non-linear regression software that employed the marquardt algorithm and the least squares method  (GraphPad Software Inc)

Journal: Journal of Biomedical Optics

Article Title: Discrimination of lipid composition and cellular localization in human liver tissues by stimulated Raman scattering microscopy

doi: 10.1117/1.JBO.29.1.016008

Figure Lengend Snippet: SRS spectra of cholesterol crystal, saturated CE, unsaturated CE, and TAG in the C–H region (a) and fingerprint region (b). Shaded lines: main spectral differences between saturated CE (green) and unsaturated CE (blue). (c) Ternary plot of the calculated fractions of three-component mixtures using SRS imaging and the spectral unmixing algorithm. Black circles: lipid standard mixture fractions. Red dots: calculated lipid fractions.

Article Snippet: A nonnegative linear least-squares spectral unmixing algorithm (MATLAB Isqnonneg) was applied to both the C–H and the fingerprint region spectra for spectral unmixing of each pixel in lipid droplets.

Techniques: Imaging

Journal: Journal of Biomedical Optics

Article Title: Discrimination of lipid composition and cellular localization in human liver tissues by stimulated Raman scattering microscopy

doi: 10.1117/1.JBO.29.1.016008

Figure Lengend Snippet: Schematic diagram of the image analysis pipeline and representative SRS images and lipid composition maps of a liver tissue section from a patient with NASH. From the SRS images at 2850 (a) and 2930 cm − 1 (b), we use the R 2850 / 2930 ratiometric image to create a binary lipid map (c). We then perform the linear least-squares spectral unmixing analysis on both C–H and fingerprint region hyperspectral SRS images of lipid molecules (d) and (e) to generate mole percentage maps of free cholesterol, saturated CE, unsaturated CE, and TAG (f)–(i). The percentage maps of free cholesterol and saturated CE are also shown with a scale extending only from 0% to 10% (for cholesterol) and 0% to 40% (for saturated CE) rather than for 0% to 100% to make more apparent the distribution of these lipids that are present in much lower relative percentages than unsaturated CEs and TAGs. Scale bar: 50 μ m .

Article Snippet: A nonnegative linear least-squares spectral unmixing algorithm (MATLAB Isqnonneg) was applied to both the C–H and the fingerprint region spectra for spectral unmixing of each pixel in lipid droplets.

Techniques:

Journal: Cytometry. Part A : the journal of the International Society for Analytical Cytology

Article Title: Assessing FRET using Spectral Techniques

doi: 10.1002/cyto.a.22340

Figure Lengend Snippet: A: FRET spectrum (red solid line) for basal (0 μM) cAMP from Figure 2 showing estimated contributions of CFP (long-dash blue line) and YFP (short-dash green line) calculated using linear unmixing; B: the sum of the estimated CFP and YFP contributions (dashed blue line) very closely matches the FRET spectrum from A (solid red line); C: FRET efficiency calculated using the CFP peak intensity (473 nm) and the YFP peak intensity (525 nm); D: FRET efficiency calculated by linear unmixing, as shown in A, and dividing the CFP abundance by the CFP+YFP abundance (black squares); the linear unmixing FRET has been further corrected by estimating the percent of the acceptor signal that is due to direct excitation and then subtracting this percent from the total acceptor signal before dividing by the donor signal (red triangles), as shown in Eq. (13). Normalized FRET responses are shown in Figure 4. [Color figure can be viewed in the online issue which is available at wileyonlinelibrary.com]

Article Snippet: Images were analyzed using a custom script incorporating a nonnegative linear least-squares unmixing algorithm (lsqnonneg, MATLAB).

Techniques:

Journal: Cytometry. Part A : the journal of the International Society for Analytical Cytology

Article Title: Assessing FRET using Spectral Techniques

doi: 10.1002/cyto.a.22340

Figure Lengend Snippet: 1-FRET response normalized to minimum and maximum FRET levels. Error bars indicate the standard error-of-the-mean (n = 3) for each cAMP concentration. A: one-filter set method; B: two-filter set method; C: three-filter set method; D: three-filter set method and corrected for changes in CFP concentration; E: YFP-CFP peak intensity ratio; F: linear unmixing YFP-CFP ratio; G: linear unmixing YFP-CFP ratio, corrected for direct excitation of YFP. Note that panels A and C represent FRET indices, whereas panels B, D, E, F, and G represent FRET efficiencies. [Color figure can be viewed in the online issue, which is available at wileyonlinelibrary.com]

Article Snippet: Images were analyzed using a custom script incorporating a nonnegative linear least-squares unmixing algorithm (lsqnonneg, MATLAB).

Techniques: Concentration Assay

Journal: Cytometry. Part A : the journal of the International Society for Analytical Cytology

Article Title: Assessing FRET using Spectral Techniques

doi: 10.1002/cyto.a.22340

Figure Lengend Snippet: Comparison of different FRET measurement methods.

Article Snippet: Images were analyzed using a custom script incorporating a nonnegative linear least-squares unmixing algorithm (lsqnonneg, MATLAB).

Techniques: Comparison

Journal: Cytometry. Part A : the journal of the International Society for Analytical Cytology

Article Title: Assessing FRET using Spectral Techniques

doi: 10.1002/cyto.a.22340

Figure Lengend Snippet: Hyperspectral confocal microscope images were unmixed to calculate fluorophore intensities and the FRET efficiency. A: Raw hyperspectral confocal microscope image (all wavelength bands summed) of HEK-293 cells expressing the CFP-Epac-YFP probe; B: the spectral library used for linear unmixing; nonnegatively constrained linear unmixing was used to calculate images for C: Hoechst, D: CFP, and E: YFP; F: the unmixed CFP and YFP images were summed to locate expressing (transfected) cells; G: the FRET efficiency was calculated using equation 23 (note that this image was later masked so that only regions with sufficient signal were used for single-cell FRET calculations, as shown in Figure 6); H: the root-mean-square (RMS) percent error associated with linear unmixing was calculated as the RMS residual from unmixing divided by the RMS signal of the original spectral image. [Color figure can be viewed in the online issue which is available at wileyonlinelibrary.com]

Article Snippet: Images were analyzed using a custom script incorporating a nonnegative linear least-squares unmixing algorithm (lsqnonneg, MATLAB).

Techniques: Microscopy, Expressing, Transfection