Biology subjects

Pinals, R. L.

Publications and source records attributed to Pinals, R. L..

14 recordsLinked to original sources

bioRxiv2026

FRAGMENT MORPHOMETRY ANALYSIS AND SAME-COLOR-CHANNEL SEPARATION ENABLE OBJECTIVE QUANTIFICATION ACROSS BBB MODELS

Quantifying blood-brain barrier (BBB) integrity from fluorescence microscopy remains limited by subjective scoring and categorical classification methods that lack reproducibility. For objective and consistent BBB phenotyping, we present two semi-automated image-analysis pipelines that replace manual scoring with quantitative, continuous-variable measurements. Our in vitro pipeline, implemented in Python, quantifies the connectivity of tight junction structures by measuring discrete ZO-1 fragment objects within manually traced junction regions. It outputs continuous metrics including average fragment area, total junctional area, and a junctional fragmentation ratio that captures degree of ZO-1 continuity versus discontinuity. In human brain microvascular endothelial cells subjected to glycocalyx component knockdown, the pipeline detected significantly reduced fragment area (37% decrease for both CD44 and syndecan-1 (SDC1) knockdown, p = 0.0148 and 0.0084) and junctional fragmentation ratio (p = 0.0061 and 0.0137). Our in vivo pipeline integrates ilastik-based pixel classification with FIJI macro automation to quantify vascular marker colocalization and to separate vessel signal from microglial contamination within a single fluorescence channel, eliminating the need for dedicated counterstains. Applied across four mouse cohorts [young, aged, Alzheimers, traumatic brain injury (TBI)] and three brain regions [prefrontal cortex (PFC), hippocampus, midbrain], the pipeline revealed concurrent ZO-1 loss and ICAM-1 elevation in the PFC and hippocampus of aged and Alzheimers mice, with Alzheimers-specific doubling of eNOS occurring in the PFC (p = 0.0013). TBI mice showed persistent ZO-1 loss with transient ICAM-1 and eNOS changes. Both deterministic pipelines are available on GitHub and designed for adoption beyond the specific markers and systems analyzed here.

bioengineering↗

bioRxiv2025

Inflammatory reprogramming of human brain endothelial cells compromises blood-brain barrier integrity in Alzheimer's disease

Blood-brain barrier (BBB) dysfunction is an early feature of Alzheimers disease (AD), yet the endothelial gene-regulatory programs involved remain incompletely understood. We integrate postmortem human single-nucleus transcriptomics with iPSC-based BBB models to define a conserved, inflammation-driven pathway that compromises barrier integrity. We identify an NF-{kappa}B-associated endothelial gene module endoM2 that is elevated in AD, inversely correlated with cognition, and enriched for inflammation and endothelial-to-mesenchymal transition signatures. Cytokine stimulation of iPSC-derived brain endothelial cells induces morphological remodeling, lipid accumulation, junctional disruption, and transcriptomic shifts that mirror endoM2. A targeted drug screen identifies the NF-{kappa}B inhibitor BAY11-7082 as protective against cytokine-induced changes. In our perfusable iPSC-derived BBB-Chip that recapitulates human BBB signatures, single-cell profiling reveals inflammatory endothelial state-specific programs reflecting those in AD brains and demonstrates that BAY11-7082 suppresses cytokine-triggered dysfunction and reverses inflammation-associated gene activation. Together, these findings position cerebrovascular inflammation as a therapeutic target to preserve BBB integrity in AD.

neuroscience↗

bioRxiv2025

Vascular-Perfusable Human 3D Brain-on-Chip

Development and delivery of treatments for neurological diseases are limited by the tight and selective human blood-brain barrier (BBB). Although animal models have been important research and preclinical tools, the rodent BBB exhibits species differences and fails to capture the complexity of human genetics. Microphysiological systems incorporating human-derived cells hold great potential for modeling disease and therapeutic development, with advantages in screening throughput, real-time monitoring, and tunable genetic backgrounds when combined with induced pluripotent stem cell (iPSC) technology. Existing 3D BBB-on-chip systems have incorporated iPSC-derived endothelial cells but not the other major brain cell types from iPSCs, each of which contributes to brain physiology and disease. Here we developed a 3D Brain-Chip system incorporating endothelial cells, pericytes, astrocytes, neurons, microglia, and oligodendroglia from iPSCs. To enable this multicellular 3D co-culture in-chip, we designed a GelChip microfluidic platform using a 3D printing-based approach and dextran-based engineered hydrogel. Leveraging this platform, we co-cultured and characterized iPSC-derived brain-on-chips and modeled the brain microvasculature of APOE4, the strongest known genetic risk factor for sporadic Alzheimers disease. These 3D brain-on-chips provide a versatile system to assess BBB vascular morphology and function, investigate downstream neurological effects in disease, and screen therapeutics to optimize delivery to the brain. Significance StatementThe blood-brain barrier (BBB) is both a contributing factor to neurological disease and a major obstacle to its treatment, yet human-relevant models remain limited. Most existing brain-on-chip systems incorporate only subsets of BBB cell types and cannot capture the full cellular complexity of the human neurovascular unit. Here, we establish a vascular-perfusable 3D Brain-Chip using human induced pluripotent stem cell-derived brain cells including endothelial cells, pericytes, astrocytes, neurons, microglia, and oligodendroglia. This system enables systematic analysis of human genetic risk factors, such as APOE4 in Alzheimers disease, and provides a powerful platform to investigate BBB function and dysfunction and accelerate the development of more effective neurological therapies.

neuroscience↗

bioRxiv2025

Protein corona formed on lipid nanoparticles compromises delivery efficiency of mRNA cargo

Lipid nanoparticles (LNPs) are the most clinically advanced nonviral RNA-delivery vehicles, though challenges remain in fully understanding how LNPs interact with biological systems. In vivo, proteins form an associated corona on LNPs that redefines their physicochemical properties and influences delivery outcomes. Despite its importance, the LNP protein corona is challenging to study owing to the technical difficulty of selectively recovering soft nanoparticles from biological samples. Herein, we developed a quantitative, label-free mass spectrometry-based proteomics approach to characterize the protein corona on LNPs. Critically, this protein corona isolation workflow avoids artifacts introduced by the presence of endogenous nanoparticles in human biofluids. We applied continuous density gradient ultracentrifugation for protein-LNP complex isolation, with mass spectrometry for protein identification normalized to protein composition in the biofluid alone. With this approach, we quantify proteins consistently enriched in the LNP corona including vitronectin, C-reactive protein, and alpha-2-macroglobulin. We explore the impact of these corona proteins on cell uptake and mRNA expression in HepG2 human liver cells, and find that, surprisingly, increased levels of cell uptake do not correlate with increased mRNA expression in part likely due to protein corona-induced lysosomal trafficking of LNPs. Our results underscore the need to consider the protein corona in the design of LNP-based therapeutics. Abstract Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=58 SRC="FIGDIR/small/633942v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@2fef29org.highwire.dtl.DTLVardef@fe7613org.highwire.dtl.DTLVardef@1f9de01org.highwire.dtl.DTLVardef@15e5237_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2023

Engineered 3D Immuno-Glial-Neurovascular Human Brain Model

Patient-specific, human-based cellular models integrating a biomimetic blood-brain barrier (BBB), immune, and myelinated neuron components are critically needed to enable accelerated, translationally relevant discovery of neurological disease mechanisms and interventions. By engineering a novel brain-mimicking 3D hydrogel and co-culturing all six major brain cell types derived from patient iPSCs, we have constructed, characterized, and utilized a multicellular integrated brain (miBrain) immuno-glial-neurovascular model with in vivo-like hallmarks inclusive of neuronal activity, functional connectivity, barrier function, myelin-producing oligodendrocyte engagement with neurons, multicellular interactions, and transcriptomic profiles. We implemented the model to study Alzheimers Disease pathologies associated with APOE4 genetic risk. APOE4 miBrains differentially exhibit amyloid aggregation, tau phosphorylation, and astrocytic GFAP. Unlike the co-emergent fate specification of glia and neurons in organoids, miBrains integrate independently differentiated cell types, a feature we harnessed to identify that APOE4 in astrocytes promotes neuronal tau pathogenesis and dysregulation through crosstalk with microglia.

neuroscience↗

bioRxiv2023

Mapping the Morphology of DNA on Carbon Nanotube-Based Sensors in Solution using X-ray Scattering Interferometry

Single-walled carbon nanotubes (SWCNTs) with adsorbed single-stranded DNA (ssDNA) are applied as sensors to investigate biological systems, with applications ranging from clinical diagnostics to agricultural biotechnology. Unique ssDNA sequences render SWCNTs selectively responsive to target analytes. However, it remains unclear how the ssDNA conformation on the SWCNT surface contributes to their ultimate functionality, as observations have been constrained to computational models or experiments under dehydrated states that differ substantially from the aqueous biological environments in which the nanosensors are applied. Herein, we demonstrate a direct mode of measuring in-solution ssDNA geometries on SWCNTs via X-ray scattering interferometry (XSI), which leverages the interference pattern produced by AuNP tags conjugated to ssDNA on the SWCNT surface. We employ XSI to quantify distinct surface-adsorbed morphologies for two ssDNA oligomer lengths, conformational changes as a function of ionic strength, and the mechanism of dopamine sensing for a previously established ssDNA-SWCNT nanosensor, with corresponding ab initio modeling for visualization. We show that the shorter oligomer, (GT)6, adopts a highly ordered structure of stacked rings along the SWCNT axis, compared to the longer, less periodic (GT)15 wrapping. The presence of dopamine elicits a simultaneous axial elongation and radial constriction of the ssDNA closer to the SWCNT surface. Application of XSI to probe solution-phase morphologies of nanoparticle-based tools will yield insights into sensing mechanisms and inform future design strategies for polymer-functionalized SWCNT technologies.

biophysics↗

bioRxiv2022

Bridging global actin network patterns to local molecular dynamics: a combined modeling and machine learning framework

Cells rely on their cytoskeleton for key processes including division and directed motility. Actin filaments are a primary constituent of the cytoskeleton. Although actin filaments can create a variety of network architectures linked to distinct cell functions, the microscale molecular interactions that give rise to these macroscale structures are not well understood. In this work, we investigate the microscale mechanisms that produce different branched actin network structures using an iterative classification approach. First, we employ a simple yet comprehensive agent-based model that produces synthetic actin networks with precise control over the microscale dynamics. Then we apply machine learning techniques to classify actin networks based on measurable network density and geometry, identifying key mechanistic processes that lead to particular branched actin network architectures. Extensive computational experiments reveal that the most accurate method uses a combination of supervised learning based on network density and unsupervised learning based on network symmetry. This framework can potentially serve as a powerful tool to discover the molecular interactions that produce the wide variety of actin network configurations associated with normal development as well as pathological conditions such as cancer.

biophysics↗

bioRxiv2021

Supervised Learning Model Predicts Protein Adsorption to Nanotubes

Engineered nanoparticles are advantageous for numerous biotechnology applications, including biomolecular sensing and delivery. However, testing the compatibility and function of nanotechnologies in biological systems requires a heuristic approach, where unpredictable biofouling via protein corona formation often prevents effective implementation. Moreover, rational design of biomolecule-nanoparticle conjugates requires prior knowledge of such interactions or extensive experimental testing. Toward better applying engineered nanoparticles in biological systems, herein, we develop a random forest classifier (RFC) trained with proteomic mass spectrometry data that identifies proteins that adsorb to nanoparticles, based solely on the proteins amino acid sequence. We model proteins that populate the corona of a single-walled carbon nanotube (SWCNT)-based optical nanosensor and study whether there is a relationship between the proteins amino acid-based properties and the proteins adsorption to SWCNTs. We optimize the classifier and characterize the classifier performance against other models. To evaluate the predictive power of our model, we apply the classifier to rapidly identify proteins with high binding affinity to SWCNTs, followed by experimental validation. We further determine protein features associated with increased likelihood of SWCNT binding: high content of solvent-exposed glycine residues and non-secondary structure-associated amino acids. Conversely, proteins with high content of leucine residues and beta-sheet-associated amino acids are less likely to form the SWCNT protein corona. The classifier presented herein provides a step toward undertaking the otherwise intractable problem of predicting protein-nanoparticle interactions, which is needed for more rapid and effective translation of nanobiotechnologies from in vitro synthesis to in vivo use. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=66 SRC="FIGDIR/small/449132v2_ufig1.gif" ALT="Figure 1"> View larger version (22K): org.highwire.dtl.DTLVardef@bbc175org.highwire.dtl.DTLVardef@9a0b94org.highwire.dtl.DTLVardef@16e22e1org.highwire.dtl.DTLVardef@1b40a84_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2020

Transcriptomic and morphological response of SIM-A9 mouse microglia to carbon nanotube neuro-sensors

Single-walled carbon nanotubes (SWCNT) are used in neuroscience for deep-brain imaging, neuron activity recording, measuring brain morphology, and imaging neuromodulation. However, the extent to which SWCNT-based probes impact brain tissue is not well understood. Here, we study the impact of (GT)6-SWCNT dopamine nanosensors on SIM-A9 mouse microglial cells and show SWCNT-induced morphological and transcriptomic changes in these brain immune cells. Next, we introduce a strategy to passivate (GT)6-SWCNT nanosensors with PEGylated phospholipids to improve both biocompatibility and dopamine imaging quality. We apply these passivated dopamine nanosensors to image electrically stimulated striatal dopamine release in acute mouse brain slices, and show that slices labeled with passivated nanosensor exhibit higher fluorescence response to dopamine and measure more putative dopamine release sites. Hence, this facile modification to SWCNT-based dopamine probes provides immediate improvements to both biocompatibility and dopamine imaging functionality with an approach that is readily translatable to other SWCNT-based neurotechnologies.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioengineering↗

bioRxiv2020

Connecting actin polymer dynamics across multiple scales

Actin is an intracellular protein that constitutes a primary component of the cellular cytoskeleton and is accordingly crucial for various cell functions. Actin assembles into semi-flexible filaments that cross-link to form higher order structures within the cytoskeleton. In turn, the actin cytoskeketon regulates cell shape, and participates in cell migration and division. A variety of theoretical models have been proposed to investigate actin dynamics across distinct scales, from the stochastic nature of protein and molecular motor dynamics to the deterministic macroscopic behavior of the cytoskeleton. Yet, the relationship between molecular-level actin processes and cellular-level actin network behavior remains understudied, where prior models do not holistically bridge the two scales together. In this work, we focus on the dynamics of the formation of a branched actin structure as observed at the leading edge of motile eukaryotic cells. We construct a minimal agent-based model for the microscale branching actin dynamics, and a deterministic partial differential equation model for the macroscopic network growth and bulk diffusion. The microscale model is stochastic, as its dynamics are based on molecular level effects. The effective diffusion constant and reaction rates of the deterministic model are calculated from averaged simulations of the microscale model, using the mean displacement of the network front and characteristics of the actin network density. With this method, we design concrete metrics that connect phenomenological parameters in the reaction-diffusion system to the biochemical molecular rates typically measured experimentally. A parameter sensitivity analysis in the stochastic agent-based model shows that the effective diffusion and growth constants vary with branching parameters in a complementary way to ensure that the outward speed of the network remains fixed. These results suggest that perturbations to microscale rates can have significant consequences at the macroscopic level, and these should be taken into account when proposing continuum models of actin network dynamics.

biophysics↗

bioRxiv2020

Protein Corona Composition and Dynamics on Carbon Nanotubes in Blood Plasma and Cerebrospinal Fluid

When a nanoparticle enters a biological environment, the surface is rapidly coated with proteins to form a "protein corona". Presence of the protein corona surrounding the nanoparticle has significant implications for applying nanotechnologies within biological systems, affecting outcomes such as biodistribution and toxicity. Herein, we measure protein corona formation on single-stranded DNA wrapped single-walled carbon nanotubes (ssDNA-SWCNTs), a high-aspect ratio nanoparticle ideal for sensing and delivery applications, and polystyrene nanoparticles, a model nanoparticle system. The protein corona of each nanoparticle is studied in human blood plasma and cerebrospinal fluid. We characterize corona composition by proteomic mass spectrometry to determine abundant and differentially enriched vs. depleted corona proteins. High-binding corona proteins on ssDNA-SWCNTs include proteins involved in lipid binding and transport (clusterin and apolipoprotein A-I), complement activation (complement C3), and blood coagulation (fibrinogen). Of note, albumin is the most common blood protein (55% w/v), yet exhibits low-binding affinity towards ssDNA-SWCNTs, displaying 1300-fold lower bound concentration relative to native plasma. We investigate the role of electrostatic and entropic interactions driving selective protein corona formation, and find that hydrophobic interactions drive inner corona formation, while shielding of electrostatic interactions allows for outer corona formation. Lastly, we study real-time binding of proteins on ssDNA-SWCNTs and find relative agreement between proteins that are enriched and bind strongly, such as fibrinogen, and proteins that are depleted and bind marginally, such as albumin. Interestingly, certain proteins express contrary behavior in single-protein experiments than within the whole biofluid, highlighting the importance of cooperative mechanisms driving selective corona adsorption on the SWCNT surface. Knowledge of the protein corona composition, dynamics, and structure informs translation of engineered nanoparticles from in vitro design to effective in vivo application.

bioengineering↗

bioRxiv2019

Surface Modification Effects on Single-Walled Carbon Nanotubes for Multimodal Optical Applications

Optical nanoscale technologies often implement covalent or noncovalent strategies for the modification of nanoparticles, whereby both functionalizations are leveraged for multimodal applications but can affect the intrinsic fluorescence of nanoparticles. Specifically, single-walled carbon nanotubes (SWCNTs) can enable real-time imaging and cellular delivery; however, the introduction of covalent SWCNT sidewall functionalizations often attenuates SWCNT fluorescence. Herein, we leverage recent advances in SWCNT covalent functionalization chemistries that preserve the SWCNTs pristine graphitic lattice and intrinsic fluorescence and demonstrate that such covalently functionalized SWCNTs maintain fluorescence-based molecular recognition of neurotransmitter and protein analytes. We show that the covalently modified SWCNT nanosensor fluorescence response towards its analyte is preserved for certain nanosensors, presumably dependent on the steric hindrance introduced by the covalent functionalization that hinders noncovalent interactions with the SWCNT surface. We further demonstrate that these SWCNT nanosensors can be functionalized via their covalent handles to self-assemble on passivated microscopy slides, and discuss future use of these dual-functionalized SWCNT materials for multiplexed applications.

bioengineering↗

bioRxiv2019

Corona exchange dynamics on carbon nanotubes by multiplexed fluorescence monitoring

Noncovalent adsorption of DNA on nanoparticles has led to their widespread implementation as gene delivery tools and optical probes. Yet, the behavior and stability of DNA-nanoparticle complexes once applied in biomolecule-rich, in vivo environments remains unpredictable, whereby biocompatibility testing usually occurs in serum. Here, we demonstrate time-resolved measurements of exchange dynamics between solution-phase and adsorbed corona-phase DNA and protein biomolecules on single-walled carbon nanotubes (SWCNTs). We capture real-time binding of fluorophore-labeled biomolecules, utilizing the SWCNT surface as a fluorescence quencher, and apply this corona exchange assay to study protein corona dynamics on ssDNA-SWCNT-based dopamine sensors. We study exchange of two blood proteins, albumin and fibrinogen, adsorbing to and competitively displacing (GT)6 vs. (GT)15 ssDNA from ssDNA-SWCNTs. We find that (GT)15 binds to SWCNTs with a higher affinity than (GT)6 and that fibrinogen interacts with ssDNA-SWCNTs more strongly than albumin. Albumin and fibrinogen cause a 52.2% and 78.2% attenuation of the dopamine nanosensor response, coinciding with 0.5% and 3.7% desorption of (GT)6, respectively. Concurrently, the total surface-adsorbed fibrinogen mass is 168% greater than that of albumin. Binding profiles are fit to a competitive surface exchange model which recapitulates the experimental observation that fibrinogen has a higher affinity for SWCNTs than albumin, with a fibrinogen on-rate constant 1.61-fold greater and an off-rate constant 0.563-fold smaller than that of albumin. Our methodology presents a generic route to assess real-time corona exchange on nanoparticles in solution phase, and more broadly motivates testing of nanoparticle-based technologies in blood plasma rather than the more ubiquitously-tested serum conditions.

bioengineering↗

bioRxiv2019

Graphene Quantum Dot Oxidation Governs Noncovalent Biopolymer Adsorption

The graphene quantum dot (GQD) is a carbon allotrope with a planar surface amenable for functionalization and nanoscale dimensions that confer photoluminescent properties. Collectively, these properties render GQDs an advantageous platform for nanobiotechnology applications, including as optical biosensors and delivery platforms. In particular, noncovalent functionalization offers a route to reversible modification and preservation of the pristine GQD substrate. However, a clear paradigm for GQD noncovalent functionalization has yet to be realized. Herein, we demonstrate the feasibility of noncovalent polymer adsorption to the GQD surface, with a specific focus on single-stranded DNA (ssDNA). We study how GQD oxidation level affects the propensity for polymer adsorption by synthesizing and characterizing four types of GQD substrates and investigating noncovalent polymer association to these substrates. Distinct adsorption methods are developed for successful ssDNA attachment based upon the GQDs initial level of oxidation. ssDNA adsorption to the GQD is confirmed by atomic force microscopy, by inducing ssDNA desorption, and with molecular dynamics simulations. ssDNA is determined to adsorb strongly to no-oxidation GQDs, weakly to low-oxidation GQDs, and not at all for heavily oxidized GQDs. We hypothesize that high GQD oxygen content disrupts the graphitic carbon domains responsible for stacking with the aromatic ssDNA bases, thus preventing the formation of stable polymer-GQD complexes. Finally, we develop a more generic adsorption platform and assess how the GQD system is tunable by modifying both the polymer sequence and type.

bioengineering↗