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An Automated Microwell Platform for Large-Scale Single Cell RNA-Seq

View Article: PubMed Central - PubMed

ABSTRACT

Recent developments have enabled rapid, inexpensive RNA sequencing of thousands of individual cells from a single specimen, raising the possibility of unbiased and comprehensive expression profiling from complex tissues. Microwell arrays are a particularly attractive microfluidic platform for single cell analysis due to their scalability, cell capture efficiency, and compatibility with imaging. We report an automated microwell array platform for single cell RNA-Seq with significantly improved performance over previous implementations. We demonstrate cell capture efficiencies of >50%, compatibility with commercially available barcoded mRNA capture beads, and parallel expression profiling from thousands of individual cells. We evaluate the level of cross-contamination in our platform by both tracking fluorescent cell lysate in sealed microwells and with a human-mouse mixed species RNA-Seq experiment. Finally, we apply our system to comprehensively assess heterogeneity in gene expression of patient-derived glioma neurospheres and uncover subpopulations similar to those observed in human glioma tissue.

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Phenotypic heterogeneity in glioma neurospheres reflects glioma patient subtypes.(A) t-SNE clustering analysis of >2,200 individual TS543 glioma neurosphere cell expression profiles showing two distinct subpopulations of cells. Assignment of individual cells to the red or blue clusters was accomplished by nearest-neighbor density analysis (see Methods). (B) Same t-SNE clustering analysis shown in (A) but colorized according expression of classifier marker genes that are characteristic of the Proneural subtype of glioblastoma (see Methods for mathematical details). Red cells have high expression of the Proneural classifier genes and blue cells have low expression of the classifier genes. (C) Same as (B) but using the classifier marker genes that are characteristic of the Classical subtype of glioblastoma. (D) Same as (B) but using classifier marker genes that are characteristic of the Mesenchymal subtype of glioblastoma.
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f4: Phenotypic heterogeneity in glioma neurospheres reflects glioma patient subtypes.(A) t-SNE clustering analysis of >2,200 individual TS543 glioma neurosphere cell expression profiles showing two distinct subpopulations of cells. Assignment of individual cells to the red or blue clusters was accomplished by nearest-neighbor density analysis (see Methods). (B) Same t-SNE clustering analysis shown in (A) but colorized according expression of classifier marker genes that are characteristic of the Proneural subtype of glioblastoma (see Methods for mathematical details). Red cells have high expression of the Proneural classifier genes and blue cells have low expression of the classifier genes. (C) Same as (B) but using the classifier marker genes that are characteristic of the Classical subtype of glioblastoma. (D) Same as (B) but using classifier marker genes that are characteristic of the Mesenchymal subtype of glioblastoma.

Mentions: Glioma neurospheres represent an important model system for brain tumors because, in many cases, they more effectively preserve the phenotypic and genotypic features of tumors than conventional monolayer cultures23. They have been widely used to study drug response, glioma stem cells, and tumor progression as xenograft models232425. However, to our knowledge, glioma neurospheres have not been analyzed comprehensively by single cell RNA-Seq to determine the extent of phenotypic heterogeneity and co-occurrence of cellular subpopulations within a single culture. Expression profiling of surgical specimens from glioma patients by The Cancer Genome Atlas has established classifier gene sets that stratify tumors into distinct subtypes26. Recent studies employing bulk expression analysis of regional heterogeneity27 and single cell RNA-Seq28 have shown that gene signatures corresponding to different patient subtypes co-occur within individual gliomas. We analyzed single cell expression profiles obtained from TS543 cells, a glioma neurosphere line that most closely resembles the Proneural glioma subtype and harbors amplification of PDGFRA, a genetic alteration associated with Proneural gliomas29. We used unsupervised dimensionality reduction and density-based cluster assignment that was uninformed of the identities of the glioma classifier genes (taken from Table S3 of Verhaak et al.26) to show that individual TS543 cells are comprised of at least two clear phenotypic subpopulations (Fig. 4A). For simplicity, we refer to these subpopulations as the red cluster and blue cluster. The median number of molecules detected per cell in the red and blue clusters was 11,382 and 9,771, respectively, suggesting that coverage is not a major driver of the separation between these two subpopulations. As expected, we found that Proneural genes are more commonly expressed in the majority of TS543 cells than genes from either the Classical or Mesenchymal subtypes. However, when we project expression of subtype-specific genes onto our clustering analysis, we find considerable expression heterogeneity among the classifier genes. For example, above-median expression of the Proneural classifier genes (Fig. 4B) is significantly enriched in the blue cluster (p < 10−6, hypergeometric test) whereas above-median expression of both Classical (Fig. 4C) and Mesenchymal (Fig. 4D) genes is significantly enriched in the red cluster (p < 10−6 for both gene sets). This phenomenon is reminiscent of the “hybrid cellular states” observed in by Patel et al. among individual cells in human glioblastoma tissue specimens28. Hence, our results suggest that glioma neurosphere cultures can recapitulate the subtype-specific expression heterogeneity found in human glioma tissue.


An Automated Microwell Platform for Large-Scale Single Cell RNA-Seq
Phenotypic heterogeneity in glioma neurospheres reflects glioma patient subtypes.(A) t-SNE clustering analysis of >2,200 individual TS543 glioma neurosphere cell expression profiles showing two distinct subpopulations of cells. Assignment of individual cells to the red or blue clusters was accomplished by nearest-neighbor density analysis (see Methods). (B) Same t-SNE clustering analysis shown in (A) but colorized according expression of classifier marker genes that are characteristic of the Proneural subtype of glioblastoma (see Methods for mathematical details). Red cells have high expression of the Proneural classifier genes and blue cells have low expression of the classifier genes. (C) Same as (B) but using the classifier marker genes that are characteristic of the Classical subtype of glioblastoma. (D) Same as (B) but using classifier marker genes that are characteristic of the Mesenchymal subtype of glioblastoma.
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f4: Phenotypic heterogeneity in glioma neurospheres reflects glioma patient subtypes.(A) t-SNE clustering analysis of >2,200 individual TS543 glioma neurosphere cell expression profiles showing two distinct subpopulations of cells. Assignment of individual cells to the red or blue clusters was accomplished by nearest-neighbor density analysis (see Methods). (B) Same t-SNE clustering analysis shown in (A) but colorized according expression of classifier marker genes that are characteristic of the Proneural subtype of glioblastoma (see Methods for mathematical details). Red cells have high expression of the Proneural classifier genes and blue cells have low expression of the classifier genes. (C) Same as (B) but using the classifier marker genes that are characteristic of the Classical subtype of glioblastoma. (D) Same as (B) but using classifier marker genes that are characteristic of the Mesenchymal subtype of glioblastoma.
Mentions: Glioma neurospheres represent an important model system for brain tumors because, in many cases, they more effectively preserve the phenotypic and genotypic features of tumors than conventional monolayer cultures23. They have been widely used to study drug response, glioma stem cells, and tumor progression as xenograft models232425. However, to our knowledge, glioma neurospheres have not been analyzed comprehensively by single cell RNA-Seq to determine the extent of phenotypic heterogeneity and co-occurrence of cellular subpopulations within a single culture. Expression profiling of surgical specimens from glioma patients by The Cancer Genome Atlas has established classifier gene sets that stratify tumors into distinct subtypes26. Recent studies employing bulk expression analysis of regional heterogeneity27 and single cell RNA-Seq28 have shown that gene signatures corresponding to different patient subtypes co-occur within individual gliomas. We analyzed single cell expression profiles obtained from TS543 cells, a glioma neurosphere line that most closely resembles the Proneural glioma subtype and harbors amplification of PDGFRA, a genetic alteration associated with Proneural gliomas29. We used unsupervised dimensionality reduction and density-based cluster assignment that was uninformed of the identities of the glioma classifier genes (taken from Table S3 of Verhaak et al.26) to show that individual TS543 cells are comprised of at least two clear phenotypic subpopulations (Fig. 4A). For simplicity, we refer to these subpopulations as the red cluster and blue cluster. The median number of molecules detected per cell in the red and blue clusters was 11,382 and 9,771, respectively, suggesting that coverage is not a major driver of the separation between these two subpopulations. As expected, we found that Proneural genes are more commonly expressed in the majority of TS543 cells than genes from either the Classical or Mesenchymal subtypes. However, when we project expression of subtype-specific genes onto our clustering analysis, we find considerable expression heterogeneity among the classifier genes. For example, above-median expression of the Proneural classifier genes (Fig. 4B) is significantly enriched in the blue cluster (p < 10−6, hypergeometric test) whereas above-median expression of both Classical (Fig. 4C) and Mesenchymal (Fig. 4D) genes is significantly enriched in the red cluster (p < 10−6 for both gene sets). This phenomenon is reminiscent of the “hybrid cellular states” observed in by Patel et al. among individual cells in human glioblastoma tissue specimens28. Hence, our results suggest that glioma neurosphere cultures can recapitulate the subtype-specific expression heterogeneity found in human glioma tissue.

View Article: PubMed Central - PubMed

ABSTRACT

Recent developments have enabled rapid, inexpensive RNA sequencing of thousands of individual cells from a single specimen, raising the possibility of unbiased and comprehensive expression profiling from complex tissues. Microwell arrays are a particularly attractive microfluidic platform for single cell analysis due to their scalability, cell capture efficiency, and compatibility with imaging. We report an automated microwell array platform for single cell RNA-Seq with significantly improved performance over previous implementations. We demonstrate cell capture efficiencies of &gt;50%, compatibility with commercially available barcoded mRNA capture beads, and parallel expression profiling from thousands of individual cells. We evaluate the level of cross-contamination in our platform by both tracking fluorescent cell lysate in sealed microwells and with a human-mouse mixed species RNA-Seq experiment. Finally, we apply our system to comprehensively assess heterogeneity in gene expression of patient-derived glioma neurospheres and uncover subpopulations similar to those observed in human glioma tissue.

No MeSH data available.


Related in: MedlinePlus