67 and CC3 expression were negative in dormant mammospheres, demonstrating a viable, but non-proliferative, steady-state phenotype.

Metabolic imaging revealed unique metabolic phenotypes across the tumor development stages that were consistent with the gold standard assays. While primary mammospheres, overexpressing Her2, maintained increased glucose uptake (“Warburg effect”), after Her2 downregulation, regressing and residual disease mammospheres appeared to switch to oxidative phosphorylation. Interestingly, in mammospheres where Her2 overexpression was turned back on to model recurrence, glucose uptake was lowest, indicating a potential change in substrate preference following the reactivation of Her2, re-eliciting growth. These findings highlight the importance of imaging metabolic adaptations to gain insight into residual and recurrent disease’s fundamental behaviors.

This work paved the way for similar studies in vivo using a mammary window chamber with the ultimate goal of informing the potential impact of metabolically-targeted therapies on tumor dormancy and recurrence.

In Aim 2, 2-NBDG and TMRE imaging was applied to in vivo mammary tumors as they transitioned from primary tumors, through regression and dormancy, to regrowth as recurrent tumors. Two tumor models varying in periods of dormancy (termed slow recurring and fast recurring tumors) were selected to characterize the importance of either axis of metabolism in the context of recurrent disease. When comparing the glucose demand and mitochondrial membrane potential levels between slow and fast recurring tumors, both sets of primary tumors behaved similarly to the primary mammosphere cultures: increased 2-NBDG indicating highly glycolytic tumors with low TMRE indicating little mitochondrial activity. Following acute Her2 downregulation, there was an increase of mitochondrial activity that remained relatively constant through regression, dormancy, and recurrence for both tumor types. However, glucose uptake varied between the two tumor types following Her2 downregulation. The mice bearing slow-recurring tumors showed a resurgence of glucose uptake during recurrence; conversely, the mice bearing fast-recurring tumors maintained decreased glucose levels continually following Her2 downregulation. Because the fast-recurring tumors did not have a meaningful change in glucose uptake during recurrence, it was hypothesized that the fast-recurring tumors might have reprogrammed to use fatty acids as a fuel source. Indeed, inhibiting fatty acid oxidation in these tumors resulted in increased glucose uptake during regression. Additionally, following this acute change in metabolism due to the inhibition of fatty acid oxidation, the tumor’s dormancy period prior to recurrence was prolonged, pointing to lipids as a crucial fuel source for residual disease and recurrence in aggressive breast cancer.

Aim 2 showed the importance of lipid metabolism in residual disease and recurrence. Additionally, other groups have also shown increased reliance on fatty acid oxidation in breast cancer residual disease following oncogene downregulation. Thus, Aim 3 established a method of visualizing long-chain fatty acid uptake in breast cancer murine models. Until now, the ability to monitor such uptake has been limited to in vitro and ex vivo approaches. Here, an imaging strategy that combines a fluorescently labeled palmitate molecule, Bodipy FL c16, and intravital, optical imaging was developed to measure exogenous fatty acid uptake. Because the palmitate’s 16th carbon is fluorescently labeled, immediate degradation of the Bodipy dye during fatty acid oxidation (β-oxidation) is prevented, allowing for fatty acid to be visualized through fluorescence imaging.

This technique was validated in two breast cancer models: a MYC-overexpressing transgenic triple-negative breast cancer (TNBC) model, previously reported to dramatically upregulate fatty acid oxidation intermediates, and the murine model of the 4T1 family, a group of sibling tumor lines with a reported wide range of metabolic phenotypes.

Using a genetically engineered mouse-derived xenograft allowed for fatty acid uptake levels to be captured during MYC-overexpression and following oncogene downregulation. Similar to the previously described genetically engineered model, this model used doxycycline addition and withdrawal to modulate MYC expression.

Through in vivo Bodipy FL c16 imaging, fatty acid uptake was found to be increased in MYC-high tumors. This model showcased two critically needed features for clinically relevant study of fatty acid uptake: 1) longitudinal metabolite tracking in a single animal shown through intra-animal decreases in fatty acid uptake following MYC-downregulation; and 2) providing a link between oncogene expression, which can be modulated therapeutically, and metabolic endpoints. This decreased uptake is indicative of a less aggressive state and correlates with a visible reduction in tumor volume. Additionally, this method found an increased fatty acid uptake in tumors with high metastatic potential, as well as the ability of the system to monitor inhibition efficacy, potentially allowing for therapeutic pharmaceutical testing of drug efficacy.

This fast and dynamic approach to image fatty acid uptake in vivo is a tool relevant to study tumor metabolic reprogramming or the effectiveness of drugs targeting lipid metabolism.

Targeting a tumor’s metabolic dependencies is a clinically actionable therapeutic approach, but identifying subtypes of tumors that are likely to respond remains difficult. The work presented here indicates that an optical platform to image 2-NBDG, TMRE, and Bodipy FL c16 longitudinally is well suited to characterize breast cancer residual disease and recurrence’s critical metabolic features and to pinpoint metabolic vulnerabilities for potential treatments. While the primary goal was to develop an imaging strategy for the unprecedented assessment of residual and recurrent disease at high resolution in in vitro and in vivo models, this innovation also fits within the broader framework of existing metabolic assessment techniques and provides a systematic way to connect in vitro studies to whole-body imaging within the context of preclinical pharmacology research.

Future work will focus on establishing a combined imaging strategy for simultaneous imaging of all three endpoints, transitioning imaging to a hand-held microscope for wide-spread adoption and rapid metabolic phenotyping of clinical samples, and integrating optical spectroscopy with this imaging platform to track the long-term effects therapy has on an individual tumor’s metabolism. The third will enable the ability to retrospectively look for changes in primary and regressing phenotypes that might foreshadow dormant behavior or the risk of early recurrence.

Advances in the development of miniaturized, autonomous general-purpose sensing systems for applications such as medical diagnostics, biological and chemical analysis, and point-of-care testing have driven the emergence of lab-on a-chip (LOC) systems, which integrate sample preparation and sensing. To realize LOC systems, enabling technologies are needed to carry out sample preparation and manipulation at the chip-scale, and sensing technologies that can be integrated with the chip-scale fluidic sample preparation platform.

The integration of sample preparation and sensing is key to LOC systems. Electrowetting-on-dielectric (EWD) fluidic technology enables digital droplet manipulation at the chip-scale with standard microfabrication manufacturing techniques, with the advantages of non-bulky systems, low cost and portability [1]. EWD system have been integrated with optical, mechanical and electrochemical sensing mechanisms to realize a miniaturized LOC [2]–[4].Fluorescence sensing is one of the most widely used types of analyte sensing for biochemical targets due to its high sensitivity and specificity [2]. Intensity-based fluorescence sensing provides fast, localized detection that can be correlated to the concentration of the test analyte, thus providing quantitative detection information. Most current microfluidic LOC systems that utilize fluorescent sensing use large external microscopes or bulky filters and lenses for extracting the quantitative information, thus compromising on the portability and cost-effectiveness. The realization of optical fluorescence sensing integrated directly into microfluidic-based LOC systems can enable the systems to become more self-contained, portable, and potentially low cost.

Fluorescence sensing is an effective method of identifying target species, and the integration of fluorescence sensing into a programmable digital microfluidic platform is the goal of the thesis work described herein. Silicon photodetectors (PDs) are an excellent choice for optical sensing due to their high responsivity at visible wavelengths, which corresponds to the emission wavelengths of many fluorophores. Additionally, using Si PDs enables the use of standard microfabrication processes that can be scaled for mass manufacturing. Furthermore, using thin film Si photodetectors provides the ability to be bonded to a chosen substrate for further system integration in small spaces where conventional photodetectors will not fit, without significantly altering the spatial configuration of the region where the sensing occurs.

This dissertation presents the design and fabrication of annular, thin film Si photodetectors heterogeneously bonded to a pyrex substrate for fluorescence sensing, with a longer-term goal of integration into an EWD to realize a LOC system. Herein we design, fabricate, and test the performance of PD fluorescence sensors with thin film Si PD testing, followed by PD integration into an EWD microfluidic system. This system is simple and low cost, because it does not utilize filters to block the optical pump beam selectively from the PD, but rather, uses a novel optical design to suppress the fluorescence pump signal with the bottom plate of the microfluidic system, so that the signal to noise ratio (SNR) of the fluorescence signal to the pump signal is high. This device has the potential to be applied as a miniature, non-invasive, multi-target sensor.

[Figure 1: Cross-section schematic of the integrated thin film Si fluorescence sensor with a bottom plate designed for integration with an EWD system. PD thickness not drawn to scale.]

The target EWD system for integration with the fluorescence sensor has a top plate and a bottom plate, with the thin film Si PD integrated onto the top plate. The PD sensor is a thin film annular silicon PD bonded to the pyrex top plate, as shown in Figure 1, which can also serve as a ground electrode for the microfluidic platform. The optical pump signal, delivered herein through an optical fiber, is coupled to the droplet under test (containing the fluorophore) through the pyrex, through a thin metallization on the pyrex to minimize stray light, and then through the aperture in the photodetector. Light delivery can also be carried out using an integrated optical (LED or laser) source with or without an optical waveguide. The droplet is sandwiched between the top plate and the bottom plate. Fluorophore concentrations ranging from 0.3 μM to 20 μM were tested with different bottom plate substrates and herein, we discuss high SNR detection for droplet sizes as low as 10 μL over a time period of microseconds. Next, the integration of the fluorescence sensor with a digital microfluidic system is discussed. Finally, the performance of the sensor is evaluated, followed by conclusions and thoughts for future work.

Current techniques for nucleic acid analysis often involve extensive sample preparation that requires skilled personnel and multiple purification steps. In this dissertation, we introduce an on-chip, isothermal, post-hybridization labeling and signal amplification technique that can directly interrogate unmodified DNA and RNA samples on a microarray format, eliminating the need for microarray sample pre-processing.

We name this technique Surface Initiated Enzymatic Polymerization (SIEP), where we exploit the ability of a template independent DNA polymerase called Terminal Deoxynucleotidyl Transferase (TdT) to catalyze the formation of long single-stranded DNA (ssDNA) chain from the 3'-end of a short DNA primer, which is tethered on the surface, and TdT's ability to incorporate unnatural reporter nucleotides, such as fluorescent nucleotides. We hypothesize that polymerization of a long ssDNA chain while incorporating multiple fluorescent nucleotides on target DNA or RNA hybridized to probe printed on a surface will provide a simple and powerful, isothermal method for on-chip labeling and signal amplification.

We developed the SIEP methodology by first characterizing TdT biochemical reaction to polymerize long homopolymer ssDNA (> 1000 bases) starting from the 3'-OH of ten bases oligonucleotides. We found that the preferred monomers (deoxynucleotide, dNTP) are dATP and dTTP, and that the length of the ssDNA extension is determined by the ratio of input monomer (dNTP) to initiator (short oligonucleotides). We also investigated TdT's ability to incorporate fluorescent dNTPs into a ssDNA chain by examining the effect of the molar ratios of fluorescent dNTP to natural dNTP on the initiation efficiency, the degree of fluorophore incorporation, the length and the polydispersity of the polymerized DNA strand. These experiments allowed us to incorporate up to ~50 fluorescent Cy3-labeled dNTPs per kilobase into a ssDNA chain. With the goal of using SIEP as an on-chip labeling method, we also quantified TdT mediated signal amplification on the surface by immobilizing ssDNA oligonucleotide initiators on a glass surface followed by SIEP of DNA. The incorporation of multiple fluorophores into the extended DNA chain by SIEP translated to a up to ~45 fold increase in signal amplification compared to the incorporation of a single fluorophore.

SIEP was then employed to detect hybridization of DNA (25 bases), short miRNA (21 bases) and long mRNA (1400 bases) by the post-hybridization, on-chip polymerization of fluorescently labeled ssDNA that was grown from the 3'-OH of hybridized target strands. A dose-response curve for detection of DNA hybridization by SIEP was generated, with a ~1 pM limit of detection (LOD) and a 2-log linear dynamic range while the detection of short miRNA and fragmented mRNA targets resulted in ~2 pM and ~10 pM LOD, respectively with a 3-log linear dynamic range.

We further developed SIEP for colorimetric detection by exploiting the presence of negatively charged phosphate backbone on the surface as target DNA or RNA hybridizes on the immobilized probe. The net negative charge on the surface is further increased by TdT catalyzed polymerization of long ssDNA. We then used positively charged gold nanoparticles as reporters, which can be further amplified through electroless metallization, creating DNA spots that are visible by eye. We observed an increase of 100 fold in LOD due to SIEP amplification.

Overall, we demonstrated the use of SIEP methodology to label unmodified target DNA and RNA on chip, which can be detected through fluorescence signal or colorimetric signal of metallized DNA spots. This methodology is straightforward and versatile, is compatible with current microarray technology, and can be implemented using commercially available reagents.

Biosensors incorporating proteins as molecular recognition elements for analytes are used in clinical diagnostics, as biological research tools, and to detect chemical threats and pollutants. This work describes the application of protein engineering techniques to address three aspects in the design of protein-based biosensors; the transduction of binding into an observable, the manipulation of affinities, and the diversification of specificities. The periplasmic glucose-binding protein from the hyperthermophile Thermotoga maritima (tmGBP) was fused with green fluorescent protein variants to construct a fluorescent ratiometric sensor that is sufficiently robust to detect glucose up to 67°C. Ligand-binding affinities of tmGBP were changed by altering a C-terminal helical domain that tunes ligand binding affinity through conformational coupling effects. This method was extended to the Escherichia coli arabinose-binding protein. Computational design techniques were used to diversify the specificity of the E. coli maltose-binding protein (ecMBP) to bind ibuprofen, a non-steroidal antiinflammatory drug. These designs ranged in affinity from 0.24 to 0.8 mM and function as reagentless fluorescent sensors. The ligand affinities of ecMBP are tuned by complex interactions that control conformational coupling. These experiments demonstrate that long-range conformational effects as well as molecular recognition interactions need to be considered in the design of high-affinity receptors.