Robust RNA-Seq of aRNA-amplified single cell material collected by patch clamp

Most single cell RNA sequencing protocols start with single cells dispersed from intact tissue. High-throughput processing of the separated cells is enabled using microfluidics platforms. However, dissociation of tissue results in loss of information about cell location and morphology and potentially alters the transcriptome. An alternative approach for collecting RNA from single cells is to re-purpose the electrophysiological technique of patch clamp recording. A hollow patch pipette is attached to individual cells, enabling the recording of electrical activity, after which the cytoplasm may be extracted for single cell RNA-Seq (“Patch-Seq”). Since the tissue is not disaggregated, the location of cells is readily determined, and the morphology of the cells is maintained, making possible the correlation of single cell transcriptomes with cell location, morphology and electrophysiology. Recent Patch-Seq studies utilizes PCR amplification to increase amount of nucleic acid material to the level required for current sequencing technologies. PCR is prone to create biased libraries – especially with the extremely high degrees of exponential amplification required for single cell amounts of RNA. We compared a PCR-based approach with linear amplifications and demonstrate that aRNA amplification (in vitro transcription, IVT) is more sensitive and robust for single cell RNA collected by a patch clamp pipette. We used 5 metrics to assess the RNA-Seq data of amplified products. The first two metrics were total mapping rate defined as a fraction of raw reads mapped to the genome and transcriptome (GenCode v22, GRCh38.p2) and transcriptome mapping ratio calculated as a percentage of mapped reads which mapped to the transcriptome, excluding both rRNA (ribosomal RNA) and mtRNA (mitochondrial RNA). The third metric was gene model discovery rate, assessed as the number of genes with more than 5 mapped reads detected per 3 million mapped reads. A Recent single cell RNA-Seq (scRNA-Seq) study indicated that more than one million reads are required to analyze the variance in expression[2]. We used 3 million as our baseline for our analysis to increase the gene complexity because we are targeting highly complex neuronal cells. With single cell amount (10pg) and sub-single cell amount (5pg) of RNA processed with several different library construction methods, we compared these five metrics . Please note that the 'SMARTER-UCSD-UHR'* and 'Upenn_UHR10pg*' data columns (in the 'Partek_Hugo_SCUHRDS_transcript_counts.xlsx') contain re-analyzed data from publicly available data (GSE56638).