Integrative analysis of cell state changes in lung fibrosis with peripheral protein biomarkers.
Integrative analysis of cell state changes in lung fibrosis with peripheral protein biomarkers.
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DOI：
10.15252/emmm.202012871
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发表时间：
2021-04-09
影响因子：
11.1
通讯作者：
Schiller HB
中科院分区：
文献类型：
--
作者：
Mayr CH;Simon LM;Leuschner G;Ansari M;Schniering J;Geyer PE;Angelidis I;Strunz M;Singh P;Kneidinger N;Reichenberger F;Silbernagel E;Böhm S;Adler H;Lindner M;Maurer B;Hilgendorff A;Prasse A;Behr J;Mann M;Eickelberg O;Theis FJ;Schiller HB
患病器官中细胞状态变化与外周蛋白质特征的对应性目前尚不清楚。在这里，我们生成并整合了来自多个大型肺纤维化患者队列的单细胞转录组学和蛋白质组学数据。在三个独立的队列中整合233，638个单细胞转录组（n = 61），使我们能够获得细胞类型比例的变化和45种细胞类型的肺纤维化中改变的一组强大的核心基因。对肺灌洗液（n = 124）和血浆（n = 141）蛋白质组的质谱分析鉴定了与诊断、肺功能和损伤状态相关的不同蛋白质特征。一种新的SSTR 2+周细胞状态与疾病的严重程度相关，并反映在灌洗液中的补体调节因子CFHR 1的水平增加。我们进一步发现CRTAC 1是灌洗液和血浆中肺泡2型上皮细胞健康状态的生物标志物。使用交叉模态分析和机器学习，我们确定了生物标志物的细胞来源，并证明了模态之间的信息传递可以正确预测疾病状态，这表明通过体液蛋白质组的纵向采样进行临床细胞状态监测的可行性。来自肺组织的单细胞RNA-seq数据和来自独立肺纤维化患者队列体液的蛋白质组学数据的多模式整合揭示了与疾病进展期间细胞状态变化相对应的生物标志物特征。
The correspondence of cell state changes in diseased organs to peripheral protein signatures is currently unknown. Here, we generated and integrated single‐cell transcriptomic and proteomic data from multiple large pulmonary fibrosis patient cohorts. Integration of 233,638 single‐cell transcriptomes (n = 61) across three independent cohorts enabled us to derive shifts in cell type proportions and a robust core set of genes altered in lung fibrosis for 45 cell types. Mass spectrometry analysis of lung lavage fluid (n = 124) and plasma (n = 141) proteomes identified distinct protein signatures correlated with diagnosis, lung function, and injury status. A novel SSTR2+ pericyte state correlated with disease severity and was reflected in lavage fluid by increased levels of the complement regulatory factor CFHR1. We further discovered CRTAC1 as a biomarker of alveolar type‐2 epithelial cell health status in lavage fluid and plasma. Using cross‐modal analysis and machine learning, we identified the cellular source of biomarkers and demonstrated that information transfer between modalities correctly predicts disease status, suggesting feasibility of clinical cell state monitoring through longitudinal sampling of body fluid proteomes. Multi‐modal integration of single‐cell RNA‐seq data from lung tissue and proteomic data from body fluids across independent lung fibrosis patient cohorts revealed biomarker signatures that correspond with cell state changes during disease progression.
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影响因子：
16.6
作者：
Angelidis, Ilias;Simon, Lukas M.;Schiller, Herbert B.
通讯作者：
Schiller, Herbert B.
影响因子：
4.4
作者：
Cox, Juergen;Neuhauser, Nadin;Mann, Matthias
通讯作者：
Mann, Matthias
影响因子：
4.8
作者：
Breed, DR;Margraf, LR;Mendelson, CR
通讯作者：
Mendelson, CR
影响因子：
4.3
作者：
Cox TR;Erler JT
通讯作者：
Erler JT
影响因子：
168.9
作者：
Fischer, Aryeh;du Bois, Roland
通讯作者：
du Bois, Roland