Real-time PCR quantitation of clostridia in feces of autistic children.
A PPLIED AND E NVIRONMENTAL M ICROBIOLOGY,Nov.2004,p.6459–6465Vol.70,No.11 0099-2240/04/$08.00ϩ0DOI:10.1128/AEM.70.11.6459–6465.2004Copyright©2004,American Society for Microbiology.All Rights Reserved.Real-Time PCR Quantitation of Clostridia in Feces ofAutistic ChildrenYuli Song,1*Chengxu Liu,1and Sydney M.Finegold2,3,4Research Service1and Infectious Diseases Section,2VA Medical Center West Los Angeles,andDepartment of Medicine3and Department of Microbiology,Immunology,andMolecular Genetics,4UCLA School of Medicine,Los Angeles,CaliforniaReceived11February2004/Accepted27June2004Based on the hypothesis that intestinal clostridia play a role in late-onset autism,we have been character-izing clostridia from stools of autistic and control children.We applied the TaqMan real-time PCR procedureto detect and quantitate three Clostridium clusters and one Clostridium species,C.bolteae,in stool specimens.Group-and species-specific primers targeting the16S rRNA genes were designed,and specificity of the primerswas confirmed with DNA from related bacterial strains.In this procedure,a linear relationship exists betweenthe threshold cycle(CT )fluorescence value and the number of bacterial cells(CFU).The assay showed highsensitivity:as few as2cells of members of cluster I,6cells of cluster XI,4cells of cluster XIVab,and0.6cell of C.bolteae could be detected per PCR.Analysis of the real-time PCR data indicated that the cell count differences between autistic and control children for C.bolteae and the following Clostridium groups were statistically significant:mean counts of C.bolteae and clusters I and XI in autistic children were46-fold(P؍0.01),9.0-fold(P؍0.014),and3.5-fold(P؍0.004)greater than those in control children,respectively,but not for cluster XIVab(2.6؋108CFU/g in autistic children and4.8؋108CFU/g in controls;respectively).More subjects need to be studied.The assay is a rapid and reliable method,and it should have great potential for quantitation of other bacteria in the intestinal tract.Autism is a complex disease with unclear causes.Many au-tistic subjects exhibit a range of gut disorders,which include constipation,diarrhea,retention of gas,and abdominal pain and discomfort.Abnormal gut microflora may play a role in these problems.Research into the characteristics of the gut flora in autism has been limited.In our initial studies that characterized the fecal bacterial composition by culturing,we noted abnormalities in the fecal bacterial composition of chil-dren with autism compared to age-and sex-matched controls. We found higher counts of clostridia overall and more species of clostridia in stools of autistic children than in healthy chil-dren(11).In particular,Clostridium bolteae,a novel species that we described previously(29;called Clostridium clostridio-forme in reference11),caught our attention because it was cultured from5of15autistic children,but none of8controls. However,it is well known that traditional culture-based meth-ods,while very important,result in a significant underestima-tion of bacteria present in fecal samples(14,19,30). Molecular techniques introduced in microbial ecology have made it possible to study the composition of intestinalflora in a culture-independent way based on the detection of rRNA genes.Although these methods,such asfluorescent in situ hybridization(12,13,19),denaturing gradient gel electro-phoresis,temperature gradient gel electrophoresis(8,10,28), and the16S rRNA gene clone library method(15,27,30),have been applied successfully for studying the ecology of intestinal flora,PCR analysis using specific primers achieves the most sensitive results as well as providing ease of use and speed(23, 24,32).Most recently,real-time quantitative PCR has been used for the specific detection and quantitation of selected bacteria from fecal DNA(1,2,4,9,16,18,21,22,25,33). Few studies have reported on using real-time PCR for quan-titation of clostridia in different environments.Belanger et al.(2)and Kimura et al.(17)reported on the successful quanti-tation of Clostridium difficile in feces and Clostridium botulinum type E infish samples using specific primers and probes tar-geted to toxin genes,respectively.A very recent study investi-gated the feasibility of using16S rRNA gene-targeted specific primers and probes for quantitation of major intestinal bacte-ria,including certain Clostridum species by real-time PCR(26). In this study,we evaluated the suitability of a real-time PCR (5Јnuclease PCR assay)to detect and quantitate C.bolteae and some Clostridium groups(clusters)in fecal specimens of autis-tic and control children.MATERIALS AND METHODSBacterial strains and fecal specimens.All reference strains used in this study are listed in Table1.The strains were obtained from different sources,as indicated in Table1:ATCC is the American Type Culture Collection,CCUG is the Culture Collection,University of Go¨teborg,DSM is the Deutsche Sammlung von Mikroorganismen und Zellkulturen,and WAL is the Wadsworth Anaerobe Laboratory.All the strains were cultivated on brucella agar(Anaerobe Systems, Morgan Hill,Calif.)supplemented with5%sheep blood and incubated anaer-obically at37°C under N2(86%),H2(7%),and CO2(7%)gas phase in an anaerobic incubator.The WAL strains are human fecal isolates;they were identified by phenotypic testing and16S rRNA sequencing(11).Fecal specimens were obtained at Rush Children’s Hospital,Chicago,Ill., under the jurisdiction of its Institutional Review Board(IRB)and with written informed consent by a parent or guardian.Our IRB approved receipt of these specimens by our laboratory.Stool specimens were packed in dry ice and shipped to our laboratory by overnight air express.The entire fecal specimen was ho-*Corresponding author.Mailing address:VA Medical Center WestLos Angeles,11301Wilshire Blvd.,Room E3-237,Bldg.304,Los Angeles,CA90073.Phone:(310)478-3711,ext.49151.Fax:(310) 268-4458.E-mail:yulis1@.6459mogenized by use of a sterile stainless steel Waring blender,and aliquots of each specimen were frozen atϪ80°C until DNA was extracted.Extraction of DNA from pure cultures and feces.Genomic DNA from all bacterial strains was purified from cultures with a QIAamp DNA extraction kit, (QIAGEN,Valencia,Calif.),and the concentration was determined by spectro-photometer(A260).Duplicate samples of genomic DNA(20ng)from each bacterium were amplified for40cycles with each of four real-time PCR primer/ probe sets(Table2)to assess specificity.One-milliliter aliquots of stool(previously diluted1:3in sterile bi-distilled water and thoroughly homogenized under anaerobic conditions in an anaerobic chamber)were centrifuged at14,000ϫg for3min to pellet fecal bacterial cells. The supernatant was carefully removed and discarded.Two hundred milligrams of cell pellet was transferred to a fresh tube and subjected to DNA extraction by using a commercial extraction system(QIAamp DNA stool mini kit;QIAGEN) according to the instructions of the manufacturer.Our pilot studies have shown that this QIAamp product produces high-quality DNA free of PCR-inhibiting substances.DNA extraction was performed in duplicate.Design of oligonucleotide primers and probe.Sequences of the16S rRNA genes of the organisms of interest and of closely related bacteria were aligned with CLUSTAL-W(http://genome.kribb.re.kr)and inspected for regions of con-served and variable sequences.Based on the multialignment analysis data,four pairs of16S rRNA gene-targeted cluster-and species-specific primers were designed,one each for Clostridium clusters I,XI,and XIVab(7)and C.bolteae,respectively.They were designed from a collection of128Clostridium16S rRNA sequences obtained from the GenBank database.Potential candidates for PCR primers were compared to the aligned SSU_rRNA database of the Ribosomal Database Project(RDP)using the CHECK_PROBE utility(20)and were com-pared to all available16S ribosomal DNA(rDNA)sequences by using the BLAST database search program(/BLAST)(3).The prim-ers were used in combination with internal nonspecific probes(Table2).The primers’target regions were located as near as possible to the probe in order to enable an efficient hydrolysis of the probe during primer elongation by the polymerase.The primer and probe sequences were analyzed for T m(melting temperature),secondary structure formation,GϩC content,and primer-dimer formation with NetPrimer analysis software( /netprimer).The probe was5Јlabeled with carboxyfluorescein(FAM)as the reporter dye and Black Hole quencher(BHQ)as the3Јquencher dye.The BHQ dye was used as the quencher dye because of its broad quenching spectrum and a lower signal/noise ratio than that of other quenching dyes.SmartCycler real-time PCR assay.The amplification reactions were carried out in a total volume of25l,which consisted of2l of DNA samples and23l of the master mixture.The latter contained1ϫpuReTaq Ready-To Go PCR bead(2.5U of puReTaq DNA polymerase,10mM Tris-HCl[pH9.0at room temperature],50mM KCl,1.5mM MgCl2,200M deoxynucleoside triphos-phate[dNTP],and stabilizer bovine serum albumin[Amersham Biosciences, Piscataway,N.J.]),300nM each primer,200nMfluorescence-labeled probe,andTABLE1.Strains used in this studyStrain Source a Strain Source aAnaerococcus spp.UG31351T UG38493T UG41932T UG46590T UG31349T Clostridium spp.C.beijerinckii..................................ATCC25752TC.bifermentans...............................ATCC638TC.bifermentans...............................WAL16469C.bolteae........................................ATCC BAA-613T C.bolteae........................................WAL14578C.bolteae........................................WAL14510C.bolteae........................................WAL16099C.bolteae........................................WAL12258C.bolteae........................................WAL7642C.butyricum...................................ATCC13732TC.butyricum...................................WAL16108C.cadaveris.....................................ATCC25783TC.carnis..........................................ATCC25777TC.celerecrescens.............................DSMZ5628TC.fallax...........................................ATCC19400TC.glycolicum...................................ATCC14880TC.glycolicum...................................WAL14424C.indolis.........................................ATCC25771TC.irregulare.....................................ATCC25756TC.limosum......................................ATCC25620TC.paraputrificum...........................ATCC25780TC.paraputrificum...........................WAL14502C.pasteurianum.............................ATCC6013TC.perfringens..................................ATCC13124TC.perfringens..................................WAL14572C.putrificum...................................ATCC25784TC.septicum.....................................ATCC12464TC.sordellii.......................................ATCC9714TC.subterminale...............................ATCC25774TC.subterminale...............................WAL16483C.symbiosum..................................ATCC14940TC.symbiosum..................................WAL14673C.tertium........................................ATCC14573TC.tertium........................................WAL16200C.tyrobutyricum.............................ATCC25755Ta All WAL strains were identified by16S rDNA sequencing.C.xylanolyticum..........................................DSMZ6555T C.acetobutylicum........................................ATCC824T C.baratii......................................................ATCC27638T C.bolteae.....................................................WAL16351 C.celatum....................................................ATCC27791T C.clostridioforme........................................ATCC25537T C.clostridioforme........................................WAL12260 C.clostridioforme........................................WAL7855 C.coccoides.................................................ATCC29236T C.cochlearium.............................................ATCC17787T C.difficile.....................................................ATCC9689T C.difficile.....................................................WAL14186 C.ghonii.......................................................ATCC25757T C.malenominatum......................................ATCC25776T C.methoxybenzovorans...............................DSMZ12182T C.novyi........................................................ATCC27606 C.oroticum..................................................ATCC13619T C.sphenoides...............................................ATCC19403T C.sporogenes...............................................ATCC19404 Eubacterium spp.E.tenue........................................................ATCC25553T Finegoldia spp.UG17636T Peptoniphilus spp.UG7835T UG9988T UG38491T UG17639T UG38492T UG31350T Peptostreptococcus spp.UG46357T Ruminococcus spp.R.flavefaciens..............................................WAL16464 UG43222T R.gnavus......................................................WAL16098 ctaris.....................................................WAL14694 UG10976T R.torques.....................................................WAL14161 Sarcina spp.UG45682T S.ventriculi..................................................WAL163766460SONG ET AL.A PPL.E NVIRON.M ICROBIOL.2.5mM MgCl2,made up to23l with distilled water.The assay was performed with the Cepheid(Sunnyvale,Calif.)SmartCycler instrument in a protocol com-prising1cycle of2min at95°C(hot start)followed by45cycles of95°C for20s for denaturation.Annealing was performed for30s at58°C for C.bolteae,63°C for Clostridium cluster I,58°C for Clostridium cluster XI,and52°C for Clostrid-ium cluster XIVab.Extension was performed at72°C for45s for all.Data analysis was performed with Cepheid software.The C T values(i.e.,the threshold cycles in which exponential amplification of PCR products wasfirst detected) were determined on the basis of the mean baseline signals during the early cycles of amplification.Standard curve.The standard curve for C.bolteae was constructed by using DNA recovered from fecal samples spiked with different numbers of C.bolteae type strain ATCC BAA-613(30to3.0ϫ107CFU mlϪ1).Briefly,C.bolteae ATCC BAA-613was collected from thioglycolate broth cultures while in loga-rithmic growth phase.Ten-fold dilutions were made in dilution blanks(Anaer-obe Systems),and the number of CFU of bacteria in each dilution was deter-mined by plating on brucella blood agar plates.All determinations were performed in triplicate.The average titer(CFU per milliliter)of three replicates was determined.One milliliter of each dilution was added to tubes containing 200-mg aliquots of a stool sample that was verified to be C.bolteae negative by both bacteriological and PCR techniques and pelleted by centrifugation.The pellets with different numbers of C.bolteae cells were subjected to DNA extrac-tion by using a QIAamp DNA Stool Mini kit.The DNA was used to establish a standard curve.To generate standard curves for each of the three Clostridium clusters,the C T values were plotted relative to the corresponding serial10-fold dilutions of template DNA extracted from cultures of representative clostridial stool isolates of different Clostridium clusters:a mixed culture of C.subterminale WAL16483, C.perfringens WAL14572,and C.paraputrificum WAL14502for cluster I;a mixed culture of C.bifermentans WAL16496,C.difficile WAL14186,and C. glycolicum WAL14424for cluster XI;and a mixed culture of C.symbiosum WAL 14673,C.bolteae WAL16351and Ruminococcus gnavus WAL16098for cluster XIVab.Briefly,1ml of the overnight thioglycolate broth cultures of these bacterial strains was subjected to DNA extraction by using the QIAamp DNA stool mini kit.Ten-fold serial dilutions of these bacterial DNAs were prepared. The corresponding CFU per PCR were calculated based on plate counts,as described above.For comparison of PCR amplification efficiencies and detection sensitivities among different experiments,slopes of the standard curves were calculated by performing a linear regression analysis with the SmartCycler software.A mixture of all PCR reagents without any DNA was used as a negative control.Amplifi-cation efficiency(E)was estimated by using the slope of the standard curve and the formula Eϭ(10Ϫ1slope)Ϫ1.A reaction with100%efficiency will generate a slope ofϪ3.32.The standard curves were used for determining the detection limits of the assays and enumeration of C.bolteae and members of each of the three Clostridium clusters in stool samples.Enumeration of Clostridium in stool samples by real-time PCR.Purified DNA from stool samples was used with optimized PCR conditions and an appropriate standard curve to enumerate the load of C.bolteae and members of different Clostridium clusters in the stool samples.Each stool sample was subjected to four PCR runs(2PCR repetitionsϫ2DNA extractions;nϭ4).The amount of DNA measured by real-time PCR was converted to cell numbers to allow comparison with the CFU data.This was accomplished by using the standard curve that was generated by plotting the C T against CFU.This approach was used because,for stool samples,it is easier to understand results in actual CFU numbers than in DNA concentrations or copy numbers.The C T standard deviations were calculated as shown in Table3.The CFU of C.bolteae and the members of each cluster were determined from C T values by using the standard curves(Fig.1and2).Statistical analysis.Parametric t tests and multivariate logistic regression and/or classification tree(CART)method(6)was used to compare mean CFU values between autistic and control samples.RESULTSSpecificity of oligonucleotide primers.The results of com-prehensive cross-specificity checks on the RDP database en-abled thefinal selection of four16S rDNA-targeted PCR primer pairs theoretically specific for C.bolteae or each of three Clostridium clusters(Table2).Degenerate PCR primers were used in order to broaden the specificity within each clus-ter.The primer pair CI-F1and CI-R2is specific for cluster I, which includes most species of the Clostridium cluster I and three species of cluster II.Primer pair CXI-F1and CXI-R2is specific for cluster XI,which includes part of Clostridium clus-ter XI.Finally,primer pair CXIV-F1and CXIV-R2is specific for cluster XIVab,which includes most species belonging to Clostridium cluster XIVab.The specificity of species-and clus-ter-specific primers was further confirmed by amplifying genomic DNA from target and phylogenetically related non-target bacterial strains(Table1)with each of the four sets of primers by conventional PCR and real-time PCR.In conven-tional PCR,the primer pairs were specific for their target species or cluster at the appropriate annealing temperatures and yielded PCR products of the expected size only from their targets.The optimized conventional PCR conditions were ap-plied to real-time PCR.In real-time PCR,nofluorescent signal was detected from non-target bacterial DNA(data not shown). Although our TaqMan probe and reverse primer for C.bolteae also showed100%homology with16S rDNA of its most closely related species,C.clostridioforme,the forward primer did notTABLE2.Sequences of oligonucleotide primers and probeTarget bacteria and primer or probe a Oligonucleotide sequence(5Ј33Ј)T m(°C)bProbe-I GTGCCAGCAGCCGCGGTAATACG72.4 Group I(Clostridium cluster I)Forward primer,CI-F1TACCHRAGGAGGAAGCCAC54.6 Reverse primer,CI-R2GTTCTTCCTAATCTCTACGCAT53.0 Group II(Clostridium cluster XI)Forward primer,CXI-F1ACGCTACTTGAGGAGGA46.5 Reverse primer,CXI-R2GAGCCGTAGCCTTTCACT52.3 Group III(Clostridium cluster XIVab)Forward primer,CXIV-F1GAWGAAGTATYTCGGTATGT46.2 Reverse primer,CXIV-R2CTACGCWCCCTTTACAC44.8 Probe-II(C.bolteae)CAGGTGGTGCATGGTTGTCGTCAG69.2 Forward primer CCTCTTGACCGGCGTGT56.7 Reverse primer CAGGTAGAGCTGGGCACTCTAGG62.1a Probe-I is an internal universal probe that corresponds to a region of the16S rRNA gene that is conserved in all eubacteria(5);it was used with three sets of cluster-specific primers.Probe-II was used with C.bolteae-specific primer set.Clostridium clusters are as described in reference7.b The Tm of DNA was determined with NetPrimer software(PREMIER Biosoft International).V OL.70,2004PCR OF CLOSTRIDIA IN FECES OF AUTISTIC CHILDREN6461show any major sequence homology(70.5%only);there wasno signal detected from DNA from C.clostridioforme strains. Standard curve and detection limits.In real-time PCR as-say,the CTat which thefluorescent signal is statistically sig-nificant above background is measured.The CTvalue is pro-portional to the amount of target DNA and hence the number of bacteria in the samples.The detection sensitivity for C. bolteae was determined by two sets of genomic DNA prepared from10-fold serial dilutions(30to3.0ϫ107CFU/ml)of C. bolteae type strain ATCC BAA-613spiked into a C.bolteae-negative stool sample.The detection sensitivities for three Clostridium clusters were determined by two sets of10-fold serial dilutions of genomic DNA prepared from representa-tives of each cluster.Representative data are shown in Fig.1and2.The results are reported as the CTnumbers versus CFU numbers.As shown in Fig.1and2,plotting the obtained CTvalues relative to the CFU of bacteria resulted in a linear correlation with square regression coefficient ofՆ0.99,demonstrating that quantification of the target DNA was possible.To evaluate the robustness of the assays,the same experiment,which included an unknown stool sample,was repeated six times on different days with two different sets of DNA.The result for C.bolteaeis given in the inset in Fig.1.The standard deviation of CT values for the unknown sample based on the six PCR runs(3PCR repetitionsϫ2DNA extractions;nϭ6)were0.08,0.37,0.22,and0.52(Table3);determined by the C.bolteae and cluster I,XI,and XIVab standard curves,respectively.This showed that both DNA extraction by the QIAamp stool DNA extraction kit and the real-time PCR assay were highly repro-ducible.The function describing the relationship between CTvalues and CFU for the assay has been calculated based on the mean values obtained from the six PCR runs(Fig.1and2).Average slopes ofϪ3.52,Ϫ3.52,Ϫ3.28,andϪ3.14were generated for C.bolteae and clusters I,XI,and XIVab,respectively.Accord-ing to the formula log EϭslopeϪ1,the standard curves gen-erated from the PCR data resulted in a reaction efficiency of above96.5%for all four PCR assays.The estimated detection limits for the primer and probe sets specific for C.bolteae and clusters I,XI,and XIVab were0.6, 2,6,and4bacteria per PCR,respectively.All nontemplate controls and all dilutions with DNA corresponding to less than these numbers of cells per reaction gave negative results. Enumeration of Clostridium in stool samples by real-time PCR.The bacterial load of C.bolteae and each Clostridium cluster in stools was estimated by real-time PCR with DNA extracted from the23samples(Table3).The reproducibility ofthe assay was high,with the standard deviation of CTvalues for stool samples ranging from0.08to0.29for C.bolteae,0.21toTABLE3.Clostridium detection in stool samples of autistic and control children by real-time PCR aSample bC.bolteae Clostridium cluster I Clostridium cluster XI Clostridium cluster XIVab Mean C T(SD)CFU/g of fecalspecimenMean C T(SD)CFU/g of fecalspecimenMean C T(SD)CFU/g of fecalspecimenMean C T(SD)CFU/g of fecalspecimenControl98-1306C33.6(0.22)(4.0Ϯ0.3)ϫ10228.6(0.31)(3.1Ϯ0.4)ϫ10525.4(0.21)(3.5Ϯ0.3)ϫ10621.7(0.33)(5.9Ϯ0.3)ϫ107 98-1332C35.2(0.21)(2.8Ϯ0.2)ϫ10231.6(0.46)(4.9Ϯ0.4)ϫ10426.3(0.27)(1.7Ϯ0.3)ϫ10618.5(0.22)(2.5Ϯ0.4)ϫ108 98-1333C36.9(0.29)(9.2Ϯ0.4)ϫ10127.6(0.45)(6.6Ϯ0.4)ϫ10524.6(0.25)(6.2Ϯ0.5)ϫ10621.0(0.23)(7.5Ϯ0.2)ϫ107 98-1334C31.4(0.18)(2.7Ϯ0.2)ϫ10328.4(0.25)(3.9Ϯ0.3)ϫ10526.4(0.31)(1.7Ϯ0.2)ϫ10618.0(0.35)(3.5Ϯ0.3)ϫ108 98-1335C31.3(0.24)(3.3Ϯ0.4)ϫ10327.6(0.46)(6.6Ϯ0.3)ϫ10526.6(0.21)(1.5Ϯ0.2)ϫ10620.5(0.26)(9.6Ϯ0.2)ϫ107 98-1342C30.8(0.22)(5.6Ϯ0.4)ϫ10331.9(0.39)(4.3Ϯ0.3)ϫ10425.7(0.13)(2.6Ϯ0.4)ϫ10618.1(0.31)(3.4Ϯ0.3)ϫ108 00-1318CϽDLϽDL28.5(0.23)(3.5Ϯ0.4)ϫ10525.5(0.12)(2.9Ϯ0.4)ϫ10622.0(0.25)(4.6Ϯ0.3)ϫ107 00-1319C28.6(0.15)(1.9Ϯ0.1)ϫ10427.1(0.21)(8.5Ϯ0.5)ϫ10523.6(0.11)(1.2Ϯ0.1)ϫ10716.5(0.38)(8.8Ϯ0.2)ϫ108 Avg(3.9Ϯ0.3)ϫ103(4.1Ϯ0.3)ϫ105(4.0Ϯ0.4)ϫ106(2.6Ϯ0.2)ϫ108 Autistic97-1301A31.5(0.17)(2.7Ϯ0.2)ϫ10324.6(0.22)(5.5Ϯ0.2)ϫ10621.4(0.08)(6.7Ϯ0.2)ϫ10718.5(0.29)(2.5Ϯ0.3)ϫ108 97-1316A30.0(0.16)(7.6Ϯ0.2)ϫ10324.4(0.24)(6.6Ϯ0.2)ϫ10622.9(0.17)(2.2Ϯ0.3)ϫ10717.4(0.41)(4.9Ϯ0.3)ϫ108 97-1321A24.1(0.10)(3.9Ϯ0.1)ϫ10525.1(0.32)(3.3Ϯ0.3)ϫ10622.5(0.13)(3.0Ϯ0.3)ϫ10718.6(0.25)(2.4Ϯ0.4)ϫ108 98-1301A28.4(0.18)(2.0Ϯ0.3)ϫ10430.9(0.44)(8.2Ϯ0.6)ϫ10425.3(0.23)(3.8Ϯ0.5)ϫ10622.4(0.09)(3.9Ϯ0.1)ϫ107 98-1343A27.7(0.22)(3.2Ϯ0.4)ϫ10425.7(0.34)(2.6Ϯ0.3)ϫ10626.6(0.22)(1.6Ϯ0.3)ϫ10618.9(0.17)(1.5Ϯ0.2)ϫ108 98-1344A29.3(0.17)(1.2Ϯ0.2)ϫ10428.2(0.21)(4.7Ϯ0.4)ϫ10524.3(0.20)(8.6Ϯ0.4)ϫ10615.5(0.30)(1.6Ϯ0.3)ϫ109 00-1311A22.9(0.10)(7.3Ϯ0.1)ϫ10527.8(0.44)(5.9Ϯ0.3)ϫ10521.4(0.14)(1.6Ϯ0.3)ϫ10618.9(0.17)(1.4Ϯ0.2)ϫ108 00-1312A33.0(0.13)(1.2Ϯ0.1)ϫ10326.1(0.38)(2.4Ϯ0.3)ϫ10625.5(0.15)(3.2Ϯ0.6)ϫ10618.7(0.24)(2.2Ϯ0.4)ϫ108 00-1313A26.0(0.08)(9.5Ϯ0.1)ϫ10426.6(0.37)(1.4Ϯ0.3)ϫ10624.6(0.22)(6.1Ϯ0.6)ϫ10617.7(0.52)(4.2Ϯ0.5)ϫ108 00-1314A26.3(0.21)(5.0Ϯ0.4)ϫ10423.3(0.24)(1.3Ϯ0.2)ϫ10722.5(0.18)(2.8Ϯ0.2)ϫ10718.5(0.22)(2.7Ϯ0.2)ϫ108 00-1315A28.0(0.15)(2.7Ϯ0.2)ϫ10429.1(0.29)(2.2Ϯ0.5)ϫ10523.8(0.15)(1.1Ϯ0.1)ϫ10720.6(0.25)(8.4Ϯ0.1)ϫ107 00-1316A31.4(0.17)(3.3Ϯ0.2)ϫ10324.8(0.30)(4.8Ϯ0.3)ϫ10623.3(0.18)(1.5Ϯ0.1)ϫ10715.1(0.34)(2.4Ϯ0.4)ϫ109 00-1317A30.4(0.19)(7.1Ϯ0.3)ϫ10323.6(0.24)(1.1Ϯ0.2)ϫ10723.8(0.13)(1.1Ϯ0.1)ϫ10722.1(0.27)(4.4Ϯ0.3)ϫ107 02-1409A22.0(0.10)(1.3Ϯ0.1)ϫ10629.9(0.31)(1.7Ϯ0.3)ϫ10524.6(0.25)(6.8Ϯ0.3)ϫ10617.3(0.42)(5.1Ϯ0.1)ϫ108 03-1404A26.6(0.13)(5.0Ϯ0.2)ϫ10425.4(0.33)(2.9Ϯ0.2)ϫ10622.4(0.07)(3.0Ϯ0.2)ϫ10718.5(0.17)(2.7Ϯ0.2)ϫ108 Avg(1.8Ϯ0.1)ϫ105(3.7Ϯ0.4)ϫ106(1.4Ϯ0.1)ϫ107(4.8Ϯ0.6)ϫ108a Mean CT represents two PCR replicates of two replicate DNA extractions(nϭ4).CFU were calculated by using the standard curve.ϽDL,below the detectionlimit of0.6CFU/reaction;SD,standard deviation.b C,control children;A,autistic children.6462SONG ET AL.A PPL.E NVIRON.M ICROBIOL.0.46for cluster I,0.07to 0.31for cluster XI,and 0.09to 0.52for cluster XIVab.The mean counts (CFU per gram [wet weight of centrifuged specimens]of feces)of C.bolteae and members of clusters I,XI,and XIVab were 1.8ϫ105Ϯ0.1ϫ105,3.7ϫ106Ϯ0.4ϫ106,1.4ϫ107Ϯ0.1ϫ107,and 4.8ϫ108Ϯ0.6ϫ108in autistic children,respectively,and were 3.9ϫ103Ϯ0.3ϫ103,4.1ϫ105Ϯ0.3ϫ105,4.0ϫ106Ϯ0.4ϫ106,and 2.6ϫ108Ϯ0.2ϫ108in controls,respectively.Analysis of the real-time PCR data indicated that the cell count differences between autistic and control children in C.bolteae and the following Clostridium groups were statistically significant:mean counts of C.bolteae and clusters I and XI in autistic children were 46-fold (P ϭ0.01),9.0-fold (P ϭ0.014),and 3.5-fold (P ϭ0.004)greater than those in control children,respectively,but the differences in cluster XIVab between au-tistic and control children were not statistically significant (P ϭ0.3).More subjects need to be studied for all groups.DISCUSSIONReal-time PCR has several advantages over conventional PCR.It focuses on the logarithmic phase of product accumu-lation rather than on the end product abundance.Therefore,it is more accurate since it is less affected by amplification effi-ciency or depletion of a reagent.In addition,it has an in-creased dynamic range for quantification of target sequence (at least 5orders of magnitude).Furthermore,without any post-PCR manipulation of the samples,cross-contamination be-tween samples is greatly reduced.Finally,PCR results canbeFIG.1.Standard curve generated by analysis of a dilution series of C.bolteae cells spiked in stools by real-time PCR.Quantitation was performed by determining the C T .The same experiment,which included a known C.bolteae -positive stool with an unknown count (F ),was repeated six times.The C T values,the CFU/PCR determined,and R 2values of each experiment are listed in the inset.Means and the standard deviation were calculated from these six experiments.},C.bolteaestandards.FIG.2.Standard curves generated by analysis of a dilution series of DNA extracted from mixed cultures of representative strains for each Clostridium cluster by real-time PCR.The C T values are plotted against the corresponding cell numbers in the PCR.Œ,Clostridium cluster I;},Clostridium cluster XI;■,Clostridium cluster XIVab.V OL .70,2004PCR OF CLOSTRIDIA IN FECES OF AUTISTIC CHILDREN 6463。
real time PCR 数据分析
real time PCR 数据分析real-timepcr数据分析无论所使用的real-timepcr是何种型号，正确的数据分析对于获得有效的实验结果都是至关重要的。
这里介绍有关real-timepcr数据分析的知识。
在探讨基本分析过程之前，先了解如何设计一个不好的实验。
如果你就是自己设计的引物和探针，那有利于下一步的工作。
但是在有些情况下，人们采用出版发行文献上的序列可以更便利。
忘记，即便就是出版物提供更多的序列也无法确保可以获得优化的实验结果。
而且排印错误的可能性也须要考量在内。
所以步入实验室之前采用blast对全部序列展开核实保证他们就是恰当的。
下订单前先检察引物和探针的序列和tm值就是实验设计的基本建议。
标准曲线是判断实验质量的重要手段。
使用一个已知的模板，pcr产物，合成的寡核苷酸或转录的rna做个标准曲线能够确定pcr的效率，敏感性，动态范围和其他的参数。
建立标准曲线时使用od260的模板样本。
模板的总量以dna分子的数量来描述，把质量转化为dna含量的公式如下：（质量（克）*阿伏伽德罗常数）每个碱基的平均值质量*模板的长度。
例如，合成70-mer的单链dna，样本质量为0.8*10?-11gm。
代入公式得：（0.8*10?-11*6.023*10?23molecules/mole）330gm/mole/base*70base。
如果使用双链的模板，则碱基的平均质量为660gm/mole/base。
标准曲线采用的模板含量从1*10?7已经开始已连续吸收7次每次吸收10倍，最终获得10个模板拷贝。
这样的浓度有利于获得最低的δrn和最高的ct。
用excel画曲线时以模板数量的对数值为x，ct（cyclethreshold）值y轴。
标准曲线的计算公式如下：y=mx+b。
y就是ct，m是斜率，x=log10templateamount，b=y-intercept。
用斜率排序出来实验效率efficiency【10?（-1/斜率）】-1。
realtime pcr操作流程
realtime pcr操作流程英文回答：Real-time PCR, also known as quantitative PCR (qPCR), is a powerful molecular biology technique used to amplify and quantify specific DNA sequences in real-time. It is widely used in various fields, including gene expression analysis, pathogen detection, and genetic testing. The workflow of a real-time PCR experiment typically consists of several key steps.1. Experimental Design:Before starting a real-time PCR experiment, it is essential to design the experiment carefully. This includes selecting the target DNA sequence to be amplified, designing specific primers and probes, and optimizing reaction conditions.2. Sample Preparation:The next step involves preparing the DNA samples for analysis. This can include extracting DNA from cells or tissues, purifying the DNA, and quantifying its concentration using a spectrophotometer or fluorometer.3. Primer and Probe Design:Specific primers and probes are designed to target the desired DNA sequence. Primers are short DNA sequences that bind to the complementary regions of the target DNA, while probes are labeled with fluorescent dyes and bind to the amplified DNA during the PCR reaction.4. PCR Reaction Setup:The PCR reaction mixture is prepared by combining the DNA template, primers, probes, and other components such as buffer, dNTPs, and DNA polymerase. The reaction mixture is then loaded into individual wells of a real-time PCR plate.5. Thermal Cycling:The real-time PCR instrument performs a series of temperature cycles to amplify the DNA. The thermal cycling typically consists of three steps: denaturation, annealing, and extension. The denaturation step separates the DNA strands, the annealing step allows the primers to bind to the target DNA, and the extension step synthesizes new DNA strands using DNA polymerase.6. Fluorescence Detection:During the PCR cycles, the real-time PCR instrument monitors the fluorescence emitted by the probes as they bind to the amplified DNA. The increase in fluorescence intensity is directly proportional to the amount of DNA amplified in each cycle.7. Data Analysis:The real-time PCR instrument continuously measures the fluorescence intensity and generates amplification curves. These curves represent the increase in DNA concentrationover the PCR cycles. The data can be analyzed using various software programs to determine the initial DNA concentration, quantify gene expression levels, or detectthe presence of specific pathogens.中文回答：实时荧光定量PCR（real-time PCR），也称为定量PCR （qPCR），是一种用于实时扩增和定量特定DNA序列的强大的分子生物学技术。
RT-PCR和realtimePCR原理及步骤
装载
将样品和试剂装入PCR板或 realtimePCR板中。
进样
将PCR板或realtimePCR板放 入仪器中开始反应。
评价和解读结果
1
数据分析
利用专业软件对荧光信号和扩增曲线进
基因表达计算
2
行分析。
比较样品之间的基因表达水平。
3
标准曲线制备
用标准品建立浓度和荧光信号之间的关 系。
实验控制和误差处理
1 防止样品污染
2 合理设计实验
避免引入外源DNA或RNA。
提前考虑样品数、重复次 数和阴性对照。
3 数据验证
CR的优缺点比较
RT-PCR
灵敏度高，适用于低表达基因，但结果需要后续凝 胶电泳分析。
realtimePCR
结果即时可见，无需后续分析，但对样品纯度和荧 光信号分析要求较高。
RT-PCR和realtimePCR的应用领域
医学诊断
RT-PCR和realtimePCR用于病 原体检测、基因突变分析和 疾病诊断。
基因表达分析
这两种技术用于研究基因调 控、蛋白质表达和细胞信号 传导。
环境监测
RT-PCR和realtimePCR可用于 检测环境中的微生物和污染 物。
RT-PCR和realtimePCR的原理
应用案例
• 基因表达差异分析 • 疾病诊断 • 新药研发 • 环境污染监测
发展前景
随着技术的不断改进，RT-PCR和realtimePCR在医学、生物学和环境科学等领 域都会有更广泛的应用。
结论与展望
RT-PCR和realtimePCR是重要的分子生物学技术，为科学研究、医学诊断和环 境监测提供了强大的工具。
RT-PCR和realtimePCR原理 及步骤
realtime pcr操作流程
realtime pcr操作流程英文回答：Real-time PCR, also known as quantitative PCR (qPCR), is a widely used technique in molecular biology to detect and quantify DNA or RNA molecules. It is a sensitive and accurate method that allows researchers to measure gene expression levels, identify genetic variations, and detect pathogens.The general workflow of a real-time PCR experiment involves several steps:1. Sample preparation: This step involves extracting DNA or RNA from the sample of interest. Various methods can be used for sample preparation, such as phenol-chloroform extraction, column-based purification kits, or automated extraction systems. The quality and purity of the extracted nucleic acids are crucial for the success of the PCR reaction.2. Primer design: Primers are short DNA sequences that specifically bind to the target DNA or RNA region of interest. They are designed using bioinformatics tools and should have high specificity and minimal secondary structures. The forward and reverse primers are designed to flank the target sequence, and ideally, they should have similar melting temperatures.3. Reaction setup: The PCR reaction mixture is prepared by combining the extracted nucleic acids, primers, a DNA polymerase enzyme, and a fluorescent dye or probe. The reaction mixture is typically prepared in a PCR tube or plate. It is important to maintain proper aseptic techniques to prevent contamination.4. Thermal cycling: The PCR reaction goes through a series of temperature cycles in a thermal cycler machine. These cycles typically include denaturation, annealing, and extension steps. The denaturation step separates the DNA double strands, the annealing step allows the primers to bind to the target sequence, and the extension stepsynthesizes new DNA strands using the primers as templates. The number of cycles depends on the initial amount oftarget nucleic acid and the desired level of amplification.5. Data collection and analysis: Real-time PCR instruments monitor the fluorescence emitted by the fluorescent dye or probe during each cycle. The fluorescence signal increases proportionally to the amount of amplified DNA or RNA. The data can be plotted in a graph called the amplification curve, which shows the exponential growth of the target sequence. The threshold cycle (Ct) value, which represents the cycle number at which the fluorescence signal crosses a predetermined threshold, is used to quantify the initial amount of target nucleic acid.Real-time PCR is a versatile technique that can be adapted to various applications, including gene expression analysis, pathogen detection, and genetic testing. Itoffers high sensitivity, specificity, and quantification accuracy, making it an essential tool in molecular biology research and diagnostic laboratories.中文回答：实时荧光定量PCR（real-time PCR），也被称为定量PCR （qPCR），是分子生物学中广泛使用的一种技术，用于检测和定量DNA或RNA分子。
【金钥匙】荧光定量PCR要点大解析
【金钥匙】荧光定量PCR要点大解析聚合酶链式反应 (PCR) 是分子生物学领域功能最强大的技术之一。
实时荧光定量PCR 是在标准PCR 技术基础上演变而成，常用于定量样本中的DNA 或RNA。
今天小析姐就和大家聊一聊荧光定量PCR的技术要点，希望能对你有所帮助。
荧光定量PCR（Real-time PCR），是指在 PCR 扩增反应体系中加入荧光基团，通过对扩增反应中每一个循环产物荧光信号的实时检测，最后通过标准曲线对未知模板进行定量分析的方法。
荧光定量 PCR 原理以探针法荧光定量PCR 为例：PCR 扩增时在加入一对引物的同时加入一个特异性的荧光探针，该探针两端分别标记一个报告荧光基团和一个淬灭荧光基团。
开始时，探针完整地结合在DNA 任意一条单链上，报告基团发射的荧光信号被淬灭基团吸收，检测不到荧光信号；PCR 扩增时，Taq 酶将探针酶切降解，使报告荧光基团和淬灭荧光基团分离，从而荧光监测系统可接收到荧光信号，即每扩增一条DNA 链，就有一个荧光分子形成，实现了荧光信号的累积与PCR 产物形成完全同步。
传统的PCR进行检测时，扩增反应后需要进行染色处理及电泳分离，并且只能作定性分析，不能准确定量，易造成污染出现假阳性，使其应用受到限制。
实时定量PCR技术不仅实现了对模板的定量，且具有灵敏度高、特异性和可靠性更好、自动化程度高和无污染的特点，使得荧光定量PCR 逐渐取代常规PCR。
在PCR 扩增反应的最初数个循环里，荧光信号变化不大，接近一条直线，这样的直线即是基线，这条线是可以自动生成也可以手动设置的。
之后反应会进入指数增长期，这个期间扩增曲线具有高度重复性，在该期间，可设定一条荧光阈值线，它可以设定在荧光信号指数扩增阶段任意位置上，但一般会将荧光阈值的缺省设置是3-15个循环的荧光信号的标准偏差的10倍。
每个反应管内的荧光信号到达设定的阈值时所经历的循环数被称为CT值，这个值与起始浓度的对数成线性关系，且该值具有重现性。
Realtime PCR检测原理和问题处理(技术材料)
技术研究
2
★实时荧光定量PCR检测技术
（Real-time PCR) 定量PCR的数学原理
定量PCR的化学原理
定量PCR的光学原理
技术研究
3
什么是PCR？
技术研究
4
PCR的反应过程
技术研究
5
RT-PCR原理图
技术研究
6
荧光定量PCR的数学原理
1、Baseline (基线) 2、Threshold (阈值)
推荐措施：提高引物设计质量，避免形成引物二聚体 补救措施：优化引物浓度和退火温度，专设高温度的收集信号步骤
技术研究
53
实例7：重复性不佳
正常的原始数据
技术研究
54
实例7：重复性不佳
不正常的原始数据
原因：盖子未盖紧，溶液蒸发。
技术研究
55
疾病监测中常用的反应Mix
ABI公司 AgPath-IDOne Step RT-PCR Kit（货号 AM1005）
8
阈值
阈值= 基线（背景）信号的标准偏差的10倍，即 PCR扩增信 号进入相对稳定对数增长的最下限，通常设定在S型扩增曲 线的增长拐点处附近。
技术研究
9Ct值Biblioteka 扩增反应中，当荧光信号增长到大于阈值时所对应的循 环数，即PCR增长信号与 Threshold发生交汇的循环数，也是 FQ-PCR判断阴阳性和进行定量分析的依据。
16
定量PCR的数学原理 定量PCR的化学原理 定量PCR的光学原理
技术研究
17
常用的荧光标记方法
1、DNA双链特异性染料 SYBR Green 、SYTO 9、LC Green、Evagreen
2、序列特异性探针
Realtime_PCR的原理和应用
4
Real time PCR的原理
在扩增产物达到阈值线时: XCt=X0(1+Ex)Ct=M (1) XCt:荧光扩增信号达到阈值强度时扩增产物的量. 在阈值线设定以后,它是一个常数,我们设为M 方程式(1)两边同时取对数得: log M=log X0(1+Ex)Ct(2) 整理方程式(2)得: log X0= -log(1+Ex) *Ct+ log M (3) 最后结论： Log X0与Ct呈线性关系，根据样品扩增的Ct值就可计算出样品中所含的 模板量
影响效率的因素为：引物、镁离子以及探针的浓度
11
Real time PCR数据处理方法
绝对定量的标准样品：
已知拷贝数的质粒DNA，做系列稀释。
标准样品的种类： •含有和待测样品相同扩增片段的克隆质粒 •含有和待测样品相同扩增片段的cDNA •PCR的产物 •含有已知数目的与待测样品相同的扩增片段的细胞
8
Real Time PCR的方法
分子信标的优点 对目标序列有很高的特异性 用于SNP检测的最灵敏的试剂之一 荧光背景低
分子信标的缺点 设计困难 无终点分析功能 只能用于一个特定的目标 价格较高
9
Real time PCR数据处理方法
绝对定量检测起始模板数的精确拷贝数， ������ 标准曲线法
SYBR-Green 的优点 使用方便 ---不必设计复杂的引物 没有序列特异性 ---可以用于不同的模板 便宜 灵敏 SYBR-Green 的缺点
特异性差，会与非特异性产物结合
7
Real Time PCR的方法
Taqman 的优点 对目标序列有很高的特异性 ---特别适合于SNP检测 与Molecular Beacons 相比设计相 对简单 Taqman 的缺点 价格较高 只适合于一个特定的目标 不能进行融解曲线分析
realtimePCR和RT-PCR详解及其区别要点
real-time PCR技术的原理及应用摘要：一、实时荧光定量PCR原理（一）定义：在PCR反应体系中加入荧光基团，利用荧光信号累积实时监测整个PCR进程，最后通过标准曲线对未知模板进行定量分析的方法。
（二）实时原理 1、常规PCR技术：对PCR扩增反应的终点产物进行定量和定性分析无法对起始模板准一、实时荧光定量PCR原理（一）定义：在PCR反应体系中加入荧光基团，利用荧光信号累积实时监测整个PCR进程，最后通过标准曲线对未知模板进行定量分析的方法。
（二）实时原理1、常规PCR技术：对PCR扩增反应的终点产物进行定量和定性分析无法对起始模板准确定量，无法对扩增反应实时检测。
2、实时定量PCR技术：利用荧光信号的变化实时检测PCR扩增反应中每一个循环扩增产物量的变化，通过Ct值和标准曲线的分析对起始模板进行定量分析3、如何对起始模板定量？通过Ct值和标准曲线对起始模板进行定量分析.4、几个概念：（1）扩增曲线：（2）荧光阈值：（3）Ct值：CT值的重现性：5、定量原理：理想的PCR反应： X=X0*2n非理想的PCR反应： X=X0 (1+Ex)nn：扩增反应的循环次数X：第n次循环后的产物量X0：初始模板量Ex：扩增效率5、标准曲线6、绝对定量1）确定未知样品的 C(t)值2）通过标准曲线由未知样品的C(t)值推算出其初始量7、DNA的荧光标记：二、实时荧光定量PCR的几种方法介绍方法一：SYBR Green法（一）工作原理1、SYBR Green 能结合到双链DNA的小沟部位2、SYBR Green 只有和双链DNA结合后才发荧光3、变性时，DNA双链分开，无荧光4、复性和延伸时，形成双链DNA， SYBR Green 发荧光，在此阶段采集荧光信号。
PCR反应体系的建立及优化:1、SYBR Green 使用浓度:太高抑制Taq酶活性，太低，荧光信号太弱，不易检测2、Primer：引物的特异性高，否则扩增有杂带，定量不准3、MgCl2的浓度:可以降低到1.5mM,以减少非特异性产物4、反应Buffer 体系的优化5、反应温度和时间参数:由酶和引物决定6、其他与常规PCR相同（二）应用范围1、起始模板的测定；2、基因型的分析；3、融解曲线分析：可以优化PCR反应的条件，对常规PCR有指导意义，如对primer的评价；可以区分单一引物、引物二聚体、变异产物、多种产物。
PCR发展过程
PCR 发展过程 ——QX100TM Droplet DigitalTM PCR1st2nd3rdPCR QualitativeReal-time PCR Relative QuantitationDroplet Digital PCR Absolute Quantitation自从 1985 年美国科学家 Kary Mullis 发明 PCR 方法以来，该方法已经成为生命科学研究 领域中最常规的实验方法之一。
PCR 方法就是在体外采用酶促反应来特异性合成特定核酸 片段达到富集的目的，PCR 扩增产物可用于下游多种应用，如克隆表达、芯片杂交、突变 检测、测序等等。
最传统的“第一代 PCR”采用琼脂糖电泳的方式对 PCR 产物进行分析，但存在着操作繁 琐、只适用于定性研究、交叉污染风险大等不足。
为了避免上述传统 PCR 的缺陷，并对基 因的表达水平进行定量分析，1992 年诞生了所谓“第二代 PCR”的荧光定量实时 PCR。
定量 PCR(quantification PCR)在 PCR 扩增的同时对反应体系中的荧光信号进行实时收 集，通过三个参数间（荧光信号-Cq 值-靶基因的起始浓度）的关系，最终确定靶基因的拷 贝数或基因的表达水平。
由于定量 PCR 的分析最终结果依赖于 Cq 值，2009 年 The MIQE Guides(Clinical Chemistry 55 :4 611-622)的发表明确了定量实验整个流程标准化的重要 性，否则就会出现实验结果无法重复，甚至结果互相矛盾的状况。
更重要的该文献还给出了 如何规范定量 PCR 流程的 checklist，其核心目的就是在规范的实验流程、统一的设计思路 下，得到的结果才具有可重复性，也具有可比性。
但依赖于 Cq 值仍然是目前定量 PCR 最 大的技术瓶颈，在这个意义上所谓的“定量”也只是相对的。
而且在低拷贝靶分子、模板浓 度差异细微的条件下，其检测的灵敏度、精确度都受到了限制。
Real-time PCR for mRNA quantitation
INTRODUCTIONThe advent of real-time PCR and real-time reverse transcription PCR (real-time RT-PCR) has dramatically changed the field of measuring gene expression. Real-time PCR is the technique of collecting data throughout the PCR process as it occurs, thus combining amplification and detection into a single step. This is achieved using a variety of different fluorescent chemistries that correlate PCR product concentration to fluorescence intensity (1). Reactions are characterized by the point in time (or PCR cycle) where the target amplification is first detected. This value is usually referred to as cycle threshold (C t), the time at which fluorescence intensity is greater than background fluorescence. Conse-quently, the greater the quantity of target DNA in the starting material, the faster a significant increase in fluorescent signal will appear, yielding a lower C t (2).There are many benefits of using real-time PCR over other methods to quantify gene expression. It can produce quantitative data with an accurate dynamic range of 7 to 8 log orders of magnitude (3) and does not require post-amplification manipu-lation. Real-time PCR assays are 10,000- to 100,000-fold more sensitivethan RNase protection assays (4),1000-fold more sensitive than dot blothybridization (5), and can even detect asingle copy of a specific transcript (6).In addition, real-time PCR assays canreliably detect gene expression differ-ences as small as 23% between samples(7) and have lower coefficients ofvariation (cv; SYBR® Green at 14.2%;TaqMan® at 24%) than end point assayssuch as band densitometry (44.9%)and probe hybridization (45.1%) (8).Real-time PCR can also discriminatebetween messenger RNAs (mRNAs)with almost identical sequences,requires much less RNA templatethan other methods of gene expressionanalysis, and can be relatively high-throughput given the proper equipment.The major disadvantage to real-timePCR is that it requires expensiveequipment and reagents. In addition,due to its extremely high sensitivity,sound experimental design and an in-depth understanding of normalizationtechniques are imperative for accurateconclusions.The general steps performed duringa real-time PCR experiment, from RNAisolation to data analysis, are outlinedin Figure 1. This review is intendedto provide an overview of the manyfacets of real-time PCR, highlightingPCR theory, quantification methodsand models, data normalization, typesof detection chemistry, and causes ofvariation.THEORY OF REAL-TIME PCRPCR can be broken into four majorphases (Figure 2): the linear groundphase, early exponential phase, log-linear (also known as exponential)phase, and plateau phase (9). Duringthe linear ground phase (usuallythe first 10–15 cycles), PCR is justbeginning, and fluorescence emissionat each cycle has not yet risen abovebackground. Baseline fluorescenceis calculated at this time. At the earlyexponential phase, the amount offluorescence has reached a thresholdwhere it is significantly higher (usually10 times the standard deviation of thebaseline) than background levels. Thecycle at which this occurs is known asC t in ABI Prism®literature (AppliedBiosystems, Foster City, CA, USA) orcrossing point (CP) in LightCycler®literature (Roche Applied Science,Indianapolis, IN, USA) (2,10). Thisvalue is representative of the startingcopy number in the original templateand is used to calculate experimentalresults (2). During the log-linear phase,Real-time PCR for mRNA quantitationMarisa L. Wong and Juan F. MedranoReal-time PCR has become one of the most widely used methods of gene quantitation because it has a large dynamic range, boasts tremendous sensitivity, can be highly sequence-specific, has little to no post-amplification processing, and is amenable to increasing sample throughput. However, optimal benefit from these advantages requires a clear understanding of the many options available for running a real-time PCR experiment. Starting with the theory behind real-time PCR, this review discusses the key components of a real-time PCR experiment, including one-step or two-step PCR, absolute versus relative quantitation, mathematical mod-els available for relative quantitation and amplification efficiency calculations, types of normalization or data correction, and detection chemistries. In addition, the many causes of variation as well as methods to calculate intra- and inter-assay variation are addressed.University of California, Davis, CA, USABioTechniques 39:__-__ (July 2005)PCR reaches its optimal amplification period with the PCR product doubling after every cycle in ideal reaction conditions. Finally, the plateau stage is reached when reaction components become limited and the fluorescence intensity is no longer useful for data calculation (11).One-Step Versus Two-Step Real-Time PCRWhen quantifying mRNA, real-time PCR can be performed as either a one-step reaction, where the entire reaction from cDNA synthesis to PCR amplification is performed in a single tube, or as a two-step reaction, wherereverse transcription and PCR ampli-fication occur in separate tubes. Thereare several pros and cons associatedwith each method. One-step real-time PCR is thought to minimizeexperimental variation because bothenzymatic reactions occur in a singletube. However, this method uses anRNA starting template, which is proneto rapid degradation if not handledproperly. Therefore, a one-step reactionmay not be suitable in situations wherethe same sample is assayed on severaloccasions over a period of time. One-step protocols are also reportedly lesssensitive than two-step protocols (12).Two-step real-time PCR separatesthe reverse transcription reaction fromthe real-time PCR assay, allowingseveral different real-time PCR assayson dilutions of a single cDNA. Becausethe process of reverse transcriptionis notorious for its highly variablereaction efficiency (13), using dilutionsfrom the same cDNA template ensuresthat reactions from subsequent assayshave the same amount of templateas those assayed earlier. Data fromtwo-step real-time PCR is quite repro-ducible with Pearson correlationcoefficients ranging from 0.974 to0.988 (14). A two-step protocol may bepreferred when using a DNA bindingdye (such as SYBR Green I) becauseit is easier to eliminate primer-dimersthrough the manipulation of meltingtemperatures (T m s) (14). However,two-step protocols allow for increasedopportunities of DNA contamination inreal-time PCR.TYPES OF REAL-TIMEQUANTIFICATIONAbsolute QuantitationAbsolute quantitation uses seriallydiluted standards of known concen-trations to generate a standard curve.The standard curve produces a linearrelationship between C t and initialamounts of total RNA or cDNA,allowing the determination of theconcentration of unknowns basedon their C t values (2). This methodassumes all standards and sampleshave approximately equal amplifi-cation efficiencies (15). In addition,the concentration of serial dilutionsshould encompass the levels in theexperimental samples and stay withinthe range of accurately quantifiable anddetectable levels specific for both thereal-time PCR machine and assay.The PCR standard is a fragmentof double-stranded DNA (dsDNA),single-stranded DNA (ssDNA), orcRNA bearing the target sequence.A simple protocol for constructing acRNA standard for one-step PCR canbe found in Fronhoffs et al. (16), whilea DNA standard for two-step real-timePCR can be synthesized by cloning thetarget sequence into a plasmid (17),Figure 1. Steps performed when measuring gene expression using real-time PCR. RNA is first isolated and characterized for quantity and integrity. If performing a one-step reaction, RNA is used as a template for the real-time PCR assay, and reverse transcription occurs during the as-say. During a two-step reaction, cDNA is first synthesized and then used as a PCR template. The steps performed on the real-time PCR machine are shown in blue, the time during which raw fluorescence data are collected, adjusted, and manipulated to generate the output data used foranalysis. For normalizing results with multiple housekeeping genes, a normalization factor must be calculated for each individual sample. Dividing the fluorescent data by its normalization factor produces the normalized data, which is followed by statistical analysis.purifying a conventional PCR product (18), or directly synthesizing the target nucleic acid. The standard used must be a pure species. DNA standards have been shown to have a larger quantifi-cation range and greater sensitivity, reproducibility, and stability than RNA standards (19). However, a DNA standard cannot be used for a one-step real-time RT-PCR due to the absence of a control for the reverse transcription efficiency (20). Relative QuantitationDuring relative quantitation, changes in sample gene expression are measured based on either an external standard or a reference sample, also known as a calibrator (21). When using a calibrator, the results are expressed as a target/reference ratio. There are numerous mathematical models available to calculate the mean normalized gene expression from relative quantitation assays. Depending on the method employed, these can yield different results and thus discrepant measures of standard error (22,23). Table 1 shows a comparison of the different methods, with an explanation of each method to follow.Table 1. Characteristics of Relative Quantitation Methods Methods (Reference)Amplification Efficiency Correction Amplification Efficiency Calculation Amplification Efficiency Assumptions AutomatedExcel-BasedProgramStandard Curve (31)no standard curve no experimental sample variation noComparative C t (2-∆∆Ct ) (21)yes standard curve reference = target noPfaffl et al.(26)yes standard curve sample = control REST aQ-Gene (23)yes standard curve sample = control Q-Geneb Gentle et al . (7)yesraw dataresearcher defines log-linear phase noLiu and Saint (22)yes raw datareference and target genes can have different efficiencies no DART-PCR (30)yes raw datastatistically defined log-linear phaseDART-PCR c C t , cycle threshold, DART-PCR, data analysis for real-time PCR; REST, relative expression software tool.a b c /cgi/content/full/31/14/e73/DC1Amplification efficiency. Ampli-fication efficiency of the reaction is an important consideration when performing relative quantitation. Past methods of calculating gene expression have assumed the amplification efficiency of the reaction is ideal, or 1, meaning the PCR product concentration doubles during every cycle within the exponential phase of the reaction (24). However, many PCRs do not have ideal amplification efficiencies, and calcula-tions without an appropriate correction factor may overestimate starting concentration (22). Current mathe-matical models make assumptions of reaction kinetics and usually require its accurate measurement (7,21,22,25,26).Traditionally, amplification efficiency of a reaction is calculated using data collected from a standard curve with the following formula (27): [Eq. 1]Exponential amplification = 10(-1/slope) Efficiency = [10(-1/slope)]-1The amplification efficiency of the reaction varies from being relatively stable in the early exponential phase and gradually declining to zero (22).This decay is due to the depletion of PCR components, the decline of polymerase activity, and competition with PCR products. Calcu-lation of amplification efficiency using a standard curve is not reflective of thischanging efficiency (28) and may overes-timate efficiencies (9). Because PCR results are based on C t , which are determined very early in the exponential phase of the reaction,these differences inamplification efficiency usually generate onlyminor differences in C tvalue (20). Nonetheless, after 26 cycles, a 5% difference in ampli-fication efficiency can result in a 2-fold difference of PCRproduct concentration (29).There are several alternate methods of calculating amplification efficiency based on raw data collected during PCR (7,9,22,25,30). During the exponential phase, the absolute fluores-cence increase at each PCR cycle for each individual sample reflects the true reaction kinetics of that sample. Consequently, data collected during the exponential phase can be log-trans-formed and plotted with the slope of the regression line representing the sample’s amplification efficiency. In the Liu and Saint (22) method, the individual researcher designates which cycles have exponential characteristics, while the method proposed by Tichopad et al. (9) uses a statistical calculation to define the period of exponential growth. Amplification efficiency calculated from raw data analysis is reportedly more accurate than when derived from a standard curve (9,25).Standard curve method for relative quantification. The quantity of each experimental sample is first deter-mined using a standard curve and then expressed relative to a single calibrator sample (31). The calibrator is desig-nated as 1-fold, with all experimentallyderived quantities reported as an n-fold difference relative to the calibrator. Because sample quantity is divided by calibrator quantity, standard curve units are eliminated, requiring only the relative dilution factors of the standards for quantification. This method is often applied when the amplification efficiencies of the reference and target genes are unequal (22). It is also the simplest method of quantification because it requires no preparation of exogenous standards, no quantification of calibrator samples, and is not based on complex mathematics. However, because this method does not incor-porate an endogenous control (usually a housekeeping gene), results must still be normalized.Comparative C t(2-∆∆Ct) method. The comparative C t method is a mathematical model that calculates changes in gene expression as a relative fold difference between an experi-mental and calibrator sample. While this method includes a correction for nonideal amplification efficiencies (i.e., not 1; Reference 21), the ampli-fication kinetics of the target gene and reference gene assays must be approxi-mately equal (32) because different efficiencies will generate errors when using this method (22). Consequently, a validation assay must be performed where serial dilutions are assayed for the target and reference gene and the results plotted with the log input concentration for each dilution on the x-axis, and the difference in C t (target-reference) for each dilution on the y-axis. If the absolute value of the slope of the line is less than 0.1, the comparative C t method may be used (21). The PCR product size should be kept small (less than 150 bp) and the reaction rigorously optimized (25). Because the comparative C t method does not require a standard curve, it is useful when assaying a large number of samples since all reaction wells are filled with sample reactions rather than standards.Pfaffl model. The Pfaffl model (26) combines gene quantification and normalization into a single calcu-lation. This model incorporates the amplification efficiencies of the target and reference (normalization) genes to correct for differences between the two assays. The relative expressionsoftware tool (REST©), which runsin Microsoft®Excel, automates dataanalysis using this model (33). RESTuses the Pairwise Fixed ReallocationRandomization Test©to calculateresult significance and will indicate ifthe reference gene used is suitable fornormalization.Q-Gene. Q-Gene is a fully compre-hensive Microsoft Excel-based softwareapplication that aids in the entireprocess of a real-time PCR experiment,from experimental planning and setupthrough data analysis and graphicalpresentation (23). Q-Gene calculatesthe mean normalized gene expressionwith standard errors using two differentmathematical models, both correctingfor amplification efficiencies. Thecalculated expression values are thencompared between two matched groupsto determine the expression of a samplerelative to a calibrator. The program alsoincludes several statistical tests such asthe paired or unpaired Student’s t-test,a Mann-Whitney U-test, Wilcoxonsigned-rank test, together withPearson’s correlation analysis to fullyassess the significance of experimentalresults. When running large or complexreal-time PCR experiments, having anorganized and automated method suchas Q-Gene can significantly expeditedata processing and management.Gentle et al. Gentle et al. (7)designed one of the first models inwhich both fold changes betweensamples and amplification efficienciesof experimental versus control samplesare calculated without the use ofstandard curves. Linear regressionanalyses of the mean of the raw logfluorescence data collected during theexponential phase of the PCR are usedto calculate the amplification efficiencyof each sample. By graphing the controland experimental samples together,they show that the vertical distancebetween the control and experimentallines is the log of the fold differencebetween the two, with the slopes ofthe lines representing the log of theiramplification efficiencies (7).Liu and Saint. Liu and Saint (22)developed a sigmoidal mathematicalmodel to quantitate and normalize geneexpression. Similar to Gentle et al. (7),this method calculates amplificationefficiencies from the actual slope ofthe amplification plot rather than astandard curve. The authors found thismethod was more accurate than thecomparative C t method with regard tothe varying amplification efficiencythroughout the PCR because the userFigure 2. Phases of the PCR amplification curve. The PCR amplification curve charts the accumula-tion of fluorescent emission at each reaction cycle. The curve can be broken into four different phases: the linear ground, early exponential, log-linear, and plateau phases. Data gathered from these phases are important for calculating background signal, cycle threshold (C t), and amplification efficiency. Rn is the intensity of fluorescent emission of the reporter dye divided by the intensity of fluorescent emissionof the passive dye (a reference dye incorporated into the PCR master mix to control for differences in master mix volume). ∆Rn is calculated as the difference in Rn values of a sample and either no template control or background, and thus represents the magnitude of signal generated during PCR. This graph was generated with ABI Prism SDS version 1.9 software (Applied Biosystems).defines which PCR cycles experience exponential growth and are used for the calculation (22).Amplification plot method. The amplification plot method uses a simple algorithm to calculate the amplification efficiency of every sample individually within the real-time PCR assay. These data are then used in the calculation forexpression quantitation (30). To easedata handling, Peirson et al. (30) havedeveloped a Microsoft Excel workbookentitled Data Analysis for Real-TimePCR (DART-PCR) that quickly calcu-lates all results from raw data.Absolute or Relative Quantitation:Pros and ConsAbsolute quantitation is consideredto be more labor-intensive than relativequantitation because of the necessityto create reliable standards for quanti-tation and include these standardsin every PCR (19). However, whenperforming relative quanti-tation, the data (C t) used forcomparison are arbitraryvalues and only applicableto the samples run withinthe same PCR. To comparesamples between twodifferent PCRs, it is necessaryto include a reference controlin every plate or run. In caseswhere data compared areassayed on different daysor in different laboratories,absolute quantitation may bepreferred because results arebased on a constant. In termsof fold-change data, absoluteand relative quantitationmethods produce comparableresults (30).ControlsThere are several typesof controls that ensure theintegrity of every step of thereal-time PCR process. DNAcontamination in the samplemay be accounted for with aminus reverse transcriptioncontrol. However, whenone has numerous samples,an alternate method toprevent the detection ofgenomic DNA is to designthe target PCR product tospan an exon/exon boundary.Variation in the efficiency ofthe reverse transcriptase aswell as the amount of RNAadded into the reaction canbe accounted for using anendogenous control, whichis a nucleic acid alreadypresent in an individualsample. The use of endog-enous controls is discussedin detail in the sectionentitled Normalization. PCRmaster mix volume has beenFigure 3. Real-time PCR detection chemistries. Probe sequences are shown in blue while target DNA sequences are shown in black. Primers are indicated by horizontal arrowheads. Not all unlabeled PCR primers are shown. Oligo,oligonucleotide.shown to be a factor in PCR amplifi-cation efficiency such that differences in master mix volume in reactions using the same amount of starting template have different amplification efficiencies (22). A passive reference dye (such as ROX) is often included in the master mix to account for subtle differences in PCR master mix volumes as well as non-PCR-related fluctuations in fluorescence signal. Problems with the PCR master mix itself can be accounted for using an exogenous control, which is a synthesized construct of charac-terized RNA or DNA spiked into each reaction (34).NormalizationNormalization of gene expression data is used to correct sample-to-sample variation. Starting material obtained from different individuals usually varies in tissue mass or cell number, RNA integrity or quantity, or experimental treatment. Under ideal conditions, mRNA levels can be standardized to cell number, but when using whole tissue samples, this type of normalization is impossible (35). Therefore, real-time PCR results are usually normalized against a control gene that may also serve as a positivecontrol for the reaction. The idealcontrol gene should be expressed inan unchanging fashion regardless ofexperimental conditions, includingdifferent tissue or cell types, devel-opmental stage, or sample treatment.Because there is no one gene that meetsthis criterion for every experimentalcondition, it is necessary to validatethe expression stability of a controlgene for the specific requirements of anexperiment prior to its use for normal-ization (36).Housekeeping genes (mRNA).Traditionally, genes thought to havestable expression have been employedas controls in gene expression assays.Due to the increased sensitivity anddynamic range of real-time PCR overtraditional quantitation techniques,many of the well-known housekeepinggenes such as GAPDH andβ-actin havebeen shown to be affected by differenttreatments, biological processes, andeven different tissues or cell types(reviewed in depth in Reference 11).Consequently, normalization with asingle housekeeping gene can falselybias results. When using a house-keeping gene for normalization, it isabsolutely imperative to validate itsstability with one’s own samples ratherthan relying on previously publishedmaterials.Ribosomal RNA (rRNA). rRNAis another possible reference gene fornormalization. Of the two main rRNAs,28S and 18S, 28S is considered morerepresentative of mRNA integritybecause 18S may remain intact insamples with degraded mRNA (37).There are several problems with using28S rRNA to normalize mRNA genemeasurements. rRNAs are transcribedwith a different polymerase thanmRNAs, so changes in polymeraseactivity may not affect both types ofRNA expression equally (38). This islikely reflected in the fact that rRNAexpression tends to be less affectedby treatments that significantly altermRNA expression (39). Varying ratiosof rRNA to mRNA have been reported(40) and, given the extreme abundanceof 28S rRNA in a total RNA sample [ina 10-µg total RNA sample, on average2 µg are 18S rRNA and 5.5 µg are 28SrRNA (Technical Bulletin #151, www./techlib/tb/tb_151.html;Reference 40a)], it may be impossibleto accurately measure both 28S anda rare transcript in the same RNA orcDNA dilution. Lastly, rRNA, whichTable 2. Characteristics of Detection ChemistriesDetection Chemistries Specificity MultiplexCapabilitySpecificOligonucleotideRequiredAllelicDiscriminationCostDNA Binding Dyes two PCR primers No No No$Hybridization Probe Four Oligonucleotide Method two PCR primers;two specific probesYes Yes Yes$$$Hybridization Probe Three Oligonucleotide Method two PCR primers;one specific probeYes Yes Yes$$$Hydrolysis Probes two PCR primers;one specific probeYes Yes Yes$$$Molecular Beacons two PCR primers;one specific probeYes Yes Yes$$$Scorpions one PCR primer;one primer/probeYes Yes Yes$$$ Sunrise Primers two PCR primers Yes Yes Yes$$$ LUX Primers two PCR primers Yes Yes No$$ $$$, very expensive; $$, moderately expensive; $, inexpensive. LUX, light upon extension.lacks a poly(A) tail, cannot be measured if an oligo(dT) or gene-specific primer has been used for reverse transcription.Total RNA. Gene expression measurements may be normalized against total RNA concentration (11). RNA quantitation can be performed via RiboGreen®RNA (Molecular Probes, Eugene, OR, USA) quantifi-cation or the Agilent 2100 BioAnalyzer (Agilent Technologies, Palo Alto, CA, USA); spectrophotometry may not have the sensitivity and accuracy required for this measurement. There are several inherent problems with this approach: total RNA levels are affected by cellular processes, RNA quality and reverse transcription efficiency are not considered, normalization is only as accurate as the RNA quantifi-cation, and, in situations where RNA is extracted from a microdissected tissue, all recovered RNA may be needed for the real-time PCR assay itself (35).Multiple mRNAs. Given the many disadvantages of using cell number, mRNA, rRNA, or total RNA for normalization purposes, a new method of employing multiple housekeeping genes has emerged to minimize these problems (19,35,41). Multiple housekeeping genes are assayed and a normalization factor is calculated from the geometric mean of their expression levels (19,35). In this method, the expression stability of several (10–13) different housekeeping genes in the samples of interest are measured to identify the genes most suitable for an individual experiment. Microarray results may be exploited to identify potential normalization candidates (42). A list of housekeeping genes can be found in Vandesompele et al.(35). The expression stability of candidate control genes is determined with either geNorm (35) or BestKeeper (19), which are both Microsoft Excel applets that estimate gene stability through numerous pair-wise compar-isons. geNorm can be downloaded at medgen.ugent.be/~jvdesomp/genorm, and BestKeeper can be downloaded at . While the use of multiple housekeeping genes may be the most labor-intensive method, it is also the most conservative method of data normalization.DETECTION CHEMISTRIESA diagram of all real-time PCRdetection chemistries discussed in thisreview can be seen in Figure 3, with acomparison of their characteristics inTable 2.DNA Binding DyesDNA binding dyes emit fluores-cence when bound to dsDNA (Figure3A). As the double-stranded PCRproduct accumulates during cycling,more dye can bind and emit fluores-cence. Thus, the fluorescence intensityincreases proportionally to dsDNAconcentration (43). This technique isvery flexible because one dye can beused for different gene assays. Conse-quently, multiplexing reactions is notpossible. Because DNA binding dyesdo not bind in a sequence-specificmanner, these assays are prone to falsepositives (44). Accurate results demanda specific PCR, which can be confirmedvia dissociation curve analysis, wherethe presence of different PCR productsis reflected in the number of first-derivative melting peaks (45) or gelanalysis (46). A protocol for SYBRGreen I PCR master mix can be foundin Ramos-Payen et al. (47).Hybridization ProbesHybridization probes can be utilizedin either a four or three oligonucle-otide manner (for a short review, seeReference 48) (Figure 3B). The fouroligonucleotide method consists of twoPCR primers and two sequence-specificprobes that bind adjacent to eachother in a head-to-tail arrangement.The upstream probe is labeled withan acceptor dye on the 3′ end, and thedownstream probe with a donor dye onthe 5′ end (49), allowing the donor andacceptor fluorophores to experiencean increase in fluorescence resonanceenergy transfer (FRET) when bound(48). The three oligonucleotide methodis similar to the four oligonucleotidemethod, except that the upstream PCRprimer is labeled with an acceptor dyeon the 3′end, and thus replaces thefunction of one of the probes from thefour oligonucleotide method.In both cases, the downstream probecan be designed to cover a mutation siteand discriminate between known allelesand detect new alleles simultaneously(50). Alleles are identified and differ-entiated via dissociation curve (48). Asingle melting curve can distinguishup to four different T m s, and six differ-ently labeled probes may be multi-plexed, theoretically allowing a run of24 assays in a single tube (48). Whilemultiplex reactions are theoretically asimple way to increase the efficiencyof data collection, in reality it is avery technically challenging processthat requires extensive optimization toensure that reactions do not competewith each other (34).Hydrolysis ProbesHydrolysis probes, exemplified bythe TaqMan chemistry, also knownas 5′nuclease assay, fluoresce uponprobe hydrolysis to detect PCRproduct accumulation (Figure 3C).The sequence-specific probe is labeledwith a reporter dye on the 5′ end anda quencher dye on the 3′end (24),which allows the quencher to reducethe reporter fluorescence intensity byFRET when the probe is intact (51).While both hydrolysis and hybrid-ization probes rely on FRET to alterthe intensity of fluorescence emission,the energy transfer works in oppositemanners in these two chemistries.FRET reduces fluorescence intensityin hydrolysis probes and increasesintensity in hybridization probes.When annealed to the target sequence,the bound and quenched probe will bedegraded by the DNA polymerase’s 5′nuclease ability during the extensionstep of the PCR. Probe degradationallows for separation of the reporterfrom the quencher dye, resultingin increased fluorescence emission(2,24).Minor groove binders (MGBs), suchas dihydrocyclopyrroloindole tripeptide(DPI3), may be added to these probes toincrease their T m and allow the use of ashorter probe (52). These probes are notonly less expensive to produce but havereduced background fluorescence and alarger dynamic range due to increasedefficiency of reporter quenching (52).。