Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells
Open accessAbstract
It has long been the dream of biologists to map gene expression at the single-cell level. With such data one might track heterogeneous cell sub-populations, and infer regulatory relationships between genes and pathways. Recently, RNA sequencing has achieved single-cell resolution. What is limiting is an effective way to routinely isolate and process large numbers of individual cells for quantitative in-depth sequencing. We have developed a high-throughput droplet-microfluidic approach for barcoding the RNA from thousands of individual cells for subsequent analysis by next-generation sequencing. The method shows a surprisingly low noise profile and is readily adaptable to other sequencing-based assays. We analyzed mouse embryonic stem cells, revealing in detail the population structure and the heterogeneous onset of differentiation after leukemia inhibitory factor (LIF) withdrawal. The reproducibility of these high-throughput single-cell data allowed us to deconstruct cell populations and infer gene expression relationships. VIDEO ABSTRACT.
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The C1 system allows direct confirmation of single cell capture events.
“Notably, in the context of this study, the C1 system also allows for direct confirmation of single cell capture events, in contrast to most other microfluidic-based approaches322.”
From Batch effects and the effective design of single-cell gene expression studies (2017) · cites this paper as [22]
The scale and precision of single-cell studies has continued to increase, reaching tens of thousands of cells for massively parallel scRNA-seq and millions of cells for mass cytometry protein measurements.
“The scale and precision of such studies has continued to increase, reaching tens of thousands of cells for massively parallel scRNA-seq30,31 and millions of cells for mass cytometry signature protein measurements32–34.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [31]
Using the latest protocols for massively parallel scRNA-seq and mass cytometry, a single laboratory can now readily collect tens of thousands to hundreds of thousands of single-cell RNA-seq profiles and tens of millions of single-cell protein profiles.
“Using the latest protocols for massively parallel scRNA-seq30,31 and mass cytometry32–34, a single laboratory can now readily collect tens of thousands to hundreds of thousands of single-cell RNA-seq profiles and tens of millions of single-cell protein profiles.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [31]
Massively parallel scRNA-seq methods such as Drop-seq can be applied at scale cost-effectively but require intact single-cell suspensions.
“massively parallel scRNA-seq methods, such as Drop-seq7 and related methods8–10 can be readily applied at scale11 in a cost-effective manner12, but require intact single cell suspension as input.”
From Massively parallel single-nucleus RNA-seq with DroNc-seq (2017) · cites this paper as [10]
Droplet and well-based single-cell RNA-sequencing technologies can isolate and barcode cell transcriptomes at high throughput and relatively low cost.
“Droplet microfluidic (Klein et al., 2015; Macosko et al., 2015; Zheng et al., 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, high-throughput isolation and barcoding of cell transcriptomes.”
From Scrublet: computational identification of cell doublets in single-cell transcriptomic data (2019) · cites this paper as Klein et al., 2015
Differences between human and mouse genomic sequence provide an independent way to detect human-mouse doublets.
“This dataset, though not representative of most single-cell experiments, provides a useful test case because the differences between human and mouse genomic sequence provide an independent way to detect doublets (Klein et al., 2015; Macosko et al., 2015).”
From Scrublet: computational identification of cell doublets in single-cell transcriptomic data (2019) · cites this paper as Klein et al., 2015
In typical single-cell RNA-sequencing experiments, at least a few percent of capture events are multiplets.
“In a typical scRNA-seq experiment, at least several percent of all capture events are multiplets (Cao et al., 2017; Klein et al., 2015; Macosko et al., 2015; Zheng et al., 2017).”
From Scrublet: computational identification of cell doublets in single-cell transcriptomic data (2019) · cites this paper as Klein et al., 2015
The single-cell transcriptomics field is divided into two major categories: microfluidics droplet-based and microwell plate-based.
“The field has rapidly developed ever since and is divided into two major categories: microfluidics droplet-based transcriptomics7–10 and microwell plate-based11–13 transcriptomics (Box 1).”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [8]
Preliminary results from Klein et al (2015) suggest that downstream analysis is robust to the exact choice of the number of HVGs.
“Preliminary results from Klein et al (2015) suggest that downstream analysis is robust to the exact choice of the number of HVGs.”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Klein et al (2015)
Other protocols have sacrificed full-length coverage to sequence part of the cDNA primer, enabling early cell-specific barcoding and increasing scRNA-seq library generation throughput by one to three orders of magnitude.
“Other protocols have sacrificed full-length coverage in order to sequence part of the primer used for cDNA generation. This enables early barcoding of libraries (i.e., the incorporation of cell-specific barcodes), allowing for multiplexing the cDNA amplification and thereby increasing the throughput of scRNAseq library generation by one to three orders of magnitude (Hashimshony et al., 2012; Jaiti”
From Comparative Analysis of Single-Cell RNA Sequencing Methods (2017) · cites this paper as Klein et al., 2015
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- Authors
- Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W.
- Affiliations
- Harvard University (US)
- Corresponding author
- Weitz, David A.
- Kirschner, Marc W.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2015-05
- Volume
- 161
- Issue
- 5
- Pages
- 1187-1201
- ISSN
- 0092-8674 (ISSN-L)
- PMC
- PMC4441768
- OpenAlex
- W2135937351
- Cited by
- 3,356
- References
- 74
- Citations by year
- 2026: 170 · 2025: 238 · 2024: 316 · 2023: 310 · 2022: 355 · 2021: 420 · 2020: 404 · 2019: 343 · 2018: 335 · 2017: 237 · 2016: 169 · 2015: 50 · 2014: 2 · 2013: 2 · 2012: 1
- FWCI
- 114.84
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (1310266, 1420570)
- Wellcome Trust
- National Institutes of Health (DMR-1420570, HD073104)
- Defense Advanced Research Projects Agency (HR0011‐11‐C‐0093, HR0011)
- Harvard Stem Cell Institute
- Materials Research Science and Engineering Center, Harvard University (DMR- 1420570, 1420570, DMR-1310266)
- National Heart, Lung, and Blood Institute (P01HL120839)
- National Institute of General Medical Sciences (P01GM096971, R01GM026875, R01GM103785)
- National Institute of Allergy and Infectious Diseases (R21AI101291)
- National Institute of Diabetes and Digestive and Kidney Diseases (R21DK098818)
- National Institute of Biomedical Imaging and Bioengineering (R01EB014703)
- Division of Materials Research (DMR 14–20570, DMR-1310266, 1420570, 1310266)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD073104)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Physical Sciences › Engineering › Biomedical Engineering › Innovative Microfluidic and Catalytic Techniques Innovation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single-cell transcriptomics, microfluidics, mouse embryonic stem cells, stochastic gene expression, cell isolation
- MeSH terms
- Animals, Sequence Analysis, RNA, Gene Expression Profiling, Microfluidic Analytical Techniques, Mice, Embryonic Stem Cells, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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