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Abbkine Inc cell cycle analysis kit
Proteomic profiling of EEDJF-treated colorectal cancer (CRC) cells. (A–C) Effects of EEDJF on the viability of HCT116, SW480, and HaCaT cells. (D) Volcano plot showing differentially expressed proteins (DEPs) between control and EEDJF-treated groups. (E) Hierarchical clustering <t>analysis</t> of DEPs. (F) Gene Ontology (GO) enrichment analysis of DEPs. (G) Top 20 enriched KEGG pathways associated with DEPs. (H) Quantitative analysis of <t>cell</t> <t>cycle-related</t> proteins. Data are presented as mean ± SD from three independent biological experiments. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.
Cell Cycle Analysis Kit, supplied by Abbkine Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/cell+cycle+analysis+kit/pmc13265501-96-1-7?v=Abbkine+Inc
Average 86 stars, based on 1 article reviews
cell cycle analysis kit - by Bioz Stars, 2026-06
86/100 stars

Images

1) Product Images from "Integrated multi-omics analysis suggests the involvement of PI3K-Akt/p21 signaling in the anti-colorectal cancer effects of Diaphragma Juglandis Fructus extract"

Article Title: Integrated multi-omics analysis suggests the involvement of PI3K-Akt/p21 signaling in the anti-colorectal cancer effects of Diaphragma Juglandis Fructus extract

Journal: Frontiers in Pharmacology

doi: 10.3389/fphar.2026.1833123

Proteomic profiling of EEDJF-treated colorectal cancer (CRC) cells. (A–C) Effects of EEDJF on the viability of HCT116, SW480, and HaCaT cells. (D) Volcano plot showing differentially expressed proteins (DEPs) between control and EEDJF-treated groups. (E) Hierarchical clustering analysis of DEPs. (F) Gene Ontology (GO) enrichment analysis of DEPs. (G) Top 20 enriched KEGG pathways associated with DEPs. (H) Quantitative analysis of cell cycle-related proteins. Data are presented as mean ± SD from three independent biological experiments. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.
Figure Legend Snippet: Proteomic profiling of EEDJF-treated colorectal cancer (CRC) cells. (A–C) Effects of EEDJF on the viability of HCT116, SW480, and HaCaT cells. (D) Volcano plot showing differentially expressed proteins (DEPs) between control and EEDJF-treated groups. (E) Hierarchical clustering analysis of DEPs. (F) Gene Ontology (GO) enrichment analysis of DEPs. (G) Top 20 enriched KEGG pathways associated with DEPs. (H) Quantitative analysis of cell cycle-related proteins. Data are presented as mean ± SD from three independent biological experiments. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Techniques Used: Control

EEDJF induces G1/S cell cycle arrest in HCT116 cells. (A) Western blot analysis of cell cycle–related proteins (p21, Cyclin D1, and CDK4) following EEDJF treatment. (B) Densitometric quantification of protein expression shown in (A) . Band intensities were quantified using ImageJ software and normalized to β-actin. (C) Flow cytometric analysis of cell cycle distribution after EEDJF treatment. (D,E) Quantitative distribution of cells in G1, S, and G2/M phases. Data are presented as mean ± SD from three independent biological experiments. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.
Figure Legend Snippet: EEDJF induces G1/S cell cycle arrest in HCT116 cells. (A) Western blot analysis of cell cycle–related proteins (p21, Cyclin D1, and CDK4) following EEDJF treatment. (B) Densitometric quantification of protein expression shown in (A) . Band intensities were quantified using ImageJ software and normalized to β-actin. (C) Flow cytometric analysis of cell cycle distribution after EEDJF treatment. (D,E) Quantitative distribution of cells in G1, S, and G2/M phases. Data are presented as mean ± SD from three independent biological experiments. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Techniques Used: Western Blot, Expressing, Software



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Image Search Results


Proteomic profiling of EEDJF-treated colorectal cancer (CRC) cells. (A–C) Effects of EEDJF on the viability of HCT116, SW480, and HaCaT cells. (D) Volcano plot showing differentially expressed proteins (DEPs) between control and EEDJF-treated groups. (E) Hierarchical clustering analysis of DEPs. (F) Gene Ontology (GO) enrichment analysis of DEPs. (G) Top 20 enriched KEGG pathways associated with DEPs. (H) Quantitative analysis of cell cycle-related proteins. Data are presented as mean ± SD from three independent biological experiments. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Journal: Frontiers in Pharmacology

Article Title: Integrated multi-omics analysis suggests the involvement of PI3K-Akt/p21 signaling in the anti-colorectal cancer effects of Diaphragma Juglandis Fructus extract

doi: 10.3389/fphar.2026.1833123

Figure Lengend Snippet: Proteomic profiling of EEDJF-treated colorectal cancer (CRC) cells. (A–C) Effects of EEDJF on the viability of HCT116, SW480, and HaCaT cells. (D) Volcano plot showing differentially expressed proteins (DEPs) between control and EEDJF-treated groups. (E) Hierarchical clustering analysis of DEPs. (F) Gene Ontology (GO) enrichment analysis of DEPs. (G) Top 20 enriched KEGG pathways associated with DEPs. (H) Quantitative analysis of cell cycle-related proteins. Data are presented as mean ± SD from three independent biological experiments. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Article Snippet: The cell cycle analysis kit was from Abbkine (Wuhan, China).

Techniques: Control

EEDJF induces G1/S cell cycle arrest in HCT116 cells. (A) Western blot analysis of cell cycle–related proteins (p21, Cyclin D1, and CDK4) following EEDJF treatment. (B) Densitometric quantification of protein expression shown in (A) . Band intensities were quantified using ImageJ software and normalized to β-actin. (C) Flow cytometric analysis of cell cycle distribution after EEDJF treatment. (D,E) Quantitative distribution of cells in G1, S, and G2/M phases. Data are presented as mean ± SD from three independent biological experiments. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Journal: Frontiers in Pharmacology

Article Title: Integrated multi-omics analysis suggests the involvement of PI3K-Akt/p21 signaling in the anti-colorectal cancer effects of Diaphragma Juglandis Fructus extract

doi: 10.3389/fphar.2026.1833123

Figure Lengend Snippet: EEDJF induces G1/S cell cycle arrest in HCT116 cells. (A) Western blot analysis of cell cycle–related proteins (p21, Cyclin D1, and CDK4) following EEDJF treatment. (B) Densitometric quantification of protein expression shown in (A) . Band intensities were quantified using ImageJ software and normalized to β-actin. (C) Flow cytometric analysis of cell cycle distribution after EEDJF treatment. (D,E) Quantitative distribution of cells in G1, S, and G2/M phases. Data are presented as mean ± SD from three independent biological experiments. *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Article Snippet: The cell cycle analysis kit was from Abbkine (Wuhan, China).

Techniques: Western Blot, Expressing, Software

Broad-spectrum antitumor activity of B6ADC (A) Tumor suppression efficacy of B6ADC in multiple pancreatic cancer CDX models ( n = 5∼7/group). (B) Tumor suppression efficacy of B6ADC in four cell lines with varying TROP2/c-Met expression levels ( n = 5∼7/group). Scale bar, 50 μm. (C) Tumor suppression effect of a single 2.2 mg/kg dose of B6ADC in SPC-A1 lung adenocarcinoma-bearing mice with a tumor volume of 1,200 mm 3 ( n = 5∼7/group). (D) Comparison of tumor suppression between B6ADC and the combination of parental “TROP2ADC and c-MetADC” in SPC-A1 tumor-bearing mice (initial tumor volume: 600 mm 3 ) ( n = 6/group). See also . (E) Comparison of tumor suppression between B6ADC and the combination of “marketed TROP2ADC (SG) and marketed c-MetADC (Teliso-V)” in SPC-A1 tumor-bearing mice (initial tumor volume: 600 mm 3 ) ( n = 5∼7/group). See also . (F) Transmission electron microscopy images showing apoptosis in BxPC-3 tumors on day 7 after treatment with B6ADC and the control. Apoptotic features (e.g., chromatin condensation and nuclear fragmentation) were observed in the B6ADC-treated group ( n = 3 independent experiments). Scale bar, 2 μm. See also . Statistical analysis was performed using two-way ANOVA. The data are presented as the means ± SDs. ∗ p < 0.05, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, and p > 0.05 (no significance, ns).

Journal: Cell Reports Medicine

Article Title: A bispecific nanobody-drug conjugate targeting TROP2 and c-Met for low-concentration, single-dose treatment of pancreatic cancer

doi: 10.1016/j.xcrm.2026.102688

Figure Lengend Snippet: Broad-spectrum antitumor activity of B6ADC (A) Tumor suppression efficacy of B6ADC in multiple pancreatic cancer CDX models ( n = 5∼7/group). (B) Tumor suppression efficacy of B6ADC in four cell lines with varying TROP2/c-Met expression levels ( n = 5∼7/group). Scale bar, 50 μm. (C) Tumor suppression effect of a single 2.2 mg/kg dose of B6ADC in SPC-A1 lung adenocarcinoma-bearing mice with a tumor volume of 1,200 mm 3 ( n = 5∼7/group). (D) Comparison of tumor suppression between B6ADC and the combination of parental “TROP2ADC and c-MetADC” in SPC-A1 tumor-bearing mice (initial tumor volume: 600 mm 3 ) ( n = 6/group). See also . (E) Comparison of tumor suppression between B6ADC and the combination of “marketed TROP2ADC (SG) and marketed c-MetADC (Teliso-V)” in SPC-A1 tumor-bearing mice (initial tumor volume: 600 mm 3 ) ( n = 5∼7/group). See also . (F) Transmission electron microscopy images showing apoptosis in BxPC-3 tumors on day 7 after treatment with B6ADC and the control. Apoptotic features (e.g., chromatin condensation and nuclear fragmentation) were observed in the B6ADC-treated group ( n = 3 independent experiments). Scale bar, 2 μm. See also . Statistical analysis was performed using two-way ANOVA. The data are presented as the means ± SDs. ∗ p < 0.05, ∗∗∗ p < 0.001, ∗∗∗∗ p < 0.0001, and p > 0.05 (no significance, ns).

Article Snippet: Cell Cycle and Apoptosis Analysis Kit , BEYOTIME , Cat#C1052.

Techniques: Activity Assay, Expressing, Comparison, Transmission Assay, Electron Microscopy, Control

Single-cell transcriptomics reveals heterogeneity of the cell cycle dynamics underlying NE lineage plasticity (A and B) UMAP visualization of single-cell transcriptomes from the in-house (A, 64,756 cells) and public (B, 201,720 cells) scRNA-seq cohorts, colored by major cell type annotations. The zoomed views show the UMAP visualizations of epithelial cells, classified into lu/ba, NE-like, and NE subtypes. (C) Dot plot showing the expression patterns of lu/ba, NE-like, and NE signature genes across epithelial cell subtypes from the in-house and public cohorts. (D) KEGG pathway enrichment analysis of DEGs from in-house scRNA-seq cohort, showing the top enriched pathways for NE-like vs. lu/ba epithelial cells and NE vs. lu/ba epithelial cells, with cell cycle-related pathways prominently enriched in both comparisons. (E) NMF-based similarity matrix of integrated epithelial cells from both in-house and public scRNA-seq cohorts was constructed with NE differentiation-associated genes from the MEmidnightblue module were identified via WGCNA. (F) UMAP visualization of epithelial cells from (E) colored by dominant MP assignment. (G) Heatmap showing the observed-to-expected ratio (R o/e) of meta-program-assigned epithelial cells across lu/ba, NE-like, and NE subtypes. (H) Sankey diagram showing the correspondence between NE/NE-like subtype annotations and dominant MP assignments. NE-like cells are predominantly associated with MP1, whereas NE-like cells align with MP2. (I) GSEA plot showing the enrichment scores for the G2/M and G1/S cell cycle phases for the MP1 and MP2 groups. NES, normalized enrichment score. (J) Cell cycle phases of lu/ba, NE-like, and NE epithelial cells from the in-house and public scRNA-seq cohorts.

Journal: Cell Reports Medicine

Article Title: Targeting TPX2-dependent lineage plasticity by CDK4/6 inhibition reverses therapy resistance in neuroendocrine bladder carcinoma

doi: 10.1016/j.xcrm.2026.102712

Figure Lengend Snippet: Single-cell transcriptomics reveals heterogeneity of the cell cycle dynamics underlying NE lineage plasticity (A and B) UMAP visualization of single-cell transcriptomes from the in-house (A, 64,756 cells) and public (B, 201,720 cells) scRNA-seq cohorts, colored by major cell type annotations. The zoomed views show the UMAP visualizations of epithelial cells, classified into lu/ba, NE-like, and NE subtypes. (C) Dot plot showing the expression patterns of lu/ba, NE-like, and NE signature genes across epithelial cell subtypes from the in-house and public cohorts. (D) KEGG pathway enrichment analysis of DEGs from in-house scRNA-seq cohort, showing the top enriched pathways for NE-like vs. lu/ba epithelial cells and NE vs. lu/ba epithelial cells, with cell cycle-related pathways prominently enriched in both comparisons. (E) NMF-based similarity matrix of integrated epithelial cells from both in-house and public scRNA-seq cohorts was constructed with NE differentiation-associated genes from the MEmidnightblue module were identified via WGCNA. (F) UMAP visualization of epithelial cells from (E) colored by dominant MP assignment. (G) Heatmap showing the observed-to-expected ratio (R o/e) of meta-program-assigned epithelial cells across lu/ba, NE-like, and NE subtypes. (H) Sankey diagram showing the correspondence between NE/NE-like subtype annotations and dominant MP assignments. NE-like cells are predominantly associated with MP1, whereas NE-like cells align with MP2. (I) GSEA plot showing the enrichment scores for the G2/M and G1/S cell cycle phases for the MP1 and MP2 groups. NES, normalized enrichment score. (J) Cell cycle phases of lu/ba, NE-like, and NE epithelial cells from the in-house and public scRNA-seq cohorts.

Article Snippet: Cell cycle analysis kit , MultiScience , Cat#CCS012.

Techniques: Single-cell Transcriptomics, Single Cell, Expressing, Construct

Mapping reveals TPX2 as a representative and functionally relevant driver of NE-like cells (A) Violin plot showing correlations between NE-like cell top markers and the NE-like Sig score across multiple BLCA transcriptomic cohorts. TPX2, highlighted in red, shows the strongest correlation among the markers analyzed. (B and C) Spatial transcriptomic analysis showing the expression of TPX2 and NE-like Sig scores across spatial locations in bladder tumor tissue (B) and their positive correlation (C). R indicates the Spearman correlation coefficient. (D) Representative mIF image of NEBC tissue showing TPX2 (magenta), INSM1 (cyan), and DAPI (blue). The inset highlights TPX2 + (red arrowheads) and INSM1 + (white arrowheads) cells, which demonstrated minimal colocalization. Scale bars, 50 μm. (E and F) Representative mIF images (E) and quantification (F) showing the proportion of TPX2 + panCK + tumor cells across different tumor grades (Tis/Ta/T1: n = 55; T2: n = 46; T3: n = 43; T4: n = 35). Scale bars, 50 μm. (G and H) Flow cytometry (G) and quantification (H) showing the proportions of T24 and UM-UC-3 cells in different cell cycle phases 48 h after transfection with control siRNA or TPX2 siRNA ( n = 3 per group). (I and J) Flow cytometry (I) and quantification (J) showing the proportions of T24 and UM-UC-3 cell lines in different cell cycle phases in the empty vector control and TPX2-oe groups ( n = 3 per group). (K) qPCR analysis was performed to evaluate changes in the expression levels of NE markers following TPX2 knockdown in the T24 cell line. The data are presented as the means ± SDs in (H, J, and K). The box inside violin plot (A) represents the interquartile range (IQR), with the middle line as the median and the hinges corresponding to the 25th and 75th percentiles. p values were determined by one-way ANOVA in (F, H, and K) and by two-tailed unpaired Student’s t test in (J).

Journal: Cell Reports Medicine

Article Title: Targeting TPX2-dependent lineage plasticity by CDK4/6 inhibition reverses therapy resistance in neuroendocrine bladder carcinoma

doi: 10.1016/j.xcrm.2026.102712

Figure Lengend Snippet: Mapping reveals TPX2 as a representative and functionally relevant driver of NE-like cells (A) Violin plot showing correlations between NE-like cell top markers and the NE-like Sig score across multiple BLCA transcriptomic cohorts. TPX2, highlighted in red, shows the strongest correlation among the markers analyzed. (B and C) Spatial transcriptomic analysis showing the expression of TPX2 and NE-like Sig scores across spatial locations in bladder tumor tissue (B) and their positive correlation (C). R indicates the Spearman correlation coefficient. (D) Representative mIF image of NEBC tissue showing TPX2 (magenta), INSM1 (cyan), and DAPI (blue). The inset highlights TPX2 + (red arrowheads) and INSM1 + (white arrowheads) cells, which demonstrated minimal colocalization. Scale bars, 50 μm. (E and F) Representative mIF images (E) and quantification (F) showing the proportion of TPX2 + panCK + tumor cells across different tumor grades (Tis/Ta/T1: n = 55; T2: n = 46; T3: n = 43; T4: n = 35). Scale bars, 50 μm. (G and H) Flow cytometry (G) and quantification (H) showing the proportions of T24 and UM-UC-3 cells in different cell cycle phases 48 h after transfection with control siRNA or TPX2 siRNA ( n = 3 per group). (I and J) Flow cytometry (I) and quantification (J) showing the proportions of T24 and UM-UC-3 cell lines in different cell cycle phases in the empty vector control and TPX2-oe groups ( n = 3 per group). (K) qPCR analysis was performed to evaluate changes in the expression levels of NE markers following TPX2 knockdown in the T24 cell line. The data are presented as the means ± SDs in (H, J, and K). The box inside violin plot (A) represents the interquartile range (IQR), with the middle line as the median and the hinges corresponding to the 25th and 75th percentiles. p values were determined by one-way ANOVA in (F, H, and K) and by two-tailed unpaired Student’s t test in (J).

Article Snippet: Cell cycle analysis kit , MultiScience , Cat#CCS012.

Techniques: Expressing, Flow Cytometry, Transfection, Control, Plasmid Preparation, Knockdown, Two Tailed Test