Biology subjects

Yang, S. J.

Publications and source records attributed to Yang, S. J..

8 recordsLinked to original sources

bioRxiv2025

Brain-derived extracellular vesicle microRNAs in Lewy body and Alzheimer's disease

INTRODUCTIONRobust plasma-based biomarkers to distinguish Lewy body disease (LBD) and Alzheimers 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. METHODSWe 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 (A{beta}40, A{beta}42, tau, p-Tau181, p-Tau231) via SIMOA. RESULTSWe 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 cross-validated accuracy = 0.95 and area under the curve (AUC) = 0.96. DISCUSSIONBrain-derived EVs offer accurate and accessible miRNA biomarkers for the differential diagnosis of LBD and AD.

bioengineering↗

bioRxiv2024

LCL: Contrastive Learning for Lineage Barcoded scRNA-seq Data

Deciphering how cells commit to future fates is essential for developing precision therapeutics that can reprogram stem cells or modulate immune functions. However, isolating these fate-determining signals in single-cell lineage tracing (scLT) remains challenging because differentiation programs are often confounded by unrelated processes like the cell cycle. To address this, we introduce Lineage-aware Contrastive Learning (LCL), a framework that treats inheritable lineage barcodes as a "natural" data augmentation to isolate subtle, lineage-specific signals. LCL utilizes a semi-supervised architecture to align unlabeled cells, facilitating the transfer of lineage structures to clinical datasets where explicit barcoding is unavailable. We demonstrate LCLs utility by predicting future cell-type compositions from early-time points, effectively modeling longitudinal fate commitment from cross-sectional data. Benchmarking on hematopoietic and fibroblast systems shows that LCL significantly outperforms standard models like scVI, establishing contrastive learning as a scalable paradigm for understanding and potentially manipulating cellular differentiation.

genomics↗

bioRxiv2023

Identifying Neural Signatures of Dopamine Signaling with Machine Learning

The emergence of new tools to image neurotransmitters, neuromodulators, and neuropeptides has transformed our understanding of the role of neurochemistry in brain development and cognition, yet analysis of this new dimension of neurobiological information remains challenging. Here, we image dopamine modulation in striatal brain tissue slices with near infrared catecholamine nanosensors (nIRCat) and implement machine learning to determine which features of dopamine modulation are unique to changes in stimulation strength, and to different neuroanatomical regions. We trained a support vector machine and a random forest classifier to determine whether recordings were made from the dorsolateral striatum (DLS) versus the dorsomedial striatum (DMS) and find that machine learning is able to accurately distinguish dopamine release that occurs in DLS from that occurring in DMS in a manner unachievable with canonical statistical analysis. Furthermore, our analysis determines that dopamine modulatory signals including the number of unique dopamine release sites and peak dopamine released per stimulation event are most predictive of neuroanatomy yet note that integrated neuromodulator amount is the conventional metric currently used to monitor neuromodulation in animal studies. Lastly, our study finds that machine learning discrimination of different stimulation strengths or neuroanatomical regions is only possible in adult animals, suggesting a high degree of variability in dopamine modulatory kinetics during animal development. Our study highlights that machine learning could become a broadly-utilized tool to differentiate between neuroanatomical regions, or between neurotypical and disease states, with features not detectable by conventional statistical analysis.

neuroscience↗

bioRxiv2022

Synaptic scale dopamine disruption in Huntington's Disease model mice imaged with near infrared catecholamine nanosensors

Dopamine neuromodulation is a critical process that facilitates learning, motivation, and motor control. Disruption of these processes has been implicated in several neurological and psychiatric disorders including Huntingtons Disease (HD). While several treatments for physical and psychiatric HD symptoms target dopaminergic neuromodulation, the mechanism by which dopaminergic dysfunction occurs during HD is unknown. This is partly due to limited capability to visualize dopamine dynamics at the spatiotemporal resolution of both neuromodulator release (ms) and dopaminergic boutons ({micro}m). Here we employ near-infrared fluorescent catecholamine nanosensors (nIRCats) to image dopamine release within the brain striatum of R6/2 Huntingtons Disease Model (R6/2) mice. We find that stimulated dorsal striatal dopamine release decreases with progressive motor degeneration and that these decreases are primarily driven by a decrease in the number of dopamine hotspots combined with decreased release intensity and decreased release fidelity. Using nIRCats high spatial resolution, we show that dopamine hotspots in late HD show increased ability to add new dopamine hotspots at high extracellular calcium concentrations and track individual dopamine hotspots over repeated stimulations and pharmacological wash to measure dopamine hotspots release fidelity. Compellingly, we demonstrate that antagonism of D2-autoreceptors using Sulpiride and direct blocking of Kv1.2 channels using 4-Aminopyradine (4-AP) increases the fidelity of dopamine hotspot activity in WT striatum but not in late HD striatum, indicating that D2-autoreceptor regulation of dopamine release through Kv1.2 channels is compromised in late HD. These findings, enabled by nIRCats, provide a more detailed look into how dopamine release is disrupted and dysregulated during Huntingtons Disease to alter the coverage of dopamine modulation across the dorsal striatum. SIGNIFICANCE STATEMENTHuntingtons Disease (HD) is a neurodegenerative disorder with no cure. Dysfunction of dopamine signaling is known to deteriorate in HD but has not been studied at the spatial level of individual release sites. Here, we image dopamine release from individual hotspots in brain slices from R6/2 HD mice at early and late disease timepoints with dopamine nanosensors. We track single dopamine hotspots and find that dopamine hotspot number, release intensity, and release fidelity decrease in HD, and demonstrate that changes in D2-autoreceptor regulation manifest through changes in hotspot release fidelity thus compromising dopamine coverage across the dorsal lateral striatum. These findings highlight dopaminergic neurons in cortico-striatal signaling during HD as a promising new therapeutic target for HD treatment.

neuroscience↗

bioRxiv2020

Deep learning and automated Cell Painting reveal Parkinson's disease-specific signatures in primary patient fibroblasts

Drug discovery for diseases such as Parkinsons disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 Parkinsons disease patients and matched healthy controls, creating the largest publicly available Cell Painting image dataset to date at 48 terabytes. We use fixed weights from a convolutional deep neural network trained on ImageNet to generate deep embeddings from each image and train machine learning models to detect morphological disease phenotypes. Our platforms robustness and sensitivity allow the detection of individual-specific variation with high fidelity across batches and plate layouts. Lastly, our models confidently separate LRRK2 and sporadic Parkinsons disease lines from healthy controls (receiver operating characteristic area under curve 0.79 (0.08 standard deviation)), supporting the capacity of this platform for complex disease modeling and drug screening applications.

cell biology↗

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↗

bioRxiv2019

E-cadherin binds glycosylated sodium-taurocholate cotransporting polypeptide to facilitate hepatitis B virus entry

Hepatitis B virus (HBV) continues to pose a serious public health risk and is one of the major causes of chronic liver disease and hepatocellular carcinoma. Current antiviral therapy does not effectively eradicate HBV and, thus, further investigation into the mechanisms employed by HBV to allow for invasion of host cells, is critical for the development of novel therapeutic agents. Sodium-taurocholate cotransporting polypeptide (NTCP) has been identified as a functional receptor for HBV. However, the specific mechanism by which HBV and NTCP interact remains unclear. Herein we show that the expression of E-cadherin was upregulated in cells expressing HBV, while knockdown of E-cadherin in HepG2-NTCP cells, HepaRG cells and primary human hepatocytes served to significantly inhibit infection by HBV and HBV pseudotyped particles. Alternatively, exogenous E-cadherin expression was found to significantly enhance HBV uptake by HepaRG cells. Further, mechanistic studies identified glycosylated NTCP localized to the cell membrane via E-cadherin binding, which subsequently allowed for more efficient binding between NTCP and the preS1 of the large HBV surface proteins. E-cadherin was also found to play a key role in establishing and maintaining hepatocyte polarity, which is essential for efficient HBV infection. These observations suggest that E-cadherin facilitates HBV entry through regulation of NTCP distribution and hepatocyte polarity.\n\nAuthor SummaryHepatitis B Virus (HBV) still seriously endangers public health. It is very important to understand the mechanism of HBV invading host cells for developing new therapy target. Sodium-taurocholate cotransporting polypeptide (NTCP) is the key receptor mediating HBV invasion, while other molecules also exhibit important roles in ensuring efficient and productive HBV infection. This study reports that E-cadherin facilitates HBV entry by directly interacting with glycosylated NTCP to mediate its distribution on the hepatocyte membrane and also affects the efficacy of HBV invasion by influncing hepatocyte polarity.

microbiology↗

bioRxiv2018

Imaging Striatal Dopamine Release Using a Non-Genetically Encoded Near-Infrared Fluorescent Catecholamine Nanosensor

Neuromodulation plays a critical role in brain function in both health and disease. New optical tools, and their validation in biological tissues, are needed that can image neuromodulation with high spatial and temporal resolution, which will add an important new dimension of information to neuroscience research. Here, we demonstrate the use of a catecholamine nanosensor with fluorescent emission in the 1000-1300 nm near-infrared window to measure dopamine transmission in ex vivo brain slices. These near-infrared catecholamine nanosensors (nIRCats) represent a broader class of nanosensors that can be synthesized from non-covalent conjugation of single wall carbon nanotubes (SWNT) with single strand oligonucleotides. We show that nIRCats can be used to detect catecholamine efflux in brain tissue driven by both electrical stimulation or optogenetic stimulation. Spatial analysis of electrically-evoked signals revealed dynamic regions of interest approximately 2 microns in size in which transients scaled with simulation intensity. Optogenetic stimulation of dopaminergic terminals produced similar transients, whereas optogenetic stimulation of glutamatergic terminals showed no effect on nIRCat signal. Bath application of nomifensine prolonged nIRCat fluorescence signal, consistent with reuptake blockade of dopamine. We further show that the chemically synthetic molecular recognition elements of nIRCats permit measurement of dopamine dynamics in the presence of dopamine receptor agonists and antagonists. These nIRCat nanosensors may be advantageous for future use because i) they do not require virus delivery, gene delivery, or protein expression, ii) their near-infrared fluorescence facilitates imaging in optically scattering brain tissue and is compatible for use in conjunction with other optical neuroscience tool sets, iii) the broad availability of unique near-infrared colors have the potential for simultaneous detection of multiple neurochemical signals, and iv) they are compatible with pharmacology. Together, these data suggest nIRCats and other nanosensors of this class can serve as versatile new optical tools to report dynamics of extracellular neuromodulation in the brain.

neuroscience↗