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Identification of Distinct Tumor Subpopulations in Lung Adenocarcinoma via Single-Cell RNA-seq.

Min JW, Kim WJ, Han JA, Jung YJ, Kim KT, Park WY, Lee HO, Choi SS - PLoS ONE (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Biotechnology, College of Biomedical Science, and Institute of Bioscience & Biotechnology, Kangwon National University, Chuncheon, 200-701, Korea.

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.

No MeSH data available.


Related in: MedlinePlus

Plotting the expression of seed genes and G64.The log2 fold-change in the expression of a given gene among the single cells compared to the average expression level of the given in the 34 single cells was plotted for seed genes (upper panel) and for the genes in G64 (lower panel). Lines of different color indicate anonymous, distinct genes included in G64.
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pone.0135817.g002: Plotting the expression of seed genes and G64.The log2 fold-change in the expression of a given gene among the single cells compared to the average expression level of the given in the 34 single cells was plotted for seed genes (upper panel) and for the genes in G64 (lower panel). Lines of different color indicate anonymous, distinct genes included in G64.

Mentions: From the remaining 5,587 x 5,587 gene-to-gene correlations, the gene groups containing at least five genes displaying the highest correlations based on a Pearson’s correlation coefficient of (r) > = 0.9 were collected. From this analysis, a total of 20 genes were identified as ‘seed genes’ (in bold in S1 Table). Then, a weaker threshold, r > = 0.75, was applied for the 20 seed genes to expand the gene set, ultimately producing a cluster of 64 genes termed G64 (Fig 2 and S1 Table).


Identification of Distinct Tumor Subpopulations in Lung Adenocarcinoma via Single-Cell RNA-seq.

Min JW, Kim WJ, Han JA, Jung YJ, Kim KT, Park WY, Lee HO, Choi SS - PLoS ONE (2015)

Plotting the expression of seed genes and G64.The log2 fold-change in the expression of a given gene among the single cells compared to the average expression level of the given in the 34 single cells was plotted for seed genes (upper panel) and for the genes in G64 (lower panel). Lines of different color indicate anonymous, distinct genes included in G64.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4549254&req=5

pone.0135817.g002: Plotting the expression of seed genes and G64.The log2 fold-change in the expression of a given gene among the single cells compared to the average expression level of the given in the 34 single cells was plotted for seed genes (upper panel) and for the genes in G64 (lower panel). Lines of different color indicate anonymous, distinct genes included in G64.
Mentions: From the remaining 5,587 x 5,587 gene-to-gene correlations, the gene groups containing at least five genes displaying the highest correlations based on a Pearson’s correlation coefficient of (r) > = 0.9 were collected. From this analysis, a total of 20 genes were identified as ‘seed genes’ (in bold in S1 Table). Then, a weaker threshold, r > = 0.75, was applied for the 20 seed genes to expand the gene set, ultimately producing a cluster of 64 genes termed G64 (Fig 2 and S1 Table).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Medical Biotechnology, College of Biomedical Science, and Institute of Bioscience & Biotechnology, Kangwon National University, Chuncheon, 200-701, Korea.

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.

No MeSH data available.


Related in: MedlinePlus