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Peptide-encoding mRNA barcodes for the high-throughput in vivo screening of libraries of lipid nanoparticles for mRNA delivery

Nature Biomedical Engineering volume 7, pages 901–910 (2023)Cite this article

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Abstract

Developing safe and effective nanoparticles for the delivery of messenger RNA (mRNA) is slow and expensive, partly due to the lack of predictive power of in vitro screening methods and the low-throughput nature of in vivo screening. While DNA barcoding and batch analysis present methods for increasing in vivo screening throughput, they can also result in incomplete or misleading measures of efficacy. Here, we describe a high-throughput and accurate method for the screening of pooled nanoparticle formulations within the same animal. The method uses liquid chromatography with tandem mass spectrometry to detect peptide barcodes translated from mRNAs in nanoparticle-transfected cells. We show the method’s applicability by evaluating a library of over 400 nanoparticle formulations with 384 unique ionizable lipids using only nine mice to optimize the formulation of a biodegradable lipid nanoparticle for mRNA delivery to the liver. Barcoding lipid nanoparticles with peptide-encoding mRNAs may facilitate the rapid development of nanoparticles for mRNA delivery to specific cells and tissues.

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Fig. 1: Overview of the peptide barcoding assay.
Fig. 2: Validation and expansion of the peptide barcoding assay.
Fig. 3: Synthesis of 384 ionizable lipids with biodegradable ester linkages and extensive tail branching.
Fig. 4: Evaluation of an ionizable lipid series using peptide barcoding.
Fig. 5: Optimization and characterization of lead lipid, RM133-3.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. All data generated or analysed during the study are available from the corresponding author on reasonable request.

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Acknowledgements

We acknowledge project funding from Translate Bio (Lexington, MA, USA) and the Marble Center for Cancer Nanomedicine, as well as support from the Cancer Center Support (core) (grant no. P30-CA14051) from the National Cancer Institute. We thank the Koch Institute Swanson Biotechnology Center for technical support, specifically the Biopolymers & Proteomics core and Preclinical Imaging and Testing core.

Author information

Author notes

  1. These authors contributed equally: Luke H. Rhym, Rajith S. Manan.

Authors and Affiliations

  1. David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

    Luke H. Rhym, Rajith S. Manan, Antonius Koller, Georgina Stephanie & Daniel G. Anderson

  2. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Luke H. Rhym, Rajith S. Manan & Daniel G. Anderson

Authors
  1. Luke H. Rhym
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  2. Rajith S. Manan
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Contributions

L.H.R. developed the peptide barcoding concept. L.H.R., R.S.M. and D.G.A. conceived the study and designed experiments. L.H.R. and G.S. performed all in vitro and in vivo barcoding, hEPO and FLuc assays, including LNP formulation and animal work. A.K. developed and performed the LC–MS/MS method for all samples. R.S.M. designed and synthesized the combinatorial ionizable lipid library screened in this study. L.H.R., R.S.M. and D.G.A. contributed to the analysis and interpretation of the results and to the writing of the paper.

Corresponding author

Correspondence to Daniel G. Anderson.

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Competing interests

Three of the authors (L.H.R., R.S.M. and D.G.A.) have filed a patent (US provisional application 63/289,343) on the technology described in this manuscript. The remaining authors declare no competing interests.

Peer review

Peer review information

Nature Biomedical Engineering thanks Xiangrong Song and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Comparison of formulations containing DOPE or DSPC.

a Data of Fig. 4 replotted with DOPE formulations highlighted in red and DSPC formulations highlighted in blue. b Comparison of LNPs with identical molar compositions, differing only in phospholipid identity. Values shown are mean of n = 3 mice/group, with error bars representing the SD. * p < 0.05, ** p < 0.005, *** p < 0.0005.

Extended Data Fig. 2 Pilot screen using a small subset of the combinatorial library.

a A list of esters (left) and amines (right) used to generate lipids for the pilot screen. b Peptide barcoding analysis of the pilot library. Only lipids that resulted in a peptide dot product > 0.8 are shown. c Individual particle analysis using the top four hits, showing that the top three lipids from the peptide barcoding analysis do not result in significant protein production. d FLuc assay confirming that FLuc mRNA-containing LNPs composed of any of the three ‘false positives’ alone do not result in detectable protein expression, whereas a 1:1 mixture of hEPO mRNA-containing cKK-e12 LNPs and FLuc mRNA-containing ‘false positive’ LNPs results in robust protein expression. Values shown are the mean of n = 3 mice/group, with error bars representing the SD. The FLuc assay of panel d was performed twice with similar results.

Supplementary information

Supplementary Information

Supplementary Methods, Figs. 1–5 and Tables 1–4.

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Rhym, L.H., Manan, R.S., Koller, A. et al. Peptide-encoding mRNA barcodes for the high-throughput in vivo screening of libraries of lipid nanoparticles for mRNA delivery. Nat. Biomed. Eng 7, 901–910 (2023). https://doi.org/10.1038/s41551-023-01030-4

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