RNA-Seq: Less Lumping, More Splitting

Single-Cell RNA-Seq Detects Subtle Differences between Cellular Subtypes, Demands Specialized Methods of Data Analysis

Genetic Engineering News by Richard A. Stein, M.D., Ph.D

Categorization has preoccupied biologists going back to the days of classical Greece, when no less a figure than Aristotle classified living things by asking successive narrowing questions such as, “Is it animal or vegetable?” and “How many legs does it have?”

At times, biological categorization has gone awry, resulting in famous gaffes (Aristotle himself said that spiders had six legs and thus qualified as insects)—yet the practice has endured.

More recently, biological categorization was taken up by Charles Darwin, who distinguished between splitters and lumpers. Splitters, he said, are “those who make many species,” whereas lumpers are “those who make few.”

Today, categorization is reaching ever deeper into the stuff of life. For example, it is establishing categories based on differences in gene expression that occur from cell to cell. To find these differences, latter-day Aristotles and Darwins are relying on a new technique: RNA sequencing, or RNA-seq. It promises, as did earlier exercises in categorization, to reveal new entities and previously unknown relationships among them. For example, it may distinguish cell subtypes that are more or less significant at different stages of development, or in different states of health and disease.

Originally, RNA-seq was more of a lumper. It began with bulk RNA-seq approaches, which measure average gene expression levels across cell populations. Increasingly, however, RNA-seq is becoming more of a splitter. The ultimate in transcriptome-level splitting is single-cell RNA-seq technology.

Bulk RNA-seq and single-cell RNA-seq technologies are both capable of providing deep, rapid, and unbiased analyses of the transcriptome, and both are becoming routine in the study of gene expression. Single-cell RNA-seq, however, surpasses bulk RNA-seq in terms of the kinds of information it can generate. It is single-cell RNA-seq that can identify novel cell types, characterize tumor heterogeneity, and follow the cell-fate decisions that shape development. If, however, single-cell RNA-seq is to deliver all these advantages, it must be backed by specialized methods of data analysis.

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