mouse breast cancer cell lines 4t1 Search Results


90
Inserm Transfert 4t1 mouse breast cancer cell line
Different volumes of WT mice blood collected either within ( A ) heparin tubes or ( B ) EDTA tubes were filtered with ScreenCell Cyto kits. Similar volumes of PyMT cancer mice blood were also processed with ScreenCell Cyto kits ( C ). To evaluate the correct morphology of cancer cells in EDTA tubes, MCF7, and <t>4T1</t> breast cancer cell lines were spiked into WT mice blood and processed with ScreenCell Cyto kits. The images were taken using 40X magnification ( D ). To evaluate the recovery rate of ScreenCell technology, either 0, 5, or 10 MCF7 cells were spiked into 100 µl of blood samples of healthy WT mice. Captured cancer cells were counted on Cyto ISs and recovery rate was calculated in percentage. Data are presented as mean ± SEM (n = 6) ( E ). All experiments were repeated at least 2 times.
4t1 Mouse Breast Cancer Cell Line, supplied by Inserm Transfert, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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90
Pasteur Institute 4t1 (mouse breast cancer) cell lines
MTT viability assay for ( A ) MCF7 and ( B ) <t>4T1</t> cell lines treated with different formulations of ferulic acid (FA)-loaded cyclodextrin nanosponges (NS) and various interval times at 570 nm.
4t1 (Mouse Breast Cancer) Cell Lines, supplied by Pasteur Institute, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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90
Nacalai 4t1 mouse breast cancer cell line
a. Heatmap showing the Pearson correlation coefficients between the clinical characteristics (age and albumin levels) and the module eigengenes generated by WGCNA. In each column, the upper number indicates the Pearson correlation coefficient and the lower number in parentheses indicates a p -value calculated by WGCNA. See also Extended Data Fig. 2a, b for scale independence and mean connectivity of this analysis. b. Bar plot showing log 2 fold changes of module-3 genes between metastatic breast cancer patients (MBC) and healthy volunteers (HV). Genes showing negative correlations to albumin levels are shown in green. Genes showing positive correlations to albumin levels are shown in orange. Genes are ordered according to their correlation to albumin levels. The names of the top10 genes most strongly negatively correlating with albumin levels are indicated. c. The plasma albumin levels of sham-operated and <t>4T1</t> breast cancer-bearing mice measured 14 days after transplantation. Data are presented as the mean ± SEM. The p- value is shown (non-paired, two-tailed Student t -test). n = 11 for sham-operated mice, n = 11 for 4T1-bearing mice. d. Heatmap of module-3 genes showing the same direction of changes in the 4T1 breast cancer model compared to the human datasets. Genes are ordered according to their correlation to albumin levels in humans. The names of the top10 genes most strongly negatively correlating with albumin levels in humans are indicated. See also Extended Data Fig. 2c, d for the mouse data.
4t1 Mouse Breast Cancer Cell Line, supplied by Nacalai, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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90
Shanghai Model Organisms Center 4t1 mouse breast cancer cell line
a. Heatmap showing the Pearson correlation coefficients between the clinical characteristics (age and albumin levels) and the module eigengenes generated by WGCNA. In each column, the upper number indicates the Pearson correlation coefficient and the lower number in parentheses indicates a p -value calculated by WGCNA. See also Extended Data Fig. 2a, b for scale independence and mean connectivity of this analysis. b. Bar plot showing log 2 fold changes of module-3 genes between metastatic breast cancer patients (MBC) and healthy volunteers (HV). Genes showing negative correlations to albumin levels are shown in green. Genes showing positive correlations to albumin levels are shown in orange. Genes are ordered according to their correlation to albumin levels. The names of the top10 genes most strongly negatively correlating with albumin levels are indicated. c. The plasma albumin levels of sham-operated and <t>4T1</t> breast cancer-bearing mice measured 14 days after transplantation. Data are presented as the mean ± SEM. The p- value is shown (non-paired, two-tailed Student t -test). n = 11 for sham-operated mice, n = 11 for 4T1-bearing mice. d. Heatmap of module-3 genes showing the same direction of changes in the 4T1 breast cancer model compared to the human datasets. Genes are ordered according to their correlation to albumin levels in humans. The names of the top10 genes most strongly negatively correlating with albumin levels in humans are indicated. See also Extended Data Fig. 2c, d for the mouse data.
4t1 Mouse Breast Cancer Cell Line, supplied by Shanghai Model Organisms Center, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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90
Pasteur Institute mouse breast epithelial 4t1 cell line
a. Heatmap showing the Pearson correlation coefficients between the clinical characteristics (age and albumin levels) and the module eigengenes generated by WGCNA. In each column, the upper number indicates the Pearson correlation coefficient and the lower number in parentheses indicates a p -value calculated by WGCNA. See also Extended Data Fig. 2a, b for scale independence and mean connectivity of this analysis. b. Bar plot showing log 2 fold changes of module-3 genes between metastatic breast cancer patients (MBC) and healthy volunteers (HV). Genes showing negative correlations to albumin levels are shown in green. Genes showing positive correlations to albumin levels are shown in orange. Genes are ordered according to their correlation to albumin levels. The names of the top10 genes most strongly negatively correlating with albumin levels are indicated. c. The plasma albumin levels of sham-operated and <t>4T1</t> breast cancer-bearing mice measured 14 days after transplantation. Data are presented as the mean ± SEM. The p- value is shown (non-paired, two-tailed Student t -test). n = 11 for sham-operated mice, n = 11 for 4T1-bearing mice. d. Heatmap of module-3 genes showing the same direction of changes in the 4T1 breast cancer model compared to the human datasets. Genes are ordered according to their correlation to albumin levels in humans. The names of the top10 genes most strongly negatively correlating with albumin levels in humans are indicated. See also Extended Data Fig. 2c, d for the mouse data.
Mouse Breast Epithelial 4t1 Cell Line, supplied by Pasteur Institute, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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90
Irvine Scientific mouse breast cancer cell line 4t1.2-neu
a. Heatmap showing the Pearson correlation coefficients between the clinical characteristics (age and albumin levels) and the module eigengenes generated by WGCNA. In each column, the upper number indicates the Pearson correlation coefficient and the lower number in parentheses indicates a p -value calculated by WGCNA. See also Extended Data Fig. 2a, b for scale independence and mean connectivity of this analysis. b. Bar plot showing log 2 fold changes of module-3 genes between metastatic breast cancer patients (MBC) and healthy volunteers (HV). Genes showing negative correlations to albumin levels are shown in green. Genes showing positive correlations to albumin levels are shown in orange. Genes are ordered according to their correlation to albumin levels. The names of the top10 genes most strongly negatively correlating with albumin levels are indicated. c. The plasma albumin levels of sham-operated and <t>4T1</t> breast cancer-bearing mice measured 14 days after transplantation. Data are presented as the mean ± SEM. The p- value is shown (non-paired, two-tailed Student t -test). n = 11 for sham-operated mice, n = 11 for 4T1-bearing mice. d. Heatmap of module-3 genes showing the same direction of changes in the 4T1 breast cancer model compared to the human datasets. Genes are ordered according to their correlation to albumin levels in humans. The names of the top10 genes most strongly negatively correlating with albumin levels in humans are indicated. See also Extended Data Fig. 2c, d for the mouse data.
Mouse Breast Cancer Cell Line 4t1.2 Neu, supplied by Irvine Scientific, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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90
Fuxiang Biotechnology Co Ltd mouse breast cancer cell line 4t1
a. Heatmap showing the Pearson correlation coefficients between the clinical characteristics (age and albumin levels) and the module eigengenes generated by WGCNA. In each column, the upper number indicates the Pearson correlation coefficient and the lower number in parentheses indicates a p -value calculated by WGCNA. See also Extended Data Fig. 2a, b for scale independence and mean connectivity of this analysis. b. Bar plot showing log 2 fold changes of module-3 genes between metastatic breast cancer patients (MBC) and healthy volunteers (HV). Genes showing negative correlations to albumin levels are shown in green. Genes showing positive correlations to albumin levels are shown in orange. Genes are ordered according to their correlation to albumin levels. The names of the top10 genes most strongly negatively correlating with albumin levels are indicated. c. The plasma albumin levels of sham-operated and <t>4T1</t> breast cancer-bearing mice measured 14 days after transplantation. Data are presented as the mean ± SEM. The p- value is shown (non-paired, two-tailed Student t -test). n = 11 for sham-operated mice, n = 11 for 4T1-bearing mice. d. Heatmap of module-3 genes showing the same direction of changes in the 4T1 breast cancer model compared to the human datasets. Genes are ordered according to their correlation to albumin levels in humans. The names of the top10 genes most strongly negatively correlating with albumin levels in humans are indicated. See also Extended Data Fig. 2c, d for the mouse data.
Mouse Breast Cancer Cell Line 4t1, supplied by Fuxiang Biotechnology Co Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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90
Sangon Biotech 4t1 mouse breast cancer cell line
a. Heatmap showing the Pearson correlation coefficients between the clinical characteristics (age and albumin levels) and the module eigengenes generated by WGCNA. In each column, the upper number indicates the Pearson correlation coefficient and the lower number in parentheses indicates a p -value calculated by WGCNA. See also Extended Data Fig. 2a, b for scale independence and mean connectivity of this analysis. b. Bar plot showing log 2 fold changes of module-3 genes between metastatic breast cancer patients (MBC) and healthy volunteers (HV). Genes showing negative correlations to albumin levels are shown in green. Genes showing positive correlations to albumin levels are shown in orange. Genes are ordered according to their correlation to albumin levels. The names of the top10 genes most strongly negatively correlating with albumin levels are indicated. c. The plasma albumin levels of sham-operated and <t>4T1</t> breast cancer-bearing mice measured 14 days after transplantation. Data are presented as the mean ± SEM. The p- value is shown (non-paired, two-tailed Student t -test). n = 11 for sham-operated mice, n = 11 for 4T1-bearing mice. d. Heatmap of module-3 genes showing the same direction of changes in the 4T1 breast cancer model compared to the human datasets. Genes are ordered according to their correlation to albumin levels in humans. The names of the top10 genes most strongly negatively correlating with albumin levels in humans are indicated. See also Extended Data Fig. 2c, d for the mouse data.
4t1 Mouse Breast Cancer Cell Line, supplied by Sangon Biotech, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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WuXi AppTec mouse breast cancer cell line 4t1 cell line transfected with fap
a. Heatmap showing the Pearson correlation coefficients between the clinical characteristics (age and albumin levels) and the module eigengenes generated by WGCNA. In each column, the upper number indicates the Pearson correlation coefficient and the lower number in parentheses indicates a p -value calculated by WGCNA. See also Extended Data Fig. 2a, b for scale independence and mean connectivity of this analysis. b. Bar plot showing log 2 fold changes of module-3 genes between metastatic breast cancer patients (MBC) and healthy volunteers (HV). Genes showing negative correlations to albumin levels are shown in green. Genes showing positive correlations to albumin levels are shown in orange. Genes are ordered according to their correlation to albumin levels. The names of the top10 genes most strongly negatively correlating with albumin levels are indicated. c. The plasma albumin levels of sham-operated and <t>4T1</t> breast cancer-bearing mice measured 14 days after transplantation. Data are presented as the mean ± SEM. The p- value is shown (non-paired, two-tailed Student t -test). n = 11 for sham-operated mice, n = 11 for 4T1-bearing mice. d. Heatmap of module-3 genes showing the same direction of changes in the 4T1 breast cancer model compared to the human datasets. Genes are ordered according to their correlation to albumin levels in humans. The names of the top10 genes most strongly negatively correlating with albumin levels in humans are indicated. See also Extended Data Fig. 2c, d for the mouse data.
Mouse Breast Cancer Cell Line 4t1 Cell Line Transfected With Fap, supplied by WuXi AppTec, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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90
AddexBio Inc mouse breast cancer cell line (4t1)
a. Heatmap showing the Pearson correlation coefficients between the clinical characteristics (age and albumin levels) and the module eigengenes generated by WGCNA. In each column, the upper number indicates the Pearson correlation coefficient and the lower number in parentheses indicates a p -value calculated by WGCNA. See also Extended Data Fig. 2a, b for scale independence and mean connectivity of this analysis. b. Bar plot showing log 2 fold changes of module-3 genes between metastatic breast cancer patients (MBC) and healthy volunteers (HV). Genes showing negative correlations to albumin levels are shown in green. Genes showing positive correlations to albumin levels are shown in orange. Genes are ordered according to their correlation to albumin levels. The names of the top10 genes most strongly negatively correlating with albumin levels are indicated. c. The plasma albumin levels of sham-operated and <t>4T1</t> breast cancer-bearing mice measured 14 days after transplantation. Data are presented as the mean ± SEM. The p- value is shown (non-paired, two-tailed Student t -test). n = 11 for sham-operated mice, n = 11 for 4T1-bearing mice. d. Heatmap of module-3 genes showing the same direction of changes in the 4T1 breast cancer model compared to the human datasets. Genes are ordered according to their correlation to albumin levels in humans. The names of the top10 genes most strongly negatively correlating with albumin levels in humans are indicated. See also Extended Data Fig. 2c, d for the mouse data.
Mouse Breast Cancer Cell Line (4t1), supplied by AddexBio Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


Different volumes of WT mice blood collected either within ( A ) heparin tubes or ( B ) EDTA tubes were filtered with ScreenCell Cyto kits. Similar volumes of PyMT cancer mice blood were also processed with ScreenCell Cyto kits ( C ). To evaluate the correct morphology of cancer cells in EDTA tubes, MCF7, and 4T1 breast cancer cell lines were spiked into WT mice blood and processed with ScreenCell Cyto kits. The images were taken using 40X magnification ( D ). To evaluate the recovery rate of ScreenCell technology, either 0, 5, or 10 MCF7 cells were spiked into 100 µl of blood samples of healthy WT mice. Captured cancer cells were counted on Cyto ISs and recovery rate was calculated in percentage. Data are presented as mean ± SEM (n = 6) ( E ). All experiments were repeated at least 2 times.

Journal: Scientific Reports

Article Title: Fast and efficient isolation of murine circulating tumor cells using screencell technology for pre-clinical analyzes

doi: 10.1038/s41598-024-66032-x

Figure Lengend Snippet: Different volumes of WT mice blood collected either within ( A ) heparin tubes or ( B ) EDTA tubes were filtered with ScreenCell Cyto kits. Similar volumes of PyMT cancer mice blood were also processed with ScreenCell Cyto kits ( C ). To evaluate the correct morphology of cancer cells in EDTA tubes, MCF7, and 4T1 breast cancer cell lines were spiked into WT mice blood and processed with ScreenCell Cyto kits. The images were taken using 40X magnification ( D ). To evaluate the recovery rate of ScreenCell technology, either 0, 5, or 10 MCF7 cells were spiked into 100 µl of blood samples of healthy WT mice. Captured cancer cells were counted on Cyto ISs and recovery rate was calculated in percentage. Data are presented as mean ± SEM (n = 6) ( E ). All experiments were repeated at least 2 times.

Article Snippet: 4T1 mouse breast cancer cell line (passages 12–15) and MCF7 human breast cancer cell line (passages 7–10) were received from Inserm U1197 (Paul Brousse Hospital, Villejuif, France) and cultured in Dulbecco’s modified Eagle medium (DMEM) (Gibco, Life Technologies, USA) supplemented with 10% FBS (Pan biotech, Aidenbach, Germany) and 1% Penicillin/Streptomycin (P/S) (Thermofisher Scientific, Waltham, MA, USA).

Techniques:

This figure represents the 4T1 cancer cells isolated using ScreenCell Cyto kits either immediately after the blood draw (left image, 40X) or after 24 h of preservation at 4 °C in EDTA tubes (right image, 40X). This experiment was performed 4 times. T0: time zero.

Journal: Scientific Reports

Article Title: Fast and efficient isolation of murine circulating tumor cells using screencell technology for pre-clinical analyzes

doi: 10.1038/s41598-024-66032-x

Figure Lengend Snippet: This figure represents the 4T1 cancer cells isolated using ScreenCell Cyto kits either immediately after the blood draw (left image, 40X) or after 24 h of preservation at 4 °C in EDTA tubes (right image, 40X). This experiment was performed 4 times. T0: time zero.

Article Snippet: 4T1 mouse breast cancer cell line (passages 12–15) and MCF7 human breast cancer cell line (passages 7–10) were received from Inserm U1197 (Paul Brousse Hospital, Villejuif, France) and cultured in Dulbecco’s modified Eagle medium (DMEM) (Gibco, Life Technologies, USA) supplemented with 10% FBS (Pan biotech, Aidenbach, Germany) and 1% Penicillin/Streptomycin (P/S) (Thermofisher Scientific, Waltham, MA, USA).

Techniques: Isolation, Preserving

ScreenCell Cyto kits can isolate both single and cluster CTCs. ( A ) After spiking into WT mice blood, both single and cluster 4T1 cells were captured by ScreenCell Cyto kits. ( B ) Single and cluster CTCs were also efficiently captured from breast cancer mice blood. In all conditions, CTCs were distinguishable from normal epithelial cells according to their morphological features. These experiments were performed at least 3 times. All images are taken using 40X magnification, except for the CTC cluster (lower right) which is in 20X.

Journal: Scientific Reports

Article Title: Fast and efficient isolation of murine circulating tumor cells using screencell technology for pre-clinical analyzes

doi: 10.1038/s41598-024-66032-x

Figure Lengend Snippet: ScreenCell Cyto kits can isolate both single and cluster CTCs. ( A ) After spiking into WT mice blood, both single and cluster 4T1 cells were captured by ScreenCell Cyto kits. ( B ) Single and cluster CTCs were also efficiently captured from breast cancer mice blood. In all conditions, CTCs were distinguishable from normal epithelial cells according to their morphological features. These experiments were performed at least 3 times. All images are taken using 40X magnification, except for the CTC cluster (lower right) which is in 20X.

Article Snippet: 4T1 mouse breast cancer cell line (passages 12–15) and MCF7 human breast cancer cell line (passages 7–10) were received from Inserm U1197 (Paul Brousse Hospital, Villejuif, France) and cultured in Dulbecco’s modified Eagle medium (DMEM) (Gibco, Life Technologies, USA) supplemented with 10% FBS (Pan biotech, Aidenbach, Germany) and 1% Penicillin/Streptomycin (P/S) (Thermofisher Scientific, Waltham, MA, USA).

Techniques:

DNA concentration from WT mouse blood with or without spiked  4T1  cells isolated by ScreenCell MB device.

Journal: Scientific Reports

Article Title: Fast and efficient isolation of murine circulating tumor cells using screencell technology for pre-clinical analyzes

doi: 10.1038/s41598-024-66032-x

Figure Lengend Snippet: DNA concentration from WT mouse blood with or without spiked 4T1 cells isolated by ScreenCell MB device.

Article Snippet: 4T1 mouse breast cancer cell line (passages 12–15) and MCF7 human breast cancer cell line (passages 7–10) were received from Inserm U1197 (Paul Brousse Hospital, Villejuif, France) and cultured in Dulbecco’s modified Eagle medium (DMEM) (Gibco, Life Technologies, USA) supplemented with 10% FBS (Pan biotech, Aidenbach, Germany) and 1% Penicillin/Streptomycin (P/S) (Thermofisher Scientific, Waltham, MA, USA).

Techniques: Concentration Assay, Isolation

4T1 cells were spiked into WT mice blood and filtered with ScreenCell MB kits. ISs were then directly inserted into 24 well plates and cultured for 10 days. The images of D0, D5, and D10 with RAL coloration are taken using 40X, and D10 without coloration is taken with 20X magnification. This experiment was performed 4 times. D: Day.

Journal: Scientific Reports

Article Title: Fast and efficient isolation of murine circulating tumor cells using screencell technology for pre-clinical analyzes

doi: 10.1038/s41598-024-66032-x

Figure Lengend Snippet: 4T1 cells were spiked into WT mice blood and filtered with ScreenCell MB kits. ISs were then directly inserted into 24 well plates and cultured for 10 days. The images of D0, D5, and D10 with RAL coloration are taken using 40X, and D10 without coloration is taken with 20X magnification. This experiment was performed 4 times. D: Day.

Article Snippet: 4T1 mouse breast cancer cell line (passages 12–15) and MCF7 human breast cancer cell line (passages 7–10) were received from Inserm U1197 (Paul Brousse Hospital, Villejuif, France) and cultured in Dulbecco’s modified Eagle medium (DMEM) (Gibco, Life Technologies, USA) supplemented with 10% FBS (Pan biotech, Aidenbach, Germany) and 1% Penicillin/Streptomycin (P/S) (Thermofisher Scientific, Waltham, MA, USA).

Techniques: Cell Culture

MTT viability assay for ( A ) MCF7 and ( B ) 4T1 cell lines treated with different formulations of ferulic acid (FA)-loaded cyclodextrin nanosponges (NS) and various interval times at 570 nm.

Journal: International Journal of Nanomedicine

Article Title: Improving the solubility and in vitro cytotoxicity (anticancer activity) of ferulic acid by loading it into cyclodextrin nanosponges

doi: 10.2147/IJN.S206350

Figure Lengend Snippet: MTT viability assay for ( A ) MCF7 and ( B ) 4T1 cell lines treated with different formulations of ferulic acid (FA)-loaded cyclodextrin nanosponges (NS) and various interval times at 570 nm.

Article Snippet: MCF7 (human breast cancer) and 4T1 (mouse breast cancer) cell lines were purchased from Pasteur Institute of Iran.

Techniques: MTT Viability Assay

4T1 cell lines treated with different formulations: control cells ( A ); ferulic acid, 250 µM ( B ); nanosponges, 250 µM ( C ); ferulic acid-loaded nanosponges, 250 µM ( D ). Microscopic magnification ×10 K, after 24 hrs of incubation.

Journal: International Journal of Nanomedicine

Article Title: Improving the solubility and in vitro cytotoxicity (anticancer activity) of ferulic acid by loading it into cyclodextrin nanosponges

doi: 10.2147/IJN.S206350

Figure Lengend Snippet: 4T1 cell lines treated with different formulations: control cells ( A ); ferulic acid, 250 µM ( B ); nanosponges, 250 µM ( C ); ferulic acid-loaded nanosponges, 250 µM ( D ). Microscopic magnification ×10 K, after 24 hrs of incubation.

Article Snippet: MCF7 (human breast cancer) and 4T1 (mouse breast cancer) cell lines were purchased from Pasteur Institute of Iran.

Techniques: Incubation

a. Heatmap showing the Pearson correlation coefficients between the clinical characteristics (age and albumin levels) and the module eigengenes generated by WGCNA. In each column, the upper number indicates the Pearson correlation coefficient and the lower number in parentheses indicates a p -value calculated by WGCNA. See also Extended Data Fig. 2a, b for scale independence and mean connectivity of this analysis. b. Bar plot showing log 2 fold changes of module-3 genes between metastatic breast cancer patients (MBC) and healthy volunteers (HV). Genes showing negative correlations to albumin levels are shown in green. Genes showing positive correlations to albumin levels are shown in orange. Genes are ordered according to their correlation to albumin levels. The names of the top10 genes most strongly negatively correlating with albumin levels are indicated. c. The plasma albumin levels of sham-operated and 4T1 breast cancer-bearing mice measured 14 days after transplantation. Data are presented as the mean ± SEM. The p- value is shown (non-paired, two-tailed Student t -test). n = 11 for sham-operated mice, n = 11 for 4T1-bearing mice. d. Heatmap of module-3 genes showing the same direction of changes in the 4T1 breast cancer model compared to the human datasets. Genes are ordered according to their correlation to albumin levels in humans. The names of the top10 genes most strongly negatively correlating with albumin levels in humans are indicated. See also Extended Data Fig. 2c, d for the mouse data.

Journal: bioRxiv

Article Title: Low albumin status accompanies multi-layered immunosuppressive phenotypes in metastatic breast cancer patients

doi: 10.1101/2023.09.05.556440

Figure Lengend Snippet: a. Heatmap showing the Pearson correlation coefficients between the clinical characteristics (age and albumin levels) and the module eigengenes generated by WGCNA. In each column, the upper number indicates the Pearson correlation coefficient and the lower number in parentheses indicates a p -value calculated by WGCNA. See also Extended Data Fig. 2a, b for scale independence and mean connectivity of this analysis. b. Bar plot showing log 2 fold changes of module-3 genes between metastatic breast cancer patients (MBC) and healthy volunteers (HV). Genes showing negative correlations to albumin levels are shown in green. Genes showing positive correlations to albumin levels are shown in orange. Genes are ordered according to their correlation to albumin levels. The names of the top10 genes most strongly negatively correlating with albumin levels are indicated. c. The plasma albumin levels of sham-operated and 4T1 breast cancer-bearing mice measured 14 days after transplantation. Data are presented as the mean ± SEM. The p- value is shown (non-paired, two-tailed Student t -test). n = 11 for sham-operated mice, n = 11 for 4T1-bearing mice. d. Heatmap of module-3 genes showing the same direction of changes in the 4T1 breast cancer model compared to the human datasets. Genes are ordered according to their correlation to albumin levels in humans. The names of the top10 genes most strongly negatively correlating with albumin levels in humans are indicated. See also Extended Data Fig. 2c, d for the mouse data.

Article Snippet: 4T1 mouse breast cancer cell line was cultured and maintained in RPMI1640 (nacalai tesque) supplemented with 10% fetal bovine serum (FBS) (Sigma-Aldrich, Saint Louis, Missouri, USA), 1% penicillin/streptomycin (nacalai tesque) in a 5% CO 2 tissue culture incubator at 37°C.

Techniques: Generated, Clinical Proteomics, Transplantation Assay, Two Tailed Test

a. Heatmap of select module-2 genes showing the same direction of changes in the 4T1 breast cancer model compared to the human datasets. Genes are ordered according to their correlation to albumin levels in humans. The names of the top10 genes most strongly positively correlating with albumin levels in humans are indicated. b. Correlation of plasma albumin levels and log 2 normalized scores of CD8A in PBMC. c. Correlation of plasma albumin levels and log 2 normalized scores of KLRG1 in PBMC. d. Correlation of plasma albumin levels and log 2 normalized scores of CRTAM in PBMC. e. UMAP plot for CD8A . f. UMAP plot for KLRG1 . g. UMAP plot for CRTAM h. Estimated abundance of CD8 + T cells in PBMC calculated by ImmuCell-AI and its correlation to plasma albumin levels. i. Estimated abundance of γδ T cells in PBMC calculated by ImmuCell-AI and its correlation to plasma albumin levels. See also Extended Data Fig. 4b for the data related to natural killer cells. See also Extended Data Fig. 4a, c, d for the mouse data for CD8 + T cells, γδ T cells, and natural killer cells, respectively. j. Correlation of plasma albumin levels and log 2 normalized scores of TRDV2 in PBMC. k. UMAP plot for TRDV2 . b-d, j. Scores are normalized to the average scores of five healthy volunteers. b-d, h, i, j. The Pearson correlation coefficients and p -values obtained by simple regression analysis (GraphPad Prism) are shown. e-g, k. The data are retrieved from previously published single-cell RNA-seq (scRNA-seq) datasets from PBMC of Japanese healthy volunteers ( n = 3 (females)) . Clusters corresponding to CD8 + T cells ( e-g ) and γδ T cells ( k ) are highlighted.

Journal: bioRxiv

Article Title: Low albumin status accompanies multi-layered immunosuppressive phenotypes in metastatic breast cancer patients

doi: 10.1101/2023.09.05.556440

Figure Lengend Snippet: a. Heatmap of select module-2 genes showing the same direction of changes in the 4T1 breast cancer model compared to the human datasets. Genes are ordered according to their correlation to albumin levels in humans. The names of the top10 genes most strongly positively correlating with albumin levels in humans are indicated. b. Correlation of plasma albumin levels and log 2 normalized scores of CD8A in PBMC. c. Correlation of plasma albumin levels and log 2 normalized scores of KLRG1 in PBMC. d. Correlation of plasma albumin levels and log 2 normalized scores of CRTAM in PBMC. e. UMAP plot for CD8A . f. UMAP plot for KLRG1 . g. UMAP plot for CRTAM h. Estimated abundance of CD8 + T cells in PBMC calculated by ImmuCell-AI and its correlation to plasma albumin levels. i. Estimated abundance of γδ T cells in PBMC calculated by ImmuCell-AI and its correlation to plasma albumin levels. See also Extended Data Fig. 4b for the data related to natural killer cells. See also Extended Data Fig. 4a, c, d for the mouse data for CD8 + T cells, γδ T cells, and natural killer cells, respectively. j. Correlation of plasma albumin levels and log 2 normalized scores of TRDV2 in PBMC. k. UMAP plot for TRDV2 . b-d, j. Scores are normalized to the average scores of five healthy volunteers. b-d, h, i, j. The Pearson correlation coefficients and p -values obtained by simple regression analysis (GraphPad Prism) are shown. e-g, k. The data are retrieved from previously published single-cell RNA-seq (scRNA-seq) datasets from PBMC of Japanese healthy volunteers ( n = 3 (females)) . Clusters corresponding to CD8 + T cells ( e-g ) and γδ T cells ( k ) are highlighted.

Article Snippet: 4T1 mouse breast cancer cell line was cultured and maintained in RPMI1640 (nacalai tesque) supplemented with 10% fetal bovine serum (FBS) (Sigma-Aldrich, Saint Louis, Missouri, USA), 1% penicillin/streptomycin (nacalai tesque) in a 5% CO 2 tissue culture incubator at 37°C.

Techniques: Clinical Proteomics, RNA Sequencing