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anti human cd45 antibody  (Cell Signaling Technology Inc)


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    Structured Review

    Cell Signaling Technology Inc anti human cd45 antibody
    a , Schematic illustrating the principle of QuEST for quantifying molecule numbers and surface densities. b , Isolation and detection of fluorescence-labelled single molecules by TIRFM. c , Workflow for quantifying single-molecule fluorescence intensity. d , e , Intensity distribution within a 5×5-pixel region ( d ) and corresponding mean fluorescence intensity ( e ) of single <t>CD45</t> AF405, UCHT-1 AF488, ICAM-1 AF568, and UCHT-1 AF647 molecules. f , Workflow for quantifying fluorescence intensity of multiple molecules. g , Quantification of UCHT-1 and ICAM-1 densities on supported lipid bilayers (SLBs) containing different percentages of Ni–NTA using QuEST. h , i , Schematic ( h ) and quantification results ( i ) determining the linear dynamic range of DOPE with different fluorescence levels. j , Linear density ranges of UCHT-1 and ICAM-1 under different fluorescence conditions. k , T cells clustering UCHT-1 and ICAM-1 on SLBs, with dynamic molecular densities quantified. l , Relative errors in quantifying UCHT-1 and ICAM-1 densities using QuEST. Data in g and i are presented as mean ± SD; data in e and k are presented as mean ± SEM. Statistical significance was assessed using an unpaired two-tailed Student’s t -test (**** P ≤ 0.0001; ns, not significant). In e , data were obtained from 16 single molecules. In g and i , data were obtained from three independent TIRF images. The structure of UCHT-1 was derived from PDB entry 3FZU, and the structure of ICAM-1 was generated using AlphaFold3 from the corresponding protein sequence.
    Anti Human Cd45 Antibody, supplied by Cell Signaling Technology Inc, used in various techniques. Bioz Stars score: 94/100, based on 47 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Images

    1) Product Images from "Quantitative extrapolation from single-tags (QuEST) immunofluorescence microscopy to derive TCR signalosome stoichiometries in human primary T cells"

    Article Title: Quantitative extrapolation from single-tags (QuEST) immunofluorescence microscopy to derive TCR signalosome stoichiometries in human primary T cells

    Journal: bioRxiv

    doi: 10.64898/2026.03.28.715001

    a , Schematic illustrating the principle of QuEST for quantifying molecule numbers and surface densities. b , Isolation and detection of fluorescence-labelled single molecules by TIRFM. c , Workflow for quantifying single-molecule fluorescence intensity. d , e , Intensity distribution within a 5×5-pixel region ( d ) and corresponding mean fluorescence intensity ( e ) of single CD45 AF405, UCHT-1 AF488, ICAM-1 AF568, and UCHT-1 AF647 molecules. f , Workflow for quantifying fluorescence intensity of multiple molecules. g , Quantification of UCHT-1 and ICAM-1 densities on supported lipid bilayers (SLBs) containing different percentages of Ni–NTA using QuEST. h , i , Schematic ( h ) and quantification results ( i ) determining the linear dynamic range of DOPE with different fluorescence levels. j , Linear density ranges of UCHT-1 and ICAM-1 under different fluorescence conditions. k , T cells clustering UCHT-1 and ICAM-1 on SLBs, with dynamic molecular densities quantified. l , Relative errors in quantifying UCHT-1 and ICAM-1 densities using QuEST. Data in g and i are presented as mean ± SD; data in e and k are presented as mean ± SEM. Statistical significance was assessed using an unpaired two-tailed Student’s t -test (**** P ≤ 0.0001; ns, not significant). In e , data were obtained from 16 single molecules. In g and i , data were obtained from three independent TIRF images. The structure of UCHT-1 was derived from PDB entry 3FZU, and the structure of ICAM-1 was generated using AlphaFold3 from the corresponding protein sequence.
    Figure Legend Snippet: a , Schematic illustrating the principle of QuEST for quantifying molecule numbers and surface densities. b , Isolation and detection of fluorescence-labelled single molecules by TIRFM. c , Workflow for quantifying single-molecule fluorescence intensity. d , e , Intensity distribution within a 5×5-pixel region ( d ) and corresponding mean fluorescence intensity ( e ) of single CD45 AF405, UCHT-1 AF488, ICAM-1 AF568, and UCHT-1 AF647 molecules. f , Workflow for quantifying fluorescence intensity of multiple molecules. g , Quantification of UCHT-1 and ICAM-1 densities on supported lipid bilayers (SLBs) containing different percentages of Ni–NTA using QuEST. h , i , Schematic ( h ) and quantification results ( i ) determining the linear dynamic range of DOPE with different fluorescence levels. j , Linear density ranges of UCHT-1 and ICAM-1 under different fluorescence conditions. k , T cells clustering UCHT-1 and ICAM-1 on SLBs, with dynamic molecular densities quantified. l , Relative errors in quantifying UCHT-1 and ICAM-1 densities using QuEST. Data in g and i are presented as mean ± SD; data in e and k are presented as mean ± SEM. Statistical significance was assessed using an unpaired two-tailed Student’s t -test (**** P ≤ 0.0001; ns, not significant). In e , data were obtained from 16 single molecules. In g and i , data were obtained from three independent TIRF images. The structure of UCHT-1 was derived from PDB entry 3FZU, and the structure of ICAM-1 was generated using AlphaFold3 from the corresponding protein sequence.

    Techniques Used: Isolation, Fluorescence, Two Tailed Test, Derivative Assay, Generated, Sequencing



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    tSNE analysis of megakaryocytes (A) Gating strategy post concatenation. Segregation of samples based on treatment function, gate for red blood cells <t>(CD45</t> vs CD235), megakaryocytes (CD45 vs CD41/CD61) and subsequently gated for maturation marker CD42. (B) Selection of parameters for generating a tSNE plot. (C) Example of the two samples files generated into tSNE plot. (D) Automatic overlay of markers of interest in the panel. (E) Legend for changing colors. (F) Interpretation of results for the samples.
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    Image Search Results


    3D light sheet and 2D multi-cyclic imaging data comparison (Human OvCa) (A) Imaris 3D surface rendering of autofluorescence (cyan) and CD326 positive cells (red). (B) Imaris 3D surface rendering of autofluorescence (cyan) with target plane in yellow. (C) Light sheet guided target plane selection representing CD326 positive cell (purple), CD45 positive cells (red), and CD3 positive cells (green). (D) DAPI overview image of selected tissue slice for 2D MACSima™ imaging. (E) Magnified merged six color multiparameter MICS image with CD45 (green), CD326 (cyan), FOLR1 (purple), Collagen III (red), Collagen IV (red), and CD31 (yellow). (F–L) Single staining MICS images (white) of DAPI (F), CD45 (G), CD326 (H), FOLR1 (I), Collagen III (J), Collagen IV (K), and CD31 (L) (gray) (see “Antibodies”). Scale bars: (A–F) 1 mm; (E) 250 μm; (F–L) 500 μm.

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    doi: 10.1016/j.xpro.2025.104296

    Figure Lengend Snippet: 3D light sheet and 2D multi-cyclic imaging data comparison (Human OvCa) (A) Imaris 3D surface rendering of autofluorescence (cyan) and CD326 positive cells (red). (B) Imaris 3D surface rendering of autofluorescence (cyan) with target plane in yellow. (C) Light sheet guided target plane selection representing CD326 positive cell (purple), CD45 positive cells (red), and CD3 positive cells (green). (D) DAPI overview image of selected tissue slice for 2D MACSima™ imaging. (E) Magnified merged six color multiparameter MICS image with CD45 (green), CD326 (cyan), FOLR1 (purple), Collagen III (red), Collagen IV (red), and CD31 (yellow). (F–L) Single staining MICS images (white) of DAPI (F), CD45 (G), CD326 (H), FOLR1 (I), Collagen III (J), Collagen IV (K), and CD31 (L) (gray) (see “Antibodies”). Scale bars: (A–F) 1 mm; (E) 250 μm; (F–L) 500 μm.

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    a , Schematic illustrating the principle of QuEST for quantifying molecule numbers and surface densities. b , Isolation and detection of fluorescence-labelled single molecules by TIRFM. c , Workflow for quantifying single-molecule fluorescence intensity. d , e , Intensity distribution within a 5×5-pixel region ( d ) and corresponding mean fluorescence intensity ( e ) of single CD45 AF405, UCHT-1 AF488, ICAM-1 AF568, and UCHT-1 AF647 molecules. f , Workflow for quantifying fluorescence intensity of multiple molecules. g , Quantification of UCHT-1 and ICAM-1 densities on supported lipid bilayers (SLBs) containing different percentages of Ni–NTA using QuEST. h , i , Schematic ( h ) and quantification results ( i ) determining the linear dynamic range of DOPE with different fluorescence levels. j , Linear density ranges of UCHT-1 and ICAM-1 under different fluorescence conditions. k , T cells clustering UCHT-1 and ICAM-1 on SLBs, with dynamic molecular densities quantified. l , Relative errors in quantifying UCHT-1 and ICAM-1 densities using QuEST. Data in g and i are presented as mean ± SD; data in e and k are presented as mean ± SEM. Statistical significance was assessed using an unpaired two-tailed Student’s t -test (**** P ≤ 0.0001; ns, not significant). In e , data were obtained from 16 single molecules. In g and i , data were obtained from three independent TIRF images. The structure of UCHT-1 was derived from PDB entry 3FZU, and the structure of ICAM-1 was generated using AlphaFold3 from the corresponding protein sequence.

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    Article Title: Quantitative extrapolation from single-tags (QuEST) immunofluorescence microscopy to derive TCR signalosome stoichiometries in human primary T cells

    doi: 10.64898/2026.03.28.715001

    Figure Lengend Snippet: a , Schematic illustrating the principle of QuEST for quantifying molecule numbers and surface densities. b , Isolation and detection of fluorescence-labelled single molecules by TIRFM. c , Workflow for quantifying single-molecule fluorescence intensity. d , e , Intensity distribution within a 5×5-pixel region ( d ) and corresponding mean fluorescence intensity ( e ) of single CD45 AF405, UCHT-1 AF488, ICAM-1 AF568, and UCHT-1 AF647 molecules. f , Workflow for quantifying fluorescence intensity of multiple molecules. g , Quantification of UCHT-1 and ICAM-1 densities on supported lipid bilayers (SLBs) containing different percentages of Ni–NTA using QuEST. h , i , Schematic ( h ) and quantification results ( i ) determining the linear dynamic range of DOPE with different fluorescence levels. j , Linear density ranges of UCHT-1 and ICAM-1 under different fluorescence conditions. k , T cells clustering UCHT-1 and ICAM-1 on SLBs, with dynamic molecular densities quantified. l , Relative errors in quantifying UCHT-1 and ICAM-1 densities using QuEST. Data in g and i are presented as mean ± SD; data in e and k are presented as mean ± SEM. Statistical significance was assessed using an unpaired two-tailed Student’s t -test (**** P ≤ 0.0001; ns, not significant). In e , data were obtained from 16 single molecules. In g and i , data were obtained from three independent TIRF images. The structure of UCHT-1 was derived from PDB entry 3FZU, and the structure of ICAM-1 was generated using AlphaFold3 from the corresponding protein sequence.

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    Techniques: Isolation, Fluorescence, Two Tailed Test, Derivative Assay, Generated, Sequencing

    tSNE analysis of megakaryocytes (A) Gating strategy post concatenation. Segregation of samples based on treatment function, gate for red blood cells (CD45 vs CD235), megakaryocytes (CD45 vs CD41/CD61) and subsequently gated for maturation marker CD42. (B) Selection of parameters for generating a tSNE plot. (C) Example of the two samples files generated into tSNE plot. (D) Automatic overlay of markers of interest in the panel. (E) Legend for changing colors. (F) Interpretation of results for the samples.

    Journal: STAR Protocols

    Article Title: Protocol for generating megakaryocytes from patient induced pluripotent stem cells for disease modeling and compound screening

    doi: 10.1016/j.xpro.2026.104393

    Figure Lengend Snippet: tSNE analysis of megakaryocytes (A) Gating strategy post concatenation. Segregation of samples based on treatment function, gate for red blood cells (CD45 vs CD235), megakaryocytes (CD45 vs CD41/CD61) and subsequently gated for maturation marker CD42. (B) Selection of parameters for generating a tSNE plot. (C) Example of the two samples files generated into tSNE plot. (D) Automatic overlay of markers of interest in the panel. (E) Legend for changing colors. (F) Interpretation of results for the samples.

    Article Snippet: Anti-human CD45 antibody (APC-vio 770); 1:50 , Miltenyi Biotec , CAT#130-110-773, RRID: AB_2905022.

    Techniques: Marker, Selection, Generated