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cd3  (fluidigm)


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    fluidigm cd3
    The sequential gating strategy was used to identify immune cell populations from healthy human samples. Healthy donor samples, either ( A ) cryopreserved peripheral blood mononuclear cells (cPBMCs) or ( B ) fresh whole blood (WB), were stimulated for 15 min, fixed, palladium-barcoded, and stained with a comprehensive panel of 19 (for cPBMCs) or 20 (for whole blood) surface markers. Within the single-cell gate, non-granulocytes (leukocytes) were defined as CD66b-CD45 + , while granulocytes were confirmed solely in whole blood as CD66b + CD45-. Lymphocytes were resolved using <t>CD3</t> and CD56 [T cells (CD3 + CD56-)], NKT cells (CD3 + CD56 + ), and NK cells (CD3-CD56 + ). B cells were identified as CD19 + CD20 +/- within the CD3-CD56- gate, further confirmed by negative expression of CD123 and CD11c. Both T and B cells were further subset based on the expression of CD4, CD8, CD45RA, CD27, and CD25 or IgD and CD27, respectively. Non-T, non-B, and non-NK cells (CD45 + CD3-CD19-CD56-CD20-) were separated based on their expression of CD11c and HLA-DR. Total DCs were defined as CD11c + HLA-DR + and can be further defined through the expression of CD123 (plasmacytoid DCs). Monocytes were resolved within this non-lymphocyte gate based on their expression of CD14 and CD16.
    Cd3, supplied by fluidigm, used in various techniques. Bioz Stars score: 94/100, based on 26 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/3170001b/Anti-Human+CD3+(UCHT1)-170Er/pmc12645211-43-3-5
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    Images

    1) Product Images from "Dual Phospho-CyTOF Workflows for Comparative JAK/STAT Signaling Analysis in Human Cryopreserved PBMCs and Whole Blood"

    Article Title: Dual Phospho-CyTOF Workflows for Comparative JAK/STAT Signaling Analysis in Human Cryopreserved PBMCs and Whole Blood

    Journal: Bio-protocol

    doi: 10.21769/BioProtoc.5512

    The sequential gating strategy was used to identify immune cell populations from healthy human samples. Healthy donor samples, either ( A ) cryopreserved peripheral blood mononuclear cells (cPBMCs) or ( B ) fresh whole blood (WB), were stimulated for 15 min, fixed, palladium-barcoded, and stained with a comprehensive panel of 19 (for cPBMCs) or 20 (for whole blood) surface markers. Within the single-cell gate, non-granulocytes (leukocytes) were defined as CD66b-CD45 + , while granulocytes were confirmed solely in whole blood as CD66b + CD45-. Lymphocytes were resolved using CD3 and CD56 [T cells (CD3 + CD56-)], NKT cells (CD3 + CD56 + ), and NK cells (CD3-CD56 + ). B cells were identified as CD19 + CD20 +/- within the CD3-CD56- gate, further confirmed by negative expression of CD123 and CD11c. Both T and B cells were further subset based on the expression of CD4, CD8, CD45RA, CD27, and CD25 or IgD and CD27, respectively. Non-T, non-B, and non-NK cells (CD45 + CD3-CD19-CD56-CD20-) were separated based on their expression of CD11c and HLA-DR. Total DCs were defined as CD11c + HLA-DR + and can be further defined through the expression of CD123 (plasmacytoid DCs). Monocytes were resolved within this non-lymphocyte gate based on their expression of CD14 and CD16.
    Figure Legend Snippet: The sequential gating strategy was used to identify immune cell populations from healthy human samples. Healthy donor samples, either ( A ) cryopreserved peripheral blood mononuclear cells (cPBMCs) or ( B ) fresh whole blood (WB), were stimulated for 15 min, fixed, palladium-barcoded, and stained with a comprehensive panel of 19 (for cPBMCs) or 20 (for whole blood) surface markers. Within the single-cell gate, non-granulocytes (leukocytes) were defined as CD66b-CD45 + , while granulocytes were confirmed solely in whole blood as CD66b + CD45-. Lymphocytes were resolved using CD3 and CD56 [T cells (CD3 + CD56-)], NKT cells (CD3 + CD56 + ), and NK cells (CD3-CD56 + ). B cells were identified as CD19 + CD20 +/- within the CD3-CD56- gate, further confirmed by negative expression of CD123 and CD11c. Both T and B cells were further subset based on the expression of CD4, CD8, CD45RA, CD27, and CD25 or IgD and CD27, respectively. Non-T, non-B, and non-NK cells (CD45 + CD3-CD19-CD56-CD20-) were separated based on their expression of CD11c and HLA-DR. Total DCs were defined as CD11c + HLA-DR + and can be further defined through the expression of CD123 (plasmacytoid DCs). Monocytes were resolved within this non-lymphocyte gate based on their expression of CD14 and CD16.

    Techniques Used: Staining, Expressing

    This bar graph illustrates the average frequency of total (%) of the major immune cell subsets in donor-matched ( A ) cPBMCs and ( B ) whole blood from (n = 3) healthy individuals. The data points represent the average frequency of total (%) of each subset, with error bars indicating the standard deviation from triplicate samples. Major immune subsets, including B cells, monocytes, dendritic cells (DCs), NK cells, NKT cells, and T cells, were identified using a comprehensive high-dimensional immunophenotyping strategy defined after Gaussian parameter cleanup (Figure 2) and doublet exclusion. These lineages were identified through expression of markers such as CD3, CD19, CD56, CD14, CD11c, HLA-DR, and CD66b, following the detailed gating strategy outlined in Figure 3. As expected, cPBMCs have a higher proportional representation of lymphocytes (T cells, B cells, NK, and NKT cells) and monocytes but lack granulocytes, whereas whole blood maintains the complete cellular repertoire, including a larger proportion of granulocytes and what appears to be a lower percentage of lymphocyte or monocyte subsets due to being expressed as a percentage of total leukocytes.
    Figure Legend Snippet: This bar graph illustrates the average frequency of total (%) of the major immune cell subsets in donor-matched ( A ) cPBMCs and ( B ) whole blood from (n = 3) healthy individuals. The data points represent the average frequency of total (%) of each subset, with error bars indicating the standard deviation from triplicate samples. Major immune subsets, including B cells, monocytes, dendritic cells (DCs), NK cells, NKT cells, and T cells, were identified using a comprehensive high-dimensional immunophenotyping strategy defined after Gaussian parameter cleanup (Figure 2) and doublet exclusion. These lineages were identified through expression of markers such as CD3, CD19, CD56, CD14, CD11c, HLA-DR, and CD66b, following the detailed gating strategy outlined in Figure 3. As expected, cPBMCs have a higher proportional representation of lymphocytes (T cells, B cells, NK, and NKT cells) and monocytes but lack granulocytes, whereas whole blood maintains the complete cellular repertoire, including a larger proportion of granulocytes and what appears to be a lower percentage of lymphocyte or monocyte subsets due to being expressed as a percentage of total leukocytes.

    Techniques Used: Standard Deviation, Expressing

    Related Articles

    other:


    Article Title: Dual Phospho-CyTOF Workflows for Comparative JAK/STAT Signaling Analysis in Human Cryopreserved PBMCs and Whole Blood
    Article Snippet: 170 Er , CD3 , Standard BioTools , 3170001B , UCHT1 , 17.5.

    Article Title: Mass Cytometry Assessment of Cell Phenotypes and Signaling States in Human Whole Blood
    Article Snippet: 170 Er , CD3 , UCHT1 , Fluidigm 3170001B.

    Article Title: Single-cell transcriptome and crosstalk analysis reveals immune alterations and key pathways in the bone marrow of knee OA patients
    Article Snippet: Anti-Human CD3 (UCHT1)-170Er , Fluidigm Corporation , Cat3170001B.

    Virus:

    Article Title: Anatomical, subset, and HIV-dependent expression of viral sensors and restriction factors.
    Article Snippet: .. REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies CD57 Biolegend Cat#359602; RRID:AB_2562403 HLADR Thermo Fisher Scientific Cat#Q22158; RRID:AB_2556514 CD25 BD Biosciences Cat#555430; RRID:AB_395824 CD19 Standard BioTools Cat#3142001B; RRID:AB_3661857 IFI16 Santa Cruz Biotechnology Cat#sc-8023; RRID:AB_627775 CCR5 Standard BioTools Cat#3144007A; RRID:AB_2892770 IFITM1 Proteintech Cat#60074-1-Ig; RRID:AB_2233405 CD8 Standard BioTools Cat#3146001B; RRID:AB_3661846 cGAS Cell Signaling Technology Cat#79978S; RRID:AB_2905508 IFIT3 Novus Cat#NBP2-71006; RRID:AB_3094693 AIM2 Santa Cruz Biotechnology Cat#sc-293174; RRID:AB_3665129 HSA (CD24) Standard BioTools Cat#3150009B; RRID:AB_2916042 LAG3 Standard BioTools Cat#3150030B; RRID:AB_3661851 pSTING Cell Signaling Technology Cat#40818; RRID:AB_2799187 SLFN11 Santa Cruz Biotechnology Cat#sc-374339; RRID:AB_10989536 MX2 Santa Cruz Biotechnology Cat#sc-271527; RRID:AB_10649506 IFIT1 Novus Cat#NBP2-71005; RRID:AB_3363102 pIRF3 Cell Signaling Technology Cat#29047; RRID:AB_2773013 SAMHD1 Proteintech Cat#12586-1-AP; RRID:AB_2183496 TRIM28 R and D Systems Cat#MAB7785; RRID:AB_3096992 PAF1 Santa Cruz Biotechnology Cat#sc-514491; RRID:AB_3665128 CCR7 Standard BioTools Cat#3159003A; RRID:AB_2938859 MX1 Cell Signaling Technology Cat#62815; RRID:AB_3665127 CD45RO Biolegend Cat#304239; RRID:AB_2563752 CD69 Standard BioTools Cat#3162001B; RRID:AB_3096016 TLR9 Novus Cat#NBP2-24729; RRID:AB_3272891 CXCR5 Standard BioTools Cat#3164029B; RRID:AB_3665126 pSAMHD1 Sigma-Aldrich Cat#MABF934; RRID:AB_3665125 PQBP1 Santa Cruz Biotechnology Cat#sc-376039; RRID:AB_10989350 CD27 Standard BioTools Cat#3167002B; RRID:AB_3094744 PD1 BD Biosciences Cat#562138; RRID:AB_10897007 CD45RA Standard BioTools Cat#3169008B; RRID:AB_3665124 CD3 Standard BioTools Cat3170001B; RRID:AB_2811085 RIGI Novus Cat#NBP2-61849; RRID:AB_3351257 CD38 Standard BioTools Cat#3172007B; RRID:AB_2756288 BRD4 Abcam Cat#ab182446; RRID:AB_3665123 CD4 Standard BioTools Cat#3174004B; RRID:AB_3661864 CD14 Biolegend Cat#301843; RRID:AB_2562813 CD127 Standard BioTools Cat#3176004B; RRID:AB_3665122 TIGIT Standard BioTools Cat#3209013B; RRID:AB_2905649 Bacterial and virus strains HIV-F4.HSA (NL-HSA.6ATRi-C.109FPB4.ecto) Cavrois et al.34 N/A HIV-F4.HSA virions containing BlaM-Vpr Cavrois et al.34,67 N/A (Continued on next page) 16 Cell Reports 44, 115202, January 28, 2025 .. REAGENT or RESOURCE SOURCE IDENTIFIER GraphPad Prism (Version 10.2.0) http://www.graphpad.com/ RRID:SCR_002798 RStudio (2022.07.2 + 576 "Spotted Wakerobin" Release) https://rstudio.com/ RRID:SCR_000432 R Project for Statistical Computing (Version 4.2.1) http://www.r-project.org/ RRID:SCR_001905 CUHIMSR/CytofBatchAdjust Schuyler et al.95; https://github.com/ CUHIMSR/CytofBatchAdjust N/A ggplot2 https://cran.r-project.org/web/ packages/ggplot2/index.html RRID:SCR_014601 Rtsne https://github.com/jkrijthe/Rtsne RRID:SCR_016342 Seurat Hao et al.96; https://satijalab.org/ seurat/get_started.html RRID:SCR_016341 Harmony Korsunsky et al.97; https://github.com/ immunogenomics/harmony RRID:SCR_022206 RandomForest Package in R Breiman98; https://cran.r-project. org/web/packages/randomForest/ RRID:SCR_015718 lme4 Bates et al.99; https://cran.r-project. org/web/packages/lme4/index.html RRID:SCR_015654 emmeans https://CRAN.R-project.org/ package=emmeans RRID:SCR_018734



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    The sequential gating strategy was used to identify immune cell populations from healthy human samples. Healthy donor samples, either ( A ) cryopreserved peripheral blood mononuclear cells (cPBMCs) or ( B ) fresh whole blood (WB), were stimulated for 15 min, fixed, palladium-barcoded, and stained with a comprehensive panel of 19 (for cPBMCs) or 20 (for whole blood) surface markers. Within the single-cell gate, non-granulocytes (leukocytes) were defined as CD66b-CD45 + , while granulocytes were confirmed solely in whole blood as CD66b + CD45-. Lymphocytes were resolved using CD3 and CD56 [T cells (CD3 + CD56-)], NKT cells (CD3 + CD56 + ), and NK cells (CD3-CD56 + ). B cells were identified as CD19 + CD20 +/- within the CD3-CD56- gate, further confirmed by negative expression of CD123 and CD11c. Both T and B cells were further subset based on the expression of CD4, CD8, CD45RA, CD27, and CD25 or IgD and CD27, respectively. Non-T, non-B, and non-NK cells (CD45 + CD3-CD19-CD56-CD20-) were separated based on their expression of CD11c and HLA-DR. Total DCs were defined as CD11c + HLA-DR + and can be further defined through the expression of CD123 (plasmacytoid DCs). Monocytes were resolved within this non-lymphocyte gate based on their expression of CD14 and CD16.

    Journal: Bio-protocol

    Article Title: Dual Phospho-CyTOF Workflows for Comparative JAK/STAT Signaling Analysis in Human Cryopreserved PBMCs and Whole Blood

    doi: 10.21769/BioProtoc.5512

    Figure Lengend Snippet: The sequential gating strategy was used to identify immune cell populations from healthy human samples. Healthy donor samples, either ( A ) cryopreserved peripheral blood mononuclear cells (cPBMCs) or ( B ) fresh whole blood (WB), were stimulated for 15 min, fixed, palladium-barcoded, and stained with a comprehensive panel of 19 (for cPBMCs) or 20 (for whole blood) surface markers. Within the single-cell gate, non-granulocytes (leukocytes) were defined as CD66b-CD45 + , while granulocytes were confirmed solely in whole blood as CD66b + CD45-. Lymphocytes were resolved using CD3 and CD56 [T cells (CD3 + CD56-)], NKT cells (CD3 + CD56 + ), and NK cells (CD3-CD56 + ). B cells were identified as CD19 + CD20 +/- within the CD3-CD56- gate, further confirmed by negative expression of CD123 and CD11c. Both T and B cells were further subset based on the expression of CD4, CD8, CD45RA, CD27, and CD25 or IgD and CD27, respectively. Non-T, non-B, and non-NK cells (CD45 + CD3-CD19-CD56-CD20-) were separated based on their expression of CD11c and HLA-DR. Total DCs were defined as CD11c + HLA-DR + and can be further defined through the expression of CD123 (plasmacytoid DCs). Monocytes were resolved within this non-lymphocyte gate based on their expression of CD14 and CD16.

    Article Snippet: 170 Er , CD3 , Standard BioTools , 3170001B , UCHT1 , 17.5.

    Techniques: Staining, Expressing

    This bar graph illustrates the average frequency of total (%) of the major immune cell subsets in donor-matched ( A ) cPBMCs and ( B ) whole blood from (n = 3) healthy individuals. The data points represent the average frequency of total (%) of each subset, with error bars indicating the standard deviation from triplicate samples. Major immune subsets, including B cells, monocytes, dendritic cells (DCs), NK cells, NKT cells, and T cells, were identified using a comprehensive high-dimensional immunophenotyping strategy defined after Gaussian parameter cleanup (Figure 2) and doublet exclusion. These lineages were identified through expression of markers such as CD3, CD19, CD56, CD14, CD11c, HLA-DR, and CD66b, following the detailed gating strategy outlined in Figure 3. As expected, cPBMCs have a higher proportional representation of lymphocytes (T cells, B cells, NK, and NKT cells) and monocytes but lack granulocytes, whereas whole blood maintains the complete cellular repertoire, including a larger proportion of granulocytes and what appears to be a lower percentage of lymphocyte or monocyte subsets due to being expressed as a percentage of total leukocytes.

    Journal: Bio-protocol

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    doi: 10.21769/BioProtoc.5512

    Figure Lengend Snippet: This bar graph illustrates the average frequency of total (%) of the major immune cell subsets in donor-matched ( A ) cPBMCs and ( B ) whole blood from (n = 3) healthy individuals. The data points represent the average frequency of total (%) of each subset, with error bars indicating the standard deviation from triplicate samples. Major immune subsets, including B cells, monocytes, dendritic cells (DCs), NK cells, NKT cells, and T cells, were identified using a comprehensive high-dimensional immunophenotyping strategy defined after Gaussian parameter cleanup (Figure 2) and doublet exclusion. These lineages were identified through expression of markers such as CD3, CD19, CD56, CD14, CD11c, HLA-DR, and CD66b, following the detailed gating strategy outlined in Figure 3. As expected, cPBMCs have a higher proportional representation of lymphocytes (T cells, B cells, NK, and NKT cells) and monocytes but lack granulocytes, whereas whole blood maintains the complete cellular repertoire, including a larger proportion of granulocytes and what appears to be a lower percentage of lymphocyte or monocyte subsets due to being expressed as a percentage of total leukocytes.

    Article Snippet: 170 Er , CD3 , Standard BioTools , 3170001B , UCHT1 , 17.5.

    Techniques: Standard Deviation, Expressing

    KEY RESOURCES TABLE

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    Figure Lengend Snippet: KEY RESOURCES TABLE

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    Figure Lengend Snippet:

    Article Snippet: CD3-170Er (clone UCHT1, 1:100 dilution) , Standard BioTools , Cat# 3170001B.

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    doi: 10.1016/j.isci.2024.110827

    Figure Lengend Snippet:

    Article Snippet: Anti-Human CD3 (UCHT1)-170Er , Fluidigm Corporation , Cat#3170001B.

    Techniques: Recombinant, Software