Seq-Well: A Portable, Low-Cost Platform for High-Throughput Single-Cell RNA-Seq of Low-Input Samples

By Todd M. Gierahn, M. Wadsworth, T. Hughes, Bryan D. Bryson, Andrew W. Butler, Rahul Satija, S. Fortune, J. C. Love, A. Shalek. Nature Methods, 2017

702 citations recorded by the source corpus.

Single-cell RNA-seq can precisely resolve cellular states, but applying this method to low-input samples is challenging. Here, we present Seq-Well, a portable, low-cost platform for massively parallel single-cell RNA-seq. Barcoded mRNA capture beads and single cells are sealed in an array of subnanoliter wells using a semipermeable membrane, enabling efficient cell lysis and transcript capture. We use Seq-Well to profile thousands of primary human macrophages exposed to Mycobacterium tuberculosis.

Subfields: Epidemiology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/5005a2551e2d77bbb4b9164239b7059944ac1fbf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Epidemiology

In Molecular Biology

Citations in the corpus

DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors (2018, background): “Today, sequencing thousands to hundreds of thousands of cells is routine due to the advent of droplet microfluidics and nanowell-based sequencing strategies (Macosko et al., 2015; Klein et al., 2015; Zheng et al., 2017; Gierahn et al., 2017; Takara Bio USA, 2018).”
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications (2017, neutral): “Reference [63] [57] [39] [10] [64] [45] [46] [47] [101] [38] Haque et al.”
SARS-CoV-2 Receptor ACE2 Is an Interferon-Stimulated Gene in Human Airway Epithelial Cells and Is Detected in Specific Cell Subsets across Tissues (2020, positive): “In a previously unpublished dataset consisting of NHP (Macaca mulatta) lung tissue collected after necropsy of healthy adult animals and analyzed by using SeqWell v1 (Gierahn et al., 2017), we recovered at least 17 distinct major cell types, including various lymphoid, myeloid, and stromal populatio”
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama (2019, methodology): “SeqWell analysis was performed as previously described 54 .”
Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data (2018, background): “, 2017) and well-based (Cao et al., 2017; Gierahn et al., 2017; Han et al., 2018; Rosenberg et al., 2018) technologies now enable the relatively inexpensive, highthroughput isolation and barcoding of cell transcriptomes.”
Systems Biology of Cancer Metastasis (2019, background): “The recent decreases in the cost and concomitant increases in the portability and flexibility of single-cell technology will facilitate a deeper understanding of tumor cell heterogeneity in metastasis(Bush et al., 2017; Gierahn et al., 2017).”
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, positive): “One conspicuous diﬀerence among these scRNA-seq methods is that some of them can produce full-length (or nearly full-length) transcript sequencing data (e.g., Smart-seq2, SUPeR-seq, and MATQ-seq), whereas others only capture and sequence the 3 -end [such as Drop-seq, Seq-Well and DroNC-seq, SPLiT-se”
Wound Healing: A Cellular Perspective. (2019, background): “For example, single-cell RNA sequencing can detect more than 5,000 genes/cell (140) and offers comprehensiveness.”
Batch effects in single-cell RNA sequencing data are corrected by matching mutual nearest neighbours (2018, background): “The decreasing cost of scRNA-seq experiments 1–4 has encouraged the establishment of large-scale projects such as the Human Cell Atlas, which profile the transcriptomes of thousands to millions of cells.”