ABSTRACT High-quality single-cell RNA-sequencing (RNA-seq) with spatial resolution remains challenging. Laser capture microdissection (LCM) is a widely used, potent approach to isolate arbitrarily targeted cells from tissue sections for comprehensive transcriptomics. Here, we developed DRaqL (direct RNA recovery and quenching for LCM), an experimental approach for efficient lysis of single cells isolated by LCM from alcohol- and formalin-fixed sections without RNA purification. Single-cell RNA-seq combined with DRaqL allowed transcriptomic profiling from alcohol-fixed sections with efficiency comparable to that of profiling from freshly dissociated cells, together with effective exon– exon junction profiling. Furthermore, the combination of DRaqL and protease treatment enabled robust and efficient single-cell transcriptome analysis from tissue sections strongly fixed with formalin. Applying this method to mouse ovarian sections, we revealed a transcriptomic continuum of growing oocytes quantitatively associated with oocyte size, and detected oocyte-specific splice isoforms. In addition, our statistical model revealed heterogeneity of the relationship between the transcriptome of oocytes and their size, resulting in identification of a size–transcriptome relationship anomaly in a subset of oocytes. Finally, we identified genes that were differentially expressed in granulosa cells in association with the histological affiliations of granulosa cells to the oocytes, suggesting distinct epigenetic regulations and cell-cycle activities governing the germ–soma relationship. Thus, we developed a versatile, efficient approach for robust single-cell cDNA amplification from tissue sections and provided an experimental platform conducive to high-quality transcriptomics, thereby revealing histology-associated transcriptomic heterogeneity in folliculogenesis in ovarian tissues.

Histology-associated transcriptomic heterogeneity in ovarian folliculogenesis revealed by quantitative single-cell RNA-sequencing for tissue sections with DRaqL