Longitudinal gene expression from dried blood microsamples: a pilot study

Understanding dynamic immune–tumor interactions is essential for improving cancer detection, monitoring treatment response, and identifying early signs of relapse. However, current immune monitoring strategies in oncology rely heavily on venous blood sampling, limiting longitudinal resolution and increasing cost and patient burden. Molecular profiling approaches such as RNA sequencing provide deep biological insight but are not traditionally compatible with frequent, remote sampling. HemoPrint addresses this limitation by enabling scalable, decentralized immune transcriptomic profiling using dried blood microsamples.

HemoPrint integrates volumetric capillary microsampling, RNA stabilization, automated extraction, and a targeted RNA-sequencing workflow optimized for robust gene expression quantification from small blood volumes. The platform generates high-quality transcriptomic data with strong concordance to matched venous blood samples, while enabling high-frequency longitudinal sampling. In oncology, this creates opportunities to track systemic immune responses during therapy, monitor minimal residual disease–associated immune changes, and capture early immune shifts linked to disease progression or treatment toxicity.

To translate transcriptomic data into clinically relevant immune metrics, we apply computational deconvolution methods trained on single-cell RNA sequencing reference datasets. This enables estimation of circulating immune cell fractions directly from HemoPrint bulk transcriptomes, providing a scalable alternative to flow cytometry or single-cell profiling. The combination of remote sampling and computational immune phenotyping enables monitoring of tumor–immune ecosystem dynamics in real-world settings.

Together, HemoPrint and transcriptomic deconvolution support a shift toward longitudinal, patient-centric immune monitoring in cancer, enabling earlier detection of disease transitions, improved therapy monitoring, and scalable generation of real-world immune-oncology data to support precision oncology and translational research.