Adapting the Smart-seq2 Protocol for Robust Single Worm RNA-seq

Serra, Lorrayne; Chang, Dennis; Macchietto, Marissa; Williams, Katherine; Murad, Rabi; Lu, Dihong; Dillman, Adler R.; Mortazavi, A

doi:10.21769/bioprotoc.2729

Cited by 35 publications

(40 citation statements)

References 12 publications

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“…Though we anticipate that alignment and mapping methodology may also influence tagged-end single-cell RNA-seq protocols, we exclude such samples from our analyses here since the processing methodologies for such protocols are considerably different from those used in bulk RNA-seq quantification (and are typically performed at the gene level). However, we do include full-length singlecell datasets in our analysis because the alignment and mapping methods we compare here are frequently used in conjunction with existing transcript-level quantification tools to process full-length single-cell data, as suggested by several existing studies [29,30]. Before further processing, we applied adapter and (light) quality trimming using Trim-Galore [31,32] 3 .…”

Section: Randomly Sampled Experiments From Ncbi Databasementioning

confidence: 99%

Alignment and mapping methodology influence transcript abundance estimation

et al. 2020

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Background The accuracy of transcript quantification using RNA-seq data depends on many factors, such as the choice of alignment or mapping method and the quantification model being adopted. While the choice of quantification model has been shown to be important, considerably less attention has been given to comparing the effect of various read alignment approaches on quantification accuracy. Results We investigate the influence of mapping and alignment on the accuracy of transcript quantification in both simulated and experimental data, as well as the effect on subsequent differential expression analysis. We observe that, even when the quantification model itself is held fixed, the effect of choosing a different alignment methodology, or aligning reads using different parameters, on quantification estimates can sometimes be large and can affect downstream differential expression analyses as well. These effects can go unnoticed when assessment is focused too heavily on simulated data, where the alignment task is often simpler than in experimentally acquired samples. We also introduce a new alignment methodology, called selective alignment, to overcome the shortcomings of lightweight approaches without incurring the computational cost of traditional alignment. Conclusion We observe that, on experimental datasets, the performance of lightweight mapping and alignment-based approaches varies significantly, and highlight some of the underlying factors. We show this variation both in terms of quantification and downstream differential expression analysis. In all comparisons, we also show the improved performance of our proposed selective alignment method and suggest best practices for performing RNA-seq quantification.

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Section: Randomly Sampled Experiments From Ncbi Databasementioning

confidence: 99%

Alignment and mapping methodology influence transcript abundance estimation

et al. 2020

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“…Previous scRNA-seq techniques include Smart-seq [60,148], designed primer-based sequencing (DP-seq) [61], and Quartz-seq [62], and each of them exhibits prominent advantages and disadvantages. Smart-seq is a method based on a full-length cDNA amplification strategy ( Fig.…”

Section: Rna-seqmentioning

confidence: 99%

Principles and innovative technologies for decrypting noncoding RNAs: from discovery and functional prediction to clinical application

Sun

Chen

2020

J Hematol Oncol

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Noncoding RNAs (ncRNAs) are a large segment of the transcriptome that do not have apparent protein-coding roles, but they have been verified to play important roles in diverse biological processes, including disease pathogenesis. With the development of innovative technologies, an increasing number of novel ncRNAs have been uncovered; information about their prominent tissue-specific expression patterns, various interaction networks, and subcellular locations will undoubtedly enhance our understanding of their potential functions. Here, we summarized the principles and innovative methods for identifications of novel ncRNAs that have potential functional roles in cancer biology. Moreover, this review also provides alternative ncRNA databases based on highthroughput sequencing or experimental validation, and it briefly describes the current strategy for the clinical translation of cancer-associated ncRNAs to be used in diagnosis.

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“…To prepare single-cell cDNA, all the samples were amplified using the Smart-Seq2 method (23), and a cDNA product of 1–2 kb in length was obtained. Subsequently, single-cell cDNA was purified with the Ampure XP kit (Beckman Coulter, Inc., Brea, CA, USA).…”

Section: Methodsmentioning

confidence: 99%

Differential expression profiles of long non‑coding RNAs during the mouse pronuclear stage under normal gravity and simulated microgravity

et al. 2018

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Pronuclear migration, which is the initial stage of embryonic development and the marker of zygote formation, is a crucial process during mammalian preimplantation embryonic development. Recent studies have revealed that long non-coding RNAs (lncRNAs) serve an important role in early embryonic development. However, the functional regulation of lncRNAs in this process has yet to be elucidated, largely due to the difficulty of assessing gene expression alterations during the very short time in which pronuclear migration occurs. It has previously been reported that migration of the pronucleus of a zygote can be obstructed by simulated microgravity. To investigate pronuclear migration in mice, a rotary cell culture system was employed, which generates simulated microgravity, in order to interfere with murine pronuclear migration. Subsequently, lncRNA sequencing was performed to investigate the mechanism underlying this process. In the present study, a comprehensive analysis of lncRNA profile during the mouse pronuclear stage was conducted, in which 3,307 lncRNAs were identified based on single-cell RNA sequencing data. Furthermore, 52 lncRNAs were identified that were significantly differentially expressed. Subsequently, 10 lncRNAs were selected for validation by reverse transcription-quantitative polymerase chain reaction, in which the same relative expression pattern was observed. The results revealed that 12 lncRNAs (lnc006745, lnc007956, lnc013100, lnc013782, lnc017097, lnc019869, lnc025838, lnc027046, lnc005454, lnc007956, lnc019410 and lnc019607), with tubulin β 4B class IVb or actinin α 4 as target genes, may be associated with the expression of microtubule and microfilament proteins. Binding association was confirmed using a dual-luciferase reporter assay. Finally, Gene Ontology analysis revealed that the target genes of the differentially expressed lncRNAs participated in cellular processes associated with protein transport, binding, catalytic activity, membrane-bounded organelle, protein complex and the cortical cytoskeleton. These findings suggested that these lncRNAs may be associated with migration of the mouse pronucleus.

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Adapting the Smart-seq2 Protocol for Robust Single Worm RNA-seq

Cited by 35 publications

References 12 publications

Alignment and mapping methodology influence transcript abundance estimation

Alignment and mapping methodology influence transcript abundance estimation

Principles and innovative technologies for decrypting noncoding RNAs: from discovery and functional prediction to clinical application

Differential expression profiles of long non‑coding RNAs during the mouse pronuclear stage under normal gravity and simulated microgravity

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