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non small cell lung cancer cell lines pc 9  (ATCC)


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    Structured Review

    ATCC non small cell lung cancer cell lines pc 9
    IFN-γ responsiveness correlates DNA damage and repair responses in NSCLC cell lines (A) Relative cell viability of A549 <t>or</t> <t>PC-9</t> treated with the indicated concentration of IFN-γ for 24 h are shown. Data are presented as mean ± SD. (B) Reactome analysis of the GSE180942 dataset was performed using differential CRISPR β-scores under IFN-γ treatment (Δβ = PC-9 − A549). Bars indicate the normalized enrichment score (NES) for each pathway indicated. Positive NES denotes pathways whose constituent genes are more essential in A549, whereas negative NES denotes pathways more essential in PC-9 upon IFN-γ treatment.
    Non Small Cell Lung Cancer Cell Lines Pc 9, supplied by ATCC, used in various techniques. Bioz Stars score: 99/100, based on 31404 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    1) Product Images from "ATM inhibition restores IFN-γ sensitivity and induces ferroptosis in NSCLC via DNA damage response"

    Article Title: ATM inhibition restores IFN-γ sensitivity and induces ferroptosis in NSCLC via DNA damage response

    Journal: Biochemistry and Biophysics Reports

    doi: 10.1016/j.bbrep.2026.102568

    IFN-γ responsiveness correlates DNA damage and repair responses in NSCLC cell lines (A) Relative cell viability of A549 or PC-9 treated with the indicated concentration of IFN-γ for 24 h are shown. Data are presented as mean ± SD. (B) Reactome analysis of the GSE180942 dataset was performed using differential CRISPR β-scores under IFN-γ treatment (Δβ = PC-9 − A549). Bars indicate the normalized enrichment score (NES) for each pathway indicated. Positive NES denotes pathways whose constituent genes are more essential in A549, whereas negative NES denotes pathways more essential in PC-9 upon IFN-γ treatment.
    Figure Legend Snippet: IFN-γ responsiveness correlates DNA damage and repair responses in NSCLC cell lines (A) Relative cell viability of A549 or PC-9 treated with the indicated concentration of IFN-γ for 24 h are shown. Data are presented as mean ± SD. (B) Reactome analysis of the GSE180942 dataset was performed using differential CRISPR β-scores under IFN-γ treatment (Δβ = PC-9 − A549). Bars indicate the normalized enrichment score (NES) for each pathway indicated. Positive NES denotes pathways whose constituent genes are more essential in A549, whereas negative NES denotes pathways more essential in PC-9 upon IFN-γ treatment.

    Techniques Used: Concentration Assay, CRISPR

    Inhibition of ATM restores NSCLC cells to IFN-γ by inducing DNA damage response (A) Cell viability of A549 (left panel) or PC-9 (right panel) treated with IFN-γ (1000 ng/ml) and/or KU-55933 (10 μM) for 24 h are shown. Data are presented as mean ± SD. * p < 0.05. (B) Expression of γH2AX and b-Actin (loading control) in A549 (left panel) or PC-9 (right panel) cells treated with IFN-γ (1000 ng/ml) and/or KU-55933 (10 μM) for 24 h are shown.
    Figure Legend Snippet: Inhibition of ATM restores NSCLC cells to IFN-γ by inducing DNA damage response (A) Cell viability of A549 (left panel) or PC-9 (right panel) treated with IFN-γ (1000 ng/ml) and/or KU-55933 (10 μM) for 24 h are shown. Data are presented as mean ± SD. * p < 0.05. (B) Expression of γH2AX and b-Actin (loading control) in A549 (left panel) or PC-9 (right panel) cells treated with IFN-γ (1000 ng/ml) and/or KU-55933 (10 μM) for 24 h are shown.

    Techniques Used: Inhibition, Expressing, Control

    Inhibition of ATM in combination with IFN-γ induce ferroptosis in NSCLCs Cell viability of A549 (A) or PC-9 (B) treated with the indicated combination of IFN-γ (1000 ng/ml), KU-55933 (10 μM), Ferrostatin-1 (5 μM), and Liproxstatin-1 (5 μM) for 24 h are shown. Data are presented as mean ± SD. * p < 0.05.
    Figure Legend Snippet: Inhibition of ATM in combination with IFN-γ induce ferroptosis in NSCLCs Cell viability of A549 (A) or PC-9 (B) treated with the indicated combination of IFN-γ (1000 ng/ml), KU-55933 (10 μM), Ferrostatin-1 (5 μM), and Liproxstatin-1 (5 μM) for 24 h are shown. Data are presented as mean ± SD. * p < 0.05.

    Techniques Used: Inhibition



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    ATCC non small cell lung cancer cell lines pc 9
    IFN-γ responsiveness correlates DNA damage and repair responses in NSCLC cell lines (A) Relative cell viability of A549 <t>or</t> <t>PC-9</t> treated with the indicated concentration of IFN-γ for 24 h are shown. Data are presented as mean ± SD. (B) Reactome analysis of the GSE180942 dataset was performed using differential CRISPR β-scores under IFN-γ treatment (Δβ = PC-9 − A549). Bars indicate the normalized enrichment score (NES) for each pathway indicated. Positive NES denotes pathways whose constituent genes are more essential in A549, whereas negative NES denotes pathways more essential in PC-9 upon IFN-γ treatment.
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    Internalization of SD-EVLPs and its inhibitory effects on lung cancer cell proliferation (A) Internalization of SD-EVLPs by lung cancer cells; (B, C) CCK-8 assays assessing the effects of SD-EVLPs on lung cancer cell viability; (D–G) EdU assays evaluating the proliferation of <t>A549</t> and NCI-H1299 cells; (H–I) Colony formation assays assessing the proliferative capacity of A549 and NCI-H1299 cells. Data are presented as the mean ± SEM, n = 3 independent experiments. Compared with the control group, * P < 0.05, ** P < 0.01, and *** P < 0.001.
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    Internalization of SD-EVLPs and its inhibitory effects on lung cancer cell proliferation (A) Internalization of SD-EVLPs by lung cancer cells; (B, C) CCK-8 assays assessing the effects of SD-EVLPs on lung cancer cell viability; (D–G) EdU assays evaluating the proliferation of <t>A549</t> and NCI-H1299 cells; (H–I) Colony formation assays assessing the proliferative capacity of A549 and NCI-H1299 cells. Data are presented as the mean ± SEM, n = 3 independent experiments. Compared with the control group, * P < 0.05, ** P < 0.01, and *** P < 0.001.
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    A: classification of CCEs in different phenotypes based on the analysis of longitudinal imaging data. Red: CellTrace™ Far Red, blue: Annexin V, green: EGFR. B: UMAP based on the transcriptomic data from 10,604 CCEs containing <t>A549</t> cells treated with 10 µM Olmutinib. The colors represent different transcriptomic clusters. C: UMAP based on the transcriptomic data (same as panel B) colored according to the imaging-derived CCE classification in panel A. 2,328 CCEs that could not be accurately classified were excluded from the analysis. D: proportion of CCEs (y axis) belonging to each imaging-based phenotype (indicated by the color) within each gene expression cluster (x axis). E: Upset plot showing overlap of significant GSEA pathway enrichments across three classification strategies. The combination of transcriptomic clustering with imaging classification identified 15 unique pathways not found in either single-modality strategy. F: significant interaction effects (p_adj < 0.05) between RNA clusters and imaging phenotypes on the prediction of drug resistance pathway modules (G2M checkpoint, E2F targets, MYC targets, DNA repair, EMT) (see Methods). The daughter cell resistant phenotype showed 7 out of 14 total significant interactions, indicating that pathway activities are maximally explained by the combination of transcript state and the daughter cell resistant phenotypic classification. G: Confusion matrix for elastic net prediction of imaging phenotypes from gene expression. H: STRING PPI network for top 50 positive coefficient genes (associated with daughter cell resistance). I: STRING PPI network for top 50 negative coefficient genes (inversely associated with daughter cell resistance). J: Selected differentially expressed genes between expression-defined clusters (x axis). The color represents the average expression (scaled per gene) and the size of the circle indicates the percentage of CCEs expressing the gene. Cluster 2 showed strong enrichment for cell division pathways and overexpressed the proliferation marker TOP2A. Cluster 3 exhibited activation of multiple EGFR bypass pathways with overexpression of EPHA7 (64), HGF (65), ERBB2 (66), and AXL(67), all capable of activating MAPK signaling independently of EGFR. Cluster 5 displayed enrichment of p53 targets, including upregulation of quiescence-associated genes such as GADD45A, REDD1, ATF3, SFN, and BTG2.
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    ATCC a549 lung cancer cells
    A: classification of CCEs in different phenotypes based on the analysis of longitudinal imaging data. Red: CellTrace™ Far Red, blue: Annexin V, green: EGFR. B: UMAP based on the transcriptomic data from 10,604 CCEs containing <t>A549</t> cells treated with 10 µM Olmutinib. The colors represent different transcriptomic clusters. C: UMAP based on the transcriptomic data (same as panel B) colored according to the imaging-derived CCE classification in panel A. 2,328 CCEs that could not be accurately classified were excluded from the analysis. D: proportion of CCEs (y axis) belonging to each imaging-based phenotype (indicated by the color) within each gene expression cluster (x axis). E: Upset plot showing overlap of significant GSEA pathway enrichments across three classification strategies. The combination of transcriptomic clustering with imaging classification identified 15 unique pathways not found in either single-modality strategy. F: significant interaction effects (p_adj < 0.05) between RNA clusters and imaging phenotypes on the prediction of drug resistance pathway modules (G2M checkpoint, E2F targets, MYC targets, DNA repair, EMT) (see Methods). The daughter cell resistant phenotype showed 7 out of 14 total significant interactions, indicating that pathway activities are maximally explained by the combination of transcript state and the daughter cell resistant phenotypic classification. G: Confusion matrix for elastic net prediction of imaging phenotypes from gene expression. H: STRING PPI network for top 50 positive coefficient genes (associated with daughter cell resistance). I: STRING PPI network for top 50 negative coefficient genes (inversely associated with daughter cell resistance). J: Selected differentially expressed genes between expression-defined clusters (x axis). The color represents the average expression (scaled per gene) and the size of the circle indicates the percentage of CCEs expressing the gene. Cluster 2 showed strong enrichment for cell division pathways and overexpressed the proliferation marker TOP2A. Cluster 3 exhibited activation of multiple EGFR bypass pathways with overexpression of EPHA7 (64), HGF (65), ERBB2 (66), and AXL(67), all capable of activating MAPK signaling independently of EGFR. Cluster 5 displayed enrichment of p53 targets, including upregulation of quiescence-associated genes such as GADD45A, REDD1, ATF3, SFN, and BTG2.
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    ATCC a549 human lung cancer cells
    A: classification of CCEs in different phenotypes based on the analysis of longitudinal imaging data. Red: CellTrace™ Far Red, blue: Annexin V, green: EGFR. B: UMAP based on the transcriptomic data from 10,604 CCEs containing <t>A549</t> cells treated with 10 µM Olmutinib. The colors represent different transcriptomic clusters. C: UMAP based on the transcriptomic data (same as panel B) colored according to the imaging-derived CCE classification in panel A. 2,328 CCEs that could not be accurately classified were excluded from the analysis. D: proportion of CCEs (y axis) belonging to each imaging-based phenotype (indicated by the color) within each gene expression cluster (x axis). E: Upset plot showing overlap of significant GSEA pathway enrichments across three classification strategies. The combination of transcriptomic clustering with imaging classification identified 15 unique pathways not found in either single-modality strategy. F: significant interaction effects (p_adj < 0.05) between RNA clusters and imaging phenotypes on the prediction of drug resistance pathway modules (G2M checkpoint, E2F targets, MYC targets, DNA repair, EMT) (see Methods). The daughter cell resistant phenotype showed 7 out of 14 total significant interactions, indicating that pathway activities are maximally explained by the combination of transcript state and the daughter cell resistant phenotypic classification. G: Confusion matrix for elastic net prediction of imaging phenotypes from gene expression. H: STRING PPI network for top 50 positive coefficient genes (associated with daughter cell resistance). I: STRING PPI network for top 50 negative coefficient genes (inversely associated with daughter cell resistance). J: Selected differentially expressed genes between expression-defined clusters (x axis). The color represents the average expression (scaled per gene) and the size of the circle indicates the percentage of CCEs expressing the gene. Cluster 2 showed strong enrichment for cell division pathways and overexpressed the proliferation marker TOP2A. Cluster 3 exhibited activation of multiple EGFR bypass pathways with overexpression of EPHA7 (64), HGF (65), ERBB2 (66), and AXL(67), all capable of activating MAPK signaling independently of EGFR. Cluster 5 displayed enrichment of p53 targets, including upregulation of quiescence-associated genes such as GADD45A, REDD1, ATF3, SFN, and BTG2.
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    ATCC cell cultures human lung cancer cell line a549
    A: classification of CCEs in different phenotypes based on the analysis of longitudinal imaging data. Red: CellTrace™ Far Red, blue: Annexin V, green: EGFR. B: UMAP based on the transcriptomic data from 10,604 CCEs containing <t>A549</t> cells treated with 10 µM Olmutinib. The colors represent different transcriptomic clusters. C: UMAP based on the transcriptomic data (same as panel B) colored according to the imaging-derived CCE classification in panel A. 2,328 CCEs that could not be accurately classified were excluded from the analysis. D: proportion of CCEs (y axis) belonging to each imaging-based phenotype (indicated by the color) within each gene expression cluster (x axis). E: Upset plot showing overlap of significant GSEA pathway enrichments across three classification strategies. The combination of transcriptomic clustering with imaging classification identified 15 unique pathways not found in either single-modality strategy. F: significant interaction effects (p_adj < 0.05) between RNA clusters and imaging phenotypes on the prediction of drug resistance pathway modules (G2M checkpoint, E2F targets, MYC targets, DNA repair, EMT) (see Methods). The daughter cell resistant phenotype showed 7 out of 14 total significant interactions, indicating that pathway activities are maximally explained by the combination of transcript state and the daughter cell resistant phenotypic classification. G: Confusion matrix for elastic net prediction of imaging phenotypes from gene expression. H: STRING PPI network for top 50 positive coefficient genes (associated with daughter cell resistance). I: STRING PPI network for top 50 negative coefficient genes (inversely associated with daughter cell resistance). J: Selected differentially expressed genes between expression-defined clusters (x axis). The color represents the average expression (scaled per gene) and the size of the circle indicates the percentage of CCEs expressing the gene. Cluster 2 showed strong enrichment for cell division pathways and overexpressed the proliferation marker TOP2A. Cluster 3 exhibited activation of multiple EGFR bypass pathways with overexpression of EPHA7 (64), HGF (65), ERBB2 (66), and AXL(67), all capable of activating MAPK signaling independently of EGFR. Cluster 5 displayed enrichment of p53 targets, including upregulation of quiescence-associated genes such as GADD45A, REDD1, ATF3, SFN, and BTG2.
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    ATCC lung cancer cell line a549
    A: classification of CCEs in different phenotypes based on the analysis of longitudinal imaging data. Red: CellTrace™ Far Red, blue: Annexin V, green: EGFR. B: UMAP based on the transcriptomic data from 10,604 CCEs containing <t>A549</t> cells treated with 10 µM Olmutinib. The colors represent different transcriptomic clusters. C: UMAP based on the transcriptomic data (same as panel B) colored according to the imaging-derived CCE classification in panel A. 2,328 CCEs that could not be accurately classified were excluded from the analysis. D: proportion of CCEs (y axis) belonging to each imaging-based phenotype (indicated by the color) within each gene expression cluster (x axis). E: Upset plot showing overlap of significant GSEA pathway enrichments across three classification strategies. The combination of transcriptomic clustering with imaging classification identified 15 unique pathways not found in either single-modality strategy. F: significant interaction effects (p_adj < 0.05) between RNA clusters and imaging phenotypes on the prediction of drug resistance pathway modules (G2M checkpoint, E2F targets, MYC targets, DNA repair, EMT) (see Methods). The daughter cell resistant phenotype showed 7 out of 14 total significant interactions, indicating that pathway activities are maximally explained by the combination of transcript state and the daughter cell resistant phenotypic classification. G: Confusion matrix for elastic net prediction of imaging phenotypes from gene expression. H: STRING PPI network for top 50 positive coefficient genes (associated with daughter cell resistance). I: STRING PPI network for top 50 negative coefficient genes (inversely associated with daughter cell resistance). J: Selected differentially expressed genes between expression-defined clusters (x axis). The color represents the average expression (scaled per gene) and the size of the circle indicates the percentage of CCEs expressing the gene. Cluster 2 showed strong enrichment for cell division pathways and overexpressed the proliferation marker TOP2A. Cluster 3 exhibited activation of multiple EGFR bypass pathways with overexpression of EPHA7 (64), HGF (65), ERBB2 (66), and AXL(67), all capable of activating MAPK signaling independently of EGFR. Cluster 5 displayed enrichment of p53 targets, including upregulation of quiescence-associated genes such as GADD45A, REDD1, ATF3, SFN, and BTG2.
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    Image Search Results


    IFN-γ responsiveness correlates DNA damage and repair responses in NSCLC cell lines (A) Relative cell viability of A549 or PC-9 treated with the indicated concentration of IFN-γ for 24 h are shown. Data are presented as mean ± SD. (B) Reactome analysis of the GSE180942 dataset was performed using differential CRISPR β-scores under IFN-γ treatment (Δβ = PC-9 − A549). Bars indicate the normalized enrichment score (NES) for each pathway indicated. Positive NES denotes pathways whose constituent genes are more essential in A549, whereas negative NES denotes pathways more essential in PC-9 upon IFN-γ treatment.

    Journal: Biochemistry and Biophysics Reports

    Article Title: ATM inhibition restores IFN-γ sensitivity and induces ferroptosis in NSCLC via DNA damage response

    doi: 10.1016/j.bbrep.2026.102568

    Figure Lengend Snippet: IFN-γ responsiveness correlates DNA damage and repair responses in NSCLC cell lines (A) Relative cell viability of A549 or PC-9 treated with the indicated concentration of IFN-γ for 24 h are shown. Data are presented as mean ± SD. (B) Reactome analysis of the GSE180942 dataset was performed using differential CRISPR β-scores under IFN-γ treatment (Δβ = PC-9 − A549). Bars indicate the normalized enrichment score (NES) for each pathway indicated. Positive NES denotes pathways whose constituent genes are more essential in A549, whereas negative NES denotes pathways more essential in PC-9 upon IFN-γ treatment.

    Article Snippet: The human non-small cell lung cancer cell lines PC-9 (kindly gifted from Dr. Kiura, Okayama University, Japan) and A549 (CCL-185, obtained from American Type Culture Collection) were used.

    Techniques: Concentration Assay, CRISPR

    Inhibition of ATM restores NSCLC cells to IFN-γ by inducing DNA damage response (A) Cell viability of A549 (left panel) or PC-9 (right panel) treated with IFN-γ (1000 ng/ml) and/or KU-55933 (10 μM) for 24 h are shown. Data are presented as mean ± SD. * p < 0.05. (B) Expression of γH2AX and b-Actin (loading control) in A549 (left panel) or PC-9 (right panel) cells treated with IFN-γ (1000 ng/ml) and/or KU-55933 (10 μM) for 24 h are shown.

    Journal: Biochemistry and Biophysics Reports

    Article Title: ATM inhibition restores IFN-γ sensitivity and induces ferroptosis in NSCLC via DNA damage response

    doi: 10.1016/j.bbrep.2026.102568

    Figure Lengend Snippet: Inhibition of ATM restores NSCLC cells to IFN-γ by inducing DNA damage response (A) Cell viability of A549 (left panel) or PC-9 (right panel) treated with IFN-γ (1000 ng/ml) and/or KU-55933 (10 μM) for 24 h are shown. Data are presented as mean ± SD. * p < 0.05. (B) Expression of γH2AX and b-Actin (loading control) in A549 (left panel) or PC-9 (right panel) cells treated with IFN-γ (1000 ng/ml) and/or KU-55933 (10 μM) for 24 h are shown.

    Article Snippet: The human non-small cell lung cancer cell lines PC-9 (kindly gifted from Dr. Kiura, Okayama University, Japan) and A549 (CCL-185, obtained from American Type Culture Collection) were used.

    Techniques: Inhibition, Expressing, Control

    Inhibition of ATM in combination with IFN-γ induce ferroptosis in NSCLCs Cell viability of A549 (A) or PC-9 (B) treated with the indicated combination of IFN-γ (1000 ng/ml), KU-55933 (10 μM), Ferrostatin-1 (5 μM), and Liproxstatin-1 (5 μM) for 24 h are shown. Data are presented as mean ± SD. * p < 0.05.

    Journal: Biochemistry and Biophysics Reports

    Article Title: ATM inhibition restores IFN-γ sensitivity and induces ferroptosis in NSCLC via DNA damage response

    doi: 10.1016/j.bbrep.2026.102568

    Figure Lengend Snippet: Inhibition of ATM in combination with IFN-γ induce ferroptosis in NSCLCs Cell viability of A549 (A) or PC-9 (B) treated with the indicated combination of IFN-γ (1000 ng/ml), KU-55933 (10 μM), Ferrostatin-1 (5 μM), and Liproxstatin-1 (5 μM) for 24 h are shown. Data are presented as mean ± SD. * p < 0.05.

    Article Snippet: The human non-small cell lung cancer cell lines PC-9 (kindly gifted from Dr. Kiura, Okayama University, Japan) and A549 (CCL-185, obtained from American Type Culture Collection) were used.

    Techniques: Inhibition

    Internalization of SD-EVLPs and its inhibitory effects on lung cancer cell proliferation (A) Internalization of SD-EVLPs by lung cancer cells; (B, C) CCK-8 assays assessing the effects of SD-EVLPs on lung cancer cell viability; (D–G) EdU assays evaluating the proliferation of A549 and NCI-H1299 cells; (H–I) Colony formation assays assessing the proliferative capacity of A549 and NCI-H1299 cells. Data are presented as the mean ± SEM, n = 3 independent experiments. Compared with the control group, * P < 0.05, ** P < 0.01, and *** P < 0.001.

    Journal: Frontiers in Oncology

    Article Title: Selaginella doederleinii -derived extracellular vesicle-like particles suppress lung cancer with ferroptosis-associated changes and modulation of the FABP4/PPARG/GPX4 axis

    doi: 10.3389/fonc.2026.1829211

    Figure Lengend Snippet: Internalization of SD-EVLPs and its inhibitory effects on lung cancer cell proliferation (A) Internalization of SD-EVLPs by lung cancer cells; (B, C) CCK-8 assays assessing the effects of SD-EVLPs on lung cancer cell viability; (D–G) EdU assays evaluating the proliferation of A549 and NCI-H1299 cells; (H–I) Colony formation assays assessing the proliferative capacity of A549 and NCI-H1299 cells. Data are presented as the mean ± SEM, n = 3 independent experiments. Compared with the control group, * P < 0.05, ** P < 0.01, and *** P < 0.001.

    Article Snippet: Human lung cancer cell lines A549 (Cat. No.: CL-0016) and NCI-H1299 (Cat. No.: CL-0165) were purchased from Wuhan Procell Life Science & Technology Co., Ltd. Four-week-old male BALB/c nude mice (SPF grade) were purchased from Hunan Silaike Jingda Laboratory Animal Co., Ltd. (Changsha, China), with the Animal Production License No.: SCXK (Xiang) 2021-0002.

    Techniques: CCK-8 Assay, Control

    SD-EVLPs inhibit migration and invasion of lung cancer cells (A–D) Wound healing assays assessing the effect of SD-EVLPs on the migration of A549 and NCI-H1299 cells; (E, F) Transwell assays evaluating the invasion ability of A549 and NCI-H1299 cells; (G–I) Western blot analysis of E-cadherin and N-cadherin protein expression. Data are presented as the mean ± SEM, n = 3 independent experiments. Compared with the control group, * P < 0.05, ** P < 0.01, and *** P < 0.001.

    Journal: Frontiers in Oncology

    Article Title: Selaginella doederleinii -derived extracellular vesicle-like particles suppress lung cancer with ferroptosis-associated changes and modulation of the FABP4/PPARG/GPX4 axis

    doi: 10.3389/fonc.2026.1829211

    Figure Lengend Snippet: SD-EVLPs inhibit migration and invasion of lung cancer cells (A–D) Wound healing assays assessing the effect of SD-EVLPs on the migration of A549 and NCI-H1299 cells; (E, F) Transwell assays evaluating the invasion ability of A549 and NCI-H1299 cells; (G–I) Western blot analysis of E-cadherin and N-cadherin protein expression. Data are presented as the mean ± SEM, n = 3 independent experiments. Compared with the control group, * P < 0.05, ** P < 0.01, and *** P < 0.001.

    Article Snippet: Human lung cancer cell lines A549 (Cat. No.: CL-0016) and NCI-H1299 (Cat. No.: CL-0165) were purchased from Wuhan Procell Life Science & Technology Co., Ltd. Four-week-old male BALB/c nude mice (SPF grade) were purchased from Hunan Silaike Jingda Laboratory Animal Co., Ltd. (Changsha, China), with the Animal Production License No.: SCXK (Xiang) 2021-0002.

    Techniques: Migration, Western Blot, Expressing, Control

    RT-qPCR and Western blot analysis of ferroptosis-related gene expression in lung cancer cells (A, B) RT-qPCR analysis of GPX4 and SLC7A11 mRNA expression in A549 cells; (C, D) RT-qPCR analysis of GPX4 and SLC7A11 mRNA expression in NCI-H1299 cells; (E–H) Western blot analysis of GPX4 and xCT protein expression in lung cancer cells; (I) TEM assessment of mitochondrial ultrastructure. Data are presented as the mean ± SEM, n = 3 independent experiments. Compared with the control group, * P < 0.05, ** P < 0.01, and *** P < 0.001.

    Journal: Frontiers in Oncology

    Article Title: Selaginella doederleinii -derived extracellular vesicle-like particles suppress lung cancer with ferroptosis-associated changes and modulation of the FABP4/PPARG/GPX4 axis

    doi: 10.3389/fonc.2026.1829211

    Figure Lengend Snippet: RT-qPCR and Western blot analysis of ferroptosis-related gene expression in lung cancer cells (A, B) RT-qPCR analysis of GPX4 and SLC7A11 mRNA expression in A549 cells; (C, D) RT-qPCR analysis of GPX4 and SLC7A11 mRNA expression in NCI-H1299 cells; (E–H) Western blot analysis of GPX4 and xCT protein expression in lung cancer cells; (I) TEM assessment of mitochondrial ultrastructure. Data are presented as the mean ± SEM, n = 3 independent experiments. Compared with the control group, * P < 0.05, ** P < 0.01, and *** P < 0.001.

    Article Snippet: Human lung cancer cell lines A549 (Cat. No.: CL-0016) and NCI-H1299 (Cat. No.: CL-0165) were purchased from Wuhan Procell Life Science & Technology Co., Ltd. Four-week-old male BALB/c nude mice (SPF grade) were purchased from Hunan Silaike Jingda Laboratory Animal Co., Ltd. (Changsha, China), with the Animal Production License No.: SCXK (Xiang) 2021-0002.

    Techniques: Quantitative RT-PCR, Western Blot, Gene Expression, Expressing, Control

    SD-EVLPs modulate the FABP4/PPARG/GPX4-associated pathway in lung cancer cells (A–D) RT-qPCR analysis of FABP4 and PPARG mRNA expression in A549 and NCI-H1299 cells; (E–H) Western blot analysis of FABP4 and PPARG protein expression in A549 and NCI-H1299 cells. Data are presented as the mean ± SEM, n = 3 independent experiments. Compared with the control group, * P < 0.05, ** P < 0.01, and *** P < 0.001.

    Journal: Frontiers in Oncology

    Article Title: Selaginella doederleinii -derived extracellular vesicle-like particles suppress lung cancer with ferroptosis-associated changes and modulation of the FABP4/PPARG/GPX4 axis

    doi: 10.3389/fonc.2026.1829211

    Figure Lengend Snippet: SD-EVLPs modulate the FABP4/PPARG/GPX4-associated pathway in lung cancer cells (A–D) RT-qPCR analysis of FABP4 and PPARG mRNA expression in A549 and NCI-H1299 cells; (E–H) Western blot analysis of FABP4 and PPARG protein expression in A549 and NCI-H1299 cells. Data are presented as the mean ± SEM, n = 3 independent experiments. Compared with the control group, * P < 0.05, ** P < 0.01, and *** P < 0.001.

    Article Snippet: Human lung cancer cell lines A549 (Cat. No.: CL-0016) and NCI-H1299 (Cat. No.: CL-0165) were purchased from Wuhan Procell Life Science & Technology Co., Ltd. Four-week-old male BALB/c nude mice (SPF grade) were purchased from Hunan Silaike Jingda Laboratory Animal Co., Ltd. (Changsha, China), with the Animal Production License No.: SCXK (Xiang) 2021-0002.

    Techniques: Quantitative RT-PCR, Expressing, Western Blot, Control

    A: classification of CCEs in different phenotypes based on the analysis of longitudinal imaging data. Red: CellTrace™ Far Red, blue: Annexin V, green: EGFR. B: UMAP based on the transcriptomic data from 10,604 CCEs containing A549 cells treated with 10 µM Olmutinib. The colors represent different transcriptomic clusters. C: UMAP based on the transcriptomic data (same as panel B) colored according to the imaging-derived CCE classification in panel A. 2,328 CCEs that could not be accurately classified were excluded from the analysis. D: proportion of CCEs (y axis) belonging to each imaging-based phenotype (indicated by the color) within each gene expression cluster (x axis). E: Upset plot showing overlap of significant GSEA pathway enrichments across three classification strategies. The combination of transcriptomic clustering with imaging classification identified 15 unique pathways not found in either single-modality strategy. F: significant interaction effects (p_adj < 0.05) between RNA clusters and imaging phenotypes on the prediction of drug resistance pathway modules (G2M checkpoint, E2F targets, MYC targets, DNA repair, EMT) (see Methods). The daughter cell resistant phenotype showed 7 out of 14 total significant interactions, indicating that pathway activities are maximally explained by the combination of transcript state and the daughter cell resistant phenotypic classification. G: Confusion matrix for elastic net prediction of imaging phenotypes from gene expression. H: STRING PPI network for top 50 positive coefficient genes (associated with daughter cell resistance). I: STRING PPI network for top 50 negative coefficient genes (inversely associated with daughter cell resistance). J: Selected differentially expressed genes between expression-defined clusters (x axis). The color represents the average expression (scaled per gene) and the size of the circle indicates the percentage of CCEs expressing the gene. Cluster 2 showed strong enrichment for cell division pathways and overexpressed the proliferation marker TOP2A. Cluster 3 exhibited activation of multiple EGFR bypass pathways with overexpression of EPHA7 (64), HGF (65), ERBB2 (66), and AXL(67), all capable of activating MAPK signaling independently of EGFR. Cluster 5 displayed enrichment of p53 targets, including upregulation of quiescence-associated genes such as GADD45A, REDD1, ATF3, SFN, and BTG2.

    Journal: bioRxiv

    Article Title: Scalable longitudinal imaging and transcriptomics of cells in dynamic enclosures

    doi: 10.64898/2026.05.05.723030

    Figure Lengend Snippet: A: classification of CCEs in different phenotypes based on the analysis of longitudinal imaging data. Red: CellTrace™ Far Red, blue: Annexin V, green: EGFR. B: UMAP based on the transcriptomic data from 10,604 CCEs containing A549 cells treated with 10 µM Olmutinib. The colors represent different transcriptomic clusters. C: UMAP based on the transcriptomic data (same as panel B) colored according to the imaging-derived CCE classification in panel A. 2,328 CCEs that could not be accurately classified were excluded from the analysis. D: proportion of CCEs (y axis) belonging to each imaging-based phenotype (indicated by the color) within each gene expression cluster (x axis). E: Upset plot showing overlap of significant GSEA pathway enrichments across three classification strategies. The combination of transcriptomic clustering with imaging classification identified 15 unique pathways not found in either single-modality strategy. F: significant interaction effects (p_adj < 0.05) between RNA clusters and imaging phenotypes on the prediction of drug resistance pathway modules (G2M checkpoint, E2F targets, MYC targets, DNA repair, EMT) (see Methods). The daughter cell resistant phenotype showed 7 out of 14 total significant interactions, indicating that pathway activities are maximally explained by the combination of transcript state and the daughter cell resistant phenotypic classification. G: Confusion matrix for elastic net prediction of imaging phenotypes from gene expression. H: STRING PPI network for top 50 positive coefficient genes (associated with daughter cell resistance). I: STRING PPI network for top 50 negative coefficient genes (inversely associated with daughter cell resistance). J: Selected differentially expressed genes between expression-defined clusters (x axis). The color represents the average expression (scaled per gene) and the size of the circle indicates the percentage of CCEs expressing the gene. Cluster 2 showed strong enrichment for cell division pathways and overexpressed the proliferation marker TOP2A. Cluster 3 exhibited activation of multiple EGFR bypass pathways with overexpression of EPHA7 (64), HGF (65), ERBB2 (66), and AXL(67), all capable of activating MAPK signaling independently of EGFR. Cluster 5 displayed enrichment of p53 targets, including upregulation of quiescence-associated genes such as GADD45A, REDD1, ATF3, SFN, and BTG2.

    Article Snippet: Human lung cancer A549 cells were purchased from ATCC (CCL-185), and cultured in DMEM supplemented with 10% FBS and 1% Pen-Strep.

    Techniques: Imaging, Derivative Assay, Gene Expression, Expressing, Marker, Activation Assay, Over Expression