Search for: All records

Extracellular vesicles (EVs) have been identified as promising biomarkers for the noninvasive diagnosis of various diseases. However, challenges in separating EVs from soluble proteins have resulted in variable EV recovery rates and low purities. Here, we report a high-yield ( > 90%) and rapid ( < 10 min) EV isolation method calledFLocculation viaOrbitalAcousticTrapping (FLOAT). The FLOAT approach utilizes an acoustofluidic droplet centrifuge to rotate and controllably heat liquid droplets. By adding a thermoresponsive polymer flocculant, nanoparticles as small as 20 nm can be rapidly and selectively concentrated at the center of the droplet. We demonstrate the ability of FLOAT to separate urinary EVs from the highly abundant Tamm-Horsfall protein, addressing a significant obstacle in the development of EV-based liquid biopsies. Due to its high-yield nature, FLOAT reduces biofluid starting volume requirements by a factor of 100 (from 20 mL to 200 µL), demonstrating its promising potential in point-of-care diagnostics.

 

The addition of surface acoustic wave (SAW) technologies to microfluidics has greatly advanced lab-on-a-chip applications due to their unique and powerful attributes, including high-precision manipulation, versatility, integrability, biocompatibility, contactless nature, and rapid actuation. However, the development of SAW microfluidic devices is limited by complex and time-consuming micro/nanofabrication techniques and access to cleanroom facilities for multistep photolithography and vacuum-based processing. To simplify the fabrication of SAW microfluidic devices with customizable dimensions and functions, we utilized the additive manufacturing technique of aerosol jet printing. We successfully fabricated customized SAW microfluidic devices of varying materials, including silver nanowires, graphene, and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS). To characterize and compare the acoustic actuation performance of these aerosol jet printed SAW microfluidic devices with their cleanroom-fabricated counterparts, the wave displacements and resonant frequencies of the different fabricated devices were directly measured through scanning laser Doppler vibrometry. Finally, to exhibit the capability of the aerosol jet printed devices for lab-on-a-chip applications, we successfully conducted acoustic streaming and particle concentration experiments. Overall, we demonstrated a novel solution-based, direct-write, single-step, cleanroom-free additive manufacturing technique to rapidly develop SAW microfluidic devices that shows viability for applications in the fields of biology, chemistry, engineering, and medicine.

 

While mesenchymal stem cells (MSCs) have gained enormous attention due to their unique properties of self-renewal, colony formation, and differentiation potential, the MSC secretome has become attractive due to its roles in immunomodulation, anti-inflammatory activity, angiogenesis, and anti-apoptosis. However, the precise stimulation and efficient production of the MSC secretome for therapeutic applications are challenging problems to solve. Here, we report on Acoustofluidic Interfaces for the Mechanobiological Secretome of MSCs: AIMS. We create an acoustofluidic mechanobiological environment to form reproducible three-dimensional MSC aggregates, which produce the MSC secretome with high efficiency. We confirm the increased MSC secretome is due to improved cell-cell interactions using AIMS: the key mediator N-cadherin was up-regulated while functional blocking of N-cadherin resulted in no enhancement of the secretome. After being primed by IFN-γ, the secretome profile of the MSC aggregates contains more anti-inflammatory cytokines and can be used to inhibit the pro-inflammatory response of M1 phenotype macrophages, suppress T cell activation, and support B cell functions. As such, the MSC secretome can be modified for personalized secretome-based therapies. AIMS acts as a powerful tool for improving the MSC secretome and precisely tuning the secretory profile to develop new treatments in translational medicine.

 

Newly developed acoustic technologies are playing a transformational role in life science and biomedical applications ranging from the activation and inactivation of mechanosensitive ion channels for fundamental physiological processes to the development of contact-free, precise biofabrication protocols for tissue engineering and large-scale manufacturing of organoids. Here, we provide our perspective on the development of future acoustic technologies and their promise in addressing critical challenges in biomedicine.

 

Machine learning image recognition and classification of particles and materials is a rapidly expanding field. However, nanomaterial identification and classification are dependent on the image resolution, the image field of view, and the processing time. Optical microscopes are one of the most widely utilized technologies in laboratories across the world, due to their nondestructive abilities to identify and classify critical micro-sized objects and processes, but identifying and classifying critical nano-sized objects and processes with a conventional microscope are outside of its capabilities, due to the diffraction limit of the optics and small field of view. To overcome these challenges of nanomaterial identification and classification, we developed an intelligent nanoscope that combines machine learning and microsphere array-based imaging to: (1) surpass the diffraction limit of the microscope objective with microsphere imaging to provide high-resolution images; (2) provide large field-of-view imaging without the sacrifice of resolution by utilizing a microsphere array; and (3) rapidly classify nanomaterials using a deep convolution neural network. The intelligent nanoscope delivers more than 46 magnified images from a single image frame so that we collected more than 1000 images within 2 seconds. Moreover, the intelligent nanoscope achieves a 95% nanomaterial classification accuracy using 1000 images of training sets, which is 45% more accurate than without the microsphere array. The intelligent nanoscope also achieves a 92% bacteria classification accuracy using 50 000 images of training sets, which is 35% more accurate than without the microsphere array. This platform accomplished rapid, accurate detection and classification of nanomaterials with miniscule size differences. The capabilities of this device wield the potential to further detect and classify smaller biological nanomaterial, such as viruses or extracellular vesicles. 

sEV subpopulations and nanoparticles are directly fractionated via acoustic virtual wave-pillars without any sample preprocessing.