FAP protein, human

FAPs were isolated by FACS as described^{34 (link)}, with minor modifications, as follows. Total hindlimb muscle was harvested, minced with scissors for 7–9 min and then incubated for 60–75 min at 37 °C in Dulbecco’s Modified Eagle Medium (DMEM) (Life Technologies) supplemented with 700–800 U/ml Collagenase Type II (Worthington Biochemical) and 0.3 U/ml Dispase (Invitrogen) with gentle agitation every 15 min. Enzymatic digestion was then quenched by addition of DMEM containing 20% HyClone™ characterized fetal bovine serum (FBS) (GE Healthcare; Lot# A00168), followed by serial filtration of the cell suspension through 100 µm and 70-µm cell strainers (Falcon). The cell suspension was then centrifuged at 800×g for 5 min and resuspended in 10% FBS in 1× Dulbecco’s phosphate-buffered saline (DPBS) (Gibco) for antibody staining. Anti-CD31 (1:100, Miltenyi Biotech, #130-097-418) and anti-CD45 (1:100, Miltenyi Biotech, #130-052-301) microbead conjugated antibodies were used to deplete endothelial and hematopoietic cells, respectively, via MACS LS Separation columns (Miltenyi Biotech) loaded in a QuadroMACS Separator (Miltenyi Biotech), according to manufacturer’s instructions. Following magnetic depletion, the remaining cell suspension was incubated with fluorescently conjugated SCA-1-V450 (1:400, BD Horizon, #560653) and PDGFRα-APC (1:100, eBioscience, #17-1401-81) antibodies for 30 min on ice, centrifuged at 800×g for 5 min, and resuspended in 2% FBS in DPBS following two washes with 1× DPBS. Samples were filtered through 35-µm cell strainers (Falcon) and 50 µg/mL 7-AAD (BioLegend) was added to a final concentration of 0.50 µg/mL immediately prior to sorting to distinquish between live/dead cells. Fluorescence-minus-one (FMO) controls were used to test for non-specific staining and generate strict sort gates to minimize cross contamination between populations. FAPs were isolated based on co-staining for PDGFRα and SCA-1 as shown in Supplementary Fig. 2. To distinguish between Acvr1^R206H/+ and wild-type FAPs, we assessed the enriched hindlimb PDGFRα+ SCA1+ subpopulation for tdTomato and GFP expression, which provides a direct readout of recombination status at the Acvr1^R206H and R26^NG loci, respectively. For the tdTomato-FMOs, depending on the experimental mouse line being analyzed, either R26^NG/+;Tie2-Cre or R26^NG/+;Pdgfrα-Cre mice were utilized. Acvr1^tnR206H/+ mice were used for the GFP FMOs. Sorting and analysis was done on a FACS Aria II (BD Biosciences) equipped with 407, 488, and 633 lasers. Acvr1^R206H and R26^NG gates were confirmed based on verification of tdTomato and GFP expression via fluorescence microscopy.

Each subject was imaged twice at six angles. Individual images were assembled at the highest possible resolution into a single 3D mesh composite of the face using InSpeck FAPS and EM 6.0 software. Twenty-nine 3D landmarks were placed on the meshes using a novel automated landmarking method. A manuscript describing the automated landmarking method has been submitted for publication. Details of the method and the automated landmarking algorithm are available at https://www.facebase.org/facial_landmarking/ [28 ], and landmarks were then subjected to Procrustes superimposition for morphometric analysis [28 –30 ]. Images from 163 subjects in the GWAS cohort were landmarked manually as they could not be landmarked automatically. This was mostly due to imaging artifacts on non-critical regions of the face that do not interfere with manual landmark placement. Superficial artifacts (smiling, squinting, open mouth, etc.) in the landmark data were corrected using a multiple linear regression in which all factors and their interactions were considered. The resulting residuals were mean centered and used for downstream phenotype derivation.
A 3D "skew" artifact (coordinated asymmetric displacement of landmarks due to image assembly) was identified by principal components analysis (PCA) of the landmark coordinates and was removed by regressing out the PC scores from the landmark data. Unlike manual landmarking, automated landmarking produces a non-normal distribution of measurement error; therefore, outliers were detected using the combination of Procrustes distance from the mean and the within-landmark variance of distances from each landmark mean. The cleaned landmark data were then used to calculate linear distances and multivariate measures to be used as phenotypes. Linear distance phenotypes were calculated as the distance between their defining landmarks, multiplied by the centroid size of each individual's landmark configuration. Centroid size, by definition, is the mean squared distance of each landmark from the geometric center. Allometry is shape variation related to size [31 (link),32 ], and was calculated using regression scores corresponding to size independent of age. To calculate multivariate measures, we regressed out age and size variation in symmetrized landmark data, and the first five PCs of a PCA were used as phenotypes. S1 File shows 3D faces that correspond to the extremes of variation in allometry and the first 5PCs. To calculate the MidfaceModPC1 phenotype we used the RV method to identify the set of spatially contiguous landmarks that maximized the ratio of covariation among themselves to covariation with landmarks outside of that set [33 (link)]. The resulting set around the midface was then subjected to PCA, and the first PC represented MidfaceModPC1. For all variables, measurements greater than four standard deviations from the mean were excluded from analyses. All morphometric analyses were performed in MorphoJ [34 (link)] or in R using the Geomorph [35 ] and Morpho [36 ] packages. Head circumference was measured directly using a tape measure on a subset of 2,676 subjects. Phenotype data were deposited in the FaceBase data Hub (FaceBase: https://www.facebase.org/; FB00000667.01).

Human CAFs were harvested from freshly resected non-small cell lung carcinoma (NSCLC) tumour tissues. Tumours from 16 patients were included in this study (Table 1). The Regional Ethical Committee approved the study, and all patients provided written informed consent. Fibroblasts from tumours were isolated using the out-growth method and characterized by specific antibodies. Briefly, tumour resections were collected and cut into 1-1.5 mm³ pieces. Enzymatic digestion of tissues was carried out for 1.5 h with collagenase (Cat. no. C-9407 Sigma-Aldrich, St. Louise, MO, USA), at a final concentration of 0.8 mg/mL. Pure fibroblast cultures were obtained by selective cell detachment from the primary culture mix, and by further cell propagation in the presence of 10% FBS. Cells were grown at 3% oxygen levels and used for experiments after the second passage (2-3 weeks). Antibodies: FITC-conjugated anti-human α-SMA (smooth muscle α-actin) antibody (Abcam; Cat. # ab8211), FITC-conjugated anti-IgG antibody (negative control) and anti-human FAP (Fibroblast Activation Protein) α-antibody (Abcam; Cat. # ab53066).

We extracted the single-cell RNA sequencing data used in this paper from Gene Expression Omnibus (GEO; GSE138826) (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE138826; GSE138826_expression_matrix.txt) (Oprescu et al., 2020 (link)). The preliminary analyses of processed scRNA-seq data were analysed using the Seurat suite (version 4.0.3) standard workflow in RStudio Version 1.2.5042 and R version 4.0.3. First, we applied initial quality control to Oprescu et al., 2020 (link) dataset. We kept all the features (genes) expressed at least in five cells and cells with more than 200 genes detected. Otherwise, we filtered out the cells. Second, we verified nUMIs_RNA (>200 and < 4,000) and percent.mt. (less than 5%) Third, UMIs were normalized to counts-per-ten-thousand log-transformed (normalization.method = LogNormalize). The log-normalized data were then used to find variable genes (FindVariableFeatures) and scaled (ScaleData). Finally, Principal Component Analysis (PCA) was run (RunPCA) on the scaled data using highly variable features or genes. Elbowplot were used to decide the number of principal components (PCs) to use for unsupervised graph-based clustering and dimensional reduction plot (UMAP) embedding of all cells or further subclustering analyses (i.e., FAPs) using the standard FindNeighbors, FindClusters, and RunUMAP workflow. We used 30 PCs and a resolution of 0.6 to visualize a Uniform manifold approximation and projection (UMAP) dimensionality reduction plot generated on the same set of PCs used for clustering. We decided the resolution value for FindClusters on a supervised basis after considering clustering output from a range of resolutions (0.4, 0.6, 0.8, and 1.2). We used a resolution of 0.6. Our initial clustering analysis returned 29 clusters (clusters 0–28). We identified cell populations and lineage-specific marker genes for the analyzed dataset using the FindAllMarkers function with logfc.threshold = 0.25, test.use = “wilcox,” and max.cells.per.ident = 1,000. We then plotted the top 10 expressed genes, grouped by orig.ident and seurat_clusters using the DoHeatmap function. We determine cell lineages and cell types based on the expression of canonical genes. We also inspected the clusters (in Figures 2, 3) for hybrid or not well-defined gene expression signatures. Clusters that had similar canonical marker gene expression patterns were merged.
For Mesenchymal Clusters (group of FAPs + DiffFibroblasts + Tenocytes obtained in Figure 2) we used PCs 20 and a resolution of 20 to visualize on the UMAP plot. Our mesenchymal subclustering analysis returned 10 clusters (clusters 0–9). Cell populations and lineage-specific marker genes were identified for the analyzed dataset using the FindAllMarkers function with logfc.threshold = 0.25 and max.cells.per.ident = 1,000. We then plotted the top eight expressed genes, grouped by orig.ident and seurat_clusters using the DoHeatmap function. The identity of the returned cell clusters was then annotated based on known marker genes (see details about cell type and cell lineage definitions in the main text, Results section). Individual cell clusters were grouped to represent cell lineages and types better. Finally, figures were generated using Seurat and ggplot2 R packages. We also used dot plots because they reveal gross differences in expression patterns across different cell types and highlight moderately or highly expressed genes.
To validate our initial skeletal muscle single-cell analysis, we explored three publicly available scRNAseq datasets (McKellar et al., 2021 (link); Yang et al., 2022 (link); Zhang et al., 2022 (link)). Zhang et al. dataset was explored using R/ShinyApp (https://mayoxz.shinyapps.io/Muscle), McKellar et al. (2021) (link) using their web tool developed http://scmuscle.bme.cornell.edu/, and Yang et al. using their Single Cell Metab Browser http://scmetab.mit.edu/. All the figures used were downloaded from the websites (Supplementary Figure S6).
The scRNAseq pipeline used for MuSC subclustering was developed following previous studies (Oprescu et al., 2020 (link); Contreras et al., 2021a (link)). To perform unsupervised MuSC subclustering, we used Seurat’s subset function FindClusters, followed by dimensionality reduction and UMAP visualization (DimPlot) in Seurat.

One-step digestion of skeletal muscle tissue for fibro-adipogenic progenitor isolation was performed as described before with few modifications (Contreras et al., 2020 (link)). Briefly, skeletal muscles from both hindlimbs of female wild type mice were carefully dissected, washed with ice-cold DMEM, and cut into small pieces with blades until a homogeneous, paste-like slurry was formed. Seven ml of digestion solution containing collagenase type II (265 Unit/mL, Worthington, DC, United States), 0.5 U of Dispase (Cat. No. 07913, STEMCELL™ Technologies, Canada), 0.05 mg/mL of DNaseI (Cat. No. 10104159001, Roche/Sigma-Aldrich, 100 mg from bovine), and 1% BSA (Sigma-Aldrich Pty Ltd., A3311-50G) dissolved in DMEM (Cat. No. 10566016) was added to two hindlimbs and the preparation was placed on a water bath with constant rotation at 37°C for 45 min and intermittent vortexing every 15 min. Tissue preparations were gently pipetted up and down 5–10 times to enhance muscle dissociation with a 10 mL Stripette^® serological pipette on ice. Ice-cold FACS buffer was added to make the final volume up to 30 mL volume and samples were then passed through 100 μm cell strainer sequentially after gentle mixing. Following centrifugation at 600 g for 6 min at 4°C, the pellet was resuspended in 10 mL of growth media (20 ng/mL of basic Fibroblast Growth Factor (Milteny Biotec, Cat. No. 130-093-843) and 10% heat-inactivated fetal bovine serum (v/v) (FBS; Hyclone, UT, United States) in DMEM (Cat. No. 10566016) and supplemented with antibiotics (Penicillin-Streptomycin Cat. no. 15140122, Gibco by Life Technologies) and cells were pre-plated onto 100 mm plastic tissue culture dish for 2 h and grown at 37°C in 5% CO₂ as previously described (Contreras et al., 2019a (link)). After 2 h of FAP pre-plating the supernatant media was removed to culture muscle stem cells (see Muscle stem cell enrichment and myotube differentiation protocol below) and replaced with fresh growth media. FAP CFU-F assay was performed with cells seeded at a density of 250/cm² in growth media in a 12-well plate coated with Corning Matrigel Matrix hESC qualified (Cat. No. 354277) prepared in cold DMEM/F-12 as per the provider’s instructions. Cultured FAPs were allowed to grow for about 7 days before splitting them. CFU-F experimental outline is shown in Figure 6B. FAPs were used not further than passage 1. CFU-F averages were obtained from three technical replicates/samples using three female mice. CFU-F photos were taken using an iPhone XR 12MP Wide camera.

Fatty acid dried blood spot (DBS) was obtained to track supplementation compliance and ensure adequate LC n-3 PUFA membrane incorporation. A drop of blood was collected from each participant via finger stick on filter paper pre-treated with a preservation solution (Fatty Acid Preservative Solution, FAPS™) and allowed to dry at room temperature for ~15 minutes. At the conclusion of the study, the DBS were shipped overnight on dry ice to OmegaQuant (Sioux Falls, SD, USA) for fatty acid analysis. Based on their standard laboratory protocol, fatty acids were identified by comparison with a standard mixture of fatty acids characteristic of RBC (GLC OQ-A, NuCheck Prep, Elysian, MN, USA) and used to construct individual fatty acid calibration curves. Fatty acid composition was expressed as a percent of total identified fatty acids. PRE and POST values of EPA, DHA, and the omega-3 index (O3i) were reported. The O3i is defined as the sum of EPA and DHA adjusted by a regression equation (r = 0.96) to predict the RBC O3i.