Selection of Cell-Type Specific Cpgs for Fibroblasts (A) Split of Total Samples (N = 579) Into Training (N = 409) and Validation Set (N = 170)

Supplemental Figure S1: Selection of cell-type specific CpGs for fibroblasts (A) Split of total samples (n = 579) into training (n = 409) and validation set (n = 170). (B) Multidimensional scaling (MDS) plot of the validation data (n = 170) shows that samples cluster by cell type. For the analysis, all CpGs shared between the 450K and the EPIC BeadChip were included (except XY chromosomes). (C) Chromosomal location and association with corresponding genes for the selected fibroblast-specific CpGs: lncRNA (RP11-60A8.1) and leucine rich repeats and immunoglobulin like domains 1 (LRIG1). Shown are mean β values from the training data set of neighboring CpGs for the respective cell types. The red lines depict the relevant CpGs. (D) DNAm levels (β values) of the two selected CpGs from the FibroScore in the lung fibrosis dataset GSE63704 (450K BeadChip) (1). Two-sided t-test: *** p < 0.001. (E) DNAm levels (β values) of the two selected CpGs from the FibroScore in the liver cirrhosis dataset GSE60753 (450K BeadChip) (2). Two-sided t-test: : ** p < 0.01, NS = not significant. 1

Supplemental Figure S2: Genomic context of cell-type-specific CpG sites Chromosomal location and association with corresponding genes for the selected cell-type-specific CpGs: Dynamin 2 (DNM2), Myosin IG (MYO1G) encodes for the human minor histocompatibility antigen HA-2, which is only expressed in hematopoietic cells (3), Stathmin 1 (STMN1), DNA polymerase epsilon catalytic subunit A (POLE), WSC Domain Containing 1 (WSCD1), ArfGAP With GTPase Domain, Ankyrin Repeat And PH Domain 1 (AGAP1) is associated with neurodevelopmental disorders and overexpressed in brain (4), RAS-Associated Protein RAB3A (RAB3A) is also highly expressed in brain (4). Shown are mean β values from the training data set of neighboring CpGs for the respective cell types. The red lines depict the relevant CpGs.

Supplemental Figure S3: Comparison of different deconvolution methods (A) In addition to the NNLS model, two alternative deconvolution approaches were considered for in vitro neuron-glia-DNA mixes from dataset GSE41826 (5): non-negative matrix factorization (NMF) and EpiDISH (6, 7). Comparison of expected versus predicted DNA proportions by deconvolution with different algorithms (NNLS, NMF and EpiDISH) (6, 7). (B) The predicted cell fractions for the neuron-glia-DNA mixes from dataset GSE41826 (5) are depicted based on the eight cell-type-specific CpGs for NMF and EpiDISH. The best correlation of the predicted cell fractions with the real mixture of neurons/glia was observed for the NNLS-based deconvolution (Figure 4), while EpiDISH did not misclassify any other cell type. (C) Cell type DNA mixes from dataset GSE122126 (8). Comparison of expected versus predicted DNA proportions by deconvolution with different reference matrices. (D) Pyrosequencing of DNA mixes. Comparison of expected versus predicted DNA proportions by deconvolution. 3

Supplemental Figure S4: Deconvolution of cell mixtures based on individual cell-type-specific CpGs (A) Deconvolution of normal tissues from the TCGA database (450K BeadChip). Shown are estimated cellular fractions. The relevant CpG for neurons was not available for TCGA datasets and therefore left out. (B) Deconvolution of healthy tissues based on pyrosequencing of DNAm at the eight relevant CpGs.

Supplemental Table S1. 450k/EPIC Illumina BeadChip datasets used in this study

NUMBER OF GSE SAMPLE TYPE REFERENCES SAMPLES Training dataset GSE34486* endothelial cells 10 (9) GSE40699 muscle cells, epithelial cells, hepatocytes, fibroblasts, 26 (10) astrocyte, endothelial cells GSE41933 mesenchymal stromal cells 12 (11) GSE43976 leukocytes 10 (12) GSE50222 leukocytes 8 (13) GSE52025 fibroblasts 2 (14) GSE52112 mesenchymal stromal cells 34 (15) GSE58622 adipocytes 10 (16) GSE59065 leukocytes 20 (17) GSE59091 induced pluripotent stem cells, fibroblasts 12 (18) GSE59250 leukocytes 60 (19) GSE59796 leukocytes 4 (20) GSE60753 hepatocytes 15 (2) GSE63409 leukocytes 30 (21) GSE65078 induced pluripotent stem cells, fibroblasts 8 (22) GSE68134 induced pluripotent stem cells, fibroblasts 6 (23) GSE71955 leukocytes 20 (24) GSE74877 epithelial cells, melanocytes, mesenchymal stromal 9 (25) cells, fibroblasts, endothelial cells GSE77135 fibroblasts 20 (26) GSE79144 glia, neurons 18 (27) GSE79695 mesenchymal stromal cells 12 (28) GSE82234 endothelial cells 3 (29) GSE85647 leukocytes 6 (30) GSE87095 leukocytes 10 (31) GSE87177 endothelial cells 1 (32) GSE88824 leukocytes 16 (33) GSE92843 epithelial cells 1 (34) GSE95096 fibroblasts 1 (35) GSE98203 neurons 10 (36) GSE99716 endothelial cells 1 (37) GSE103253* endothelial cells 10 (38) GSE107226 fibroblasts 4 (39)

Validation dataset GSE34486* endothelial cells 6 (9) GSE51921 iPSCs, fibroblasts 6 (40) GSE53302 muscle stem cells 6 (41) GSE68851 fibroblasts 12 (42) GSE71244 leukocytes 24 (43) GSE74486 glia, neurons 15 (44) GSE85566 epithelial cells 6 (45) GSE86258 fibroblasts 7 (46) GSE86829 fibroblasts 3 (47) GSE87797 mesenchymal stromal cells 12 (48) GSE103253* endothelial cells 15 (38) GSE104287 leukocytes 8 (49) GSE106099 feto-placental endothelial cells 12 (50) GSE109042 buccal epithelial cells 6 (51) GSE111396 fibroblasts 14 (52) GSE122126 adipocytes, neurons, hepatocytes, endothelial cells, 18 (13 EPIC) (8) epithelial cells, leukocytes

Other datasets GSE41826 glia-neuron DNA mixes 9 (5) GSE60753 normal and cirrhotic liver 100 (2) GSE63704 normal and fibrotic lung 80 (1) GSE122126 cell type DNA mixes 8 (8) * Samples from these studies were considered either for training or validation sets

Supplemental Table S2. Association of fibroblast-associated DNAm with overall survival in cancer

TCGA SURV_PVAL SURV_PVAL SURV_PVAL CANCER TYPE PROJECT CG18096962 CG18005280 FIBROSCORE TCGA-ACC Adrenocortical Carcinoma 0.0001* 0.7525 0.0003* TCGA-MESO Mesothelioma 0.2466 0.0001* 0.0011* TCGA-HNSC Head and Neck Squamous Cell Carcinoma 0.7474 0.0153* 0.0050* TCGA-PCPG Pheochromocytoma and Paraganglioma 0.1130 0.9490 0.0064* TCGA-KICH Kidney Chromophobe 0.0170* 0.1673 0.0119* TCGA-KIRC Kidney Renal Clear Cell Carcinoma 0.2481 0.0103* 0.0619 TCGA-BLCA Bladder Urothelial Carcinoma 0.3357 0.2406 0.0623 TCGA-THYM Thymoma 0.1658 0.1807 0.1081 TCGA-PRAD Prostate Adenocarcinoma 0.7707 0.9890 0.1087 TCGA-SARC Sarcoma 0.1208 0.3432 0.1390 TCGA-UCEC Uterine Corpus Endometrial Carcinoma 0.6565 0.7381 0.1453 TCGA-LGG Brain Lower Grade Glioma 0.0113* 0.0177* 0.1704 TCGA-THCA Thyroid Carcinoma 0.1991 0.5390 0.2141 TCGA-LUAD Lung Adenocarcinoma 0.4578 0.3120 0.2541 TCGA-BRCA Breast Invasive Carcinoma 0.4685 0.8774 0.2661 TCGA-ESCA Esophageal Carcinoma 0.7665 0.4520 0.3506 TCGA-READ Rectum Adenocarcinoma 0.6664 0.2644 0.4358 TCGA-LIHC Liver Hepatocellular Carcinoma 0.5432 0.8678 0.4505 TCGA-PAAD Pancreatic Adenocarcinoma 0.9590 0.0090* 0.4868 TCGA-TGCT Testicular Germ Cell Tumors 0.5297 0.7229 0.4901 TCGA-LAML Acute Myeloid Leukemia 0.2399 0.9467 0.4939 TCGA-CHOL Cholangiocarcinoma 0.2521 0.2828 0.5361 Lymphoid Neoplasm Diffuse Large B-cell TCGA-DLBC 0.8924 0.4410 0.5602 Lymphoma TCGA-UCS Uterine Carcinosarcoma 0.0179* 0.0865 0.6019 TCGA-GBM Glioblastoma Multiforme 0.7417 0.3277 0.6096 TCGA-SKCM Skin Cutaneous Melanoma 0.6751 0.7819 0.6126 TCGA-UVM Uveal Melanoma 0.3799 0.0001* 0.6207 Cervical Squamous Cell Carcinoma & TCGA-CESC 0.5976 0.3391 0.6233 Endocervical Adenocarcinoma TCGA-OV Ovarian Serous Cystadenocarcinoma 0.6395 0.0922 0.6395 TCGA-COAD Colon Adenocarcinoma 0.7778 0.7443 0.7918 TCGA-LUSC Lung Squamous Cell Carcinoma 0.9646 0.4866 0.9541 TCGA-KIRP Kidney Renal Papillary Cell Carcinoma 0.2736 0.0455* 0.9929 * p < 0.05

Supplemental Table S3. Application for cell type deconvolution

An application for cell type deconvolution is provided as separate Excel tool. The mean DNAm values for the cell-type-specific CpGs of the Illumina BeadChip training dataset are given as reference matrix. Furthermore, the application allows NNLS predictions to estimate the cellular composition in independent datasets. This table was generated in analogy to the NNLS application for Epi-Blood-Count (53).

Supplemental Table S4. Primer DNA sequences used for pyrosequencing

NAME DNA SEQUENCE cg18096962_Forward GAGTATTGGGTTTATTTAGTTTTAGGAT cg18096962_Reverse_Biotin TCAAATTCTATTTACTACCCTCTTCC cg18096962_Sequencing GTTTTTATTTTTGAG cg18005280_Forward TATTGGTGTTATTGGGGGAGG cg18005280_Reverse_Biotin CCCACAACCATTCTAAAACAATC cg18005280_Sequencing TGTTATTGGGGGAGG cg10673833_Forward TGTTGTTAGGGTTGGAAGTTAATTT cg10673833_Reverse_Biotin CACCAACCTCCTCCAATACTAATATAA cg10673833_Sequencing GGGGAGGATTTAGT cg06421238_Forward TTGTGGGGGATGGGTAGT cg06421238_Reverse_Biotin ACCTCCTCCCTACAAATCCTATATCT cg06421238_Sequencing GATAAAGTTTAGGAAGAGGTT cg06631999_Forward_Biotin GGGTTGTTTTTTGGTTATTAGAGTTAGGTA cg06631999_Reverse CTCTTCTTTCCAATTTTTTCCAAATAATC cg06631999_Sequencing CTCTAACTCAATCCCTAAATAC cg23068797_Forward_Biotin TTTTTGGGTTTAGGAGGAATGTT cg23068797_Reverse CCAACTAATACCACATCTAAAACTATTTACAATAC cg23068797_Sequencing AATACCACATCTAAAACTATTTAC cg27309098_Forward_Biotin GAGGAAATTGAGGTTTAGAGATATGAA cg27309098_Reverse CTAAAATCAAACTTAAAATACAACTCCTTAATA cg27309098_Sequencing CAAAAAATTTTTACC cg27197524_Forward_Biotin GAAGAATTTGAATTTTAGGGAAGAAGTAT cg27197524_Reverse CCCAACAAACAAAAACAAAAATTCAATA cg27197524_Sequencing TACTAAACTCTAAAACCTAC cg21548464_Forward GTTTTTTAGTTGGGATTTATTTAGATTTGT cg21548464_Reverse_Biotin AACCCTTACCATCTTCTACCTAAACT cg21548464_Sequencing CATATTCAAAATTCTCATCAT cg09998451_Forward_Biotin GGGAATTTTGTATTTTAGTTGTGGATTTTT cg09998451_Reverse TAAACCTCAATTAACCCCTACTCAA cg09998451_Sequencing GTAAAATTTTGTTTGAT

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