Highly parallel and efficient single cell mRNA sequencing with paired picoliter chambers

ScRNA-seq has the ability to reveal accurate and precise cell types and states. Existing scRNA-seq platforms utilize bead-based technologies uniquely barcoding individual cells, facing practical challenges for precious samples with limited cell number. Here, we present a scRNA-seq platform, named Paired-seq, with high cells/beads utilization efficiency, cell-free RNAs removal capability, high gene detection ability and low cost. We utilize the differential flow resistance principle to achieve single cell/barcoded bead pairing with high cell utilization efficiency (95%). The integration of valves and pumps enables the complete removal of cell-free RNAs, efficient cell lysis and mRNA capture, achieving highest mRNA detection accuracy (R = 0.955) and comparable sensitivity. Lower reaction volume and higher mRNA capture and barcoding efficiency significantly reduce the cost of reagents and sequencing. The single-cell expression profile of mES and drug treated cells reveal cell heterogeneity, demonstrating the enormous potential of Paired-seq for cell biology, developmental biology and precision medicine.


Statistics
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For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
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Data analysis
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

Data
Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Chaoyong Yang Mar 20, 2020 NIS-Elements Basic Research for microscopy imaging scRNA-seq data was analyzed using the 'Seurat' R package (V2.3.4), 'monocle' R package (V2.6.4), R (V3.4.4). scRNA-seq expression library FASTQs were pre-processed using python (V3.7.0). scRNA-seq gene name were tagged using Drop-seq software TagReadWithGeneExon (V1.13) scRNA-seq FASTQs were aligned using STAR (V2.5) with references hg19 and mm10 The sequencing data presented in this paper have been deposited in the Sequence Read Archive ( Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

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Life sciences study design
All studies must disclose on these points even when the disclosure is negative. For technical characteristic, we used 3 biological replicates for each outcome. This is a widely used standard for statistic analysis. For single-cell RNAseq, we isolated cells from individual cell preparations for each timepoint. We aimed to collect as many cells as possible for analysis, but decided to randomly down-sample each timepoint to 100 cells to balance the samples for easier statistical analysis and cleaner visualization of the data. For ERCC sample, we isolated 55 samples for highly deep sequencing that was comparable to analysis in other platforms.
No data was excluded from the analysis. Filtering and quality control of single cell RNA-seq data is described in the Methods.
All experimental steps are detailed in the manuscript to ensure replication. All data analyses are available online in reproducible format. We have not attempted study replication.
The study is exploratory and descriptive to demonstrate the feasibility and efficiency of a new scRNA-seq method, and no case control comparisons were performed, so no randomization was considered.
Not applicable, the manuscript concerns a new method for preparation of single cell RNA sequencing sample.
NIH 3T3: ATCC CRL-1658, purchased from National Infrastructure of Cell Line Resource. K562: ATCC CCL-243, purchased from National Infrastructure of Cell Line Resource. J1 mouse embryonic stem cell (J1 mES cell): derived from the mouse 129 s4 / SvJae strains called J1. They were kindly provided by Stem Cell Bank, Chinese Academy of Sciences.
NIH 3T3/K562/J1 mES: Cells were authenticated at their source (e.g., ATCC) prior to acquisition, but no extra authentication was employed in this study. Single-cell gene expression profiles match expectations from literature-supported marker genes for each cell line.