Comparative optimization of polysaccharide-based nanoformulations for cardiac RNAi therapy

Ionotropic gelation is widely used to fabricate targeting nanoparticles (NPs) with polysaccharides, leveraging their recognition by specific lectins. Despite the fabrication scheme simply involves self-assembly of differently charged components in a straightforward manner, the identification of a potent combinatory formulation is usually limited by structural diversity in compound collections and trivial screen process, imposing crucial challenges for efficient formulation design and optimization. Herein, we report a diversity-oriented combinatory formulation screen scheme to identify potent gene delivery cargo in the context of precision cardiac therapy. Distinct categories of cationic compounds are tested to construct RNA delivery system with an ionic polysaccharide framework, utilizing a high-throughput microfluidics workstation coupled with streamlined NPs characterization system in an automatic, step-wise manner. Sequential computational aided interpretation provides insights in formulation optimization in a broader scenario, highlighting the usefulness of compound library diversity. As a result, the out-of-bag NPs, termed as GluCARDIA NPs, are utilized for loading therapeutic RNA to ameliorate cardiac reperfusion damages and promote the long-term prognosis. Overall, this work presents a generalizable formulation design strategy for polysaccharides, offering design principles for combinatory formulation screen and insights for efficient formulation identification and optimization.


Fig. S2 .
Fig. S2.The representative schematic of the pre-mixed automatic microfluidics system.

Fig. S4 .
Fig. S4.Cell viability on RAW 264.7 cells after treatment with representative nanosystems for 48 h (n=3 replicates).Data are presented as mean ± SD.Source Data are provided in the Source Data File.

Fig. S5 .
Fig. S5.Computational assisted step-wise interpretation of formulation screening process.(a) Categories of different samples in the screening process for determining the NPs size and formation.(b) RTs+ (R maximal index weighted by intrinsic-state) of MolDes of each cationic compound.(c) The corresponding ROC curve of MolDes with the lowest p-value.(d) Molecular dynamic simulation was performed to compare the number of contacts in KALA-EEPG NPs and Transportan-EEPG NPs.(e) R-square values from MolDes describing hydrophobicity, polar surface area and positive charges.

Fig. S9 .
Fig. S9.Western blotting analysis was performed to evaluate the gene silencing efficacy of GluCARDIA-siGAPDH NPs in heart and liver (n=3 mice).Source Data are provided in the Source Data File.

Fig. S12 .
Fig. S12.Gene Set Enrichment Analysis (GSEA) was performed based on the RNA-seq datasets.Positive and negative enrichment score indicate higher and lower expression respectively.(a) IR vs. GluCARDIA-siIRF3, negative association between GluCARDIA-siIRF3 treatment and dilated cardiomyopathy was observed.(b) TGF-b pathway analysis, (c) cardiac muscle contraction analysis, (d) TNF pathway analysis, and (e) IL-17 pathway analysis showed GluCARDIA-siIRF3 may inhibited fibrosis progress, promoted cardiac contraction and facilitated the inflammation resolution, comparing to GluCARDIA-siNC group.

Fig. S13 .
Fig. S13.Representative TUNEL staining of cardiac myocytes in heart sections from IR and GluCARDIA-siIRF3 NPs (n=3 mice).Scale bar: 50 μm.Source Data are provided in the Source Data File.
* -represents non-obvious NPs formation between oppositely charged species.