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. Researchers at the Baylor College of Medicine have developed 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, the researchers detect subclusters among synaptosomes that are associated with neuronal subtypes and characterize the landscape of transcript splicing that occurs within synapses. They 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, these results show that this platform provides a high-throughput, single-synaptosome transcriptome profiling tool that will facilitate future discoveries in neuroscience.
Overview of MATQ-Drop and the performance in species-mixing experiment
a, Reaction scheme of MATQ-Drop. In situ reverse transcription and poly(A) tailing are performed on the fixed nuclei, which are then encapsulated in droplets with barcoded hydrogel beads. Inside the droplet, barcoded dT20 primers are enzymatically released from the beads to capture the poly(A) tail of cDNA released from the nuclei. After the barcoded second-strand synthesis has been accomplished, the emulsion is broken and the product can be amplified and sequenced. b, Identification of the barcodes representing true nuclei in the species-mixing experiment. Barcodes are ordered from the largest to the smallest UMI counts. On the UMI counts versus barcode rank plot, the knee point (162, red dashed line) indicates the threshold for true nuclei. c, Species annotation of the 162 nuclei identified. d, Species specificity of UMIs. e, Fractions of UMIs in exons and introns (mean ± s.d.). f,g, Detection sensitivity of MATQ-Drop in UMI counts (f) and gene counts (g). h,i, Comparison of detection sensitivity between MATQ-Drop and other major snRNA-seq methods for single NIH/3T3 nuclei, UMI detection (h) and gene detection (i) (P values calculated using two-sided Student’s t-test). d,f–i, Boxplot shows the center line and median, box limits the upper and lower quartiles, whiskers the 1.5× interquartile range (IQR) and points the outliers.
Availability – The analysis code customized for MATQ_Drop sequencing data is available at https://github.com/zonglab/MATQ_Drop.