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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

Cell-cycle regulation is a major functional class of G64 genes.A total of 40 genes in the G64 module were mapped to cell-cycle genes in CycleBase (http://www.cyclebase.org/). Each of the 40 cell-cycle genes was accordingly allocated to one of the cell-cycle stages (e.g., G1, S, G2, or M, colored in yellow, green, blue, and red, respectively).
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pone.0135817.g006: Cell-cycle regulation is a major functional class of G64 genes.A total of 40 genes in the G64 module were mapped to cell-cycle genes in CycleBase (http://www.cyclebase.org/). Each of the 40 cell-cycle genes was accordingly allocated to one of the cell-cycle stages (e.g., G1, S, G2, or M, colored in yellow, green, blue, and red, respectively).

Mentions: Thus, we examined whether the G64 genes were expressed during a specific cell-cycle stage and whether the two groups of single cells, i.e., the G64 up-regulation group and the G64 down-regulation group, actually represent two different types of cells, i.e., dividing cells and non-dividing cells. Forty G64 genes could be assigned to each corresponding cell-cycle stage based on mapping to the cell-cycle genes in CycleBase (http://www.cyclebase.org/Downloads). In fact, the 40 genes were not restricted to a single cell-cycle stage but rather were found in all different types of cell-cycle stage categories (e.g., the G1, S, G2, and M phases) (Fig 6). Based on this result, all 13 single cells exhibiting G64 up-regulation may actually express different cell-cycle genes acting in different cell-cycle stages, whereas the 21 single cells exhibiting G64 down-regulation express few of these cell-cycle genes. This result indicates that the subpopulations of the 34 single cells stratified by G64 expression are not distinct due to their cell division capabilities. Consistently, as shown in Figs 4 and 5, the differential co-expression of G64 genes was applicable to not only the intra-tumoral level but also the tissue level of the tumors (i.e., different groups of thousands of single cells).


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)

Cell-cycle regulation is a major functional class of G64 genes.A total of 40 genes in the G64 module were mapped to cell-cycle genes in CycleBase (http://www.cyclebase.org/). Each of the 40 cell-cycle genes was accordingly allocated to one of the cell-cycle stages (e.g., G1, S, G2, or M, colored in yellow, green, blue, and red, respectively).
© Copyright Policy
Related In: Results  -  Collection

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

pone.0135817.g006: Cell-cycle regulation is a major functional class of G64 genes.A total of 40 genes in the G64 module were mapped to cell-cycle genes in CycleBase (http://www.cyclebase.org/). Each of the 40 cell-cycle genes was accordingly allocated to one of the cell-cycle stages (e.g., G1, S, G2, or M, colored in yellow, green, blue, and red, respectively).
Mentions: Thus, we examined whether the G64 genes were expressed during a specific cell-cycle stage and whether the two groups of single cells, i.e., the G64 up-regulation group and the G64 down-regulation group, actually represent two different types of cells, i.e., dividing cells and non-dividing cells. Forty G64 genes could be assigned to each corresponding cell-cycle stage based on mapping to the cell-cycle genes in CycleBase (http://www.cyclebase.org/Downloads). In fact, the 40 genes were not restricted to a single cell-cycle stage but rather were found in all different types of cell-cycle stage categories (e.g., the G1, S, G2, and M phases) (Fig 6). Based on this result, all 13 single cells exhibiting G64 up-regulation may actually express different cell-cycle genes acting in different cell-cycle stages, whereas the 21 single cells exhibiting G64 down-regulation express few of these cell-cycle genes. This result indicates that the subpopulations of the 34 single cells stratified by G64 expression are not distinct due to their cell division capabilities. Consistently, as shown in Figs 4 and 5, the differential co-expression of G64 genes was applicable to not only the intra-tumoral level but also the tissue level of the tumors (i.e., different groups of thousands of single cells).

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