Identify cell types at single-cell level
Since many years biologists are capable of sequencing the transcriptome profiles of tissues or cell cultures. However, in classical bulk RNA-sequencing experiments, the resulting profiles are an average of the entire population. Information on rare cells or subtle differences are lost within that population. Single-cell RNA sequencing allows you to take a snapshot of unique transcriptomes for individual cells, hundreds to thousands of cells at the time. These unique transcriptome profiles help scientists in the search for uncharted cell types like rare stem cells, finding minor differences between (sub)-populations, and assessing heterogeneity in cohorts of cells.
The need to classify cell types
Classifying cells into types is increasingly viewed as a requirement to gain a detailed understanding of how tissues function and interact, or to reveal mechanisms underlying pathological states. Knowledge of what cell types exist facilitates applications such as labeling specific types, cross-species comparisons, insight into heterogeneity, and implicating roles for specific cell types in diseases.
Find cell types in your data
Cell type classification based on single-cell RNA sequencing involves partitioning the data into “clusters” of single cells. Here, each cluster is defined by a unique gene expression signature compared to other clusters and therefore represents a distinct cell type. It must be noted that it is possible that this single-cell RNA sequencing-based cluster represent more of a molecular state, rather than a distinct cell type. Therefore, it is important to think about experiments to validate your results obtained with single-cell RNA sequencing.
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