scholarly journals Massively parallel and time-resolved RNA sequencing in single cells with scNT-Seq

2021 ◽  
Author(s):  
Qi Qiu ◽  
Peng Hu ◽  
Hao Wu

Abstract Single-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal dynamics of RNA biogenesis and decay. Here we present single-cell metabolically labeled new RNA tagging sequencing (scNT-Seq), a method for massively parallel analysis of newly-transcribed and pre-existing RNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking newly-transcribed RNAs with T-to-C substitutions. The steps of the protocol are (1) metabolically labeling of cells with 4sU, (2) co-encapsulating individual cell with a barcoded oligo-dT primer coated bead in a nanoliter-scale droplet, (3) performing one-pot 4sU chemical conversion on pooled barcoded beads, and (4) reverse transcription, cDNA amplification, tagmentation, indexing PCR, and sequencing. scNT-Seq provides a broadly applicable strategy to investigate dynamic biological systems at single-cell resolution.

Author(s):  
Qi Qiu ◽  
Peng Hu ◽  
Kiya W. Govek ◽  
Pablo G. Camara ◽  
Hao Wu

ABSTRACTSingle-cell RNA sequencing offers snapshots of whole transcriptomes but obscures the temporal dynamics of RNA biogenesis and decay. Here we present single-cell new transcript tagging sequencing (scNT-Seq), a method for massively parallel analysis of newly-transcribed and pre-existing RNAs from the same cell. This droplet microfluidics-based method enables high-throughput chemical conversion on barcoded beads, efficiently marking metabolically labeled newly-transcribed RNAs with T-to-C substitutions. By simultaneously measuring new and old transcriptomes, scNT-Seq reveals neuronal subtype-specific gene regulatory networks and time-resolved RNA trajectories in response to brief (minutes) versus sustained (hours) neuronal activation. Integrating scNT-Seq with genetic perturbation reveals that DNA methylcytosine dioxygenases may inhibit stepwise transition from pluripotent embryonic stem cell state to intermediate and totipotent two-cell-embryo-like (2C-like) states by promoting global RNA biogenesis. Furthermore, pulse-chase scNT-Seq enables transcriptome-wide measurements of RNA stability in rare 2C-like cells. Time-resolved single-cell transcriptomic analysis thus opens new lines of inquiry regarding cell-type-specific RNA regulatory mechanisms.


2020 ◽  
Vol 17 (10) ◽  
pp. 991-1001 ◽  
Author(s):  
Qi Qiu ◽  
Peng Hu ◽  
Xiaojie Qiu ◽  
Kiya W. Govek ◽  
Pablo G. Cámara ◽  
...  

2017 ◽  
Author(s):  
Peng Hu ◽  
Emily Fabyanic ◽  
Zhaolan Zhou ◽  
Hao Wu

Massively parallel single-cell RNA sequencing can precisely resolve cellular diversity in a high-throughput manner at low cost, but unbiased isolation of intact single cells from complex tissues, such as adult mammalian brains, is challenging. Here, we integrate sucrose-gradient assisted nuclear purification with droplet microfluidics to develop a highly scalable single-nucleus RNA-Seq approach (sNucDrop-Seq), which is free of enzymatic dissociation and nucleus sorting. By profiling ~11,000 nuclei isolated from adult mouse cerebral cortex, we demonstrate that sNucDrop-Seq not only accurately reveals neuronal and non-neuronal subtype composition with high sensitivity, but also enables analysis of long non-coding RNAs and transient states such as neuronal activity-dependent transcription at single-cell resolution in vivo.


2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sunny Z. Wu ◽  
Daniel L. Roden ◽  
Ghamdan Al-Eryani ◽  
Nenad Bartonicek ◽  
Kate Harvey ◽  
...  

Abstract Background High throughput single-cell RNA sequencing (scRNA-Seq) has emerged as a powerful tool for exploring cellular heterogeneity among complex human cancers. scRNA-Seq studies using fresh human surgical tissue are logistically difficult, preclude histopathological triage of samples, and limit the ability to perform batch processing. This hindrance can often introduce technical biases when integrating patient datasets and increase experimental costs. Although tissue preservation methods have been previously explored to address such issues, it is yet to be examined on complex human tissues, such as solid cancers and on high throughput scRNA-Seq platforms. Methods Using the Chromium 10X platform, we sequenced a total of ~ 120,000 cells from fresh and cryopreserved replicates across three primary breast cancers, two primary prostate cancers and a cutaneous melanoma. We performed detailed analyses between cells from each condition to assess the effects of cryopreservation on cellular heterogeneity, cell quality, clustering and the identification of gene ontologies. In addition, we performed single-cell immunophenotyping using CITE-Seq on a single breast cancer sample cryopreserved as solid tissue fragments. Results Tumour heterogeneity identified from fresh tissues was largely conserved in cryopreserved replicates. We show that sequencing of single cells prepared from cryopreserved tissue fragments or from cryopreserved cell suspensions is comparable to sequenced cells prepared from fresh tissue, with cryopreserved cell suspensions displaying higher correlations with fresh tissue in gene expression. We showed that cryopreservation had minimal impacts on the results of downstream analyses such as biological pathway enrichment. For some tumours, cryopreservation modestly increased cell stress signatures compared to freshly analysed tissue. Further, we demonstrate the advantage of cryopreserving whole-cells for detecting cell-surface proteins using CITE-Seq, which is impossible using other preservation methods such as single nuclei-sequencing. Conclusions We show that the viable cryopreservation of human cancers provides high-quality single-cells for multi-omics analysis. Our study guides new experimental designs for tissue biobanking for future clinical single-cell RNA sequencing studies.


2019 ◽  
Author(s):  
Imad Abugessaisa ◽  
Shuhei Noguchi ◽  
Melissa Cardon ◽  
Akira Hasegawa ◽  
Kazuhide Watanabe ◽  
...  

AbstractAnalysis and interpretation of single-cell RNA-sequencing (scRNA-seq) experiments are compromised by the presence of poor quality cells. For meaningful analyses, such poor quality cells should be excluded to avoid biases and large variation. However, no clear guidelines exist. We introduce SkewC, a novel quality-assessment method to identify poor quality single-cells in scRNA-seq experiments. The method is based on the assessment of gene coverage for each single cell and its skewness as a quality measure. To validate the method, we investigated the impact of poor quality cells on downstream analyses and compared biological differences between typical and poor quality cells. Moreover, we measured the ratio of intergenic expression, suggesting genomic contamination, and foreign organism contamination of single-cell samples. SkewC is tested in 37,993 single-cells generated by 15 scRNA-seq protocols. We envision SkewC as an indispensable QC method to be incorporated into scRNA-seq experiment to preclude the possibility of scRNA-seq data misinterpretation.


2016 ◽  
Author(s):  
Hannah R. Dueck ◽  
Rizi Ai ◽  
Adrian Camarena ◽  
Bo Ding ◽  
Reymundo Dominguez ◽  
...  

AbstractRecently, measurement of RNA at single cell resolution has yielded surprising insights. Methods for single-cell RNA sequencing (scRNA-seq) have received considerable attention, but the broad reliability of single cell methods and the factors governing their performance are still poorly known. Here, we conducted a large-scale control experiment to assess the transfer function of three scRNA-seq methods and factors modulating the function. All three methods detected greater than 70% of the expected number of genes and had a 50% probability of detecting genes with abundance greater than 2 to 4 molecules. Despite the small number of molecules, sequencing depth significantly affected gene detection. While biases in detection and quantification were qualitatively similar across methods, the degree of bias differed, consistent with differences in molecular protocol. Measurement reliability increased with expression level for all methods and we conservatively estimate the measurement transfer functions to be linear above ~5-10 molecules. Based on these extensive control studies, we propose that RNA-seq of single cells has come of age, yielding quantitative biological information.


2021 ◽  
Author(s):  
Nicole C. Rondeau ◽  
JJ L. Miranda

We detected precise coordination of RNA levels between two latent genes of the Kaposi sarcoma-associated herpesvirus (KSHV) using single-cell RNA sequencing. LANA and vIL6 are expressed during latency by different promoters on remote regions of the episome.…


Lab on a Chip ◽  
2018 ◽  
Vol 18 (5) ◽  
pp. 775-784 ◽  
Author(s):  
Hui-Sung Moon ◽  
Kwanghwi Je ◽  
Jae-Woong Min ◽  
Donghyun Park ◽  
Kyung-Yeon Han ◽  
...  

We developed a modified high-throughput droplet barcoding technique for single-cell Drop-Seq via introduction of hydrodynamic ordering in a spiral microchannel.


Genes ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 240 ◽  
Author(s):  
Prashant N. M. ◽  
Hongyu Liu ◽  
Pavlos Bousounis ◽  
Liam Spurr ◽  
Nawaf Alomran ◽  
...  

With the recent advances in single-cell RNA-sequencing (scRNA-seq) technologies, the estimation of allele expression from single cells is becoming increasingly reliable. Allele expression is both quantitative and dynamic and is an essential component of the genomic interactome. Here, we systematically estimate the allele expression from heterozygous single nucleotide variant (SNV) loci using scRNA-seq data generated on the 10×Genomics Chromium platform. We analyzed 26,640 human adipose-derived mesenchymal stem cells (from three healthy donors), sequenced to an average of 150K sequencing reads per cell (more than 4 billion scRNA-seq reads in total). High-quality SNV calls assessed in our study contained approximately 15% exonic and >50% intronic loci. To analyze the allele expression, we estimated the expressed variant allele fraction (VAFRNA) from SNV-aware alignments and analyzed its variance and distribution (mono- and bi-allelic) at different minimum sequencing read thresholds. Our analysis shows that when assessing positions covered by a minimum of three unique sequencing reads, over 50% of the heterozygous SNVs show bi-allelic expression, while at a threshold of 10 reads, nearly 90% of the SNVs are bi-allelic. In addition, our analysis demonstrates the feasibility of scVAFRNA estimation from current scRNA-seq datasets and shows that the 3′-based library generation protocol of 10×Genomics scRNA-seq data can be informative in SNV-based studies, including analyses of transcriptional kinetics.


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