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 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.

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.

Multi tissue processing for single cell sequencing of human immune cells v1

2021 ◽  
Author(s):  
Daniel Rainbow ◽  
Sarah Howlett ◽  
Lorna Jarvis ◽  
Joanne Jones
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Immune Cells ◽  
Human Tissues ◽  
Single Cell Sequencing ◽  
Tissue Processing ◽  
Simultaneous Processing ◽  
Single Cell Rna Sequencing ◽  
Human Immune

This protocol has been developed for the simultaneous processing of multiple human tissues to extract immune cells for single cell RNA sequencing using the 10X platform, and ideal for atlasing projects. Included in this protocol are the steps needed to go from tissue to loading the 10X Chromium for single cell RNA sequencing and includes the hashtag and CiteSeq labelling of cells as well as the details needed to stimulate cells with PMA+I.

scholarly journals Demuxalot: scaled up genetic demultiplexing for single-cell sequencing

2021 ◽  
Author(s):  
Alex Rogozhnikov ◽  
Pavan Ramkumar ◽  
Saul Kato ◽  
Sean Escola
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Single Nucleotide Variants ◽  
Single Nucleotide ◽  
Single Cell Sequencing ◽  
Additional Information ◽  
Genotype Information ◽  
Single Cell Rna Sequencing ◽  
Genotype Inference

Demultiplexing methods have facilitated the widespread use of single-cell RNA sequencing (scRNAseq) experiments by lowering costs and reducing technical variations. Here, we present demuxalot: a method for probabilistic genotype inference from aligned reads, with no assumptions about allele ratios and efficient incorporation of prior genotype information from historical experiments in a multi-batch setting. Our method efficiently incorporates additional information across reads originating from the same transcript, enabling up to 3x more calls per read relative to naive approaches. We also propose a novel and highly performant tradeoff between methods that rely on reference genotypes and methods that learn variants from the data, by selecting a small number of highly informative variants that maximize the marginal information with respect to reference single nucleotide variants (SNVs). Our resulting improved SNV-based demultiplex method is up to 3x faster, 3x more data efficient, and achieves significantly more accurate doublet discrimination than previously published methods. This approach renders scRNAseq feasible for the kind of large multi-batch, multi-donor studies that are required to prosecute diseases with heterogeneous genetic backgrounds.

scholarly journals PRIME: a probabilistic imputation method to reduce dropout effects in single-cell RNA sequencing

2020 ◽  
Vol 36 (13) ◽  
pp. 4021-4029
Author(s):  
Hyundoo Jeong ◽  
Zhandong Liu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Rna Sequencing ◽  
Expression Profiles ◽  
Expression Patterns ◽  
Imputation Method ◽  
Supplementary Information ◽  
Single Cell Sequencing ◽  
Depth Analysis ◽  
Single Cell Rna Sequencing

Abstract Summary Single-cell RNA sequencing technology provides a novel means to analyze the transcriptomic profiles of individual cells. The technique is vulnerable, however, to a type of noise called dropout effects, which lead to zero-inflated distributions in the transcriptome profile and reduce the reliability of the results. Single-cell RNA sequencing data, therefore, need to be carefully processed before in-depth analysis. Here, we describe a novel imputation method that reduces dropout effects in single-cell sequencing. We construct a cell correspondence network and adjust gene expression estimates based on transcriptome profiles for the local subnetwork of cells of the same type. We comprehensively evaluated this method, called PRIME (PRobabilistic IMputation to reduce dropout effects in Expression profiles of single-cell sequencing), on synthetic and eight real single-cell sequencing datasets and verified that it improves the quality of visualization and accuracy of clustering analysis and can discover gene expression patterns hidden by noise. Availability and implementation The source code for the proposed method is freely available at https://github.com/hyundoo/PRIME. Supplementary information Supplementary data are available at Bioinformatics online.

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.

A Highly Sensitive, Accurate, and Automated Single-Cell RNA Sequencing Platform with Digital Microfluidics

2020 ◽  
Vol 92 (12) ◽  
pp. 8599-8606
Author(s):  
Xing Xu ◽  
Qianqian Zhang ◽  
Jia Song ◽  
Qingyu Ruan ◽  
Weidong Ruan ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Digital Microfluidics ◽  
Sequencing Platform ◽  
Highly Sensitive ◽  
Single Cell Rna Sequencing

#71: Single-cell RNA Sequencing Analysis of Zika Virus Infection in Human Stem Cell-Derived Neuroprogenitor Cells and Cerebral Organoids

2021 ◽  
Vol 10 (Supplement_1) ◽  
pp. S14-S14
Author(s):  
K E Ocwieja ◽  
T K Hughes ◽  
J M Antonucci ◽  
A L Richards ◽  
A C Stanton ◽  
...  
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Rna Sequencing ◽  
Zika Virus ◽  
Molecular Mechanisms ◽  
Sequencing Analysis ◽  
Human Stem Cell ◽  
Zikv Infection ◽  
Single Cell Sequencing ◽  
Single Cell Rna Sequencing

Abstract Background The molecular mechanisms underpinning the neurologic and congenital pathologies caused by Zika virus (ZIKV) infection remain poorly understood. It is also unclear why congenital ZIKV disease was not observed prior to the recent epidemics in French Polynesia and the Americas, despite evidence that the Zika virus has actively circulated in parts of Africa and Asia since 1947 and 1966, respectively. Methods Due to advances in stem cell-based technologies, we can now model ZIKV infections of the central nervous system in human stem cell-derived neuroprogenitor cells and cerebral organoids, which recapitulate complex three-dimensional neural architecture. We apply Seq-Well—a simple, portable platform for massively parallel single-cell RNA sequencing—to characterize these neural models infected with ZIKV. We detect and quantify host mRNA transcripts and viral RNA with single-cell resolution, thereby defining transcriptional features of both uninfected and infected cells. Results In neuroprogenitor cells, single-cell sequencing reveals that while uninfected bystander cells strongly upregulate interferon pathway genes, these are largely suppressed in cells infected with ZIKV within the same culture dish. In our organoid model, single-cell sequencing allows us to identify multiple cellular populations, including neuroprogenitor cells, intermediate progenitor cells, and terminally differentiated neurons. In this model of the developing brain, we identify preferred tropisms of ZIKV infection. Our data additionally reveal differences in cell-type frequencies and gene expression within organoids infected by historic and contemporary ZIKV strains from a variety of geographic locations. Conclusions These findings may help explain phenotypic differences attributed to the viruses, including variable propensities to cause microcephaly. Overall, our work provides insight into normal and diseased human brain development and suggests that both virus replication and host response mechanisms underlie the neuropathology of ZIKV infection.

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.

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