Simple electroanalytical method for determination of guanosine in biological sample

A poly glutamic acid film modified electrode exhibited a catalytic response toguanosine oxidation potential and higher peak current value. Linear concentration curve was obtained in the concentration interval of 1.0 a 10.0 μmol L-1 in 0.04 mol L-1 B-R buffer pH 2.0 with a detection limit of 0.198 μmol L-1. The electrode was used for the determination of guanosine in the potential of +1.1 V (vs. Ag/AgCl) using differential pulse voltammetry (DPV) at urine sample with good recovery. © 2010 by CEE.

Charge Injection through Nanocomposite Electrode in Microfluidic Channel for Electrical Lysis of Biological Cells

Several concepts have been developed in the recent years for nanomaterial based integrated MEMS platform in order to accelerate the process of biological sample preparation followed by selective screening and identification of target molecules. In this context, there exist several challenges which need to be addressed in the process of electrical lysis of biological cells. These are due to (i) low resource settings while achieving maximal lysis (ii) high throughput of target molecules to be detected (iii) automated extraction and purification of relevant molecules such as DNA and protein from extremely small volume of sample (iv) requirement of fast, accurate and yet scalable methods (v) multifunctionality toward process monitoring and (vi) downward compatibility with already existing diagnostic protocols. This paper reports on the optimization of electrical lysis process based on various different nanocomposite coated electrodes placed in a microfluidic channel. The nanocomposites are synthesized using different nanomaterials like Zinc nanorod dispersion in polymer. The efficiency of electrical lysis with various different electrode coatings has been experimentally verified in terms of DNA concentration, amplification and protein yield. The influence of the coating thickness on the injection current densities has been analyzed. We further correlate experimentally the current density vs. voltage relationship with the extent of bacterial cell lysis. A coupled multiphysics based simulation model is used to predict the cell trajectories and lysis efficiencies under various electrode boundary conditions as estimated from experimental results. Detailed in-situ fluorescence imaging and spectroscopy studies are performed to validate various hypotheses.

Determination of rare earth elements in biological samples by inductively coupled plasma mass spectrometry

A method for the determiantion of rare earth elements in biological sampels by inductively coupled plasma mass spectrometry was developed. Oxide ion yield of the rare earth elements (RFE) decreased with the increasing of RF power and the sampling depth, or with the decreasing of carrier gas flow rate. The spectral interference arising from (PrO)-Pr-141-O-16 on Gd-157 must be corrected. if the concentration of Ba was high enough, it was necessary to correct the spectral interference arising from (BO)-B-135-O-16 on Eu-151, and it was not necessary to correct spectral interference arising from (NdO)-Nd-143-O-16 on Tb-159 etc. in the biological samples under the selected operation parameters. In the biological sample, the major matrix elements, such as K, Na and Ca, result in the suppression of REEs signals and the suppression degree of the Ca is grezter than that of the K and Na. The mussel sample was digested by thd dry ashing, wet digestion with HNO3 + H2O2 and HNO3 + HClO4, respectively. The analytical results of REEs were consistent with each other. Detection limits for REEs are 0.001 similar to 0.013 mu g/L. Recoveries of standard addition are 91.7% similar to 125%. REEs in biological samples were determined directly without separation and preconcentration procedure.

Metabolite profiling of biological extracts

Metabolite profiling, HPLC, LC-QTOF-MS, GC-MS. A workflow will be presented for comprehensive metabolomics using LC- and GC-MS. Metabolomics is an emerging field in the suite of ‘omic’ approaches for Systems Biology. The goal of metabolomics is to detect the presence of all small-molecules in a biological sample. This presents a significant challenge due to the chemical diversity and large concentration range of metabolites. Currently, there is no single method which enables the entire metabolome to be analysed, therefore a suite of analytical approaches are required to increase the coverage of detected metabolites. The routinely used techniques for metabolite profiling are LC- and GC-MS and NMR. Here we present complementary approaches using MS hyphenated to different chromatographic techniques. GC-MS represent the most robust standardised technique for high throughput metabolite profiling however there are still no standard LC-based methods for profiling. Polar compounds represent the most challenging aspect of LC-based metabolomics. A robust chromatographic technique for profiling polar compounds using HILIC chromatography and QTOF-MS will be presented as well as the complimentary reverse phase LC-MS method. The polar separation was carried out using a diamond hydride column. This unique stationary phase provides stable retention times and fast re-equilibration which contrasts to other forms of HILIC stationary phases. These LC-based methods will be compared to the well established GC-MS method as well as NMRbased profiling.

Development of Original Analytical Methods for the Determination of Drugs of Abuse in Biological Samples

Drug abuse is a major global problem which has a strong impact not only on the single individual but also on the entire society. Among the different strategies that can be used to address this issue an important role is played by identification of abusers and proper medical treatment. This kind of therapy should be carefully monitored in order to discourage improper use of the medication and to tailor the dose according to the specific needs of the patient. Hence, reliable analytical methods are needed to reveal drug intake and to support physicians in the pharmacological management of drug dependence. In the present Ph.D. thesis original analytical methods for the determination of drugs with a potential for abuse and of substances used in the pharmacological treatment of drug addiction are presented. In particular, the work has been focused on the analysis of ketamine, naloxone and long-acting opioids (buprenorphine and methadone), oxycodone, disulfiram and bupropion in human plasma and in dried blood spots. The developed methods are based on the use of high performance liquid chromatography (HPLC) coupled to various kinds of detectors (mass spectrometer, coulometric detector, diode array detector). For biological sample pre-treatment different techniques have been exploited, namely solid phase extraction and microextraction by packed sorbent. All the presented methods have been validated according to official guidelines with good results and some of these have been successfully applied to the therapeutic drug monitoring of patients under treatment for drug abuse.

Electrochemical sensing strategies for the detection of interactions between biological macromolecules

Electrochemical biosensors provide an attractive means to analyze the content of a biological sample due to the direct conversion of a biological event to an electronic signal, enabling the development of cheap, small, portable and simple devices, that allow multiplex and real-time detection. At the same time nanobiotechnology is drastically revolutionizing the biosensors development and different transduction strategies exploit concepts developed in these field to simplify the analysis operations for operators and end users, offering higher specificity, higher sensitivity, higher operational stability, integrated sample treatments and shorter analysis time. The aim of this PhD work has been the application of nanobiotechnological strategies to electrochemical biosensors for the detection of biological macromolecules. Specifically, one project was focused on the application of a DNA nanotechnology called hybridization chain reaction (HCR), to amplify the hybridization signal in an electrochemical DNA biosensor. Another project on which the research activity was focused concerns the development of an electrochemical biosensor based on a biological model membrane anchored to a solid surface (tBLM), for the recognition of interactions between the lipid membrane and different types of target molecules.

(Tables 1+4) Sample site substratum and morphometrics and damage levels of different populations of Laternula elliptica around Antarctica

We document differences in shell damage and shell thickness in a bivalve mollusc (Laternula elliptica) from seven sites around Antarctica with differing exposures to ice movement. These range from 60% of the sea bed impacted by ice per year (Hangar Cove, Antarctic Peninsula) to those protected by virtually permanent sea ice cover (McMurdo Sound). Patterns of shell damage consistent with blunt force trauma were observed in populations where ice scour frequently occurs; damage repair frequencies and the thickness of shells correlated positively with the frequency of iceberg scour at the different sites with the highest repair rates and thicker shells at Hangar Cove (74.2% of animals damaged) compared to the other less impacted sites (less than 10% at McMurdo Sound). Genetic analysis of population structure using Amplified Fragment Length Polymorphisms (AFLPs) revealed no genetic differences between the two sites showing the greatest difference in shell morphology and repair rates. Taken together, our results suggest that L. elliptica exhibits considerable phenotypic plasticity in response to geographic variation in physical disturbance.

Sparse Sample Detection Using Magnetic Bead Manipulation on a Digital Microfluidic Device

This thesis demonstrates a new way to achieve sparse biological sample detection, which uses magnetic bead manipulation on a digital microfluidic device. Sparse sample detection was made possible through two steps: sparse sample capture and fluorescent signal detection. For the first step, the immunological reaction between antibody and antigen enables the binding between target cells and antibody-­‐‑ coated magnetic beads, hence achieving sample capture. For the second step, fluorescent detection is achieved via fluorescent signal measurement and magnetic bead manipulation. In those two steps, a total of three functions need to work together, namely magnetic beads manipulation, fluorescent signal measurement and immunological binding. The first function is magnetic bead manipulation, and it uses the structure of current-­‐‑carrying wires embedded in the actuation electrode of an electrowetting-­‐‑on-­‐‑dielectric (EWD) device. The current wire structure serves as a microelectromagnet, which is capable of segregating and separating magnetic beads. The device can achieve high segregation efficiency when the wire spacing is 50µμm, and it is also capable of separating two kinds of magnetic beads within a 65µμm distance. The device ensures that the magnetic bead manipulation and the EWD function can be operated simultaneously without introducing additional steps in the fabrication process. Half circle shaped current wires were designed in later devices to concentrate magnetic beads in order to increase the SNR of sample detection. The second function is immunological binding. Immunological reaction kits were selected in order to ensure the compatibility of target cells, magnetic bead function and EWD function. The magnetic bead choice ensures the binding efficiency and survivability of target cells. The magnetic bead selection and binding mechanism used in this work can be applied to a wide variety of samples with a simple switch of the type of antibody. The last function is fluorescent measurement. Fluorescent measurement of sparse samples is made possible of using fluorescent stains and a method to increase SNR. The improved SNR is achieved by target cell concentration and reduced sensing area. Theoretical limitations of the entire sparse sample detection system is as low as 1 Colony Forming Unit/mL (CFU/mL).

Metabolite profiling and pathway analysis of acute hepatitis rats by UPLC-ESI MS combined with pattern recognition methods

BACKGROUND & AIMS Metabolomics is comprehensive analysis of low-molecular-weight endogenous metabolites in a biological sample. It could enable mapping of perturbations of early biochemical changes in diseases and hence provide an opportunity to develop predictive biomarkers that could provide valuable insights into the mechanisms of diseases. The aim of this study was to elucidate the changes in endogenous metabolites and to phenotype the metabolic profiling of d-galactosamine (GalN)-inducing acute hepatitis in rats by UPLC-ESI MS. METHODS The systemic biochemical actions of GalN administration (ip, 400 mg/kg) have been investigated in male wistar rats using conventional clinical chemistry, liver histopathology and metabolomic analysis of UPLC- ESI MS of urine. The urine was collected predose (-24 to 0 h) and 0-24, 24-48, 48-72, 72-96 h post-dose. Mass spectrometry of the urine was analysed visually and via conjunction with multivariate data analysis. RESULTS Results demonstrated that there was a time-dependent biochemical effect of GalN dosed on the levels of a range of low-molecular-weight metabolites in urine, which was correlated with developing phase of the GalN-inducing acute hepatitis. Urinary excretion of beta-hydroxybutanoic acid and citric acid was decreased following GalN dosing, whereas that of glycocholic acid, indole-3-acetic acid, sphinganine, n-acetyl-l-phenylalanine, cholic acid and creatinine excretion was increased, which suggests that several key metabolic pathways such as energy metabolism, lipid metabolism and amino acid metabolism were perturbed by GalN. CONCLUSION This metabolomic investigation demonstrates that this robust non-invasive tool offers insight into the metabolic states of diseases.

A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data

Background: A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript profiling data remains an important yet challenging endeavor. A transcript profile specifies the abundances of many genes in a biological sample of interest. Prevailing strategies for learning the structure of a genetic network from high-dimensional transcript profiling data assume sparsity and linearity. Many methods consider relatively small directed graphs, inferring graphs with up to a few hundred nodes. This work examines large undirected graphs representations of genetic networks, graphs with many thousands of nodes where an undirected edge between two nodes does not indicate the direction of influence, and the problem of estimating the structure of such a sparse linear genetic network (SLGN) from transcript profiling data. Results: The structure learning task is cast as a sparse linear regression problem which is then posed as a LASSO (l1-constrained fitting) problem and solved finally by formulating a Linear Program (LP). A bound on the Generalization Error of this approach is given in terms of the Leave-One-Out Error. The accuracy and utility of LP-SLGNs is assessed quantitatively and qualitatively using simulated and real data. The Dialogue for Reverse Engineering Assessments and Methods (DREAM) initiative provides gold standard data sets and evaluation metrics that enable and facilitate the comparison of algorithms for deducing the structure of networks. The structures of LP-SLGNs estimated from the INSILICO1, INSILICO2 and INSILICO3 simulated DREAM2 data sets are comparable to those proposed by the first and/or second ranked teams in the DREAM2 competition. The structures of LP-SLGNs estimated from two published Saccharomyces cerevisae cell cycle transcript profiling data sets capture known regulatory associations. In each S. cerevisiae LP-SLGN, the number of nodes with a particular degree follows an approximate power law suggesting that its degree distributions is similar to that observed in real-world networks. Inspection of these LP-SLGNs suggests biological hypotheses amenable to experimental verification. Conclusion: A statistically robust and computationally efficient LP-based method for estimating the topology of a large sparse undirected graph from high-dimensional data yields representations of genetic networks that are biologically plausible and useful abstractions of the structures of real genetic networks. Analysis of the statistical and topological properties of learned LP-SLGNs may have practical value; for example, genes with high random walk betweenness, a measure of the centrality of a node in a graph, are good candidates for intervention studies and hence integrated computational – experimental investigations designed to infer more realistic and sophisticated probabilistic directed graphical model representations of genetic networks. The LP-based solutions of the sparse linear regression problem described here may provide a method for learning the structure of transcription factor networks from transcript profiling and transcription factor binding motif data.

Bentsodiatsepiinien ja amfetamiinijohdannaisten seulonta virtsasta mikropilarisähkösumutusmassaspektrometrian avulla

Väärinkäytettyjen aineiden seulontaan käytetyn menetelmän tulee olla herkkä, selektiivinen, yksinkertainen, nopea ja toistettava. Työn tavoitteena oli kehittää yksinkertainen, mutta herkkä, esikäsittelymenetelmä bentsodiatsepiinien ja amfetamiinijohdannaisten kvalitatiiviseen seulomiseen virtsasta mikropilarisähkösumutussirun (μPESI) avulla, mikä tarjoaisi vaihtoehdon seulonnassa käytetyille immunologisille menetelmille, joiden herkkyys ja selektiivisyys ovat puutteellisia. Tavoitteena oli samalla tarkastella mikropilarisähkösumutussirun toimivuutta biologisten näytteiden analyysissa. Esikäsittely optimoitiin erikseen bentsodiatsepiineille ja amfetamiinijohdannaisille. Käytettyjä esikäsittelymenetelmiä olivat neste-nesteuutto, kiinteäfaasiuutto Oasis HLB-patruunalla ja ZipTip®-pipetinkärjellä sekä laimennus ja suodatus ilman uuttoa. Mittausten perusteella keskityttiin optimoimaan ZipTip®-uuttoa. Optimoinnissa tutkittavia yhdisteitä spiikattiin 0-virtsaan niiden ennaltamääritetyn raja-arvon verran, bentsodiatsepiineja 200 ng/ml ja amfetamiinijohdannaisia 300 ng/ml. Bentsodiatsepiinien kohdalla optimoitiin kutakin uuton vaihetta ja optimoinnin tuloksena näytteen pH säädettiin arvoon 5, faasi kunnostettiin asetonitriililla, tasapainotettiin ja pestiin veden (pH 5) ja asetonitriilin (10 % v/v) seoksella ja eluoitiin asetonitriilin, muurahaishapon ja veden (95:1:4 v/v/v) seoksella. Amfetamiinijohdannaisten uutossa optimoitiin näytteen ja liuottimien pH-arvoja ja tuloksena näytteen pH säädettiin arvoon 10, faasi kunnostettiin veden ja ammoniumvetykarbonaatin(pH 10, 1:1 v/v) seoksella, tasapainotettiin ja pestiin asetonitriilin ja veden (1:5 v/v) seoksella ja eluoitiin metanolilla. Optimoituja uuttoja testattiin Yhtyneet Medix Laboratorioista toimitetuilla autenttisilla virtsanäytteillä ja saatuja tuloksia verrattiin kvantitatiivisen GC/MS-analyysin tuloksiin. Bentsodiatsepiininäytteet hydrolysoitiin ennen uuttoa herkkyyden parantamiseksi. Autenttiset näytteet analysoitiin Q-TOF-laitteella Viikissä. Lisäksi hydrolysoidut bentsodiatsepiininäytteet mitattiin Yhtyneet Medix Laboratorioiden TOF-laitteella. Kehitetty menetelmä vaatii tulosten perusteella lisää optimointia toimiakseen. Ongelmana oli etenkin toistoissa ilmennyt tulosten hajonta. Manuaalista näytteensyöttöä tulisi kehittää toistettavammaksi. Autenttisten bentsodiatsepiininäytteiden analyysissa ongelmana olivat virheelliset negatiiviset tulokset ja amfetamiinijohdannaisten analyysissa virheelliset positiiviset tulokset. Virheellisiä negatiivisia tuloksia selittää menetelmän herkkyyden puute ja virheellisiä positiivisia tuloksia mittalaitteen, sirujen tai liuottimien likaantuminen.

A metabolomic approach to studying mechanisms of polymeric gene delivery to retinal pigment epithelial cells

Tiivistelmä ReferatAbstract Metabolomics is a rapidly growing research field that studies the response of biological systems to environmental factors, disease states and genetic modifications. It aims at measuring the complete set of endogenous metabolites, i.e. the metabolome, in a biological sample such as plasma or cells. Because metabolites are the intermediates and end products of biochemical reactions, metabolite compositions and metabolite levels in biological samples can provide a wealth of information on on-going processes in a living system. Due to the complexity of the metabolome, metabolomic analysis poses a challenge to analytical chemistry. Adequate sample preparation is critical to accurate and reproducible analysis, and the analytical techniques must have high resolution and sensitivity to allow detection of as many metabolites as possible. Furthermore, as the information contained in the metabolome is immense, the data set collected from metabolomic studies is very large. In order to extract the relevant information from such large data sets, efficient data processing and multivariate data analysis methods are needed. In the research presented in this thesis, metabolomics was used to study mechanisms of polymeric gene delivery to retinal pigment epithelial (RPE) cells. The aim of the study was to detect differences in metabolomic fingerprints between transfected cells and non-transfected controls, and thereafter to identify metabolites responsible for the discrimination. The plasmid pCMV-β was introduced into RPE cells using the vector polyethyleneimine (PEI). The samples were analyzed using high performance liquid chromatography (HPLC) and ultra performance liquid chromatography (UPLC) coupled to a triple quadrupole (QqQ) mass spectrometer (MS). The software MZmine was used for raw data processing and principal component analysis (PCA) was used in statistical data analysis. The results revealed differences in metabolomic fingerprints between transfected cells and non-transfected controls. However, reliable fingerprinting data could not be obtained because of low analysis repeatability. Therefore, no attempts were made to identify metabolites responsible for discrimination between sample groups. Repeatability and accuracy of analyses can be influenced by protocol optimization. However, in this study, optimization of analytical methods was hindered by the very small number of samples available for analysis. In conclusion, this study demonstrates that obtaining reliable fingerprinting data is technically demanding, and the protocols need to be thoroughly optimized in order to approach the goals of gaining information on mechanisms of gene delivery.

Application of metabolomics to plant genotype discrimination using statistics and machine learning

Janet Taylor, Ross D King, Thomas Altmann and Oliver Fiehn (2002). Application of metabolomics to plant genotype discrimination using statistics and machine learning. 1st European Conference on Computational Biology (ECCB). (published as a journal supplement in Bioinformatics 18: S241-S248).

Local examination of skin diffusion using FTIR spectroscopic imaging and multivariate target factor analysis

In the context of trans-dermal drug delivery it is very important to have mechanistic insight into the barrier function of the skin's stratum corneum and the diffusion mechanisms of topically applied drugs. Currently spectroscopic imaging techniques are evolving which enable a spatial examination of various types of samples in a dynamic way. ATR-FTIR imaging opens up the possibility to monitor spatial diffusion profiles across the stratum corneum of a skin sample. Multivariate data analyses methods based on factor analysis are able to provide insight into the large amount of spectroscopically complex and highly overlapping signals generated. Multivariate target factor analysis was used for spectral resolution and local diffusion profiles with time through stratum corneum. A model drug, 4-cyanophenol in polyethylene glycol 600 and water was studied. Results indicate that the average diffusion profiles between spatially different locations show similar profiles despite the heterogeneous nature of the biological sample and the challenging experimental set-up.

Design, recruitment, logistics, and data management of the GEHA (Genetics of Healthy Ageing) project

In 2004, the integrated European project GEHA (Genetics of Healthy Ageing) was initiated with the aim of identifying genes involved in healthy ageing and longevity. The first step in the project was the recruitment of more than 2500 pairs of siblings aged 90 years or more together with one younger control person from 15 areas in 11 European countries through a coordinated and standardised effort. A biological sample, preferably a blood sample, was collected from each participant, and basic physical and cognitive measures were obtained together with information about health, life style, and family composition. From 2004 to 2008 a total of 2535 families comprising 5319 nonagenarian siblings were identified and included in the project. In addition, 2548 younger control persons aged 50-75 years were recruited. A total of 2249 complete trios with blood samples from at least two old siblings and the younger control were formed and are available for genetic analyses (e.g. linkage studies and genome-wide association studies). Mortality follow-up improves the possibility of identifying families with the most extreme longevity phenotypes. With a mean follow-up time of 3.7 years the number of families with all participating siblings aged 95 years or more has increased by a factor of 5 to 750 families compared to when interviews were conducted. Thus, the GEHA project represents a unique source in the search for genes related to healthy ageing and longevity.