![]() Single cells are dropped into 96 or 384 well plates containing a hypotonic lysis buffer containing Triton-X100 with excess ribonuclease inhibitor to stabilize RNA. Cell separation for Smart-Seq2 is typically achieved by fluorescence-activated cell sorting (FACS), enabling a wide variety of cell types to be isolated in the same experiment if necessary. Before conducting a single-cell experiment, it is recommended to trial single-cell separation methods and assess cell viability, for example processing a polymerase chain reaction (PCR) strip of eight cells, instead of a full 96 well plate. As with other methods relying on oligo dT priming, Smart-Seq2 transcriptomes show a significant 3′-prime bias.Ĭell culture-based approaches allow for easy separation of single cells using routine trypsinization methods, with liver perfusions leading to single-cell solutions and blood readily providing access to single cells. Therefore, batch effects need to be considered in the experimental design. ![]() However, because of the manual nature of the protocol, processing of cell numbers is limited to the hundreds with either one 96 or one 384 well plates being processed at the same time, causing technical variability from experiment to experiment because of the many pipetting steps. Therefore, it is readily available to a wide community of researchers. Typically, this protocol provides good coverage of the transcriptome with rarer transcripts being detectable and does not need any specialist equipment. This protocol takes 2 days hands-on wet-lab work where clean-up steps can be automated if a robot is available. ![]() They optimized the SMART (Switching Mechanism at 5′ End of RNA Template) workflow, which is based on high-fidelity reverse transcription, template switching and preamplification for increased complementary DNA (cDNA) yield for single-cell analysis. One of the most successful methods for single-cell gene expression was reported by Picelli and colleagues. We highlight pros and cons of each method and summarize important considerations to help design successful single-cell transcriptome experiments. In this review, we discuss the experimental design for single-cell transcriptome analysis based on the two most popular protocols used for scRNA-seq: Smart-seq2 and 10X Chromium 3′ sequencing. ![]() While scRNA-seq offers new avenues to explore thus far unanswerable questions, it is important to consider experimental design carefully before conducting the study to avoid confounding factors and to be able to draw sound conclusions based on biological variation rather than technical artefacts associated with batch processing of samples. Signalling pathways critical to the development of cerebral cortex were recently uncovered by low coverage single-cell mRNA sequencing, and liver tissue was spatially reconstructed based on transcriptomes of single hepatocytes, highlighting division of labour within different spatial zones of the organ. In haematology, single-cell transcriptomics was applied to elucidate age-related changes to the blood system and address heterogeneity of ageing. Single-cell RNA sequencing (scRNA-seq) has been at the forefront of method development both in the laboratory and computationally to provide robust methods for downstream data analysis.Ī recent flurry of papers highlighted the potential of this technology. Single-cell genomic technologies have revolutionized the way scientists can interrogate heterogeneous tissues or rare subpopulations of cells. Single-cell RNA sequencing, Smart-Seq 2, 10x Chromium, Drop-Seq, experimental design Introduction ![]()
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