Synergistic siRNA Loading of Extracellular Vesicles Enables Functional Delivery into Cells

小干扰RNA 脂质体 化学 生物物理学 小泡 核糖核酸 离心 药物输送 内化 细胞 胞外囊泡 Zeta电位 细胞生物学 微泡 纳米技术 纳米颗粒 材料科学 生物化学 生物 小RNA 基因 有机化学
作者
Josepha Roerig,Franziska Mitrach,Maximilian Schmid,Gerd Hause,Michael C. Hacker,Christian Wölk,Michaela Schulz‐Siegmund
出处
期刊:Small methods [Wiley]
卷期号:6 (12) 被引量:24
标识
DOI:10.1002/smtd.202201001
摘要

RNA interference opened new approaches for disease treatment but safe and efficient cell delivery remains a bottleneck. Extracellular vesicles (EVs) are known to naturally shuttle RNA. Due to their potent cell internalization and low-cost scalability, milk-derived EVs in particular are considered promising RNA delivery systems. However, low drug loading currently impedes their use. Here, innovative exogenous loading strategies for small interfering RNA (siRNA) are explored and systematically compared regarding encapsulation efficiency, loading capacity, and loading concentration. Firstly, siRNA is pre-accumulated in liposomes or stabilized calcium phosphate nanoparticles (CaP-NP). The selected systems, which exhibited neutral or negative zeta potentials, are then applied for EV loading. Secondly, EVs are concentrated and applied to protocols known for liposome loading: dehydration-rehydration of vesicles, based on freeze-drying, and mixing by dual asymmetric centrifugation (DAC) after ultracentrifugation. Additionally, DAC after EV ultracentrifugation is combined with CaP-NP leading to a synergistic loading performance. The balance between energy input for siRNA loading and EV integrity is evaluated by monitoring the EV size, marker proteins, and morphology. For the EV-based siRNA formulation via DAC plus CaP-NP, EV properties are sufficiently maintained to protect the siRNA from degradation and deliver cell-death siRNA dose-dependently in Caco-2 cells.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
CodeCraft应助ste采纳,获得10
1秒前
美琦完成签到,获得积分10
1秒前
3秒前
3秒前
麦益颖发布了新的文献求助20
3秒前
务实蓝天发布了新的文献求助10
4秒前
Woodward发布了新的文献求助30
6秒前
田様应助Gabrielle采纳,获得10
6秒前
7秒前
车间我发布了新的文献求助10
7秒前
大模型应助鲁丁丁采纳,获得10
8秒前
脑洞疼应助麦益颖采纳,获得10
10秒前
echo完成签到 ,获得积分10
10秒前
研究僧发布了新的文献求助10
11秒前
14秒前
Ava应助在封我就急眼啦采纳,获得20
14秒前
温柔又莲完成签到,获得积分10
14秒前
14秒前
15秒前
17秒前
17秒前
18秒前
沫沫发布了新的文献求助10
18秒前
研友_8WEa2n发布了新的文献求助10
19秒前
拼搏的夜阑完成签到,获得积分10
19秒前
ok的发布了新的文献求助10
20秒前
鲁丁丁发布了新的文献求助10
21秒前
21秒前
21秒前
22秒前
22秒前
科研通AI5应助andrele采纳,获得10
22秒前
橙子皮发布了新的文献求助10
24秒前
遇见未来发布了新的文献求助10
24秒前
26秒前
科研助手6应助lenon采纳,获得10
27秒前
27秒前
wanci应助科研通管家采纳,获得10
27秒前
打打应助科研通管家采纳,获得10
27秒前
星辰大海应助科研通管家采纳,获得10
27秒前
高分求助中
Worked Bone, Antler, Ivory, and Keratinous Materials 1000
Algorithmic Mathematics in Machine Learning 500
Разработка метода ускоренного контроля качества электрохромных устройств 500
建筑材料检测与应用 370
Getting Published in SSCI Journals: 200+ Questions and Answers for Absolute Beginners 300
Advances in Underwater Acoustics, Structural Acoustics, and Computational Methodologies 300
The Monocyte-to-HDL ratio (MHR) as a prognostic and diagnostic biomarker in Acute Ischemic Stroke: A systematic review with meta-analysis (P9-14.010) 240
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3830108
求助须知:如何正确求助?哪些是违规求助? 3372647
关于积分的说明 10473699
捐赠科研通 3092210
什么是DOI,文献DOI怎么找? 1701974
邀请新用户注册赠送积分活动 818688
科研通“疑难数据库(出版商)”最低求助积分说明 771030