workflow integrated raw image preprocessing  (MathWorks Inc)

Journal: bioRxiv

Article Title: A Set of Novel Far-Red Fluorescent Proteins for Temporal Domain Multiplexing and Super-Resolution Imaging

doi: 10.1101/2025.06.01.657196

Figure Lengend Snippet: TDM imaging of mammalian cells. (a) Workflow of temporal domain multiplexing, (i) Multi-frame time series acquisition of Hella cell co-transfect with two FPs expressed in the nucleus and cytoplasm, the Raw data set is processed by background subtraction and denoising to produce the input dataset, the input dataset is uploaded to the Temporal domain multiplexing MATLAB script for unmixing to produced output images of individual channels. (b) Phasor plot and lifetime for Mito-mCardinal (green) (1.593ns) and H2B-mfRFP (red) (0.922ns) (c) Reference photobleaching curve trace for Mito-mCardinal (green) and H2B-mfRFP (red). (d) Unmixing results of FLIM microscopy (top), The raw image was unmix using phasor plot analysis resulting in two different FPs Mito-mCardinal (green) and H2B-mfRFP (red) TDM (bottom) unmixing result from the raw image (gray) was supplied to MATLAB script for linear unmixing of each reference at each pixel (the input images are brief movies obtained from single channel imaging containing two different but spectrally similar (far-red) fluorescent proteins. the output image is a composite of Mito-mCardinal (green) and H2B-mfRFP (red). Scale bar 50µm. (e) Cross-talk plot showing the fluorescence intensity contribution of mito (magenta) and H2B (red) in FLIM mito-mCardinal channel (above) and FLIM mfRFP channel (below), in each plot, the y-axis represents the fluorescence intensity of selected pixel, and the x-axis represent the fluorophore in each channel. (f) Per pixel Line profile analysis of FLIM raw image (grey) and TDM raw image (orange) Statistics Spearman correlation (0.95859) the y-axis represents the normalized fluorescence intensity, and the x-axis represent the length (g) Per pixel Line profile analysis of FLIM mito-mCardinal image (grey) and TDM mito-mCardinal image (orange) Statistical Spearman correlation (0.88849) the y-axis represents the normalized fluorescence intensity, and the x-axis represent the length. (h) Per pixel Line profile analysis of FLIM H2B-mCardinal image (grey) and TDM H2B-mCardinal image (orange) Statistics Spearman correlation (0.881771) the y-axis represents the normalized fluorescence intensity, and the x-axis represent the length. (i) Cross-talk plot showing the fluorescence intensity contribution of mito (magenta) and H2B (red) in TDM mito-mCardinal channel (above) and TDM mfRFP channel (below), in each plot, the y-axis represents the fluorescence intensity of selected pixel, and the x-axis represent the fluorophore in each channel. (j) Simulation of Temporal domain multiplexing with two far-red fluorescent proteins, A 21-frame line plot obtained from previously acquired HeLa cell ROI illustrating the unique photobleaching curve of mfRFP and mfRFP-A that contribute to the overall signals, Data are shown as ± mean standard deviation SD. Illumination(594nm at 100% laser power, 500ms Exposure time. The line plot is subsequently used as reference trace for the unmixing algorithms (k)Acquired brief movie (Time series image) of HeLa cell expressing H2B-mfRFP and mfRFP-A-cytoplasm acquired using 594nmSoRA excitation laser channel. The unmixing result (MATLAB) the signal contribution of each FPS (OUTPUT) to their respective corresponding structures and the merged image depicting the complete HeLa cells structure (H2B and cytoplasm). Note that the image colors used above red-H2B and green-cytoplasm are pseudo colors to enhance visualization and both H2B and the cytoplasm were indeed red. Scale bar 25μm. (l) crosstalk plot showing the fluorescence intensity contributions of mfRFP and mfRFP-A in the TDM output channels, the y-axis represents the fluorescence intensity of selected pixel, and the x-axis represent the fluorophore in each channel. (m) Acquired photobleaching reference traces of mCardinal and mfRFP-A in life HeLa-cell, n=5 cells from 2 briefed movies, 41 frames long, from 1 cell culture batch. Data are shown as ± mean standard deviation SD. Illumination(594nm at 100% laser power, 500ms Exposure time (n), The input image was first refined via background subtraction (ImageJ) followed by denoising (Nikon Element AR software), the input image was supplied to MATLAB script for linear unmixing of each reference at each pixel (the input images are brief movies obtained from single channel imaging(red), containing two different but spectrally similar (far-red) fluorescent proteins. the output image is a composite of mfRFP-H2B and Mito-mCardinal. (Scale-Bar 100um). (o) crosstalk plot showing the fluorescence intensity contributions of mCardinal and mfRFP-A in the TDM output channels, the y-axis represents the fluorescence intensity of selected pixel, and the x-axis represent the fluorophore in each channel. (p) Simulation of TDM using Confocal microscope acquired photobleaching reference traces of H2B-mCardinal, H2B-mRuby2 and H2B-mfRFP in fixed HeLa-cell, 25 frames long (q), the input image was supplied to MATLAB script for linear unmixing of each reference at each pixel (the input images are brief movies obtained from single channel imaging(red), containing three different but spectrally similar (far-red) fluorescent proteins of H2B-mCardina, H2B-mruby2 and H2B-mfRFP, and overlay image is a composite. (Scale-Bar 100um). (r) crosstalk plot showing the fluorescence intensity contributions of H2B-mCardinal, H2B-mRuby2 and H2B-mfRFPin the TDM output channels, the y-axis represents the fluorescence intensity of selected pixel, and the x-axis represent the fluorophore in each channel. (s) Temporal domain multiplexing of six z-plain projection of two red fluorescent proteins(FPs). (A) Acquired photobleaching reference traces of mfRFP and mCardinal in life HeLa-cell, n=10 cells from 2 briefed movies, 41 frames long, from 1 cell culture batch. Data are shown as ± mean standard deviation SD. Illumination(594nm at 100% laser power, 500ms Exposure time). (t) Demultiplexing of co-expressed Keratin-targeted mCardinal and H2B-targeted mfRFP in HeLa cell. the raw image (top) was supplied to MATLAB script for linear unmixing of each reference at each pixel, the Merge TDM image (bottom) is a composite of mfRFP-H2B and mCardinal-Keratin. The output image mfRFP-H2B and mCardinal-Keratin are products of linear un-mixing (Scale-Bar 100um) (u) 3D image obtained from the z-stack of the unmixed z-planes images.

Article Snippet: The workflow integrated raw image preprocessing, computational decomposition, and validation using MATLAB (MathWorks R2020b), Nikon NIS-Elements AR (version 5.30.00 64-bit), and Leica STELLARIS FLIM microscopy.

Techniques: Imaging, Multiplexing, Produced, Microscopy, Fluorescence, Standard Deviation, Expressing, Cell Culture, Software

Journal: Biomedicines

Article Title: Comparative Proteomic Assessment of Normal vs. Polyhydramnios Amniotic Fluid Based on Computational Analysis

doi: 10.3390/biomedicines10081821

Figure Lengend Snippet: Comparative analysis of 2DE proteome maps characteristic for normal and polyhydramnios pregnancies. The proteins were resolved by 2DE, pH range 3–11, and Excel Gel SDS, gradient 8–18%. 2DE images of proteins of amniotic fluid of normal pregnancy (AFN, G1; blue in G1 + G2 and amniotic fluid of polyhydramnios pregnancy (AFP, G2; orange in G1 + G2) were superposed and presented in G1 + G2. Arrows and numbers in the 2DE maps indicate the positions of proteins supplied to MALDI-TOF MS/MS and identified. Spot labels are the same as in . Molecular weight (Mw) markers are presented on the left. Representative images from one of three experiments showing similar results are shown.

Article Snippet: The prototype is executed in MatlabTM environment (The MathWorks, Inc., Natick, MA, USA) and has integral original 2DE gel image processing algorithms as tools for specific tasks: Image preparation tools: image cropping (to remove excess areas), spot labelling, master gel selection (to align the group of gels), molecular mass markers’ calibration (to delineate area of MM marker and input of MM values), pI calibration (to input positions of known pI values); Image preprocessing related tools: image smoothing (to eliminate impulse noise), background elimination (to remove variations of background staining), individual image warping (to straighten protein migration paths); Image segmentation tools: 2DE image splitting (to split image into primary segments), segmented area evaluation (to highlight uncertain segmentations of protein spots for the user), editing of segments (to manually edit protein spots in order to remove false negatives and positives of segmentation by merging, splitting, adding, or removing areas); Image alignment tools: initial registration (to automatically detect some high confident control points for initial image registration), spot pairing and concluding image alignment (to find correspondences between spots), manual editing of alignment vectors (to remove mismatches and add new matches between images); Quantitative analysis tools: spot quantification (to measure normalized quantities of spots), changes evaluation (to calculate change ratios); Visualization tools: 3D viewer (to display small area of image as surface), image fusion (to display overlay of two images using pseudocolors).

Techniques: Tandem Mass Spectroscopy, Molecular Weight

Journal: Biomedicines

Article Title: Comparative Proteomic Assessment of Normal vs. Polyhydramnios Amniotic Fluid Based on Computational Analysis

doi: 10.3390/biomedicines10081821

Figure Lengend Snippet: The summarized search results (by UniProt, Expasy) of proteins identified from 2DE gels representing protein maps of amniotic fluid of normal and polyhydramnios pregnancies fractionated in the pI 3–11 range. An increase in spot intensity yields a positive fold-change and a decrease accordingly a negative fold-change in AFN/AFP (marked as G1/G2). a ) AC—accession number; b ) Score—protein Score C.I. %; c ) Match—Matching (sequence coverage, %); d ) TP—Theoretical Peptides; e ) DP—Digest Peptides; f ) FC—Fold Change.

Article Snippet: The prototype is executed in MatlabTM environment (The MathWorks, Inc., Natick, MA, USA) and has integral original 2DE gel image processing algorithms as tools for specific tasks: Image preparation tools: image cropping (to remove excess areas), spot labelling, master gel selection (to align the group of gels), molecular mass markers’ calibration (to delineate area of MM marker and input of MM values), pI calibration (to input positions of known pI values); Image preprocessing related tools: image smoothing (to eliminate impulse noise), background elimination (to remove variations of background staining), individual image warping (to straighten protein migration paths); Image segmentation tools: 2DE image splitting (to split image into primary segments), segmented area evaluation (to highlight uncertain segmentations of protein spots for the user), editing of segments (to manually edit protein spots in order to remove false negatives and positives of segmentation by merging, splitting, adding, or removing areas); Image alignment tools: initial registration (to automatically detect some high confident control points for initial image registration), spot pairing and concluding image alignment (to find correspondences between spots), manual editing of alignment vectors (to remove mismatches and add new matches between images); Quantitative analysis tools: spot quantification (to measure normalized quantities of spots), changes evaluation (to calculate change ratios); Visualization tools: 3D viewer (to display small area of image as surface), image fusion (to display overlay of two images using pseudocolors).

Techniques: Sequencing, Clinical Proteomics, Membrane

Journal: Biomedicines

Article Title: Comparative Proteomic Assessment of Normal vs. Polyhydramnios Amniotic Fluid Based on Computational Analysis

doi: 10.3390/biomedicines10081821

Figure Lengend Snippet: The summarized search results (by UniProt, Expasy) of proteins identified from 2DE gels representing protein maps of amniotic fluid of polyhydramnios pregnancy fractionated in pI 3–11 and pI 4–7 range. a ) AC—accession number; b ) Score—protein Score C.I. %; c ) Match—Matching (sequence coverage, %); d ) TP—Theoretical Peptides; e ) DP—Digest Peptides; f ) FC—Fold Change.

Article Snippet: The prototype is executed in MatlabTM environment (The MathWorks, Inc., Natick, MA, USA) and has integral original 2DE gel image processing algorithms as tools for specific tasks: Image preparation tools: image cropping (to remove excess areas), spot labelling, master gel selection (to align the group of gels), molecular mass markers’ calibration (to delineate area of MM marker and input of MM values), pI calibration (to input positions of known pI values); Image preprocessing related tools: image smoothing (to eliminate impulse noise), background elimination (to remove variations of background staining), individual image warping (to straighten protein migration paths); Image segmentation tools: 2DE image splitting (to split image into primary segments), segmented area evaluation (to highlight uncertain segmentations of protein spots for the user), editing of segments (to manually edit protein spots in order to remove false negatives and positives of segmentation by merging, splitting, adding, or removing areas); Image alignment tools: initial registration (to automatically detect some high confident control points for initial image registration), spot pairing and concluding image alignment (to find correspondences between spots), manual editing of alignment vectors (to remove mismatches and add new matches between images); Quantitative analysis tools: spot quantification (to measure normalized quantities of spots), changes evaluation (to calculate change ratios); Visualization tools: 3D viewer (to display small area of image as surface), image fusion (to display overlay of two images using pseudocolors).

Techniques: Sequencing, Membrane

Journal: Biomedicines

Article Title: Comparative Proteomic Assessment of Normal vs. Polyhydramnios Amniotic Fluid Based on Computational Analysis

doi: 10.3390/biomedicines10081821

Figure Lengend Snippet: Comparative analysis of 2DE protein maps corresponding amniotic fluid of polyhydramnios pregnancy fractionated in different pI range. ( A ) proteins corresponding amniotic fluid of polyhydramnios pregnancy (AFP), fractionated in different pI range: pI 3–11 (G2, blue in G2 + G3) and pI 4–7 (G3, orange in G2 + G3) range and Excel Gel SDS, gradient 8–18%. Arrows and numbers in the 2DE maps indicate the positions of proteins supplied to MALDI-TOF MS/MS and identified. Spot labels for the proteins fractionated in the range pI 3–11 are the same as in . Spot labels for the proteins fractionated in the range pI 4–7 are the same as in . ( B ) Computational analysis of several protein groups is performed to evaluate their distribution in different pI value ranges. It shows that the same protein level with different pI changes because of modification level. In , the proteins’ spot distribution corresponding to the different modification level is presented (column— Share, %). Representative images from one of three experiments showing similar results are shown.

Article Snippet: The prototype is executed in MatlabTM environment (The MathWorks, Inc., Natick, MA, USA) and has integral original 2DE gel image processing algorithms as tools for specific tasks: Image preparation tools: image cropping (to remove excess areas), spot labelling, master gel selection (to align the group of gels), molecular mass markers’ calibration (to delineate area of MM marker and input of MM values), pI calibration (to input positions of known pI values); Image preprocessing related tools: image smoothing (to eliminate impulse noise), background elimination (to remove variations of background staining), individual image warping (to straighten protein migration paths); Image segmentation tools: 2DE image splitting (to split image into primary segments), segmented area evaluation (to highlight uncertain segmentations of protein spots for the user), editing of segments (to manually edit protein spots in order to remove false negatives and positives of segmentation by merging, splitting, adding, or removing areas); Image alignment tools: initial registration (to automatically detect some high confident control points for initial image registration), spot pairing and concluding image alignment (to find correspondences between spots), manual editing of alignment vectors (to remove mismatches and add new matches between images); Quantitative analysis tools: spot quantification (to measure normalized quantities of spots), changes evaluation (to calculate change ratios); Visualization tools: 3D viewer (to display small area of image as surface), image fusion (to display overlay of two images using pseudocolors).

Techniques: Tandem Mass Spectroscopy, Modification

Journal: Biomedicines

Article Title: Comparative Proteomic Assessment of Normal vs. Polyhydramnios Amniotic Fluid Based on Computational Analysis

doi: 10.3390/biomedicines10081821

Figure Lengend Snippet: Identified AFP proteins—those expressions are higher in comparison to proteome associated with normal pregnancy in 2DE gels with pI 3–11 ranges (right panel) and AFP proteins, the spots number of which (proportionate to modification) in AFP pI 4–7 differ in comparison to fractionated in pI 3–11 range (left panel).

Article Snippet: The prototype is executed in MatlabTM environment (The MathWorks, Inc., Natick, MA, USA) and has integral original 2DE gel image processing algorithms as tools for specific tasks: Image preparation tools: image cropping (to remove excess areas), spot labelling, master gel selection (to align the group of gels), molecular mass markers’ calibration (to delineate area of MM marker and input of MM values), pI calibration (to input positions of known pI values); Image preprocessing related tools: image smoothing (to eliminate impulse noise), background elimination (to remove variations of background staining), individual image warping (to straighten protein migration paths); Image segmentation tools: 2DE image splitting (to split image into primary segments), segmented area evaluation (to highlight uncertain segmentations of protein spots for the user), editing of segments (to manually edit protein spots in order to remove false negatives and positives of segmentation by merging, splitting, adding, or removing areas); Image alignment tools: initial registration (to automatically detect some high confident control points for initial image registration), spot pairing and concluding image alignment (to find correspondences between spots), manual editing of alignment vectors (to remove mismatches and add new matches between images); Quantitative analysis tools: spot quantification (to measure normalized quantities of spots), changes evaluation (to calculate change ratios); Visualization tools: 3D viewer (to display small area of image as surface), image fusion (to display overlay of two images using pseudocolors).

Techniques: Comparison, Modification, Expressing, Clinical Proteomics, Membrane