The client wanted to study the expression of their target gene within two known indications and to uncover other diseases where the target has a similar mode of action. Identifying comparable indications could strengthen the business case for further development.

Our Approach

We collected and explored single-cell data, compared gene expression across indications and identified new disease opportunities.

• Data collection & exploration: Gathered public single-cell datasets and metadata, explored gene-expression landscapes and analysed cell-type abundances across indications.
• Cross-indication comparison: Compared target expression across healthy and diseased tissues, charted disease-to-healthy ratios and identified diseases with similar expression profiles.

The Outcome

• Identified potential new indications where the target shows comparable expression patterns.
• Provided a prioritised list of diseases and recommended confirmation through targeted single-cell analyses.
• Enabled the client to expand their indication landscape based on existing data.

Why It Matters

Mining public single-cell datasets can reveal new therapeutic opportunities without the need for additional experiments. This case illustrates how comparative expression analysis informs strategic decisions in small-molecule development.