A web server for comparative analysis of single-cell RNA-seq data

ABSTRACTMultiplexing of samples in single-cell RNA-seq studies allows significant reduction of experimental costs, straightforward identification of doublets, increased cell throughput, and reduction of sample-specific batch effects. Recently published multiplexing techniques using oligo-conjugated antibodies or - lipids allow barcoding sample-specific cells, a process called ‘hashing’. Here, we compare the hashing performance of TotalSeq-A and -C antibodies, custom synthesized lipids and MULTI-seq lipid hashes in four cell lines, both for single-cell RNA-seq and single-nucleus RNA-seq. Hashing efficiency was evaluated using the intrinsic genetic variation of the cell lines. Benchmarking of different hashing strategies and computational pipelines indicates that correct demultiplexing can be achieved with both lipid- and antibody-hashed human cells and nuclei, with MULTISeqDemux as the preferred demultiplexing function and antibody-based hashing as the most efficient protocol on cells. Antibody hashing was further evaluated on clinical samples using PBMCs from healthy and SARS-CoV-2 infected patients, where we demonstrate a more affordable approach for large single-cell sequencing clinical studies, while simultaneously reducing batch effects.

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scQuery: a web server for comparative analysis of single-cell RNA-seq data

10.1101/323238 ◽

2018 ◽

Author(s):

Amir Alavi ◽

Matthew Ruffalo ◽

Aiyappa Parvangada ◽

Zhilin Huang ◽

Ziv Bar-Joseph

Keyword(s):

Single Cell ◽

Large Scale ◽

Web Server ◽

Cell Types ◽

Marker Genes ◽

Heterogeneous Environments ◽

Rna Seq ◽

Cell Type ◽

Small Set ◽

Unique Cell

SummarySingle cell RNA-Seq (scRNA-seq) studies often profile upward of thousands of cells in heterogeneous environments. Current methods for characterizing cells perform unsupervised analysis followed by assignment using a small set of known marker genes. Such approaches are limited to a few, well characterized cell types. To enable large scale supervised characterization we developed an automated pipeline to download, process, and annotate publicly available scRNA-seq datasets. We extended supervised neural networks to obtain efficient and accurate representations for scRNA-seq data. We applied our pipeline to analyze data from over 500 different studies with over 300 unique cell types and show that supervised methods greatly outperform unsupervised methods for cell type identification. A case study of neural degeneration data highlights the ability of these methods to identify differences between cell type distributions in healthy and diseased mice. We implemented a web server that compares new datasets to collected data employing fast matching methods in order to determine cell types, key genes, similar prior studies, and more.

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Comparative analysis of droplet-based ultra-high-throughput single-cell RNA-seq systems

10.1101/313130 ◽

2018 ◽

Cited By ~ 2

Author(s):

Xiannian Zhang ◽

Tianqi Li ◽

Feng Liu ◽

Yaqi Chen ◽

Jiacheng Yao ◽

...

Keyword(s):

Developmental Biology ◽

Stem Cell ◽

Comparative Analysis ◽

Single Cell ◽

High Throughput ◽

Single Cells ◽

Detailed Comparison ◽

Entire Body ◽

Rna Seq ◽

Distinguishing Features

SummarySince its establishment in 2009, single-cell RNA-seq has been a major driver behind progress in biomedical research. In developmental biology and stem cell studies, the ability to profile single cells confers particular benefits. While most studies still focus on individual tissues or organs, the recent development of ultra-high-throughput single-cell RNA-seq has demonstrated potential power in characterizing more complex systems or even the entire body. However, although multiple ultra-high-throughput single-cell RNA-seq systems have attracted attention, no systematic comparison of these systems has been performed. Here, we focus on three widely used droplet-based ultra-high-throughput single-cell RNA-seq systems, inDrop, Drop-seq, and 10X Genomics Chromium. While each system is capable of profiling single-cell transcriptomes, their detailed comparison revealed the distinguishing features and suitable applications for each system.

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