Multi-seq Barcoding
Multi-seq Barcoding - Related Videos
09:54
RNA-Seq
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2023
Among different methods to evaluate gene expression, the high-throughput sequencing of RNA, or RNA-seq. is particularly attractive, as it can be performed and analyzed without relying on prior available genomic information. During RNA-seq, RNA isolated from samples of interest is used to generate a DNA library, which is then amplified and sequenced. Ultimately, RNA-seq can determine which genes are expressed, the levels of their expression, and the presence of any previously unknown transcripts.
13:14
Genetic Barcoding with Fluorescent Proteins for Multiplexed Applications
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Since the discovery of the green fluorescent protein gene, fluorescent proteins have impacted molecular cell biology. This protocol describes how expression of distinct fluorescent proteins through genetic engineering is used for barcoding individual cells. The procedure enables tracking distinct populations in a cell mixture, which is ideal for multiplexed applications.
11:59
Competitive Genomic Screens of Barcoded Yeast Libraries
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We have developed comprehensive, unbiased genome-wide screens to understand gene-drug and gene-environment interactions. Methods for screening these mutant collections are presented.
12:59
BEST: Barcode Enabled Sequencing of Tetrads
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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.
05:07
Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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2025
This protocol allows initial quality control for RNA-seq experiments for wet-lab biologists with limited bioinformatics experience.
FAQs
Lipid-modified anchor oligonucleotides associate with cell membranes, while complementary co-anchors provide the matching molecular sequences needed to attach sample-specific barcodes. This arrangement places the barcode on the cell surface before pooling. Because the label remains associated with the cell during droplet-based RNA sequencing, its sample identity can later be linked to that cell’s transcript data.
During droplet-based RNA sequencing, the barcode tags are captured alongside cellular transcripts. Computational demultiplexing then examines the barcode information to assign sequenced cells back to their samples of origin. This separates biological or experimental groups after sequencing, allowing a pooled library to retain distinctions that would otherwise be lost when samples are combined.
Co-capturing the barcode with cellular RNA connects two kinds of information in the same sequencing result: the cell’s molecular expression profile and the sample from which it came. That connection enables researchers to interpret transcriptomic differences according to treatment, replicate, or cell population while analyzing material from multiple samples within one pooled experiment.
Each sample receives a distinct barcode sequence before the cells are pooled. After sequencing, those sequence differences provide the basis for computational demultiplexing, meaning the assignment of individual cell profiles to their original samples. Sample-specific labeling therefore preserves experimental group information across a shared sequencing workflow rather than treating the pooled cells as one undifferentiated population.
The workflow begins by labeling cells from each sample with lipid-modified anchor oligonucleotides and complementary co-anchors carrying sample-specific barcode sequences. The labeled samples are then pooled and processed with droplet-based RNA sequencing. Finally, barcode and transcript information are analyzed computationally to recover sample identities and interpret the resulting single-cell profiles.
In neuroscience, researchers can use Multi-seq Barcoding when they need to compare neuronal and glial populations across multiple samples in one library. It is also suited to experiments containing different treatment conditions or biological replicates. Pooling these labeled samples increases throughput while retaining sample-of-origin information for downstream single-cell transcriptomic analysis.
Recovered barcode identities allow neuronal and glial transcript profiles to be organized by their original sample, treatment condition, or experimental replicate. Researchers can therefore examine cellular populations while preserving the experimental grouping needed for comparison. The method is especially useful when several neuroscience samples must be analyzed together without sacrificing their distinct sample labels.