Droplet-based Sequencing
Droplet-based Sequencing - Related Videos
07:55
High Throughput Yeast Strain Phenotyping with Droplet-Based RNA Sequencing
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Cited by 3 •
2020
A bottleneck in the ‘design-build-test’ cycle of microbial engineering is the speed at which we can perform functional screens of strains. We describe a high-throughput method for strain screening applied to hundreds to thousands of yeast cells per experiment that utilizes droplet-based RNA sequencing.
13:10
Title Cell Encapsulation by Droplets
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Cited by 4 •
2007
12:59
BEST: Barcode Enabled Sequencing of Tetrads
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Cited by 4 •
2014
Barcode Enabled Sequencing of Tetrads (BEST) replaces the manual processes of isolating, disrupting and spacing tetrads. BEST isolates tetrads by fluorescence-activated cell sorting onto agar plates, separates the spores by agitation with glass beads, and determines which randomly arrayed colonies were derived from the same original tetrad using molecular barcodes.
08:57
Amplicon Sequencing using the Long-Read Sequencing Technologies
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2025
This protocol was optimized for targeted deep sequencing of 18 drug-resistance regions in Mycobacterium tuberculosis using a long-read sequencing platform, followed by analysis with a tuberculosis-specific bioinformatics pipeline designed for long-read data.
14:16
Fluorescence detection methods for microfluidic droplet platforms
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Cited by 20 •
2011
Droplet-based microfluidic platforms are promising candidates for high throughput experimentation since they are able to generate picoliter, self-compartmentalized vessels inexpensively at kHz rates. Through integration with fast, sensitive and high resolution fluorescence spectroscopic methods, the large amounts of information generated within these systems can be efficiently extracted, harnessed and utilized.
FAQs
Identity is preserved by isolating individual cells, nucleic acid molecules, or reaction mixtures in separate aqueous droplets and associating their nucleic acid copies with molecular identifiers. When the resulting material is sequenced, those identifiers allow reads to be assigned to the original source. This linkage enables researchers to distinguish molecular profiles that would be combined in bulk measurements.
Barcoded beads or oligonucleotides provide the identifiers that label nucleic acid copies within droplets. Cell lysis releases cellular material, while reverse transcription converts RNA into complementary nucleic acid copies that can receive those identifiers. Together, these steps connect sequence information to the cell or source from which it originated, supporting transcript-level analysis across many individual samples.
Bulk sequencing combines material from many cells, so signals from uncommon populations or distinct cellular states can be diluted within an overall average. By keeping sources separated, droplet-based sequencing supports comparisons among individual cells and identifies molecular differences, rare cell populations, and gene-expression patterns that may remain hidden when all samples are analyzed together.
A typical workflow partitions cells, nucleic acid molecules, or reaction mixtures into microfluidic droplets, with selected droplets containing barcoded beads or oligonucleotides. Cells can then undergo lysis, and RNA can undergo reverse transcription so copies receive molecular identifiers. The labeled material is subsequently sequenced, and reads are assigned to their original source for analysis.
Researchers may choose this approach when they need to examine many individual cells or reaction mixtures in parallel rather than obtain only a population-level average. It is suited to single-cell transcriptomics, targeted DNA analysis, and investigations of cellular heterogeneity. The method is especially informative when rare populations or differing gene-expression patterns are biologically important.
The approach can provide molecular profiles linked to individual source cells or samples, including gene-expression patterns, targeted DNA information, and evidence of cellular heterogeneity. Its scalable design allows thousands of samples to be processed simultaneously while reducing reagent use. These features support broad comparisons across samples and improve the chance of detecting uncommon molecular or cellular populations.