Additional file 1 of Discovery of a new marker to identify myeloid cells associated with metastatic breast tumours
Creators: name: Bokil, Ansooya A.
name: Le Boulvais Børkja, Mathieu
name: Wolowczyk, Camilla
name: Lamsal, Apsana
name: Prestvik, Wenche S.
name: Nonstad, Unni
name: Pettersen, Kristine
name: Andersen, Sonja B.
name: Bofin, Anna M.
name: Bjørkøy, Geir
name: Hak, Sjoerd
name: Giambelluca, Miriam S.
Publisher: name: figshare
Year: 2024
Subjects: subject: Biophysics
subject: Medicine
subject: Cell Biology
subject: Immunology
subject: FOS: Clinical medicine
subject: Biological Sciences not elsewhere classified
subject: Cancer
subject: Hematology
subject: Plant Biology
subject: FOS: Biological sciences

description: Additional file 1: Figure S1. A Principal component analysis (PCA) of the RNA-sequencing of CD45+ and CD45− sorted cells from 67NR and 66cl4 tumours (N = 5). B Volcano plot of 67NR CD45+ cells vs 66cl4 CD45+ cells showing the differential genes expression (N = 5). C Gene ontology Molecular function analysis of the top 500 overexpressed genes in 66cl4 CD45+ indicates a significant association of these genes with CXC chemokine receptor activity and cytokines binding. D Gene ontology Cellular component analysis of the top 500 overexpressed genes in 66cl4 CD45+ shows a strong association of these genes with extracellular space and the external side of the plasma membrane. Figure S2. Arginase gene and protein expression at 2.5X and single cells at 400X. A–D show a representative image of in situ hybridisation (A, B) and immunohistochemistry (C, D). The spatial distribution of ARG1 gene and protein in 67NR and 66cl4 tumours is shown at 2.5X magnification. It shows the difference in ARG1 mRNA and protein abundance and distribution in 67NR and 66cl4 tumours. E, F show a representative image of ARG1 protein by IHC at 400X magnification, and the panel to the right shows a zoom-in of the ARG1-positive cell. Figure S3. Flow cytometry gating strategies on dissociated tumours and organs to identify ARG1-positive cells. To investigate the source of ARG1 in 67NR and 66cl4 tumours, antibodies mentioned in Materials and Methods were used. Forward/side angle light scatter (FSCA/SSC-A) was used to exclude debris, followed by a forward scatter height (FSC-H)/forward scatter area (FSC-A) to exclude doublets and Zombie Aqua (live dead marker)/FSC to exclude dead cells. A Gating strategy to identify ARG1-positive cells in immune and non-immune populations. The single and live cells were plotted for ARG1 vs CD45 (immune cell marker) to identify if immune (CD45+) or nonimmune (CD45−) cells were the primary source of ARG1. B Gating strategy to identify ARG1-positive immune cells. Single/live cells were plotted for CD45 vs FSC to distinguish between immune (CD45+) and non-immune (CD45−) cells. Further, the immune cells were plotted for ARG1 vs CD11b (myeloid cell marker) to identify if myeloid (CD11b+) or lymphoid (CD11b-) cells were the primary ARG1 source. FMOs were used to gate positive populations. Figure S4. Flow cytometry gating strategies on dissociated tumours/organs to identify immune subpopulations. Gating strategy to assess myeloid cell subpopulations in tumours. The resulting population of single, live cells were plotted for CD45 (immune cell marker) to distinguish between immune (CD45+) and non-immune (CD45−) cells. Immune cells were plotted for CD11b vs Ly6G (myeloid cell marker/neutrophil marker) to identify different immune cell populations. The plot revealed three distinct CD11b+ cell populations of interest: 1 subset of CD11b+/Ly6G− and 2 population subsets of CD11b+/Ly6G+ cells. The two CD11b+/Ly6G+ cell populations could be distinguished based on the level of Ly6G as cells with intermediate or high Ly6G expression. These populations were labelled CD11b+/Ly6G−, CD11b+/Ly6G intermediate and CD11b+/Ly6G high. Figure S5. The frequencies of Ly6C+ vs F4/80+ cells in the 3 Ly6G populations: Ly6G−, LyG6+ intermediate, Ly6G+ high, in 67NR and 66cl4 tumours. Values are mean ± SEM of at least 3 independent experiments. Figure S6. Flow cytometry gating strategies on dissociated tumours and organs to identify ARG1-positive immune subpopulations. Gating approach to identify ARG1 source in CD11b+ subsets. Single, live cells were plotted for CD45 (immune cell marker), followed by CD11b (myeloid cell marker). The resulting myeloid cells were plotted for ARG1 vs Ly6G (neutrophils marker) to identify the CD45+/CD11b+ population source of ARG1. FMOs were used to gate positive populations. Figure S7. No elevated levels of ARG1-positive cells were found in blood, bone marrow and spleen in tumour-bearing mice. A The myeloid cell population (CD11b+) in blood, bone marrow, lung, and spleen have a similar composition based on Ly6G expression level in healthy and tumour-bearing animals. Blood (control mice N = 9, 67NR N = 9, 66cl4 N = 11), bone marrow (control mice N = 6, 67NR N = 6, 66cl4 N = 6), lungs (control mice N = 4, 67NR N = 4, 66cl4 N = 3) and spleen (control mice N = 3, 67NR N = 4, 66cl4 N = 3). Bars represent the mean ± SEM. Statistical analysis was performed using the Kruskal–Wallis test followed by Dunn’s multiple comparisons (ns, p &gt; 0.05; *p &lt; 0.05). B Representative flow cytometry plots representing ARG1-positive cells in immune cells (CD45+) from blood, bone marrow, lungs, and spleen of healthy and tumour-bearing mice. Figure S8. ARG1 protein quantification in plasma by ELISA. Bars represent mean ± SEM, and each data point represents a single animal (control N = 10, 67NR N = 14; 66cl4 N = 14). Statistical significance was determined using one-way ANOVA followed by Kruskal–Wallis test multiple comparisons. **p &lt; 0.005. Table S1. Gene Ontology analysis of Biological Process elevated in 66cl4 CD45+ cells compared with 67NR CD45+ cells. Table S2. Fn1, Tnbs1 and Arg1 expression levels as transcripts per million in the CD45+ and CD45- cells isolated from 67NR and 66cl4 tumours. Table S3. Frequencies of ARG1-positive cells in the blood, bone marrow, lungs and spleen. Table S4. Parameter estimates for the log-normal accelerated failure time model, unadjusted and adjusted for other variables.