Liquid chromatography (LC) in tandem with mass spectrometry (MS) is a measurement tool for obtain information about the compounds in the investigated extracts. There were already developed methods for processing and analysis of measured data sets. However, only partial problems of processing/analysis task were handled independently. Therefore, the rst part describes existing methods and techniques commonly used in the LC-MS for the processing and analysis today. In this thesis an approach based on the theory of systems is used for description of abstract model above the measured data. This model encapsulated all processing/analysis steps into appropriate and consistent mathematical space. The creation of this model via description of the measurement device and data outputs is introduced. Abstract model of LC-MS data set is used to decompose the measurement into three partial contributions, the analyte signal, the random noise and the systemic noise. The separation process of the signal could be estimated using the probabilistic approach. That probabilistic approach to the LC-MS analysis was implemented in the developed software, which was published in the Bioinformatics Journal.

Tissues are heterogeneous ecosystems comprised of various cell types. For example, in tumor tissues, malignant cancer cells are surround by various non-malignant stromal cells. Proteins, especially N-linked glycoproteins, are key players in tumor microenvironment and respond to many extracellular stimuli for involving and regulating intercellular signaling. Understanding the human proteome and glycoproteome in heterogeneous tissues with spatial resolution are meaningful for exploring intercellular signaling networks and discovering protein biomarkers for various diseases, such as cancer. In this study, we aimed to develop new liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based analytical methods for spatially-resolved proteome and glycoproteome profiling in tissue samples, and apply them for profiling potential biomarkers for pancreatic cancer. We first systematically and synchronously optimized the LC-MS parameters to increase peptide sequencing efficiency in data dependent proteomics. Taking advantage of its hybrid instrument design with various mass analyzer and fragmentation strageties, the Orbitrap Fusion mass spectrometer was used for systematically comparing the popular high-high approach by using orbitrap for both MS1 and MS2 scans and high-low approach by using orbitrap for MS1 scan and ion trap for MS2 scans. High-high approach outperformed high-low approach in terms of better saturation of the scan cycle and higher MS2 identification rate. We then systematically optimized various MS parameters for high-high approach. We investigated the influence of isolation window and injection time on scan speed and MS2 identification rate. We then explored how to properly set dynamic exclusion time according to the chromatography peak width. Furthermore, we found that the orbitrap analyzer, rather than the analytical column, was easily saturated with higher peptide loading amount, thus limited the dynamic range of MS1-based quantification. Finally, by using the optimized LC-MS parameters, more than 9000 proteins and 110,000 unique peptides were identified by using 10 hours of effective LC gradient time. The study therefore illustrated the importance of synchronizing LC-MS precursor targeting and high-resolution fragment detection for high-efficient data dependent proteomics. Understanding the tumor heterogeneity through spatially resolved proteome profiling is meaningful for biomedical research. Laser capture microdissection (LCM) is a powerful technology for exploring local cell populations without losing spatial information. Here, we designed an immunohistochemistry (IHC)-based workflow for cell type-resolved proteome analysis of tissue samples. Firstly, targeted cell type was stained by IHC using antibody targeting cell-type specific marker to improve accuracy and efficiency of LCM. Secondly, to increase protein recovery from chemically crosslinked IHC tissues, we optimized a decrosslinking procedure to seamlessly combine with the integrated spintip-based sample preparation technology SISPROT. This newly developed approach, termed IHC-SISPROT, has comparable performance with traditional H&E staining-based proteomic analysis. High sensitivity and reproducibility of IHC-SISPROT was achieved by combining with data independent proteomic analysis. This IHC-SISPROT workflow was successfully applied for identifying 6660 and 6052 protein groups from cancer cells and cancer- associated fibroblasts (CAFs) by using only 5 mm 2 and 12 μm thickness of hepatocellular carcinoma tissue section. Bioinformatic analysis revealed the enrichment of cell type-specific ligands and receptors and potentially new communications between cancer cells and CAFs by these signaling proteins. Therefore, IHC-SISPROT is sensitive and accurate proteomic approach for spatial profiling of cell type-specific proteome from tissues. N-linked glycoproteins are promising candidates for diagnostic and prognostic biomarkers and therapeutic targets. They often locate at plasma membrane and extracellular space with distinct cell type distribution in tissue microenvironment. Due to access to only low microgram of proteins and low abundance of glycoproteins in tissue sections harvested by LCM, region- and cell type-resolved glycoproteome analysis of tissue sections remains challenging. Here we designed a fully integrated spintip-based glycoproteomic approach (FISGlyco) which achieved all the steps for glycoprotein enrichment, digestion, deglycosylation and desalting in a single spintip device. Sample loss is significantly reduced and the total processing time is reduced to 4 hours, while detection sensitivity and label-free quantification precision is greatly improved. 607 N-glycosylation sites were successfully identified and quantified from only 5 μg of mouse brain proteins. By seamlessly combining with LCM, the first region-resolved N-glycoproteome profiling of four mouse brain regions, including isocortex, hippocampus, thalamus, and hypothalamus, was achieved, with 1,875, 1,794, 1,801, and 1,417 N-glycosites identified, respectively. Our approach could be a generic approach for region and even cell type specific glycoproteome analysis of tissue sections. Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with five year survival rate of around 8%. No effective biomarkers and targeted therapy are one of the major reasons for this urgent clinical situation. To explore potential protein biomarkers and drug targets located at intercellular space of pancreatic tumor microenvironment, we established chemical proteomic approach for deep glycoproteome profiling of PDAC clinical tissue samples based on the above- mentioned new proteomic methods. Taking advantage of a long chain biotin- hydrazide probe with less space hindrance, the new method outperformed traditional hydrazide chemistry method in terms of sensitivity, time efficiency and glycoproteome coverage. The method was successfully applied to enrich and validate LIF and its receptors as potential biomarkers for PDAC. In addition, to explore the full map of pancreatic tumor microenvironment glycoproteome with diagnostic and therapeutic values, we collected 114 pancreatic tissues, including 30 PDAC tumor tissues, 30 adjacent non-tumor (NT) tissues, 32 chronic pancreatitis tissues and 22 normal pancreatic tissues, and systematically profiled their glycoprotein expression pattern by using the developed glycoproteomic strategy. The deepest glycoproteome of PDAC was achieved, which covered the majority of previously reported glycoprotein biomarkers and drug targets for PDAC. Importantly, we discovered many new glycoproteins with differential expression in PDAC and normal tissue types. Moreover, LCM-based cell-type proteome profiling was achieved for 13 PDAC tissue samples, which covered more than 8000 proteins for both pancreatic stromal cells and pancreatic cancer cells in each sample. We therefore provided a valuable resource for screening novel and cancer specific glycoprotein biomarkers for pancreatic cancer with spatial resolution

Tissues are heterogeneous ecosystems comprised of various cell types. For example, in tumor tissues, malignant cancer cells are surround by various non-malignant stromal cells. Proteins, especially N-linked glycoproteins, are key players in tumor microenvironment and respond to many extracellular stimuli for involving and regulating intercellular signaling. Understanding the human proteome and glycoproteome in heterogeneous tissues with spatial resolution are meaningful for exploring intercellular signaling networks and discovering protein biomarkers for various diseases, such as cancer. In this study, we aimed to develop new liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based analytical methods for spatially-resolved proteome and glycoproteome profiling in tissue samples, and apply them for profiling potential biomarkers for pancreatic cancer. We first systematically and synchronously optimized the LC-MS parameters to increase peptide sequencing efficiency in data dependent proteomics. Taking advantage of its hybrid instrument design with various mass analyzer and fragmentation strageties, the Orbitrap Fusion mass spectrometer was used for systematically comparing the popular high-high approach by using orbitrap for both MS1 and MS2 scans and high-low approach by using orbitrap for MS1 scan and ion trap for MS2 scans. High-high approach outperformed high-low approach in terms of better saturation of the scan cycle and higher MS2 identification rate. We then systematically optimized various MS parameters for high-high approach. We investigated the influence of isolation window and injection time on scan speed and MS2 identification rate. We then explored how to properly set dynamic exclusion time according to the chromatography peak width. Furthermore, we found that the orbitrap analyzer, rather than the analytical column, was easily saturated with higher peptide loading amount, thus limited the dynamic range of MS1-based quantification. Finally, by using the optimized LC-MS parameters, more than 9000 proteins and 110,000 unique peptides were identified by using 10 hours of effective LC gradient time. The study therefore illustrated the importance of synchronizing LC-MS precursor targeting and high-resolution fragment detection for high-efficient data dependent proteomics. Understanding the tumor heterogeneity through spatially resolved proteome profiling is meaningful for biomedical research. Laser capture microdissection (LCM) is a powerful technology for exploring local cell populations without losing spatial information. Here, we designed an immunohistochemistry (IHC)-based workflow for cell type-resolved proteome analysis of tissue samples. Firstly, targeted cell type was stained by IHC using antibody targeting cell-type specific marker to improve accuracy and efficiency of LCM. Secondly, to increase protein recovery from chemically crosslinked IHC tissues, we optimized a decrosslinking procedure to seamlessly combine with the integrated spintip-based sample preparation technology SISPROT. This newly developed approach, termed IHC-SISPROT, has comparable performance with traditional H&E staining-based proteomic analysis. High sensitivity and reproducibility of IHC-SISPROT was achieved by combining with data independent proteomic analysis. This IHC-SISPROT workflow was successfully applied for identifying 6660 and 6052 protein groups from cancer cells and cancer- associated fibroblasts (CAFs) by using only 5 mm 2 and 12 μm thickness of hepatocellular carcinoma tissue section. Bioinformatic analysis revealed the enrichment of cell type-specific ligands and receptors and potentially new communications between cancer cells and CAFs by these signaling proteins. Therefore, IHC-SISPROT is sensitive and accurate proteomic approach for spatial profiling of cell type-specific proteome from tissues. N-linked glycoproteins are promising candidates for diagnostic and prognostic biomarkers and therapeutic targets. They often locate at plasma membrane and extracellular space with distinct cell type distribution in tissue microenvironment. Due to access to only low microgram of proteins and low abundance of glycoproteins in tissue sections harvested by LCM, region- and cell type-resolved glycoproteome analysis of tissue sections remains challenging. Here we designed a fully integrated spintip-based glycoproteomic approach (FISGlyco) which achieved all the steps for glycoprotein enrichment, digestion, deglycosylation and desalting in a single spintip device. Sample loss is significantly reduced and the total processing time is reduced to 4 hours, while detection sensitivity and label-free quantification precision is greatly improved. 607 N-glycosylation sites were successfully identified and quantified from only 5 μg of mouse brain proteins. By seamlessly combining with LCM, the first region-resolved N-glycoproteome profiling of four mouse brain regions, including isocortex, hippocampus, thalamus, and hypothalamus, was achieved, with 1,875, 1,794, 1,801, and 1,417 N-glycosites identified, respectively. Our approach could be a generic approach for region and even cell type specific glycoproteome analysis of tissue sections. Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with five year survival rate of around 8%. No effective biomarkers and targeted therapy are one of the major reasons for this urgent clinical situation. To explore potential protein biomarkers and drug targets located at intercellular space of pancreatic tumor microenvironment, we established chemical proteomic approach for deep glycoproteome profiling of PDAC clinical tissue samples based on the above- mentioned new proteomic methods. Taking advantage of a long chain biotin- hydrazide probe with less space hindrance, the new method outperformed traditional hydrazide chemistry method in terms of sensitivity, time efficiency and glycoproteome coverage. The method was successfully applied to enrich and validate LIF and its receptors as potential biomarkers for PDAC. In addition, to explore the full map of pancreatic tumor microenvironment glycoproteome with diagnostic and therapeutic values, we collected 114 pancreatic tissues, including 30 PDAC tumor tissues, 30 adjacent non-tumor (NT) tissues, 32 chronic pancreatitis tissues and 22 normal pancreatic tissues, and systematically profiled their glycoprotein expression pattern by using the developed glycoproteomic strategy. The deepest glycoproteome of PDAC was achieved, which covered the majority of previously reported glycoprotein biomarkers and drug targets for PDAC. Importantly, we discovered many new glycoproteins with differential expression in PDAC and normal tissue types. Moreover, LCM-based cell-type proteome profiling was achieved for 13 PDAC tissue samples, which covered more than 8000 proteins for both pancreatic stromal cells and pancreatic cancer cells in each sample. We therefore provided a valuable resource for screening novel and cancer specific glycoprotein biomarkers for pancreatic cancer with spatial resolution

Indiana University-Purdue University Indianapolis (IUPUI) / The aim of this study is to identify metabolic transformations in breast cancer through urinary volatile organic compounds in mammary pad or bone tumor mice models. Subsequently, it focuses on investigating the efficacy of therapeutic intervention through identified potential biomarkers. Methods for monitoring tumor development and treatment responses have technologically advanced over the years leading to significant increase in percent survival rates. Although these modalities are reliable, it would be beneficial to observe disease progression from a new perspective to gain greater understanding of cancer pathogenesis. Analysis of cellular energetics affected by cancer using bio-fluids can non-invasively help in prognosis and selection of treatment regimens. The hypothesis is altered profiles of urinary volatile metabolites is directly related to disrupted metabolic pathways. Additionally, effectiveness of treatments can be indicated through changes in concentration of metabolites. In this ancillary experiment, mouse urine specimens were analyzed using gas chromatography-mass spectrometry, an analytical chemistry tool in identifying volatile organic compounds. Female BALB/c mice were injected with 4T1.2 murine breast tumor cells in the mammary fat pad. Consecutively, 4T1.2 cells were injected in the right iliac artery of BALB/c mice and E0771 tumor cells injected in the tibia of C57BL/6 mice to model bone tumor. The effect of two different modes of treatment: chemical drug and mechanical stimulation was investigated through changes in compound profiles. Chemical drug therapy was conducted with dopamine agents, Triuoperazine, Fluphenazine and a statin, Pitavastatin. Mechanical stimulation included tibia and knee loading at the site of tumor cell injection were given to mice. A biological treatment mode included administration of A5 osteocyte cell line. A set of potential volatile organic compounds biomarkers differentiating mammary pad or bone confined tumors from healthy controls was identified using forward feature selection. Effect of treatments was demonstrated through hierarchical heat maps and multivariate data analysis. Compounds identified in series of experiments belonged to the class of terpenoids, precursors of cholesterol molecules. Terpene synthesis is a descending step of mevalonate pathway suggesting its potential role in cancer pathogenesis. This thesis demonstrates the ability of urine volatilomics to indicate signaling pathways inflicted in tumors. It proposes a concept of using urine to detect tumor developments at two distinct locations as well as to monitor treatment efficacy.

Indiana University-Purdue University Indianapolis (IUPUI) / In Headspace Solid-Phase Microextraction (Headspace SPME), a sample is heated to encourage a portion of the analyte into the headspace of a vial. A coated fiber is introduced into the sample headspace and the analyte is adsorbed onto the fiber coating. Total Vaporization Solid-Phase Microextraction (TV-SPME) is a technique that is derived from this technique. In TV-SPME, liquid samples are completely vaporized allowing for better adsorption and fewer matrix effects. This method does not require any sample preparation, utilizes minimal supplies and can be automated, making it both an efficient and cost-effective method. Chapter 1 will discuss the theory of SPME and TV-SPME. In Chapter 2, the detection of ɣ-hydroxybutyric acid (GHB) and ɣ-butyrolactone (GBL) in beverages is discussed. The detection of these compounds in beverages is of importance because these drugs may be used to facilitate sexual assault. This crime utilizes substances that cause sedation and memory loss. The derivatization of GHB as well as the properties that make GHB difficult to detect will be discussed. Chapter 3 will discuss the detection of methamphetamine and amphetamine (as their trifluoroacetyl derivatives), GBL, and the trimethylsilyl derivative of GHB in human urine. Amphetamine is a metabolite of methamphetamine, therefore, both drugs should be identified within biological samples. GHB and GBL are metabolites of one another and interconvert when in aqueous solution. This interconversion will be discussed. Chapter 4 will cover method optimization of the Total Vaporization Solid-Phase Microextraction method. Analytes of interest for these analyses were methamphetamine, amphetamine, GHB, and GBL. The optimal extraction temperature ranging from 60-160°C of each drug will be discussed as well as why higher temperatures may not be suitable for this method. A limit of detection study for methamphetamine and amphetamine will also be covered. Chapter 5, the future work chapter, will discuss future analyses using the Total Vaporization Solid-Phase Microextraction method including the analysis of powder materials, plant material, and toxicological samples. Powder material will include the analysis of individual powdered drugs as well as realistic drug mixtures. Some analyses on individual powder samples has already been completed and will be shown. Plant material will include the analysis of naturally occurring compounds found in marijuana plants as well as synthetic cannabinoids. Toxicological samples will expand on previously mentioned urine samples to include drugs such as benzoylecgonine and THC-COOH.

No description available.

No description available.

Traditional proteomics studies can measure many protein biomarkers simultaneously from a single patient-derived sample, promising the possibility of syndromic diagnoses of multiple diseases sharing common symptoms. However, precious cellular-level information is lost in conventional bulk-scale studies that measure tissues comprising many types of cells. As single cells are the building blocks of organisms and are easier to biopsy than traditional bulk samples, performing proteomics on a single-cell level would benefit clinicians and patients. Single-cell proteomics, combined with mass spectrometry imaging, can be used to analyze cells in their microenvironment, preserving spatial information. We have previously used laser-capture microdissection to isolate single motor neurons from tissue and analyze them in our single-cell proteomics platform. However, our sampled population of cells was necessarily limited by the low throughput of the measurement platform, and by the sensitivity of our liquid chromatography-mass spectrometry system to debris introduced in the laser-capture microdissection isolation workflow. In the work described in this dissertation, we dramatically improved the throughput of single-cell proteomics, created a method for removing insoluble debris that clogged our liquid chromatography-mass spectrometry system, and developed a high-performance, low-cost method for nanoflow gradient formation. Together, these methodologies will increase the depth of information and the number of biological replicates that can measured in single-cell proteomics. We hope that these technologies will be applied to future liquid chromatography systems to enable large scale single-cell proteomics studies of tissues. This will reveal the cellular origins of disease on a multimolecular level, while keeping important spatial information. Thus, we expect the technologies and ideas developed here to play a key role in understanding the cellular proteomics in biomedical and clinical settings.

No description available.

The main objective of this research was to identify the compounds responsible for persistent odors in biosolids during long-term storage using olfactometry measurements and to determine their generation pattern with regard to time of appearance and decline using gas chromatography-mass spectrometry (GC-MS). Another objective of this study was to investigate the effect of various digestion and dewatering techniques on odors and determine if there is a correlation between the peak concentration and time of appearance of short-tem organic sulfur odors and persistent odors. Headspace analysis was used to quantify short-term odor-causing organic sulfur compounds and persistent odors from compounds such as indole, skatole, butyric acid and p-cresol for an incubation period up to 150 days. A unique odor generation pattern was observed for each of the compounds analyzed for all the dewatered cakes tested in this study. Dewatered cake samples were also analyzed to determine their detection threshold by a trained odor panel and the results were consistent with the general pattern of odor generation observed in this study. Positive correlations were observed between the peak concentration of organic sulfur and persistent odor compounds whereas little or no relationship was observed between their times of appearance. The type of sludge used in digestion (primary sludge, WAS and mix) was found to affect the production of odor-causing compounds significantly. Primary sludge produces the highest odors followed by mix. WAS was found to produce biosolids with a low odor concentration. Positive correlation was observed between odor concentration and digestion SRT. Significant reduction in odor concentration was observed when the SRT was increased from 12-days to 25-days. At 45-day SRT, further reduction in odors was not very significant. Moreover, the results from this study indicate that methanogens play an important role in the degradation of both organic sulfur and persistent odors. Although the highest odors during biosolids incubation came from sulfur compounds, the persistent odors must be managed as part of a comprehensive sludge management approach. / Master of Science