Automated Nucleic Acid Purification for Large Samples

Advancements in the fields of genomic screening, molecular pathology and clinical research have resulted in a major increase in the demand for high quality DNA and RNA. This escalating demand has resulted in a sample preparation bottleneck and an emphasis on the development of new technologies to automate the purification process. Gentra has developed the AUTOPURE LS™ nucleic acid purification instrument, a platform capable of high-throughput sample purification from large samples, such as 10 mL whole blood. This article presents data showing the equivalency of DNA purified using manual and automated processing.     

A completely automated sample preparation instrument and consumable device for isolation and purification of nucleic acids

Molecular diagnostic analysis and life science studies are dependent on the ability to effectively prepare samples for analysis. We report the development of a system that enables robust sample preparation of nucleic acids. To enable completely automated sample preparation, a consumable cartridge and consumable module system were developed to emulate every step of the sample preparation process. This included enzyme and reagent addition, temperature-controlled incubations, noncontact mixing of enzymes and reagents, buffer exchanges, and sample elution. Using this system, completely automated methods were developed for the purification of viral RNA and DNA from plasma and whole blood and of bacterial genomic DNA from water and whole blood. Extracted nucleic acids were detected and quantified using real-time PCR. The data indicate that automated viral DNA extraction was more efficient than sample extractions performed using a manual process, whereas automated total RNA extraction from the same samples was equivalent to controls. Additionally, we found that the process for bacterial genomic DNA extraction from either water or whole blood was equivalent to the manual extraction processes. We conclude the instrument, consumable cartridge, and reagent system enables easy, cost-effective, and robust sample preparation regardless of the experience of the operator.     

Automated purification of dye terminator sequencing reactions: an approach to high-throughput capillary electrophoresis sequencing of large templates

Demands for higher quantity and quality of sequence data during genome sequencing projects have led to a need for completely automated reagent systems designed to isolate, process, and analyze DNA samples. While much attention has been given to methodologies aimed at increasing the throughput of sample preparation and reaction setup, purification of the products of sequencing reactions has received less scrutiny despite the profound influence that purification has on sequence quality. Commonly used and commercially available sequencing reaction cleanup methods are not optimal for purifying sequencing reactions generated from larger templates, including bacterial artificial chromosomes (BACs) and those generated by rolling circle amplification. Theoretically, these methods would not remove the original template since they only exclude small molecules and retain large molecules in the sample. If the large template remains in the purified sample, it could understandably interfere with electrokinetic injection and capillary performance. We demonstrate that the use of MagneSil^® paramagnetic particles (PMPs) to purify ABI PRISM^® BigDye^® sequencing reactions increases the quality and read length of sequences from large templates. The high-quality sequence data obtained by our procedure is independent of the size of template DNA used and can be completely automated on a variety of automated platforms.     

Extraction of genomic DNA and detection of single nucleotide polymorphism genotyping utilizing an integrated magnetic bead-based microfluidic platform

This study presents a new magnetic bead-based microfluidic platform, which integrates three major modules for rapid leukocytes purification, genomic DNA (gDNA) extraction and fast analysis of genetic gene. By utilizing microfluidic technologies and magnetic beads conjugated with CD_15/45 antibodies, leukocytes in a human whole blood sample can be first purified and concentrated, followed by extraction of gDNA utilizing surface-charge switchable, DNA-specific, magnetic beads in the lysis solution. Then, specific genes associated with genetic diseases can be amplified by an on-chip polymerase chain reaction (PCR) process automatically. The whole pretreatment process including the leukocytes purification and gDNA extraction can be performed in an automatic fashion with the incorporation of the built bio-separators consisting of microcoils array within less than 20 min. The detection of single nucleotide polymorphism (SNP) genotyping of methylenetetra-hydrofolate reductase (MTHFR) C677T region associated with an increased risk of genetic diseases was further performed to demonstrate the capability of the proposed system. The extracted gDNA can be transported into a micro PCR chamber for on-chip fast nucleic acid amplification of detection genes with minimum human intervention. Hence, the developed system may provide a powerful automated platform for pretreatment of human leukocytes, gDNA extraction and fast analysis of genetic gene.     

An evolutionary computational model applied to cluster analysis of DNA microarray data

This paper proposes a new hierarchical clustering method using genetic algorithms for the analysis of gene expression data. This method is based on the mathematical proof of several results, showing its effectiveness with regard to other clustering methods. Genetic algorithms applied to cluster analysis have disclosed good results on biological data and many studies have been carried out in this sense, although most of them are focused on partitional clustering methods. Even though there are few studies that attempt to use genetic algorithms for building hierarchical clustering, they do not include constraints that allow us to reduce the complexity of the problem. Therefore, these studies become intractable problems for large data sets. On the other hand, the deterministic hierarchical clustering methods generally face the problem of convergence towards local optimums due to their greedy strategy. The method introduced here is an alternative to solve some of the problems existing methods face. The results of the experiments have shown that our approach can be very effective in cluster analysis of DNA microarray data.     

A Study of a High-Throughput Plasmid DNA Purification System

An automated process that incorporates Millipore's Plasmid Miniprep₉₆ Montáge™ Kit with the Apogent Discoveries PlateMate Plus® and Tango™ automated high-throughput dispensing systems has been developed for purifying plasmid DNA. To test the efficacy of this process, parameters such as the reproducibility and consistency of the purified DNA quantity and quality as well as the purification speed were analyzed. The purification time for two plates of the Plasmid Miniprep₉₆ Kit (192 samples) was approximately 60 minutes using a PlateMate Plus equipped with 96 disposable tips and the Tango system equipped with 96 RB (resin bead) syringes. High uniformity and consistency in DNA yields (determined by spectrophotometric analysis) and quality (determined by gel electrophoresis analysis) among the different wells were observed. The purified plasmid DNA samples sequenced at an exceptional level with an average PHRED Q > 20 of 819 ± 25.*Millipore and Montage are the trademarks of Millipore Corporation     

Integral characteristics of genetic code

Numerous data acquired for complete DNA sequences of large genomes (human and others) created the problem of their studies by biomathematics. This work presents theoretical foundations for the solution of certain problems and, primarily, for estimations of large sets of genes. The major issue is the so-called natural blockage of genes, when all five sequences of codons, alternative to gene sequence, contain multiple stoppages of protein synthesis. Theorem 1 establishes the potential of such blockage for standard genetic code. Theorem 2 establishes the code potential that is utilized for peculiar records of genetic information, the so-called overlapping genes. The interrelation is established between integral characteristics of genetic codes that can be derived on the basis of the aforementioned theorems.     

Automated genomic and proteomic applications on the Biomek NX laboratory automation workstation

This technical paper describes the utilization of a new automated liquid handler from Beckman Coulter, Inc., the Biomek^® NX Laboratory Automation Workstation, for genomic and proteomic applications. For genomic applications, methodology for plasmid DNA purification using Promega Wizard^® SV 96 reagents was developed for the Biomek NX. A single plate of bacterial pellets can be processed to purified plasmid DNA without user interaction after initial setup. DNA quantity and quality were assessed by spectrophotometric analysis, restriction digestion, PCR (The PCR process is covered by patents owned by Roche Molecular Systems, Inc., and F. Hoffman La Roche, Ltd.), and capillary sequencing. Additionally, the plasmid preparation method was used to purify plasmid DNA from bacterial clones isolated in a bacterial two-hybrid screening procedure. In this case, the system quickly and efficiently prepared clones for rapid identification of target sequences. For proteomic applications, His-tag proteins were purified from bacterial cultures in a 96-well plate format. Following purification, a Bradford assay was used to determine the quantitative yields of the His-tag protein products in each of the aliquots from the purified samples. The AD 340 Automated Labware Positioner (ALP), an integrated absorbance reader, was used for absorbance measurements in the Bradford assay. Given the placement of this ALP on the deck of the Biomek NX, the entire process of protein purification and quantitation was performed in a complete walk-away automated format. Results obtained when purifying proteins, from both uninduced and induced bacterial cultures, on the worksurface of the Biomek NX will be described.     

An automatic microfluidic system that continuously performs the systematic evolution of ligands by exponential enrichment

The systematic evolution of ligands by exponential enrichment (SELEX) technique has been extensively used to screen molecule-specific aptamers from combinatorial libraries of synthetic nucleic acids. Aptamers are single-stranded DNA or RNA, which have a high affinity to a large variety of molecules ranging from small drugs or metabolites to cells. Therefore, they have a variety of promising applications such as for diagnostics and targeted therapeutics. In this study, a new microfluidic chip was developed to perform continuous screening of DNA-based aptamers in an automatic format. When compared with the existing manual procedure, the developed microfluidic chip has several advantages including a rapid and efficient screening process, automation, and less consumption of samples/reagents. Experimental data showed that an aptamer specific to alpha-fetoprotein was successfully screened from a random DNA pool. The entire screening process (five continuous, repetitive rounds) can be completed within 6?h, which is much faster than the traditional methods (more than 15?h). An automatic, rapid and efficient SELEX process was performed by this developed microfluidic chip, which may enable a generalized platform for the fast screening of DNA-based biomarkers in the future.     

A high-throughput microchromatography platform for quantitative analytical scale protein sample preparation

Protein analysis (using either specific protein quantitation by methods such as HPLC and immunoassays or structural analysis by methods such as LC-MS) usually requires significant sample preparation, including quantitative purification of the target protein from complex sample matrices and potentially enzymatic treatment or labeling. We have developed platform for high-throughput microchromatography, capable of running 96 or more small volume samples in parallel, producing from 10 pg to 100 μg of purified protein from each sample. The platform is based on disposable cartridge devices with 5 μL packed bed of resin. The cartridges may be operated as spin columns or run on a modified 96-channel liquid handler with ultra-low dead volume syringes that directly connect to the cartridges, providing very precisely controlled positive-displacement flow control. A major application is quantitative purification of target proteins using affinity or physical chromatography. Using large diameter nonporous beads, standard microplate enzyme-linked immunosorbent assay reagents can be used to perform 30-min immunoassays. Enzymatic digestion methods have also been developed on the system for application in glycan profiling.     

Integration of supervised ART-based neural networks with a hybrid genetic algorithm

In this paper, two evolutionary artificial neural network (EANN) models that are based on integration of two supervised adaptive resonance theory (ART)-based artificial neural networks with a hybrid genetic algorithm (HGA) are proposed. The search process of the proposed EANN models is guided by a knowledge base established by ART with respect to the training data samples. The EANN models explore the search space for “coarse” solutions, and such solutions are then refined using the local search process of the HGA. The performances of the proposed EANN models are evaluated and compared with those from other classifiers using more than ten benchmark data sets. The applicability of the EANN models to a real medical classification task is also demonstrated. The results from the experimental studies demonstrate the effectiveness and usefulness of the proposed EANN models in undertaking pattern classification problems.     

DNA sequence comparison by a novel probabilistic method

This paper proposes a novel method for comparing DNA sequences. By using a graphical representation, we are able to construct the probability distributions of DNA sequences. These probability distributions can then be used to make similarity studies by using the symmetrised Kullback-Leibler divergence. After presenting our method, we test it using six DNA sequences taken from the threonine operons of Escherichia coli K-12 and Shigella flexneri. Our approach is then used to study the evolution of primates using mitochondrial DNA data. Our method allows us to reconstruct a phylogenetic tree for primate evolution. In addition, we use our technique to analyze the classification and phylogeny of the Tomato Yellow Leaf Curl Virus (TYLCV) based on its whole genome sequences. These examples show that large volumes of DNA sequences can be handled more easily and more quickly by our approach than by the existing multiple alignment methods. Moreover, our method, unlike other approaches, does not require human intervention, because it can be applied automatically.     

Genomic mining for complex disease traits with “random chemistry”

Our rapidly growing knowledge regarding genetic variation in the human genome offers great potential for understanding the genetic etiology of disease. This, in turn, could revolutionize detection, treatment, and in some cases prevention of disease. While genes for most of the rare monogenic diseases have already been discovered, most common diseases are complex traits, resulting from multiple gene–gene and gene-environment interactions. Detecting epistatic genetic interactions that predispose for disease is an important, but computationally daunting, task currently facing bioinformaticists. Here, we propose a new evolutionary approach that attempts to hill-climb from large sets of candidate epistatic genetic features to smaller sets, inspired by Kauffman’s “random chemistry” approach to detecting small auto-catalytic sets of molecules from within large sets. Although the algorithm is conceptually straightforward, its success hinges upon the creation of a fitness function able to discriminate large sets that contain subsets of interacting genetic features from those that don’t. Here, we employ an approximate and noisy fitness function based on the ReliefF data mining algorithm. We establish proof-of-concept using synthetic data sets, where individual features have no marginal effects. We show that the resulting algorithm can successfully detect epistatic pairs from up to 1,000 candidate single nucleotide polymorphisms in time that is linear in the size of the initial set, although success rate degrades as heritability declines. Research continues into seeking a more accurate fitness approximator for large sets and other algorithmic improvements that will enable us to extend the approach to larger data sets and to lower heritabilities.     

Multi-view fuzzy clustering with minimax optimization for effective clustering of data from multiple sources

Multi-view data clustering refers to categorizing a data set by making good use of related information from multiple representations of the data. It becomes important nowadays because more and more data can be collected in a variety of ways, in different settings and from different sources, so each data set can be represented by different sets of features to form different views of it. Many approaches have been proposed to improve clustering performance by exploring and integrating heterogeneous information underlying different views. In this paper, we propose a new multi-view fuzzy clustering approach called MinimaxFCM by using minimax optimization based on well-known Fuzzy c means. In MinimaxFCM the consensus clustering results are generated based on minimax optimization in which the maximum disagreements of different weighted views are minimized. Moreover, the weight of each view can be learned automatically in the clustering process. In addition, there is only one parameter to be set besides the fuzzifier. The detailed problem formulation, updating rules derivation, and the in-depth analysis of the proposed MinimaxFCM are provided here. Experimental studies on nine multi-view data sets including real world image and document data sets have been conducted. We observed that MinimaxFCM outperforms related multi-view clustering approaches in terms of clustering accuracy, demonstrating the great potential of MinimaxFCM for multi-view data analysis.     

A two-stage electrophoretic microfluidic device for nucleic acid collection and enrichment

Sample purification and enrichment is an important and usually time-consuming step for on-chip nucleic acid detection and analysis. This paper presents an electrophoretic DNA focusing method in microfluidic devices to enrich nucleic acid concentration by around 2700-fold. The electrical waveforms applied to five individual electrodes are such designed that DNAs move successively to the collection electrodes at high speed, while the interferences from bubbles due to electrohydrolysis are minimized. In a spiral channel with a total length of 48 cm, 1 ml DNA sample is purified and enriched by 57 times at a flow rate of 30 μl/min at first. The captured DNAs are then released and transported to the second microfluidic chamber where DNAs are collected and concentrated by 49 times. Thus, in about 40 min, the two-stage device can extract DNAs from 1 ml sample volume and enrich its concentration by 2790-fold. A trade-off exists between the process throughput and the DNA collection efficiency. A DNA capture efficiency of 99.7 % is reached when the flow rate is 1 μl/min, and the maximum DNA capture throughput is achieved at a flow rate of 30 μl/min. As a platform technology, the device can be integrated into bio-sensing and genetic analysis assays for DNA extraction and pre-concentration.     

Impact of pre-analytical factors on the proteomic analysis of formalin-fixed paraffin-embedded tissue

Formalin-fixed paraffin-embedded (FFPE) tissue samples represent a tremendous potential resource for biomarker discovery, with large numbers of samples in hospital pathology departments and links to clinical information. However, the cross-linking of proteins and nucleic acids by formalin fixation has hampered analysis and proteomic studies have been restricted to using frozen tissue, which is more limited in availability as it needs to be collected specifically for research. This means that rare disease subtypes cannot be studied easily. Recently, improved extraction techniques have enabled analysis of FFPE tissue by a number of proteomic techniques. As with all clinical samples, pre-analytical factors are likely to impact on the results obtained, although overlooked in many studies. The aim of this review is to discuss the various pre-analytical factors, which include warm and cold ischaemic time, size of sample, fixation duration and temperature, tissue processing conditions, length of storage of archival tissue and storage conditions, and to review the studies that have considered these factors in more detail. In those areas where investigations are few or non-existent, illustrative examples of the possible importance of specific factors have been drawn from studies using frozen tissue or from immunohistochemical studies of FFPE tissue.     

A Fully Automated Process Using a Magnetic Particle Based Kit for Removal of Dye Terminators from Sequencing Reactions

Prolinx,® Inc. of Bothell, WA has developed the RapXtract™ 384 Dye Terminator Removal Kit for full automation of DNA sequencing reaction purification. The RapXtract product line is based upon proprietary superparamagnetic particle technology that eliminates the need for centrifugation, vacuum filtration, or modified primers to achieve purification of sequencing reactions. The kit described here is pre-dispensed in a 384-well microtiter plate and run on the TECAN GENESIS Workstation 150 (Tecan U.S. Inc., Research Triangle Park, NC). This system enables rapid purification of up to 384 sequencing reactions in a single run.As the completion of the Human Genome Project nears, it is imperative for biotechnology and pharmaceutical companies to increase throughput of DNA sequencing in order to be competitive in the drug discovery and validation process. The “race to market” requires a shift from standard DNA sequencing processes-including DNA sequencing reaction purification-towards complete walk-away automation for all steps.Existing sequencing reaction purification methods (Table 1) require considerable resources including: plastic and other laboratory consumables; specialized equipment, such as high-speed centrifuges or vacuum filtration apparatus; and labor-intensive protocols requiring large amounts of technician time. As a result, walk-away automation of standard purification methods is difficult and expensive.     

Cascade of genetic algorithm and decision tree for cancer classification on gene expression data

Abstract: Cancer classification, through gene expression data analysis, has produced remarkable results, and has indicated that gene expression assays could significantly aid in the development of efficient cancer diagnosis and classification platforms. However, cancer classification, based on DNA array data, remains a difficult problem. The main challenge is the overwhelming number of genes relative to the number of training samples, which implies that there are a large number of irrelevant genes to be dealt with. Another challenge is from the presence of noise inherent in the data set. It makes accurate classification of data more difficult when the sample size is small. We apply genetic algorithms (GAs) with an initial solution provided by t statistics, called t‐GA, for selecting a group of relevant genes from cancer microarray data. The decision‐tree‐based cancer classifier is built on the basis of these selected genes. The performance of this approach is evaluated by comparing it to other gene selection methods using publicly available gene expression data sets. Experimental results indicate that t‐GA has the best performance among the different gene selection methods. The Z‐score figure also shows that some genes are consistently preferentially chosen by t‐GA in each data set.     

An analysis of the effects of sample size on classification performance of a histogram based cluster analysis procedure

A non-parametric unsupervised program was developed to identify clusters in multidimensional data by mode analysis using histograms. An implicit assumption in the histogram approach is that a relatively large number of samples is required to insure an accurate classification. Tests with randomly generated data show that the assumption is not true, i.e. a small number of samples does not necessarily result in a poor classification, nor does a relatively large number of samples guarantee the best classification. The histogram classifier was compared to two parametric classifiers, maximum likelihood and K-means clustering. Results from timing the classifiers show that, although the parametric classifiers are more efficient for a small number of samples, the histogram approach uses less CPU time for a large number of samples.     

Rapid detection of bacterial cell from whole blood: Integration of DNA sample preparation into single micro-PCR chip

A novel bacterial cell detection method from blood samples has been developed for molecular diagnostics. Functional integration of DNA sample preparation into polymerase chain reaction (PCR) chip enabled detection of pathogenic bacterial cells in a single microchip. Surface-modified micropillars possessing affinity for bacterial cells were fabricated inside a PCR chip, and reaction conditions were optimized to render the microchip with high surface-to-volume ratio PCR-compatible. After bacterial cells were captured on the micropillars from whole blood and PCR inhibitors were washed out, PCR mixture was injected to allow real-time amplification of DNA extracted from the isolated cells. Cell enrichment effect produced by volume reduction from large initial sample to small micro-PCR chip chamber led to increased detection sensitivity. Moreover, the developed method from sample preparation to detection of bacterial cells from whole blood took less than 1 h. These results demonstrated that the surface-modified pillar-packed microchip would be a practical approach for integration into Lab-On-a-Chip (LOC) to enable point-of-care genetic analysis.