Journal: Methods in molecular biology (Clifton, N.J.)

Article Title: Intravital Imaging of Tumor Cell Motility in the Tumor Microenvironment Context

doi: 10.1007/978-1-4939-7701-7_14

Figure Lengend Snippet: Intravital Systems Microscopy: Multiparametric SVM classification of tumor cell locomotion and microenvironmental parameters (a) Identifying and quantifying fast-locomoting cells. Left panels, raw images of an individual z-section from the 4D stack (at 0 and 60 min; 0′ and 60′). Middle panels, 0′ was subtracted from 60′, resulting in Δ60 (top); image was then thresholded/binarized (bottom). Right panels, results of motility analysis including quantification of fast locomoting cells (top) and the overlay with the 0′ image (bottom). Scale bar 50 μm. (b) Identifying and quantifying invadopodia in slow locomoting cells. Raw images of a cell at time 0, with fully extended invadopodium at 3 min and partially retracted invadopodium at 15 min. Overlays Δ3 and Δ15 show invadopodia extension highlighted in magenta. Scale bar 10 μm. (c) Binarized images used for extraction of microenvironmental parameters. Image in Fig. 1c was separated and thresholded, resulting in binary images of collagen (magenta), tumor cells (green), macrophages (cyan) and blood vessels (red). (c′) Collagen fiber map (left) and dimensionless straightness histogram (right) from ctFIRE software. (d) 3D projection of SVM classification results. Red spheres denote slow locomotion, blue-fast locomotion, green- misclassifications. The size of the spheres indicates the number of locomoting cells in the FOV. Dmax (μm) stands for the diameter of the largest blood vessel in the FOV, macrophages (%) and collagen (%) stand for the thresholded area in respective channels

Article Snippet: The ctFIRE software requires MATLAB compiler runtime (MCR 7.17 2012a) installation.

Techniques: Microscopy, Extraction, Software

Journal: Nucleic Acids Research

Article Title: TeloTool: a new tool for telomere length measurement from terminal restriction fragment analysis with improved probe intensity correction

doi: 10.1093/nar/gkt1315

Figure Lengend Snippet: Typical work flow for TRF analysis with TeloTool. Raw TRF scans in 8- or 16-bit TIFF formats can be loaded directly into TeloTool and easily rotated and cropped for analysis. Lanes are then detected automatically, ‘Filter for lane number’ and ‘Filter for lane width’ sliders can be used to fine tune lane recognition. Marker bands are then detected and a fit is applied to determine the molecular weight over the gel image, incorrectly identified bands must be removed. A Gauss curve is fitted to each lane and data are automatically subject to probe intensity corrections. The user can choose to display the resulting data for the fitted raw data or the probe-corrected data. After analysis, data are presented in a graph displaying mean and range values along with exportable length values in Microsoft Excel format.

Article Snippet: TeloTool was developed in Matlab (Mathworks) and runs on a 64 bit windows platform, which requires installation of the MATLAB Compiler Runtime [version 7.17 (R2012a), freely available at the Mathworks web page http://www.mathworks.com/products/compiler/mcr/ ].

Techniques: Marker, Molecular Weight

Journal: Nucleic Acids Research

Article Title: TeloTool: a new tool for telomere length measurement from terminal restriction fragment analysis with improved probe intensity correction

doi: 10.1093/nar/gkt1315

Figure Lengend Snippet: Optional probe correction by TeloTool. The intensity analysis of one lane usually results in an asymmetric profile ( A ). The falling flank (high molecular weight) is especially heterogeneous and prone to local maxima. The binding probability of the probe is nearly linear in the range of smaller telomeres, i.e. they contain the largest number of validly bound probes. Therefore, the rising flank of the data is used to mathematically model the intensity-corrected profile. First, the rising flank is extracted, mirrored on the x -axis (correction of profile symmetry based on the rising flank) and subsequently fitted with a first-order Gaussian function ( B ). The Gaussian fit is used for the correction of the falling flank of the intensity profile. TeloTool provides two different correction methods. (i) For each point of the falling flank, TeloTool calculates the mean between the fitted Gaussian function and the original data. (ii) TeloTool mixes two different Gaussian functions; one is obtained by fitting the original data and a second Gaussian is fitted into the mirrored left flank profile. Finally, the intensity-corrected profile ( C ) is fitted with another Gaussian function and the mean, sigma and fit quality of the fit is displayed in the result section ( D ). µ r and µ c —mean telomere length for the corrected (c) and uncorrected (r) data set.

Article Snippet: TeloTool was developed in Matlab (Mathworks) and runs on a 64 bit windows platform, which requires installation of the MATLAB Compiler Runtime [version 7.17 (R2012a), freely available at the Mathworks web page http://www.mathworks.com/products/compiler/mcr/ ].

Techniques: Molecular Weight, Binding Assay

Journal: Nucleic Acids Research

Article Title: TeloTool: a new tool for telomere length measurement from terminal restriction fragment analysis with improved probe intensity correction

doi: 10.1093/nar/gkt1315

Figure Lengend Snippet: Comparison of intergel variance between TeloTool and Telometric and effects of probe correction. ( A ) To test the variation of the same sample between different TRF gels, 31 lanes containing the Col-0 accession have been analyzed by both programs. TeloTool’s mean intergel variation for corrected data does not differ from Telometric’s median intergel variation. However, there are vast differences between intergel variation for uncorrected data (KS-Test, two-sided, α at 0.05, *** P < 0.005). ( B ) Comparison of the statistical parameters of both programs (visual display of the data can be found in panel A). TM—Telometric; TT—TeloTool, mu—mean of all the mean telomere lengths, sigma—standard deviation and SIR—semi-interquartile range. ( C ) Effect of probe correction from both programs. The corrected and uncorrected data of five different accessions was plotted and the linear regression graph calculated. Telometric’s telomere length values, calculated from probe-corrected data, decrease in size in comparison with the uncorrected data. This suggests that the formula that calculates the probe correction in Telometric leads to a vast underestimation of telomere length at longer telomeres. The respective data can be found in Supplementary Table S1 . PCC, Pearson’s correlation coefficient.

Article Snippet: TeloTool was developed in Matlab (Mathworks) and runs on a 64 bit windows platform, which requires installation of the MATLAB Compiler Runtime [version 7.17 (R2012a), freely available at the Mathworks web page http://www.mathworks.com/products/compiler/mcr/ ].

Techniques: Standard Deviation

Journal: Nucleic Acids Research

Article Title: TeloTool: a new tool for telomere length measurement from terminal restriction fragment analysis with improved probe intensity correction

doi: 10.1093/nar/gkt1315

Figure Lengend Snippet: Visualization of measurements from TeloTool and Telometric on TRF smears ( A ) Comparison of the maximum intensity, TeloTool mean and Telometric median in representative Southern blots of the six tested accessions (Col-0, Pro-0, Est-1, Cvi-1, Ler-2). Telometric’s calculations lead to greater median values for the uncorrected (raw) data and smaller values for the probe-corrected data when compared with the respective mean estimated by TeloTool. The quantification of this effect can be found in C. The colored dots represent respective mean or median parameters calculated by the two programs. ( B ) The intensity profiles with the fitted Gaussian curves for the Ler-2, Col-0 and Pro-0 lanes in A are displayed. The color-coded parameters describe the respective Gaussian curves. TeloTool’s probe correction leads to the production of distribution data that can be better described by a Gaussian function, i.e. the fit quality of the uncorrected data (Q r ) compared with the corrected one (Q c ) increases. kb—kilobases; µ r and µ c —mean length of the telomere for the corrected (c) and uncorrected (r) data set. s r and s c —standard deviation of telomere length; dashed black line—raw data; solid black line—Gaussian curve fitted to raw data; dashed red line—probe intensity corrected data; solid black line—Gaussian curve fitted to corrected data.

Article Snippet: TeloTool was developed in Matlab (Mathworks) and runs on a 64 bit windows platform, which requires installation of the MATLAB Compiler Runtime [version 7.17 (R2012a), freely available at the Mathworks web page http://www.mathworks.com/products/compiler/mcr/ ].

Techniques: Standard Deviation

Journal: Nucleic Acids Research

Article Title: TeloTool: a new tool for telomere length measurement from terminal restriction fragment analysis with improved probe intensity correction

doi: 10.1093/nar/gkt1315

Figure Lengend Snippet: Reproducibility of measurements from TeloTool. The same gel with a TRF for Col-0 ( A ) and Pro-0 ( B ) was analyzed 10 times with both programs and the resulting mean telomere lengths were plotted as a box plot. Measurements from TeloTool were found to be highly reproducible, whereas mean and median results calculated by Telometric varied by as much as 2 kb. TM, Telometric; TT, TeloTool, mu, mean of all the mean telomere lengths, sigma, standard deviation; SIR, semi-interquartile range.

Article Snippet: TeloTool was developed in Matlab (Mathworks) and runs on a 64 bit windows platform, which requires installation of the MATLAB Compiler Runtime [version 7.17 (R2012a), freely available at the Mathworks web page http://www.mathworks.com/products/compiler/mcr/ ].

Techniques: Standard Deviation