ACS NanoBioengineeringPeer-reviewed journal article
Lipid Nanoparticle Barcoding for Multiplexed Single-Cell RNA Sequencing
Yujun FengiD, Donglai CheniD, Catherine C. ApplegateiD, Natalia Gonzalez MedinaiD, Chia-Wei KuoiD, Opeyemi H. ArogundadeiD, Chris L WrightiD, Fangxiu Xu, Jenny M. DrnevichiD, Andrew M. SmithiD
Abstract
Sample multiplexing is an emerging method in single-cell RNA sequencing (scRNA-seq) that addresses high costs and batch effects. Current multiplexing schemes use DNA labels to barcode cell samples but are limited in their stability and extent of labeling across heterogeneous cell populations. Here, we introduce Nanocoding using lipid nanoparticles (LNPs) for high barcode labeling density in multiplexed scRNA-seq. LNPs reduce dependencies on cell surface labeling mechanisms due to multiple controllable means of cell uptake, amplifying barcode loading 10-100-fold and allowing both protection and efficient release by dissolution. In cultured cell lines and heterogeneous cells from tissue digests, Nanocoding occurs in 40 minutes with stability after sample mixing and requires only commercially available reagents without novel chemical modifications. In spleen digests, 6-plex barcoded samples show minimal unlabeled cells, with all barcodes giving bimodal count distributions. Challenging samples containing lipid-rich debris and heterogeneous cells from adipose tissue of obese rodents show more than 95% labeling with all known subtypes identified. Using Nanocoding, we investigate gene expression changes related to aging in adipose tissue, profiling cells that could not be readily identified with current direct conjugate methods using lipid or antibody conjugates. This ease of generating and tuning these constructs may afford efficient and robust whole-sample multiplexing with minimal sample crosstalk.
- organism: mouse
- organism: rat
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: experimental assay
- Citations
- 4
- FWCI
- 1.20
- Field percentile
- top 24%
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