Work Description

Title: Single-molecule microscopy image data and analysis files for "Ultra-specific and Amplification-free Quantification of Mutant DNA by Single-molecule Kinetic Fingerprinting" Open Access Deposited

http://creativecommons.org/licenses/by-nc/4.0/
Attribute Value
Methodology
  • Single-Molecule Recognition through Equilibrium Poisson Sampling (SiMREPS) experiments were performed on an Olympus IX-81 objective-type TIRF microscope equipped with a 60X oil-immersion objective (APON 60XOTIRF, 1.49 NA) with both Cell^TIRF and z-drift control modules, and an EMCCD camera (IXon 897, Andor), using MetaMorph acquisition software (Molecular Devices). Transient binding of a fluorescent probe oligonucleotide to DNA molecules immobilized to the surface of a custom-built sample cell was monitored for 10 min under TIRF illumination by 640 nM laser light with a 500 ms exposure time (1200 total frames), and recorded as a stack of TIF images (movie). Movie files were analyzed using custom scripts written in MATLAB, and the QuB software suite (State University of New York at Buffalo) to: 1) identify the locations of immobilized candidate DNA molecules and extract a fluorescence intensity versus time trace for each, 2) determine the number of binding and dissociation events (Nb+d) and the median fluorescent probe bound (τbound,median) and unbound (τunbound,median) time for each candidate molecule, which together comprise the kinetic fingerprint of the candidate molecule, and 3) evaluate the kinetic fingerprint and data quality of each candidate to arrive at the final number of immobilized DNA molecules of a specific sequence.
Description
  • This work contains the experimental data and associated analysis that are described in the research publication entitled "Ultra-specific and Amplification-free Quantification of Mutant DNA by Single-molecule Kinetic Fingerprinting". This work contains multiple zip files, each of which represents one of the principal experiment groups presented in the publication. Each experiment group contains movie and analysis files corresponding to various experimental conditions related to that experiment group.
Creator
Depositor
  • palund@umich.edu
Contact information
Discipline
Funding agency
  • Other Funding Agency
Keyword
Citations to related material
Resource type
Last modified
  • 02/14/2020
Published
  • 08/13/2018
Language
DOI
  • https://doi.org/10.7302/Z2CZ35DF
License
To Cite this Work:
Hayward, S., Lund, P., Kang, Q., Johnson-Buck, A., Tewari, M., Walter, N. (2018). Single-molecule microscopy image data and analysis files for "Ultra-specific and Amplification-free Quantification of Mutant DNA by Single-molecule Kinetic Fingerprinting" [Data set]. University of Michigan - Deep Blue. https://doi.org/10.7302/Z2CZ35DF

Relationships

Files (Count: 20; Size: 104 GB)

% Create averaged image for spot localization in STORM image
% Alex Johnson-Buck 2014(?)
%
% Modify to batch process multiple movies, Paul Lund 2015

clear all
close all

%% Begin PARAMETERS TO SET

% directory = 'Y:\PaulLund\SiMREPS\Movies\RA3_2015-11-30\';
% filename = 'RA3_miR16_250fM_A-1.tif';

% infilename = strcat(directory,filename);

% channel = 'Cy5';
% channel = 'Cy3';
channel = 'whole';

overwrite = 'yes';
% overwrite = 'no';

% stdfactor = 2.5; % Threshold for particle finding-default for T790M
stdfactor = 2;

straight_intensity_cutoff = 0;
ithreshold = 0.1;

%Frames to use in averaging
startframe = 1;
% endframe = 500;
endframe = 1200;

plotcolor = 'r';

selectionbyflucts = 1;

analyze_all = 'y'; %Analyze all traces without filtering based on model results
% analyze_all = 'n';
% analysis = 'manual'; % Allow uesr to review traces individually and evaluate choice of thresholds
analysis = 'auto';

% mincounts = 1000; % For Cy5 in 5/25/2012
% mincounts = 1000; % For Cy3 in 5/25/2012
% mincounts = 200; % For expt 5/22/2012
% mincounts = 5000;

% driftcorr = 'yes';
driftcorr = 'no';
driftbins = 10;

edgePx = 30; %Number of border pixels to exclude (>=10)

% maxL = 500; % Maximum intensity value allowed for the "low" (no molecule) state
%% Get Input File Names
fprintf(1,'Please select all the movie files for peak finding\n')
[TestFileName, TestPathName] = uigetfile('*.tif','Please select all your movie files','Multiselect','on');
% This is just in case the user selects only one file (converts array ->
% cell array), because the rest of the code expects a cell array.
if ~iscell(TestFileName)
G = cell(1);
G{1} = TestFileName;
TestFileName = G;
end
%% Loop through all selected movie files
nfiles = length(TestFileName);
for n=1:nfiles
close all;
FileName = strcat(TestPathName,TestFileName{n});
disp(strcat('Now working on:',TestFileName{n}));

outfilename = strcat(FileName(1,1:(size(FileName,2)-4)),'_',channel,'_avg_bgsub.tif');
tracesname = strcat(FileName(1,1:(size(FileName,2)-4)),'_',channel,'_traces.dat');
coordsname = strcat(FileName(1,1:(size(FileName,2)-4)),'_',channel,'_coords.dat');
coordsname2 = strcat(FileName(1,1:(size(FileName,2)-4)),'_',channel,'_coords.txt');
% Check for files containing the molecule numbers, coordinates, and
% intervals for fitting. If they exist; add to them. If not, start at
% beginning.
if exist(strcat(FileName(1,1:(size(FileName,2)-4)),'_',channel,'_molintervals.mat'))==2 && strcmp(overwrite,'yes')~=1
molnum = load(strcat(FileName(1,1:(size(FileName,2)-4)),'_',channel,'_molnum.mat'));
molnum = molnum.molnum;
molcoords = load(strcat(FileName(1,1:(size(FileName,2)-4)),'_',channel,'_molcoords.mat'));
molcoords = molcoords.molcoords;
molintervals = load(strcat(FileName(1,1:(size(FileName,2)-4)),'_',channel,'_molintervals.mat'));
molintervals = molintervals.molintervals;
mm = size(molnum,1)+1;
elseif exist(strcat(FileName(1,1:(size(FileName,2)-4)),'_',channel,'_molnum.mat'))==2
molnum = load(strcat(FileName(1,1:(size(FileName,2)-4)),'_',channel,'_molnum.mat'));
molnum = molnum.molnum;
molcoords = load(strcat(FileName(1,1:(size(FileName,2)-4)),'_',channel,'_molcoords.mat'));
molcoords = molcoords.molcoords;
molintervals = cell(1,0);
overwrite = 'no';
mm=1;
else
molnum = zeros(0,1);
molcoords = zeros(0,2);
molintervals = cell(1,0);
mm = 1;
end


binsize = endframe-startframe+1;
% binsize = 300; % Number of frames to include in average


% Read TIF info

mov1info = imfinfo(FileName,'tif');

frames = size(mov1info,1);
xdim = mov1info(1,1).Width;
ydim = mov1info(1,1).Height;

if endframe > frames
endframe = frames;
end

if endframe <= frames && startframe >= 1

if strcmp(driftcorr,'yes')==1
[driftxy]=drift_corr_tif(FileName,1,driftbins,1);
for dd = 1:size(driftxy,1)
for ff = 1:size(driftxy,2)
driftorigin = driftxy{dd,ff}(round((endframe+startframe)/2),1:2);
for ii = 1:size(driftxy{dd,ff})
driftxy{dd,ff}(ii,:)=driftxy{dd,ff}(ii,:)-driftorigin;
end
end
end
else
driftxy{1,1}=zeros(frames,2);
end



if exist(outfilename) ~= 2 || strcmp(overwrite,'yes') ==1 % Only create averaged image if doesn't already exist

tic;
imagesum = zeros(ydim,xdim);
if selectionbyflucts == 1 % Generate average image by finding intensity fluctuations between frames
fmap = zeros(ydim,xdim);
for frame = startframe:endframe
adata = imread(FileName,'tif','Index',frame);
adata = im2double(adata);

if frame > startframe
adiff = abs(adata-lastframe);
adiff = adiff - imopen(adiff,strel('disk',15)); % This step is one speed bottleneck
astd = std(reshape(adiff, size(adiff,1)*size(adiff,2),1));
if straight_intensity_cutoff == 1
threshold = ithreshold;
else
threshold = stdfactor*astd;
end
adiff = imhmax(adiff,threshold,4); % This step is another speed bottleneck

fmap = fmap + adiff;
end

% imagesum = (imagesum + adata);
lastframe = adata;
% disp(strcat('Working: frame ',num2str(frame)));
end
imageavg = fmap/binsize;
imageavg = imageavg./max(max(imageavg));
imshow(imageavg);
% imageavg = im2uint16(imageavg);
else
for frame = startframe:endframe % Otherwise, generate image by averaging and thresholding only
adata = imread(FileName,'tif','Index',frame);
adata = im2double(adata);

imagesum = (imagesum + adata);
end
imageavg = imagesum/binsize;
end
imageavg = imageavg-imopen(imageavg,strel('disk',15));
imageavg = imageavg-min(min(imageavg));

imageavg2 = imageavg/max(max(imageavg));

tfinish1 = toc;

disp(strcat('Finished averaging',{' '},num2str(endframe-startframe+1),{' '},'frames in',{' '},num2str(tfinish1),{' '},'seconds.'));

imshow(imageavg2);


pause(0.01);


% imageavg = im2uint16(imageavg);

imwrite(im2uint8(imageavg2),outfilename,'tif');

clear adata;

clear imageavg2;

else


disp('Averaged image exists; skipping...');

% Read averaged TIF

adata = imread(outfilename, 'tif');

imageavg = im2double(adata);

imageavg2 = (imageavg-min(min(imageavg)));
imageavg2 = imageavg2/max(max(imageavg2));

imshow(imageavg2);
clear imageavg2;
end



%*********************************************

%% Find local maxima in averaged image and record coordinates

if strcmp(channel,'whole')==1
aIm = imageavg;

else

if strcmp(channel,'Cy5')==1
% Rect = [257 0 256 512];
Rect = [xdim/2+1 0 xdim/2 ydim];
elseif strcmp(channel,'Cy3')==1
% Rect = [0 0 256 512];
Rect = [0 0 xdim/2 ydim];
else
Rect = [0 0 xdim ydim];
end

aIm = imcrop(imageavg, Rect);
end

aImlinear = reshape(aIm,1,size(aIm,1)*size(aIm,2));
aImstd = std(aImlinear);
aImmedian = median(aImlinear);
% end
% Hard intensity cutoff -- ignore pixels below cutoff intensity
Icutoff = aImmedian; %+ stdfactor*aImstd; % In my experience, histogramming the Cy5 channel intensities showed that the median is a reasonable cutoff.
% Icutoff = ithreshold;
% aImcut = aIm-Icutoff;
aImcut = aIm;

aImcut2 = aImcut/max(max(aImcut));

imshow(aImcut2);

pause(0.01);

hold on

% clear aImcut2;

for i = 1:size(aImcut,1)
for j = 1:size(aImcut,2)
if aImcut(i,j)<0
aImcut(i,j)=0;
end
end
end

if straight_intensity_cutoff == 1
threshold = ithreshold;
else
threshold = aImstd*stdfactor;
end
aImSupp = imhmax(aImcut,threshold, 8); %Suppress local maxima
% imshow(aImSupp);
aImrMax = imregionalmax(aImSupp);
% imshow(aImrMax);
regions = bwlabel(aImrMax);
% figure(2)
% imshow(regions);
centroids = regionprops(regions, 'centroid');
RegArr = struct2cell(centroids);
RegMat = cell2mat(RegArr.');

if exist(coordsname) == 2 && strcmp(overwrite,'yes')~=1 % Record molecule coordinates if coordinates file doesn't already exist

coordscell = load(coordsname,'-mat');
X = round(coordscell.coords(:,1));
Y = round(coordscell.coords(:,2));
molecules = coordscell.coords;
if min(molecules(:,1)) > xdim/2-1
% molecules(:,1)=molecules(:,1)-256;
molecules(:,1)=molecules(:,1)-xdim/2;
end

nummols=size(molecules,1);

else

nummols=size(RegMat,1);
molecules = zeros(0,2);
for n = 1:nummols
if RegMat(n,1)>edgePx && RegMat(n,1)edgePx && RegMat(n,2)190
% disp(RegMat(n,:));

% end
end
end
% for m = 1:nummols
% molnum(m,1)=m;
% end
% molecules = cat(2,molnum,RegMat);
nummols=size(molecules,1);

% clear RegMat RegArr

end

plot(molecules(:,1),molecules(:,2), 'wo');

hold off

startframe=1;
endframe=frames;

% **********************************************

%% Create intensity traces of molecules

if exist(tracesname) ~= 2 || exist(coordsname) ~= 2 || strcmp(overwrite,'yes')==1

tic;

X = zeros(nummols,1);
Y = zeros(nummols,1);

for m = 1:nummols

if strcmp(channel,'Cy5')==1
X(m) = molecules(m,1)+xdim/2;
else
X(m) = molecules(m,1);
end
Y(m) = molecules(m,2);

end


if endframe > frames
endframe=frames;
end


traces=zeros(nummols,frames);

for i = startframe:endframe

frame = i;

b = imread(FileName,'tif','Index',frame);

bnew = b;

for m = 1:nummols
% disp('Molecule:');
% disp(num2str(m));
% disp(num2str(X(m)));
% disp(num2str(Y(m)));
Xr(m)=round(X(m)+driftxy{1,1}(i,1)); %apply xy drift correction for molecule m with coordinates [X(m) Y(m)]
Yr(m)=round(Y(m)+driftxy{1,1}(i,2));
region = bnew(Yr(m)-10:Yr(m)+10,Xr(m)-10:Xr(m)+10); %extract pixel intensities for 21 x 21 pixel box, centered around molecule coordinates
region_bg = double(region); % convert to numbers
region_bg(9:13,9:13) = nan(5,5); %exclude 5x5 pixel box centered on molecule from region considered for background
% if i == startframe
background = nanmedian(reshape(region_bg,1,size(region_bg,1)*size(region_bg,2))); % Find median intensity value in the surrounding background region box (region_bg)
% end
particle = double(region(9:13,9:13))-background; % Subtract bg value from 5x5 box centered on molecule
I = sum(sum(particle)); %Sum background-corrected pixel intensities in the 5x5 box that contains the molecule
% traceframe = cat(2,i,I);
traces(m,i) = I; % Store molecule intensity for ith frame
end %for m = 1:nummol
% figure(1)
% imshow(particle/max(max(particle)), 'InitialMagnification', 1000);
% pause(0.02)
% imsum = imsum+bnew;
if mod(i,10)==0
disp('Done with frame:');
disp(frame);
% figure(1)
% region = region/max(max(bnew));
% imshow(region, 'InitialMagnification', 1000);
% figure(2)
% plot(trace(:,1),trace(:,2))
% xlim([0 max(trace(:,1))]);
% ylim([0 max(trace(:,2))]);
end
end %for i = startframe:endframe
% imsum = imsum/(endframe-startframe);
% imshow(imsum);
coords = cat(2,round(X),round(Y));
tfinish2 = toc;
disp(strcat('Finished generating trace data from',{' '},num2str(endframe-startframe+1),{' '},'frames in',{' '},num2str(tfinish2),{' '},'seconds.'));;
save(coordsname, 'coords');
save(tracesname, 'traces');
% Save coordinates as text file
save(coordsname2,'coords','-ascii') ;
else
coordscell = load(coordsname,'-mat');
X = coordscell.coords(:,1);
Y = coordscell.coords(:,2);
tracescell = load(tracesname,'-mat');
traces = tracescell.traces;
end %if

%% Analyze and/or save trace data
m = 1;
% mm = 1;
tprob_list = zeros(0,1);
firstmol = 1;
while m <= nummols
if m < 1
m = 1;
end
if size(molnum,1)>size(molintervals,2) % If the molecule list is longer than the list of intervals, go to the next molecule in the list that has no corresponding intervals
mm = size(molintervals,2)+1;
m = molnum(mm,1);
end
figure(1)
imshow(aImcut2);
hold on
plot(molecules(:,1),molecules(:,2), 'wo');
plot(molecules(m,1),molecules(m,2), 'ro');
hold off
% figure(2)
figure(2)
% [Ihist,Ibins]=hist(traces(m,:)-min(min(traces(m,:))),15);
[Ihist,Ibins]=hist(traces(m,:),30);
bar(Ibins,Ihist);


figure(3)

% if strcmp(channel,'Cy3')==1
plot(traces(m,:), plotcolor, 'LineWidth', 2);
% else
% plot(traces(m,:), 'r', 'LineWidth', 2);
% end
xlim([startframe-1 endframe+1]);
ylim([-200 max(traces(m,:))+100]);
xlabel('Frame');
ylabel('Fluorescence (A.U.)');
text(0.45, 0.9, sprintf('Candidate %d',m),'units', 'normalized', 'FontSize', 16, 'FontWeight', 'bold');
text(0.45, 0.8, strcat('(',num2str(X(m)),',',num2str(Y(m)),')'),'units','normalized','FontSize', 16, 'FontWeight', 'bold');
% hold on


% disp(sprintf('Candidate %d',m));
% disp(strcat('(',num2str(X(m)),',',num2str(Y(m)),')'));

if strcmp(analysis,'manual')==1 %Display each trace to let user inspect and analyze
user1 = input('Command? n = next, p = previous, s = skip to trace, j = save image, a = analyze (n)', 's');
elseif strcmp(analysis,'auto')==1 % Automatically save all traces
user1 = 'n';
else
user1 = 'a';
end

if strcmp(user1,'p') == 1
m = m-1;
hold off
elseif strcmp(user1,'s') == 1
m = input('What trace would you like to skip to?');
if m < 1
m = 1;
elseif m > nummols
m = nummols;
end
elseif strcmp(user1,'j')==1
figure(2)
pause(0.1);
set(gcf, 'PaperPositionMode', 'auto');%Make sure on-screen dimensions are preserved in output file
print('-f2', '-r600', '-djpeg', strcat(FileName(1,1:(size(FileName,2)-4)),'_cand_',num2str(m),'_',channel,'_hist.jpg'));
figure(3)
pause(0.1);
set(gcf, 'PaperPositionMode', 'auto');%Make sure on-screen dimensions are preserved in output file
print('-f3', '-r300', '-djpeg', strcat(FileName(1,1:(size(FileName,2)-4)),'_cand_',num2str(m),'_',channel,'_trace.jpg'));
m = m+1;
elseif strcmp(user1,'a') == 1
FRET = mat2cell(traces(m,:),1);
% FRET = mat2cell(traces(m,:));
% FRET = mat2cell(traces(m,1:500));
% vbFRET_no_gui;
hold on
plot(x_hat{1,3});
% plot(abs(x_hat{1,3}-traces(m,:)'),'g');
hold off
figure(4)
[pop,state]=hist(x_hat{1,3});
bar(state,pop);
[C,I] = max(bestOut{1,3}.m);
Hind = I;
[C2,I2] = min(bestOut{1,3}.m);
Lind = I2;
Mind = 6-I-I2;
Htmat = bestOut{1,3}.Wa(Hind,:);
Htmat(Hind)=0; % Remove the high-to-high transition from consideration
[dummy,H_fastest] = max(Htmat); % Find the fastest transition to/from the high state
ycenter = 0;
if size(molnum,1)>size(molintervals,2) || strcmp(analysis,'manual')==1 % If this is not the first set to be analyzed, analyze all molecules from molnum list
discard = 'n';
if bestOut{1,3}.m(H_fastest) mincounts
if bestOut{1,3}.m(H_fastest)mincounts % Otherwise, if the mid state is at least mincounts in intensity, take this as the true single-molecule signature
ycenter = bestOut{1,3}.m(Mind);
tprob = sum(bestOut{1,3}.Wa(Mind,:))-bestOut{1,3}.Wa(Mind,Mind);
tprob = tprob/frames;
discard = 'n';
else
discard = 'y';
end
else
discard = 'y'; % Otherwise, discard this molecule
ycenter = 0;
end
else
discard = 'y';
ycenter = 0;
end
% ycenter = mincounts;
% for bb = 1:size(state,2)
% if state(1,bb) ybot && traces(m,n) < ytop
if x_hat{1,3}(n) > ybot && x_hat{1,3}(n) < ytop
goodframes = cat(1,goodframes,n);
end
end
if size(goodframes,1)~=0
intervals = zeros(0,2);
k=1;
intervals(k,1)=goodframes(k,1);
for p = 1:size(goodframes,1)-1;
if goodframes(p+1,1)==goodframes(p,1)+1
else
intervals(k,2)=goodframes(p,1);
k = k+1;
intervals(k,1)=goodframes(p+1,1);
end
end
intervals(k,2)=goodframes(size(goodframes,1),1);
dwellt_on = zeros(size(intervals,1),1);
dwellt_off = zeros(size(intervals,1)-1,1);
for mol = 1:size(intervals,1)
dwellt_on(mol) = intervals(mol,2)-intervals(mol,1) + 1;
if mol > 1
dwellt_off(mol-1) = intervals(mol,1)-intervals(mol-1,2) - 1;
end
end
% intervals
% disp(sprintf('[%d %d;', intervals(1,1), intervals(1,2)));
% for s = 2:size(intervals,1)-1
% disp(sprintf('%d %d;', intervals(s,1),intervals(s,2)));
% end
% disp(sprintf('%d %d];', intervals(size(intervals,1),1), intervals(size(intervals,1),2)));
% Wanorm = bestOut{1,3}.Wa/max(max(bestOut{1,3}.Wa))
% Wa = bestOut{1,3}.Wa

int_string = sprintf('[%d %d;', intervals(1,1), intervals(1,2));
for s = 2:size(intervals,1)-1
string1 = sprintf('%d %d;', intervals(s,1),intervals(s,2));
int_string = cat(2,int_string,string1);
end
int_string = cat(2,int_string,sprintf('%d %d];', intervals(size(intervals,1),1), intervals(size(intervals,1),2)));
disp('intervals:');
disp(num2str(size(intervals,1)))
disp(int_string);
% pause;
% offdiagsum = sum(sum(bestOut{1,3}.Wa))-(bestOut{1,3}.Wa(1,1)+bestOut{1,3}.Wa(2,2)+bestOut{1,3}.Wa(3,3)); %Sum of off-diagonal transition probabilities

% offdiagsum = sum(sum(bestOut{1,2}.Wa))-(bestOut{1,2}.Wa(1,1)+bestOut{1,2}.Wa(2,2));
% keepyn = input('Keep this molecule? (y)','s');
% meddeviation = median(abs(x_hat{1,2}-traces(m,:)'))
if strcmp(discard,'n')==1
tprob_list = cat(1,tprob_list,tprob);
if strcmp(analyze_all,'y')==1
keepyn='y';
elseif tprob < 0.025 && size(molnum,1)==size(molintervals,2) % Check that transitions are frequent
keepyn = 'n';
disp('Do not keep');
% pause(0.5);
else
keepyn = 'y';
disp('Keep');
end
else
keepyn = 'n';
disp('Do not keep');
end
% keepyn = 'n';
if strcmp(keepyn,'n')==0

if size(molnum,1)==size(molintervals,2)
molnum(mm,1) = m;
molcoords(mm,1:2)=[X(m) Y(m)];
end
molintervals{mm}=intervals;
mm = mm + 1;
figure(2)
pause(0.1);
set(gcf, 'PaperPositionMode', 'auto');%Make sure on-screen dimensions are preserved in output file
print('-f2', '-r600', '-djpeg', strcat(FileName(1,1:(size(FileName,2)-4)),'_cand_',num2str(m),'_',channel,'_hist.jpg'));
figure(3)
pause(0.1);
set(gcf, 'PaperPositionMode', 'auto');%Make sure on-screen dimensions are preserved in output file
print('-f3', '-r300', '-djpeg', strcat(FileName(1,1:(size(FileName,2)-4)),'_cand_',num2str(m),'_',channel,'_trace.jpg'));
save(strcat(FileName(1,1:(size(FileName,2)-4)),'_',channel,'_molintervals.mat'),'molintervals');
save(strcat(FileName(1,1:(size(FileName,2)-4)),'_',channel,'_molnum.mat'),'molnum');
save(strcat(FileName(1,1:(size(FileName,2)-4)),'_',channel,'_molcoords.mat'),'molcoords');
disp('Recorded molecule.');
else
disp('Discarded.');
end
% hold off
% user1 = input('Command? n = next, p = previous (n)', 's');
pause(0.5);
user1 = 'n';
if strcmp(user1,'p')==1
m = m-1;
% hold off
else
m = m+1;
% hold off
end
else
% hold off
m = m+1;
end
else
m = m+1;
% hold off
end

end



else

disp('Error: Check averaging interval. Movie was NOT analyzed.');

end
end %for n = 1:nfiles

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