Exponential scaling of single-cell RNA-seq in the past decade

By Valentine Svensson, Roser Vento-Tormo, Sarah A. Teichmann. 2017

866 citations recorded by the source corpus.

Measurement of the transcriptomes of single cells has been feasible for only a few years, but it has become an extremely popular assay. While many types of analysis can be carried out and various questions can be answered by single-cell RNA-seq, a central focus is the ability to survey the diversity of cell types in a sample. Unbiased and reproducible cataloging of gene expression patterns in distinct cell types requires large numbers of cells. Technological developments and protocol improvements have fueled consistent and exponential increases in the number of cells that can be studied in single-cell RNA-seq analyses. In this Perspective, we highlight the key technological developments that have enabled this growth in the data obtained from single-cell RNA-seq experiments.

Subfields: Immunology, Molecular Biology

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Citations in the corpus

Falco: high-speed FastQC emulation for quality control of sequencing data (2019, neutral): “New sequencing protocols are constantly being introduced 7,8 , and as the cost of sequencing per base decreases, sequencing data is growing in abundance, dataset size and read length 9 .”
Computational principles and challenges in single-cell data integration (2021, background): “As sequencing cost decreases and technologies improve, we anticipate that multimodal datasets will follow a trend similar to that seen with scRNA-seq, where in the span of less than 10 years the size of experiments increased from the order of tens to potentially millions of cells 139 .”
A comparison of automatic cell identification methods for single-cell RNA sequencing data (2019, background): “Rapid and continuous technological advances over the past decade have allowed scRNA-seq technologies to scale to thousands of cells per experiment [1].”
RNA Sequencing Data: Hitchhiker's Guide to Expression Analysis (2018, methodology): “Two main experimental approaches are used: platebased, where cells are sorted into individual wells for lysis and library preparation; or, dropletbased, where each cell is absorbed (together with reagents) and processed within an oil droplet [181] .”
Generalizable and Scalable Visualization of Single-Cell Data Using Neural Networks (2018, background): “Fortunately, advances in automatic cell isolation and multiplex sequencing have led to an exponential growth in the number of cells sequenced for individual studies (Svensson et al., 2018) (Figure 1A).”
Large Scale Foundation Model on Single-cell Transcriptomics (2023, neutral): “9 and 10).”
Inferring spatial and signaling relationships between cells from single cell transcriptomic data (2020, methodology): “(1) in Methods), which returns an optimal transport plan γ* 2 R connecting the two datasets (Fig.”