Single-cell Mrna
Single-cell Mrna - Related Videos
11:22
Measuring the Kinetics of mRNA Transcription in Single Living Cells
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Cited by 6 •
2011
RNA polymerase II transcriptional kinetics are measured on specific genes in living cells. mRNAs transcribed from the gene of interest are fluorescently tagged and using Fluorescence Recovery After Photobleaching (FRAP) the in vivo kinetics of transcriptional elongation are obtained.
08:33
Cell-Free In Vitro mRNA Translation In Drosophila Lysates
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2026
Here, we provide details on how to prepare lysates and perform in vitro translation using Drosophila embryos.
14:49
Single Read and Paired End mRNA-Seq Illumina Libraries from 10 Nanograms Total RNA
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Cited by 30 •
2011
Here we describe a method for preparation of both single read and paired end Illumina mRNA-Seq sequencing libraries for gene expression analysis based on T7 linear RNA amplification. This protocol requires only 10 nanograms of starting total RNA and generates highly consistent libraries representing whole transcripts.
04:52
Increasing cDNA Yields from Single-cell Quantities of mRNA in Standard Laboratory Reverse Transcriptase Reactions using Acoustic Microstreaming
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Cited by 11 •
2011
We describe a novel method for increasing cDNA yield from single-cell quantities of mRNA in otherwise standard laboratory reverse transcription reactions. The novelty resides in the use of a micromixer, which utilizes the phenomenon of acoustic microstreaming, to mix fluids at microliter scales more effectively than shaking, vortexing or trituration.
08:30
Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells
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Cited by 4 •
2020
Here we presented a multiplexed single cell mRNA sequencing method to profile gene expression in mouse embryonic tissues. The droplet-based single cell mRNA sequencing (scRNA-Seq) method in combination with multiplexing strategies can profile single cells from multiple samples simultaneously, which significantly reduces reagent costs and minimizes experimental batch effects.
FAQs
During partitioning, molecular barcodes associate captured mRNA with the cell that produced it. After conversion to complementary DNA, sequencing reads retain those barcode assignments. Computational analysis can then group reads by barcode and compare transcript profiles across cells. This preserves cell-specific information that would be obscured if all molecules were combined before analysis.
Single-cell mRNA measurements preserve differences among individual cells instead of averaging them across an entire population. That resolution can expose cellular heterogeneity, rare cell states, and distinct responses within engineered populations. In bioengineering, these distinctions help determine whether a population behaves uniformly or contains subgroups with different functional or developmental characteristics.
Complementary DNA provides the molecular form used for downstream sequencing after mRNA capture. Because barcode information is associated with this converted material, sequencing reads can be assigned back to their cells of origin. The conversion therefore connects the original transcript collection to computational comparisons of gene-expression profiles across individual cells.
Profiles from individual cells can be compared after cells experience controlled environments, allowing researchers to assess whether responses are shared across the population or restricted to particular cells. This approach can reveal heterogeneous environmental responses that a population-level measurement may conceal. The resulting information supports evaluation of how cellular systems behave under designed conditions.
A typical workflow begins by isolating or partitioning cells, followed by capturing their mRNA. The captured molecules are converted into complementary DNA, and molecular barcodes connect sequencing reads to the originating cells. Computational analysis then compares transcript profiles across cells. Together, these stages preserve cell-level identity while producing data suitable for population and subpopulation analysis.
Bioengineers can use the method to characterize the composition of an engineered cell population and identify rare cellular states within it. It is also useful for evaluating differentiation or therapeutic production when cells may not perform identically. By resolving transcript profiles at the individual-cell level, researchers gain evidence about population heterogeneity and system performance.
Single-cell transcript profiles provide information about how different cells behave within an engineered system and how they respond to controlled environments. Bioengineers can use those observations to assess cellular heterogeneity, identify desirable or unexpected states, and refine the design of tissues, biomaterials, or cell-based systems. The measurements therefore connect molecular behavior with engineering decisions.