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Nissen supersegger
Supersegger, supplied by Nissen, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/supersegger/supersegger/pm40470839-311-17-10
Average 90 stars, based on 1 article reviews

supersegger - by Bioz Stars, 2026-09

90/100 stars

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Article Title: Pseudomonas putida Coordinates the Expression of two Manganese Oxidases and Optimizes Manganese Oxide Precipitation in Response to Aqueous Mn(II).
Article Snippet: 2013, 4 (1), No. 1757. (39) Stylianidou, S.; Brennan, C.; Nissen, S. B.; Kuwada, N. J.; Wiggins, P. A. SuperSegger: Robust Image Segmentation, Analysis and Lineage Tracking of Bacterial Cells.

Bacteria:

Article Title: Plasmid partitioning driven by collective migration of ParA between nucleoid lobes
Article Snippet: .. S. Stylianidou, C. Brennan, S. B. Nissen, N. J. Kuwada, P. A. Wiggins, SuperSegger: robust image segmentation, analysis and lineage tracking of bacterial cells: Robust segmentation and analysis of bacteria. ..

Flow Cytometry:

Article Title: BactMAP: An R package for integrating, analyzing and visualizing bacterial microscopy data
Article Snippet: .. SuperSegger (Stylianidou, Brennan, Nissen, Kuwada, & Wiggins, ) includes a set of plotting and filtering tools inspired by flow cytometry analysis (Cass, Stylianidou, Kuwada, Traxler, & Wiggins, ). ..

Image Search Results

To characterize the performances of the competing packages, we first analyzed performance under the best case scenario: the proliferation of cells, with normal morphology, from a single cell to a monolayered microcolony under ideal imaging conditions. Panel A: Competing pipelines generate distinct cellular boundaries. The panel shows a phase contrast image of a microcolony. Competing cell segmentations are shown for a representative magnified region. All the pipelines, except CellProfiler (purple), lead to the same number of cells and are therefore acceptable for colony-scale analysis. The remaining pipelines generate significantly different cell boundaries at a sub-cellular resolution. Panel B: Lineage tree as generated by OmniSegger. Panel C: Quantitation of cell number. CellProfiler (purple) over-segments the cells to such a great extent that it roughly double counts cells. OmniSegger, SuperSegger, and DeLTA all show comparable performance. Panel D: Sub-cellular structure. Panel A visually illustrates the difference in the segmented cellular boundaries. To emphasize the biological significance of these differences, we generated histograms of cellular width measured by each pipeline and compared these to the true average cellular width (dotted line, inferred from cell contact). OmniSegger both generates a measurement with the smallest bias (1%) as well as the narrowest distribution ( σ / μ = 6%).

Journal: PLOS Computational Biology

Article Title: OmniSegger: A time-lapse image analysis pipeline for bacterial cells

doi: 10.1371/journal.pcbi.1013088

Figure Lengend Snippet: To characterize the performances of the competing packages, we first analyzed performance under the best case scenario: the proliferation of cells, with normal morphology, from a single cell to a monolayered microcolony under ideal imaging conditions. Panel A: Competing pipelines generate distinct cellular boundaries. The panel shows a phase contrast image of a microcolony. Competing cell segmentations are shown for a representative magnified region. All the pipelines, except CellProfiler (purple), lead to the same number of cells and are therefore acceptable for colony-scale analysis. The remaining pipelines generate significantly different cell boundaries at a sub-cellular resolution. Panel B: Lineage tree as generated by OmniSegger. Panel C: Quantitation of cell number. CellProfiler (purple) over-segments the cells to such a great extent that it roughly double counts cells. OmniSegger, SuperSegger, and DeLTA all show comparable performance. Panel D: Sub-cellular structure. Panel A visually illustrates the difference in the segmented cellular boundaries. To emphasize the biological significance of these differences, we generated histograms of cellular width measured by each pipeline and compared these to the true average cellular width (dotted line, inferred from cell contact). OmniSegger both generates a measurement with the smallest bias (1%) as well as the narrowest distribution ( σ / μ = 6%).

Article Snippet: SuperSegger [ ] , ✓ , ML-informed , Traditional , ✓ , mat , MATLAB , Linux,.

Techniques: Imaging, Generated, Quantitation Assay

Panel A: Visualization of proliferation. Frames from a phase-contrast time-lapse of a growing wild-type E. coli colony treated with a sub-MIC of hydroxyurea. Hydroxyurea inhibits DNA synthesis and results in a phenotype of cell filamentation. Panel B: Competing pipelines generate distinct cellular boundaries. Pipeline performance varies greatly for cells with unusual morphologies, not only at sub-cellular resolution, but at a colony scale. For this challenge, we focus on fatal errors defined as those that affect the cell number and prevent temporal linking without hand correction. Panel C: Cumulative fatal error defined. We measure performance as cumulative number of fatal errors. The red cell is over segmented in the i th frame, generating two new cells (yellow and cyan) before fusing back into the original cell (purple). The corrected segmentation is shown below. This segmentation error in frame i increases the cumulative error N by 1. Note that neither erroneously narrow cell boundaries nor a late (or early) call of a cell division event constitutes a fatal error. Panel D: Performance of competing packages measured by cumulative fatal errors. The OmniSegger analysis is error free for 100 min of imaging (20 frames). DeLTA also results in a tractable analysis, although the analysis requires the correction of over 20 cells in a single microcolony. The performance of the SuperSegger and CellProfiler pipelines are so poor for cells of unusual morphology as to make these analyses intractable.

Journal: PLOS Computational Biology

Article Title: OmniSegger: A time-lapse image analysis pipeline for bacterial cells

doi: 10.1371/journal.pcbi.1013088

Figure Lengend Snippet: Panel A: Visualization of proliferation. Frames from a phase-contrast time-lapse of a growing wild-type E. coli colony treated with a sub-MIC of hydroxyurea. Hydroxyurea inhibits DNA synthesis and results in a phenotype of cell filamentation. Panel B: Competing pipelines generate distinct cellular boundaries. Pipeline performance varies greatly for cells with unusual morphologies, not only at sub-cellular resolution, but at a colony scale. For this challenge, we focus on fatal errors defined as those that affect the cell number and prevent temporal linking without hand correction. Panel C: Cumulative fatal error defined. We measure performance as cumulative number of fatal errors. The red cell is over segmented in the i th frame, generating two new cells (yellow and cyan) before fusing back into the original cell (purple). The corrected segmentation is shown below. This segmentation error in frame i increases the cumulative error N by 1. Note that neither erroneously narrow cell boundaries nor a late (or early) call of a cell division event constitutes a fatal error. Panel D: Performance of competing packages measured by cumulative fatal errors. The OmniSegger analysis is error free for 100 min of imaging (20 frames). DeLTA also results in a tractable analysis, although the analysis requires the correction of over 20 cells in a single microcolony. The performance of the SuperSegger and CellProfiler pipelines are so poor for cells of unusual morphology as to make these analyses intractable.

Article Snippet: SuperSegger [ ] , ✓ , ML-informed , Traditional , ✓ , mat , MATLAB , Linux,.

Techniques: DNA Synthesis, Imaging

(A) Fluorescence microscopy analyses of live bacterial cells showing wild type 630Δ erm (no reporter) and 630Δ erm carrying mNeonGreen coupled to either the slpA or cwp2 promoters after overnight growth in TY media. Phase-contrast microscopy was used to visualize the cells. Blue arrows highlight cells where the mNeonGreen signal appears reduced relative to other cells in the population for the P slpA::mNG strain. Pink arrows highlight lower levels of mNeonGreen signal in the forespore of cells undergoing sporulation. The merge of phase-contrast and mNeonGreen pseudo-colored in yellow images is shown. (B) SuperSegger-based quantification of the mean fluorescent intensity for each cell is shown as a black dot on the scatterplot. The magenta and blue dots represent the median fluorescent intensity for the first and second biological replicates, respectively. The gray line represents the mean fluorescence value for each reporter based on the average of the two biological replicate’s median value . N.R. indicates wild type with no reporter. (C) OD 600 growth curve of the indicated strains during growth in TY broth. The graph shown is a single biological replicate that is representative of three biological replicates.

Journal: bioRxiv

Article Title: Development of a dual fluorescent reporter system in Clostridioides difficile reveals a division of labor between virulence and transmission gene expression

doi: 10.1101/2022.03.03.482933

Figure Lengend Snippet: (A) Fluorescence microscopy analyses of live bacterial cells showing wild type 630Δ erm (no reporter) and 630Δ erm carrying mNeonGreen coupled to either the slpA or cwp2 promoters after overnight growth in TY media. Phase-contrast microscopy was used to visualize the cells. Blue arrows highlight cells where the mNeonGreen signal appears reduced relative to other cells in the population for the P slpA::mNG strain. Pink arrows highlight lower levels of mNeonGreen signal in the forespore of cells undergoing sporulation. The merge of phase-contrast and mNeonGreen pseudo-colored in yellow images is shown. (B) SuperSegger-based quantification of the mean fluorescent intensity for each cell is shown as a black dot on the scatterplot. The magenta and blue dots represent the median fluorescent intensity for the first and second biological replicates, respectively. The gray line represents the mean fluorescence value for each reporter based on the average of the two biological replicate’s median value . N.R. indicates wild type with no reporter. (C) OD 600 growth curve of the indicated strains during growth in TY broth. The graph shown is a single biological replicate that is representative of three biological replicates.

Article Snippet: Images were quantitatively analyzed using the SuperSegger pipeline ( ) in MATLAB with the supplied ‘60x’ analysis settings.

Techniques: Fluorescence, Microscopy

(A) Fluorescence microscopy analyses of fixed cells for wild type 630Δ erm (no reporter) and 630Δ erm carrying mScarlet coupled to either the slpA or cwp2 constitutive promoters after overnight growth in TY broth. Phase-contrast microscopy was used to visualize all bacterial cells. The merge of phase-contrast and mScarlet pseudo-colored in magenta is shown. The P slpA::mSc signal was adjusted for brightness/contrast because this reporter is so much brighter than P cwp2::mSc . (B) SuperSegger-based quantification of the mean fluorescent intensity for each cell is shown as a black dot on the scatterplot. The magenta and blue dots represent the median fluorescent intensity for the first and second biological replicates, respectively. The gray line represents the mean fluorescence value for each reporter based on the average of the two biological replicate’s median value . N.R. indicates wild type with no reporter. (C) Fluorescent intensities of overnight TY cultures of the indicated strains after exposure to oxygen over the course of 36 hours.

Journal: bioRxiv

Article Title: Development of a dual fluorescent reporter system in Clostridioides difficile reveals a division of labor between virulence and transmission gene expression

doi: 10.1101/2022.03.03.482933

Figure Lengend Snippet: (A) Fluorescence microscopy analyses of fixed cells for wild type 630Δ erm (no reporter) and 630Δ erm carrying mScarlet coupled to either the slpA or cwp2 constitutive promoters after overnight growth in TY broth. Phase-contrast microscopy was used to visualize all bacterial cells. The merge of phase-contrast and mScarlet pseudo-colored in magenta is shown. The P slpA::mSc signal was adjusted for brightness/contrast because this reporter is so much brighter than P cwp2::mSc . (B) SuperSegger-based quantification of the mean fluorescent intensity for each cell is shown as a black dot on the scatterplot. The magenta and blue dots represent the median fluorescent intensity for the first and second biological replicates, respectively. The gray line represents the mean fluorescence value for each reporter based on the average of the two biological replicate’s median value . N.R. indicates wild type with no reporter. (C) Fluorescent intensities of overnight TY cultures of the indicated strains after exposure to oxygen over the course of 36 hours.

Article Snippet: Images were quantitatively analyzed using the SuperSegger pipeline ( ) in MATLAB with the supplied ‘60x’ analysis settings.

Techniques: Fluorescence, Microscopy

(A) Fluorescence microscopy analyses of live cells from strains carrying P tcdA::mNG toxin gene reporters grown overnight in TY broth relative to a promoter-less mNG construct integrated into the pyrE locus. Phase-contrast microscopy was used to visualize all bacterial cells, and the nucleoid was stained with Hoechst. The merge of Hoechst (blue) and mNeonGreen pseudo-colored in yellow is shown. Sporulating cells based on Hoescht stain with decreased toxin reporter levels are highlighted with magenta arrows. (B) SuperSegger-based quantification of the mean fluorescent intensity of each cell is shown as a black dot on the scatterplot. Individual cell intensities were quantified from three biological replicates with at least two fields of view per strain per replicate. The magenta, yellow, and blue dots represent the median intensity for the first, second, and third biological replicates, respectively. The gray line represents the mean value of each replicate’s median value. N.R. indicates a strain harboring mNeonGreen with no upstream promoter region integrated into the pyrE locus. Percentage “Toxin-ON” is displayed ± the standard deviation. “Toxin-ON” cells were calculated using the 99 th percentile of the Δ tcdR signal as a cutoff (value displayed as a blue dotted line). A minority of points (<1%) are outside the limits of the scatterplot; axes were adjusted to provide an optimal view of the scatterplot trends. Statistical significance was determined relative to wild type using a one-way ANOVA and Tukey’s test. *p < 0.1; ** p < 0.01. (C) Histogram analysis of single-cell fluorescent intensities for the P tcdA::mNG reporter in wildtype vs. ΔtcdR . The 99 th percentile cutoff in Δ tcdR used to define cells as “Toxin-ON” is shown by the blue dotted line. Plotted data is a compilation of three biological replicates.

Journal: bioRxiv

Article Title: Development of a dual fluorescent reporter system in Clostridioides difficile reveals a division of labor between virulence and transmission gene expression

doi: 10.1101/2022.03.03.482933

Figure Lengend Snippet: (A) Fluorescence microscopy analyses of live cells from strains carrying P tcdA::mNG toxin gene reporters grown overnight in TY broth relative to a promoter-less mNG construct integrated into the pyrE locus. Phase-contrast microscopy was used to visualize all bacterial cells, and the nucleoid was stained with Hoechst. The merge of Hoechst (blue) and mNeonGreen pseudo-colored in yellow is shown. Sporulating cells based on Hoescht stain with decreased toxin reporter levels are highlighted with magenta arrows. (B) SuperSegger-based quantification of the mean fluorescent intensity of each cell is shown as a black dot on the scatterplot. Individual cell intensities were quantified from three biological replicates with at least two fields of view per strain per replicate. The magenta, yellow, and blue dots represent the median intensity for the first, second, and third biological replicates, respectively. The gray line represents the mean value of each replicate’s median value. N.R. indicates a strain harboring mNeonGreen with no upstream promoter region integrated into the pyrE locus. Percentage “Toxin-ON” is displayed ± the standard deviation. “Toxin-ON” cells were calculated using the 99 th percentile of the Δ tcdR signal as a cutoff (value displayed as a blue dotted line). A minority of points (<1%) are outside the limits of the scatterplot; axes were adjusted to provide an optimal view of the scatterplot trends. Statistical significance was determined relative to wild type using a one-way ANOVA and Tukey’s test. *p < 0.1; ** p < 0.01. (C) Histogram analysis of single-cell fluorescent intensities for the P tcdA::mNG reporter in wildtype vs. ΔtcdR . The 99 th percentile cutoff in Δ tcdR used to define cells as “Toxin-ON” is shown by the blue dotted line. Plotted data is a compilation of three biological replicates.

Article Snippet: Images were quantitatively analyzed using the SuperSegger pipeline ( ) in MATLAB with the supplied ‘60x’ analysis settings.

Techniques: Fluorescence, Microscopy, Construct, Staining, Standard Deviation

(A) Fluorescence microscopy analyses of sporulating cultures of the indicated strains grown for 15 hrs on SMC sporulation agar followed by fixation. Phase contrast microscopy was used to visualize all bacterial cells. The merge of Hoechst (blue) and mScarlet (magenta) is shown. (B) SuperSegger-based quantification of the mean fluorescent intensity of each cell is shown as a black dot on the scatterplot. Individual cell intensities were quantified from three biological replicates, with at least two fields of view per strain per replicate. The magenta, yellow, and blue dots represent the median intensity for the first, second, and third biological replicates, respectively. The gray line represents the mean value of each replicate’s median value. N.R. indicates a strain harboring mScarlet with no upstream promoter region integrated into the pyrE locus. Percentage “Sporulation-ON” is displayed ± the standard deviation and was calculated using 1000 RFU signal as a cutoff (value displayed as a blue dotted line). This cut-off was determined using histogram analyses . A minority of points (< 1%) are outside the limits of the scatterplot; axes were adjusted to provide an optimal view of the scatterplot trends. Statistical significance was determined relative to wild type using a one-way ANOVA and Tukey’s test. *p<0.1; ** p < 0.01. (C) Histogram analysis of mean single-cell fluorescent intensities for the P sipL::mSc reporter in wildtype, ΔtcdR and Δ rstA demonstrates a bimodal distribution of cells as “Sporulation-ON” vs. “Sporulation-OFF”. Blue dotted line indicates the determined cutoff value of 1000 RFU which was also confirmed by visual inspection of phase-contrast images. Plotted data is a compilation of three biological replicates.

Journal: bioRxiv

Article Title: Development of a dual fluorescent reporter system in Clostridioides difficile reveals a division of labor between virulence and transmission gene expression

doi: 10.1101/2022.03.03.482933

Figure Lengend Snippet: (A) Fluorescence microscopy analyses of sporulating cultures of the indicated strains grown for 15 hrs on SMC sporulation agar followed by fixation. Phase contrast microscopy was used to visualize all bacterial cells. The merge of Hoechst (blue) and mScarlet (magenta) is shown. (B) SuperSegger-based quantification of the mean fluorescent intensity of each cell is shown as a black dot on the scatterplot. Individual cell intensities were quantified from three biological replicates, with at least two fields of view per strain per replicate. The magenta, yellow, and blue dots represent the median intensity for the first, second, and third biological replicates, respectively. The gray line represents the mean value of each replicate’s median value. N.R. indicates a strain harboring mScarlet with no upstream promoter region integrated into the pyrE locus. Percentage “Sporulation-ON” is displayed ± the standard deviation and was calculated using 1000 RFU signal as a cutoff (value displayed as a blue dotted line). This cut-off was determined using histogram analyses . A minority of points (< 1%) are outside the limits of the scatterplot; axes were adjusted to provide an optimal view of the scatterplot trends. Statistical significance was determined relative to wild type using a one-way ANOVA and Tukey’s test. *p<0.1; ** p < 0.01. (C) Histogram analysis of mean single-cell fluorescent intensities for the P sipL::mSc reporter in wildtype, ΔtcdR and Δ rstA demonstrates a bimodal distribution of cells as “Sporulation-ON” vs. “Sporulation-OFF”. Blue dotted line indicates the determined cutoff value of 1000 RFU which was also confirmed by visual inspection of phase-contrast images. Plotted data is a compilation of three biological replicates.

Article Snippet: Images were quantitatively analyzed using the SuperSegger pipeline ( ) in MATLAB with the supplied ‘60x’ analysis settings.

Techniques: Fluorescence, Microscopy, Standard Deviation

(A) Fluorescence microscopy analyses of fixed bacterial cells after overnight growth in TY liquid media. Dual reporter strains contain P sipL::mScarlet and P tcdA::mNeonGreen. Dual reporter analyses were visualized in WT, Δ tcdR , Δ spo0A and Δ rstA strain backgrounds. Phase-contrast microscopy was used to visualize all bacterial cells. The merge of mNeonGreen (yellow) with mScarlet (magenta) signals are shown. Cells expressing both P sipL::mScarlet and P tcdA::mNeonGreen are highlighted with blue arrows. (B-C) SuperSegger-based quantification of P tcdA::mNG and P sipL::mSc reporters shows the mean fluorescent intensity of each cell as a black dot on the scatterplot for the indicated reporters. Individual cell intensities were quantified from three biological replicates, with at least two fields of view per strain per replicate. The magenta, yellow, and blue dots represent the median intensity for the first, second, and third biological replicates, respectively. The gray line represents the mean value of each replicate’s median value. Percentage “Toxin-ON” is displayed ± the standard deviation. “Toxin-ON” cells were calculated using the 99 th percentile of the Δ tcdR signal as a cutoff (value displayed as a pink dotted line). Percentage “Sporulation-ON” was calculated using 1000 RFU signal as a cutoff (value displayed as a pink dotted line; green dotted line represents the 500 RFU cut-off for “Sporulation-ON” cells for Δ tcdR and Δ spo0A ). This cut-off was determined using the histogram analyses in . A minority of points (<1%) are outside the limits of the scatterplot; axes were adjusted to provide an optimal view of the scatterplot trends. Statistical significance was determined relative to wild type using a one-way ANOVA and Tukey’s test. ***p<0.001, ** p < 0.01. (D) Scatterplot analyses show single cell mean fluorescent intensities with P sipL::mSc on the x-axis and PtcdA::mNG on the y-axis. “Toxin-ON” cutoff is represented by the yellow dotted line which indicates 99 th percentile of the Δ tcdR signal. “Sporulation-ON” cutoff is represented by the magenta dotted line at 1000 RFU based on the histogram bimodal distribution analyses. Percentages indicate Toxin-ON/Sporulation-OFF in the top left quadrant, Toxin-ON/Sporulation-ON in the top right quadrant, Toxin-OFF/Sporulation-OFF in the bottom left quadrant and Toxin-OFF/Sporulation-ON in the bottom right quadrant. A minority of points (<1%) are outside the limits of the scatterplot; axes were adjusted to provide an optimal view of the scatterplot trends.

Journal: bioRxiv

Article Title: Development of a dual fluorescent reporter system in Clostridioides difficile reveals a division of labor between virulence and transmission gene expression

doi: 10.1101/2022.03.03.482933

Figure Lengend Snippet: (A) Fluorescence microscopy analyses of fixed bacterial cells after overnight growth in TY liquid media. Dual reporter strains contain P sipL::mScarlet and P tcdA::mNeonGreen. Dual reporter analyses were visualized in WT, Δ tcdR , Δ spo0A and Δ rstA strain backgrounds. Phase-contrast microscopy was used to visualize all bacterial cells. The merge of mNeonGreen (yellow) with mScarlet (magenta) signals are shown. Cells expressing both P sipL::mScarlet and P tcdA::mNeonGreen are highlighted with blue arrows. (B-C) SuperSegger-based quantification of P tcdA::mNG and P sipL::mSc reporters shows the mean fluorescent intensity of each cell as a black dot on the scatterplot for the indicated reporters. Individual cell intensities were quantified from three biological replicates, with at least two fields of view per strain per replicate. The magenta, yellow, and blue dots represent the median intensity for the first, second, and third biological replicates, respectively. The gray line represents the mean value of each replicate’s median value. Percentage “Toxin-ON” is displayed ± the standard deviation. “Toxin-ON” cells were calculated using the 99 th percentile of the Δ tcdR signal as a cutoff (value displayed as a pink dotted line). Percentage “Sporulation-ON” was calculated using 1000 RFU signal as a cutoff (value displayed as a pink dotted line; green dotted line represents the 500 RFU cut-off for “Sporulation-ON” cells for Δ tcdR and Δ spo0A ). This cut-off was determined using the histogram analyses in . A minority of points (<1%) are outside the limits of the scatterplot; axes were adjusted to provide an optimal view of the scatterplot trends. Statistical significance was determined relative to wild type using a one-way ANOVA and Tukey’s test. ***p<0.001, ** p < 0.01. (D) Scatterplot analyses show single cell mean fluorescent intensities with P sipL::mSc on the x-axis and PtcdA::mNG on the y-axis. “Toxin-ON” cutoff is represented by the yellow dotted line which indicates 99 th percentile of the Δ tcdR signal. “Sporulation-ON” cutoff is represented by the magenta dotted line at 1000 RFU based on the histogram bimodal distribution analyses. Percentages indicate Toxin-ON/Sporulation-OFF in the top left quadrant, Toxin-ON/Sporulation-ON in the top right quadrant, Toxin-OFF/Sporulation-OFF in the bottom left quadrant and Toxin-OFF/Sporulation-ON in the bottom right quadrant. A minority of points (<1%) are outside the limits of the scatterplot; axes were adjusted to provide an optimal view of the scatterplot trends.

Article Snippet: Images were quantitatively analyzed using the SuperSegger pipeline ( ) in MATLAB with the supplied ‘60x’ analysis settings.

Techniques: Fluorescence, Microscopy, Expressing, Standard Deviation

(A) Fluorescence microscopy analyses of fixed bacterial cells after growth on TY plates. Dual reporter strains contain P sipL::mScarlet and P tcdA::mNeonGreen. Dual reporter analyses were visualized in WT, Δ tcdR , Δ spo0A and Δ rstA strain backgrounds. Phase contrast microscopy was used to visualize all bacterial cells. The merge of mNeonGreen (yellow) with mScarlet (magenta) signals are shown. (B-C) SuperSegger-based quantification of P tcdA::mNG and P sipL::mSc reporters shows the mean fluorescent intensity of each cell as a black dot on the scatterplot for the indicated reporters. Individual cell intensities were quantified from three biological replicates, with at least two fields of view per strain per replicate. The magenta, yellow, and blue dots represent the median intensity for the first, second, and third biological replicates, respectively. The gray line represents the mean value of each replicate’s median value. Percentage “Toxin-ON” is displayed ± the standard deviation. “Toxin-ON” cells were calculated using the 99 th percentile of the Δ tcdR signal as a cutoff (value displayed as a blue dotted line). Percentage “Sporulation-ON” was calculated using 1000 RFU signal as a cutoff (value displayed as a pink dotted line; green dotted line represents the 500 RFU cut-off for “Sporulation-ON” cells for Δ tcdR and Δ spo0A ). This cut-off was determined using the histogram analyses in . A minority of points (<1%) are outside the limits of the scatterplot; axes were adjusted to provide an optimal view of the scatterplot trends. Statistical significance was determined relative to wild type using a one-way ANOVA and Tukey’s test. ***p<0.001, ** p < 0.01. (D) Scatterplot analyses show single cell mean fluorescent intensities with P sipL::mSc on the x-axis and PtcdA::mNG on the y-axis. “Toxin-ON” cutoff is represented by the yellow dotted line which indicates 99 th percentile of the Δ tcdR signal. “Sporulation-ON” cutoff is represented by the pink dotted line at 1000 RFU based on the histogram bimodal distribution analyses. Percentages indicate Toxin-ON/Sporulation-OFF in the top left quadrant, Toxin-ON/Sporulation-ON in the top right quadrant, Toxin-OFF/Sporulation-OFF in the bottom left quadrant and Toxin-OFF/Sporulation-ON in the bottom right quadrant. A minority of points (<1%) are outside the limits of the scatterplot; axes were adjusted to provide an optimal view of the scatterplot trends.

Journal: bioRxiv

Article Title: Development of a dual fluorescent reporter system in Clostridioides difficile reveals a division of labor between virulence and transmission gene expression

doi: 10.1101/2022.03.03.482933

Figure Lengend Snippet: (A) Fluorescence microscopy analyses of fixed bacterial cells after growth on TY plates. Dual reporter strains contain P sipL::mScarlet and P tcdA::mNeonGreen. Dual reporter analyses were visualized in WT, Δ tcdR , Δ spo0A and Δ rstA strain backgrounds. Phase contrast microscopy was used to visualize all bacterial cells. The merge of mNeonGreen (yellow) with mScarlet (magenta) signals are shown. (B-C) SuperSegger-based quantification of P tcdA::mNG and P sipL::mSc reporters shows the mean fluorescent intensity of each cell as a black dot on the scatterplot for the indicated reporters. Individual cell intensities were quantified from three biological replicates, with at least two fields of view per strain per replicate. The magenta, yellow, and blue dots represent the median intensity for the first, second, and third biological replicates, respectively. The gray line represents the mean value of each replicate’s median value. Percentage “Toxin-ON” is displayed ± the standard deviation. “Toxin-ON” cells were calculated using the 99 th percentile of the Δ tcdR signal as a cutoff (value displayed as a blue dotted line). Percentage “Sporulation-ON” was calculated using 1000 RFU signal as a cutoff (value displayed as a pink dotted line; green dotted line represents the 500 RFU cut-off for “Sporulation-ON” cells for Δ tcdR and Δ spo0A ). This cut-off was determined using the histogram analyses in . A minority of points (<1%) are outside the limits of the scatterplot; axes were adjusted to provide an optimal view of the scatterplot trends. Statistical significance was determined relative to wild type using a one-way ANOVA and Tukey’s test. ***p<0.001, ** p < 0.01. (D) Scatterplot analyses show single cell mean fluorescent intensities with P sipL::mSc on the x-axis and PtcdA::mNG on the y-axis. “Toxin-ON” cutoff is represented by the yellow dotted line which indicates 99 th percentile of the Δ tcdR signal. “Sporulation-ON” cutoff is represented by the pink dotted line at 1000 RFU based on the histogram bimodal distribution analyses. Percentages indicate Toxin-ON/Sporulation-OFF in the top left quadrant, Toxin-ON/Sporulation-ON in the top right quadrant, Toxin-OFF/Sporulation-OFF in the bottom left quadrant and Toxin-OFF/Sporulation-ON in the bottom right quadrant. A minority of points (<1%) are outside the limits of the scatterplot; axes were adjusted to provide an optimal view of the scatterplot trends.

Article Snippet: Images were quantitatively analyzed using the SuperSegger pipeline ( ) in MATLAB with the supplied ‘60x’ analysis settings.

Techniques: Fluorescence, Microscopy, Standard Deviation

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