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mouse breast cancer cell line emt6  (ATCC)


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    ATCC mouse breast cancer cell line emt6
    Genome-wide CRISPR-Cas9 screen identifies genetic regulators of CD47 expression in murine cancer cell lines. (A) Schematic of the FACS-based CRISPR screening pipeline. Mouse cancer cells (B16F10A, MC38, and <t>EMT6)</t> were transduced with the mTKO genome-wide CRISPR-Cas9 library, followed by puromycin selection and passaging for 5 days to ensure stable sgRNA integration. Cells were stained with fluorophore-conjugated anti-CD47 antibodies and sorted by FACS into the top 30% (CD47 high ) and bottom 30% (CD47 low ) populations. Genomic DNA was extracted, and sgRNA abundance was determined by NGS and analyzed using DrugZ to calculate NormZ scores. (B) Ranked NormZ scores of sgRNAs in B16F10A, MC38, and EMT6 cells. Negative NormZ scores indicate positive regulators of CD47 (gene knockouts reduce CD47 expression), while positive NormZ scores indicate negative regulators (gene knockouts increase CD47 expression). CD47 itself ranked as the top positive regulator in all three cell lines, validating the screen’s robustness. (C) GO enrichment analysis of significant negative regulators of CD47 expression (|NormZ| > 3) in B16F10A and MC38 cells. Negative regulators were significantly enriched in pathways related to mitochondrial signaling, including oxidative phosphorylation, mitochondrial translation, and respiratory chain complex assembly.
    Mouse Breast Cancer Cell Line Emt6, supplied by ATCC, used in various techniques. Bioz Stars score: 99/100, based on 739 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/mouse breast cancer cell line emt6/product/ATCC
    Average 99 stars, based on 739 article reviews
    mouse breast cancer cell line emt6 - by Bioz Stars, 2026-03
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    1) Product Images from "FACS-based genome-wide CRISPR screening platform identifies modulators of CD47"

    Article Title: FACS-based genome-wide CRISPR screening platform identifies modulators of CD47

    Journal: Frontiers in Immunology

    doi: 10.3389/fimmu.2025.1684539

    Genome-wide CRISPR-Cas9 screen identifies genetic regulators of CD47 expression in murine cancer cell lines. (A) Schematic of the FACS-based CRISPR screening pipeline. Mouse cancer cells (B16F10A, MC38, and EMT6) were transduced with the mTKO genome-wide CRISPR-Cas9 library, followed by puromycin selection and passaging for 5 days to ensure stable sgRNA integration. Cells were stained with fluorophore-conjugated anti-CD47 antibodies and sorted by FACS into the top 30% (CD47 high ) and bottom 30% (CD47 low ) populations. Genomic DNA was extracted, and sgRNA abundance was determined by NGS and analyzed using DrugZ to calculate NormZ scores. (B) Ranked NormZ scores of sgRNAs in B16F10A, MC38, and EMT6 cells. Negative NormZ scores indicate positive regulators of CD47 (gene knockouts reduce CD47 expression), while positive NormZ scores indicate negative regulators (gene knockouts increase CD47 expression). CD47 itself ranked as the top positive regulator in all three cell lines, validating the screen’s robustness. (C) GO enrichment analysis of significant negative regulators of CD47 expression (|NormZ| > 3) in B16F10A and MC38 cells. Negative regulators were significantly enriched in pathways related to mitochondrial signaling, including oxidative phosphorylation, mitochondrial translation, and respiratory chain complex assembly.
    Figure Legend Snippet: Genome-wide CRISPR-Cas9 screen identifies genetic regulators of CD47 expression in murine cancer cell lines. (A) Schematic of the FACS-based CRISPR screening pipeline. Mouse cancer cells (B16F10A, MC38, and EMT6) were transduced with the mTKO genome-wide CRISPR-Cas9 library, followed by puromycin selection and passaging for 5 days to ensure stable sgRNA integration. Cells were stained with fluorophore-conjugated anti-CD47 antibodies and sorted by FACS into the top 30% (CD47 high ) and bottom 30% (CD47 low ) populations. Genomic DNA was extracted, and sgRNA abundance was determined by NGS and analyzed using DrugZ to calculate NormZ scores. (B) Ranked NormZ scores of sgRNAs in B16F10A, MC38, and EMT6 cells. Negative NormZ scores indicate positive regulators of CD47 (gene knockouts reduce CD47 expression), while positive NormZ scores indicate negative regulators (gene knockouts increase CD47 expression). CD47 itself ranked as the top positive regulator in all three cell lines, validating the screen’s robustness. (C) GO enrichment analysis of significant negative regulators of CD47 expression (|NormZ| > 3) in B16F10A and MC38 cells. Negative regulators were significantly enriched in pathways related to mitochondrial signaling, including oxidative phosphorylation, mitochondrial translation, and respiratory chain complex assembly.

    Techniques Used: Genome Wide, CRISPR, Expressing, Transduction, Selection, Passaging, Staining, Phospho-proteomics

    DNAJC13 is a conserved positive regulator of CD47 expression identified across multiple cancer cell lines. (A) Venn diagram of significant positive regulators of CD47 expression identified in the genome-wide CRISPR screen. CD47 and DNAJC13 were the only two genes consistently identified as significant positive regulators (|NormZ| > 3) across all three cell lines (B16F10A, MC38, and EMT6). (B) Correlation of DNAJC13 and CD47 expression in human cancers. Scatter plots generated from TCGA datasets via GEPIA2 show a positive correlation between DNAJC13 and CD47 expression in breast cancer (BRCA), colon adenocarcinoma (COAD), and skin cutaneous melanoma (SKCM). Pearson correlation coefficients (r) and p-values are indicated. (C) Western blot validation of DNAJC13 regulation of CD47 expression. CRISPR-Cas9–mediated DNAJC13 knockout (three independent sgRNAs: DNAJC13#1, #2, #3) markedly reduced CD47 protein levels compared with vector control in B16F10A, MC38, and EMT6 cells. GAPDH was used as a loading control. (D) Flow cytometry analysis of surface CD47 expression. Representative FACS histograms show reduced surface CD47 expression in DNAJC13 KO cells (green, yellow, and orange peaks; three independent sgRNAs) compared with vector controls (red) in B16F10A, MC38, and EMT6 cells. Isotype control is shown in blue. Quantification of Mean fluorescence intensity (MFI) is shown on the right.
    Figure Legend Snippet: DNAJC13 is a conserved positive regulator of CD47 expression identified across multiple cancer cell lines. (A) Venn diagram of significant positive regulators of CD47 expression identified in the genome-wide CRISPR screen. CD47 and DNAJC13 were the only two genes consistently identified as significant positive regulators (|NormZ| > 3) across all three cell lines (B16F10A, MC38, and EMT6). (B) Correlation of DNAJC13 and CD47 expression in human cancers. Scatter plots generated from TCGA datasets via GEPIA2 show a positive correlation between DNAJC13 and CD47 expression in breast cancer (BRCA), colon adenocarcinoma (COAD), and skin cutaneous melanoma (SKCM). Pearson correlation coefficients (r) and p-values are indicated. (C) Western blot validation of DNAJC13 regulation of CD47 expression. CRISPR-Cas9–mediated DNAJC13 knockout (three independent sgRNAs: DNAJC13#1, #2, #3) markedly reduced CD47 protein levels compared with vector control in B16F10A, MC38, and EMT6 cells. GAPDH was used as a loading control. (D) Flow cytometry analysis of surface CD47 expression. Representative FACS histograms show reduced surface CD47 expression in DNAJC13 KO cells (green, yellow, and orange peaks; three independent sgRNAs) compared with vector controls (red) in B16F10A, MC38, and EMT6 cells. Isotype control is shown in blue. Quantification of Mean fluorescence intensity (MFI) is shown on the right.

    Techniques Used: Expressing, Genome Wide, CRISPR, Generated, Western Blot, Biomarker Discovery, Knock-Out, Plasmid Preparation, Control, Flow Cytometry, Fluorescence

    DNAJC13 negatively correlates with macrophage infiltration and regulates macrophage-mediated phagocytosis. (A) Correlation between gene expression and macrophage infiltration in human cancers. Scatter plots generated using TIMER2 show negative correlations between CD47 expression (top row) or DNAJC13 expression (bottom row) and macrophage infiltration levels in breast cancer (BRCA), colon adenocarcinoma (COAD), and skin cutaneous melanoma (SKCM). Spearman’s correlation coefficients (Rho) and p-values are indicated. (B, C) DNAJC13 loss enhances macrophage phagocytosis in vitro . Flow cytometry analysis of macrophage-mediated phagocytosis in co-culture assays. DNAJC13 knockout (KO) or CD47 KO cancer cells (B16F10A, MC38, EMT6) were co-cultured with (B) RAW 264.7 or (C) J774 macrophages for the indicated time. Representative FACS plots show increased phagocytosis (higher engulfment rate) in DNAJC13-KO and CD47-KO groups compared with vector controls.
    Figure Legend Snippet: DNAJC13 negatively correlates with macrophage infiltration and regulates macrophage-mediated phagocytosis. (A) Correlation between gene expression and macrophage infiltration in human cancers. Scatter plots generated using TIMER2 show negative correlations between CD47 expression (top row) or DNAJC13 expression (bottom row) and macrophage infiltration levels in breast cancer (BRCA), colon adenocarcinoma (COAD), and skin cutaneous melanoma (SKCM). Spearman’s correlation coefficients (Rho) and p-values are indicated. (B, C) DNAJC13 loss enhances macrophage phagocytosis in vitro . Flow cytometry analysis of macrophage-mediated phagocytosis in co-culture assays. DNAJC13 knockout (KO) or CD47 KO cancer cells (B16F10A, MC38, EMT6) were co-cultured with (B) RAW 264.7 or (C) J774 macrophages for the indicated time. Representative FACS plots show increased phagocytosis (higher engulfment rate) in DNAJC13-KO and CD47-KO groups compared with vector controls.

    Techniques Used: Gene Expression, Generated, Expressing, In Vitro, Flow Cytometry, Co-Culture Assay, Knock-Out, Cell Culture, Plasmid Preparation



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    Genome-wide CRISPR-Cas9 screen identifies genetic regulators of CD47 expression in murine cancer cell lines. (A) Schematic of the FACS-based CRISPR screening pipeline. Mouse cancer cells (B16F10A, MC38, and <t>EMT6)</t> were transduced with the mTKO genome-wide CRISPR-Cas9 library, followed by puromycin selection and passaging for 5 days to ensure stable sgRNA integration. Cells were stained with fluorophore-conjugated anti-CD47 antibodies and sorted by FACS into the top 30% (CD47 high ) and bottom 30% (CD47 low ) populations. Genomic DNA was extracted, and sgRNA abundance was determined by NGS and analyzed using DrugZ to calculate NormZ scores. (B) Ranked NormZ scores of sgRNAs in B16F10A, MC38, and EMT6 cells. Negative NormZ scores indicate positive regulators of CD47 (gene knockouts reduce CD47 expression), while positive NormZ scores indicate negative regulators (gene knockouts increase CD47 expression). CD47 itself ranked as the top positive regulator in all three cell lines, validating the screen’s robustness. (C) GO enrichment analysis of significant negative regulators of CD47 expression (|NormZ| > 3) in B16F10A and MC38 cells. Negative regulators were significantly enriched in pathways related to mitochondrial signaling, including oxidative phosphorylation, mitochondrial translation, and respiratory chain complex assembly.
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    Genome-wide CRISPR-Cas9 screen identifies genetic regulators of CD47 expression in murine cancer cell lines. (A) Schematic of the FACS-based CRISPR screening pipeline. Mouse cancer cells (B16F10A, MC38, and <t>EMT6)</t> were transduced with the mTKO genome-wide CRISPR-Cas9 library, followed by puromycin selection and passaging for 5 days to ensure stable sgRNA integration. Cells were stained with fluorophore-conjugated anti-CD47 antibodies and sorted by FACS into the top 30% (CD47 high ) and bottom 30% (CD47 low ) populations. Genomic DNA was extracted, and sgRNA abundance was determined by NGS and analyzed using DrugZ to calculate NormZ scores. (B) Ranked NormZ scores of sgRNAs in B16F10A, MC38, and EMT6 cells. Negative NormZ scores indicate positive regulators of CD47 (gene knockouts reduce CD47 expression), while positive NormZ scores indicate negative regulators (gene knockouts increase CD47 expression). CD47 itself ranked as the top positive regulator in all three cell lines, validating the screen’s robustness. (C) GO enrichment analysis of significant negative regulators of CD47 expression (|NormZ| > 3) in B16F10A and MC38 cells. Negative regulators were significantly enriched in pathways related to mitochondrial signaling, including oxidative phosphorylation, mitochondrial translation, and respiratory chain complex assembly.
    Mouse Breast Cancer Cell Lines, supplied by ATCC, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Genome-wide CRISPR-Cas9 screen identifies genetic regulators of CD47 expression in murine cancer cell lines. (A) Schematic of the FACS-based CRISPR screening pipeline. Mouse cancer cells (B16F10A, MC38, and <t>EMT6)</t> were transduced with the mTKO genome-wide CRISPR-Cas9 library, followed by puromycin selection and passaging for 5 days to ensure stable sgRNA integration. Cells were stained with fluorophore-conjugated anti-CD47 antibodies and sorted by FACS into the top 30% (CD47 high ) and bottom 30% (CD47 low ) populations. Genomic DNA was extracted, and sgRNA abundance was determined by NGS and analyzed using DrugZ to calculate NormZ scores. (B) Ranked NormZ scores of sgRNAs in B16F10A, MC38, and EMT6 cells. Negative NormZ scores indicate positive regulators of CD47 (gene knockouts reduce CD47 expression), while positive NormZ scores indicate negative regulators (gene knockouts increase CD47 expression). CD47 itself ranked as the top positive regulator in all three cell lines, validating the screen’s robustness. (C) GO enrichment analysis of significant negative regulators of CD47 expression (|NormZ| > 3) in B16F10A and MC38 cells. Negative regulators were significantly enriched in pathways related to mitochondrial signaling, including oxidative phosphorylation, mitochondrial translation, and respiratory chain complex assembly.
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    ATCC 2d monolayer cell culture a mouse breast cancer cell line emt 6
    Genome-wide CRISPR-Cas9 screen identifies genetic regulators of CD47 expression in murine cancer cell lines. (A) Schematic of the FACS-based CRISPR screening pipeline. Mouse cancer cells (B16F10A, MC38, and <t>EMT6)</t> were transduced with the mTKO genome-wide CRISPR-Cas9 library, followed by puromycin selection and passaging for 5 days to ensure stable sgRNA integration. Cells were stained with fluorophore-conjugated anti-CD47 antibodies and sorted by FACS into the top 30% (CD47 high ) and bottom 30% (CD47 low ) populations. Genomic DNA was extracted, and sgRNA abundance was determined by NGS and analyzed using DrugZ to calculate NormZ scores. (B) Ranked NormZ scores of sgRNAs in B16F10A, MC38, and EMT6 cells. Negative NormZ scores indicate positive regulators of CD47 (gene knockouts reduce CD47 expression), while positive NormZ scores indicate negative regulators (gene knockouts increase CD47 expression). CD47 itself ranked as the top positive regulator in all three cell lines, validating the screen’s robustness. (C) GO enrichment analysis of significant negative regulators of CD47 expression (|NormZ| > 3) in B16F10A and MC38 cells. Negative regulators were significantly enriched in pathways related to mitochondrial signaling, including oxidative phosphorylation, mitochondrial translation, and respiratory chain complex assembly.
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    Genome-wide CRISPR-Cas9 screen identifies genetic regulators of CD47 expression in murine cancer cell lines. (A) Schematic of the FACS-based CRISPR screening pipeline. Mouse cancer cells (B16F10A, MC38, and EMT6) were transduced with the mTKO genome-wide CRISPR-Cas9 library, followed by puromycin selection and passaging for 5 days to ensure stable sgRNA integration. Cells were stained with fluorophore-conjugated anti-CD47 antibodies and sorted by FACS into the top 30% (CD47 high ) and bottom 30% (CD47 low ) populations. Genomic DNA was extracted, and sgRNA abundance was determined by NGS and analyzed using DrugZ to calculate NormZ scores. (B) Ranked NormZ scores of sgRNAs in B16F10A, MC38, and EMT6 cells. Negative NormZ scores indicate positive regulators of CD47 (gene knockouts reduce CD47 expression), while positive NormZ scores indicate negative regulators (gene knockouts increase CD47 expression). CD47 itself ranked as the top positive regulator in all three cell lines, validating the screen’s robustness. (C) GO enrichment analysis of significant negative regulators of CD47 expression (|NormZ| > 3) in B16F10A and MC38 cells. Negative regulators were significantly enriched in pathways related to mitochondrial signaling, including oxidative phosphorylation, mitochondrial translation, and respiratory chain complex assembly.

    Journal: Frontiers in Immunology

    Article Title: FACS-based genome-wide CRISPR screening platform identifies modulators of CD47

    doi: 10.3389/fimmu.2025.1684539

    Figure Lengend Snippet: Genome-wide CRISPR-Cas9 screen identifies genetic regulators of CD47 expression in murine cancer cell lines. (A) Schematic of the FACS-based CRISPR screening pipeline. Mouse cancer cells (B16F10A, MC38, and EMT6) were transduced with the mTKO genome-wide CRISPR-Cas9 library, followed by puromycin selection and passaging for 5 days to ensure stable sgRNA integration. Cells were stained with fluorophore-conjugated anti-CD47 antibodies and sorted by FACS into the top 30% (CD47 high ) and bottom 30% (CD47 low ) populations. Genomic DNA was extracted, and sgRNA abundance was determined by NGS and analyzed using DrugZ to calculate NormZ scores. (B) Ranked NormZ scores of sgRNAs in B16F10A, MC38, and EMT6 cells. Negative NormZ scores indicate positive regulators of CD47 (gene knockouts reduce CD47 expression), while positive NormZ scores indicate negative regulators (gene knockouts increase CD47 expression). CD47 itself ranked as the top positive regulator in all three cell lines, validating the screen’s robustness. (C) GO enrichment analysis of significant negative regulators of CD47 expression (|NormZ| > 3) in B16F10A and MC38 cells. Negative regulators were significantly enriched in pathways related to mitochondrial signaling, including oxidative phosphorylation, mitochondrial translation, and respiratory chain complex assembly.

    Article Snippet: Mouse melanoma cell line B16F10A, mouse colon cancer cell line MC38, mouse breast cancer cell line EMT6, two mouse macrophage cells J774A-1, RAW264.7 are purchased from the American Type Culture Collection (ATCC).

    Techniques: Genome Wide, CRISPR, Expressing, Transduction, Selection, Passaging, Staining, Phospho-proteomics

    DNAJC13 is a conserved positive regulator of CD47 expression identified across multiple cancer cell lines. (A) Venn diagram of significant positive regulators of CD47 expression identified in the genome-wide CRISPR screen. CD47 and DNAJC13 were the only two genes consistently identified as significant positive regulators (|NormZ| > 3) across all three cell lines (B16F10A, MC38, and EMT6). (B) Correlation of DNAJC13 and CD47 expression in human cancers. Scatter plots generated from TCGA datasets via GEPIA2 show a positive correlation between DNAJC13 and CD47 expression in breast cancer (BRCA), colon adenocarcinoma (COAD), and skin cutaneous melanoma (SKCM). Pearson correlation coefficients (r) and p-values are indicated. (C) Western blot validation of DNAJC13 regulation of CD47 expression. CRISPR-Cas9–mediated DNAJC13 knockout (three independent sgRNAs: DNAJC13#1, #2, #3) markedly reduced CD47 protein levels compared with vector control in B16F10A, MC38, and EMT6 cells. GAPDH was used as a loading control. (D) Flow cytometry analysis of surface CD47 expression. Representative FACS histograms show reduced surface CD47 expression in DNAJC13 KO cells (green, yellow, and orange peaks; three independent sgRNAs) compared with vector controls (red) in B16F10A, MC38, and EMT6 cells. Isotype control is shown in blue. Quantification of Mean fluorescence intensity (MFI) is shown on the right.

    Journal: Frontiers in Immunology

    Article Title: FACS-based genome-wide CRISPR screening platform identifies modulators of CD47

    doi: 10.3389/fimmu.2025.1684539

    Figure Lengend Snippet: DNAJC13 is a conserved positive regulator of CD47 expression identified across multiple cancer cell lines. (A) Venn diagram of significant positive regulators of CD47 expression identified in the genome-wide CRISPR screen. CD47 and DNAJC13 were the only two genes consistently identified as significant positive regulators (|NormZ| > 3) across all three cell lines (B16F10A, MC38, and EMT6). (B) Correlation of DNAJC13 and CD47 expression in human cancers. Scatter plots generated from TCGA datasets via GEPIA2 show a positive correlation between DNAJC13 and CD47 expression in breast cancer (BRCA), colon adenocarcinoma (COAD), and skin cutaneous melanoma (SKCM). Pearson correlation coefficients (r) and p-values are indicated. (C) Western blot validation of DNAJC13 regulation of CD47 expression. CRISPR-Cas9–mediated DNAJC13 knockout (three independent sgRNAs: DNAJC13#1, #2, #3) markedly reduced CD47 protein levels compared with vector control in B16F10A, MC38, and EMT6 cells. GAPDH was used as a loading control. (D) Flow cytometry analysis of surface CD47 expression. Representative FACS histograms show reduced surface CD47 expression in DNAJC13 KO cells (green, yellow, and orange peaks; three independent sgRNAs) compared with vector controls (red) in B16F10A, MC38, and EMT6 cells. Isotype control is shown in blue. Quantification of Mean fluorescence intensity (MFI) is shown on the right.

    Article Snippet: Mouse melanoma cell line B16F10A, mouse colon cancer cell line MC38, mouse breast cancer cell line EMT6, two mouse macrophage cells J774A-1, RAW264.7 are purchased from the American Type Culture Collection (ATCC).

    Techniques: Expressing, Genome Wide, CRISPR, Generated, Western Blot, Biomarker Discovery, Knock-Out, Plasmid Preparation, Control, Flow Cytometry, Fluorescence

    DNAJC13 negatively correlates with macrophage infiltration and regulates macrophage-mediated phagocytosis. (A) Correlation between gene expression and macrophage infiltration in human cancers. Scatter plots generated using TIMER2 show negative correlations between CD47 expression (top row) or DNAJC13 expression (bottom row) and macrophage infiltration levels in breast cancer (BRCA), colon adenocarcinoma (COAD), and skin cutaneous melanoma (SKCM). Spearman’s correlation coefficients (Rho) and p-values are indicated. (B, C) DNAJC13 loss enhances macrophage phagocytosis in vitro . Flow cytometry analysis of macrophage-mediated phagocytosis in co-culture assays. DNAJC13 knockout (KO) or CD47 KO cancer cells (B16F10A, MC38, EMT6) were co-cultured with (B) RAW 264.7 or (C) J774 macrophages for the indicated time. Representative FACS plots show increased phagocytosis (higher engulfment rate) in DNAJC13-KO and CD47-KO groups compared with vector controls.

    Journal: Frontiers in Immunology

    Article Title: FACS-based genome-wide CRISPR screening platform identifies modulators of CD47

    doi: 10.3389/fimmu.2025.1684539

    Figure Lengend Snippet: DNAJC13 negatively correlates with macrophage infiltration and regulates macrophage-mediated phagocytosis. (A) Correlation between gene expression and macrophage infiltration in human cancers. Scatter plots generated using TIMER2 show negative correlations between CD47 expression (top row) or DNAJC13 expression (bottom row) and macrophage infiltration levels in breast cancer (BRCA), colon adenocarcinoma (COAD), and skin cutaneous melanoma (SKCM). Spearman’s correlation coefficients (Rho) and p-values are indicated. (B, C) DNAJC13 loss enhances macrophage phagocytosis in vitro . Flow cytometry analysis of macrophage-mediated phagocytosis in co-culture assays. DNAJC13 knockout (KO) or CD47 KO cancer cells (B16F10A, MC38, EMT6) were co-cultured with (B) RAW 264.7 or (C) J774 macrophages for the indicated time. Representative FACS plots show increased phagocytosis (higher engulfment rate) in DNAJC13-KO and CD47-KO groups compared with vector controls.

    Article Snippet: Mouse melanoma cell line B16F10A, mouse colon cancer cell line MC38, mouse breast cancer cell line EMT6, two mouse macrophage cells J774A-1, RAW264.7 are purchased from the American Type Culture Collection (ATCC).

    Techniques: Gene Expression, Generated, Expressing, In Vitro, Flow Cytometry, Co-Culture Assay, Knock-Out, Cell Culture, Plasmid Preparation