Limits...
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

Heat map combined with the hierarchical clustering analysis of G64 expression in the 34 single cells.(A) The 34 single cells were clustered into two subgroups (i.e., single cells displaying G64 down-regulation [left] and single cells displaying G64 up-regulation [right]). See also the dendrogram and black and red flat bars at the top of the heat map. The single cells displaying G64 up-regulation are labeled ‘red’ at the bottom of the heat map to ensure that the single cells exhibiting up-regulated G64 expression could be compared with the single cells shown in the other figures. (B) Principal component analysis of G64 expression in the 34 single cells (see the Materials and Methods section). The same subgroups shown in (A) were clustered. Blue and orange dots represent single cells exhibiting G64 down- and up-regulation, respectively.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4549254&req=5

pone.0135817.g003: Heat map combined with the hierarchical clustering analysis of G64 expression in the 34 single cells.(A) The 34 single cells were clustered into two subgroups (i.e., single cells displaying G64 down-regulation [left] and single cells displaying G64 up-regulation [right]). See also the dendrogram and black and red flat bars at the top of the heat map. The single cells displaying G64 up-regulation are labeled ‘red’ at the bottom of the heat map to ensure that the single cells exhibiting up-regulated G64 expression could be compared with the single cells shown in the other figures. (B) Principal component analysis of G64 expression in the 34 single cells (see the Materials and Methods section). The same subgroups shown in (A) were clustered. Blue and orange dots represent single cells exhibiting G64 down- and up-regulation, respectively.

Mentions: Hierarchical clustering was performed on the 34 single cells using G64. The expression fold-change for each gene in each single cell was estimated by dividing the FPKM value in each cell by the average FPKM value of the 34 single cells, followed by log2 transformation. Interestingly, the 34 single cells derived from a single LADC tumor region were divided into two distinct subpopulations according to the expression of G64 (Fig 3A). PCA confirmed this separation (Fig 3B). This result may indicate that single cells derived from even a single tumor are not identical in their gene expression characteristics, and this phenomenon may be related to the different physiological responses of distinct tumor cell subpopulations to drugs or treatments.


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)

Heat map combined with the hierarchical clustering analysis of G64 expression in the 34 single cells.(A) The 34 single cells were clustered into two subgroups (i.e., single cells displaying G64 down-regulation [left] and single cells displaying G64 up-regulation [right]). See also the dendrogram and black and red flat bars at the top of the heat map. The single cells displaying G64 up-regulation are labeled ‘red’ at the bottom of the heat map to ensure that the single cells exhibiting up-regulated G64 expression could be compared with the single cells shown in the other figures. (B) Principal component analysis of G64 expression in the 34 single cells (see the Materials and Methods section). The same subgroups shown in (A) were clustered. Blue and orange dots represent single cells exhibiting G64 down- and up-regulation, respectively.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0135817.g003: Heat map combined with the hierarchical clustering analysis of G64 expression in the 34 single cells.(A) The 34 single cells were clustered into two subgroups (i.e., single cells displaying G64 down-regulation [left] and single cells displaying G64 up-regulation [right]). See also the dendrogram and black and red flat bars at the top of the heat map. The single cells displaying G64 up-regulation are labeled ‘red’ at the bottom of the heat map to ensure that the single cells exhibiting up-regulated G64 expression could be compared with the single cells shown in the other figures. (B) Principal component analysis of G64 expression in the 34 single cells (see the Materials and Methods section). The same subgroups shown in (A) were clustered. Blue and orange dots represent single cells exhibiting G64 down- and up-regulation, respectively.
Mentions: Hierarchical clustering was performed on the 34 single cells using G64. The expression fold-change for each gene in each single cell was estimated by dividing the FPKM value in each cell by the average FPKM value of the 34 single cells, followed by log2 transformation. Interestingly, the 34 single cells derived from a single LADC tumor region were divided into two distinct subpopulations according to the expression of G64 (Fig 3A). PCA confirmed this separation (Fig 3B). This result may indicate that single cells derived from even a single tumor are not identical in their gene expression characteristics, and this phenomenon may be related to the different physiological responses of distinct tumor cell subpopulations to drugs or treatments.

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