CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq

By T. Hashimshony, N. Senderovich, Gal Avital, A. Klochendler, Yaron de Leeuw, Leon Anavy, D. Gennert, Shuqiang Li, K. Livak, O. Rozenblatt-Rosen and 3 more. Genome Biology, 2016

1,034 citations recorded by the source corpus.

Single-cell transcriptomics requires a method that is sensitive, accurate, and reproducible. Here, we present CEL-Seq2, a modified version of our CEL-Seq method, with threefold higher sensitivity, lower costs, and less hands-on time. We implemented CEL-Seq2 on Fluidigm’s C1 system, providing its first single-cell, on-chip barcoding method, and we detected gene expression changes accompanying the progression through the cell cycle in mouse fibroblast cells. We also compare with Smart-Seq to demonstrate CEL-Seq2’s increased sensitivity relative to other available methods. Collectively, the improvements make CEL-Seq2 uniquely suited to single-cell RNA-Seq analysis in terms of economics, resolution, and ease of use.

Subfields: Immunology, Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/f7748d5671759e83efb9905e185005d57e9e4d37

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Citations in the corpus

Fast Batch Alignment of Single Cell Transcriptomes Unifies Multiple Mouse Cell Atlases into an Integrated Landscape (2018, methodology): “The experiments came from four independent studies, and were performed using a combination of both droplet 32 and plate 33,34 based methods.”
scPred: accurate supervised method for cell-type classification from single-cell RNA-seq data (2019, methodology): “We generated a training reference cohort of scRNA-seq data from a total of 4292 cells from three independent studies undertaken by Muraro et al. [25], Segerstolpe et al. [3], and Xin et al. [26] that had sequenced cells using CELseq2 [27], Smart-Seq2 [28], and SMARTer [29], respectively.”
A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure (2016, neutral): “…high-throughput droplet microfluidics that barcode the RNA from thousands of individual cells, implementing a sensitive linear amplification method for single-cell transcriptome library construction (cell expression by linear amplification and sequencing [CEL-seq]) (Hashimshony et al., 2012, 2016).”