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Linxian Li

Chinese University of Hong Kong

ORCID: 0000-0002-3451-577X

Publishes on Advanced biosensing and bioanalysis techniques, RNA Interference and Gene Delivery, Surface Modification and Superhydrophobicity. 88 papers and 4.7k citations.

88Publications
4.7kTotal Citations

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Top publicationsby citations

HOLMESv2: A CRISPR-Cas12b-Assisted Platform for Nucleic Acid Detection and DNA Methylation Quantitation
Linxian Li, Shiyuan Li, Na Wu et al.|ACS Synthetic Biology|2019
Cited by 746

-cleavage activity against collateral single-stranded DNA (ssDNA) and employed the activity to develop a rapid nucleic acid detection system, namely HOLMES (one-hour low-cost multipurpose highly efficient system). Here, with the employment of thermophilic CRISPR-Cas12b, we create HOLMESv2 for four different applications: (1) specifically discriminating single nucleotide polymorphism (SNP); (2) simply detecting virus RNA, human cell mRNA and circular RNA; (3) conveniently quantitating target nucleic acids with a one-step system combined with LAMP amplification in a constant temperature, thus avoiding cross-contamination; (4) accurately quantitating target DNA methylation degree with the combination of Cas12b detection and bisulfite treatment. These results highlight the potential of HOLMESv2 as a promising platform for both molecular diagnostics and epigenetics applications.

UV‐Triggered Dopamine Polymerization: Control of Polymerization, Surface Coating, and Photopatterning
Xin Du, Linxian Li, Junsheng Li et al.|Advanced Materials|2014
Cited by 368Open Access

UV irradiation is demonstrated to initiate dopamine polymerization and deposition on different surfaces under both acidic and basic pH. The observed acceleration of the dopamine polymerization is explained by the UV-induced formation of reactive oxygen species that trigger dopamine polymerization. The UV-induced dopamine polymerization leads to a better control over polydopamine deposition and formation of functional polydopamine micropatterns.

Synergistic lipid compositions for albumin receptor mediated delivery of mRNA to the liver
Lei Miao, Jiaqi Lin, Yuxuan Huang et al.|Nature Communications|2020
Cited by 327Open Access

Lipid-like nanoparticles (LNPs) have potential as non-viral delivery systems for mRNA therapies. However, repeated administrations of LNPs may lead to accumulation of delivery materials and associated toxicity. To address this challenge, we have developed biodegradable lipids which improve LNPs clearance and reduce toxicity. We modify the backbone structure of Dlin-MC3-DMA by introducing alkyne and ester groups into the lipid tails. We evaluate the performance of these lipids when co-formulated with other amine containing lipid-like materials. We demonstrate that these formulations synergistically facilitate robust mRNA delivery with improved tolerability after single and repeated administrations. We further identify albumin-associated macropinocytosis and endocytosis as an ApoE-independent LNP cellular uptake pathway in the liver. Separately, the inclusion of alkyne lipids significantly increases membrane fusion to enhance mRNA release, leading to synergistic improvement of mRNA delivery. We believe that the rational design of LNPs with multiple amine-lipids increases the material space for mRNA delivery.

Design of self-assembly dipeptide hydrogels and machine learning via their chemical features
Fei Li, Jinsong Han, Tian Cao et al.|Proceedings of the National Academy of Sciences|2019
Cited by 183Open Access

Hydrogels that are self-assembled by peptides have attracted great interest for biomedical applications. However, the link between chemical structures of peptides and their corresponding hydrogel properties is still unclear. Here, we showed a combinational approach to generate a structurally diverse hydrogel library with more than 2,000 peptides and evaluated their corresponding properties. We used a quantitative structure-property relationship to calculate their chemical features reflecting the topological and physicochemical properties, and applied machine learning to predict the self-assembly behavior. We observed that the stiffness of hydrogels is correlated with the diameter and cross-linking degree of the nanofiber. Importantly, we demonstrated that the hydrogels support cell proliferation in culture, suggesting the biocompatibility of the hydrogel. The combinatorial hydrogel library and the machine learning approach we developed linked the chemical structures with their self-assembly behavior and can accelerate the design of novel peptide structures for biomedical use.