Using nanodrop for analysing biological samples other than nucleotides

Using nanodrop for analysing biological samples other than nucleotides

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I am a 3rd timer postdoctoral fellow with some experience in molecular biology and biochemistry, but major skills in Zoology and Natural History. I am studying some natural extracts, and isolating compounds.

Some biological samples come in limited amounts (e.g. invertebrate haemolymph), hampering analytical methods that rely on larger amounts. For instance, establishing UV-Vis spectra of extracts is a usual non-destructive method for approaching unknown samples and estimating general parameters, done with a spectrophotometer.

The microvolume-scaled spectrophotometer system popularly known as 'Nanodrop' has been around labs since almost two decades now. Its use has been usually limited to fast purity & concentration estimations for DNA & RNA in molecular biology labs.

I am considering using the nanodrop to estimate parameter of other biological and chemical samples. The manual says most commonly used solvents are compatible with the system. I have however yet found no-one else who has tried using Nanodrop for different applications.

Please, anyone here who has experimented using a Nanodrop system with other biological samples and extracts could comment on the experience?

The Nanodrop is a generic UV-visible spectrophotometer. According to the manufacturer, the latest model can measure absorbance from 190 to 850 nm. Its dynamic range is also very good: from about 0.1 to about 60 in absorbance. Therefore, as long as you don't use an incompatible solvent, you can measure anything that absorbs in this wavelength range. I use it very often for purified proteins. It also works well for turbid suspensions (like bacterial cultures; reading optical density at 600 nm gives an idea of turbidity).

It is easy to use, and of course the low volume requirement is a big advantage. One drawback is that it won't let you fine tune certain parameters like a "real" spectrophotometer would allow (bandwidth, gain, etc.). But you can definitely get decent spectra for characterization of mixtures and concentration estimation of pure samples.

I used the Nanodrop for the measurement of the absorbance at 600 nm of bacterial cultures, as well as nucleic acid preparations. However, I switched to using another spectrophotometer (Spetrophotometer, Ultrospec 2100 pro, UV/Visible Spectrophotometer, Amersham Biosciences) for my bacterial cultures because I trust it more due to using a cuvette containing 1 ml of bacteria instead of just 1 microlitre of culture.

After completing my tests, I have decided to come back to answer my own question.

A Nanodrop machine has served my purpose quite efficiently, as far as I can judge. I used have used it to evaluate the degree of purity of a natural alkaloid extract from insects, using synthetic alkaloid analogues as controls. See our published paper discussing the method here, along with raw data.

We have noticed no alterations nor cross contaminations to colleagues' DNA/RNA samples running in parallel, highlighting on the fact that I properly cleaned the equipment between use.

Therefore, yes: I recommend using the Nanodrop as a cheap & quick method to scan biological samples, provided chemicals and solvents used in cleaning the pedestal are compatible with the manual.

Hope this discussion help others dare!

A comparison of five methods for extraction of bacterial DNA from human faecal samples

The purity of DNA extracted from faecal samples is a key issue in the sensitivity and usefulness of biological analyses such as PCR for infectious pathogens and non-pathogens. We have compared the relative efficacy of extraction of bacterial DNA (both Gram negative and positive origin) from faeces using four commercial kits (FastDNA® kit, Bio 101 Nucleospin® C+T kit, Macherey-Nagal Quantum Prep® Aquapure Genomic DNA isolation kit, Bio-Rad QIAamp® DNA stool mini kit, Qiagen) and a non-commercial guanidium isothiocyanate/silica matrix method. Human faecal samples were spiked with additional known concentrations of Lactobacillus acidophilus or Bacteroides uniformis, the DNA was then extracted by each of the five methods, and tested in genus-specific PCRs. The Nucleospin® method was the most sensitive procedure for the extraction of DNA from a pure bacterial culture of Gram-positive L. acidophilus (10 4 bacteria/PCR), and QIAamp® and the guanidium method were most sensitive for cultures of Gram-negative B. uniformis (10 3 bacteria/PCR). However, for faecal samples, the QIAamp® kit was the most effective extraction method and led to the detection of bacterial DNA over the greatest range of spike concentrations for both B. uniformis and L. acidophilus in primary PCR reactions. A difference in extraction efficacy was observed between faecal samples from different individuals. The use of appropriate DNA extraction kits or methods is critical for successful and valid PCR studies on clinical, experimental or environmental samples and we recommend that DNA extraction techniques are carefully selected with particular regard to the specimen type.

High stability of microRNAs in tissue samples of compromised quality

Degradation of tissue samples limits performing RNA-based molecular studies, but little is known about the potential usefulness of samples of compromised quality for studies focused on miRNAs. In this work we analyze a series of cryopreserved tissue samples (n = 14), frozen samples that underwent a severe thawing process (n = 10), and their paired formalin-fixed paraffin-embedded (FFPE) tissue samples (n = 24) from patients with breast cancer obtained during primary surgical resection and collected in 2011. Quality and integrity analyses of the total and small fraction of RNA were carried out. Recovery of specific RNA molecules (miRNAs hsa-miR-21, hsa-miR-125b, and hsa-miR-191 snoRNA RNU6B and mRNAs GAPDH and HPRT1) was also analyzed by quantitative RT-PCR. Our results suggest that visualisation of the small RNA electrophoretic profiles obtained using the Agilent 2100 bioanalyzer makes it possible to differentiate between the three groups of samples (optimally frozen, thawed, and FFPE). We demonstrate that specific miRNA molecules can be similarly recovered from different tissue sample sources, which supports their high degree of stability. We conclude that miRNAs are robustly detected irrespective of the quality of the tissue sample. In this regard, a word of caution should be raised before degraded samples are discarded: although prior quality assessment of the biological material to be analyzed is recommended, our work demonstrates that degraded tissue samples are also suitable for miRNA studies.

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Bisulfite genomic sequencing of DNA from dried blood spot microvolume samples

DNA methylation is an important event in epigenetic changes in cells, and a fundamental regulator of gene transcription. Bisulfite genomic sequencing is a powerful technique used in studies of DNA methylation. However, the established procedures often require relatively large amounts of DNA. In everyday practice, samples submitted for analysis might contain very small amounts of poor quality material, as is often the case with forensic stain samples. In this study, we assess a modified, more efficient method of bisulfite genomic sequencing. Genomic DNA extracted from 3-mm dried blood spots using QIAamp micro kit was treated with sodium bisulfite (using EpiTect kit). Subsequent methylation-specific PCR (MSP) followed by DNA sequencing displayed the differentially methylated region of imprinted gene SNRPN. Our results show that this new combination of efficient DNA extraction and bisulfite treatment provides high quality conversion of unmethylated cytosine to uracil for bisulfite genomic sequencing analysis. This reliable method substantially improves the DNA methylation analysis of forensic stain samples.


The prevalence of the mutations detected in complex DNA mixture has traditionally been limited to approximately 20% using Sanger sequencing [21, 22]. The development of specific mutation enrichment or detection strategies has greatly increased this sensitivity [3, 23, 24], but impaired the breadth of the assay. The UDT-Seq approach presented here offers a streamlined method to implement in clinical care massively parallel sequencing of cancer mutational hotspots in heterogeneous samples. The simultaneous sequencing of a calibration sample enhances the robustness of the assay and therefore the reliability of the results. We have shown that this approach can comprehensively detect low prevalence mutations by screening 71,081 DNA positions located in cancer mutational hotspots. The sensitivity of the assay down to mutations present at 5% prevalence permits detection of mutations in heterogeneous or poor quality samples with rare mutated clones, low cellularity, or contamination with stroma or immune cell infiltration, all of which are commonly seen in clinical samples. Importantly, our data suggest that in order to increase the reliability and identify mutations present at less than 5% prevalence, the accuracy of the next generation sequencing technology needs to increase, with improvements of both chemistry, instrument and bioinformatics analysis. Increasing sequence depth coverage only is unlikely to solve the systematic bias observed that limits the ability to accurately measure the abundance of alleles present at less than 5% prevalence. This is exemplified by the notable improvement in the substitution rate observed on the MiSeq instrument, where the samples were sequenced at lower depth.

The UDT-Seq assay will enable high throughput molecular testing for a large number of cancer patients that have samples that are incompatible with current comprehensive diagnostic procedures. By integrating this tool in institutional master clinical protocols, it can immediately enable focused clinical confirmatory sequencing for selection of patients for targeted treatments or clinical trials testing novel targeted therapies or repurposing of approved drugs. Going forward we expect that this tool will be deployed in clinical testing, further facilitating its use for clinical management of patients. Additionally, UDT-Seq will empower the study of clonal selection in cancer metastasis, recurrence and progression. Comparison of initial UDT-Seq profiles with disease outcomes may identify novel targets that, with therapeutic intervention, can prolong survival or reduce mortality. Lastly, similar to other approaches [25], UDT-Seq can also be used to establish a personalized molecular signature of tumor driver and/or passenger mutations that can be used to monitor for recurrence or response in circulating DNA in plasma or urine by more sensitive methods.

Applications in inherited metabolic diseases

With the multi-metabolite quantitative abilities of metabolomics, the future of IMD diagnosis may be found in the developing area of metabolomics. Targeted MS-based metabolomics is already widely used and implemented in IMD newborn screening national programs worldwide (Therrell et al 2015). Several IMD are routinely screened using targeted MS-based metabolomics methods such as organic acidurias, aminoacidopathies, and fatty acid oxidation disorders (Pitt et al 2002 Pitt 2009 Pitt 2010 Spacil et al 2013 Auray-Blais et al 2014). However, combining the already existing tools with actionable data analysis strategies, metabolomics is very appealing for better and effective diagnosis. For example, an integrated strategy for IMD screening, using both targeted and untargeted approaches, have been recently proposed by Miller et al. The method provides actionable diagnostic information for IMD. The authors have successfully diagnosed 21 IMD disorders using plasma metabolite measurements through metabolomics (Miller et al 2015). For more details on metabolomics potential in IMD, the reader may refer to recent comprehensive reviews (Piras et al 2016 Tebani et al 2016a, b).


Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L. & Wold, B. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5, 621–628 (2008).

Ozsolak, F. & Milos, P.M. RNA sequencing: advances, challenges and opportunities. Nat. Rev. Genet. 12, 87–98 (2011).

Wang, Z., Gerstein, M. & Snyder, M. RNA-seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).

Cloonan, N. et al. Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat. Methods 5, 613–619 (2008).

't Hoen, P.A. et al. Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms. Nucleic Acids Res. 36, e141 (2008).

van Iterson, M. et al. Relative power and sample size analysis on gene expression profiling data. BMC Genomics 10, 439 (2009).

Sirbu, A., Kerr, G., Crane, M. & Ruskin, H.J. RNA-seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering. PLoS ONE 7, e50986 (2012).

Bradford, J.R. et al. A comparison of massively parallel nucleotide sequencing with oligonucleotide microarrays for global transcription profiling. BMC Genomics 11, 282 (2010).

Marioni, J.C., Mason, C.E., Mane, S.M., Stephens, M. & Gilad, Y. RNA-seq: an assessment of technical reproducibility and comparison with gene expression arrays. Genome Res. 18, 1509–1517 (2008).

Agarwal, A. et al. Comparison and calibration of transcriptome data from RNA-Seq and tiling arrays. BMC Genomics 11, 383 (2010).

Bottomly, D. et al. Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays. PLoS ONE 6, e17820 (2011).

Raghavachari, N. et al. A systematic comparison and evaluation of high density exon arrays and RNA-seq technology used to unravel the peripheral blood transcriptome of sickle cell disease. BMC Med. Genomics 5, 28 (2012).

Liu, S., Lin, L., Jiang, P., Wang, D. & Xing, Y. A comparison of RNA-Seq and high-density exon array for detecting differential gene expression between closely related species. Nucleic Acids Res. 39, 578–588 (2011).

Hansen, K.D., Brenner, S.E. & Dudoit, S. Biases in Illumina transcriptome sequencing caused by random hexamer priming. Nucleic Acids Res. 38, e131 (2010).

Gao, L., Fang, Z., Zhang, K., Zhi, D. & Cui, X. Length bias correction for RNA-seq data in gene set analyses. Bioinformatics 27, 662–669 (2011).

Oshlack, A. & Wakefield, M.J. Transcript length bias in RNA-seq data confounds systems biology. Biol. Direct 4, 14 (2009).

Roberts, A., Trapnell, C., Donaghey, J., Rinn, J.L. & Pachter, L. Improving RNA-Seq expression estimates by correcting for fragment bias. Genome Biol. 12, R22 (2011).

Risso, D., Schwartz, K., Sherlock, G. & Dudoit, S. GC-content normalization for RNA-Seq data. BMC Bioinformatics 12, 480 (2011).

Pickrell, J.K. et al. Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature 464, 768–772 (2010).

Shi, L. et al. The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat. Biotechnol. 24, 1151–1161 (2006).

Canales, R.D. et al. Evaluation of DNA microarray results with quantitative gene expression platforms. Nat. Biotechnol. 24, 1115–1122 (2006).

Patterson, T.A. et al. Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project. Nat. Biotechnol. 24, 1140–1150 (2006).

Lappalainen, T. et al. Transcriptome and genome sequencing uncovers functional variation in humans. Nature (in the press) doi:10.1038/nature12531 (2013).

Marco-Sola, S., Sammeth, M., Guigo, R. & Ribeca, P. The GEM mapper: fast, accurate and versatile alignment by filtration. Nat. Methods 9, 1185–1188 (2012).

Pantano, L., Estivill, X. & Marti, E. SeqBuster, a bioinformatic tool for the processing and analysis of small RNAs datasets, reveals ubiquitous miRNA modifications in human embryonic cells. Nucleic Acids Res. 38, e34 (2010).

Kosters, W.A. & Laros, J.F.J. Metrics for mining multisets. in Research and Development in Intelligent Systems XXIV, Proceedings of AI-2007 (Eds. Bramer, M., Coenen, F. & Petridis, M.) 293–303 (Springer, 2007).

Gordon, D. & Finch, S.J. Consequences of error. in Encyclopedia of Genetics, Genomics, Proteomics and Bioinformatics (Eds. Jorde, L., Little, P., Dunn, M. & Subramaniam, S.) (Wiley Online Library, 2006).

Aird, D. et al. Analyzing and minimizing PCR amplification bias in Illumina sequencing libraries. Genome Biol. 12, R18 (2011).

Stegle, O., Parts, L., Durbin, R. & Winn, J. A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. PLOS Comput. Biol. 6, e1000770 (2010).

Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500–507 (2012).

Parts, L. et al. Extent, causes, and consequences of small RNA expression variation in human adipose tissue. PLoS Genet. 8, e1002704 (2012).

Benjamini, Y. & Speed, T.P. Summarizing and correcting the GC content bias in high-throughput sequencing. Nucleic Acids Res. 40, e72 (2012).

Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).

Huang, J., Chen, J., Lathrop, M. & Liang, L. A tool for RNA sequencing sample identity check. Bioinformatics 1463–1464 (2013).

Westra, H.J. et al. MixupMapper: correcting sample mix-ups in genome-wide datasets increases power to detect small genetic effects. Bioinformatics 27, 2104–2111 (2011).

Leek, J.T. & Storey, J.D. Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet. 3, e161 (2007).

Fehrmann, R.S. et al. Trans-eQTLs reveal that independent genetic variants associated with a complex phenotype converge on intermediate genes, with a major role for the HLA. PLoS Genet. 7, e1002197 (2011).

Montgomery, S.B. et al. Transcriptome genetics using second generation sequencing in a Caucasian population. Nature 464, 773–777 (2010).

Griebel, T. et al. Modelling and simulating generic RNA-Seq experiments with the flux simulator. Nucleic Acids Res. 40, 10073–10083 (2012).

Jurka, J. et al. Repbase Update, a database of eukaryotic repetitive elements. Cytogenet. Genome Res. 110, 462–467 (2005).

Karolchik, D. et al. The UCSC Table Browser data retrieval tool. Nucleic Acids Res. 32, D493–D496 (2004).

Berninger, P., Gaidatzis, D., van, N.E. & Zavolan, M. Computational analysis of small RNA cloning data. Methods 44, 13–21 (2008).

Robinson, M.D., McCarthy, D.J. & Smyth, G.K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).


PCR was performed using 2.5 U Taq polymerase (Fermentas Life Science, MD) in 50 μl of 1× reaction buffer (10 mM Tris-HCl, 50 mM KCl, 0.08% Nonidet P-40, pH 8.8) containing 200 ng template DNA, 20 pmol of each primer, 2.5 mM of each 4 dNTP (Promega Life Science, Madison), and 1.5 mM MgCl2. The template was the pMEP4 plasmid DNA containing the breakpoint cluster region of the human MLL gene (from Peter Aplan, NIH, Bethesda see Fig. 1), throughout the experiments. All primers listed in Table 1 were purchased from Integrated DNA Technologies (Coralville, IA positions of the primers are shown in Fig. 1). In the first reaction cycle, denaturation was at 94°C for 3 min, annealing was for 2 min, polymerization at 72°C for 1.5 min this was followed by 35 cycles, 50 s each. In the case of the PCR reaction defined by primers pleft–p 4.2 (length 725 bp), p3.2–p 4.2 (541 bp), pIL1–pM6 (574 bp), and pM3–pM6 (357 bp), the annealing temperatures were 62, 62, 56, and 61°C, respectively. The PCR products were cleaned with QIAquick PCR Purification Kit (Qiagen, Valencia, CA), eluted in 50 μl sterile TE (10 mM Tris, 2 mM EDTA, pH 8) and analyzed on 2% agarose gels run in 1× TAE (0.04 M Tris, 0.02 M acetic acid, 0.01 M EDTA, pH 8) and stained with ethidium bromide.

Position of restriction sites and primers defining the amplified regions within the MLL bcr of the pMEP4 plasmid. Star: PvuII restriction site arrowhead: XbaI restriction site.

pleft sense 5′–CTC TGA ATC TCC CGC AAT GT–3′
pleft-FITC sense 5′–(FITC)-CTC TGA ATC TCC CGC AAT GT–3′
pleft-6FAM sense 5′–(6FAM)-CTC TGA ATC TCC CGC AAT GT–3′
p(A/G)-Cy3 sense 5′–(Cy3)-CTC TGG ATC TCC CGC AAT GT–3′
p3.2 sense 5′–GTG TAT TGC CAA GTC TGT TGT GAG–3′
p3.2-Cy3 sense 5′–(Cy3)-GTG TAT TGC CAA GTC TGT TGT GAG–3′
pIL1-biotin sense 5′–(biotin)-ATA TGA ATA CTC ATC ACT GAG TGC CTT TGG C–3′
p4.2 antisense 5′–TCT GCC TCC AAA GTT CAA GCG ATT–3′
p4.2-biotin antisense 5′–(biotin)-TCT GCC TCC AAA GTT CAA GCG ATT–3′
pM6 antisense 5′–AGC GAA CAC ACT TGG TAC AGA TC–3′
pM6-6FAM antisense 5′–(6FAM)-AGC GAA CAC ACT TGG TAC AGA TC–3′

Preparation of Single-Stranded DNA by Linear Amplification

Amplification was performed at the conditions described earlier but using either the pIL1-biotin (sense) or the pM6-6FAM (antisense) primer alone. Unlabeled ds PCR products of 574 or 357 bp length were used as template DNA in these experiments.

Hybridization of PCR Products

The longer 6FAM- and, in a separate test tube, the shorter Cy3 labeled PCR product were mixed with equal amounts of the biotin labeled longer product, in TE supplemented with 1 M NaCl. The two samples were denatured at 95°C for 5 min, then reannelaled at room temperature (RT) for 2 h. The DINAMelt server ( was used to predict the conformation of the reannealed molecules.

Enzymatic Cleavage at Single-Stranded Regions by S1 Nuclease

One hundred nanograms of PCR product was digested by 0.00001–1 U S1 nuclease (Promega Life Science) in 50 μl 1× S1 buffer (50 mM Na-acetate pH 4.5, 280 mM NaCl, 4.5 mM ZnSO4) at 20°C for 40 min., in the dark. The reactions were stopped by the addition of 125 μl of 0.1 M EDTA. DNA was precipitated for 2 h after the addition of 125 μl of 0.3 M Na-acetate and 750 μl −20°C abs. ethanol. After centrifugation, DNA was dissolved in 50 μl phosphate buffered saline (PBS, pH 7.4).

Chemical Cleavage at Single-Stranded Regions by Hydroxylamine/Piperidine Treatment

One hundred nanograms of PCR product dissolved in 2 μl TE was added to different amounts of hydroxylamine, in a final volume of 100 μl set by PBS (pH 6.0), and treated at 37°C for 2 h in the dark. The 0.5 M hydroxylamine stock solution was prepared in 2 M tetraethylammonium chloride (pH 6.0, adjusted by dietilamin). Reaction was stopped by the addition of 125 μl of 0.1 M EDTA. DNA was precipitated, dissolved in 50 μl sterile dH2O, and mixed with an equal volume of 1% piperidine, and incubated at 90°C for 30 min in the dark. Finally, DNA was precipitated and dissolved in 50 μl of PBS.

Cleavage by PvuII

Two hundred nanograms of the hybrids formed between the 6FAM- and biotin labeled PCR products were digested with 10 U PvuII restriction endonuclease (Fermentas Life Sciences). Restriction sites are as shown in Figure 1.

Generation of Single Nucleotide Mismatch for S1 Analysis

Cy3 labeled PCR products carrying a transversion were prepared by PCR using the p(A/G)-Cy3 sense and p4.2 antisense primers. The p(A/G) primers contained an A→G transversion in position 5. The Cy3 labeled PCR product containing the transversion and the biotin labeled original PCR product amplified using the pleft and p4.2-biotin primers were mixed with equal volume of a buffer containing 50 mM Tris, 2 mM EDTA, 25 mM NaCl, pH 8.0 and denatured at 94°C for 3 min, incubated at 72°C for 2 min, then slowly cooled down to RT. One hundred nanograms of these hybrids were digested by S1 nuclease (used at 0.0001–1 U) in 1× S1 buffer, in a final volume of 50 μl.

Generation and Detection of Nicks

Two micrograms of unlabeled or, in some experiments, FITC labeled PCR products were digested with 10 U Xba I restriction enzyme (Promega Life Science), in 50 μl of 1× Buffer D. (Restriction sites are as shown in Fig. 1). Equal amounts of Xba I digested (FITC labeled or unlabeled) and of the biotin labeled PCR products were mixed and incubated in TE supplemented with 1 M NaCl, at 95°C for 5 min, then at RT for 2 h. One microgram of the hybrid formed between the Xba I digested and the nondigested, biotin labeled PCR products were treated with 10 U of E. coli DNA polymerase I (Fermentas Life Science), in 25 μl of 1× polymerase buffer (50 mM Tris-HCl, pH 7.5, 10 mM MgCl2, 1 mM DTT) containing dATP, dCTP, dGTP each at 1 mM, dTTP at 0.1 mM and fuorescein–12–dUTP (Fermentas Life Science) at 1 mM final concentration, at 16°C for 2 h.

Binding of PCR Products to Microbeads, Flow Cytometric Analysis

Ten thousand streptavidin coated polymeric microbeads (Polyscience,Warrington, PA 6 μm diameter) were added to 50 μl of the PCR products in PBS and incubated at RT for 40 min in the dark. The microbeads were washed twice in 500 μl of PBS, then resuspended in 500 μl of PBS. The microbeads carrying fluorescent dye labeled PCR products were measured by a Becton-Dickinson FACScan flow cytometer (Mountain View, CA). Fluorescence signals were detected through the 530/30 and the 585/42 interference filter of the instrument, designated as FL1 and FL2 channels, respectively. The applied laser power was 15 mW. The data collected were analyzed by BDIS Cell Quest 3.3 software (Becton-Dickinson). Statistical analyses of the differences between the mean fluorescence intensities were performed using two-sided t-test, in the case of at least three independent measurements and when the variances of the compared samples were not significantly different according to the F-test. A significance level of <0.05 is indicated in the figures by *, a level of <0.01 by **, and a level of <0.001 by ***.


Genomic DNA was extracted from whole blood using the Qiamp mini isolation kit (Qiagen) concentrations were determined by UV spectrophotometry using a Nanodrop 1000 (Nanodrop Technologies Inc.). The same method was used for all samples and the PCR product amplification and sequencing were performed in three batches of 96, 96 and 48 samples (regular SNPs) and four batches of 95 for the CNV-affected SNP.

All targets were amplified using a commercial PCR master mix (Promega, Southampton, UK). For all amplicons (except the FCGR3B fragment) initial denaturation was at 95°C for 30 s, followed by 30 cycles of 95°C for 15 s, 58°C for 15 s, 72°C for 30 s and a final 300 s extension at 72°C. We determined that the method worked best with templates from PCR reactions that had not yet reached a plateau in product formation. Therefore, all amplifications were limited to 30 cycles. For the FCGR3B sequencing template, co-amplification of FCGR3A was avoided by first amplifying a 2.5 kb FCGR3B-specific fragment, using an annealing temperature of 57°C and an extension time of 4 min for 25 cycles. The long PCR products were then diluted 200-fold and used as template in a second PCR reaction using nested primers to amplify a 730 bp fragment (annealing at 60°C, extension 1 min, over 30 cycles). All primer sequences are listed in Supplementary Tables 1 and 2 .

Amplicons were purified using magnetic beads (Charge Switch PCR clean-up kit, Invitrogen, Carlsbad, USA) according to the manufacturer's instructions. Sequencing was performed using the BigDye 3.1 kit (Applied Biosystems, Foster City, USA). Sequencing reactions were ethanol-precipitated and re-suspended in HiDi formamide (Applied Biosystems) before analysis on a 3130xl genetic analyzer with a 36 cm capillary array.

The QSVanalyzer program was written in Visual Basic using Microsoft Visual Studio 2005. Statistical analysis of results was performed using the R environment, version 2.7.0 (

Association between rs619586 (A/G) polymorphism in the gene encoding lncRNA-MALAT1 with type 2 diabetes susceptibility among the Isfahan population in Iran

Type 2 diabetes mellitus (T2DM) is a global human disease that affects millions of people. Long non-coding RNAs (LncRNAs) are transcripts with more than two-hundred nucleotides that play essential roles in the management of mRNAs. In the present study, we examined whether the rs619586 (A/G) polymorphism in the gene encoding lncRNA-MALAT1 is associated with the susceptibility to T2DM among the Isfahan population, Iran.


To this end, a case-control study was conducted on 200 healthy persons and 200 patients with T2DM. The genomic DNA was extracted from blood samples to strengthen the intended fragments containing rs619586 SNP. Using polymerase chain reaction (PCR)-restriction fragment length polymorphism (RFLP), the wild allele (A) and the mutant allele (G) were examined.

Result and conclusion

Results indicated that the mutant allele (G) and mutant genotypes (AG/GG) were absent in T2DM patients. This absence suggests that the rs619586 (A/G) polymorphism in the gene encoding lncRNA-MALAT1 might not be associated with the susceptibility to T2DM among the Isfahan population.


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