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Scalable microfluidics for single-cell RNA printing and sequencing.

Bose S, Wan Z, Carr A, Rizvi AH, Vieira G, Pe'er D, Sims PA - Genome Biol. (2015)

Bottom Line: We then develop a scalable technology for genome-wide, single-cell RNA-Seq.Our device generates pooled libraries from hundreds of individual cells with consumable costs of $0.10-$0.20 per cell and includes five lanes for simultaneous experiments.We anticipate that this system will serve as a general platform for single-cell imaging and sequencing.

View Article: PubMed Central - PubMed

Affiliation: Department of Systems Biology, Columbia University Medical Center, New York, NY, 10032, USA. sb3438@columbia.edu.

ABSTRACT
Many important biological questions demand single-cell transcriptomics on a large scale. Hence, new tools are urgently needed for efficient, inexpensive manipulation of RNA from individual cells. We report a simple platform for trapping single-cell lysates in sealed, picoliter microwells capable of printing RNA on glass or capturing RNA on beads. We then develop a scalable technology for genome-wide, single-cell RNA-Seq. Our device generates pooled libraries from hundreds of individual cells with consumable costs of $0.10-$0.20 per cell and includes five lanes for simultaneous experiments. We anticipate that this system will serve as a general platform for single-cell imaging and sequencing.

No MeSH data available.


Related in: MedlinePlus

Analysis of single-cell RNA-Seq data. a Gene body distribution for uniquely mapped reads showing that we are primarily sequencing the 3′-end of transcripts, as expected. b Histogram of the number of genes detected per cell for the 396 single-cell profiles used in all subsequent analysis of Experiment 1 and 247 single-cell profiles used in all subsequent analysis of Experiment 2
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Fig5: Analysis of single-cell RNA-Seq data. a Gene body distribution for uniquely mapped reads showing that we are primarily sequencing the 3′-end of transcripts, as expected. b Histogram of the number of genes detected per cell for the 396 single-cell profiles used in all subsequent analysis of Experiment 1 and 247 single-cell profiles used in all subsequent analysis of Experiment 2

Mentions: To demonstrate that our device is actually producing useful single-cell RNA-Seq profiles, we examined several key metrics. Our library preparation protocol is based on CEL-Seq [9] where, rather than sequencing the full gene body and normalizing by transcript length, the 3′-end of transcripts are sequenced and counted. Figure 5a shows the expected distribution of mapping positions for 3′-end sequencing, with most reads mapping to the 3′-UTRs or coding sequences. Subsequent analysis to demonstrate cell type separation using our dataset will rely on the 396 single-cell profiles that we obtained with the highest coverage. Although we detect only 635 genes on average across all cells profiled in Experiment 1, we detect an average of 876 genes from the top 396 cells (Fig. 5b). Hence, the 204 cells that we discard from subsequent analysis have an average of approximately 170 genes detected per cell. Similarly, for Experiment 2, we detect an average of 1,030 genes from the top 247 single-cell profiles (Fig. 5b), but only approximately 530 genes on average across all cells. Despite these shortcomings, recent studies have demonstrated cell or phenotype separation from low coverage single-cell RNA-Seq data [12, 15]. Indeed, our detection efficiency is at least comparable if not better than previously reported methods for large-scale single-cell RNA-Seq using pooled barcode library preparation, where detection of hundreds of molecules per cell was reported [12] (whereas we are reporting detection of hundreds of genes).Fig. 5


Scalable microfluidics for single-cell RNA printing and sequencing.

Bose S, Wan Z, Carr A, Rizvi AH, Vieira G, Pe'er D, Sims PA - Genome Biol. (2015)

Analysis of single-cell RNA-Seq data. a Gene body distribution for uniquely mapped reads showing that we are primarily sequencing the 3′-end of transcripts, as expected. b Histogram of the number of genes detected per cell for the 396 single-cell profiles used in all subsequent analysis of Experiment 1 and 247 single-cell profiles used in all subsequent analysis of Experiment 2
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4487847&req=5

Fig5: Analysis of single-cell RNA-Seq data. a Gene body distribution for uniquely mapped reads showing that we are primarily sequencing the 3′-end of transcripts, as expected. b Histogram of the number of genes detected per cell for the 396 single-cell profiles used in all subsequent analysis of Experiment 1 and 247 single-cell profiles used in all subsequent analysis of Experiment 2
Mentions: To demonstrate that our device is actually producing useful single-cell RNA-Seq profiles, we examined several key metrics. Our library preparation protocol is based on CEL-Seq [9] where, rather than sequencing the full gene body and normalizing by transcript length, the 3′-end of transcripts are sequenced and counted. Figure 5a shows the expected distribution of mapping positions for 3′-end sequencing, with most reads mapping to the 3′-UTRs or coding sequences. Subsequent analysis to demonstrate cell type separation using our dataset will rely on the 396 single-cell profiles that we obtained with the highest coverage. Although we detect only 635 genes on average across all cells profiled in Experiment 1, we detect an average of 876 genes from the top 396 cells (Fig. 5b). Hence, the 204 cells that we discard from subsequent analysis have an average of approximately 170 genes detected per cell. Similarly, for Experiment 2, we detect an average of 1,030 genes from the top 247 single-cell profiles (Fig. 5b), but only approximately 530 genes on average across all cells. Despite these shortcomings, recent studies have demonstrated cell or phenotype separation from low coverage single-cell RNA-Seq data [12, 15]. Indeed, our detection efficiency is at least comparable if not better than previously reported methods for large-scale single-cell RNA-Seq using pooled barcode library preparation, where detection of hundreds of molecules per cell was reported [12] (whereas we are reporting detection of hundreds of genes).Fig. 5

Bottom Line: We then develop a scalable technology for genome-wide, single-cell RNA-Seq.Our device generates pooled libraries from hundreds of individual cells with consumable costs of $0.10-$0.20 per cell and includes five lanes for simultaneous experiments.We anticipate that this system will serve as a general platform for single-cell imaging and sequencing.

View Article: PubMed Central - PubMed

Affiliation: Department of Systems Biology, Columbia University Medical Center, New York, NY, 10032, USA. sb3438@columbia.edu.

ABSTRACT
Many important biological questions demand single-cell transcriptomics on a large scale. Hence, new tools are urgently needed for efficient, inexpensive manipulation of RNA from individual cells. We report a simple platform for trapping single-cell lysates in sealed, picoliter microwells capable of printing RNA on glass or capturing RNA on beads. We then develop a scalable technology for genome-wide, single-cell RNA-Seq. Our device generates pooled libraries from hundreds of individual cells with consumable costs of $0.10-$0.20 per cell and includes five lanes for simultaneous experiments. We anticipate that this system will serve as a general platform for single-cell imaging and sequencing.

No MeSH data available.


Related in: MedlinePlus