Mobile Menu

New tool links individual cells with disease risk in scRNA-seq data

Written by Lauren Robertson, Science Writer

Genome-wide association studies (GWAS) often provide researchers with an insight into how genetic variants contribute to a particular disease. By integrating scRNA-seq with GWAS data, we can identify which risk variants impact disease. But identifying how these risk variants are linked to specific tissues and cell types is still a challenge, and current techniques often overlook the heterogeneity seen across different cells within a cell type.

Now, in a study published in Nature, a team from Harvard T.H. Chan School of Public Health have developed a new tool called “single-cell disease relevance score” (or scDRS) that promises to evaluate the disease enrichment of individual cells within scRNA-seq data.

Scoring a cell’s disease relevance

scDRS works by assessing whether a particular cell has excess expression levels across a set of disease genes derived from GWASs. How? First, scDRS forms a set of putative disease genes from GWAS data using an existing gene scoring method (MAGMA18). It then quantifies the aggregate expression of these genes in each cell to generate cell-specific raw disease scores. Finally, the tool normalizes the raw disease scores and computes cell-level P values.

To show how their tool would measure up in real-life scenarios, the team used several extensive simulations and applied it to 74 diseases/traits as well as 16 RNA-seq datasets. This allowed an analysis of cell-type-disease associations and within-cell-type association heterogeneity, including the heterogeneity of T cells in autoimmune diseases and hepatocytes in metabolic traits.

The true heterogeneity of a disease-associated cell

All in all, scDRS identified 80 associated cell-type-disease pairs and found significant within-cell-type disease-association heterogeneity in more than 50% of these pairs. Cell types were generally grouped with their corresponding disease or trait, as can be seen in Figure 1 below.

Figure 1 Disease associations at the cell type-level.
Each row represents a disease/trait and each column represents a cell type. Heatmap colours for each cell type-disease pair denote the proportion of significantly associated cells. Squares denote significant cell type-disease associations. Cross symbols denote significant heterogeneity in association with disease across individual cells within a given cell type. Abbreviated cell type names include red blood cell (RBC), granulocyte monocyte progenitor (GMP), medium spiny neuron (MSN), and oligodendrocyte precursor cell (OPC). Neuron refers to neuronal cells with undetermined subtypes (whereas MSN and interneuron (non-overlapping with neuron) refer to neuronal cells with those inferred subtypes).

Besides these somewhat anticipated results, a few findings stood out to the authors. For example, granulocyte monocyte progenitors were found to be strongly associated with multiple sclerosis – potentially highlighting the role of myeloid cells in the disease. They also found that oligodendrocytes and their precursor cells were associated with multiple brain-related diseases or traits.

Of particular note, their analysis detected significant within-cell-type association heterogeneity, including T cells with immune diseases, neurons with brain-related diseases/traits, and hepatocytes with metabolic traits. The hope is that these findings may enable researchers to target relevant cell populations in future in vitro experiments and elucidate the molecular mechanisms through which GWAS risk variants impact disease.

A look to the future

Though exciting, the study is not without its limitations. The authors note that statistical correlation itself does not necessarily mean these cells or cell types are involved in disease – though it is highly likely. They also highlight that scDRS may perform better when used with broader cell atlases rather than more narrow collections of cells.

Still, the work demonstrates the value of associating individual cells to disease, assessing heterogeneity across cells within predefined cell types, and associating these to diseases. Moving forward, the team already have their sights set on further experiments: “Although we have primarily focused on the associations involving a single disease/trait, further investigation of differences between diseases/traits within the same category is an important future direction.”