Single-microbe Total-RNA-seq (MATQ-PG Preps)
Single Synaptosome RNA-seq (MATQ-PG Preps)
We are dedicated to delivering specialized services and comprehensive kits designed for ultra-high throughput and high-sensitivity profiling of single-cell/nucleus and synaptosome transcriptomes.
Synapses are crucial structures that mediate signal transmission between neurons in complex neural circuits and display considerable morphological and electrophysiological heterogeneity. So far we still lack a high-throughput method to profile the molecular heterogeneity among individual synapses. In the present study, we develop a droplet-based single-cell (sc) total-RNA-sequencing platform, called Multiple-Annealing-and-Tailing-based Quantitative scRNA-seq in Droplets, for transcriptome profiling of individual neurites, primarily composed of synaptosomes. In the synaptosome transcriptome, or ‘synaptome’, profiling of both mouse and human brain samples, we detect subclusters among synaptosomes that are associated with neuronal subtypes and characterize the landscape of transcript splicing that occurs within synapses. We extend synaptome profiling to synaptopathy in an Alzheimer’s disease (AD) mouse model and discover AD-associated synaptic gene expression changes that cannot be detected by single-nucleus transcriptome profiling. Overall, our results show that this platform provides a high-throughput, single-synaptosome transcriptome profiling tool that will facilitate future discoveries in neuroscience.
The quantification of transcriptional variation in single cells, particularly within the same cell population, is currently limited by the low sensitivity and high technical noise of single-cell RNA-seq assays. We report multiple annealing and dC-tailing-based quantitative single-cell RNA-seq (MATQ-seq), a highly sensitive and quantitative method for single-cell sequencing of total RNA. By systematically determining technical noise, we show that MATQ-seq captures genuine biological variation between whole transcriptomes of single cells.
