Insights

Evaluation of single cell RNA sequencing from FFPE tissue using 10x Genomics Chromium Fixed RNA Profiling

Single cell RNA sequencing (scRNA-seq) uncovers tissue heterogeneity by providing gene expression measurements in individual cells within the tissue. A current limitation of scRNA-seq is the requirement to isolate viable cells from fresh or cryopreserved solid tumor biopsies. Herein, this study describes and characterizes the utilization of prepared single cells from FFPE tissue sections to establish sample requirements, key quality control (QC) metrics, and reproducibility of a robust single cell fixed RNA profiling kit for FFPE tissues.

 

To accomplish this, FFPE blocks varying in age and quality (i.e., DV200) were sectioned to either 25 or 50 µm for processing. One or two FFPE tissue curls per sample were dissociated into single cell suspensions using the Miltenyi FFPE Tissue Dissociation Kit, processed through the Chromium Fixed RNA Profiling workflow, sequenced using the Illumina NovaSeq, and analyzed by Cell Ranger 7.0.1.

 

All samples yielded sufficient cells for probe hybridization and for targeting the capture of 10,000 cells. The total number of genes detected was highly reproducible between replicates and ranged between 14,000 and 18,000 per tissue type, indicating robustness of the workflow across tissues. Notably, DV200 scores were observed to strongly correlate with key QC metrics, specifically cell capture efficiency and RNA species diversity. While FFPE curl number and size input variables were found to have no significant impact on QC metrics, the effect of DV200 was observed within each tissue type, indicating a strong driver of metric variability.

 

These combined findings support the use of the 10x Genomics Fixed RNA Profiling workflow to obtain high quality single cell gene expression data from FFPE tissues. These data also demonstrate that meaningful gene expression data is produced by multiple tissue types and input amounts, but samples with the highest DV200 produce the most robust data.

 

Watch this poster presentation to learn more.