{"paper_id":"4183dd99-616c-417d-b983-41896db8b85d","body_text":"Single Cell RNA Sequencing Identifies IGFBP5 And QKI In Ciliated Epithelial Cell Genes Associated With Severe COPD | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Single Cell RNA Sequencing Identifies IGFBP5 And QKI In Ciliated Epithelial Cell Genes Associated With Severe COPD Xiuying Li, Guillaume Noell, Tracy Tabib, Alyssa D Gregory, Humberto E Trejo Bittar, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-100834/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Apr, 2021 Read the published version in Respiratory Research → Version 1 posted 10 You are reading this latest preprint version Abstract Background : Whole lung tissue transcriptomic profiling studies in chronic obstructive pulmonary disease (COPD) have led to the identification of several genes associated with the severity of airflow limitation and/or the presence of emphysema, however, the cell types driving these gene expression signatures remain unidentified. Methods : To determine cell specific transcriptomic changes in severe COPD, we conducted single-cell RNA sequencing (scRNA seq) on n= 29,961 cells from the peripheral lung parenchymal tissue of nonsmoking subjects without underlying lung disease (n=3) and patients with severe COPD (n=3). The cell type composition and cell specific gene expression signature was assessed. Gene set enrichment analysis (GSEA) was used to identify the specific cell types contributing to the previously reported transcriptomic signatures. Results : T-distributed stochastic neighbor embedding and clustering of scRNA seq data revealed a total of 17 distinct populations. Among them, the populations with more differentially expressed genes in cases vs. controls (log fold change >|0.4| and FDR=0.05) were: monocytes (n=1499); macrophages (n=868) and ciliated epithelial cells (n= 590), respectively. Using GSEA, we found that only ciliated and cytotoxic T cells manifested a trend towards enrichment of the previously reported 127 regional emphysema gene signatures (normalized enrichment score [NES] = 1.28 and =1.33, FDR= 0.085 and =0.092 respectively). Among the significantly altered genes present in ciliated epithelial cells of the COPD lungs, QKI and IGFBP5 protein levels were also found to be altered in the COPD lungs. Conclusions : scRNA seq is useful to identify transcriptional changes and possibly individual protein levels that may contribute to the development of emphysema in a cell-type specific manner. Pulmonology single cell RNA-seq COPD cigarette smoke Figures Figure 1 Figure 2 Background Chronic obstructive pulmonary disease (COPD) is a common respiratory disorder characterized by irreversible expiratory airflow limitation in response to inhalation of noxious stimuli ( e.g ., cigarette smoke) [ 1 ]. COPD is the third leading cause of death [ 2 ] and a major economic burden in the United States [ 3 ]. COPD is a heterogeneous disorder that can manifest with multiple clinical phenotypes, including emphysema (destructive enlargement of the airspaces distal to terminal bronchioles), chronic bronchitis, and small airway disease [ 4 – 7 ]. Over the last few decades, several lung homogenates and airway transcriptomic studies have been conducted in order to reveal molecular pathways linked to the pathogenesis of smoking-related airflow limitations and emphysema [ 8 – 12 ]. These studies have resulted in the identification of several biological processes, now believed to be associated with smoking-related FEV1 decline [ 13 – 15 ], including 1) chronic immune response and inflammation [ 16 , 17 ]; 2) imbalance of proteases/anti-proteases [ 18 ]; 3) oxidative stress [ 19 , 20 ]; 4) cellular senescence (permanent loss of proliferative capacity) [ 21 , 22 ]; and 5) lung epithelial cell (LEC) apoptosis. In one of the most comprehensive transcriptomic analyses of smoking-related emphysema to date, Campbell, et al. profiled gene expression in 8 separate regions from 6 emphysematous lungs, based on degree of emphysema, and compared transcriptome with 2 non-diseased lungs (8 regions × 8 lungs = 64 samples). They identified a total of 127 genes with expression levels significantly correlating with the emphysema severity [ 23 ]. Many genes upregulated with increased emphysema severity were involved in inflammation ( e.g ., the B-cell receptor signaling), while those downregulated with increasing disease severity were implicated in tissue repair ( e.g ., the transforming growth factor beta (TGFβ) pathway) [ 23 ]. This 127 gene emphysema signature was enriched in the transversal studies of lung tissue of patients with severe COPD and emphysema [ 13 , 15 ]. However, it remains to be elucidated which specific cell types contribute most to this smoking-related emphysematous and small airflow damage transcriptome signature. Here, we used scRNA-seq technology to identify lung cell-type specific gene expression signatures associated with airflow limitation and/or emphysema. We examined the single-cell transcriptomes of cell populations from lung tissue samples obtained from a representative selection of 3 ex-smokers with severe COPD/emphysema and 3 nonsmokers without any history of lung disease. We compared our findings with previously reported whole lung tissue homogenate airflow limitation and emphysema signatures, and experimentally validated the key associated genes. Methods Human Lung Tissue Samples For scRNA seq, fresh lung parenchymal tissue samples were obtained from the upper lobes of three nonsmoking subjects without underlying lung disease who underwent warm autopsies and three patients with severe COPD who received lung transplantation (Table 1 ). For immunoblot analysis (Fig. 2 A), frozen lung parenchymal tissue obtained from smokers without any history of lung disease (n = 5), or very severe COPD (n = 7), both were provided by the University of Pittsburgh, Lung Tissue Research Consortium (LTRC), respectively (Table 5 ). Data from the Lung Genomics Research Consortium (LGRC) Cohort was used for gene expression analysis of QKI and IGFBP5 (Table 6 ) . The COPD Specialized Center for Clinically Oriented Research (SCCOR) cohort was utilized for comparison of serum IGFBP5 levels between 40 patients with COPD and 40 smokers without clinically evident COPD (Table 7 ) . Table 1 Three normal nonsmokers and three patients with severe COPD. ID Age Sex Smoking (Pack-years) FEV1/FVC FEV1% ref. DLCO % ref. NL 1 57 F None NA NA NA NL 2 18 M None NA NA NA NL 3 23 F None NA NA NA COPD 1 65 F 60 0.33 33 14 COPD 2 62 F 75 0.29 22 53 COPD 3 61 F 45 0.25 21 20 Table 4 GSEA enrichment results of the different clusters with the differential gene expression of the LTRC (COPD Gold 4 vs. Non-smokers). SIZE ES NES NOM p-value FDR q-value FWER p-value Mast cells 70 -0.62 -1.72 0.00 0.07 0.04 Proliferating macrophages 164 -0.56 -1.71 0.01 0.04 0.05 Monocytes 103 -0.63 -1.58 0.03 0.10 0.12 FBPB4 macrophages 27 -0.61 -1.46 0.04 0.15 0.23 AT2 80 -0.56 -1.56 0.06 0.09 0.14 B cells 48 -0.47 -1.42 0.07 0.14 0.28 Club 83 -0.51 -1.38 0.09 0.16 0.32 Fibroblasts 245 -0.49 -1.45 0.12 0.13 0.24 Cytotoxic T cells 30 -0.54 -1.33 0.13 0.16 0.39 Low quality T cells 126 -0.45 -1.38 0.13 0.14 0.33 Ciliated 55 -0.48 -1.17 0.30 0.30 0.57 T cells 36 -0.42 -1.14 0.32 0.30 0.60 Endothelial 94 -0.36 -0.93 0.53 0.51 0.76 AT1 221 -0.12 -0.56 0.97 0.99 0.92 NK cells 23 -0.21 -0.55 0.97 0.93 0.92 Table 5 Clinical and Demographics of the control and COPD subjects (Immunoblot Analysis) Control COPD (GOLD Stage 4) Number of subjects 5 7 Age, yr, mean (SD) 62.0 (10.0) 62.0 (2.8) Gender 3M/2F 4M/3F Smoking (pack-years) 35 (10.6) 50.7 (23.1) Table 6 Clinical and Demographics of the Lung Genomics Research Consortium (LGRC) Cohort (used for QKI and IGFBP5 gene expression analysis) Control COPD Number of subjects 108 219 Age, yr, mean (SD) 63.6 (11.4) 64.7 (9.7) Gender (% Female) 55% 43% Smoking status, (%) Never 30% 5.50% Current 1.90% 6.40% Ever 58% 86.70% Unknown 10.10% 1.40% Pulmonary function, mean (SD) FEV1, % Predicted 95 (12.6) 50.6 (24.0) FVC, % Predicted 94.4 (13.1) 73,8 (19.5) DLCO, % Predicted 84.1 (16.7) 56.6 (23.1) Emphysema, % mean (SD) 15.4 (17.1) DLCO = diffusing capacity of the lung for carbon monoxide Table 7 Clinical and Demographics of the SCCOR Cohort (used for IGFBP5 ELISA) Control COPD Number of subjects 40 40 Age, yr, mean (SD) 66.7 (7.5) 67.5 (6.4) Gender (% Female) 42.50% 42.50% Current Smoking (%) 47.50% 40% Pack Years (SD) 50.3 (30.5) 65.4 (29.1) Pulmonary function, mean (SD) FEV1, % Predicted 99.1 ± 14.7% 70.7 ± 18.8% FEV1/FVC Ratio 0.763 ± 0.037 0.535 ± 0.104 Emphysema %F950* 0.005 ± 0.003 0.107 ± 0.086 * % low-attenuation area defined as the fraction of voxels less than − 950 Houndsfield Unit of total voxels identified in regions of the lung. Preparation of single-cell libraries, sequencing, and analysis The whole lung tissue samples were processed as described previously [ 24 ]. Briefly, lung tissue samples were digested, cell suspensions laded into the Chromium instrument (10X Genomics, Pleasanton, CA), and the resulting barcoded cDNAs were used to construct libraries. RNA-seq was conducted on all mixed samples as a pool. The total number of reads for the 3 single cell COPD samples was: 456,870,504 reads. Cell-gene specific molecular identifier counting matrices were generated and analyzed using Seurat [ 25 ] to identify distinct cell populations [ 26 ] and hierarchically clustered using Cluster 3.0 [ 27 ]. Reagents and Antibodies Chemicals were obtained from Sigma Chemical and Calbiochem. Polyvinylidene difluoride membranes were obtained from Bio-Rad. ECL Plus was obtained from Amersham. Antibodies were obtained from various sources: HPRT1 and EPAS1 antibodies were obtained from Cell Signaling; QKI, RTN4, STOM, IGFBP5 and secondary antibodies (horseradish peroxidase-conjugated anti-rabbit or anti-mouse Ig) were obtained from Santa Cruz Biotechnology. Immunoblot analysis Human lung tissues (control and severe COPD groups) were thawed and homogenized in RIPA buffer using a Bullet Blender (next advance). Samples were centrifuged at 4 °C for 10 min at 12 000G and resuspended in protein loading buffer. For western blot, about 30 ug of proteins were separated by SDS-PAGE gel and transferred into a nitrocellulose membrane. Membranes were blocked and incubated with primary antibodies and the appropriated secondary antibody HRP conjugated. The signal was acquired with Chemi-Doc MP (Bio‐Rad) using WesternBright Sirius HRP substrate (advansta). ELISA for IGFBP5 The IGFBP5 Human ELISA Kit was purchased from Thermal Fisher Scientific. Serum IGFBP5 levels were measured from 40 control smokers and 40 smokers with COPD according to the vendors instructions. Gene Ontology enrichment and GSEA Gene Set Enrichment Analysis (GSEA) was used to identify similarities with previously published emphysema and severity signatures [ 28 ]. The gene ontology biological process enrichment was done in R with the ClusterProfiler package [ 29 ], and the ontologies were summarized and visualized with the Revigo package [ 30 ]. Results 1. Single cell RNA sequencing reveals 17 distinct cell clusters from human lungs with severe COPD. We conducted single cell transcriptomic analysis of all cells obtained from the whole parenchymal lung tissue of three nonsmokers without underlying lung disease and three patients with severe COPD (demographic data: Table 1 ). The pathology with the H&E staining confirmed the presence of moderate to severe emphysematous changes in all three COPD patients (see Supplementary Figure S1 ). We examined a total of 29,961 cells from six subjects (3914–5920 cells/sample). All the samples were pooled and analyzed together to gain the power to detect rare cell types as described previously [ 24 ]. t-distributed stochastic neighbor embedding (t-SNE) blots were generated using statistically significant principal components and cells were clustered using an unbiased graph-based clustering algorithm (smart local moving [SLM] clustering), which in identified 17 distinct types of cells (Fig. 1 A). Cells from disease conditions and the individual subjects were indicated in different colors (Figs. 1 B and 1 C, respectively ). SLM clustering was made according to the distinct gene expression patterns based on various cell types in the lung (Fig. 1 D). The cell clusters that cannot be reliably classified due to fewer unique molecular identifiers were referred as “low quality” cells. Cell clusters were predominantly identified as: FABP4 as a cell cluster of FABP4 macrophages (#0); S100A8 as a cluster of monocytes (#1); CD3D as cytotoxic T cells (#2); GNLY as NK cells (#3); CLDN5 as endothelial cells (#4); LTB as T cells (#5); IGKC as B cells (#6); CAPS as ciliated epithelial cells (#7); SFTPC as alveolar type 2 epithelial (AT2) cells (#8); MALAT1 as low quality T cells (#9); RSPH1 as low quality cells (#10); DCN as fibroblasts (#11); SCGB1A1 as club epithelial cells (#12); AGER as alveolar type 1 epithelial (AT1) cells (#13); CCL21 as lymphatic endothelial cells (#14); TPSAB1 as mast cells (#15); KIAA0101 as proliferating macrophages (#16). These and other markers provided strong transcriptome signatures for each cell cluster (see Supplementary Table 1 , shown graphically in feature plots (Fig. 1 E). The prevalence of individual cell types between normal nonsmokers and patients with COPD is shown in Table 2 . None of the difference in the abundance of individual cell types between normal nonsmokers and COPD patients reached statistical significance (Wilcoxon), however, a trend existed for decreased percentages of macrophages, endothelial cells, AT2 cells, and fibroblasts in the COPD lungs. Table 2 Identified clusters, number of differentially expressed genes in Case vs. control, mean % of cells belonging to this cluster both in cases and controls, fold change differences between cluster percentage and the p value of the comparison of the cluster frequency in cases vs. controls both with t-test and Wilcoxon-test. Cluster Population # DE genes up down Control % of cells COPD % of cells Fold Change P value (t.test) P value (Wilcox) 0 FABP4 Macrophages 868 620 248 27.97 ± 10.74 5.15 ± 1.57 5.43 0.02 0.057 1 Monocytes 1499 677 822 23.80 ± 12.90 11.24 ± 4.84 2.12 0.18 0.23 2 Cytotoxic T cells 539 165 374 11.18 ± 6.48 21.86 ± 9.80 -1.96 0.14 0.11 3 NK cells 431 233 198 3.85 ± 2.89 22.14 ± 27.41 -5.75 0.23 0.4 4 Endothelial 242 185 57 9.17 ± 3.90 2.37 ± 0.52 3.87 0.03 0.057 5 T cells 213 64 149 2.11 ± 1.00 11.68 ± 9.02 -5.54 0.08 0.23 6 B Cells 153 30 123 0.99 ± 0.59 6.80 ± 7.95 -6.84 0.19 0.23 7 Ciliated 590 433 157 1.84 ± 0.98 6.18 ± 4.36 -3.35 0.10 0.23 8 AT2 476 34 442 4.12 ± 2.52 0.96 ± 0.32 -4.28 0.09 0.057 9 Low quality T cells 41 14 27 1.75 ± 0.68 3.71 ± 2.07 -2.12 0.13 0.11 10 Low quality cells 596 369 227 2.02 ± 0.95 2.83 ± 1.50 -1.40 0.41 0.63 11 Fibroblasts 96 87 9 3.17 ± 1.62 0.27 ± 0.07 11.60 0.03 0.057 12 Club cells 98 70 28 1.35 ± 0.81 2.72 ± 2.16 -2.02 0.29 0.63 13 AT1 68 59 9 2.60 ± 1.91 0.46 ± 0.36 5.71 0.12 0.11 14 Lymphatic-endothelial 27 20 7 1.93 ± 1.10 0.33 ± 0.29 5.85 0.06 0.23 15 Mast cells 32 25 7 1.32 ± 0.79 0.91 ± 0.24 1.44 0.44 0.63 16 Proliferative macrophages 111 55 56 0.84 ± 0.35 0.39 ± 0.25 2.17 0.12 0.11 2. Gene expression in 17 individual types of cells in severe emphysematous lungs. For each cluster, the differential gene expression among the cells of the 3 COPD patients vs. the controls was computed using a Wilcoxon test. This analysis identified the number of differentially expressed genes per cluster when applying the filter of log fold change >|0.4| and FDR = 0.05 ( Supplementary Table S2 ). Interestingly, the major populations showing the largest differences in gene expression were: monocytes, macrophages, low quality cells, ciliated epithelial cells, T cells, AT2 cells, and NK cells (Table 2 ). Most gene expression changes corresponded to an increased transcription (up-regulation) in patients with COPD (Table 2 ). 3. The Gene Ontology enrichment Gene expression in 17 individual types of cells from severe emphysematous lungs. Next, we performed the functional enrichment with the upregulated or downregulated genes in COPD for each population for GO biological processes ( Supplementary Tables 3 and 4). Results for the upregulated genes in COPD (i.e., macrophages, monocytes, ciliated epithelial cells and NK cells), have been summarized with Revigo and are shown as Treemaps ( Supplementary Figures S2-S5 ). Of note, these four individual cell types shared ontologies related to the activation of T cells, defense response and three of them (not macrophages) also presented ontologies related to the viral life cycle. 4. Single cell contribution to the 127 emphysema-related gene signatures Next, we identified how the transcriptomic changes of each cell type contribute to the previously described 127 gene signatures of emphysema reported by Campbell et al [ 23 ]. We computed the overlap using GSEA (Table 3 ). Three individual cell types were enriched with a nominal p value < 0.05 and an FDR < 0.1 ( i.e. , ciliated epithelial cells, cytotoxic T cells, and low quality T cells). The genes in the core enrichment for these 3 individual cell types and the values of differentially expressed genes in Campbell’s data set are shown in Supplementary Table 5 . EPAS1, QKI and STOM were differentially expressed core genes in all three-cell types ( Supplementary Table 5 ). Table 3 GSEA enrichment of the different clusters with the 127 gene signature Cluster ES NES NOM p-val FDR q-val FWER p-val Ciliated 0.33 1.33 0.00 0.09 0.00 Cytotoxic T 0.33 1.29 0.00 0.09 0.00 Low quality T cells 0.47 1.29 0.00 0.11 0.00 AT2 0.39 1.27 0.09 0.18 0.04 Fibroblasts 0.40 1.31 0.10 0.18 0.05 T cells 0.31 1.17 0.10 0.21 0.05 Proliferative macrophages 0.37 1.23 0.10 0.22 0.05 B cells -0.24 -0.97 0.18 0.28 0.09 NK 0.31 1.05 0.29 0.37 0.14 AT1 0.33 1.00 0.43 0.52 0.22 Monocytes 0.20 0.84 0.68 0.77 0.36 Macrophages -0.22 -0.92 0.70 0.83 0.37 Club -0.20 -0.82 0.76 0.88 0.39 Low quality -0.25 -0.78 0.88 1.00 0.43 Endothelial -0.17 -0.53 0.89 1.00 0.44 Mast cells 0.28 0.77 0.92 1.00 0.47 As a complementary analysis, we showed which individual cell types differentially expressed the 127 emphysema related genes ( Supplementary Table 6 and Supplementary Fig. 6 ). FCN3, RTN4 and CCR7 were differentially expressed in a total of 5 individual cell types, and EPAS1, QKI and STOM in the 4 individual cell types. The individual cell types with more differentially expressed genes were: monocytes (n = 10), AT2 cells (n = 9), macrophages (n = 8), ciliated cells (n = 5), and T cells (n = 5). 5. Comparison with severe airflow limitation genes. Next, we determined whether the gene expression changes observed in the individual cell types represent the previously identified gene signatures in whole lung tissue of patients with severe airflow limitation. To achieve this, we assessed the enrichment with GSEA of the individual cell types with the differentially expressed genes between n = 17 non-smokers and n = 30 patients with GOLD stage 4 obtained from LTRC (GSE47460, GPL14550). Overall, the enrichment in all individual cell types appeared to be consistent with the genes differentially expressed in whole lung tissue. However, the enrichment was significant at a nominal p value for only 4 individual cell types (mast cells, proliferating macrophages, monocytes, and FBPB4 macrophages). The full list of genes differentially expressed in the LTRC and differentially expressed in the individual cell clusters is shown in Supplementary Table 7 . Our analysis identified several genes expressed in a distinct type of cells, which were previously reported to be differentially expressed in lung tissue homogenates according to the severity of airflow limitation [ 14 , 31 ]. These genes are FGG (AT2 cells), CCL19 (monocytes), PLA2G7 (macrophages), HP (macrophages), TNFSF13B (monocytes) and FCRLA (B cells). In addition, following genes were differentially expressed in several individual types of cells: S100A10 (n = 7), RPS10, GNG11, and CAV1 (n = 6), S100A6 (n = 5), and AGER (n = 4). 6. Quantifying protein levels of some genes significantly altered between normal and COPD lungs. To determine whether significantly altered genes between normal and COPD lungs also correlate with the related protein levels, whole parenchymal lung tissues from non-smokers without COPD ( n = 5 per group) and former-smokers with COPD GOLD stage 3 or 4 ( n = 6 or 7 per group) were evaluated for protein expression of QKI, STOM, EPAS1, and IGFBP5 by immunoblot analysis. Consistent with altered gene expression, QKI and IGFBP5 protein levels were significantly increased in the COPD lungs relative to non-smokers (Figs. 2 A and 2 B), but neither STOM nor EPAS1 protein levels were altered ( Supplementary Figure S7A ). Further, we determined whether QKI and IGFBP5 gene expressions in whole lung tissue correlated with the emphysema severity. QKI expression appreared to decrease according to % emphysema (n = 208; p = 0.0854), whereas, IGFBP5 expression significantly increased according to % emphysema (p = 00150). Since IGFBP5 is an excretory protein, we measured the serum levels of IGFBP5 in smokers with or without COPD (n = 40, each group) that were not significantly altered between the two group ( Supplementary Figure S7B ). These results suggest that some of the significantly altered genes in COPD lungs identified by scRNA seq indeed correlate with the individual protein levels in the whole lung tissue. Discussion This study uses scRNA seq from human lung homogenate in order to identify the specific cell types driving gene expression changes found in bulk RNA sequencing of patients with severe emphysema and airway obstruction. We found that: 1) t-SNE and clustering of scRNA-seq data identified a total of 17 distinct populations based on predetermined markers per cell type. Monocytes, macrophages, ciliated cells and low quality cells exhibited more differentially expressed genes in cases vs. controls relative to the other cell types; 2) GSEA revealed that the populations contributing most to the previously reported emphysema signature were ciliated cells, cytotoxic T cells and low quality T cells. While in the severe COPD LTRC signature, the populations enriched by GSEA were proliferating macrophages, mast cells, AT2 cells and monocytes; 3) key COPD associated genes were found to be expressed by specific cell types:. FGG (AT2 cells), CCL19/TNFSF13B (Monocytes) and PLA2G7 (Macrophages); 4) We verified the expression of some of the specific scRNA seq differentially expressed genes at protein level as well (i.e. QKI and IGFBP5). Previous studies Although the scRNA seq methodology has been applied to the profiling of lung tissue of patients with IPF [ 32 – 34 ], this is the first study to our knowledge which profiles lung tissue of patients with COPD. In relation to severe COPD and emphysema, several studies have reported the transcriptomic profile of lung tissue homogenates [ 14 , 31 ], but the scRNA seq has several advantages over the previously used RNA seq methods. First, scRNA seq is useful to determine specific types of cells that are responsible for the significant transcriptomic changes in a disease process (e.g., the emphysematous and/or airflow limitation signature). Second, it is unlikely that the cell composition alters the outcome such as the whole cell RNA-sEq. Third, scRNA seq may uncover an important molecular pathway unique to a specific cell type that contributes to the development of the disease. Interpretation of novel findings We identified 17 cell subtypes in lung tissue of patients with severe COPD and non-smoking controls. In relation to cell composition, differences were not statistically significant, but in agreement with previous reports. We observed an overall increase of immune cell types (T, NK and B cells) and decrease of structural cells (Fibroblasts, AT2 cells and endothelial cells) [ 35 ]. However, sampling effects inherent to scRNA seq may have contributed to the skewed proportion of distinct cell populations. Interestingly, we found that the genes up-regulated in the cell types with more differential expression (Monocytes, Macrophages, Ciliated cells, Cytotoxic T cells and AT2 cells) were related to T cell activation, antigen presentation and signaling. The role of Cytotoxic T cells (CD8+) in severe COPD has been long recognized [ 35 – 37 ] and emphysema has been proposed to be associated with a Th1 response activated by infiltrating ILC1, NK, and LTi cells [ 38 ]. Here, we expand these findings by showing that the antigen presenting cells (macrophages, monocytes and AT2 cells) are also involved in the T cell activation. Interestingly, the viral related ontologies appeared to be enriched in these cell types as well as in ciliated epithelial cells and NK cells. Further work profiling the virome in parallel with cellular phenotyping may complement these findings. In relation to the previously described 127 emphysema gene signature, our GSEA analysis showed an enrichment of genes differentially expressed by Ciliated and T cells (Cytotoxic and of low quality), suggesting an active involvement of T cells in the emphysematous tissue remodeling and accumulation of primary cilia [ 38 , 39 ]. Some of the genes associated with the homing of B cells and previously identified by homogenate tissue profiling (i.e. CCL19, TNFSF13B) [ 23 , 31 ] were found in the current study to be expressed by macrophages and monocytes. Yet in the current analysis the increase in B cells was observed in 2 of the 3 severe COPD samples, and the genes hyper expressed by the severe COPD B cells were related to the T cell activation in concordance with previous works [ 31 ]. Upregulation of gene expression for fibrinogen (FGG) is a well-recognized biomarker in COPD [ 40 ] and is produced by AT2 cells. Fibrinogen has a well-known role in both innate and T cell mediated adaptive immune responses to bacteria [ 41 ]. Similar to these previous findings, our analysis showed an upregulation of genes related to the antigen presentation in AT2 cells, suggesting a role in the stimulation and perpetuation of the observed immune response in the lung. AGER, another well-known gene associated with COPD [ 42 ], was upregulated in 4 cell types in our analysis (Macrophages, Monocytes, low quality T cells and B cells) that are associated with of immune response, suggesting a role of the RAGE axis in the chronic immune infiltrate observed in severe COPD/emphysema. In our analysis, club cells that express the COPD associated CC-16 protein, were found to be altered in the mRNA catabolism pathways, and the response to toxic substances. Further investigation is warranted to determine the impact of these alterations in cell functionality [ 43 ]. Finally, in this study, we attempted to verify whether differences in gene expression correlated with differential protein content in the cellular populations derived from COPD and normal lungs. Among several common targets, we found that COPD lungs exhibit decreased protein levels of QKI and increased protein levels of IGFBP5. Our scRNA seq data show that QKI is expressed abundantly in myeloid cells, endothelial cells, and AT1 cells, whereas IGFBP5 is expressed in ciliated cells, fibroblasts, and lymphatic endothelial cells relative to the other types of cells. QKI, a KH domain containing RNA binding protein, regulates versatile mRNA metabolism – splicing, export, stability, and protein translation [ 44 ]. The loss-of-function mutations disturb myelination and cause embryonic lethality [ 45 ]. QKI has been implicated in various disease processes, including atherosclerosis [ 46 ], tumorigenesis [ 47 ], and fibrosis [ 48 ]. IGFBP5, insulin-like growth factor binding protein 5, is one of the six proteins of the IGFBP family [ 49 ]. IGFBP proteins bind IGF-I/II and regulate their bioavailability and downstream signaling. In addition, IGFBP proteins can regulate cell growth and survival independent of IGF-I/II [ 49 ]. In particular, IGFBP5 plays a causal role in the induction of cellular senescence and inflammation [ 50 ], which may be linked to pulmonary fibrosis [ 51 ]. Further, an intergenic SNP of IGFBP5 (rs6435952) associates with airway obstruction [ 52 ]. Although IGFBP5 is a secretory protein [ 53 ], there was no significant change in the serum levels of IGFBP5 in COPD patients compared with control smokers. However, there may be excretory impairment of IGFBP5 protein in the COPD lung which remains to be determined in future studies. An in vivo animal study will be necessary to elucidate a causal role of QKI and IGFBP5 in the development of smoking-induced COPD. Limitations The main limitation of this study is the sample size as we have analyzed 4,000 to 6,000 cells/sample single cells pooled together from 3 control subjects without underlying lung disease and 3 patients with severe COPD. Accordingly, our findings are not representative of the COPD heterogeneity and we will need to increase the sample size to address this open issue in future investigations. Notwithstanding this limitation, the main focus of this work has been to use the generated data to explore which cell types express the key genes previously shown to be associated with COPD and emphysema. Conclusions We identified ciliated and CD8 + T cells as prominent cell types associated with the 127 gene signature associated with emphysema. Our findings support a prominent role of the immune response in severe COPD, with the implication of structural and antigen presenting cells in its homing and perpetuation. Finally, QKI and IGFBP5 are identified as potential COPD biomarkers, whose both gene and protein expression are significantly altered in COPD lungs relative to normal lungs. The causal role of QKI and IGFBP5 in the development of COPD/emphysema will need further investigation. List Of Abbreviations COPD, chronic obstructive pulmonary disease CS, cigarette smoke FDR, false discovery rate SEGA, gene set enrichment analysis ROS, reactive oxygen species Declarations Ethics approval and consent to participate All research involving human subjects was approved by the University of Pittsburgh institutional review board (#14010265 and 19090239). Written informed consent was obtained from all study subjects. The use of human cadaveric tissue and decedent medical records for this study was approved by the Committee for Oversight of Research and Clinical Training Involving Decedents (CORID) (#765). The written consent was obtained either via body donation registration, autopsy authorization, or provided by next of kin or legal representatives. Consent for publication Not applicable. Availability of data and material All data generated or analyzed during this study are included in this published article [and its supplementary information files]. Competing interests The authors declare that they have no completing interests. Funding This study was supported by the Merit Review Award from the US Department of Veterans Affairs (CX001048 and CX000105), AHA transformational grant to TN (19TPA34830061) and Miguel Servet Fellowship from the Instituto de Salud Carlos III (CP16/00039, PI17/00369) to RF. These founders had no role in study design, data collection and analysis, or preparation of the manuscript. Authors ’ contributions All authors have read and approved the manuscript. MR, RL, RF, and TN conceived and designed the experiments; TS, XL, RV, PS, GN, JS, YZ performed the experiments; TS, XL, TK, GN, and RF collected and analyzed the data; CZ, PS, YZ, FS, MR, JM, and TN provided reagents/materials/data analysis; XL, TS, CZ, PS, MR, PS, DC, RM, FS edited the manuscript; XL, GN, RF, and TN wrote the paper. Acknowledgements Not applicable. References Brown DW: Smoking prevalence among US veterans . 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Reyfman PA, Walter JM, Joshi N, Anekalla KR, McQuattie-Pimentel AC, Chiu S, Fernandez R, Akbarpour M, Chen CI, Ren Z et al : Single-Cell Transcriptomic Analysis of Human Lung Provides Insights into the Pathobiology of Pulmonary Fibrosis . Am J Respir Crit Care Med 2019, 199 (12):1517-1536. Xu Y, Mizuno T, Sridharan A, Du Y, Guo M, Tang J, Wikenheiser-Brokamp KA, Perl AT, Funari VA, Gokey JJ et al : Single-cell RNA sequencing identifies diverse roles of epithelial cells in idiopathic pulmonary fibrosis . JCI Insight 2016, 1 (20):e90558. Morse C, Tabib T, Sembrat J, Buschur KL, Bittar HT, Valenzi E, Jiang Y, Kass DJ, Gibson K, Chen W et al : Proliferating SPP1/MERTK-expressing macrophages in idiopathic pulmonary fibrosis . Eur Respir J 2019, 54 (2). Hogg JC, Chu F, Utokaparch S, Woods R, Elliott WM, Buzatu L, Cherniack RM, Rogers RM, Sciurba FC, Coxson HO et al : The nature of small-airway obstruction in chronic obstructive pulmonary disease . N Engl J Med 2004, 350 (26):2645-2653. 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Sun H: The interaction between pathogens and the host coagulation system . Physiology (Bethesda) 2006, 21 :281-288. Cheng DT, Kim DK, Cockayne DA, Belousov A, Bitter H, Cho MH, Duvoix A, Edwards LD, Lomas DA, Miller BE et al : Systemic soluble receptor for advanced glycation endproducts is a biomarker of emphysema and associated with AGER genetic variants in patients with chronic obstructive pulmonary disease . Am J Respir Crit Care Med 2013, 188 (8):948-957. Laucho-Contreras ME, Polverino F, Gupta K, Taylor KL, Kelly E, Pinto-Plata V, Divo M, Ashfaq N, Petersen H, Stripp B et al : Protective role for club cell secretory protein-16 (CC16) in the development of COPD . Eur Respir J 2015, 45 (6):1544-1556. Vernet C, Artzt K: STAR, a gene family involved in signal transduction and activation of RNA . Trends Genet 1997, 13 (12):479-484. Ebersole TA, Chen Q, Justice MJ, Artzt K: The quaking gene product necessary in embryogenesis and myelination combines features of RNA binding and signal transduction proteins . Nat Genet 1996, 12 (3):260-265. de Bruin RG, Shiue L, Prins J, de Boer HC, Singh A, Fagg WS, van Gils JM, Duijs JM, Katzman S, Kraaijeveld AO et al : Quaking promotes monocyte differentiation into pro-atherogenic macrophages by controlling pre-mRNA splicing and gene expression . Nat Commun 2016, 7 :10846. Mukohyama J, Isobe T, Hu Q, Hayashi T, Watanabe T, Maeda M, Yanagi H, Qian X, Yamashita K, Minami H et al : miR-221 targets QKI to enhance the tumorigenic capacity of human colorectal cancer stem cells . Cancer Res 2019. Chothani S, Schafer S, Adami E, Viswanathan S, Widjaja AA, Langley SR, Tan J, Wang M, Quaife NM, Jian Pua C et al : Widespread Translational Control of Fibrosis in the Human Heart by RNA-Binding Proteins . Circulation 2019, 140 (11):937-951. 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Supplementary Files 2020COPDscRNAseqsupplfigs.pdf Supplementary figures. Figure S1. Lung histology of three COPD cases (Hematoxylin & Eosin staining) Figure S2.. Revigo summary of biological processes enriched in Macrophages Figure S3. Revigo summary of biological processes enriched in Monocytes Figure S4. Revigo summary of biological processes enriched in Ciliated epithelial cells Figure S5. Revigo summary of biological processes enriched in NK cells Figure S6. Differentially expressed genes in the 127 gene signature per type of cell, per patient Figure S7. A: Immunoblot analysis for STOM, EPAS1, RTN4 (controls vs. COPD GOLD stage 4) B: Serum IGFBP5 measurements in controls (n=40) and COPD cases (n=40) S1top100genespercluster.xlsx Table S1:Transcriptomic markers per cell type (top 100 genes per cluster) S2DEgenesbycluster.xlsx Table S2: DE genes per cell type, log FC|0.4| and FDR S3Positiveenrichemntlogfc0.4padj0.05.xlsx Table S3: Biological process gene ontology enrichment in genes upregulated in each cluster in COPD S4Negativeenrichemntlogfc0.4padj0.05.xlsx Table S4: Biological process gene ontology enrichment in genes downregulated in each cluster in COPD S5coreenrichmentgenescampbell.xlsx Table S5: Genes present in the GSEA core enrichment with the 127 emphysema gene signature S6DEgenesincelltypesand127GS.xlsx Table S6: Genes from the 127 gene signature differentially expressed by cell type S7DEgenesinLTRCandcelltypes.xlsx Table S7: Genes from the sever COPD signature (LTRC) differentially expressed by cell type Cite Share Download PDF Status: Published Journal Publication published 06 Apr, 2021 Read the published version in Respiratory Research → Version 1 posted Review # 2 received at journal 19 Jan, 2021 Editorial decision: Major revision 19 Jan, 2021 Review # 1 received at journal 26 Dec, 2020 Reviewer # 2 agreed at journal 07 Dec, 2020 Reviewers invited by journal 06 Dec, 2020 Reviewer # 1 agreed at journal 06 Dec, 2020 Editor assigned by journal 30 Oct, 2020 First submitted to journal 29 Oct, 2020 Submission checks completed at journal 29 Oct, 2020 Editor invited by journal 29 Oct, 2020 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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using statistically significant principal components and cells were clustered using an unbiased graph-based clustering algorithm (smart local moving [SLM] clustering, which in total identified 17 distinct types of cells, distinguished by color. \\nB.\\tCells from disease conditions were indicated by different colors. \\nC.\\tCells from individual subjects were indicated by different colors. \\nD.\\tSLM clustering was made according to distinct gene expression patterns based on various cell types in the lung.\\nE.\\tSLM clustering is shown graphically in feature plots.\",\"description\":\"\",\"filename\":\"1.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-100834/v1/360bda05e20f356e1f8c4a14.jpg\"},{\"id\":3384920,\"identity\":\"0e483db0-9ba9-4103-9020-d6db9f3e0d20\",\"added_by\":\"auto\",\"created_at\":\"2020-11-04 21:22:01\",\"extension\":\"jpg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":89790,\"visible\":true,\"origin\":\"\",\"legend\":\"Quantifying protein levels of some genes significantly altered between normal and COPD lungs.\\nA.\\tWhole parenchymal lung tissues from non-smokers without COPD (n=5) and former-smokers with COPD GOLD stage 3 or 4 (n=7) were evaluated for protein expression of QKI by immunoblot analysis. The densitometry data (QKI/HPRT1) obtained from individual groups are expressed as mean  SEM. ***, p value\\u003c0.0001. \\nB.\\tWhole parenchymal lung tissues from the same groups as in (A) were evaluated for protein expression of IGFBP5 by immunoblot analysis. The densitometry data (IGFBP5/HPRT1) obtained from individual groups are expressed as mean  SEM. ***, p value\\u003c0.0001. \\nC.\\tQKI and IGFBP5 gene expression in whole lung parenchymal tissue obtained from subjects with various severities of emphysema. \",\"description\":\"\",\"filename\":\"2.jpg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-100834/v1/a079efb742b36d8619a02eba.jpg\"},{\"id\":13610555,\"identity\":\"55f7f82b-6edf-4152-9d7d-b87f1205628f\",\"added_by\":\"auto\",\"created_at\":\"2021-09-17 06:24:46\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1774773,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-100834/v1/1d5266a2-e8d4-43d9-9ffd-8d9ab375ac3a.pdf\"},{\"id\":3384919,\"identity\":\"3765b652-d162-41bc-9d19-b59770c43df6\",\"added_by\":\"auto\",\"created_at\":\"2020-11-04 21:22:01\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":7014442,\"visible\":true,\"origin\":\"\",\"legend\":\"Supplementary figures. \\nFigure S1. Lung histology of three COPD cases (Hematoxylin \\u0026 Eosin staining)\\t\\nFigure S2.. Revigo summary of biological processes enriched in Macrophages\\nFigure S3. Revigo summary of biological processes enriched in Monocytes\\nFigure S4. Revigo summary of biological processes enriched in Ciliated epithelial cells\\nFigure S5. Revigo summary of biological processes enriched in NK cells\\nFigure S6. Differentially expressed genes in the 127 gene signature per type of cell, per patient\\nFigure S7. \\nA: Immunoblot analysis for STOM, EPAS1, RTN4 (controls vs. COPD GOLD stage 4)\\nB: Serum IGFBP5 measurements in controls (n=40) and COPD cases (n=40)\",\"description\":\"\",\"filename\":\"2020COPDscRNAseqsupplfigs.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-100834/v1/7eafd0382ef68e37c2ec079a.pdf\"},{\"id\":3384921,\"identity\":\"d6505b7f-154a-44b6-9371-dfc8dc43e292\",\"added_by\":\"auto\",\"created_at\":\"2020-11-04 21:22:01\",\"extension\":\"xlsx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":136609,\"visible\":true,\"origin\":\"\",\"legend\":\"Table S1:Transcriptomic markers per cell type (top 100 genes per cluster)\",\"description\":\"\",\"filename\":\"S1top100genespercluster.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-100834/v1/2830d788d832d4052dac22c2.xlsx\"},{\"id\":3384922,\"identity\":\"4a0d0bf6-d036-494e-a401-8ddb97cbe500\",\"added_by\":\"auto\",\"created_at\":\"2020-11-04 21:22:02\",\"extension\":\"xlsx\",\"order_by\":3,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":510326,\"visible\":true,\"origin\":\"\",\"legend\":\"Table S2: DE genes per cell type, log FC|0.4| and FDR\",\"description\":\"\",\"filename\":\"S2DEgenesbycluster.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-100834/v1/b8d7d9ad9e2ff0b19be96ecb.xlsx\"},{\"id\":3384923,\"identity\":\"a488f9ca-ea01-4425-b4d5-d891804ea8de\",\"added_by\":\"auto\",\"created_at\":\"2020-11-04 21:22:02\",\"extension\":\"xlsx\",\"order_by\":4,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":100451,\"visible\":true,\"origin\":\"\",\"legend\":\"Table S3: Biological process gene ontology enrichment in genes upregulated in each cluster in COPD\",\"description\":\"\",\"filename\":\"S3Positiveenrichemntlogfc0.4padj0.05.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-100834/v1/589b6fcbf5c4d06ba43c7144.xlsx\"},{\"id\":3384924,\"identity\":\"61fb77df-ab38-40ae-a34e-889273281e43\",\"added_by\":\"auto\",\"created_at\":\"2020-11-04 21:22:02\",\"extension\":\"xlsx\",\"order_by\":5,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":160734,\"visible\":true,\"origin\":\"\",\"legend\":\"Table S4: Biological process gene ontology enrichment in genes downregulated in each cluster in COPD\",\"description\":\"\",\"filename\":\"S4Negativeenrichemntlogfc0.4padj0.05.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-100834/v1/e1d9881032f4396f608b758f.xlsx\"},{\"id\":3384925,\"identity\":\"3ea90261-1858-405a-9e56-9fd55cb0aa66\",\"added_by\":\"auto\",\"created_at\":\"2020-11-04 21:22:02\",\"extension\":\"xlsx\",\"order_by\":6,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":16056,\"visible\":true,\"origin\":\"\",\"legend\":\"Table S5: Genes present in the GSEA core enrichment with the 127 emphysema gene signature\",\"description\":\"\",\"filename\":\"S5coreenrichmentgenescampbell.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-100834/v1/018f0fca1aac15a426f2fd3a.xlsx\"},{\"id\":3384926,\"identity\":\"a7902e9e-8e93-4e31-bb02-c455dd7e5691\",\"added_by\":\"auto\",\"created_at\":\"2020-11-04 21:22:03\",\"extension\":\"xlsx\",\"order_by\":7,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":16422,\"visible\":true,\"origin\":\"\",\"legend\":\"Table S6: Genes from the 127 gene signature differentially expressed by cell type\",\"description\":\"\",\"filename\":\"S6DEgenesincelltypesand127GS.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-100834/v1/d1eebfabc5a7fc156001b48d.xlsx\"},{\"id\":3384927,\"identity\":\"510bd074-c3c2-4999-9e1e-7d5b960d02a7\",\"added_by\":\"auto\",\"created_at\":\"2020-11-04 21:22:03\",\"extension\":\"xlsx\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":37692,\"visible\":true,\"origin\":\"\",\"legend\":\"Table S7: Genes from the sever COPD signature (LTRC) differentially expressed by cell type \",\"description\":\"\",\"filename\":\"S7DEgenesinLTRCandcelltypes.xlsx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-100834/v1/50ecdfe3a8770c0f03734121.xlsx\"}],\"financialInterests\":\"\",\"formattedTitle\":\"\\u003cp\\u003eSingle Cell RNA Sequencing Identifies IGFBP5 And QKI In Ciliated Epithelial Cell Genes Associated With Severe COPD\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"Background\",\"content\":\"\\u003cp\\u003eChronic obstructive pulmonary disease (COPD) is a common respiratory disorder characterized by irreversible expiratory airflow limitation in response to inhalation of noxious stimuli (\\u003cem\\u003ee.g\\u003c/em\\u003e., cigarette smoke) [\\u003cspan class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e]. COPD is the third leading cause of death [\\u003cspan class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e] and a major economic burden in the United States [\\u003cspan class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003cp\\u003eCOPD is a heterogeneous disorder that can manifest with multiple clinical phenotypes, including emphysema (destructive enlargement of the airspaces distal to terminal bronchioles), chronic bronchitis, and small airway disease [\\u003cspan class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]. Over the last few decades, several lung homogenates and airway transcriptomic studies have been conducted in order to reveal molecular pathways linked to the pathogenesis of smoking-related airflow limitations and emphysema [\\u003cspan class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]. These studies have resulted in the identification of several biological processes, now believed to be associated with smoking-related FEV1 decline [\\u003cspan class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e], including 1) chronic immune response and inflammation [\\u003cspan class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]; 2) imbalance of proteases/anti-proteases [\\u003cspan class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]; 3) oxidative stress [\\u003cspan class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]; 4) cellular senescence (permanent loss of proliferative capacity) [\\u003cspan class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e]; and 5) lung epithelial cell (LEC) apoptosis.\\u003c/p\\u003e\\n\\u003cp\\u003eIn one of the most comprehensive transcriptomic analyses of smoking-related emphysema to date, Campbell, \\u003cem\\u003eet al.\\u003c/em\\u003e profiled gene expression in 8 separate regions from 6 emphysematous lungs, based on degree of emphysema, and compared transcriptome with 2 non-diseased lungs (8 regions\\u0026thinsp;\\u0026times;\\u0026thinsp;8 lungs\\u0026thinsp;=\\u0026thinsp;64 samples). They identified a total of 127 genes with expression levels significantly correlating with the emphysema severity [\\u003cspan class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. Many genes upregulated with increased emphysema severity were involved in inflammation (\\u003cem\\u003ee.g\\u003c/em\\u003e., the B-cell receptor signaling), while those downregulated with increasing disease severity were implicated in tissue repair (\\u003cem\\u003ee.g\\u003c/em\\u003e., the transforming growth factor beta (TGF\\u0026beta;) pathway) [\\u003cspan class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. This 127 gene emphysema signature was enriched in the transversal studies of lung tissue of patients with severe COPD and emphysema [\\u003cspan class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]. However, it remains to be elucidated which specific cell types contribute most to this smoking-related emphysematous and small airflow damage transcriptome signature.\\u003c/p\\u003e\\n\\u003cp\\u003eHere, we used scRNA-seq technology to identify lung cell-type specific gene expression signatures associated with airflow limitation and/or emphysema. We examined the single-cell transcriptomes of cell populations from lung tissue samples obtained from a representative selection of 3 ex-smokers with severe COPD/emphysema and 3 nonsmokers without any history of lung disease. We compared our findings with previously reported whole lung tissue homogenate airflow limitation and emphysema signatures, and experimentally validated the key associated genes.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec2\\\" class=\\\"Section2\\\"\\u003e\\n\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section3\\\"\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eHuman Lung Tissue Samples\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eFor scRNA seq, fresh lung parenchymal tissue samples were obtained from the upper lobes of three nonsmoking subjects without underlying lung disease who underwent warm autopsies and three patients with severe COPD who received lung transplantation (Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). For immunoblot analysis (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA), frozen lung parenchymal tissue obtained from smokers without any history of lung disease (n\\u0026thinsp;=\\u0026thinsp;5), or very severe COPD (n\\u0026thinsp;=\\u0026thinsp;7), both were provided by the University of Pittsburgh, Lung Tissue Research Consortium (LTRC), respectively (Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). Data from the Lung Genomics Research Consortium (LGRC) Cohort was used for gene expression analysis of QKI and IGFBP5 (Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e\\u003cstrong\\u003e)\\u003c/strong\\u003e. The COPD Specialized Center for Clinically Oriented Research (SCCOR) cohort was utilized for comparison of serum IGFBP5 levels between 40 patients with COPD and 40 smokers without clinically evident COPD (Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e\\u003cstrong\\u003e)\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n\\u003ctable id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption\\u003e\\n\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\n\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n\\u003cp\\u003eThree normal nonsmokers and three patients with severe COPD.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003c/caption\\u003e\\n\\u003cthead\\u003e\\n\\u003ctr\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eID\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAge\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eSex\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eSmoking\\u003c/p\\u003e\\n\\u003cp\\u003e(Pack-years)\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eFEV1/FVC\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eFEV1% ref.\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eDLCO % ref.\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/thead\\u003e\\n\\u003ctbody\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eNL 1\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e57\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eF\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd 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char=\\\".\\\"\\u003e\\n\\u003cp\\u003e65\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eF\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e60\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.33\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e33\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e14\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCOPD 2\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e62\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eF\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e75\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.29\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e22\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e53\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCOPD 3\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e61\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eF\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e45\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.25\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e21\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e20\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n\\u003ctable id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption\\u003e\\n\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e\\n\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n\\u003cp\\u003eGSEA enrichment results of the different clusters with the differential gene expression of the LTRC (COPD Gold 4 vs. Non-smokers).\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003c/caption\\u003e\\n\\u003cthead\\u003e\\n\\u003ctr\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eSIZE\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eES\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eNES\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eNOM p-value\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eFDR q-value\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eFWER p-value\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/thead\\u003e\\n\\u003ctbody\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eMast cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e70\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.62\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-1.72\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.00\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.07\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.04\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eProliferating macrophages\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e164\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.56\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-1.71\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.01\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.04\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.05\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eMonocytes\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e103\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.63\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-1.58\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.03\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.10\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.12\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eFBPB4 macrophages\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e27\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.61\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-1.46\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.04\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.15\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.23\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAT2\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e80\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.56\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-1.56\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.06\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.09\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.14\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eB cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e48\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.47\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-1.42\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.07\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.14\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.28\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eClub\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e83\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.51\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-1.38\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.09\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.16\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.32\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eFibroblasts\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e245\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.49\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-1.45\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.12\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.13\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.24\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCytotoxic T cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e30\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.54\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-1.33\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.13\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.16\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.39\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eLow quality T cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e126\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.45\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-1.38\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.13\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.14\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.33\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCiliated\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e55\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.48\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-1.17\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.30\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.30\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.57\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eT cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e36\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.42\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-1.14\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.32\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.30\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.60\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eEndothelial\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e94\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.36\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.93\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.53\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.51\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.76\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAT1\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e221\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.12\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.56\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.97\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.99\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.92\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eNK cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e23\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.21\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.55\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.97\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.93\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.92\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n\\u003ctable id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e\\u003ccaption\\u003e\\n\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e\\n\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n\\u003cp\\u003eClinical and Demographics of the control and COPD subjects (Immunoblot Analysis)\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003c/caption\\u003e\\n\\u003cthead\\u003e\\n\\u003ctr\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eControl\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCOPD (GOLD Stage 4)\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/thead\\u003e\\n\\u003ctbody\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eNumber of subjects\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e5\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e7\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAge, yr, mean (SD)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e62.0 (10.0)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e62.0 (2.8)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eGender\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e3M/2F\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e4M/3F\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eSmoking (pack-years)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e35 (10.6)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e50.7 (23.1)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n\\u003ctable id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e\\u003ccaption\\u003e\\n\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e\\n\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n\\u003cp\\u003eClinical and Demographics of the Lung Genomics Research Consortium (LGRC) Cohort (used for QKI and IGFBP5 gene expression analysis)\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003c/caption\\u003e\\n\\u003cthead\\u003e\\n\\u003ctr\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eControl\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCOPD\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/thead\\u003e\\n\\u003ctbody\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eNumber of subjects\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e108\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e219\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAge, yr, mean (SD)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e63.6 (11.4)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e64.7 (9.7)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eGender (% Female)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e55%\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e43%\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eSmoking status, (%)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eNever\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e30%\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e5.50%\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCurrent\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e1.90%\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e6.40%\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eEver\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e58%\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e86.70%\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eUnknown\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e10.10%\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e1.40%\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePulmonary function, mean (SD)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eFEV1, % Predicted\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e95 (12.6)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e50.6 (24.0)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eFVC, % Predicted\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e94.4 (13.1)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e73,8 (19.5)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eDLCO, % Predicted\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e84.1 (16.7)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e56.6 (23.1)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eEmphysema, % mean (SD)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e15.4 (17.1)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"3\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eDLCO\\u0026thinsp;=\\u0026thinsp;diffusing capacity of the lung for carbon monoxide\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n\\u003ctable id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e\\u003ccaption\\u003e\\n\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 7\\u003c/div\\u003e\\n\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n\\u003cp\\u003eClinical and Demographics of the SCCOR Cohort (used for IGFBP5 ELISA)\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003c/caption\\u003e\\n\\u003cthead\\u003e\\n\\u003ctr\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eControl\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCOPD\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/thead\\u003e\\n\\u003ctbody\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eNumber of subjects\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e40\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e40\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAge, yr, mean (SD)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e66.7 (7.5)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e67.5 (6.4)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eGender (% Female)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e42.50%\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e42.50%\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCurrent Smoking (%)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e47.50%\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e40%\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePack Years (SD)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e50.3 (30.5)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e65.4 (29.1)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePulmonary function, mean (SD)\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eFEV1, % Predicted\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e99.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;14.7%\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e70.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;18.8%\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eFEV1/FVC Ratio\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.763\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.037\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.535\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.104\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eEmphysema %F950*\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.005\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.003\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0.107\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.086\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd colspan=\\\"3\\\" align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e* % low-attenuation area defined as the fraction of voxels less than \\u0026minus;\\u0026thinsp;950 Houndsfield Unit of total voxels identified in regions of the lung.\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePreparation of single-cell libraries, sequencing, and analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe whole lung tissue samples were processed as described previously [\\u003cspan class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. Briefly, lung tissue samples were digested, cell suspensions laded into the Chromium instrument (10X Genomics, Pleasanton, CA), and the resulting barcoded cDNAs were used to construct libraries. RNA-seq was conducted on all mixed samples as a pool. The total number of reads for the 3 single cell COPD samples was: 456,870,504 reads. Cell-gene specific molecular identifier counting matrices were generated and analyzed using Seurat [\\u003cspan class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e] to identify distinct cell populations [\\u003cspan class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e] and hierarchically clustered using Cluster 3.0 [\\u003cspan class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eReagents and Antibodies\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e\\n\\u003cp\\u003eChemicals were obtained from Sigma Chemical and Calbiochem. Polyvinylidene difluoride membranes were obtained from Bio-Rad. ECL Plus was obtained from Amersham. Antibodies were obtained from various sources: HPRT1 and EPAS1 antibodies were obtained from Cell Signaling; QKI, RTN4, STOM, IGFBP5 and secondary antibodies (horseradish peroxidase-conjugated anti-rabbit or anti-mouse Ig) were obtained from Santa Cruz Biotechnology.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eImmunoblot analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e\\n\\u003cp\\u003eHuman lung tissues (control and severe COPD groups) were thawed and homogenized in RIPA buffer using a Bullet Blender (next advance). Samples were centrifuged at 4\\u0026nbsp;\\u0026deg;C for 10\\u0026nbsp;min at 12 000G and resuspended in protein loading buffer. For western blot, about 30 ug of proteins were separated by SDS-PAGE gel and transferred into a nitrocellulose membrane. Membranes were blocked and incubated with primary antibodies and the appropriated secondary antibody HRP conjugated. The signal was acquired with Chemi-Doc MP (Bio‐Rad) using WesternBright Sirius HRP substrate (advansta).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eELISA for IGFBP5\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e\\n\\u003cp\\u003eThe IGFBP5 Human ELISA Kit was purchased from Thermal Fisher Scientific. Serum IGFBP5 levels were measured from 40 control smokers and 40 smokers with COPD according to the vendors instructions.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eGene Ontology enrichment and GSEA\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e\\n\\u003cp\\u003eGene Set Enrichment Analysis (GSEA) was used to identify similarities with previously published emphysema and severity signatures [\\u003cspan class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]. The gene ontology biological process enrichment was done in R with the ClusterProfiler package [\\u003cspan class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e], and the ontologies were summarized and visualized with the Revigo package [\\u003cspan class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e1. Single cell RNA sequencing reveals 17 distinct cell clusters from human lungs with severe COPD.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe conducted single cell transcriptomic analysis of all cells obtained from the whole parenchymal lung tissue of three nonsmokers without underlying lung disease and three patients with severe COPD (demographic data: Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). The pathology with the H\\u0026amp;E staining confirmed the presence of moderate to severe emphysematous changes in all three COPD patients (see \\u003cstrong\\u003eSupplementary Figure S1\\u003c/strong\\u003e). We examined a total of 29,961 cells from six subjects (3914\\u0026ndash;5920 cells/sample). All the samples were pooled and analyzed together to gain the power to detect rare cell types as described previously [\\u003cspan class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e]. t-distributed stochastic neighbor embedding (t-SNE) blots were generated using statistically significant principal components and cells were clustered using an unbiased graph-based clustering algorithm (smart local moving [SLM] clustering), which in identified 17 distinct types of cells (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA). Cells from disease conditions and the individual subjects were indicated in different colors (Figs.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eB and \\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eC, \\u003cstrong\\u003erespectively\\u003c/strong\\u003e). SLM clustering was made according to the distinct gene expression patterns based on various cell types in the lung (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eD). The cell clusters that cannot be reliably classified due to fewer unique molecular identifiers were referred as \\u0026ldquo;low quality\\u0026rdquo; cells. Cell clusters were predominantly identified as: FABP4 as a cell cluster of FABP4 macrophages (#0); S100A8 as a cluster of monocytes (#1); CD3D as cytotoxic T cells (#2); GNLY as NK cells (#3); CLDN5 as endothelial cells (#4); LTB as T cells (#5); IGKC as B cells (#6); CAPS as ciliated epithelial cells (#7); SFTPC as alveolar type 2 epithelial (AT2) cells (#8); MALAT1 as low quality T cells (#9); RSPH1 as low quality cells (#10); DCN as fibroblasts (#11); SCGB1A1 as club epithelial cells (#12); AGER as alveolar type 1 epithelial (AT1) cells (#13); CCL21 as lymphatic endothelial cells (#14); TPSAB1 as mast cells (#15); KIAA0101 as proliferating macrophages (#16). These and other markers provided strong transcriptome signatures for each cell cluster (see \\u003cstrong\\u003eSupplementary Table\\u0026nbsp;1\\u003c/strong\\u003e, shown graphically in feature plots (Fig.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eE). The prevalence of individual cell types between normal nonsmokers and patients with COPD is shown in Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e. None of the difference in the abundance of individual cell types between normal nonsmokers and COPD patients reached statistical significance (Wilcoxon), however, a trend existed for decreased percentages of macrophages, endothelial cells, AT2 cells, and fibroblasts in the COPD lungs.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n\\u003ctable id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e\\u003ccaption\\u003e\\n\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\n\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n\\u003cp\\u003eIdentified clusters, number of differentially expressed genes in Case vs. control, mean % of cells belonging to this cluster both in cases and controls, fold change differences between cluster percentage and the p value of the comparison of the cluster frequency in cases vs. controls both with t-test and Wilcoxon-test.\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003c/caption\\u003e\\n\\u003cthead\\u003e\\n\\u003ctr\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCluster\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003ePopulation\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e# DE genes\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eup\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003edown\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eControl\\u003c/em\\u003e % of cells\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eCOPD\\u003c/em\\u003e % of cells\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eFold Change\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eP value (t.test)\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eP value (Wilcox)\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/thead\\u003e\\n\\u003ctbody\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e0\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eFABP4 Macrophages\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e868\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e620\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e248\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e27.97\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;10.74\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e5.15\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.57\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e\\u003cspan class=\\\"BoldItalicUnderline\\\"\\u003e5.43\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e\\u003cspan class=\\\"BoldItalicUnderline\\\"\\u003e0.02\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e\\u003cspan class=\\\"BoldItalicUnderline\\\"\\u003e0.057\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e1\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eMonocytes\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e1499\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e677\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e822\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e23.80\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.90\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e11.24\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.84\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e2.12\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.18\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.23\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e2\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCytotoxic T cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e539\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e165\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e374\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e11.18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.48\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e21.86\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.80\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-1.96\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.14\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.11\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e3\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eNK cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e431\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e233\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e198\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e3.85\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.89\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e22.14\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;27.41\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-5.75\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.23\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.4\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e4\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eEndothelial\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e242\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e185\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e57\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e9.17\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.90\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e2.37\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.52\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e\\u003cspan class=\\\"BoldItalicUnderline\\\"\\u003e3.87\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e\\u003cspan class=\\\"BoldItalicUnderline\\\"\\u003e0.03\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e\\u003cspan class=\\\"BoldItalicUnderline\\\"\\u003e0.057\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e5\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eT cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e213\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e64\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e149\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e2.11\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.00\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e11.68\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.02\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-5.54\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.08\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.23\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e6\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eB Cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e153\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e30\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e123\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e0.99\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.59\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e6.80\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.95\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-6.84\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.19\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.23\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e7\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCiliated\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e590\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e433\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e157\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e1.84\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.98\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e6.18\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.36\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-3.35\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.10\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.23\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e8\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAT2\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e476\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e34\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e442\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e4.12\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.52\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e0.96\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.32\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e\\u003cspan class=\\\"BoldItalicUnderline\\\"\\u003e-4.28\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e\\u003cspan class=\\\"BoldItalicUnderline\\\"\\u003e0.09\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e\\u003cspan class=\\\"BoldItalicUnderline\\\"\\u003e0.057\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e9\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eLow quality T cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e41\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e14\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e27\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e1.75\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.68\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e3.71\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.07\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-2.12\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.13\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.11\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e10\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eLow quality cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e596\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e369\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e227\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e2.02\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.95\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e2.83\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.50\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-1.40\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.41\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.63\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e11\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eFibroblasts\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e96\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e87\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e9\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e3.17\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.62\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e0.27\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.07\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e\\u003cspan class=\\\"BoldItalicUnderline\\\"\\u003e11.60\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e\\u003cspan class=\\\"BoldItalicUnderline\\\"\\u003e0.03\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e\\u003cspan class=\\\"BoldItalicUnderline\\\"\\u003e0.057\\u003c/span\\u003e\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e12\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eClub cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e98\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e70\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e28\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e1.35\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.81\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e2.72\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.16\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-2.02\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.29\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.63\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e13\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAT1\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e68\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e59\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e9\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e2.60\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.91\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e0.46\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.36\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e5.71\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.12\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.11\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e14\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eLymphatic-endothelial\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e27\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e20\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e7\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e1.93\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.10\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e0.33\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.29\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e5.85\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.06\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.23\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e15\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eMast cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e32\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e25\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e7\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e1.32\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.79\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e0.91\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.24\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e1.44\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.44\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.63\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e16\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eProliferative macrophages\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e111\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e55\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e56\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e0.84\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.35\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\"\\u003e\\n\\u003cp\\u003e0.39\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.25\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e2.17\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.12\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.11\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u0026nbsp;\\u003c/div\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003cstrong\\u003e2. Gene expression in 17 individual types of cells in severe emphysematous lungs.\\u003c/strong\\u003e\\u003c/div\\u003e\\n\\u003cp\\u003eFor each cluster, the differential gene expression among the cells of the 3 COPD patients vs. the controls was computed using a Wilcoxon test. This analysis identified the number of differentially expressed genes per cluster when applying the filter of log fold change \\u0026gt;|0.4| and FDR\\u0026thinsp;=\\u0026thinsp;0.05 (\\u003cstrong\\u003eSupplementary Table S2\\u003c/strong\\u003e). Interestingly, the major populations showing the largest differences in gene expression were: monocytes, macrophages, low quality cells, ciliated epithelial cells, T cells, AT2 cells, and NK cells (Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Most gene expression changes corresponded to an increased transcription (up-regulation) in patients with COPD (Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3. The Gene Ontology enrichment Gene expression in 17 individual types of cells from severe emphysematous lungs.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNext, we performed the functional enrichment with the upregulated or downregulated genes in COPD for each population for GO biological processes (\\u003cstrong\\u003eSupplementary Tables\\u0026nbsp;3 and 4).\\u003c/strong\\u003e Results for the upregulated genes in COPD (i.e., macrophages, monocytes, ciliated epithelial cells and NK cells), have been summarized with Revigo and are shown as Treemaps (\\u003cstrong\\u003eSupplementary Figures S2-S5\\u003c/strong\\u003e). Of note, these four individual cell types shared ontologies related to the activation of T cells, defense response and three of them (not macrophages) also presented ontologies related to the viral life cycle.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e4. Single cell contribution to the 127 emphysema-related gene signatures\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e\\n\\u003cp\\u003eNext, we identified how the transcriptomic changes of each cell type contribute to the previously described 127 gene signatures of emphysema reported by Campbell et al [\\u003cspan class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]. We computed the overlap using GSEA (Table\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Three individual cell types were enriched with a nominal p value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05 and an FDR\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.1 (\\u003cem\\u003ei.e.\\u003c/em\\u003e, ciliated epithelial cells, cytotoxic T cells, and low quality T cells). The genes in the core enrichment for these 3 individual cell types and the values of differentially expressed genes in Campbell\\u0026rsquo;s data set are shown in \\u003cstrong\\u003eSupplementary Table\\u0026nbsp;5\\u003c/strong\\u003e. EPAS1, QKI and STOM were differentially expressed core genes in all three-cell types (\\u003cstrong\\u003eSupplementary Table\\u0026nbsp;5\\u003c/strong\\u003e).\\u003c/p\\u003e\\n\\u003cdiv class=\\\"gridtable\\\"\\u003e\\n\\u003ctable id=\\\"Tab7\\\" border=\\\"1\\\"\\u003e\\u003ccaption\\u003e\\n\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e\\n\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\n\\u003cp\\u003eGSEA enrichment of the different clusters with the 127 gene signature\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003c/caption\\u003e\\n\\u003cthead\\u003e\\n\\u003ctr\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eCluster\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eES\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eNES\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eNOM p-val\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eFDR q-val\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003cth align=\\\"left\\\"\\u003e\\n\\u003cp\\u003e\\u003cem\\u003eFWER p-val\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003c/th\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/thead\\u003e\\n\\u003ctbody\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCiliated\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.33\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e1.33\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.00\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.09\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.00\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eCytotoxic T\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.33\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e1.29\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.00\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.09\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.00\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eLow quality\\u003c/p\\u003e\\n\\u003cp\\u003eT cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.47\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e1.29\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.00\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.11\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.00\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAT2\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.39\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e1.27\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.09\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.18\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.04\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eFibroblasts\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.40\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e1.31\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.10\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.18\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.05\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eT cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.31\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e1.17\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.10\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.21\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.05\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eProliferative macrophages\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.37\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e1.23\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.10\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.22\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.05\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eB cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.24\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.97\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.18\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.28\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.09\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eNK\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.31\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e1.05\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.29\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.37\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.14\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eAT1\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.33\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e1.00\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.43\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.52\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.22\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eMonocytes\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.20\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.84\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.68\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.77\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.36\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eMacrophages\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.22\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.92\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.70\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.83\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.37\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eClub\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.20\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.82\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.76\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.88\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.39\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eLow quality\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.25\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.78\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.88\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e1.00\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.43\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eEndothelial\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.17\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e-0.53\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.89\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e1.00\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.44\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003ctr\\u003e\\n\\u003ctd align=\\\"left\\\"\\u003e\\n\\u003cp\\u003eMast cells\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.28\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.77\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.92\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e1.00\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003ctd align=\\\"char\\\" char=\\\".\\\"\\u003e\\n\\u003cp\\u003e0.47\\u003c/p\\u003e\\n\\u003c/td\\u003e\\n\\u003c/tr\\u003e\\n\\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cp\\u003eAs a complementary analysis, we showed which individual cell types differentially expressed the 127 emphysema related genes (\\u003cstrong\\u003eSupplementary Table\\u0026nbsp;6 and Supplementary Fig.\\u0026nbsp;6\\u003c/strong\\u003e). FCN3, RTN4 and CCR7 were differentially expressed in a total of 5 individual cell types, and EPAS1, QKI and STOM in the 4 individual cell types. The individual cell types with more differentially expressed genes were: monocytes (n\\u0026thinsp;=\\u0026thinsp;10), AT2 cells (n\\u0026thinsp;=\\u0026thinsp;9), macrophages (n\\u0026thinsp;=\\u0026thinsp;8), ciliated cells (n\\u0026thinsp;=\\u0026thinsp;5), and T cells (n\\u0026thinsp;=\\u0026thinsp;5).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e5. Comparison with severe airflow limitation genes.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNext, we determined whether the gene expression changes observed in the individual cell types represent the previously identified gene signatures in whole lung tissue of patients with severe airflow limitation. To achieve this, we assessed the enrichment with GSEA of the individual cell types with the differentially expressed genes between n\\u0026thinsp;=\\u0026thinsp;17 non-smokers and n\\u0026thinsp;=\\u0026thinsp;30 patients with GOLD stage 4 obtained from LTRC (GSE47460, GPL14550). Overall, the enrichment in all individual cell types appeared to be consistent with the genes differentially expressed in whole lung tissue. However, the enrichment was significant at a nominal p value for only 4 individual cell types (mast cells, proliferating macrophages, monocytes, and FBPB4 macrophages). The full list of genes differentially expressed in the LTRC and differentially expressed in the individual cell clusters is shown in \\u003cstrong\\u003eSupplementary Table\\u0026nbsp;7\\u003c/strong\\u003e.\\u003c/p\\u003e\\n\\u003cp\\u003eOur analysis identified several genes expressed in a distinct type of cells, which were previously reported to be differentially expressed in lung tissue homogenates according to the severity of airflow limitation [\\u003cspan class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. These genes are FGG (AT2 cells), CCL19 (monocytes), PLA2G7 (macrophages), HP (macrophages), TNFSF13B (monocytes) and FCRLA (B cells). In addition, following genes were differentially expressed in several individual types of cells: S100A10 (n\\u0026thinsp;=\\u0026thinsp;7), RPS10, GNG11, and CAV1 (n\\u0026thinsp;=\\u0026thinsp;6), S100A6 (n\\u0026thinsp;=\\u0026thinsp;5), and AGER (n\\u0026thinsp;=\\u0026thinsp;4).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e6. Quantifying protein levels of some genes significantly altered between normal and COPD lungs.\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eTo determine whether significantly altered genes between normal and COPD lungs also correlate with the related protein levels, whole parenchymal lung tissues from non-smokers without COPD (\\u003cem\\u003en\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;5 per group) and former-smokers with COPD GOLD stage 3 or 4 (\\u003cem\\u003en\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;6 or 7 per group) were evaluated for protein expression of QKI, STOM, EPAS1, and IGFBP5 by immunoblot analysis. Consistent with altered gene expression, QKI and IGFBP5 protein levels were significantly increased in the COPD lungs relative to non-smokers (Figs.\\u0026nbsp;\\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eA and \\u003cspan class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003eB), but neither STOM nor EPAS1 protein levels were altered (\\u003cstrong\\u003eSupplementary Figure S7A\\u003c/strong\\u003e). Further, we determined whether QKI and IGFBP5 gene expressions in whole lung tissue correlated with the emphysema severity. QKI expression appreared to decrease according to % emphysema (n\\u0026thinsp;=\\u0026thinsp;208; p\\u0026thinsp;=\\u0026thinsp;0.0854), whereas, IGFBP5 expression significantly increased according to % emphysema (p\\u0026thinsp;=\\u0026thinsp;00150). Since IGFBP5 is an excretory protein, we measured the serum levels of IGFBP5 in smokers with or without COPD (n\\u0026thinsp;=\\u0026thinsp;40, each group) that were not significantly altered between the two group (\\u003cstrong\\u003eSupplementary Figure S7B\\u003c/strong\\u003e). These results suggest that some of the significantly altered genes in COPD lungs identified by scRNA seq indeed correlate with the individual protein levels in the whole lung tissue.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThis study uses scRNA seq from human lung homogenate in order to identify the specific cell types driving gene expression changes found in bulk RNA sequencing of patients with severe emphysema and airway obstruction. We found that: 1) t-SNE and clustering of scRNA-seq data identified a total of 17 distinct populations based on predetermined markers per cell type. Monocytes, macrophages, ciliated cells and low quality cells exhibited more differentially expressed genes in cases vs. controls relative to the other cell types; 2) GSEA revealed that the populations contributing most to the previously reported emphysema signature were ciliated cells, cytotoxic T cells and low quality T cells. While in the severe COPD LTRC signature, the populations enriched by GSEA were proliferating macrophages, mast cells, AT2 cells and monocytes; 3) key COPD associated genes were found to be expressed by specific cell types:. FGG (AT2 cells), CCL19/TNFSF13B (Monocytes) and PLA2G7 (Macrophages); 4) We verified the expression of some of the specific scRNA seq differentially expressed genes at protein level as well (i.e. QKI and IGFBP5).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003ePrevious studies\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e\\n\\u003cp\\u003eAlthough the scRNA seq methodology has been applied to the profiling of lung tissue of patients with IPF [\\u003cspan class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e], this is the first study to our knowledge which profiles lung tissue of patients with COPD. In relation to severe COPD and emphysema, several studies have reported the transcriptomic profile of lung tissue homogenates [\\u003cspan class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e], but the scRNA seq has several advantages over the previously used RNA seq methods. First, scRNA seq is useful to determine specific types of cells that are responsible for the significant transcriptomic changes in a disease process (e.g., the emphysematous and/or airflow limitation signature). Second, it is unlikely that the cell composition alters the outcome such as the whole cell RNA-sEq.\\u0026nbsp;Third, scRNA seq may uncover an important molecular pathway unique to a specific cell type that contributes to the development of the disease.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInterpretation of novel findings\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e\\n\\u003cp\\u003eWe identified 17 cell subtypes in lung tissue of patients with severe COPD and non-smoking controls. In relation to cell composition, differences were not statistically significant, but in agreement with previous reports. We observed an overall increase of immune cell types (T, NK and B cells) and decrease of structural cells (Fibroblasts, AT2 cells and endothelial cells) [\\u003cspan class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]. However, sampling effects inherent to scRNA seq may have contributed to the skewed proportion of distinct cell populations. Interestingly, we found that the genes up-regulated in the cell types with more differential expression (Monocytes, Macrophages, Ciliated cells, Cytotoxic T cells and AT2 cells) were related to T cell activation, antigen presentation and signaling. The role of Cytotoxic T cells (CD8+) in severe COPD has been long recognized [\\u003cspan class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u0026ndash;\\u003cspan class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e] and emphysema has been proposed to be associated with a Th1 response activated by infiltrating ILC1, NK, and LTi cells [\\u003cspan class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]. Here, we expand these findings by showing that the antigen presenting cells (macrophages, monocytes and AT2 cells) are also involved in the T cell activation. Interestingly, the viral related ontologies appeared to be enriched in these cell types as well as in ciliated epithelial cells and NK cells. Further work profiling the virome in parallel with cellular phenotyping may complement these findings.\\u003c/p\\u003e\\n\\u003cp\\u003eIn relation to the previously described 127 emphysema gene signature, our GSEA analysis showed an enrichment of genes differentially expressed by Ciliated and T cells (Cytotoxic and of low quality), suggesting an active involvement of T cells in the emphysematous tissue remodeling and accumulation of primary cilia [\\u003cspan class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]. Some of the genes associated with the homing of B cells and previously identified by homogenate tissue profiling (i.e. CCL19, TNFSF13B) [\\u003cspan class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e, \\u003cspan class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e] were found in the current study to be expressed by macrophages and monocytes. Yet in the current analysis the increase in B cells was observed in 2 of the 3 severe COPD samples, and the genes hyper expressed by the severe COPD B cells were related to the T cell activation in concordance with previous works [\\u003cspan class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e]. Upregulation of gene expression for fibrinogen (FGG) is a well-recognized biomarker in COPD [\\u003cspan class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e] and is produced by AT2 cells. Fibrinogen has a well-known role in both innate and T cell mediated adaptive immune responses to bacteria [\\u003cspan class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]. Similar to these previous findings, our analysis showed an upregulation of genes related to the antigen presentation in AT2 cells, suggesting a role in the stimulation and perpetuation of the observed immune response in the lung. AGER, another well-known gene associated with COPD [\\u003cspan class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e], was upregulated in 4 cell types in our analysis (Macrophages, Monocytes, low quality T cells and B cells) that are associated with of immune response, suggesting a role of the RAGE axis in the chronic immune infiltrate observed in severe COPD/emphysema. In our analysis, club cells that express the COPD associated CC-16 protein, were found to be altered in the mRNA catabolism pathways, and the response to toxic substances. Further investigation is warranted to determine the impact of these alterations in cell functionality [\\u003cspan class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e].\\u003c/p\\u003e\\n\\u003cp\\u003eFinally, in this study, we attempted to verify whether differences in gene expression correlated with differential protein content in the cellular populations derived from COPD and normal lungs. Among several common targets, we found that COPD lungs exhibit decreased protein levels of QKI and increased protein levels of IGFBP5.\\u003c/p\\u003e\\n\\u003cp\\u003eOur scRNA seq data show that QKI is expressed abundantly in myeloid cells, endothelial cells, and AT1 cells, whereas IGFBP5 is expressed in ciliated cells, fibroblasts, and lymphatic endothelial cells relative to the other types of cells. QKI, a KH domain containing RNA binding protein, regulates versatile mRNA metabolism \\u0026ndash; splicing, export, stability, and protein translation [\\u003cspan class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]. The loss-of-function mutations disturb myelination and cause embryonic lethality [\\u003cspan class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]. QKI has been implicated in various disease processes, including atherosclerosis [\\u003cspan class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e], tumorigenesis [\\u003cspan class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e], and fibrosis [\\u003cspan class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e]. IGFBP5, insulin-like growth factor binding protein 5, is one of the six proteins of the IGFBP family [\\u003cspan class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]. IGFBP proteins bind IGF-I/II and regulate their bioavailability and downstream signaling. In addition, IGFBP proteins can regulate cell growth and survival independent of IGF-I/II [\\u003cspan class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]. In particular, IGFBP5 plays a causal role in the induction of cellular senescence and inflammation [\\u003cspan class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e], which may be linked to pulmonary fibrosis [\\u003cspan class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e]. Further, an intergenic SNP of IGFBP5 (rs6435952) associates with airway obstruction [\\u003cspan class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e]. Although IGFBP5 is a secretory protein [\\u003cspan class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e], there was no significant change in the serum levels of IGFBP5 in COPD patients compared with control smokers. However, there may be excretory impairment of IGFBP5 protein in the COPD lung which remains to be determined in future studies. An in vivo animal study will be necessary to elucidate a causal role of QKI and IGFBP5 in the development of smoking-induced COPD.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eLimitations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003c/div\\u003e\\n\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e\\n\\u003cp\\u003eThe main limitation of this study is the sample size as we have analyzed 4,000 to 6,000 cells/sample single cells pooled together from 3 control subjects without underlying lung disease and 3 patients with severe COPD. Accordingly, our findings are not representative of the COPD heterogeneity and we will need to increase the sample size to address this open issue in future investigations. Notwithstanding this limitation, the main focus of this work has been to use the generated data to explore which cell types express the key genes previously shown to be associated with COPD and emphysema.\\u003c/p\\u003e\\n\\u003c/div\\u003e\"},{\"header\":\"Conclusions\",\"content\":\" \\u003cp\\u003eWe identified ciliated and CD8\\u0026thinsp;+\\u0026thinsp;T cells as prominent cell types associated with the 127 gene signature associated with emphysema. Our findings support a prominent role of the immune response in severe COPD, with the implication of structural and antigen presenting cells in its homing and perpetuation. Finally, QKI and IGFBP5 are identified as potential COPD biomarkers, whose both gene and protein expression are significantly altered in COPD lungs relative to normal lungs. The causal role of QKI and IGFBP5 in the development of COPD/emphysema will need further investigation.\\u003c/p\\u003e \"},{\"header\":\"List Of Abbreviations\",\"content\":\"\\u003cp\\u003eCOPD, chronic obstructive pulmonary disease\\u003c/p\\u003e \\u003cp\\u003eCS, cigarette smoke\\u003c/p\\u003e \\u003cp\\u003eFDR, false discovery rate\\u003c/p\\u003e \\u003cp\\u003eSEGA, gene set enrichment analysis\\u003c/p\\u003e \\u003cp\\u003eROS, reactive oxygen species\\u003c/p\\u003e \"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll research involving human subjects was approved by the University of Pittsburgh institutional review board (#14010265 and 19090239). \\u0026nbsp;Written informed consent was obtained from all study subjects. The use of human cadaveric tissue and decedent medical records for this study was approved by the Committee for Oversight of Research and Clinical Training Involving Decedents (CORID) (#765).\\u0026nbsp; The written consent was obtained either via body donation registration, autopsy authorization, or provided by next of kin or legal representatives. \\u003cstrong\\u003e\\u003cbr /\\u003e \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable. \\u003cstrong\\u003e\\u003cbr /\\u003e \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and material\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll data generated or analyzed during this study are included in this published article [and its supplementary information files].\\u003cstrong\\u003e\\u003cbr /\\u003e \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no completing interests. \\u003cstrong\\u003e\\u003cbr /\\u003e \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis study was supported by the Merit Review Award from the US Department of Veterans Affairs (CX001048 and CX000105), AHA transformational grant to TN (19TPA34830061) and Miguel Servet Fellowship from the Instituto de Salud Carlos III (CP16/00039, PI17/00369) to RF. These founders\\u0026nbsp;had no role in study design, data collection and analysis, or preparation of the manuscript.\\u003cstrong\\u003e \\u0026nbsp; \\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003e\\u003cbr /\\u003e \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026rsquo; \\u003c/strong\\u003e\\u003cstrong\\u003econtributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors have read and approved the manuscript. MR, RL, RF, and TN conceived and designed the experiments; TS, XL, RV, PS, GN, JS, YZ performed the experiments; TS, XL, TK, GN, and RF collected and analyzed the data; CZ, PS, YZ, FS, MR, JM, and TN provided reagents/materials/data analysis; XL, TS, CZ, PS, MR, PS, DC, RM, FS edited the manuscript; XL, GN, RF, and TN wrote the paper.\\u003cstrong\\u003e\\u003cbr /\\u003e \\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eBrown DW: \\u003cstrong\\u003eSmoking prevalence among US veterans\\u003c/strong\\u003e. \\u003cem\\u003eJ Gen Intern Med \\u003c/em\\u003e2010, \\u003cstrong\\u003e25\\u003c/strong\\u003e(2):147-149.\\u003c/li\\u003e\\n\\u003cli\\u003eHan MK: \\u003cstrong\\u003eUpdate in chronic obstructive pulmonary disease in 2010\\u003c/strong\\u003e. \\u003cem\\u003eAm J Respir Crit Care Med \\u003c/em\\u003e2011, \\u003cstrong\\u003e183\\u003c/strong\\u003e(10):1311-1315.\\u003c/li\\u003e\\n\\u003cli\\u003eGuarascio AJ, Ray SM, Finch CK, Self TH: \\u003cstrong\\u003eThe clinical and economic burden of chronic obstructive pulmonary disease in the USA\\u003c/strong\\u003e. \\u003cem\\u003eClinicoecon Outcomes Res \\u003c/em\\u003e2013, \\u003cstrong\\u003e5\\u003c/strong\\u003e:235-245.\\u003c/li\\u003e\\n\\u003cli\\u003eHogg JC: \\u003cstrong\\u003ePathophysiology of airflow limitation in chronic obstructive pulmonary disease\\u003c/strong\\u003e. \\u003cem\\u003eLancet \\u003c/em\\u003e2004, \\u003cstrong\\u003e364\\u003c/strong\\u003e(9435):709-721.\\u003c/li\\u003e\\n\\u003cli\\u003eKim V, Criner GJ: \\u003cstrong\\u003eChronic bronchitis and chronic obstructive pulmonary disease\\u003c/strong\\u003e. \\u003cem\\u003eAm J Respir Crit Care Med \\u003c/em\\u003e2013, \\u003cstrong\\u003e187\\u003c/strong\\u003e(3):228-237.\\u003c/li\\u003e\\n\\u003cli\\u003eMartinez FJ, Foster G, Curtis JL, Criner G, Weinmann G, Fishman A, DeCamp MM, Benditt J, Sciurba F, Make B\\u003cem\\u003e et al\\u003c/em\\u003e: \\u003cstrong\\u003ePredictors of mortality in patients with emphysema and severe airflow obstruction\\u003c/strong\\u003e. \\u003cem\\u003eAm J Respir Crit Care Med \\u003c/em\\u003e2006, \\u003cstrong\\u003e173\\u003c/strong\\u003e(12):1326-1334.\\u003c/li\\u003e\\n\\u003cli\\u003eMinai OA, Benditt J, Martinez FJ: \\u003cstrong\\u003eNatural history of emphysema\\u003c/strong\\u003e. \\u003cem\\u003eProc Am Thorac Soc \\u003c/em\\u003e2008, \\u003cstrong\\u003e5\\u003c/strong\\u003e(4):468-474.\\u003c/li\\u003e\\n\\u003cli\\u003eMorrow JD, Chase RP, Parker MM, Glass K, Seo M, Divo M, Owen CA, Castaldi P, DeMeo DL, Silverman EK\\u003cem\\u003e et al\\u003c/em\\u003e: \\u003cstrong\\u003eRNA-sequencing across three matched tissues reveals shared and tissue-specific gene expression and pathway signatures of COPD\\u003c/strong\\u003e. \\u003cem\\u003eRespir Res \\u003c/em\\u003e2019, \\u003cstrong\\u003e20\\u003c/strong\\u003e(1):65.\\u003c/li\\u003e\\n\\u003cli\\u003eFaner R, Morrow JD, Casas-Recasens S, Cloonan SM, Noell G, Lopez-Giraldo A, Tal-Singer R, Miller BE, Silverman EK, Agusti A\\u003cem\\u003e et al\\u003c/em\\u003e: \\u003cstrong\\u003eDo sputum or circulating blood samples reflect the pulmonary transcriptomic differences of COPD patients? A multi-tissue transcriptomic network META-analysis\\u003c/strong\\u003e. \\u003cem\\u003eRespir Res \\u003c/em\\u003e2019, \\u003cstrong\\u003e20\\u003c/strong\\u003e(1):5.\\u003c/li\\u003e\\n\\u003cli\\u003eSpira A, Beane J, Pinto-Plata V, Kadar A, Liu G, Shah V, Celli B, Brody JS: \\u003cstrong\\u003eGene expression profiling of human lung tissue from smokers with severe emphysema\\u003c/strong\\u003e. \\u003cem\\u003eAm J Respir Cell Mol Biol \\u003c/em\\u003e2004, \\u003cstrong\\u003e31\\u003c/strong\\u003e(6):601-610.\\u003c/li\\u003e\\n\\u003cli\\u003eLamontagne M, Timens W, Hao K, Bosse Y, Laviolette M, Steiling K, Campbell JD, Couture C, Conti M, Sherwood K\\u003cem\\u003e et al\\u003c/em\\u003e: \\u003cstrong\\u003eGenetic regulation of gene expression in the lung identifies CST3 and CD22 as potential causal genes for airflow obstruction\\u003c/strong\\u003e. \\u003cem\\u003eThorax \\u003c/em\\u003e2014, 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\\u003cstrong\\u003e18\\u003c/strong\\u003e(3):319-327.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"respiratory-research\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"rere\",\"sideBox\":\"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)\",\"snPcode\":\"12931\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12931/3\",\"title\":\"Respiratory Research\",\"twitterHandle\":\"@RespiratoryBMC\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"single cell RNA-seq, COPD, cigarette smoke \",\"lastPublishedDoi\":\"10.21203/rs.3.rs-100834/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-100834/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cstrong\\u003eBackground\\u003c/strong\\u003e: Whole lung tissue transcriptomic profiling studies in chronic obstructive pulmonary disease (COPD) have led to the identification of several genes associated with the severity of airflow limitation and/or the presence of emphysema, however,\\u0026nbsp;the cell types driving these gene expression signatures remain unidentified.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eMethods\\u003c/strong\\u003e: To determine cell specific transcriptomic changes in severe COPD, we conducted single-cell RNA sequencing (scRNA seq) on n= 29,961 cells from the peripheral lung parenchymal tissue of nonsmoking subjects without underlying lung disease (n=3) and patients with severe COPD (n=3). The cell type composition and cell specific gene expression signature was assessed. Gene set enrichment analysis (GSEA) was used to identify the specific cell types contributing to the previously reported transcriptomic signatures.\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eResults\\u003c/strong\\u003e: T-distributed stochastic neighbor embedding and clustering of scRNA seq data revealed a total of 17 distinct populations. Among them, the populations with more differentially expressed genes in cases vs. controls (log fold change \\u0026gt;|0.4| and FDR=0.05) were: monocytes (n=1499); macrophages (n=868) and ciliated epithelial cells (n= 590), respectively. Using GSEA, we found that only ciliated and cytotoxic T cells manifested a trend towards enrichment of the previously reported 127 regional emphysema gene signatures (normalized enrichment score [NES] = 1.28 and =1.33, FDR= 0.085 and =0.092 respectively). Among the significantly altered genes present in ciliated epithelial cells of the COPD lungs, QKI and IGFBP5 protein levels were also found to be altered in the COPD lungs. \\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\u003cp\\u003e\\u003cstrong\\u003eConclusions\\u003c/strong\\u003e: scRNA seq is useful to identify transcriptional changes and possibly individual protein levels that may contribute to the development of emphysema in a cell-type specific manner.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Single Cell RNA Sequencing Identifies IGFBP5 And QKI In Ciliated Epithelial Cell Genes Associated With Severe COPD\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2020-11-04 21:21:59\",\"doi\":\"10.21203/rs.3.rs-100834/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2021-01-20T00:00:00+00:00\",\"index\":2,\"fulltext\":\"Recommendation: Reviewer's comments unavailable due to the journal's policy.\\n\"},{\"type\":\"decision\",\"content\":\"Major revision\",\"date\":\"2021-01-20T00:00:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2020-12-27T00:00:00+00:00\",\"index\":1,\"fulltext\":\"Recommendation: Reviewer's comments unavailable due to the journal's policy.\\n\"},{\"type\":\"reviewerAgreed\",\"content\":\"\",\"date\":\"2020-12-08T00:00:00+00:00\",\"index\":2,\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2020-12-07T00:00:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"\",\"date\":\"2020-12-07T00:00:00+00:00\",\"index\":1,\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2020-10-30T12:00:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"\",\"date\":\"2020-10-29T12:00:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2020-10-29T12:00:00+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2020-10-29T12:00:00+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"respiratory-research\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"rere\",\"sideBox\":\"Learn more about [Respiratory Research](http://respiratory-research.biomedcentral.com/)\",\"snPcode\":\"12931\",\"submissionUrl\":\"https://submission.nature.com/new-submission/12931/3\",\"title\":\"Respiratory Research\",\"twitterHandle\":\"@RespiratoryBMC\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC/SO AJ\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"a5fef9f8-2b4f-46c2-a7e6-d45045c44615\",\"owner\":[],\"postedDate\":\"November 4th, 2020\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[{\"id\":979815,\"name\":\"Pulmonology\"}],\"tags\":[],\"updatedAt\":\"2021-08-18T19:42:42+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-100834\",\"link\":\"https://doi.org/10.1186/s12931-021-01675-2\",\"journal\":{\"identity\":\"respiratory-research\",\"isVorOnly\":false,\"title\":\"Respiratory Research\"},\"publishedOn\":\"2021-04-06 19:09:25\",\"publishedOnDateReadable\":\"April 6th, 2021\"},\"versionCreatedAt\":\"2020-11-04 21:21:59\",\"video\":\"\",\"vorDoi\":\"10.1186/s12931-021-01675-2\",\"vorDoiUrl\":\"https://doi.org/10.1186/s12931-021-01675-2\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-100834\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-100834\",\"identity\":\"rs-100834\",\"version\":[\"v1\"]},\"buildId\":\"cBFmMYwuxLRRLfASyISRj\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}