Barcoded Cdna Library
Barcoded Cdna Library - Related Videos
11:59
Competitive Genomic Screens of Barcoded Yeast Libraries
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Cited by 44 •
2011
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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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.
09:44
Rapid Amplification of cDNA Ends
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2023
Source: Pablo Sanchez Bosch2, Sean Corcoran2 and Katja Brückner1,2,3 1Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research 2Department of Cell and Tissue Biology, 3Cardiovascular Research Institute, University of California San Francisco, San Francisco, CA, USA Rapid Amplification of cDNA Ends (RACE) is a technique that allows amplification of full-length cDNA from mRNA by extending to the 3’ or 5’ end, even without prior knowledge of the sequence (Frohman et al.,...
12:14
A Converging Strategy for the Generation of a Virtually Sequenced cDNA Library from Unreferenced Pacific Oysters
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Cited by 2 •
2019
We describe a strategy for how to use RNA samples from unreferenced Pacific oyster specimens, and evaluate the genetic material by comparison with publicly available genome data to generate a virtually sequenced cDNA library.
13:14
Genetic Barcoding with Fluorescent Proteins for Multiplexed Applications
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Cited by 4 •
2015
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.
FAQs
The identifying sequence tag travels with the cDNA-derived read during sequencing and analysis. Because tags correspond to an original cell, sample, or experimental condition, researchers can sort reads by source before comparing transcript profiles. This assignment prevents measurements from different sources from being treated as one undifferentiated expression signal.
The barcode provides the key for separating expression measurements from developmental stages, tissues, individual cells, or experimental conditions. Once reads are assigned, profiles can be compared across those categories. This makes it possible to examine transcriptional changes associated with differentiation or embryonic development rather than only measuring an overall mixed signal.
Individual-cell tracking can reveal expression differences that would be obscured if cells were considered only as one sample. By assigning reads to individual cells, researchers can identify cell populations and examine lineage-associated expression patterns. This resolution is especially useful when developmental samples contain cells undergoing different states of differentiation.
The preparation sequence starts with cellular RNA, which is reverse-transcribed into cDNA. Barcode sequences are then added during library preparation, creating identifiable molecules for downstream sequencing. After sequencing, the tags support read assignment to the appropriate source, allowing analysis to proceed by cell, sample, or experimental condition.
Researchers compare expression profiles from cells or tissues at relevant developmental stages and then examine transcriptional changes that accompany differentiation. The resulting patterns can help identify cell populations and reveal lineage-associated expression. In this context, the library connects gene-expression measurements with developmental progression rather than treating all cells as transcriptionally equivalent.
During embryonic development, expression profiles can be compared across stages, tissues, or individual cells after reads are assigned to their original sources. These comparisons help reveal transcriptional changes that accompany developmental progression and can distinguish cell populations or lineage-associated patterns. The result is a more organized view of how gene expression changes during development.