Researchers can identify a person by sequencing RNA from individual cells

An individual’s genome is identifiable. But datasets of RNA sequences from single cells are like a blurry photocopy of the genome. The sequence numbers are small, the data are noisy, and the sequences represent only the small set of genes expressed in a given cell type. That’s why “there was a lot of debate” over whether one could identify a person based on data from a single-cell RNA sequencing experiment, says Columbia University bioinformatics professor Gamze Gürsoy.

Researchers currently treat single-cell sequencing as biochemically anonymous and share datasets publicly. The technique catalogs RNA from individual cells to detect expression differences between cell types; as it has become more affordable, studies with hundreds or thousands of research participants have become available online.

Gürsoy and her lab found that they could robustly link single-cell transcriptomes with known genomes using two such datasets from studies of autoimmune disorders (Cell 2024, DOI: 10.1016/j.cell.2024.09.012). The researchers used known statistical relationships between genomic regions and the amount of expression of related RNAs to infer an individual’s genome from as few as 1,000 cells from a blood sample. The method could allow someone with access to the data to uncover individuals in the autoimmune or control groups.

Gürsoy stresses that there’s no evidence that anyone could benefit from uncovering a person’s health status in this way, especially given laws against genetic discrimination. Still, she says, the research community has a responsibility to be proactive about protecting participants’ privacy. “Maybe we need more safeguards,” such as updates to the informed consent process and better protocols for data encryption, she says.