CoolMPS for robust sequencing of single-nuclear RNAs captured by droplet-based method

Massively-parallel single-cell and single-nucleus RNA sequencing (scRNA-seq, snRNA-seq) requires extensive sequencing to achieve proper per-cell coverage, making sequencing resources and availability of sequencers critical factors for conducting deep transcriptional profiling. CoolMPS is a novel sequencing-by-synthesis approach that relies on nucleotide labeling by re-usable antibodies, but whether it is applicable to snRNA-seq has not been tested.

Researchers at the Stanford University School of Medicine use a low-cost and off-the-shelf protocol to chemically convert libraries generated with the widely-used Chromium 10X technology to be sequenceable with CoolMPS technology. To assess the quality and performance of converted libraries sequenced with CoolMPS, the researchers generated a snRNA-seq dataset from the hippocampus of young and old mice. Native libraries were sequenced on an Illumina Novaseq and libraries that were converted to be compatible with CoolMPS were sequenced on a DNBSEQ-400RS. CoolMPS-derived data faithfully replicated key characteristics of the native library dataset, including correct estimation of ambient RNA-contamination, detection of captured cells, cell clustering results, spatial marker gene expression, inter- and intra-replicate differences and gene expression changes during aging. In conclusion, these results show that CoolMPS provides a viable alternative to standard sequencing of RNA from droplet-based libraries.

Integrated native and CooMPS-compatible snRNA-seq datasets
show no separation by library type

(A, B) UMAP representation of cells and clustering results of n = 60,0720 cells integrated from both datasets. Cells are colored by (A) cluster or (B) library type. Cells were split by library type to improve visibility. (C) Relative abundance of each cluster normalized to total cells. Relative abundances are split by library type to gauge conversion-dependent composition of clusters. n = 4 per dataset. Means ± SEM. ***P_adj < 0.001, **P_adj < 0.01, *P_adj < 0.05, two-sided Wilcoxon rank-sum test, adjusted for multiple testing. Adjusted P-values for paired, two-sided Wilcoxon test probing differential abundance per cluster are shown in red. (D) Heatmap depicting the number of common top 50 genes per cluster as detected in either integrated or native dataset. (E, F) UMAP representation of cell populations from integration dataset when (A) collapsed or (B) split by dataset and age. Circles highlight differential abundance of choroid plexus-derived cells and Pericytes.