Uncovering Nucleic Acid Payload and Size of Lipid Nanoparticles via a Single Nanoparticle Analyzer

Contributed by: Sixuan Li, Graduate Research Assistant, Wang Lab, and Yizong Hu, Postdoctoral Associate, Mao Lab at Johns Hopkins University

Lipid nanoparticles (LNPs) have broad applications as delivery vehicles for RNA vaccines and therapeutics (Chaudhary, Weissman, and Whitehead, 2021). While both the academia and industry are racing to apply RNA LNPs to various infectious disease, gene and cell therapy targets, the fundamental understanding of the structure-property-function relationships of the LNP system falls behind these pre-clinical and clinical advancements. Specifically, how RNA and lipid molecules are packaged in LNPs, how many of them can be loaded in a single LNP, and if these payload features influence their bioactivities have remained largely unknown due to a gap in quantitative methods to characterize LNPs accurately. A group of scientists at the Institute for NanoBioTechnology (INBT) of Johns Hopkins University believes that bridging this gap and providing more fundamental knowledge of these clinically promising vehicles would serve to inspire rational design strategies and ultimately help people develop more biocompatible and potent LNP systems. They recently reported their development, a fluorescent spectroscopy-based single-particle analysis platform to assess mRNA payload and lipid content in a benchmark LNP formulation at the single-nanoparticle level, on Nature Communications (Li et al., 2022) 

How to make and characterize lipid nanoparticles?

LNP manufacturing typically consists of the rapid mixing of an ethanol mixture of an ionizable lipid, a helper lipid, cholesterol, and a PEG lipid, with an acidic aqueous solution of therapeutic RNA and a subsequent purification step to remove ethanol and confer desired buffers for storage and dosing. High-resolution cryogenic transmission electron microscopy (cryo-TEM), a standard method in analyzing LNPs, captures the dynamic behaviors of LNPs during the purification step as they transform from possessing both vesicular and solid morphologies to a uniform solid (electron-dense) morphology (Arteta et al., 2018). However, in these images, one cannot directly visualize RNA molecules within the structure or distinguish an LNP loaded with RNA from those that are not due to insufficient contrast in electron transmission.

Other commonly used analytical methods, as suggested by the United States Pharmacopeia (USP), which provides guidelines for analytical procedures related to the quality of mRNA vaccines, include dynamic light scattering (DLS), Capillary gel electrophoresis (CGE), Size Exclusion Chromatography (SEC-HPLC), Reversed-phase high-performance liquid chromatography with charged aerosol detector (RP-HPLC-CAD), and RiboGreen assay. These techniques measure LNP properties such as particle size and concentration, surface charge, particle morphology, and encapsulation efficiency, but none directly quantify payload distribution and capacity. To contribute to diversifying and developing a more adequate and comprehensive quality control matrix for the industry is of significant interest.

LNP analysis platform. (a) LNP structure and composition. (b) All LNPs are screened through the single particle analysis platform. (Figure adapted from (Li et al., 2022).

Looking at LNPs one at a time

The heterogeneous nature of LNP populations, characterized by particle size, structure, and payload variations, necessitates an analytical technique that can provide single-particle resolution. These properties would otherwise be obscured by ensemble average methods (Chen et al., 2023). Recognizing this  technical gap, Sixuan Li, Yizong Hu, and their colleagues developed a multi-color fluorescence spectroscopic platform capable of characterizing LNP at single nanoparticle resolution with quantification capacity (Li et al., 2022). The platform, termed cylindrical illumination confocal spectroscopy (CICS), leverages single-molecule spectroscopy, fluorescence coincidence analysis, and quantitative deconvolution algorithms (Liu and Wang, 2008; Beh et al., 2014) to distinguish different LNP populations and quantify fluorescently labeled payloads within LNPs. The team showcased the platform's capabilities through a comprehensive characterization of mRNA LNPs based on the formulation using DLin-MC3 as the ionizable lipid, which shares the same lipid composition as Onpattro, an FDA-approved siRNA LNP drug. In their study, they opted to label the helper lipid and the mRNA, enabling the effective differentiation of LNP populations, including mRNA-encapsulating LNPs, empty LNPs, and unencapsulated mRNAs. They found that the formulation, after purification into the physiological pH of 7.4, has a significant amount of empty LNPs that are not loaded with an mRNA. The formation of such a population stems from the purification process. For the first time, it was revealed that the LNPs encapsulating mRNA contain, on average, 2.8 mRNAs per nanoparticle when the mRNA length was 1.9 kb, and the mRNA payload followed a lognormal distribution. Through CICS analysis of LNPs before and after purification, enabled by this powerful platform, Li, Hu, and their colleagues successfully depicted a payload determination mechanism, where both the initial complexation state upon the rapid mixing and the dynamic LNP behaviors during purification shape the final payload features of mRNA LNPs.

Mechanisms of determination of payload capacity and distribution of mRNA LNPs by the PEG content.The hypothesized assembly processes and characteristics of LNP formulation are based on the properties reported by the single-molecule method.Figure 2 retrieved from (Li et al., 2022).

Importantly, this platform accommodates high-throughput screening and can analyze up to 6000 particles per minute while requiring a miniature sampling volume of less than 1 μL, combining exceptional sensitivity and resolution with robust quantification capabilities.

As the developments continue, the CICS platform will soon feature in-line, concurrent nanoparticle size and payload characterizations, as well as more complex multi-color measurements to assess the co-encapsulation of different species of cargos. The team's goal is to validate the platform to characterize more LNP formulations and demonstrate its utility in characterizing other types of nanomedicines, such as nanoparticles loaded with peptides, proteins, chemotherapy drugs, etc. Ultimately, the team wishes to commercialize the platform and benefit the entire industry. Overall, this emerging CICS platform holds great promise in revealing and reporting critical quality attributes of LNPs and other therapeutic nanoparticles that have yet to be thoroughly characterized, both for exploratory and regulatory purposes.

Lab Spotlight:

BioMEMS and Single Molecule Dynamics Lab directed by Dr. Jeff Tza-Huei Wang at Johns Hopkins University focuses on the development of new technologies for molecular analysis and biomedical research via advances in micro-and nano-scale sciences. We aim to develop new methods, devices, and systems with unprecedented performance characteristics, such as sensitivity, specificity, resolution (temporal and/or spatial), multiplexing, and throughput to rectify current technological limitations in the molecular study of diseases by leveraging our engineering innovations in microfluidics, single-molecule spectroscopy, and functional nanoparticles.

Mao lab at Johns Hopkins University, directed by Prof. Hai-Quan Mao, aims to develop and translate innovative technologies through biomaterials design at the interface of nanoscience, engineering, biology, and medicine to address the unmet critical needs in healthcare. Our lab specializes in engineering nanomaterials for delivery of nucleic acid and protein therapeutics, soft tissue regeneration, and immunoengineering. Specifically, we aim to understand the structure-property-function relationship of biomaterials and technology platforms to enhance their therapeutic efficacy and develop new methods for scalable manufacturing and translation.


  • Arteta, M. Y. et al. (2018) 'Successful reprogramming of cellular protein production through mRNA delivered by functionalized lipid nanoparticles', Proceedings of the National Academy of Sciences of the United States of America, 115(15), pp. E3351–E3360. doi: 10.1073/pnas.1720542115.
  • Beh, C. W. et al. (2014) 'Direct interrogation of DNA content distribution in nanoparticles by a novel microfluidics-based single-particle analysis (supple', Nano Letters, 14(8), pp. 4729–4735. doi: 10.1021/nl5018404.
  • Chaudhary, N., Weissman, D. and Whitehead, K. A. (2021)' mRNA vaccines for infectious diseases: principles, delivery and clinical translation', Nature Reviews Drug Discovery. Springer US, 20(11), pp. 817–838. doi: 10.1038/s41573-021-00283-5.
  • Chen, Chaoxiang et al. (2023) 'Characterization of lipid-based nanomedicines at the single-particle level', Fundamental Research. Elsevier B.V., (xxxx). doi: 10.1016/j.fmre.2022.09.011.
  • Li, S. et al. (2022) 'Payload distribution and capacity of mRNA lipid nanoparticles', Nature communications. Springer US, 13(1), p. 5561. doi: 10.1038/s41467-022-33157-4.
  • Liu, K. J. and Wang, T. H. (2008) 'Cylindrical illumination confocal spectroscopy: Rectifying the limitations of confocal single spectroscopy through one-dimensional beam shaping', Biophysical Journal. Elsevier, 95(6), pp. 2964–2975. doi: 10.1529/biophysj.108.132472.

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