Identify cell types at a single-cell level
Ever since the advent of next-generation sequencing, biologists have been able to sequence the transcriptome of tissues and cell cultures.
However, in traditional bulk RNA-sequencing experiments, the resulting profiles are an average of the entire population of the cells used in the experiment. Information on rare cells or differences between cell types within that pool is averaged out.
Single-cell RNA sequencing, however, allows you to chart the transcriptomes of each individual cell, hundreds or thousands of cells at a time.
This technology is used by scientists in the search for uncharted cell types like rare therapy-resistant cancer cells, finding new (sub)populations within existing cell types, and assessing heterogeneity in cohorts of patient samples.
There are multiple single-cell sequencing platforms that can be applied to identify cell types. Use our single-cell sequencing buying guide to find which technology matches your project.
The need to classify cell types
Classifying cells is crucial to get a detailed understanding of how tissues function and interact, and to reveal mechanisms underlying pathological states.
Knowledge of which cell types exist allows us to find markers for specific (sub)cell types, and provides insight into the role of heterogeneity in disease and how particular environments can impact tumor biology.
Find cell types in your data
Cell type identification based on single-cell RNA sequencing involves partitioning the data into “clusters” of single cells. Here, a similar gene expression signature defines each cluster making it different from the cells in other clusters. The average transcriptome of all cells in that cluster can therefore be used to represent a specific cell type.
Once these transcriptome profiles are established, it is important to validate new targets that are predicted by the data with another experimental method. Likewise, it is important to determine whether they represent a stable cell type or a transient molecular state.
While this can be partially solved computationally, validation experiments are often crucial to this process. Therefore, it is important to think about follow-up experiments that can confirm the results obtained with single-cell RNA sequencing.
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