bioRxivCancer BiologyPreprint
Evaluation of single-cell RNA profiling technologies using FFPE, fresh, and frozen ccRCC tumor specimens
Simayijiang XirenayiiD, Sabrina Yvonne CampiD, Amanda GarzaiD, Yashika RustagiiD, Erica Maria PimentaiD, Erin E. ShannoniD, Anwesha NagiD, Adam Betherman, Anthony N. AnselmoiD, Rachel Trowbridge, Aaron R. ThorneriD, Jihye ParkiD, Kevin BiiD, Eliezer M. Van AlleniD
Abstract
The application of single-cell transcriptomic approaches has deepened our understanding of tumor heterogeneity, immune dynamics, and molecular programs underlying therapy response. The recent development of fixation-compatible single-cell platforms, such as 10x Genomics Flex, offers the opportunity to profile archived formalin-fixed, paraffin-embedded (FFPE) specimens, expanding access to clinically valuable samples. However, most benchmarking studies of recent single-cell RNA sequencing (scRNA-seq) technologies have relied on peripheral blood mononuclear cells, limiting their relevance to human tissues. Here, we compared three 10x single-cell RNA profiling methods, fresh tumor scRNA-seq, flash-frozen single-nucleus Multiome, and FFPE single-nucleus Flex (snFlex), using clear cell renal cell carcinoma (ccRCC) biopsies. Across methods, we observed broadly consistent cell type-specific transcriptional profiles among major ccRCC cell populations. Despite lower gene and UMI counts, snFlex reliably identified fine-grained states within CD8+ T cells, tumor-associated macrophages, and tumor compartments, comparable to those detected by scRNA-seq. Together, these findings highlight the distinct advantages of each technology depending on sample preservation type and study design, providing practical guidance for single-cell RNA profiling technology selection in translational studies using human tumor biopsies.
- organism: human
- assay: scRNA-seq
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: benchmarking
- research focus: differential expression
- approach: experimental assay
- approach: statistical model
- Citations
- 0
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