Identifying plasma-based biomarkers that can accurately differentiate Lewy body disease (LBD) from Alzheimer's disease (AD) remains a major challenge. Extracellular vesicles (EVs), which carry molecular cargo from their parent cells and can cross the blood-brain barrier, offer a new path forward. We developed the multiplexed Track-Etch magnetic NanoPOre (mTENPO) platform, a highly parallelized microfluidic technology for cell-specific EV isolation, and demonstrated independent enrichment of GluR2+ (neuron-derived) and GLAST+ (astrocyte-derived) EVs from the antemortem plasma of 137 autopsy-confirmed LBD, AD, mixed pathology, and control subjects. By integrating miRNA sequencing of GluR2+ and GLAST + EV cargo with plasma measurements of Aβ40, Aβ42, tau, p-Tau181, and p-Tau231, we identified a multimodal 15-feature panel that more comprehensively reflects brain pathology than conventional biomarkers. Using tenfold cross-validation to mitigate overfitting, the panel achieved an accuracy of 0.95 and an area under the curve of 0.96 for distinguishing LBD versus AD.

There is a critical unmet need for concussion biomarkers that predict cognitive recovery. Existing TBI biomarkers largely capture acute cellular damage rather than the multicellular repair processes that determine long-term outcome, and whether microglia causally shape divergent trajectories remains unclear. Here, we used PLX5622 to deplete microglia and other CSF1R-dependent myeloid cells in a male mouse model comparing concussion alone to concussion preconditioned by prior subconcussive impacts. Microglial depletion had no effect on cognitive outcome after concussion alone, but introduced a significant novel object recognition deficit specifically when concussion was preconditioned, revealing a history-dependent role for microglia in recovery. To identify the molecular substrates of this divergence, we profiled brain-derived GluA1/2+ extracellular vesicle (EV) miRNAs and single-nuclei transcriptomes from the injured brain. Diagnostic and injury-history EV miRNA biomarkers discriminated between concussion subtypes in microglia-intact animals but lost this discriminative power after depletion, indicating that these neuron-enriched biomarkers are influenced by microglia-dependent biology rather than injury alone. snRNA-seq further revealed that microglial depletion selectively induced transcriptional programs related to oligodendrocyte maturation after unconditioned concussion, a candidate substrate for this microglia-dependence. Finally, we identified a serum EV miRNA panel that estimated cognitive recovery across injury conditions. Together, these findings identify a history-dependent role for microglia in concussion outcome and characterize brain-derived EVs as a potentially promising tool for surveilling the neuroinflammatory processes influencing recovery.

Ultrasmall inorganic nanoparticles (sub-5 nm) have unique biomedical advantages due to rapid clearance, enhanced imaging contrast, and potent therapeutic properties. However, current synthesis methods are limited by low throughput, polydispersity, and reliance on harsh conditions such as organic solvents or high temperatures. We report a scalable, single-step aqueous synthesis using a confined impinging jet mixer (CIJM) that produces size-controlled, clinically relevant nanoparticles, including silver sulfide, silver telluride, cerium oxide, and iron oxide, under ambient conditions. The resulting nanoparticles are homogeneous, stable, and preserve their functional biological properties. We demonstrate consistent performance across scales, establishing the CIJM as a versatile and reproducible method for producing ultrasmall inorganic nanoparticles suitable for clinical translation and high-throughput biomedical applications.

Data-driven materials development requires large, well-characterized libraries of precisely defined formulations. While microfluidic platforms excel at generating highly controlled materials, their throughput is often limited by the challenge of efficiently interfacing device outputs with standard well plates. This bottleneck frequently necessitates manual transfer or non-microfluidic workflows, constraining both throughput and reproducibility. Here, we present LMNOP-bot (Libraries of Micro- and Nano-materials, OPen-source bot), an open-source robotic platform for the automated generation and collection of micro- and nanomaterial libraries from serial microfluidic outputs. Using synchronized, pressure-driven flow, LMNOP-bot enables continuous formulation and direct deposition into standard well plates. The system is low-cost (<$700, excluding pressure regulators), constructed from readily available or easily fabricated components, and designed for broad accessibility. LMNOP-bot collects ≥30 μL per formulation at a rate of one sample every four seconds, representing an approximately 50× increase in throughput over existing serial microfluidic workflows, and operates robustly for over 10,000 runs without maintenance. We demonstrate compatibility with both PDMS/glass and commercial polycarbonate devices, with seamless interfacing to 96- and 384-well plates. Repeated sampling confirms high precision and reproducibility. By removing a key bottleneck in microfluidic library generation, LMNOP-bot enables rapid, scalable, and accessible exploration of material design spaces.

Droplet-based microfluidics have accelerated the discovery of rare variants in large combinatorial protein libraries by enabling high-throughput screening in miniaturized reaction systems. However, throughput limitations and assay compatibility constrain the...

Non-viral nanomedicines, including nanoparticles (NPs) composed of lipids and polymers, represent a transformative approach to drug and gene therapy. However, clinical translation of these technologies is limited by two key barriers: the scale-up of NP formulations and the challenge of conducting predictive preclinical studies in relevant animal models. Efficient upscaling of nanomedicines, from cost and material requirement perspectives, requires manufacturing processes that can reliably provide products across the many orders of magnitude of scale from discovery (kg of product). Additionally, initial preclinical studies are often performed in mouse models for discovery; however, mid- to large-size animal models such as rabbits, pigs, sheep, and nonhuman primates are more relevant to human scale and physiology in the context of evaluating the safety, efficiency, and efficacy of therapeutic strategies proposed for use in humans across age groups. This review summarizes some current strategies to scale-up the production of nanomedicines for translational investigations. Animal models and new approach methodologies are also addressed for NP assessment and screening, including the physiological distinctions when comparing rodent models to larger species that can impact NP delivery. Current challenges are also highlighted in terms of scale-up and preclinical validation with the objective of highlighting scalable, effective nanomedicine platforms that can be considered for translation to human trials.

Microfluidic platforms produce RNA-loaded lipid nanoparticles (RNA-LNPs) with superior uniformity, encapsulation efficiency, and control over size compared to bulk methods, and unlike bulk approaches, their throughput can be scaled over...

As they became the dominant gene therapy platform, lipid nanoparticles (LNPs) experienced nearly all their innovation in varying the structure of individual molecules in LNPs. This ignored control of the spatial arrangement of molecules, which is suboptimal because supramolecular structure determines function in biology. To control LNPs' supramolecular structure, we introduce multi-stage-mixing (MSM) to successively add different molecules to LNPs. We first utilize MSM to create a core-then-shell (CTS) synthesis. CTS-LNPs display a clear core-shell structure, vastly lower frequency of LNPs containing no detectable mRNA, and improved mRNA-LNP expression. With DNA-loaded LNPs, which for decades lagged behind mRNA-LNPs due to low expression, CTS improved DNA-LNPs' protein expression by 2-3 orders of magnitude, bringing it within range of mRNA-LNPs. These results show that supramolecular arrangement is critical to LNP performance and can be controlled by mixing methodology. Further, MSM/CTS have finally made DNA-LNPs into a practical platform for long-term gene expression.

Mammography screening has improved early breast cancer detection, leading to reduced mortality and lower rates of advanced breast cancer. However, mammography has a high false positive rate that results in over a million invasive breast biopsies of benign lesions in the US each year. Therefore, there is a need for noninvasive, blood-based diagnostics that can accurately assess risk of malignancy for women with indeterminate lesions identified by mammography, such as BI-RADS category 4 breast lesions. The aim of this study is to identify biomarkers from multiplexed extracellular vesicle liquid biopsy that can accurately classify mammographically detected BI-RADS 4 lesions. We analyzed plasma from 113 prospectively enrolled subjects with BI-RADS 4 breast lesions, including 86 women with benign lesions and 27 women with malignant lesions (including 12 with stage I invasive carcinoma and 14 with ductal carcinoma in situ). None of the invasive carcinomas were metastatic. From each plasma sample, we used track etched magnetic nanopore technology to separately isolate HER2 and CD24 expressing extracellular vesicles (EVs) and measured their miRNA cargo using next-generation sequencing. We evaluated the performance of EV-miRNA biomarkers for classifying malignancy and applied LASSO classification to identify a panel of four complementary EV miRNA biomarkers that we validated by qPCR. We identified 19 differentially enriched miRNA from HER2+ EVs and 11 differentially enriched miRNA from CD24+ EVs of women with malignant lesions compared to benign lesions. We observed individual miRNA with an AUC of up to 0.87 for miR-340-5p from HER2+ EVs and 0.75 for miR-223-3p from CD24+ EVs. LASSO classification selected a panel of four complementary EV miRNA for classifying breast cancer: miR-340-5p (HER2+ EVs), miR-598-3p (CD24+), miR-15b-5p (HER2+), and miR-126-3p (CD24+). HER2+ and CD24+ EV subpopulations contain complementary biomarkers suitable for validation in larger studies that can accurately detect early-stage breast cancer among women with BI-RADS category 4 breast lesions.

Granular hydrogels are emerging as an important class of scaffolds for biomedical applications, due to their injectability and pore structure to support cellular infiltration. Past research has primarily focused on spherical microgels, which allows limited control over granular hydrogel pore size and void volume fraction; however, investigation into microgels with higher aspect ratios has allowed even higher porosity. This study explores the impact of hyaluronic acid microgel aspect ratio (ranging from 3 to 5) on granular hydrogel porosity and cellular interactions. Both simulations and experimental results show increased void volume fractions and pore sizes in granular hydrogels formed from rod-like microgels when compared to volume-matched spherical microgels, which results in increased cellular invasion with an endothelial cell spheroid migration assay. Injection of the hydrogels into a confined space alters particle packing and void space, but porosity is still higher when rod-like microgels are used, which results in increased cellular invasion when injected subcutaneously. Finally, the highest aspect ratio microgels are used as injectable granular hydrogels to treat myocardial infarction in rats and show reduced infarct area and enhanced functional outcomes when compared to untreated controls. This work provides further insight into microgel shape considerations for engineered granular hydrogels.

Lipid nanoparticles (LNPs) are the most advanced delivery system currently available for RNA therapeutics. Their development has accelerated rapidly since the success of Patisiran, the first siRNA-LNP therapeutic, and the SARS-CoV-2 mRNA vaccines that emerged during the COVID-19 pandemic. Designing LNPs with specific targeting, high potency, and minimal side effects is crucial for their successful clinical use. However, our understanding of how the composition and mixing methods influence the structure, biophysical properties, and biological activity of the resulting particles remains limited. While microfluidic technologies have significantly improved the speed and uniformity of LNP production, a major challenge that remains is that ~60-80% of mRNA-LNP formulations are unloaded (empty lipid particles). This study tackles this challenge by relating current standard characterization methods with more powerful emerging methods, including 1. multi-wavelength analytical ultracentrifugation (MWL-AUC), 2. In-line multi-angle light scattering (MALS) methods, and 3. synchrotron size-exclusion chromatography in-line with small-angle X-ray scattering (SEC-SAXS) coupled with singular-value decomposition methods (SVD). We will present the strengths and weaknesses of each approach and showcase the increased detail newer advanced methods provide by comparing LNP formulations made using two common small-scale production methods: microfluidic rapid mixing and bulk mixing. The characterization techniques employed here can enhance our understanding of LNP structure-function relationships and enable researchers to define their RNA LNP products more precisely, which can improve LNP quality and potentially accelerate pharmaceutical development.

INTRODUCTION:Robust plasma-based biomarkers to distinguish Lewy body disease (LBD) and Alzheimer's disease (AD) are currently lacking. We applied track-etch magnetic nanopore (TENPO) sorting for enrichment of brain-derived extracellular vesicle (EV) signatures as potential biomarkers to address this gap. METHODS:We analyzed plasma from 137 autopsy-confirmed patients [30 LBD, 31 AD, 30 AD/LBD, 19 AD with amygdala Lewy bodies (AD/ALB), and 27 controls], sequencing miRNAs from TENPO-isolated GluR2-positive (neuron-enriched) and GLAST-positive (astrocyte-enriched) EVs, and measuring plasma proteins (Aβ40, Aβ42, tau, p-Tau181, p-Tau231) via SIMOA. RESULTS:We identified 16 GluR2+, 8 GLAST+, and 4 protein biomarkers with differential expression (false discovery rate-corrected P value < .1) between LBD and AD. A multimodal 15-feature panel classified LBD versus AD with 10-fold crossvalidated accuracy = 0.95 and area under the curve (AUC) = 0.96. DISCUSSION:Brain-derived EVs offer accurate and accessible miRNA biomarkers for the differential diagnosis of LBD and AD.

Nanoparticles have gained attention as drug delivery vehicles for cancer treatment, but often struggle with poor tumor accumulation and penetration. Single external magnets can enhance magnetic nanoparticle delivery but are limited to superficial tumors due to the rapid decline in the magnetic field strength with distance. We previously showed that a 2-magnet device could extend targeting to greater tissue depths. Here, we improve on this approach by constructing an 8-magnet device arranged in an annular Halbach array, which facilitates radial outward movement of magnetic nanoparticles from the bore's center. Using chlorin e6-coated magnetic nanoclusters (Ce6 clusters) with densely packed cobalt-doped superparamagnetic iron oxide nanoparticles, we demonstrated nearly a 7-fold improvement in nanoparticle movement through a porous matrix compared to the 2-magnet approach. This resulted in enhanced magnetic resonance contrast, accumulation, and penetration of Ce6 clusters into 4T1 triple-negative breast tumors in mice, leading to improved photodynamic therapy and highlighting the potential therapeutic application of the 8-magnet device.

Designing lipid nanoparticle (LNP) delivery systems with specific targeting, potency and minimal side effects is crucial for their clinical use. However, traditional characterization methods, such as dynamic light scattering, cannot accurately quantify physicochemical properties of LNPs and how these are influenced by the lipid composition and mixing method. Here, we structurally characterize polydisperse LNP formulations by applying emerging solution-based biophysical methods that have higher resolution and provide biophysical data beyond size and polydispersity. These techniques include sedimentation velocity analytical ultracentrifugation, field-flow fractionation followed by multiangle light scattering and size-exclusion chromatography in line with synchrotron small-angle X-ray scattering. We show that LNPs have intrinsic polydispersity in size, RNA loading and shape, which depend on both the formulation technique and the lipid composition. Lastly, we predict LNP transfection in vitro and in vivo by examining the relationship between mRNA translation and physicochemical characteristics. Solution-based biophysical methods will be essential for determining LNP structure–function relationships, facilitating the creation of new design rules for LNPs. Guidance for optimizing lipid nanoparticle formulations is derived using sophisticated biophysical techniques.

Ultrasmall silver sulfide nanoparticles (Ag 2 S-NP) have been identified as promising contrast agents for a number of modalities and in particular for dual-energy mammography. These Ag 2 S-NP have demonstrated marked advantages over clinically available agents with the ability to generate higher contrast with high biocompatibility. However, current synthesis methods are low-throughput and highly time-intensive, limiting the possibility of large animal studies or eventual clinical use of this potential imaging agent. We herein report the use of a scalable silicon microfluidic system (SSMS) for the large-scale synthesis of Ag 2 S-NP. Using SSMS chips with 1 channel, 10 parallelized channels, and 256 parallelized channels, we determined that the Ag 2 S-NP produced were of similar quality as measured by core size, concentration, UV-visible spectrometry, and in vitro contrast generation. Moreover, by combining parallelized chips with increasing reagent concentration, we were able to increase output by an overall factor of 3,400. We also found that in vivo imaging contrast generation was consistent across synthesis methods and confirmed renal clearance of the ultrasmall nanoparticles. Finally, we found best-in-class clearance of the Ag 2 S-NP occurred within 24 hours. These studies have identified a promising method for the large-scale production of Ag 2 S-NP, paving the way for eventual clinical translation.

There is growing interest in understanding the biological implications of single cell heterogeneity and heteroplasmy of mitochondrial DNA (mtDNA), but current methodologies for single-cell mtDNA analysis limit the scale of analysis to small cell populations. Although droplet microfluidics have increased the throughput of single-cell genomic, RNA, and protein analysis, their application to sub-cellular organelle analysis has remained a largely unsolved challenge. Here, we introduce an agarose-based droplet microfluidic approach for single-cell, single-mtDNA analysis, which allows simultaneous processing of hundreds of individual mtDNA molecules within >10,000 individual cells. Our microfluidic chip encapsulates individual cells in agarose beads, designed to have a sufficiently dense hydrogel network to retain mtDNA after lysis and provide a robust scaffold for subsequent multi-step processing and analysis. To mitigate the impact of the high viscosity of agarose required for mtDNA retention on the throughput of microfluidics, we developed a parallelized device, successfully achieving ~95 % mtDNA retention from single cells within our microbeads at >700,000 drops/minute. To demonstrate utility, we analyzed specific regions of the single-mtDNA using a multiplexed rolling circle amplification (RCA) assay. We demonstrated compatibility with both microscopy, for digital counting of individual RCA products, and flow cytometry for higher throughput analysis.

Graphene Hall-effect magnetic field sensors (GHSs) exhibit high performance comparable to state-of-the-art commercial Hall sensors made from III-V semiconductors. Graphene is also amenable to CMOS-compatible fabrication processes, making GHSs attractive candidates for implementing magnetic sensor arrays for imaging fields in biosensing and scanning probe applications. However, their practical appeal is limited by response heterogeneity and drift, arising from the high sensitivity of two-dimensional (2D) materials to local device imperfections. To address this challenge, we designed a GHS array in which an individual backgate is added to each GHS, allowing the carrier density of each sensor to be electrostatically tuned independent of other sensors in the array. Compared to the constraints encountered when all devices are tuned with the same backgate, we expected that the flexibility afforded by individual tuning would allow for the array's sensitivity, uniformity, and reconfigurability to be enhanced. We fabricated an array of 16 GHSs, each with its own backgate terminal, and characterized the ability to modulate GHS carrier density and Hall sensitivity within CMOS-compatible voltage ranges. We then demonstrated that individual device tuning can be used to break the trade-off between device sensitivity and uniformity in the GHS array, allowing for enhancement of both objectives. Our results showed that GHS arrays exhibiting >30% variability under single-backgate operation could be compensated using individual tuning to achieve <2% variability with minimal impact on the array sensitivity.

The fabrication of microfluidic chips is becoming a mature field and channels can be reliably defined with micrometer‐scale precision in a variety of substrates. In addition to channel geometry, micrometer‐scale patterning of wettability in microfluidic channels is essential for many applications including multi‐phase flow stabilization and multiple emulsion generation. Unfortunately, current methods to pattern wettability in microfluidics suffer from low spatial resolution, inability for patterns to be arbitrarily defined, cumbersome procedures, and incompatibility with parallelized architectures for scaled‐up production of microfluidic generated materials. To address these issues, a method is developed to lithographically define micrometer‐scale resolution patterns of wettability on all channel surfaces (ceiling, floor, and walls) in silicon and glass microfluidic devices with complex 3D geometry. A process is reported to pattern silanes on microfluidic chips that uses photolithography and an optimized process that keeps silanized surfaces stable through the microfabrication process, including anodic bonding. The versatility of this approach is highlighted by patterning wettability of a silicon/glass device to generate both highly uniform water–in‐oil–in‐water and oil–in‐water–in‐oil double emulsions. The applicability of this process is demonstrated to the parallel generation of materials in a microfluidic chip with complex geometry, by fabricating and successfully validating parallelized double emulsion generators.

We develop a high-throughput droplet-based extracellular vesicle analysis (DEVA) assay for ultrasensitive quantification of EVs in plasma that are positive for both PD-L1 and tetraspanins.