Single Cell Rna
Single Cell Rna - Related Videos
11:26
Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
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Cited by 8 •
2017
Here, we present a combinatorial approach for classifying neuronal cell types prior to isolation and for the subsequent characterization of single-cell transcriptomes. This protocol optimizes the preparation of samples for successful RNA Sequencing (RNA-Seq) and describes a methodology designed specifically for the enhanced understanding of cellular diversity.
06:59
Nanomanipulation of Single RNA Molecules by Optical Tweezers
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Cited by 13 •
2014
Optical tweezers have been used to study RNA folding by stretching individual molecules from their 5’ and 3’ ends. Here common procedures are described to synthesize RNA molecules for tweezing, calibration of the instrument, and methods to manipulate single molecules.
14:53
Analysis of Global RNA Synthesis at the Single Cell Level following Hypoxia
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Cited by 2 •
2014
We describe a technique for analysis of global RNA synthesis in hypoxia using imaging. Click-chemistry labeling of RNA has not previously been performed under hypoxia and allows visualization of global RNA changes at the single cell level. This approach complements the existing averaged RNA techniques, allowing direct visualization of cell-to-cell changes in global RNA synthesis.
18:17
Obtaining High Quality RNA from Single Cell Populations in Human Postmortem Brain Tissue
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Cited by 39 •
2009
We describe a process using laser-capture microdissection to isolate and extract RNA from a homogeneous cell population, pyramidal neurons, in layer III of the superior temporal gyrus in postmortem human brains. We subsequently linearly amplify (T7-based) mRNA, and hybridize the sample to the Affymetrix human X3P microarray.
13:06
Analysis of Single-cell Gene Transcription by RNA Fluorescent In Situ Hybridization (FISH)
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Cited by 6 •
2012
Fluorescent in situ hybridization (FISH) to identify mRNA transcripts in individual cells allows analysis of polygenic activity such as the simultaneous transcription of more than one member of the var multigene family in Plasmodium falciparum infected erythrocytes 1. The technique is adaptable and can be used on different types of genes, cells and organisms.
FAQs
Cell-specific barcodes attach an identifying label to the complementary DNA generated from each cell’s captured RNA. During sequencing, those labels allow reads to be assigned back to their originating cells rather than combined into one sample-wide measurement. This assignment preserves cell-to-cell differences and produces separate transcriptomic profiles for downstream interpretation.
Bulk analysis averages RNA signals across all cells in a specimen, which can conceal smaller or biologically distinct populations. Single-cell measurements retain those differences, making it possible to examine separate tumor, immune, and stromal profiles. This resolution helps expose heterogeneity and identify gene-expression programs associated with progression or treatment resistance.
Comparing transcriptomic profiles across individual cells can separate malignant cells from immune and stromal populations within the same cancer sample. The resulting cellular identities and states show whether a gene-expression pattern belongs to tumor cells or to neighboring components of the tumor environment. This distinction strengthens interpretation of disease-associated signals and potential biomarkers.
The workflow begins by isolating cells from a sample and capturing RNA from individual cells. The captured RNA is then converted into complementary DNA, or cDNA, while cell-specific barcodes are incorporated. After sequencing, reads are assigned to their labeled cells to generate transcriptomic profiles that can be compared across the sample.
Single-cell transcriptomic profiles can reveal which cell populations are present and describe their molecular states. In cancer research, they help identify tumor heterogeneity and gene-expression programs linked to disease progression or treatment resistance. Because the measurements retain cellular context, researchers can connect particular expression patterns with malignant, immune, or stromal populations.
The profiles can reveal expression patterns associated with specific cellular populations, disease progression, or resistance to treatment. Researchers can use those patterns to support biomarker discovery and patient stratification, grouping patients according to relevant molecular features. The same information may guide more precisely targeted therapies by identifying cellular states that require focused intervention.