# single cell transcriptomics

> technologies to measure multiple transcripts in single cells, including single-cell RNA-seq, single nucleus RNA-seq, single cell multiplex PCR and multiplex FISH

**Wikidata**: [Q105406038](https://www.wikidata.org/wiki/Q105406038)  
**Source**: https://4ort.xyz/entity/single-cell-transcriptomics

## Summary
Single cell transcriptomics is a set of technologies that measure multiple RNA transcripts in individual cells. It includes methods like single-cell RNA-seq, single nucleus RNA-seq, single cell multiplex PCR, and multiplex FISH. These technologies enable researchers to study gene expression at the single-cell level.

## Key Facts
- Single cell transcriptomics is a subclass of both single-cell analysis and transcriptomics
- It is different from single-cell RNA-seq, though single-cell RNA-seq is one of its methods
- The field includes multiple technologies: single-cell RNA-seq, single nucleus RNA-seq, single cell multiplex PCR, and multiplex FISH
- It is also known as single-cell transcriptomic analysis or Analyse transcriptomique unicellulaire
- The field studies RNA transcripts, which are part of the broader study of transcriptomics

### Q: What is single cell transcriptomics?
A: Single cell transcriptomics is a set of technologies that measure multiple RNA transcripts in individual cells. It includes methods like single-cell RNA-seq, single nucleus RNA-seq, single cell multiplex PCR, and multiplex FISH.

### Q: How is single cell transcriptomics different from single-cell RNA-seq?
A: Single cell transcriptomics is a broader field that includes single-cell RNA-seq as one of its methods. It also encompasses other technologies like single nucleus RNA-seq, single cell multiplex PCR, and multiplex FISH.

### Q: What can single cell transcriptomics be used for?
A: Single cell transcriptomics can be used to study gene expression at the single-cell level, allowing researchers to understand cellular heterogeneity and identify rare cell types or states within a population.

## Why It Matters
Single cell transcriptomics has revolutionized our understanding of biological systems by enabling researchers to study gene expression at the individual cell level. This technology has revealed previously hidden cellular heterogeneity within tissues and organs, leading to new insights in developmental biology, cancer research, immunology, and neuroscience. By providing a more granular view of cellular function, single cell transcriptomics has helped identify rare cell populations, track cellular differentiation pathways, and understand how cells respond to various stimuli or disease states. This technology has been particularly transformative in understanding complex biological processes that were previously obscured when studying bulk tissue samples, where the average signal from many cells can mask important variations between individual cells.

## Notable For
- Enabling measurement of gene expression in individual cells rather than bulk tissue samples
- Revealing cellular heterogeneity within seemingly homogeneous cell populations
- Providing insights into rare cell types and transient cellular states
- Facilitating the discovery of new cell types and cellular differentiation pathways
- Allowing researchers to study how individual cells respond to different conditions or treatments

## Body
### Technologies Included
Single cell transcriptomics encompasses several key technologies:
- Single-cell RNA-seq: measures RNA transcripts in individual cells using next-generation sequencing
- Single nucleus RNA-seq: similar to single-cell RNA-seq but works with isolated nuclei, useful for frozen or difficult-to-dissociate tissues
- Single cell multiplex PCR: amplifies multiple target sequences simultaneously in individual cells
- Multiplex FISH: visualizes multiple RNA transcripts simultaneously in single cells using fluorescence microscopy

### Applications
Single cell transcriptomics has found applications across multiple biological disciplines:
- Developmental biology: tracking cellular differentiation and lineage relationships
- Cancer research: identifying tumor heterogeneity and rare cancer stem cells
- Immunology: characterizing immune cell populations and their responses
- Neuroscience: understanding neuronal diversity and brain development
- Disease research: identifying cellular changes associated with various pathologies

### Technical Considerations
The field requires careful consideration of several technical aspects:
- Cell isolation methods must preserve RNA integrity
- Library preparation protocols must be optimized for low-input RNA
- Data analysis requires specialized bioinformatics approaches to handle high-dimensional data
- Quality control is essential to ensure reliable measurements from individual cells
- Experimental design must account for technical variability between cells