Capillary and microchip gel electrophoresis using multiplexed fluorescence detection with both time-resolved and spectral-discrimination capabilities: applications in DNA sequencing using near-infrared fluorescence

Increasing the information content obtainable from a single assay and system miniaturization has continued to be important research areas in analytical chemistry. The research presented in this dissertation involves the development of a two-color, time-resolved fluorescence microscope for the acquisition of both steady-state and time-resolved data during capillary and microchip electrophoresis. The utility of this hybrid fluorescence detector has been demonstrated by applying it to DNA sequencing applications. Coupling color discrimination with time-resolved fluorescence offers increased multiplexing capabilities because the lifetime data adds another layer of information. An optical fiber-based fluorescence microscope was constructed, which utilized fluorescence in near-IR region, greatly simplifying the hardware and allowing superior system sensitivity. Time-resolved data was processed using electronics configured in a time-correlated single photon counting format. Cross-talk between color channels was successfully eliminated by utilizing the intrinsic time-resolved capability associated with the detector. The two-color, time-resolved microscope was first coupled to a single capillary and carried out two-color, two-lifetime sequencing of an M13 template, achieving a read length of 650 bps at a calling accuracy of 95.1%. The feasibility of using this microscope with microchips (glass-based chips) for sequencing was then demonstrated. Results from capillaries and microchips were compared, with the microchips providing faster analysis and adequate electrophoretic performance. Lifetimes of a set of fluorescent dyes were determined with favorable precision, in spite of the low loading levels associated with the microchips. The sequencing products were required to be purified and concentrated prior to electrophoretic sorting to improve data quality. PMMA-based microchips for DNA sequencing application were evaluated. The microchips were produced from thermo plastics, which allowed rapid and inexpensive production of microstructures with high aspect ratios. It was concluded that surface coating was needed on the polymer chips in order to achieve single-base resolution required for DNA sequencing. The capability of the two-color time-resolved microscope operated in a scanning mode was further explored. The successful construction of the scanner allows scanning of multi-channel microchips for high throughput processing

An axial approach to detection in capillary electrophoresis

The purpose of this research has been to develop and demonstrate visualization schemes which further the capabilities of capillary electrophoresis instrumentation. Our approach involves on-axis illumination of the compounds inside the capillary detection region and is applied to absorbance and fluorescence detection;Absorbance measurements were accomplished by focussing an incident beam of laser light into one end of the separation capillary. By utilizing signals collected over the entire length of the analyte band, this technique enhances the analytical path length of conventional absorbance detection sixty fold. The demonstrated instrument offers a fifteen-fold improvement in concentration limits of detection;Three fluorescence detection experiments are discussed; all of which involve the insertion of a small optical fiber into the capillary to introduce the excitation beam from a laser source. The first of these employs a high refractive index liquid phase to satisfy total internal reflectance along the capillary axis, thus greatly reducing light scatter from capillary walls. The second utilizes a charge-coupled device (CCD) camera for simultaneous imaging of an array of capillaries (a technique that may prove useful in high information throughput tasks such as genome sequencing). The third is basic study of fluid motion inside the capillary under conditions of pressure-driven and electroosmotic flow. In this study, the CCD is used to track nanometer-sized particles as well as fluorescent bands to give a quantitative, empirical assessment of the factors influencing capillary electrophoresis separations

Strategies for Untargeted Biomarker Discovery in Biological Fluids

The health status of an organism modulates the dynamic and complex interplay of biochemical species that make-up the body and fluids of the organism. As such, these biological fluids are routinely used for diagnostic testing, yet they are often not used to their full potential. For instance, amniotic fluid (AF), the fluid that surrounds the fetus during gestation, is collected primarily for genetic testing from women with identified risk factors. The AF proteome and/or metabolome are seldom considered and represent a largely untapped wealth of relevant clinical information. Extensive, multi-analyte data can be collected from biological samples with modern analytical instrumentation. However, sophisticated data preprocessing and analysis (i.e. chemometrics) are required to reveal the relationships between the biochemical signals and the health status. This thesis seeks to demonstrate that untargeted biomarker discovery strategies can be efficiently applied to the task of finding novel biomarkers and complement the traditional hypothesis driven approaches. In the work underlying this thesis, a chemometric data analysis strategy was developed to search for biomarkers in capillary electrophoresis (CE) separations data. The absorbance data from amniotic fluid samples (n=107) collected at 15 weeks gestation, at 195 +/- 4 nm, was normalized, time aligned with Correlation Optimized Warping and reduced to a smaller number of variables by Haar transformation. The reduced data was then classified into normal or abnormal health classes by using a Bayes classifier algorithm. The chemometric data analysis was first employed to find biomarkers of gestational diabetes mellitus (GDM) and revealed that human serum albumin (HSA) could predict the early onset of disease. The same approach was successfully used to identify cases of large-for-gestational age (LGA) with the same AF CE-UV data. It was also employed for the classification of embryos with high and low reproductive potential using in vitro fertilization (IVF) culture media analyzed by CE-UV. Overall, a chemometric method was developed to perform untargeted biomarker discovery in biological samples and provide new means to detect GDM pregnancies, LGA neonates and viable embryos in IVF. The method was successful at identifying biomarkers of interest and showed high flexibility and transferability to other biological fluids

Independent Parallel Capillary Array Separations for Rapid Second Dimension Sampling in On-Line Two-Dimensional Capillary Electrophoresis of Complex Biological Samples

Biological samples remain challenging in proteomic separations due to their complexity and large concentration dynamic range. Improvements to separation power are needed to interrogate proteomes more deeply and facilitate the advancement of biomarker discovery for personalized medicine. Current online multidimensional separations require compromise; long analysis times if the second dimension (2nd-D) must be regenerated between injections, or reduced separation efficiency if the 2nd-D is operated rapidly. Using an array of capillaries as the 2nd-D, operated in parallel, allows fast sampling of the first dimension (1st-D). This relaxes the constraints on the 2nd-D separation, allowing it to operate at optimal separation conditions that would otherwise be sacrificed for speed. This configuration allows total separation times to approximately equal to the 1st-D separation time. We have developed a novel interface that enables continuous sampling of a 1st-D separation by a 2nd-D capillary array for rapid, high peak capacity two dimensional (2D) separations, based upon automated precision positioning of capillaries. Within a laminar flow regime, a capillary electrophoresis (CE) 1st-D separation was coupled to an array of eight independent CE 2nd-D separations. The instrument terminus provides laser induced fluorescence detection via a sheath flow cuvette. Effluent transfer efficiency, from the 1st-D to the 2nd-D, and detection was optimized using visible and fluorescent dye tracers. To that end, this dissertation will discuss characterization of interface and detector parameters, including: inter capillary transverse alignment accuracy, injection distance, injection time, hydrodynamic flow rate, density considerations, inter and intra capillary differences, signal crosstalk and laser intensity. Separation performance will further be demonstrated using model protein and serum digestates. Each dimension of the 2D instrument will be operated as a one dimensional (1D) instrument to compare against an optimized commercial 1D CE instrument. These results will be used to evaluate the quality of the separations operated in on-line 2D capillary electrophoresis-to-capillary array electrophoresis (CE×CAE) mode. A novel application of the CE×CAE design will be discussed in the spirit of resolving the long standing challenge of migration time reproducibility in CE separations

Dynamic Time-Warping Correction for Shifts in Ultrahigh Resolving Power Ion Mobility Spectrometry and Structures for Lossless Ion Manipulations

Detection of arrival time shifts between ion mobility spectrometry (IMS) separations can limit achievable resolving power (Rp), particularly when multiple separations are summed or averaged, as commonly practiced in IMS. Such variations can be apparent in higher Rp measurements and are particularly evident in long path length traveling wave structures for lossless ion manipulations (SLIM) IMS due to their typically much longer separation times. Here, we explore data processing approaches employing single value alignment (SVA) and nonlinear dynamic time warping (DTW) to correct for variations between IMS separations, such as due to pressure fluctuations, to enable more effective spectrum summation for improving Rp and detection of low-intensity species. For multipass SLIM IMS separations, where narrow mobility range measurements have arrival times that can extend to several seconds, the SVA approach effectively corrected for such variations and significantly improved Rp for summed separations. However, SVA was much less effective for broad mobility range separations, such as obtained with multilevel SLIM IMS. Changes in ions’ arrival times were observed to be correlated with small pressure changes, with approximately 0.6% relative arrival time shifts being common, sufficient to result in a loss of Rp for summed separations. Comparison of the approaches showed that DTW alignment performed similarly to SVA when used over a narrow mobility range but was significantly better (providing narrower peaks and higher signal intensities) for wide mobility range data. We found that the DTW approach increased Rp by as much as 115% for measurements in which 50 IMS separations over 2 s were summed. We conclude that DTW is superior to SVA for ultra-high-resolution broad mobility range SLIM IMS separations and leads to a large improvement in effective Rp, correcting for ion arrival time shifts regardless of the cause, as well as improving the detectability of low-abundance species. Our tool is publicly available for use with universal ion mobility format (.UIMF) and text (.txt) files

Systems approach to microbial pathogenesis: complex patterns emerge from simple interactions

Biological organisms are complex systems and modeling can provide insight into their behavior by the process of recreating it. All elements may not be known of the system under study and thus, hypotheses must be made in order to create an appropriate model. These hypotheses can lead to interesting modeling results and help guide in vitro experiments. However, modeling complexity does not necessarily require complex techniques. By modeling the simplest elements of a biological system and by defining how the elements interact, it is possible to model complex behavior as emergent properties of the system. In this manner, I model simple interactions between biological elements. First, at the lowest level of complexity, is a single molecule such as an RNA. Determining RNA secondary structure is a necessary step to understand how it interacts with other molecules to affect the biological system as a whole. The structure of an RNA is formed through simple interactions between nucleotides. I developed software that aids the process of identifying sites in an RNA where nucleotide-nucleotide or nucleotide-protein binding occurs to predict RNA secondary structure more accurately. The next level of complexity is molecule-molecule interactions that result in the emergence of patterns within an organism, such as phenotypes expressed by a cell. Using agent-based modeling, I model the proteins, RNAs, and enzymes involved in a gene regulatory network that is responsible for the emergence of the competence phenotype in Bacillus subtilis. Competence is stochastically expressed due to the variable expression of genes. My agent-based model identified several possible sources for this variation: dilution events like cell division, inheritance of molecules involved in competence and most importantly, spatial temporal interactions of molecules. And lastly, I model the simple interactions between two organisms, a virus and a host cell, to understand the molecular interactions between host and pathogen that result in the replication and assembly of a virus. In this model, I successfully modeled the self-assembly of BK Virus using an agent-based model that models from transcription to translation to the encapsidation of the BKV genome within a T=7, icosahedral structure all by simple molecule-molecule interactions

Improving data extraction methods for large molecular biology datasets.

In the past, an experiment involving a pair wise comparison normally involved one or a few dependant variables. Now, 1000s of dependent variables can be measured simultaneously in a single experiment, be it detecting genes via a microarray experiment, sequencing genomes, or detecting microbial species based on DNA fragments using molecular techniques. How we analyze such large collections of data will be a major scientific focus over the next decade. Statistical methods that were once acceptable for comparing a few conditions are being revised to handle 1000?s of experiments. Molecular biology techniques that explored 1 gene or species have evolved and are now capable of generating complex datasets requiring new strategies and ways of thinking in order to discover biologically meaningful results. The central theme of this dissertation is to develop strategies that deal with a number of issues that are present in these large scale datasets. In chapter 1, I describe a microarray analytical method that can be applied to low replicate experiments. In chapter?s 2-4, the focus is how to best analyze data from ARISA (a PCR based molecular method for rapidly generating a finger print of microbial diversity). Chapter 2 focuses on qualifying ARISA data so that data will best represent its biological source, prior to further analysis. Chapter 3 focuses on how to best compare ARISA profiles to one another. Chapter 4 focuses on developing a software tool that implements the data processing and clustering strategies from chapter?s 2 and 3. The findings described herein provide the scientific community with improved analytical strategies in both the microarray and ARISA research areas