FLASH-seq protocol (V1) v1

2021 ◽  
Author(s):  
Simone Picelli ◽  
Vincent Hahaut
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Full Length ◽  
Terminal Portion ◽  
Emulsion Droplets ◽  
Hands On ◽  
Single Cell Rna Sequencing ◽  
Number Of Genes ◽  
Reproducible Analysis ◽  
And Performance

The single-cell RNA-sequencing (scRNA-seq) field has evolved tremendously since the first paper was published back in 2009. While the first methods analysed just a handful of cells, the throughput and performance rapidly increased over a very short timespan. However, it was not until the introduction of emulsion droplets methods, that the robust and reproducible analysis of thousands of cells became feasible. Despite generating data at a speed and a cost per cell that remains unmatched by full-length protocols like Smart-seq, scRNA-seq in droplets still comes with the drawback of addressing only the terminal portion of the transcripts, thus lacking the required sensitivity for comprehensively analyzing the transcriptome of individual cells. Building upon the existing Smart-seq2/3 workflows, we developed FLASH-seq (FS), a new full-length scRNA-seq method capable of detecting a significantly higher number of genes than both previous versions, requiring limited hands-on time and with a great potential for customization.

scholarly journals Lightning Fast and Highly Sensitive Full-Length Single-cell sequencing using FLASH-Seq

2021 ◽  
Author(s):  
Vincent Hahaut ◽  
Dinko Pavlinic ◽  
Cameron Cowan ◽  
Simone Picelli
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Exponential Growth ◽  
Full Length ◽  
Cdna Libraries ◽  
Single Cell Sequencing ◽  
Emulsion Droplets ◽  
Highly Sensitive ◽  
Single Cell Rna Sequencing ◽  
Processing Steps

Abstract In the last 10 years, single-cell RNA-sequencing (scRNA-seq) has undergone exponential growth. Emulsion droplets methods, such as those commercialized by 10x Genomics, have allowed researchers to analyze tens of thousands of cells in parallel in a robust and reproducible way. However, in contrast to SMART-based full-length sequencing protocols, these methods interrogate only the outer portion of the transcripts and still lack the required sensitivity for analyzing comprehensively the transcriptome of individual cells. Building upon the existing SMART-seq forerunners protocols, we developed FLASH-Seq (FS), a new scRNA-seq method which displays greater sensitivity while decreasing incubation times and reducing the number of processing steps compared to its predecessors. The entire FS protocol - from lysed cells to pooled cDNA libraries - can be performed in ~4.5 hours, is automation-friendly and can be easily miniaturized to decrease costs.

scholarly journals Lightning Fast and Highly Sensitive Full-Length Single-cell sequencing using FLASH-Seq

2021 ◽  
Author(s):  
Vincent Hahaut ◽  
Dinko Pavlinic ◽  
Cameron S Cowan ◽  
Simone Picelli
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Exponential Growth ◽  
Full Length ◽  
Cdna Libraries ◽  
Single Cell Sequencing ◽  
Emulsion Droplets ◽  
Highly Sensitive ◽  
Single Cell Rna Sequencing ◽  
Processing Steps

In the last 10 years, single-cell RNA-sequencing (scRNA-seq) has undergone exponential growth. Emulsion droplets methods, such as those commercialized by 10x Genomics, have allowed researchers to analyze tens of thousands of cells in parallel in a robust and reproducible way. However, in contrast to SMART-based full-length sequencing protocols, these methods interrogate only the outer portion of the transcripts and still lack the required sensitivity for analyzing comprehensively the transcriptome of individual cells. Building upon the existing SMART-seq forerunners protocols, we developed FLASH-Seq (FS), a new scRNA-seq method which displays greater sensitivity while decreasing incubation times and reducing the number of processing steps compared to its predecessors. The entire FS protocol - from lysed cells to pooled cDNA libraries - can be performed in ~4.5 hours, is automation-friendly and can be easily miniaturized to decrease costs.

scholarly journals Gene length and detection bias in single cell RNA sequencing protocols

2017 ◽  
Author(s):  
Belinda Phipson ◽  
Luke Zappia ◽  
Alicia Oshlack
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Full Length ◽  
Specific Gene ◽  
Gene Length ◽  
Length Bias ◽  
Single Cell Rna Sequencing ◽  
Full Length Transcript ◽  
Gene Length Bias

AbstractSingle cell RNA sequencing (scRNA-seq) has rapidly gained popularity for profiling transcriptomes of hundreds to thousands of single cells. This technology has led to the discovery of novel cell types and revealed insights into the development of complex tissues. However, many technical challenges need to be overcome during data generation. Due to minute amounts of starting material, samples undergo extensive amplification, increasing technical variability. A solution for mitigating amplification biases is to include Unique Molecular Identifiers (UMIs), which tag individual molecules. Transcript abundances are then estimated from the number of unique UMIs aligning to a specific gene and PCR duplicates resulting in copies of the UMI are not included in expression estimates. Here we investigate the effect of gene length bias in scRNA-Seq across a variety of datasets differing in terms of capture technology, library preparation, cell types and species. We find that scRNA-seq datasets that have been sequenced using a full-length transcript protocol exhibit gene length bias akin to bulk RNA-seq data. Specifically, shorter genes tend to have lower counts and a higher rate of dropout. In contrast, protocols that include UMIs do not exhibit gene length bias, and have a mostly uniform rate of dropout across genes of varying length. Across four different scRNA-Seq datasets profiling mouse embryonic stem cells (mESCs), we found the subset of genes that are only detected in the UMI datasets tended to be shorter, while the subset of genes detected only in the full-length datasets tended to be longer. We briefly discuss the role of these genes in the context of differential expression testing and GO analysis. In addition, despite clear differences between UMI and full-length transcript data, we illustrate that full-length and UMI data can be combined to reveal underlying biology influencing expression of mESCs.

scholarly journals Tissue-specific transcriptional imprinting and heterogeneity in human innate lymphoid cells revealed by full-length single-cell RNA-sequencing

Cell Research ◽  
2021 ◽  
Author(s):  
Luca Mazzurana ◽  
Paulo Czarnewski ◽  
Viktor Jonsson ◽  
Leif Wigge ◽  
Markus Ringnér ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Trajectory Analysis ◽  
Lymphoid Cells ◽  
Full Length ◽  
Tissue Specific ◽  
Single Cell Rna Sequencing ◽  
Gene Modules ◽  
The Impact ◽  
Differentiation Pathways

AbstractThe impact of the microenvironment on innate lymphoid cell (ILC)-mediated immunity in humans remains largely unknown. Here we used full-length Smart-seq2 single-cell RNA-sequencing to unravel tissue-specific transcriptional profiles and heterogeneity of CD127+ ILCs across four human tissues. Correlation analysis identified gene modules characterizing the migratory properties of tonsil and blood ILCs, and signatures of tissue-residency, activation and modified metabolism in colon and lung ILCs. Trajectory analysis revealed potential differentiation pathways from circulating and tissue-resident naïve ILCs to a spectrum of mature ILC subsets. In the lung we identified both CRTH2+ and CRTH2− ILC2 with lung-specific signatures, which could be recapitulated by alarmin-exposure of circulating ILC2. Finally, we describe unique TCR-V(D)J-rearrangement patterns of blood ILC1-like cells, revealing a subset of potentially immature ILCs with TCR-δ rearrangement. Our study provides a useful resource for in-depth understanding of ILC-mediated immunity in humans, with implications for disease.

scholarly journals Gene length and detection bias in single cell RNA sequencing protocols

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 595 ◽  
Author(s):  
Belinda Phipson ◽  
Luke Zappia ◽  
Alicia Oshlack
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Cell Types ◽  
Full Length ◽  
Specific Gene ◽  
Gene Length ◽  
Length Bias ◽  
Single Cell Rna Sequencing ◽  
Full Length Transcript ◽  
Gene Length Bias

Background: Single cell RNA sequencing (scRNA-seq) has rapidly gained popularity for profiling transcriptomes of hundreds to thousands of single cells. This technology has led to the discovery of novel cell types and revealed insights into the development of complex tissues. However, many technical challenges need to be overcome during data generation. Due to minute amounts of starting material, samples undergo extensive amplification, increasing technical variability. A solution for mitigating amplification biases is to include unique molecular identifiers (UMIs), which tag individual molecules. Transcript abundances are then estimated from the number of unique UMIs aligning to a specific gene, with PCR duplicates resulting in copies of the UMI not included in expression estimates. Methods: Here we investigate the effect of gene length bias in scRNA-Seq across a variety of datasets that differ in terms of capture technology, library preparation, cell types and species. Results: We find that scRNA-seq datasets that have been sequenced using a full-length transcript protocol exhibit gene length bias akin to bulk RNA-seq data. Specifically, shorter genes tend to have lower counts and a higher rate of dropout. In contrast, protocols that include UMIs do not exhibit gene length bias, with a mostly uniform rate of dropout across genes of varying length. Across four different scRNA-Seq datasets profiling mouse embryonic stem cells (mESCs), we found the subset of genes that are only detected in the UMI datasets tended to be shorter, while the subset of genes detected only in the full-length datasets tended to be longer. Conclusions: We find that the choice of scRNA-seq protocol influences the detection rate of genes, and that full-length datasets exhibit gene-length bias. In addition, despite clear differences between UMI and full-length transcript data, we illustrate that full-length and UMI data can be combined to reveal the underlying biology influencing expression of mESCs.

scholarly journals scPipe: a flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data

2017 ◽  
Author(s):  
Luyi Tian ◽  
Shian Su ◽  
Xueyi Dong ◽  
Daniela Amann-Zalcenstein ◽  
Christine Biben ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
R Package ◽  
Statistical Testing ◽  
Complex Data ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Reproducible Analysis ◽  
Downstream Analysis ◽  
Effective Visualization

AbstractSingle-cell RNA sequencing (scRNA-seq) technology allows researchers to profile the transcriptomes of thousands of cells simultaneously. Protocols that incorpo-rate both designed and random barcodes have greatly increased the throughput of scRNA-seq, but give rise to a more complex data structure. There is a need for new tools that can handle the various barcoding strategies used by different protocols and exploit this information for quality assessment at the sample-level and provide effective visualization of these results in preparation for higher-level analyses.To this end, we developed scPipe, a R/Bioconductor package that integrates barcode demultiplexing, read alignment, UMI-aware gene-level quantification and quality control of raw sequencing data generated by multiple 3-prime-end sequencing protocols that include CEL-seq, MARS-seq, Chromium 10X and Drop-seq. scPipe produces a count matrix that is essential for downstream analysis along with an HTML report that summarises data quality. These results can be used as input for downstream analyses including normalization, visualization and statistical testing. scPipe performs this processing in a few simple R commands, promoting reproducible analysis of single-cell data that is compatible with the emerging suite of scRNA-seq analysis tools available in R/Bioconductor. The scPipe R package is available for download from https://www.bioconductor.org/packages/scPipe.

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