求有限稀释法(limiting dilution analysis,LDA)具体步骤

求有限稀释法(limiting dilution analysis,LDA)具体步骤,第1张

有限稀释法是一种常用的克隆方法。

将需要再克隆的细胞株自培养孔内吸出并作细胞计数,计出1mL的细胞数。

用HT培养液稀释, 使细胞浓度为50~60个/mL,于96孔培养板中每孔加0.1mL(五六个细胞/孔)。

接种2排,剩余细胞悬液用HT培养液作倍比稀释,再接种2排,如此类推,直至使每孔含0.5~1个细胞。

培养7~10d后,选择单个克隆生长的阳性孔再一次进行克隆。一般需要如此重复3~5次,直至达100%阳性孔率时即可,以确保抗体由单个克隆所产生。

例如:

实验细胞克隆化培养技术—有限稀释法

一、实验目的:

1、掌握用有限稀释法进行细胞克隆化培养技术; 2、学会细胞克隆形成的辩观察技能;

3、配合抗体分泌细胞筛选技术,确定抗体分泌细胞。 二、实验原理:

克隆化培养即单细胞培养技术,用有限稀释法进行杂交瘤细胞克隆化培养,可以及时确定阳性杂交瘤细胞株,同时淘汰因发生染色体丢失或抗体的轻、重链基因分离而出现无抗体分泌阴性细胞株。

一般杂交瘤细胞需经过52—3次反复克隆后,才能达到100%细胞阳性率。

该办法亦适用于一般细胞培养中的细胞纯化、突变细胞株的选择,识别和分离。是细胞株的常用方法。

三、实验材料:

杂交瘤细胞、25毫升培养瓶、96孔细胞培养板(天津有机玻璃厂)、移液管(1毫升、5毫升、10毫升)、弯头滴管、倒置显微镜、CO2培养箱、白细胞计数板、计数器、盖玻片、防水笔、RPMI-1640、小牛血清、青链霉素、10毫升刻度离心管,00橡胶塞,饲养细胞(3-6×105/ml、见腹腔细胞的制备)。

四、实验步骤:

1、分装了每孔0.1毫升腹腔细胞的96孔细胞培养板(可提前24小时准备就绪,置CO2培养箱中备用);

2、自细胞培养瓶中收集长势良好的杂交瘤细胞(亦可自24孔培养板孔中收集),制成悬液。

3、按白细胞计数方法准确计得细胞悬液的细胞数,一般在105/毫升左右。

4、取3支10毫升刻度离心管排列在超净工作台试管架上,先用无血清培养基将细胞稀释至103/毫升,再用含15%小牛血清的完全培养基稀释到101/毫升细胞,即每0.1毫升1个细胞。

5、每孔0.1毫升细胞悬液。

6、置37℃5%CO2培养箱,4天后取出观察,并在板盖上打上标记,做好记录并统计结果。

7、继续培养时,则在第4-5天更换1/2培养基,约第7-9天可以收获培养液上清用于检测抗体。并重复检测1-2次。

8、选择单克隆生长孔,生长良好,阳性强者,转移到24孔板再做克隆经培养或扩大培养。

五、实验结果:

实验结果以总细胞孔(如96孔)中出现克隆孔统计出克隆百分率,并可进一步按单细胞孔、双细胞孔、多细胞孔分别计算出百分率。

六、注意事项:

1、注意无菌 *** 作,一旦发现污染(孔)必须及时处理;

2、计数细胞要求准确,稀释量亦要准确,否则将造成一孔多细胞克隆或克隆百分离太低;

3、在计数克隆出现率之前,不宜更换培养液,也不宜强烈振动培养板; 4、做克隆化培养的小牛血清必须是优质血清;

5、若做克隆抗体检测时,特别要保护细胞的生长状况,防止细胞生长不良甚至丢失。 七、说明:

哺乳动物细胞通过分离、稀释接种,培养在适宜于单细胞生长的培养液中,形成细胞克隆。运用这项技术可以制作细胞成活曲线,即在接种相同数量细胞的培养瓶中,加入不同浓度的化学药品,或照以不同剂量的射线,观察其对细胞的听见害程度。由此可以在哺乳类细胞中进行诱变试验及分离突变细胞。

以下是LDA的m文件函数:

你稍稍改改就能用了!

function [eigvector, eigvalue, elapse] = LDA(gnd,options,data)

% LDA: Linear Discriminant Analysis

%

% [eigvector, eigvalue] = LDA(gnd, options, data)

%

% Input:

% data - Data matrix. Each row vector of fea is a data point.

% gnd - Colunm vector of the label information for each

% data point.

% options - Struct value in Matlab. The fields in options

% that can be set:

%

%Regu - 1: regularized solution,

%a* = argmax (a'X'WXa)/(a'X'Xa+ReguAlpha*I)

% 0: solve the sinularity problem by SVD

% Default: 0

%

% ReguAlpha - The regularization parameter. Valid

% when Regu==1. Default value is 0.1.

%

%ReguType - 'Ridge': Tikhonov regularization

% 'Custom': User provided

% regularization matrix

% Default: 'Ridge'

%regularizerR - (nFea x nFea) regularization

% matrix which should be provided

% if ReguType is 'Custom'. nFea is

% the feature number of data

% matrix

%Fisherface - 1: Fisherface approach

% PCARatio = nSmp - nClass

% Default: 0

%

%PCARatio - The percentage of principal

%component kept in the PCA

%step. The percentage is

%calculated based on the

%eigenvalue. Default is 1

%(100%, all the non-zero

%eigenvalues will be kept.

%If PCARatio >1, the PCA step

%will keep exactly PCARatio principle

%components (does not exceed the

%exact number of non-zero components).

%

%

% Output:

% eigvector - Each column is an embedding function, for a new

% data point (row vector) x, y = x*eigvector

% will be the embedding result of x.

% eigvalue - The sorted eigvalue of LDA eigen-problem.

% elapse- Time spent on different steps

%

%Examples:

%

% fea = rand(50,70)

% gnd = [ones(10,1)ones(15,1)*2ones(10,1)*3ones(15,1)*4]

% options = []

% options.Fisherface = 1

% [eigvector, eigvalue] = LDA(gnd, options, fea)

% Y = fea*eigvector

%

%

% See also LPP, constructW, LGE

%

%

%

%Reference:

%

% P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, 揈igenfaces

% vs. fisherfaces: recognition using class specific linear

% projection,� IEEE Transactions on Pattern Analysis and Machine

% Intelligence, vol. 19, no. 7, pp. 711-720, July 1997.

%

% Deng Cai, Xiaofei He, Yuxiao Hu, Jiawei Han, and Thomas Huang,

% "Learning a Spatially Smooth Subspace for Face Recognition", CVPR'2007

%

% Deng Cai, Xiaofei He, Jiawei Han, "SRDA: An Efficient Algorithm for

% Large Scale Discriminant Analysis", IEEE Transactions on Knowledge and

% Data Engineering, 2007.

%

% version 2.1 --June/2007

% version 2.0 --May/2007

% version 1.1 --Feb/2006

% version 1.0 --April/2004

%

% Written by Deng Cai (dengcai2 AT cs.uiuc.edu)

%

if ~exist('data','var')

global data

end

if (~exist('options','var'))

options = []

end

if ~isfield(options,'Regu') | ~options.Regu

bPCA = 1

if ~isfield(options,'PCARatio')

options.PCARatio = 1

end

else

bPCA = 0

if ~isfield(options,'ReguType')

options.ReguType = 'Ridge'

end

if ~isfield(options,'ReguAlpha')

options.ReguAlpha = 0.1

end

end

tmp_T = cputime

% ====== Initialization

[nSmp,nFea] = size(data)

if length(gnd) ~= nSmp

error('gnd and data mismatch!')

end

classLabel = unique(gnd)

nClass = length(classLabel)

Dim = nClass - 1

if bPCA &isfield(options,'Fisherface') &options.Fisherface

options.PCARatio = nSmp - nClass

end

if issparse(data)

data = full(data)

end

sampleMean = mean(data,1)

data = (data - repmat(sampleMean,nSmp,1))

bChol = 0

if bPCA &(nSmp >nFea+1) &(options.PCARatio >= 1)

DPrime = data'*data

DPrime = max(DPrime,DPrime')

[R,p] = chol(DPrime)

if p == 0

bPCA = 0

bChol = 1

end

end

%======================================

% SVD

%======================================

if bPCA

if nSmp >nFea

ddata = data'*data

ddata = max(ddata,ddata')

[eigvector_PCA, eigvalue_PCA] = eig(ddata)

eigvalue_PCA = diag(eigvalue_PCA)

clear ddata

maxEigValue = max(abs(eigvalue_PCA))

eigIdx = find(eigvalue_PCA/maxEigValue <1e-12)

eigvalue_PCA(eigIdx) = []

eigvector_PCA(:,eigIdx) = []

[junk, index] = sort(-eigvalue_PCA)

eigvalue_PCA = eigvalue_PCA(index)

eigvector_PCA = eigvector_PCA(:, index)

%=======================================

if options.PCARatio >1

idx = options.PCARatio

if idx <length(eigvalue_PCA)

eigvalue_PCA = eigvalue_PCA(1:idx)

eigvector_PCA = eigvector_PCA(:,1:idx)

end

elseif options.PCARatio <1

sumEig = sum(eigvalue_PCA)

sumEig = sumEig*options.PCARatio

sumNow = 0

for idx = 1:length(eigvalue_PCA)

sumNow = sumNow + eigvalue_PCA(idx)

if sumNow >= sumEig

break

end

end

eigvalue_PCA = eigvalue_PCA(1:idx)

eigvector_PCA = eigvector_PCA(:,1:idx)

end

%=======================================

eigvalue_PCA = eigvalue_PCA.^-.5

data = (data*eigvector_PCA).*repmat(eigvalue_PCA',nSmp,1)

else

ddata = data*data'

ddata = max(ddata,ddata')

[eigvector, eigvalue_PCA] = eig(ddata)

eigvalue_PCA = diag(eigvalue_PCA)

clear ddata

maxEigValue = max(eigvalue_PCA)

eigIdx = find(eigvalue_PCA/maxEigValue <1e-12)

eigvalue_PCA(eigIdx) = []

eigvector(:,eigIdx) = []

[junk, index] = sort(-eigvalue_PCA)

eigvalue_PCA = eigvalue_PCA(index)

eigvector = eigvector(:, index)

%=======================================

if options.PCARatio >1

idx = options.PCARatio

if idx <length(eigvalue_PCA)

eigvalue_PCA = eigvalue_PCA(1:idx)

eigvector = eigvector(:,1:idx)

end

elseif options.PCARatio <1

sumEig = sum(eigvalue_PCA)

sumEig = sumEig*options.PCARatio

sumNow = 0

for idx = 1:length(eigvalue_PCA)

sumNow = sumNow + eigvalue_PCA(idx)

if sumNow >= sumEig

break

end

end

eigvalue_PCA = eigvalue_PCA(1:idx)

eigvector = eigvector(:,1:idx)

end

%=======================================

eigvalue_PCA = eigvalue_PCA.^-.5

eigvector_PCA = (data'*eigvector).*repmat(eigvalue_PCA',nFea,1)

data = eigvector

clear eigvector

end

else

if ~bChol

DPrime = data'*data

% options.ReguAlpha = nSmp*options.ReguAlpha

switch lower(options.ReguType)

case {lower('Ridge')}

for i=1:size(DPrime,1)

DPrime(i,i) = DPrime(i,i) + options.ReguAlpha

end

case {lower('Tensor')}

DPrime = DPrime + options.ReguAlpha*options.regularizerR

case {lower('Custom')}

DPrime = DPrime + options.ReguAlpha*options.regularizerR

otherwise

error('ReguType does not exist!')

end

DPrime = max(DPrime,DPrime')

end

end

[nSmp,nFea] = size(data)

Hb = zeros(nClass,nFea)

for i = 1:nClass,

index = find(gnd==classLabel(i))

classMean = mean(data(index,:),1)

Hb (i,:) = sqrt(length(index))*classMean

end

elapse.timeW = 0

elapse.timePCA = cputime - tmp_T

tmp_T = cputime

if bPCA

[dumpVec,eigvalue,eigvector] = svd(Hb,'econ')

eigvalue = diag(eigvalue)

eigIdx = find(eigvalue <1e-3)

eigvalue(eigIdx) = []

eigvector(:,eigIdx) = []

eigvalue = eigvalue.^2

eigvector = eigvector_PCA*(repmat(eigvalue_PCA,1,length(eigvalue)).*eigvector)

else

WPrime = Hb'*Hb

WPrime = max(WPrime,WPrime')

dimMatrix = size(WPrime,2)

if Dim >dimMatrix

Dim = dimMatrix

end

if isfield(options,'bEigs')

if options.bEigs

bEigs = 1

else

bEigs = 0

end

else

if (dimMatrix >1000 &Dim <dimMatrix/10) | (dimMatrix >500 &Dim <dimMatrix/20) | (dimMatrix >250 &Dim <dimMatrix/30)

bEigs = 1

else

bEigs = 0

end

end

if bEigs

%disp('use eigs to speed up!')

option = struct('disp',0)

if bChol

option.cholB = 1

[eigvector, eigvalue] = eigs(WPrime,R,Dim,'la',option)

else

[eigvector, eigvalue] = eigs(WPrime,DPrime,Dim,'la',option)

end

eigvalue = diag(eigvalue)

else

[eigvector, eigvalue] = eig(WPrime,DPrime)

eigvalue = diag(eigvalue)

[junk, index] = sort(-eigvalue)

eigvalue = eigvalue(index)

eigvector = eigvector(:,index)

if Dim <size(eigvector,2)

eigvector = eigvector(:, 1:Dim)

eigvalue = eigvalue(1:Dim)

end

end

end

for i = 1:size(eigvector,2)

eigvector(:,i) = eigvector(:,i)./norm(eigvector(:,i))

end

elapse.timeMethod = cputime - tmp_T

elapse.timeAll = elapse.timePCA + elapse.timeMethod


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