Analysis of canine myeloid-derived suppressor cells (MDSCs) utilizing fluorescence-activated cell sorting, RNA...

Fluorescence-activated cell sorting (FACS) is a branch of flow cytometry that allows for the isolation of specific cell populations that can then be further analyzed by single-cell RNA sequencing (scRNA-seq). When utilizing FACS for population isolation prior to sequencing, it is essential to consider the protection of RNA from RNase activity, environmental conditions, and the sorting efficiency to ensure optimum sample quality. This study aimed to optimize a previously published MDSC flow cytometry strategy to FACS sort canine Myeloid-Derived Suppressor Cells (MDSC) with various permutations of RNAlater ™ and RiboLock™ before and after FACS sorting. Concentrations of RNAlater™ greater than 2 % applied before flow analysis affected cell survival and fluorescence, whereas concentrations ≤ 2 % and time ≤ 4 h had little to no effect on cells. To shorten the procedural time and to enhance the sorting of rare populations, we used a primary PE-conjugated CD11b antibody and magnetic column. The combination of RiboLock™ pre- and post-sorting for FACS provided the best quality RNA as determined by the RNA integrity number (RIN ≥ 7) for scRNA-seq in a normal and dog and a dog with untreated oral melanoma dog. As proof of principle, we sequenced two samples, one from a normal dog another from a dog with untreated oral melanoma. Applying scRNA-Seq analysis using the 10X Genomic platform, we identified 6 clusters in the Seurat paired analysis of MDSC sorted samples. Two clusters, with the majority of the cells coming from the melanoma sample, had genes that were upregulated (> log2); these included MMP9, MMP1, HPGD, CPA3, and GATA3 and CYBB, CSTB, COX2, ATP6, and COX 17 for cluster 5 and 6 respectively. All genes have known associations with MDSCs. Further characterization using pathway analysis tools was not attempted due to the lower number of cells sequenced in the normal sample. The benefit deriving from the results of the study helped to gain data consistency when working with cells prone to RNase activity, and the scRNA-seq provided data showing transcriptional heterogeneity in MDSC populations and potentially identifying previously unreported or rare cell populations.