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tsne algorithms  (Becton Dickinson)

 
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    Becton Dickinson tsne algorithms
    Medullary FRCs have a unique phenotype and cytokine profile and expand in number during lymph node swelling. (A–D) FACS analysis of CD45−CD35− cells from pLNs of naive or OVA/Mont-immunized mice at the indicated time points. (A) Dot plots in Upper row show the gating of <t>FRCs</t> <t>(pdpn+CD31−)</t> versus BECs (pdpn−CD31+) and LECs (pdpn+CD31+) from naive pLNs. The expression of MAdCAM and BP3 in FRCs distinguishes three FRC subpopulations: MRCs, TRCs, and MedRCs. Shown at Right is the control staining when anti-MAdCAM antibody was not added. Dot plots in Lower row show a representative staining of the three FRC subsets in naive versus activated LNs, with the percentages indicated for each population (n ≥ 3). (B) Number of total FRCs and of each of the three FRC subsets per pLN upon immunization. (C) Representative histograms showing the level of BrdU incorporation in the three FRC subsets (OVA/Mont d5.5). Numbers indicate the percentage of BrdU+ cells. Black line, anti-BrdU antibody; dashed line, isotype control antibody on total FRCs. (D) Representative histograms displaying the frequency of CCL21- and CXCL13-expressing cells among the three FRC subsets found in activated pLNs. Black line, specific antibody; dashed line, no primary antibody on total FRCs. (E) The three FRC subsets and CD31+ cells (LECs/BECs) were sorted from activated pLNs and normalized transcript levels of indicated cytokine transcripts measured by qRT-PCR (means ± SD, n = 4). (F) ISH analysis for indicated cytokine transcripts (green) or the sense control (for CXCL12). (G) pLN sections stained with the indicated antibodies. The boxed areas are represented Below at higher magnification. (H) Multiparameter flow cytometry-based clustering of FRC (CD45−31−pdpn+) subsets from naive pLNs using the <t>TSNe</t> algorithms (FlowJo) to display seven parameters (FCS, SSC, Pdpn, BP3, MAdCAM-1, CXCL13, and CCL21) in a 2D representation. Colored areas were added manually. (Scale bar, 100 µm.) Results in A–D, F, and G are representative of at least three independent mice. *P < 0.05, **P < 0.01, ***P < 0.001.
    Tsne Algorithms, supplied by Becton Dickinson, 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/t-sne+algorithm/tsne+algorithms/pmc06055158-93-31-33
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    tsne algorithms - by Bioz Stars, 2026-11
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    Images

    1) Product Images from "Identification of a new subset of lymph node stromal cells involved in regulating plasma cell homeostasis"

    Article Title: Identification of a new subset of lymph node stromal cells involved in regulating plasma cell homeostasis

    Journal: Proceedings of the National Academy of Sciences of the United States of America

    doi: 10.1073/pnas.1712628115

    Medullary FRCs have a unique phenotype and cytokine profile and expand in number during lymph node swelling. (A–D) FACS analysis of CD45−CD35− cells from pLNs of naive or OVA/Mont-immunized mice at the indicated time points. (A) Dot plots in Upper row show the gating of FRCs (pdpn+CD31−) versus BECs (pdpn−CD31+) and LECs (pdpn+CD31+) from naive pLNs. The expression of MAdCAM and BP3 in FRCs distinguishes three FRC subpopulations: MRCs, TRCs, and MedRCs. Shown at Right is the control staining when anti-MAdCAM antibody was not added. Dot plots in Lower row show a representative staining of the three FRC subsets in naive versus activated LNs, with the percentages indicated for each population (n ≥ 3). (B) Number of total FRCs and of each of the three FRC subsets per pLN upon immunization. (C) Representative histograms showing the level of BrdU incorporation in the three FRC subsets (OVA/Mont d5.5). Numbers indicate the percentage of BrdU+ cells. Black line, anti-BrdU antibody; dashed line, isotype control antibody on total FRCs. (D) Representative histograms displaying the frequency of CCL21- and CXCL13-expressing cells among the three FRC subsets found in activated pLNs. Black line, specific antibody; dashed line, no primary antibody on total FRCs. (E) The three FRC subsets and CD31+ cells (LECs/BECs) were sorted from activated pLNs and normalized transcript levels of indicated cytokine transcripts measured by qRT-PCR (means ± SD, n = 4). (F) ISH analysis for indicated cytokine transcripts (green) or the sense control (for CXCL12). (G) pLN sections stained with the indicated antibodies. The boxed areas are represented Below at higher magnification. (H) Multiparameter flow cytometry-based clustering of FRC (CD45−31−pdpn+) subsets from naive pLNs using the TSNe algorithms (FlowJo) to display seven parameters (FCS, SSC, Pdpn, BP3, MAdCAM-1, CXCL13, and CCL21) in a 2D representation. Colored areas were added manually. (Scale bar, 100 µm.) Results in A–D, F, and G are representative of at least three independent mice. *P < 0.05, **P < 0.01, ***P < 0.001.
    Figure Legend Snippet: Medullary FRCs have a unique phenotype and cytokine profile and expand in number during lymph node swelling. (A–D) FACS analysis of CD45−CD35− cells from pLNs of naive or OVA/Mont-immunized mice at the indicated time points. (A) Dot plots in Upper row show the gating of FRCs (pdpn+CD31−) versus BECs (pdpn−CD31+) and LECs (pdpn+CD31+) from naive pLNs. The expression of MAdCAM and BP3 in FRCs distinguishes three FRC subpopulations: MRCs, TRCs, and MedRCs. Shown at Right is the control staining when anti-MAdCAM antibody was not added. Dot plots in Lower row show a representative staining of the three FRC subsets in naive versus activated LNs, with the percentages indicated for each population (n ≥ 3). (B) Number of total FRCs and of each of the three FRC subsets per pLN upon immunization. (C) Representative histograms showing the level of BrdU incorporation in the three FRC subsets (OVA/Mont d5.5). Numbers indicate the percentage of BrdU+ cells. Black line, anti-BrdU antibody; dashed line, isotype control antibody on total FRCs. (D) Representative histograms displaying the frequency of CCL21- and CXCL13-expressing cells among the three FRC subsets found in activated pLNs. Black line, specific antibody; dashed line, no primary antibody on total FRCs. (E) The three FRC subsets and CD31+ cells (LECs/BECs) were sorted from activated pLNs and normalized transcript levels of indicated cytokine transcripts measured by qRT-PCR (means ± SD, n = 4). (F) ISH analysis for indicated cytokine transcripts (green) or the sense control (for CXCL12). (G) pLN sections stained with the indicated antibodies. The boxed areas are represented Below at higher magnification. (H) Multiparameter flow cytometry-based clustering of FRC (CD45−31−pdpn+) subsets from naive pLNs using the TSNe algorithms (FlowJo) to display seven parameters (FCS, SSC, Pdpn, BP3, MAdCAM-1, CXCL13, and CCL21) in a 2D representation. Colored areas were added manually. (Scale bar, 100 µm.) Results in A–D, F, and G are representative of at least three independent mice. *P < 0.05, **P < 0.01, ***P < 0.001.

    Techniques Used: Expressing, Staining, BrdU Incorporation Assay, Quantitative RT-PCR, Flow Cytometry

    Related Articles

    Cytometry:

    Article Title: Identification of a new subset of lymph node stromal cells involved in regulating plasma cell homeostasis
    Article Snippet: .. The boxed areas are represented Below at higher magnification. ( H ) Multiparameter flow cytometry-based clustering of FRC (CD45 − 31 − pdpn + ) subsets from naive pLNs using the TSNe algorithms (FlowJo) to display seven parameters (FCS, SSC, Pdpn, BP3, MAdCAM-1, CXCL13, and CCL21) in a 2D representation. ..



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    Image Search Results


    Journal: Frontiers in Molecular Neuroscience

    Article Title: Innate immune activation and aberrant function in the R6/2 mouse model and Huntington’s disease iPSC-derived microglia

    doi: 10.3389/fnmol.2023.1191324

    Figure Lengend Snippet:

    Article Snippet: To visualize all live immune cells or all iMGL in a single two-dimensional map, we applied an unsupervised high-dimensional data analysis on concatenated fcs files pooling cells equally and randomly sampled from all mice or samples in each group, using the t-distributed stochastic linear embedding (t-SNE) algorithm available on Cytobank (viSNE algorithm).

    Techniques: Derivative Assay, Mass Cytometry, RNA Sequencing Assay, Transgenic Assay

    Segmentation of Ca 2+ imaging data reveals spatially similar ICs across time segments and scales. A) Schematic showing how data is parsed into equal length time segments (set of images show data time segments) for different timescales which are run through the JADE ICA algorithm independently to obtain new ICA solutions (lower images; each color represents an IC). B) Example template map based on an entire mouse’s dataset and time segment ICA map at the day timescale (left; colors show individual ICs) and Jaccard index matrix showing matching (high value) and nonmatching (low value) ICs between a time segment and template ICA map (circles). C) Examples of overlap between matching and nonmatching pairs of template and time segment ICs for circles shown in the Jaccard matrix of B (overlap shown in dark red; individual ICs blue or yellow). D) Three examples of template matching ICs (Jaccard index ≥0.5) for each of the four timescales (columns/green bars). Scale bars: 1 mm.

    Journal: Cerebral Cortex (New York, NY)

    Article Title: To be and not to be: wide-field Ca 2+ imaging reveals neocortical functional segmentation combines stability and flexibility

    doi: 10.1093/cercor/bhac523

    Figure Lengend Snippet: Segmentation of Ca 2+ imaging data reveals spatially similar ICs across time segments and scales. A) Schematic showing how data is parsed into equal length time segments (set of images show data time segments) for different timescales which are run through the JADE ICA algorithm independently to obtain new ICA solutions (lower images; each color represents an IC). B) Example template map based on an entire mouse’s dataset and time segment ICA map at the day timescale (left; colors show individual ICs) and Jaccard index matrix showing matching (high value) and nonmatching (low value) ICs between a time segment and template ICA map (circles). C) Examples of overlap between matching and nonmatching pairs of template and time segment ICs for circles shown in the Jaccard matrix of B (overlap shown in dark red; individual ICs blue or yellow). D) Three examples of template matching ICs (Jaccard index ≥0.5) for each of the four timescales (columns/green bars). Scale bars: 1 mm.

    Article Snippet: Dimensionality reduction of the IC libraries was achieved with t -distributed stochastic neighbor embedding ( t -SNE) using the Jaccard distance algorithm, plotting the position of each IC in this space as a point in a 2D graph (Matlab 2019 tsne function).

    Techniques: Imaging

    Spatial ICA of wide-field Ca 2+ imaging data produces spatially independent brain regions. A) Schematic showing the ICA workflow of concatenating data for each animal chronologically across days and trials (days are signified by different colored borders; trials are signified by overlapping images) and sending the combined dataset through the JADE ICA algorithm. B) Example template map (ground-truth to which all other ICA solutions are compared) of spatial ICs produced from running ICA on one mouse’s combined dataset (each different colored region is a single IC; scale bar: 1 mm). White lines denote major regions of the Allen Common Coordinate Framework (CCF; see ). C) Example matrix of Jaccard indices comparing the template map to itself (low off-diagonal Jaccard indices indicate good spatial separation; zero values are shown as white indicating no IC overlap). D) Frequency histograms showing the distribution of off-diagonal Jaccard indices (nonself matches) when comparing the template map to itself for each animal (bin-widths = 0.05).

    Journal: Cerebral Cortex (New York, NY)

    Article Title: To be and not to be: wide-field Ca 2+ imaging reveals neocortical functional segmentation combines stability and flexibility

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    Figure Lengend Snippet: Spatial ICA of wide-field Ca 2+ imaging data produces spatially independent brain regions. A) Schematic showing the ICA workflow of concatenating data for each animal chronologically across days and trials (days are signified by different colored borders; trials are signified by overlapping images) and sending the combined dataset through the JADE ICA algorithm. B) Example template map (ground-truth to which all other ICA solutions are compared) of spatial ICs produced from running ICA on one mouse’s combined dataset (each different colored region is a single IC; scale bar: 1 mm). White lines denote major regions of the Allen Common Coordinate Framework (CCF; see ). C) Example matrix of Jaccard indices comparing the template map to itself (low off-diagonal Jaccard indices indicate good spatial separation; zero values are shown as white indicating no IC overlap). D) Frequency histograms showing the distribution of off-diagonal Jaccard indices (nonself matches) when comparing the template map to itself for each animal (bin-widths = 0.05).

    Article Snippet: Dimensionality reduction of the IC libraries was achieved with t -distributed stochastic neighbor embedding ( t -SNE) using the Jaccard distance algorithm, plotting the position of each IC in this space as a point in a 2D graph (Matlab 2019 tsne function).

    Techniques: Imaging, Produced

    Cortex-wide maps cover similar areas across timescales. A) Brain maps from all six experimental subjects showing cumulative cortical coverage of template matching ICs across all time-windows within each of the four timescales examined (color scale shows the number of timescales where an area of cortex was covered by a spatial IC). Scale bar: 1 mm. B) Jaccard index matrices for each of the six experimental subjects showing a high degree of overlapping cortical coverage between timescales (off-diagonal comparisons).

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    Figure Lengend Snippet: Cortex-wide maps cover similar areas across timescales. A) Brain maps from all six experimental subjects showing cumulative cortical coverage of template matching ICs across all time-windows within each of the four timescales examined (color scale shows the number of timescales where an area of cortex was covered by a spatial IC). Scale bar: 1 mm. B) Jaccard index matrices for each of the six experimental subjects showing a high degree of overlapping cortical coverage between timescales (off-diagonal comparisons).

    Article Snippet: Dimensionality reduction of the IC libraries was achieved with t -distributed stochastic neighbor embedding ( t -SNE) using the Jaccard distance algorithm, plotting the position of each IC in this space as a point in a 2D graph (Matlab 2019 tsne function).

    Techniques: