Nanocoding: Lipid Nanoparticle Barcoding for Multiplexed Single-Cell RNA Sequencing

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 of heterogeneous cell populations. Here, we describe nanocoding, a technology that applies 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 upon cell lysis. In cultured cell lines and heterogeneous cells from tissue digests, nanocoding occurs in 40 min with stability after sample mixing and requires only commercially available reagents without complex chemical modifications. In spleen digests, 6-plex barcoded samples show minimal unlabeled cells, with all barcodes giving bimodal count distributions. Challenging samples from adipose tissue of obese rodents containing lipid-rich debris and heterogeneous cells 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. The ease of generating and tuning these constructs may afford efficient and robust sample multiplexing with minimal crosstalk.

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Publication Details

Journal
ACS Nano
Published
2025-06-09
DOI
https://doi.org/10.1021/acsnano.5c02111
Citations
3
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
2.42

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article

Nanocoding: Lipid Nanoparticle Barcoding for Multiplexed Single-Cell RNA Sequencing

Jenny Drnevich, D.Q. Chen, Chia-Wei Kuo, Catherine C. Applegate et al.
3 citations
ACS Nano
Single-cell and spatial transcriptomics
2.42
article

Nanocoding: Lipid Nanoparticle Barcoding for Multiplexed Single-Cell RNA Sequencing

Jenny Drnevich, D.Q. Chen, Chia-Wei Kuo, Catherine C. Applegate, Natalia Gonzalez Medina, Opeyemi H. Arogundade, Yujun Feng, Andrew M. Smith, Chris L Wright, Fangxiu Xu
article en
3 citations

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 of heterogeneous cell populations. Here, we describe nanocoding, a technology that applies 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 upon cell lysis. In cultured cell lines and heterogeneous cells from tissue digests, nanocoding occurs in 40 min with stability after sample mixing and requires only commercially available reagents without complex chemical modifications. In spleen digests, 6-plex barcoded samples show minimal unlabeled cells, with all barcodes giving bimodal count distributions. Challenging samples from adipose tissue of obese rodents containing lipid-rich debris and heterogeneous cells 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. The ease of generating and tuning these constructs may afford efficient and robust sample multiplexing with minimal crosstalk.

ACS NanoVol. 19(24)
University of Illinois Urbana-Champaign (US), Illinois College (US), Computing Center (RU), Center for Genomic Science (IT)
National Institutes of Health, Directorate for Engineering
Openalex Percentile: Top 11%
Single-cell and spatial transcriptomics
2.42
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