Single Biological Sample Research Articles

Cell-Based Method Utilizing Fluorescent Escherichia coli Auxotrophs for Quantification of Multiple Amino Acids

A cell-based assay system for simultaneous quantification of the three amino acids, phenylalanine (Phe), methionine (Met), and leucine (Leu) in a single biological sample, was developed and applied in the multiplex diagnosis of three key metabolic diseases of newborn babies. The assay utilizes three Escherichia coli auxotrophs, which grow only in the presence of the corresponding target amino acids and which contain three different fluorescent reporter plasmids that produce distinguishable fluorescence signals (red, green, and cyan) in concert with cell growth. To mixtures of the three auxotrophs, immobilized on agarose gels arrayed on a well plate, is added a test sample. Following incubation, the concentrations of the three amino acids in the sample are simultaneously determined by measuring the intensities of three fluorescence signals that correspond to the reporter plasmids. The clinical utility of this assay system was demonstrated by employing it to identify metabolic diseases of newborn babies through the quantification of Phe, Met, and Leu in clinically derived dried blood spot specimens. The general strategy developed in this effort should be applicable to the design of new assay systems for the quantification of multiple amino acids derived from complex biological samples and, as such, to expand the utilization of cell-based analytical systems that replace conventional, yet laborious methods currently in use.

Novel Biosensor for Point of Care Medical Diagnostics

Point Of Care (POC) diagnostic devices enable clinicians to provide rapid diagnoses at the time and location of care. Such POC devices are particularly relevant in world regions where access to laboratory facilities is limited or unavailable. In these applications, portability, economy, and versatility are paramount. Much of the recent research in biosensors, however, has been focused on high selectivity and high sensitivity. This focus has led to devices which require lengthy amplification schemes and/or require specialized and costly detection equipment. In addition, many of the most sensitive or selective biosensing paradigms are applicable to only a limited range of analytes. In the realm of POC diagnostics, there remains a particular need for rapid, inexpensive sensors which are portable and generalizable to a wide range of analytes. In this work, we have designed a novel biosensor which meets these needs. This sensor consists of an array of micro-posts embedded within a microfluidic channel. The post array is saturated with an aqueous solution containing well-dispersed 1-um superparamagnetic microbeads. Both the beads and the posts are chemically functionalized such that the presence of a targeted analyte will cause irreversible binding of beads to posts, which results in a detectable modulation in optical transmission. In an initial proof-of-principle, we have biotinylated both the beads and posts such that they bind in the presence of streptavidin; however, with appropriate functionalization the sensor could be designed to detect any of a wide variety of analytes with varying degrees of specificity. Our current fabrication methods would facilitate multi-channel detection from a single biological sample. While this biosensing mechanism is not as sensitive as many in development today, its generalizability and ease of use would make it ideal for point-of-care medical diagnostics in areas without access to diagnostic laboratories.

QChIPat: a quantitative method to identify distinct binding patterns for two biological ChIP-seq samples in different experimental conditions

BackgroundMany computational programs have been developed to identify enriched regions for a single biological ChIP-seq sample. Given that many biological questions are often asked to compare the difference between two different conditions, it is important to develop new programs that address the comparison of two biological ChIP-seq samples. Despite several programs designed to address this question, these programs suffer from some drawbacks, such as inability to distinguish whether the identified differential enriched regions are indeed significantly enriched, lack of distinguishing binding patterns, and neglect of the normalization between samples.ResultsIn this study, we developed a novel quantitative method for comparing two biological ChIP-seq samples, called QChIPat. Our method employs a new global normalization method: nonparametric empirical Bayes (NEB) correction normalization, utilizes pre-defined enriched regions identified from single-sample peak calling programs, uses statistical methods to define differential enriched regions, then defines binding (histone modification) pattern information for those differential enriched regions. Our program was tested on a benchmark data: histone modifications data used by ChIPDiffs. It was then applied on two study cases: one to identify differential histone modification sites for ChIP-seq of H3K27me3 and H3K9me2 data in AKT1-transfected MCF10A cells; the other to identify differential binding sites for ChIP-seq of TCF7L2 data in MCF7 and PANC1 cells.ConclusionsSeveral advantages of our program include: 1) it considers a control (or input) experiment; 2) it incorporates a novel global normalization strategy: nonparametric empirical Bayes correction normalization; 3) it provides the binding pattern information among different enriched regions. QChIPat is implemented in R, Perl and C++, and has been tested under Linux. The R package is available at http://motif.bmi.ohio-state.edu/QChIPat.

Eavesdropping on PTM cross-talk through serial enrichment

Two approaches to serially enrich protein post-translational modifications allow the detection of multiple modifications in a single biological sample using mass spectrometry.

Estimating Lifetime Risk from Spot Biomarker Data and Intraclass Correlation Coefficients (ICC)

Human biomarker measurements in tissues including blood, breath, and urine can serve as efficient surrogates for environmental monitoring because a single biological sample integrates personal exposure across all environmental media and uptake pathways. However, biomarkers represent a “snapshot” in time, and risk assessment is generally based on long-term averages. In this study, a statistical approach is proposed for estimating long-term average exposures from distributions of spot biomarker measurements using intraclass correlations based upon measurement variance components from the literature. This methodology was developed and demonstrated using a log-normally distributed data set of urinary OH-pyrene taken from our own studies. The calculations are generalized for any biomarker data set of spot measures such as those from the National Health and Nutrition Evaluation Studies (NHANES) requiring only spreadsheet calculations. A three-tiered approach depending on the availability of metadata was developed for converting any collection of spot biomarkers into an estimated distribution of individual means that can then be compared to a biologically relevant risk level. Examples from a Microsoft Excel-based spreadsheet for calculating estimates of the proportion of the population exceeding a given biomonitoring equivalent level are provided as an appendix.

Efficient recovery of proteins from multiple source samples after trizol® or trizol®LS RNA extraction and long-term storage

BackgroundSimultaneous isolation of nucleic acids and proteins from a single biological sample facilitates meaningful data interpretation and reduces time, cost and sampling errors. This is particularly relevant for rare human and animal specimens, often scarce, and/or irreplaceable. TRIzol® and TRIzol®LS are suitable for simultaneous isolation of RNA, DNA and proteins from the same biological sample. These reagents are widely used for RNA and/or DNA isolation, while reports on their use for protein extraction are limited, attributable to technical difficulties in protein solubilisation.ResultsTRIzol®LS was used for RNA isolation from 284 human colon cancer samples, including normal colon mucosa, tubulovillous adenomas, and colon carcinomas with proficient and deficient mismatch repair system. TRIzol® was used for RNA isolation from human colon cancer cells, from brains of transgenic Alzheimer’s disease mice model, and from cultured mouse cortical neurons. Following RNA extraction, the TRIzol®-chloroform fractions from human colon cancer samples and from mouse hippocampus and frontal cortex were stored for 2 years and 3 months, respectively, at −80°C until used for protein isolation.Simple modifications to the TRIzol® manufacturer’s protocol, including Urea:SDS solubilization and sonication, allowed improved protein recovery yield compared to the TRIzol® manufacturer’s protocol. Following SDS-PAGE and Ponceau and Coomassie staining, recovered proteins displayed wide molecular weight range and staining pattern comparable to those obtainable with commonly used protein extraction protocols. We also show that nuclear and cytosolic proteins can be easily extracted and detected by immunoblotting, and that posttranslational modifications, such as protein phosphorylation, are detectable in proteins recovered from TRIzol®-chloroform fractions stored for up to 2 years at −80°C.ConclusionsWe provide a novel approach to improve protein recovery from samples processed for nucleic acid extraction with TRIzol® and TRIzol®LS compared to the manufacturer`s protocol, allowing downstream immunoblotting and evaluation of steady-state relative protein expression levels. The method was validated in large sets of samples from multiple sources, including human colon cancer and brains of transgenic Alzheimer’s disease mice model, stored in TRIzol®-chloroform for up to two years. Collectively, we provide a faster and cheaper alternative to the TRIzol® manufacturer`s protein extraction protocol, illustrating the high relevance, and wide applicability, of the present protein isolation method for the immunoblot evaluation of steady-state relative protein expression levels in samples from multiple sources, and following prolonged storage.

Two-dimensional measurement of the nonlinearity parameter B/A in excised biological samples

The method previously developed for measuring the acoustic nonlinearity parameter B/A in a liquid sample with a volume as small as 0.1 ml [S. Saito, J. Acoust. Soc. Am. 127, 51(2010)] has been automated and applied to two-dimensional measurements of excised biological samples using a LabVIEW program. The focus of the sound beam is laterally shifted on the 3 × 3 mm(2) area of the sample while measuring the B/A successively. By displaying the result of 256 time repeated measurements with an interval of 0.2 mm in two dimensions, a C-mode image was generated for B/A. The images of linear properties such as density, sound speed, and attenuation coefficient are also obtained. The image, whose pattern can be different from those of the density and sound speed, has the capability to reveal the detailed structure of the B/A, which varies from region to region in a single biological sample. The application of the method to small samples is also demonstrated by measuring a thermally coagulated biological sample.

The Menstrual Cycle and Anterior Cruciate Ligament Injury Risk

The menstrual cycle and associated hormonal fluctuations are considered risk factors for non-contact anterior cruciate ligament (ACL) injuries in female athletes. Researchers have used a 'normal' 28-day cycle and relied upon menstrual history questionnaires or a biological sample (i.e. blood, saliva) taken on a single day to identify the phase of the menstrual cycle where an ACL tear has occurred. However, evidence from available studies lack adequate consideration of menstrual cycle variability that exists in the general population and neglect to acknowledge the greater prevalence of subtle menstrual disturbances in physically active and athletic women. Inter- and intra-woman menstrual cycle variability is large for total cycle, follicular phase and luteal phase length ranging from 22 to 36, 9 to 23 and 8 to 17 days, respectively (95% CI). More importantly, subtle menstrual disturbances such as anovulation and luteal phase defects are common in athletic women with a high prevalence of cycle-to-cycle variations. To complicate matters further, menstrual history questionnaires inaccurately quantify cycle length compared with prospective monitoring of cycle length, highlighting the need to implement more sophisticated methods for identifying menstrual cycle/phase characteristics. Regardless of variability and/or the presence of subtle menstrual disturbances, women may still have regularly occurring menses, making it extremely difficult to accurately identify the phase of the menstrual cycle where an ACL tear has occurred based on a menstrual history questionnaire or a single biological sample. Therefore, the assumption that normal ovarian endocrine function is synonymous with regularly occurring menses in physically active and athletic women is unjustified. Thus, definitive conclusions are not warranted regarding the association between the menstrual cycle and non-contact ACL injury risk based on currently available data. Future work in this area must incorporate methods to prospectively evaluate and accurately characterize menstrual cycle characteristics if we are to link the hormonal fluctuations of the menstrual cycle to non-contact ACL injury risk.

A distributed system for fast alignment of next-generation sequencing data.

We developed a scalable distributed computing system using the Berkeley Open Interface for Network Computing (BOINC) to align next-generation sequencing (NGS) data quickly and accurately. NGS technology is emerging as a promising platform for gene expression analysis due to its high sensitivity compared to traditional genomic microarray technology. However, despite the benefits, NGS datasets can be prohibitively large, requiring significant computing resources to obtain sequence alignment results. Moreover, as the data and alignment algorithms become more prevalent, it will become necessary to examine the effect of the multitude of alignment parameters on various NGS systems. We validate the distributed software system by (1) computing simple timing results to show the speed-up gained by using multiple computers, (2) optimizing alignment parameters using simulated NGS data, and (3) computing NGS expression levels for a single biological sample using optimal parameters and comparing these expression levels to that of a microarray sample. Results indicate that the distributed alignment system achieves approximately a linear speed-up and correctly distributes sequence data to and gathers alignment results from multiple compute clients.

A two-step approach to qPCR experimental design and software for data analysis

Materials and methods For screening, a single biological sample is used for each control and treatment group. If screening shows interesting findings, confirmation follows with more biological replicates and a definitive statistical analysis using pooled data from the screening and confirmation steps. This experimental design for qPCR reduces reagent cost and labor without sacrificing sensitivity. To quantitate gene expression, we selected the comparative Ct method due to its simplicity, intuitiveness, and popularity. For statistical analysis, however, this two-step approach raised an interesting question; which parameter should be used for statistical analysis of the data from the screening step. With a single biological sample, the statistical analysis relies on the technical replicates of qPCR. Even though it is natural to assume a normal distribution of the relative gene expression levels of biological replicates based on the central limit theorem, it is unknown for technical replicates because the Ct values of the technical replicates have been non-linearly transformed to obtain relative gene expression levels. Hence, we studied the distribution of delta-Ct and relative gene expression levels from our qPCR data and found that the distribution of delta-Ct is symmetrical and approximately normal while the distribution of relative gene expression levels is skewed. Therefore, we chose deltaCt as our parameter for statistical analysis of the technical replicates from the screening step. To automate the data analysis of the screening step, we developed REALPLOT. A text file is used to enter Ct values and gene/ group information into REALPLOT that calculates relative gene expression, performs statistical analysis, and graphically displays the screening step data. To automate the data analysis after the confirmation step, we developed REALPOOL that pools the data from both the screening and confirmation steps, performs the final statistical analysis, and generates a publishable graphical representation. REALPLOT and REALPOOL were developed using the open-source statistical R environment. They are compatible with all major computer operating systems.

From lipids analysis towards lipidomics, a new challenge for the analytical chemistry of the 21st century. Part II: Analytical lipidomics

Lipidomics is an emergent “omics” science, which is based on analysis of all lipids present in a biological system known as a lipidome. The widespread use of soft-ionization mass-spectrometric (MS) technology has enabled rapid, sensitive detection of a vast number of individual lipid molecular species from a single biological sample. Particular characteristics of different lipid classes enhance development of MS strategies that permit comprehensive analysis of the lipidome. Large-scale data sets are being produced and international initiatives have started to integrate them and combine them with other “omics” data sets, so as to obtain information about the whole biological system. Pharmaceuticals, medicine and nutrition are just some of the areas that can benefit from the study of lipidomics. Bearing in mind all these issues, this second part of a two-part review presents an overview of the state of lipidomics.

An ion-channel-containing model membrane: structural determination by magnetic contrast neutron reflectometry

To many biophysical characterisation techniques, biological membranes appear as two-dimensional structures with details of their third dimension hidden within a 5 nm profile. Probing this structure requires methods able to discriminate multiple layers a few Ångströms thick. Given sufficient resolution, neutron methods can provide the required discrimination between different biochemical components, especially when selective deuteration is employed. We have used state-of-the-art neutron reflection methods, with resolution enhancement via magnetic contrast variation to study an oriented model membrane system. The model is based on the Escherichia coli outer membrane protein OmpF fixed to a gold surface via an engineered cysteine residue. Below the gold is buried a magnetic metal layer which, in a magnetic field, displays different scattering strengths to spin-up and spin-down neutrons. This provides two independent datasets from a single biological sample. Simultaneous fitting of the two datasets significantly refines the resulting model. A β-mercaptoethanol (βME) passivating surface, applied to the gold to prevent protein denaturation, is resolved for the first time as an 8.2 ± 0.6 Å thick layer, demonstrating the improved resolution and confirming that this layer remains after OmpF assembly. The thiolipid monolayer (35.3 ± 0.5 Å), assembled around the OmpF is determined and finally a fluid DMPC layer is added (total lipid thickness 58.7 ± 0.9 Å). The dimensions of trimeric OmpF in isolation (53.6 ± 2.5 Å), after assembly of lipid monolayer (57.5 ± 0.9 Å) and lipid bilayer (58.7 ± 0.9 Å), are precisely determined and show little variation.

Perspectives for metabolomics in human nutrition: an overview

Metabolomics describes the measurement of the full complement of the products of metabolism in a single biological sample and correlating these metabolomic profiles with known physiological or pathological states. The metabolome offers the possibility of finding unique fingerprints responsible for different phenotypes. Analytical techniques such as nuclear magnetic resonance or mass spectrometry measure thousands of compounds within the metabolome simultaneously and appropriate data mining and database tools allow the finding of significant correlations between the measured metabolomes. The first direct outcome of nutritional metabolomics will be the discovery of biomarkers, which can reveal changes in health and disease but also indicate short term and long-term dietary intake. The concerted actions of nutrigenomics and metabolomics will play a crucial role in understanding how specific interactions of single nucleotide polymorphisms (SNP) influence a person's response to a diet. Finally, systems biology approaches to human nutrition combine transcriptomics, proteomics and metabolomics with the aim of understanding how diets interact within the human being.

Proteomics Can Help to Gain Insights into Metabolic Disorders According to Body Reserve Availability

Metabolic disorders are amongst the most serious medical problems that are encountered in modern societies. To unravel the molecular mechanisms that underlie the metabolic adaptations to disturbed energy balance, wild and laboratory animal models are of primary importance. Previous studies have highlighted some aspects of the metabolic and endocrine variations that are triggered by marked energy reserve depletion/repletion. A brief overview of studies addressing the adaptive mechanisms to fasting/refeeding is presented here. This review also emphasises the necessity to not only consider gene or protein expression levels but to also take into account protein structures, to enable one to fully unravel the exact steps of intermediary metabolism and/or signal transduction pathways that are modulated by nutritional transitions. In this context, proteomic analysis, which uses high-performance tools for peptide/protein separation and mass spectrometry, has emerged as an indispensable tool to elucidate the complex molecular basis of various pathophysiological processes. Proteomics provides a global view of the protein dynamics in a given tissue of any organism. It provides hundreds of protein identifications and quantifications, as well as structure characterizations, from a single complex biological sample. Therefore, proteomic analysis is detailed here in terms of its analytical approaches, strategies, methods, instrumentation, and its limitations. The benefits of performing such analysis are discussed in the context of the fasting/refeeding paradigm and expected insights into the associated molecular mechanisms. Importantly, unravelling the adaptive responses to food deprivation may provide new therapeutic targets for the treatment and/or prevention of numerous pathophysiological conditions characterized by energy depletion.

Stacking enhanced determination of steroids by CE

This study outlines a simple method for pH-mediated stacking of natural and synthetic steroids facilitated with carboxymethyl-beta-CD. Sample stacking (10 kV, 60 s) is accomplished with 23 mM carboxymethyl-beta-CD in 50 mM 3-[cyclohexylamino]-1-propanesulfonic acid buffered at pH 10. Following stacking, steroidal compounds are separated in less than 5 min with a running buffer of 13 mM hydroxypropyl-beta-CD, 30 mM SDS in 200 mM phosphate buffered at pH 2.5. Using a 60 s electrokinetic injection, the limits of detection of estradiol, ethynyl estradiol, estrone, hydroxyprogesterone, progesterone, and 11-ketotestosterone range from 2 to 14 nM. For all steroids, the within-day and day-to-day reproducibility in migration time is < or =1 and < or =2% RSD, respectively. The within-day and day-to-day reproducibility in peak area is < or =9 and < or =22% RSD, respectively. The method is applied to fish plasma and holds potential to profile multiple steroids in a single biological sample.

PhosphoBlast, a Computational Tool for Comparing Phosphoprotein Signatures among Large Datasets

Identification of specific protein phosphorylation sites provides predicative signatures of cellular activity and specific disease states such as cancer, diabetes, Alzheimer disease, and rheumatoid arthritis. Recent progress in phosphopeptide isolation technology and tandem mass spectrometry has provided the means to identify thousands of phosphorylation sites from a single biological sample. These advances now make it possible to profile global changes in the phosphoproteome at an unprecedented level. However, although this technology is generating a wealth of information, there is currently no efficient means to identify phosphoprotein signatures shared among large phosphoprotein databases. Identification of common phosphoprotein signatures found in biologically relevant systems and their conservation throughout evolution would provide valuable insight into mechanisms of signal transduction and cell function. Here we describe the development of a computational program (PhosphoBlast) that can rapidly match thousands of phosphopeptides that share phosphorylation sites within and across species. PhosphoBlast analysis of several large phosphoprotein datasets from the literature revealed common phosphorylation signatures shared across diverse experimental platforms and species. Moreover PhosphoBlast is a powerful analysis tool to identify specific phosphosite mutations. Comparison of the mouse and human phosphoproteomes revealed more than 130 specific phosphoamino acid mutations, some of which are predicted to alter protein function. Further analysis revealed that known phosphorylated amino acids are more evolutionally conserved than the Ser/Thr/Tyr amino acids not known to be phosphorylated. Together our results demonstrate that PhosphoBlast is a versatile mining tool capable of identifying related phosphorylation signatures and phosphoamino acid mutations among complex proteomics datasets in a highly efficient and accurate manner. PhosphoBlast will aid in the informatics analysis of the phosphoproteome and the identification of phosphoprotein biomarkers of disease.

Enabling Coupled Quantitative Genomics and Proteomics Analyses from Rat Spinal Cord Samples

Translational research is progressing toward combined genomics and proteomics analyses of small and precious samples. In our analyses of spinal cord material, we systematically evaluated disruption and extraction techniques to determine an optimum process for the coupled analysis of RNA and protein from a single 5-mm segment of tissue. Analyses of these distinct molecular species were performed using microarrays and high resolution two-dimensional gels, respectively. Comparison of standard homogenization with automated frozen disruption (AFD) identified negligible differences in the relative abundance of genes (44) with all genes identified by either process. Analysis on either the Affymetrix or Applied Biosystems Inc. gene array platforms provided good correlations between the extraction techniques. In contrast, the AFD technique enabled identification of more unique proteins from spinal cord tissue than did standard homogenization. Furthermore use of an optimized CHAPS/urea extraction provided better protein recovery, as shown by quantitative two-dimensional gel analyses, than did solvent precipitation during TRIzol-based RNA extraction. Thus, AFD of tissue samples followed by protein and RNA isolation from separate aliquots of the frozen powdered sample is the most effective route to ensure full, quantitative analyses of both molecular entities.

Validation of peptide epitope microarray experiments and extraction of quality data

Introduction Within the last decade, the development of antigen microarray slides has enabled the simultaneous measurement of serum reactivity to hundreds of peptides in a single biological sample. Despite this considerable scientific progress, many issues remain regarding the quality, analysis and interpretation of the data these slides produce. There is currently no accepted approach to guide data analysis, and researchers use a wide variety of statistical methods and software tools. We designed and implemented a laboratory experiment to assess the reliability and range of measurement of peptide microarray data, and present graphical and statistical procedures for pre-processing so that quality data can be extracted for addressing biological hypotheses. Methods Synthetic peptides spanning the proteins Ag85A, Ag85B, CFP10, MPT51/MPB51, TB10.4 and ESAT-6 were chosen as a paradigm to screen for serum reactivity to Mycobacteria tuberculosis (MTB). We explored various quantitative and graphical methods for presenting the responses from a slide. We replicated assays of samples from five TB-positive individuals to examine reproducibility, and used linear mixed models to investigate the various sources of variability, and to assess the range of measurement. We use our methods to extract data from the five TB-positive individuals and five healthy controls, and analyse the “normalized" responses using the freely available SAM package. Results The ratio of foreground to background signal (on a log scale) provides an appropriate response index. A two-dimensional graphical display clearly illustrates the responses from the control and peptide features on a slide. Mixed model analysis of the replicated slides found a high reproducibility of the assay between operators, days and experiments. The range of measurement was also satisfactory. Our analysis of the normalized responses from the five TB-positive patients and five healthy controls suggested that 10 of the 363 peptides assessed had significantly higher responses in the TB-positive group. Conclusions Carefully designed laboratory experiments and rigorous statistical analysis can enable the removal of technical artefacts to produce quality peptide array data for addressing biological hypotheses. These instruments, which enable valid comparisons across slides and/or batches of slides, will escort future comparative analyses targeting high content serum reactivity profiling against a broad array of B-cell epitopes.

Overlay Tool© for aCGHViewer©: An Analysis Module Built for aCGHViewer© used to Perform Comparisons of Data Derived from Different Microarray Platforms

The Overlay Tool© has been developed to combine high throughput data derived from various microarray platforms. This tool analyzes high-resolution correlations between gene expression changes and either copy number abnormalities (CNAs) or loss of heterozygosity events detected using array comparative genomic hybridization (aCGH). Using an overlay analysis which is designed to be performed using data from multiple microarray platforms on a single biological sample, the Overlay Tool© identifies potentially important genes whose expression profiles are changed as a result of losses, gains and amplifications in the cancer genome. In addition, the Overlay Tool© will incorporate loss of heterozygosity (LOH) probability data into this overlay procedure. To facilitate this analysis, we developed an application which computationally combines two or more high throughput datasets (e.g. aCGH/expression) into a single categorized dataset for visualization and interrogation using a gene-centric approach. As such, data from virtually any microarray platform can be incorporated without the need to remap entire datasets individually. The resultant categorized (overlay) data set can be conveniently viewed using our in-house visualization tool, aCGHViewer© (Shankar et al. 2006), which serves as a conduit to public databases such as UCSC and NCBI, to rapidly investigate genes of interest.

Diethylamine extraction of proteins and peptides isolated with a mono-phasic solution of phenol and guanidine isothiocyanate

Representative extraction of both RNA and protein from a single biological sample is required for reliable assessment of coordinated changes in gene and protein expression. Such a simultaneous extraction can be performed by using Trizol® Reagent. Here, we demonstrate that, as an alternative to SDS, 2% diethylamine is an effective solvent, which can be conveniently used in extraction of Trizol®-isolated proteins from various tissues. Diethylamine provides efficient extraction of proteins and compatibility with a variety of common downstream analytical applications.