Robust detection of clinically relevant features in single-cell RNA profiles of patient-matched fresh and formalin-fixed paraffin-embedded (FFPE) lung cancer tissue

Abstract

Single-cell transcriptional profiling reveals cell heterogeneity and clinically relevant traits in intra-operatively collected patient-derived tissue. However, the established approach to perform such analyses on freshly collected tissue constitutes an important limitation since it requires prospective collection and immediate processing. Therefore, the ability to perform single-cell RNA sequencing from archived tissues would be very beneficial in a clinical setting. Here, we benchmark single-cell gene expression profiles from patient-matched fresh, cryopreserved and FFPE cancer tissue. We find that fresh tissue and FFPE routine blocks can be employed for the robust detection of clinically relevant traits on the single-cell level. Specifically, single-cell maps of fresh patient tissues and corresponding FFPE tissue blocks could be integrated into common low-dimensional representations, and cell subtype clusters showed highly correlated transcriptional strengths of signaling pathways, Hallmark and clinically useful signatures, despite some variability in expression of individual genes due to technological differences. FFPE tissue blocks revealed higher cell diversity compared to fresh tissue. In contrast, single-cell profiling of cryopreserved tissue was prone to artifacts in the clinical setting. Our analysis suggests that single-cell RNA sequencing from FFPE tissues is comparable to and can replace analyses from fresh tissue. This highlights the potential of single-cell profiling in the analysis of retrospectively and prospectively collected archival pathology cohorts and dramatically increases the applicability in translational projects.