Non-biased and efficient global amplification of a single-cell cDNA library

Huang, Huan; Goto, Mari; Tsunoda, Hiroyuki; Sun, Lizhou; Taniguchi, Kiyomi; Matsunaga, Hiroko; Kambara, Hideki

doi:10.1093/nar/gkt965

Cited by 30 publications

(26 citation statements)

References 19 publications

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“…Single-cell transcriptomics started with the development of single-cell qPCR (Bengtsson et al, 2008;Eberwine et al, 1992;Huang et al, 2014;Taniguchi et al, 2009;Warren et al, 2006), which, alongside single-molecule FISH, is still the method of choice for targeted analysis of transcripts (Femino et al, 1998;Raj et al, 2006Raj et al, , 2008Tyagi and Kramer, 1996). Based on advances made in the single-cell qPCR field, whole-transcriptome analysis was performed, using microarrays (Kamme et al, 2003;Kurimoto et al, 2006Kurimoto et al, , 2007Subkhankulova et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

The Technology and Biology of Single-Cell RNA Sequencing

et al. 2015

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The differences between individual cells can have profound functional consequences, in both unicellular and multicellular organisms. Recently developed single-cell mRNA-sequencing methods enable unbiased, high-throughput, and high-resolution transcriptomic analysis of individual cells. This provides an additional dimension to transcriptomic information relative to traditional methods that profile bulk populations of cells. Already, single-cell RNA-sequencing methods have revealed new biology in terms of the composition of tissues, the dynamics of transcription, and the regulatory relationships between genes. Rapid technological developments at the level of cell capture, phenotyping, molecular biology, and bioinformatics promise an exciting future with numerous biological and medical applications.

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Section: Introductionmentioning

confidence: 99%

The Technology and Biology of Single-Cell RNA Sequencing

et al. 2015

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“…Third, an optimal workflow should be highly sensitive allowing detection of genes expressed at low levels. Various single-cell whole transcriptome amplification (WTA) methods have been developed (4, 5) permitting different types of downstream analyses utilizing qPCR (6–8), microarrays (9–11) or next generation sequencing (NGS) (12–15) as read-outs. Each of the available WTA technologies displays unique strengths and weaknesses reflected by differences in detection sensitivity (13, 14).…”

Section: Introductionmentioning

confidence: 99%

Targeted transcript quantification in single disseminated cancer cells after whole transcriptome amplification

Durst

Grujovic

Ganser

et al. 2019

Preprint

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2Gene expression analysis of rare or heterogeneous cell populations such as disseminated cancer 3 cells (DCCs) requires a sensitive method allowing reliable analysis of single cells. Therefore, we 4 developed and explored the feasibility of a quantitative PCR (qPCR) assay to analyze single-cell 5 cDNA pre-amplified using a previously established whole transcriptome amplification (WTA) 6 protocol. We carefully selected and optimized multiple steps of the protocol, e.g. re-7 amplification of WTA products, quantification of amplified cDNA yields and final qPCR 8 quantification, to identify the most reliable and accurate workflow for quantitation of gene 9 expression of the ERBB2 gene in DCCs. We found that absolute quantification outperforms 10 relative quantification. We then validated the performance of our method on single cells of 11 established breast cancer cell lines displaying distinct levels of HER2 protein. The different 12 protein levels were faithfully reflected by transcript expression across the tested cell lines 13 thereby proving the accuracy of our approach. Finally, we applied our method on patient-derived 14 breast cancer DCCs. Here, we were able to measure ERBB2 expression levels in all HER2-15 positive DCCs. In addition, we could detect ERBB2 transcript expression even in HER2-negative 16 DCCs, suggesting post-transcriptional mechanisms of HER2 loss in anti-HER2-treated DCCs. In 17 summary, we developed a reliable single-cell qPCR assay applicable to measure distinct levels of 18 ERBB2 in DCCs. 19 66(27). Moreover, protocols for preparing qPCR samples are simpler and result in higher 67 sensitivity and reproducibility as compared to NGS-based approaches (7, 27). Importantly, 68 single-cell qPCR workflows exhibit high levels of reliability and wide and dynamic detection 69 ranges, making them exceptionally well-suited for targeted gene expression analyses in singles 70 cells, where sensitivity is essential and the amount of target genes is low (7, 27). Therefore, the 71 present study aimed to develop a single-cell qPCR assay to quantify gene expression changes in 72 single cells, specifically in patient-derived DCCs. We established a workflow comprised of 73 single-cell WTA, re-amplification of single-cell cDNA, post-WTA normalization of cDNA 74 quantities and qPCR-based data analysis. The new assay provides means for measuring 75 expression levels of individual pre-selected genes in WTA products generated from single cells 76 in an accurate and reliable fashion.77 6 78 Materials and methods 79 Cell lines 80 BT-474 (ACC 64) and MCF-7 (ACC 115) breast cancer cell lines were obtained from German 81 Collection of Microorganisms and Cell Cultures (DSMZ). MCF-10A (CRL-10317), a non-82 tumorigenic mammary epithelial cell line was obtained from American Type Culture Collection 83 (ATCC). ZR-75-1 (CRL1500, ATCC) and MDA-MB-453 (ACC 65, DSMZ) cells were 84 purchased from DSMZ. The identity of all cell lines was confirmed by DNA finger printing 85 analysis utilizing the GenePrint® 10 System (Promega). B...

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“…Sequence artifacts may arise due to the formation of chimeric molecules , heteroduplex molecules , and Taq DNA polymerase error . PCR bias is thought to be due to differences amplification efficiency of templates or to the suppression of amplification in the late phases of PCR by the self‐annealing of the most templates .…”

Section: Introductionmentioning

confidence: 99%

Metagenome complexity and template length are the main causes of bias in PCR‐based bacteria community analysis

Peng

Wang

et al. 2018

J Basic Microbiol

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Multitemplate PCR is used widely for the study of microbial community diversity. Although such studies have established the abundance of different groups within many natural ecosystems, these reports are limited by uncertainties such as bias and artifacts in the PCR. Bias which is introduced by the simultaneous amplification of specific genes from complex mixtures of templates remains poorly understood. In this study, factors leading to the bias of the multitemplate PCR in bacterial communities were examined and optimized. Comparisons between PCR cycle parameters, DNA polymerases, PCR primer degeneracy, and 16S rRNA gene fragments GC content, revealed that annealing temperatures and DNA structure are predominant factors contributing to the observed bias. Pre-digestion of metagenomic DNA with the restriction enzyme Sau3A I and decreased annealing temperature reduced the bias significantly. The application of these optimized conditions to the ten-species model community in a soil sample verified the validity of these treatments.

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Non-biased and efficient global amplification of a single-cell cDNA library

Cited by 30 publications

References 19 publications

The Technology and Biology of Single-Cell RNA Sequencing

The Technology and Biology of Single-Cell RNA Sequencing

Targeted transcript quantification in single disseminated cancer cells after whole transcriptome amplification

Metagenome complexity and template length are the main causes of bias in PCR‐based bacteria community analysis

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