Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads

Abstract

High-throughput single-cell RNA-seq methods assign limited unique molecular identifier (UMI) counts as gene expression values to single cells from shallow sequence reads and detect limited gene counts. We thus developed a high-throughput single-cell RNA-seq method, Quartz-Seq2, to overcome these issues. Our improvements in the reaction steps make it possible to effectively convert initial reads to UMI counts, at a rate of 30–50%, and detect more genes. To demonstrate the power of Quartz-Seq2, we analyzed approximately 10,000 transcriptomes from in vitro embryonic stem cells and an in vivo stromal vascular fraction with a limited number of reads.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1407-3) contains supplementary material, which is available to authorized users.

CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA-sequencing

“…Similarly, Chen et al [21] conducted a more thorough investigation and concluded that negative binomial models are preferred over zero-inflated negative binomial models for modeling scRNA-seq data with UMIs. We confirmed a similar observation using our control data generated from Quartz-Seq2 [8]. Therefore, we did not take into account the effects of dropout events in this study.…”

“…In contrast to traditional RNA sequencing methods that profile the average of bulk samples, scRNA-seq has the potential to reveal heterogeneity within phenotypes of individual cells as it can distinguish the transcriptome expression of each cell by attaching a distinct cellular barcode [1,2]. In addition, several protocols have been developed that utilize unique molecular identifiers (UMIs) to more accurately quantify expression by removing duplicated counts resulting from the amplification of molecules [3][4][5][6][7][8]. The advent of library preparation for multiplexed sequencing with cellular barcod-* Correspondence: bicycle1885@gmail.com 1 Department of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo-ku, 113-8657, Tokyo, Japan Full list of author information is available at the end of the article ing and the refinement of cDNA amplification method with UMIs lead to a higher throughput and more reliable quantification of single-cell expression profiles.…”

Lasy-Seq: a high-throughput library preparation method for RNA-Seq and its application in the analysis of plant responses to fluctuating temperatures

“…All indexed samples were then pooled and purified with the same volume of AMPure XP beads (Beckman Coulter, USA) or column purification with Zymo spin column I (Zymo Research) and Membrane Binding Solution (Promega). If the number of samples was large, pooling of the RT products could be conducted by centrifuging the reaction plate set on a one well reservoir as described in a previous study 15 . The purified cDNA was dissolved in 10 μL (depending on number of pooled-samples) of nuclease-free water.…”

“…Reducing the steps in library preparation is expected to reduce sample loss caused by insufficient reaction or purification steps. To reduce the steps and amount of time taken for library preparation, previous studies have employed tagmentation with a Tn5 transposase 15-17 . Efficiency of tagmentation by transposase was reported to be largely affected by the amount of input DNA, resulting in changes in the distributions of insert length 9 .…”

Benchmarking principal component analysis for large-scale single-cell RNA-sequencing

“…For example, cellular subpopulations consisting of various tissues [2][3][4][5][6], rare cells and stem cell niches [7], continuous gene expression changes related to cell cycle progression [8], spatial coordinates [9][10][11], and differences in differentiation maturity [12,13] have been captured by many scRNA-seq studies. As the measurement of cellular heterogeneity is highly dependent on the number of cells measured simultaneously, a wide variety of large-scale scRNA-seq technologies have been developed [14], including those using cell sorting devices [15][16][17], Fludigm C1 [18][19][20][21], dropletbased technologies (Drop-Seq [2][3][4], inDrop RNA-Seq [5,6], the 10X Genomics Chromium system [22]), and *Correspondence: koki.tsuyuzaki@gmail.com; itoshi.nikaido@riken.jp 1 Laboratory for Bioinformatics Research, RIKEN Center for Biosystems Dynamics Research, Wako, Saitama, 351-0198, Japan 5 Bioinformatics Course, Master's/Doctoral Program in Life Science Innovation (T-LSI), School of Integrative and Global Majors (SIGMA), University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan Full list of author information is available at the end of the article single-cell combinatorial-indexing RNA-sequencing (sci-RNA-seq [23]). Such technologies have encouraged the establishment of several large-scale genomics consortiums, such as the Human Cell Atlas [24][25][26], Mouse Cell Atlas [27], and Tabula Muris [28].…”