Poster Presentation 39th Annual Lorne Genome Conference 2018

Transcriptional characterization of low input frozen brain samples at single-nucleus resolution using 10x Genomics Chromium microfluidics (#268)

Dulce Vargas Landin 1 2 , Rebecca Simmons 1 2 , Marina Oliva 1 2 , Daniel Poppe 1 2 , Jahnvi Pflueger 1 2 , Ryan Lister 1 2
  1. The University of Western Australia, Crawley, WA, Australia
  2. Epigenetics and Genomics Laboratory, Harry Perkins Institute of Medical Research, Perth, Western Australia, Australia

The recent development of single-nucleus RNA-sequencing (snRNA-seq) techniques has exposed the cellular diversity of complex tissues and new insights in cellular differentiation, tissue development, and human diseases. There are two leading snRNA-seq techniques, the DroNc-seq and the 10X Genomics Chromium system, both based on microfluidics to encapsulate single nuclei with unique barcodes that allow nucleus identification. These two techniques are capable of profiling thousands of nuclei and classifying them by cell types. However, they require large amounts of input material and, in the case of 10X Genomics Chromium, fresh tissue. These two disadvantages restrict their usage for clinical samples, which are usually frozen and in limited sizes. Here we present a single-nucleus isolation protocol for low input frozen samples. This protocol is compatible with the 10X Genomics Chromium microfluidics system and can be completed in 40 minutes. Using this protocol, we successfully profiled four frozen human and mouse cortices with 15-20 mg starting material. We compared the data obtained from our frozen samples to the publicly available libraries from fresh tissue generated by the 10X Genomics Chromium. With our technique, we detected a similar number of genes (~1,500), obtained a higher diversity of cell types and had lower mitochondrial transcripts (<0.05%) than in the fresh tissue samples. Overall, this protocol allows the transcriptional characterization of small frozen samples at single nucleus resolution, increasing the clinical applications of snRNA-seq.

  1. Habib, N., et al., Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat Methods, 2017. 14(10): p. 955-958.
  2. Lake, B.B., et al., A comparative strategy for single-nucleus and single-cell transcriptomes confirms accuracy in predicted cell-type expression from nuclear RNA. Sci Rep, 2017. 7(1): p. 6031.