杂志:参数优化和结果分析——METHODS25,402–408(2001)
翻译自:
Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 2-△△CT Method
Kenneth J. Livak* and Thomas D. Schmittgen
Applied Biosystems, Foster City, California 94404; and† Department of Pharmaceutical Sciences, College of Pharmacy
Washington State University, Pullman, Washington 99164-6534
摘要:
现在常用的两种分析实时定量PCR实验数据的方法是绝对定量和相对定量。绝对定量通过标准曲线计算起始模板的拷贝数;相对定量方法则是比较经过处理的样品和未经处理的样品目标转录本之间的表达差异。2-△△CT方法是实时定量PCR实验中分析基因表达相对变化的一种简便方法,即相对定量的一种简便方法。本文介绍了该方法的推导,假设及其应用。另外,在本文中我们还介绍了两种2-△△CT衍生方法的推导和应用,它们在实时定量PCR数据分析中可能会被用到。
简述:
反转录PCR(RT-PCR)是基因表达定量非常有用的一种方法(1-3)。实时PCR技术和RT-PCR的结合产生了反转录定量PCR技术(4,5)。实时定量PCR的数据分析方法有两种:绝对定量和相对定量。绝对定量一般通过定量标准曲线来确定我们所感兴趣的转录本的拷贝数;相对定量方法则是用来确定经过不同处理的样品目标转录本之间的表达差异或是目标转录本在不同时相的表达差异。
绝对定量通常在需要确定转录本绝对拷贝数的条件下使用。通过实时PCR进行绝对定量已有多篇报道(6-9),包括已发表的两篇研究论文(10,11)。在有些情况下,并不需要对转录本进行绝对定量,只需要给出相对基因表达差异即可。显然,我们说X基因在经过某种处理后表达量增加2.5倍比说该基因的表达从1000拷贝/细胞增加到2500拷贝/细胞更加直观。
用实时PCR对基因表达进行相对定量分析需要特殊的公式、假设以及对这些假设的验证。2-△△CT方法可用于定量PCR实验来计算基因表达的相对变化:2-△△CT公式的推导,以及实验设计,有效性评估在AppliedBiosystemsUserBulletinNo.2(P/N4303859)中有介绍。用2-△△CT方法分析基因表达数据在文献中也有报道(5,6)。本文介绍了该方法的推导、假设以及应用。另外,本文还介绍了2-△△CT两种衍生方法的推导和应用,它们在实时定量PCR数据分析中都可能被用到。
2-△△CT方法
2-△△CT方法的推导
PCR指数扩增的公式是:
![](data:image/png;base64,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)
Xn是第n个循环后目标分子数。
X0是初始目标分子数。
Ex是目标分子扩增效率。
n是循环数
CT代表目标扩增产物达到设定阈值所经历的循环数
因此:
![](data:image/png;base64,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)
XT是目标分子达到设定的阈值时的分子数。
CT,X是目标分子扩增达到阈值时的循环数。
Kx是一个常数
对于内参反应而言,也有同样的公式:
![](data:image/png;base64,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)
用XT除以RT得到:
![](data:image/png;base64,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)
对于使用Taqman探针的实时扩增而言,XT和RT的值由一系列因素决定:包括探针所带的荧光报导基团、探针序列对探针荧光特性的影响、探针的水解效率和纯度以及荧光阈值的设定。因此常数K并不一定等于1。
假设目标序列与内参序列扩增效率相同:
![](data:image/png;base64,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)
或:
![](data:image/png;base64,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)
XN代表经过均一化处理过的初始目标分子量;△CT表示目标基因和内标基因CT值的差异(CT,X-CT,R)整理上式得:
![](data:image/png;base64,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)
后用任一样本q的XN除以参照因子(calibrator,cb)的XN得到:
![](data:image/png;base64,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)
在这里
![](data:image/png;base64,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)
对于一个少于150bp的扩增片断而言,如果Mg2+浓度、引物都进行了适当的优化,扩增效率接近于1。因此目标序列的量通过内均一化处理之后相对于参照因子而言就是
![](data:image/png;base64,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)
2-△△CT方法的假设和应用
要使△△CT计算方法有效,目标序列和内参序列的扩增效率必须相等。看两个反应是否具有相同的扩增效率的方法是看他们模板浓度梯度稀释后扩增产物△CT如何变化。
图1显示的是cDNA样品在100倍稀释范围内的实验结果。对于每一个稀释样本,都用GAPDH和c-myc特异的荧光探针及引物进行扩增。计算出c-myc和GAPDH的平均CT值以及△CT值,通过cDNA浓度梯度的log值对△CT值作图,如果所得直线斜率绝对值接近于0,说明目标基因和内标基因的扩增效率相同,就可以通过△△CT方法进行相对定量。在图1中,直线斜率是0.047,因而假设成立,△△CT方法可以用来分析数据。如果两个扩增反应效率不同,则需要通过定量标准曲线和绝对定量的方法来进行相对定量;或者也可以重新设计引物,优化反应条件使得目标序列和内参序列具有相同的扩增效率。
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)
2-△△CT内标和参照因子的选择
使用内标基因的目的是为了对加入到反转录反应中的RNA进行均一化处理。标准的看家基因一般都可被用作内标基因。适合于实时PCR反应内标基因包括GAPDH,β-actin,β2-microglobulin以及rRNA。当然,其它的看家基因也同样能被用作内标。我们推荐在应用某一基因作为内标之前首先确证该基因的表达不会受实验处理的影响。验证实验处理是否对内标基因表达产生影响的方法在2.2部分有描述。
2-△△CT方法中参照因子的选择决定于基因表达定量实验的类型。简单的设计就是把未经处理的样品作为参照因子(calibrator)。经内标基因均一化处理后,通过2-△△CT方法计算,目标基因表达差异通过经过处理的样本相对于未经处理的样本的倍数来表示。对于未经处理的参照样,△△CT=0,而20=1。所以根据定义,未处理样本的倍数变化为1。而对于那些经过处理的样本,相对于参考因子基因表达的倍数为2-△△CT。同样的分析也可用于不同时相的基因表达差异,在这种情况下,一般选0时刻的样本作为参照因子。
有些情况下,并不是比较不同处理样本基因表达差异。例如,有的是想看某一器官中特定mRNA的表达。在这种情况下,参照因子可能是另一器官中该mRNA的表达。表1显示了大脑和肾脏总RNA中c-myc和GAPDH转录本的CT值。在这一个例子中,大脑被人为的选择为参照因子,通过计算得到肾脏c-myc表达量经GAPDH校正后相对于大脑的表达量的结果。尽管相对定量方法可用于这种组织之间的比较,但结果的生物学解释是相当复杂的。不同种类细胞中目标和参照转录本单一的相对量变化可能在任何特定的组织中都存在。
2-△△CT方法的数据分析
实时定量PCR所得到CT值可以很容易的输出到表格程序如MicrosoftExcel中去。为了显示数据分析过程,我们在这里给出了一个基因表达定量的实验数据和样本列表。通过β-actin均一化处理,我们对目标基因fos-glo-myc的表达变化进行了监测。在8h的时间范围内,在每一时间点都取3个重复样本,每一样本在cDNA合成之后都做定量PCR,数据分析用到了公式9,即:
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)
Timex表示任意时间点,Time0表示经β-actin校正后1倍量的目标基因表达。
0时刻目标基因和内标基因的平均CT(见图2第8栏)被用于公式9中。通过公式9计算出每一个样本目标基因表达通过β-actin均一化处理后相对于0时刻的倍数(见图2第9栏)。平均SD,CV由每一个时间点所取的三个重复样求得。用这种分析方法,在0时刻的平均倍数变化接近于1。我们发现通过检测在0时刻平均倍数变化是否为1可以很方便的验证三个重复样品之间是否有错误或者误差。如果得到的结果与1偏差很大,则表明存在计算错误或者是很高的实验误差。
在前面的例子中,在每一时间点上分别取了三个独立的RNA样本进行了分析。因此对每一个样本分别处理,通过2-△△CT计算后取结果的平均值就非常重要。如果是同一样本进行PCR扩增的重复,这就需要首先求出平均CT,然后再进行2-△△CT计算。怎么样计算平均值就要看目标基因和内参基因是在同一个管子中扩增还是在不同的管子中扩增。表1给出了目标基因(c-myc)和内参基因(GAPDH)在不同管中扩增的实验数据。在这里不应该把任一单个的c-myc管子和GAPDH管子作比较,而应该分别计算出c-myc和GAPDH的平均CT来计算△CT。重复实验中CT值的估计偏差通过标准的指数计算转化成后结果中相对量的变化。但其中的一个难点是CT值与相应的拷贝数成指数关系(见第4部分),因此,在后的计算中,2-△△CT的误差通过△△CT加上标准偏差和△△CT减去标准偏差来评估,这就使得求得的数值相对于平均值呈不对称分布。不对称分布是因为结果经指数处理后转化成量的线性比较造成的。
通过不同荧光染料标记的探针,我们可以在同一管中同时扩增目标序列和内标序列。表2给出了目标基因(c-myc)和内标基因(GAPDH)在同一管中扩增的实验数据。对于任意一个管子,目标基因(c-myc)和内参基因(GAPDH)扩增时加入的cDNA量都是一样多的,所以可以分别对每个管子计算△CT值,这些值取平均后再进行2-△△CT计算。在这里估计误差值也是一个不对称的范围,反映了误差经指数处理转化为线性差异。
在表1和表2中,估计误差在从△CT到△△CT的计算中未见有增加,这是因为我们把参照基因和检测基因的误差都显示出来了。我们把△CT,cb当作一个人为设定的常数来减去,得到△△CT。这样得到的结果就与图2所显示的在求平均之前对不同重复样本分别通过各自的CT值求2-△△CT所得结果相当。另一种方法是将参照基因当作没有任何误差的1倍的量,在这种情况下,平均△CT,cb的误差值被引入到每一样本的△△CT中。在表1中,肾脏中△△CT变成-2.50±0.20而经过校正的c-myc量是5.6倍,范围从4.9到5.6。而在大脑中的结果是没有误差的1倍。
2-ΔCT’方法
2-△CT’方法的推导
通过内标RNA可以对加入RNA的量的差异进行校正。2-△△CT方法的数据分析的一个特点就是能够利用实时PCR实验的一部分数据来完成这种校正。在其它的方法不能定量初始RNA量的时候:例如,在能得到的RNA量非常有限的时候或者需要处理高通量的样品的时候,这一方法的优势就格外明显。当然我们也可以利用PCR实验以外的方法来完成这种校正。常用的一种方法就是用紫外吸收来确定用于cDNA合成的RNA量,然后将相同的RNA反转录产生的cDNA用于PCR定量反应。这种外标法校正的一个应用例子就是研究某种实验处理是否影响内标基因的表达。在这里,目标基因和内标合二为一。在这个例子中,公式[2]不被公式[3]除,公式[5]变成:
![](data:image/png;base64,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)
整理得:
![](data:image/png;base64,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)
任一样品X0,q除以参照品X0,cb得:
![](data:image/png;base64,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)
在这里△CT’=CT,q - CT,cb。△CT’与前面计算中用的△CT(用目标基因CT值减去参照基因CT值)相互区别。
就象在1.1部分所描述的,如果条件优化较好,效率接近于1,内标相对于参照因子为:
![](data:image/png;base64,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)
2-△CT’方法的应用
2-△CT,方法的一个应用就是确定实验处理对某一候选内标基因的影响。为了显示这一过程,我们做了血清饥饿/诱导实验(7)。血清饥饿/诱导是研究某些mRNA降解的常用方法(8)。然而,血清可能影响一些基因的表达包括标准的看家基因的表达。
在24h血清饥饿培养之后,在NIH3T3细胞中加入15%血清诱导基因表达。从细胞中提取Poly(A)+RNA,并将之反转录成cDNA。利用SYBRGreen通过实时定量PCR检测GAPDH,β2-microglobulincDNA的量。GAPDH和β2-microglobulin各自的相对量通过2-△CT’公式
![](data:image/png;base64,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)
求得。细胞处理对于GAPDH的基因表达有明显影响,但对β2-microglobulin没有什么影响。因此β2-microglobulin很适合做血清刺激定量实验的内标,而GAPDH并不适合。这一例子向大家展示了在只研究一个基因的时候怎么用2-△CT’的方法分析基因相对表达数据。
实时PCR数据的统计学分析
实时PCR终分析的是阈值循环或C。
CT值通过PCR信号的对数值和循环数来确定。因此CT值是一个指数而非线性概念。因此,在任何统计分析中都不要用原始的CT值来表示结果。正如我们在前文中所描述的一样,PCR相对量通常和内标和参照样本一起计算而很少直接用CT值来表示,除非我们想检验重复样本之间的差别。为了向大家显示这一点,我们用SYBRGreen通过real-timePCR来检测相同cDNA的96个重复反应。所有反应组分在同一管中混好后分装到96个管中,做实时PCR分析,得到了每一个样本的CT值。为了比较样品间变化,计算了96个样本的平均±SD,如果通过原始CT值计算,平均±SD是20.00±0.193,CV为0.971%。但是如果把原始Ct值用2-CT转化成线性形式,平均±SD是9.08×10-7±1.33×10-7,CV为13.5%。从这个简单的例子我们可以看出,通过原始CT值来反映变化是错误的,应该避免。用2-CT将单个数据转化成线性形式来说明重复样本之间的变化和差异更准确可靠。
结论:
实时定量PCR实验设计和数据分析可以采用相对定量和绝对定量两种方法,研究人员在设计实时定量PCR实验分析基因表达的时候首先要问的一个问题就是:数据后会以一个什么样的形式得到。如果需要知道绝对的拷贝数,就必须用绝对定量的方法,否则只需要给出基因表达相对量就足够了。相对定量可能比绝对定量要更容易一些,因为它不需要作标准曲线。
本文所给出的公式对于每个用相对定量的方法分析基因表达差异的研究人员都足够了。下面,我们总结一下实验设计和评估中的一些重要步骤:
1.选择一个内标基因。
2.确定内标的有效性,确保它不会受到实验处理的影响。
3.通过PCR扩增目标基因和内标基因RNA或cDNA的一系列梯度稀释模板确保它们的扩增效率相同。
4.后通过2-△CT计算将统计数据转化成线性形式而不是原始CT值。
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