Kamilla-Model Sets 10-20
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The datasets used for model training and cross-validation are publicly available for download at figshare repository ( ). The information about data might be obtained from our data descriptor paper36.
Description of the dataset used in this study. The datasets from Nejedly et al. 2020 were used for model training and validation. Novel data from 12 patients from FNUSA hospital were used for pseudo-prospective testing.
Performance of the model on two datasets related to the cardinality of training data for kernel density estimates (KDE) classifier. The plot shows the area under the precision-recall curve (AUPRC) metrics. The picture shows that 100 training examples per class achieve good model performance while requiring a reasonable amount of training labels.
We first performed a bioinformatics analysis of well-known cell surface markers of CAFs from three different scRNA-seq datasets. First, we looked at cell surface markers in the Tabula Muris compendium containing nearly 100,000 cells from 20 organs and tissues from Mus musculus. Uniform Manifold Approximation and Projection (UMAP) of the different cell population demonstrated that FAPα, PDGFRα, PDGFRβ, and PDPN clustered to stromal cells of the mammary gland, while the αSMA+ cells as expected, clustered to pericytes and basal epithelial cells (Suppl. Fig. 3). We also examined the gene dipeptidyl petidease-4 (DPP4/CD26), a close homologue to FAPα , which was recently identified as a fibroblast marker of interlobular fibroblasts in healthy breast tissue and shown to display immune suppressive functions in breast cancer [10, 33]. The expression of CD26 likewise clustered to stromal cells in the healthy murine mammary gland. Finally, we examined the S100A4 gene, but as demonstrated by the UMAPs this gene was expressed by different cell types and in particular in macrophages, and we therefore decided to exclude this marker from future analyses. When simultaneously overlaying the expression of FAPα, PDGFRβ, PDGFRβ, PDPN, αSMA and CD26 onto the Tabula Muris UMAPs, these markers nicely associated with stromal fibroblasts in the health mammary gland. We then examined these six markers in two scRNA-seq studies from human breast cancers and demonstrated that all 6 genes effectively clustered to 1) the fibroblast cluster identified in 14 treatment-naïve breast cancers , and 2) the four stromal subpopulations identified in 5 patients with TNBC . These analyses demonstrate that all six markers identify CAFs in breast cancer, and we therefore designed an antibody panel (see Table 3) consisting of the 5 cell surface CAF markers: FAPα, PDGFRα, PDGFRβ, PDPN and CD26.
At present, the datasets used and analysed during the current study are available from the corresponding author on reasonable request. The flow cytometry dataset will be available in FlowRepository in the near future.
Validation of our CAF marker gene panel in single cell gene expression datasets. A) UMAP of mouse mammary gland cells (data from ) color-coded by major cell lineages. B) Violin plots showing the log-normalized expression levels of CAF marker genes in mouse mammary gland cell types. C) The average Z-score of ACTA2, FAP, PDGFRA, PDGFRB, CD26/DPP4 and PDPN overlaid on the single cell mouse mammary gland UMAP plot from A). D) Violin plot of the expression levels of the average Z-score of ACTA2, FAP, PDGFRA, PDGFRB, CD26/DPP4 and PDPN in mouse mammary gland cell types. E) Representation of 14 human breast cancers (data from ) in UMAP space coloured according to cell type. F) The average Z-score of ACTA2, FAP, PDGFRA, PDGFRB, CD26/DPP4 and PDPN overlaid on UMAP as in E). Green colour bar, average Z-score. G) Violin plot indicating the distribution of the CAF signature (ACTA2, FAP, PDGFRA, PDGFRB, CD26/DPP4 and PDPN) Z-score in breast cancer cell types. H) UMAP of five primary human breast cancer samples (data from ) coloured according to cell type. I) Expression level of the average Z-score of our CAF panel plotted onto the UMAP from H). Green colour bar, average Z-score. J) Violin plot displaying the expression of the CAF marker Z-score gene signature across all cell types annotated in this dataset.
A CAF-only clustering analysis visualized by UMAP, after the removal of all other cell types in the mouse melanoma TME, revealed three CAF subsets. Colours indicate the three CAF subpopulations, which were annotated as previously reported . (B and C) Dot plots of selected genes expressed in mouse melanoma CAF subtypes (B) and at different time points (C). Intensity of colour indicates the average expression of each gene in each cluster, and the size of the dot is the fraction of cells in the cluster expressing that gene.
The use of reference genes is required for relative quantification in gene expression analysis and since the stability of these genes could be variable depending on the experimental design, it has become indispensable to test the reliability of endogenous genes. Therefore, this study evaluated 10 reference candidate genes in two different experimental conditions in order to obtain stable genes to be used as reference in expression studies related to scrotal hernias in pigs. Two independent experiments were performed: one with 30 days-old MS115 pigs and the other with 60 days-old Landrace pigs. The inguinal ring/canal was collected, frozen and further submitted to real-time PCR analysis (qPCR). For the reference genes stability evaluation, four tools were used: GeNorm in the SLqPCR, BestKeeper, NormFinder and Comparative CT. A general ranking was generated using the BruteAggreg function of R environment. In this study, the RPL19 was one of the most reliable endogenous genes for both experiments. The breed/age effects influenced the expression stability of candidate reference genes evaluated in the inguinal ring of pigs. Therefore, this study reinforces the importance of evaluating the stability of several endogenous genes previous their use, since a consensual set of reference genes is not easily obtained. Here, two sets of genes are recommended: RPL19, RPL32 and H3F3A for 30-days MS115 and PPIA and RPL19 for the 60 days-old Landrace pigs. This is the first study using the inguinal ring tissue and the results can be useful as an indicative for other studies working with gene expression in this tissue.
Although more than two genes should be used as reference in gene expression studies , the average number of genes used is only 1.2, which means, below the recommendation [33,34]. Moreover, it is usual studies with relatively common genes such as GAPDH, β-actin and 18S RNA, without testing for stability. Given the complexity of the experimental designs and tissues to be evaluated, a broad panel of genes and tools should be used to search for the best reference genes . It is also important to note that when candidate reference genes are being evaluated, the most or least stable genes chosen are based only in that experiment, and not necessarily will happen in other conditions. Furthermore, the most stable genes found in one experiment does not mean that only those genes are stable, reinforcing the need of always testing several candidate reference genes. The use of more than three genes is indicated to reduce the selection of false endogenous genes that may impact on the reliability of the results . One example could be observed in our study, where the same tissue was collected from animals of two different lines and ages and, despite of being from the same species, two sets of genes should be used as reference: the RPL19, RPL32 and H3F3A for 30-days MS115 (E1) and PPIA and RPL19 for the 60 days-old Landrace pigs (E2). 59ce067264