title: Identification of Distinct Tumor Subpopulations in Lung Adenocarcinoma via Single-Cell RNA-seq.
abstract: Single-cell sequencing, which is used to detect clinically important tumor subpopulations, is necessary for understanding tumor heterogeneity. Here, we analyzed transcriptomic data obtained from 34 single cells from human lung adenocarcinoma (LADC) patient-derived xenografts (PDXs). To focus on the intrinsic transcriptomic signatures of these tumors, we filtered out genes that displayed extensive expression changes following xenografting and cell culture. Then, we performed clustering analysis using co-regulated gene modules rather than individual genes to minimize read drop-out errors associated with single-cell sequencing. This combined approach revealed two distinct intra-tumoral subgroups that were primarily distinguished by the gene module G64. The G64 module was predominantly composed of cell-cycle genes. E2F1 was found to be the transcription factor that most likely mediates the expression of the G64 module in single LADC cells. Interestingly, the G64 module also indicated inter-tumoral heterogeneity based on its association with patient survival and other clinical variables such as smoking status and tumor stage. Taken together, these results demonstrate the feasibility of single-cell RNA sequencing and the strength of our analytical pipeline for the identification of tumor subpopulations.
authors: Department of Medical Biotechnology, College of Biomedical Science, and Institute of Bioscience & Biotechnology, Kangwon National University, Chuncheon, 200-701, Korea.
authors: Jae-Woong
authors: JW
authors: Min
authors: School of Medicine, Kangwon National University, Chuncheon, 200-701, Korea.
authors: Woo Jin
authors: WJ
authors: Kim
authors: School of Medicine, Kangwon National University, Chuncheon, 200-701, Korea.
authors: Jeong A
authors: JA
authors: Han
authors: Department of Biological Sciences, Kangwon National University, Chuncheon, 200-701, Korea.
authors: Yu-Jin
authors: YJ
authors: Jung
authors: Samsung Genome Institute, Samsung Medical Center, Seoul, Korea.
authors: Kyu-Tae
authors: KT
authors: Kim
authors: Samsung Genome Institute, Samsung Medical Center, Seoul, Korea; Department of Molecular Cell Biology, Sungkyunkwan University, Seoul, Korea.
authors: Woong-Yang
authors: WY
authors: Park
authors: Samsung Genome Institute, Samsung Medical Center, Seoul, Korea; Department of Molecular Cell Biology, Sungkyunkwan University, Seoul, Korea.
authors: Hae-Ock
authors: HO
authors: Lee
authors: Department of Medical Biotechnology, College of Biomedical Science, and Institute of Bioscience & Biotechnology, Kangwon National University, Chuncheon, 200-701, Korea.
authors: Sun Shim
authors: SS
authors: Choi
mesh_terms: D000230:Adenocarcinoma / Q000235:genetics* / Q000473:pathology*; D000077192:Adenocarcinoma of Lung; D002453:Cell Cycle / Q000235:genetics; D016000:Cluster Analysis; D015536:Down-Regulation / Q000235:genetics; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D052138:Genes, Neoplasm; D006801:Humans; D053208:Kaplan-Meier Estimate; D016015:Logistic Models; D008175:Lung Neoplasms / Q000235:genetics* / Q000473:pathology*; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011379:Prognosis; D056910:Republic of Korea; D017423:Sequence Analysis, RNA / Q000379:methods*; D059010:Single-Cell Analysis / Q000379:methods*; D014157:Transcription Factors / Q000378:metabolism; D059467:Transcriptome / Q000235:genetics; D015854:Up-Regulation; D023041:Xenograft Model Antitumor Assays
chemical_list: D014157:Transcription Factors