Single Cell Transcriptomics
Single Cell Transcriptomics - Related Videos
07:27
Transcriptome Analysis of Single Cells
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Cited by 65 •
2011
In this article we describe a simple method for the harvesting of single cells from rat primary neuronal cultures and subsequent transcriptome analysis using aRNA amplification. This approach is generalizable to any cell type.
05:45
Generation and Downstream Analysis of Single-Cell and Single-Nuclei Transcriptomes in Brain Organoids
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2024
Here, we introduce a comprehensive protocol for the generation and downstream analysis of human brain organoids using single-cell and single-nucleus RNA sequencing.
08:53
Isolation and Transcriptome Analysis of Plant Cell Types
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Cited by 2 •
2023
The feasibility and effectiveness of high-throughput scRNA-seq methods herald a single-cell era in plant research. Presented here is a robust and complete procedure for isolating specific Arabidopsis thaliana root cell types and subsequent transcriptome library construction and analysis.
08:58
Using R, Seurat, and CellChat to Analyze a Single-Cell Transcriptomics Dataset of Mouse Skin Wound Healing
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Cited by 1 •
2025
Here, we present a step-by-step, visual workflow for analyzing a single-cell time-course transcriptomics dataset of mouse skin wound healing using R. The protocol includes a standard pipeline for dataset download, quality control, visualizations, and cell type annotations using Seurat, and cell-cell interaction analysis using CellChat.
10:05
Single-cell Transcriptomic Analyses of Mouse Pancreatic Endocrine Cells
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Cited by 3 •
2018
We describe a method for the isolation of endocrine cells from embryonic, neonatal and postnatal pancreases followed by single-cell RNA sequencing. This method allows analyses of pancreatic endocrine lineage development, cell heterogeneity and transcriptomic dynamics.
FAQs
The central advantage is resolution at the level of individual cells. A tissue-wide measurement can blend transcripts from many cell populations, whereas separate profiles preserve differences among cell states. This makes it possible to distinguish uncommon populations from abundant neighbors and to examine how cellular responses vary within the same biological sample.
Converting cellular RNA into complementary DNA creates the material that sequencing can read for transcript quantification. The resulting measurements are associated with individual captured cells, allowing expression profiles to be compared rather than merged into one tissue-wide signal. This linkage preserves cell-level variation for downstream analysis.
Computational grouping compares gene-expression profiles and assigns cells to sets with similar transcriptional patterns. These groups can represent distinct cell types or states, while relationships among profiles can help reconstruct developmental trajectories. Interpretation therefore depends not only on sequencing, but also on organizing the resulting measurements into biologically meaningful patterns.
A typical workflow begins with isolating or capturing individual cells. RNA from those cells is converted into complementary DNA, and sequencing generates transcript measurements. Computational analysis then groups cells according to their expression profiles. Separating these stages helps researchers relate experimental handling to the biological patterns identified afterward.
Comparisons across disease, environmental conditions, or treatment can reveal which cell populations change and which transcriptional programs accompany those changes. Because measurements remain linked to individual cells, the analysis can identify responses associated with particular cell types or states. This makes the method useful for studying biological responses under contrasting conditions.
In developmental biology, arranging cells by expression relationships can help reconstruct trajectories between cell states. This perspective goes beyond listing endpoint populations by connecting transcriptional programs with developmental progression and tissue organization. It can therefore support questions about how cellular diversity emerges as a biological system changes.