(93f) Bias-Free and Pure Isolation of Extracellular Nanocarriers (RNPs, EVs and lipoproteins) from Biofluids Using a High Throughput & Scalable Isoelectric Fractionation Device | AIChE

(93f) Bias-Free and Pure Isolation of Extracellular Nanocarriers (RNPs, EVs and lipoproteins) from Biofluids Using a High Throughput & Scalable Isoelectric Fractionation Device

Authors 

Sharma, H. - Presenter, University of Notre Dame,Indiana
Senapati, S., University of Notre Dame
Chang, H. C., Year
Extracellular RNAs (exRNAs) are secreted into physiological fluids (e.g., blood, urine, lymph fluids) by host cells, carrying complex cellular communication signatures and serving as promising biomarkers for various disease states. These exRNAs are protected and transported by three main classes of nanoscale molecular shuttles: extracellular vesicles (EVs), lipoproteins (LLPs), and ribonucleoproteins (RNPs), each with several subtypes (e.g., small and large EVs, HDL, LDL, VLDL). These nanocarriers contain specific functional molecules including metabolites, genetic materials, and proteins, facilitating the transfer of biomolecules from donor to recipient cells. Intercellular signaling mediated by nanocarriers plays a fundamental role in disease progression. Moreover, it has been recently recognized that the majority of circulating RNAs in blood are present in RNP complexes with RNA-binding proteins, some of which overlap with those found in vesicular EVs and LLPs as well. However, the overlapping size and density of the nanocarriers have so far prevented their efficient physical fractionation without cross contamination and at high throughput, thus impeding independent downstream molecular assays.

The current RNP isolation technologies, such as cross-linking and immunoprecipitation (CLIP) and affinity capture, yield extremely low quantities, making RNA quantification impractical. Other commonly used techniques for separating nanocarriers (LLPs and EVs) are multi-stage ultracentrifugation (UC) and nanoporous membrane-based ultrafiltration (UF). However, these conventional methods are time-consuming and labor-intensive, and they often yield low quantities, exhibit poor isolation purity, and are susceptible to clogging (e.g., UF), leading to inaccuracies in downstream analysis. Another technique known as asymmetric-flow field-flow fractionation (AF4) can separate extracellular nanoparticles based on their hydrodynamic size and offers a large dynamic range. However, similar to UC, heterogeneous nanoparticle populations with overlapping sizes cannot be effectively isolated from each other using AF4 alone. Additional steps, such as electric field-based separation, are often required in conjunction with AF4 to achieve better isolation purity. Other physical fractionation technologies, including deterministic lateral displacement, acoustofluidics, dielectrophoresis, and size-exclusion chromatography, show promise but have thus far been primarily successful in isolating larger nanocarriers like EVs, often with low purity.

In our study, we present a novel continuous isoelectric fractionation (CIF) platform capable of achieving the highest reported throughput to date (12 ml/hour), surpassing previous methods by approximately three orders of magnitude. This platform enables the unbiased isolation of exosomes, lipoproteins (HDL, LDL), and RNPs from biofluids based on their distinct isoelectric points (pI) instead of their size and density. Diverging from conventional microfluidic-based free flow isoelectric focusing strategies, our design eliminates the requirement for external input of acidic/basic solutions and internal distributors or ampholytes to maintain the pH gradient. Instead, a stable linear pH gradient is achieved and sustained on-chip through the utilization of a pair of bipolar ion-exchange membranes. The high fields induced by the IEMs prompt water splitting via the Wien effect, yielding high concentrations of H3O+and OH- ions that are spatially segregated by the transverse field. Subsequently, the low and high pH gradients are extracted and spatially extended in a trapezoidal separation chip, enhancing resolution. This scalable approach facilitates the fractionation of multiple nanocarriers spanning a wide dynamic range of pIs (minimum ΔpI of 0.3), while the modular design permits both parallel and sequential separations, a feature notably absent in prior designs. Furthermore, the integration of a machine learning-based approach enables the rapid selection of optimal pH gradients for various physiological fluids and nanocarriers, even in the presence of contamination and equipment noise. We optimize the CIF technology by fractionating various combinations of binary mixtures of exRNA nanocarriers spiked in buffer, yielding purification levels exceeding 80% and purity exceeding 90%. Moreover, we validate the platform's efficacy with diluted human plasma, urine, and saliva, achieving high-purity (plasma: >93%, urine: >95%, saliva: >97%) and high-yield (plasma: >78%, urine: >87%, saliva: >96%) isolation of RNPs from EVs and LLPs within a 30-minute timeframe, utilizing merely 0.75 ml of biofluids. These findings represent substantial advancements over the current gold standard, which typically yields less than 1% with lengthy protocol.

Furthermore, we anticipate that the CIF platform holds significant potential for future applications in exploring the heterogeneity of extracellular vesicles (EVs), particularly in fractionating different EV subtypes carrying cargoes derived from cancer cells (e.g., GPC-1, Active EGFR, AR-V7). This endeavor would necessitate a finely tuned pH gradient, which may require the utilization of multiple separation devices to achieve the requisite pH resolution. Moreover, we envision that this technology can be harnessed for the purification of various other biological nanoparticles, including virus vaccines, exosome drug carriers, amyloid-beta aggregates, and peptide assemblies. The versatility of the CIF platform makes it a promising tool for advancing research in diverse fields, ranging from cancer biology to drug delivery and neurodegenerative diseases.