Biology subjects

Chen, Y.-I.

Publications and source records attributed to Chen, Y.-I..

11 recordsLinked to original sources

bioRxiv2026

A Chemigenetic Fluorescence Lifetime Biosensor for Dopamine Sensing

Measuring the precise dynamics of specific neuromodulators in neurons is key to understanding how information is transmitted and processed in the brain. Despite the growing use of intensity-based neural activity sensors such as dLight in recent years, it remains challenging to monitor the dynamics of multiple neurochemicals simultaneously in live neurons. Unlike intensiometric sensors that are susceptible to artifacts caused by fluctuations in excitation power and rapid photobleaching, fluorescence lifetime-based sensors are less affected by these factors and can provide more stable measurements. Here we introduce dHaloLife635, a novel genetically encoded fluorescence-lifetime-based dopamine (DA) sensor that exhibits a fluorescence lifetime change up to -0.20 ns upon dopamine binding. dHaloLife635 consists of a G protein-coupled dopamine receptor (DRD1), a circularly permuted HaloTag (cpHaloTag), and a cell-permeable dye JF635 functionalized with a HaloTag ligand, a reactive chloroalkane linker (JF635-HTL). When dopamine binds to the dHaloLife635 sensor, it induces a conformational change in the cpHaloTag that alters the local environment of the conjugated dye, thereby changing its fluorescence lifetime. In live HEK293T cells and primary neurons, dHaloLife635 responds selectively to dopamine but not to serotonin, L-DOPA, or GABA, demonstrating excellent specificity. This dHaloLife635 sensor represents an initial step toward the establishment of a broader family of chemigenetic lifetime sensors that exhibit distinct fluorescence lifetime signatures upon binding their respective targets, thereby enabling highly specific and sensitive, real-time monitoring of multiple neurochemical dynamics in vivo.

bioengineering↗

bioRxiv2026

A High-Throughput Platform for Rapidly Adapting DNA Aptamers to SARS-CoV-2 Evolution

Rapid pathogen evolution threatens public health by eroding the effectiveness of vaccines, therapeutics, and diagnostic tools. Although spike protein targeting monoclonal antibodies (mAbs) were developed within 10-12 months of the initial outbreak to serve as key theranostic agents, their redesign has struggled to keep pace with viral evolution, rendering many neutralizing antibodies ineffective. Here we demonstrate a novel platform that combines a random-rational hybrid library diversification with high-throughput MiSeq screening to rapidly reprogram aptamers against emerging SARS-CoV-2 spike variants. Interactions between 3 different spike proteins and 11,806 unique aptamer variant designs were profiled within a few days. Starting from a 40-nt aptamer originally selected against wild-type (WT) spike protein, our screen identified a Delta-binding mutant with a 4-fold affinity improvement and an Omicron-binding mutant that converted undetectable binding into nanomolar affinity. We also identified a WT-selective mutant with substantially reduced affinity for Delta, as well as previously unrecognized bases that critically contribute to spike recognition. Integrating high-throughput binding data with molecular dynamics simulations further revealed sequence-dependent structural features underlying variant-specific aptamer-spike interactions. Finally, we developed a sensor based on the identified WT-selective aptamer mutant, enabling highly specific detection of the WT spike protein with robust performance. Together, this work establishes a rapid and adaptable aptamer engineering platform for rapid adaptation of aptamers to evolving pathogens in future pandemics.

bioengineering↗

bioRxiv2026

Lifetime-based multiplexed detection of viral RNA using fluorogenic aptamers

Fluorogenic aptamers (FAPs) are emerging molecular probes for viral RNA and DNA sensing. However, their use in multiplexed nucleic acid sensing has been hindered by cross-reactivity and overlapping emission spectra. Here we address these limitations by introducing a fluorescence-lifetime-based multiplexed detection strategy using variants of the DNA fluorogenic aptamer Lettuce that exhibits distinct fluorescence lifetimes when complexed with the fluorogen TO1-biotin. To effectively evolve Lettuce for diverse lifetimes, we developed a large-scale screening platform, termed FAP-FLIM-NGS (fluorogenic aptamer-based fluorescence lifetime imaging microscopy on next-generation sequencing chips), which measures the fluorescence lifetimes of [~]104 Lettuce/TO1-biotin complexes directly on an Illumina MiSeq flow cell. Using this approach, three variants with markedly different lifetimes were identified: a single mutant (smC14T, 6.0 ns) and two double mutants (dmA5T/C14T, 5.2 ns, and dmA5T/T22A, 4.4 ns). To demonstrate the utility of these Lettuce variants in multiplexed detection, a set of split Lettuce probes targeting viral RNA fragments derived from SARS-CoV-2, MERS-CoV, and influenza A were designed and tested. Phasor plot analysis confirmed that these probes can robustly distinguish individual targets as well as mixtures containing any two or all three targets purely based on distinct fluorescence lifetimes of probes, thereby overcoming the challenges of cross-reactivity and spectral overlap. Beyond this proof of concept, our findings establish a generalizable strategy for engineering FAPs with customized photophysical properties, opening new avenues for next-generation diagnostics and molecular sensing technologies.

bioengineering↗

bioRxiv2026

Mechanistic insights into the color transformation of a non-FRET substrate for RNase activity detection

DNA-templated silver nanoclusters (DNA/AgNCs) have created a new class of non-FRET DNase substrates, termed Subak, that exhibits a color change upon DNase digestion. Although Subak substrates offer advantages such as ratiometric readouts and low manufacturing costs over traditional FRET substrates, the mechanism governing AgNC color switching remains unclear. Here, using a site-specific cleavage strategy, we identify color-switching hotspots and demonstrate that AgNC transformation can be controlled by the cleavage positions within the nucleic acid host. Our data support a cleavage-driven reorganization of the AgNC coordination environment, converting a non-emissive precursor into a red-emitting cluster, rather than direct enzyme-cluster interactions. Leveraging this insight, we engineer rSubak, an RNA-incorporated Subak that displays 95 nm red shift (530 to 625 nm) upon RNase cleavage. In amplification-free CRISPR/Cas13 assays for SARS-CoV-2, influenza A (A/H5N1), and measles viruses (MV) detection, rSubak achieved a limit of detection of 0.3 pM, superior to that of the commercial RNaseAlert ([~]250 pM). Collectively, our results establish Subak as a generalizable, non-FRET platform for sensitive ratiometric reporting the activities of diverse nucleases.

bioengineering↗

bioRxiv2025

A live tumor fragment platform to assess immunotherapy response in core needle biopsies while addressing challenges of tumor heterogeneity

BackgroundImmune checkpoint inhibitors (ICIs) have revolutionized cancer treatment, providing durable and even curative responses. However, most patients do not respond and current biomarkers (eg, programmed death ligand (PD-L1), mismatch repair deficiency (dMMR)/high microsatellite instability (MSI) and tumor mutational burden) lack predictive accuracy. Ex vivo profiling of patient-derived tumor fragments shows promise as a predictive biomarker but relies on substantial surgical tissue to mitigate intra-specimen heterogeneity. Innovations are needed that address these challenges, particularly where limited tissue is available in core needle biopsies (CNBs). MethodsLive tumor fragments (LTFs) were generated from 59 human tumor resections and 31 CNBs from patients enrolled in observational clinical trials (ClinicalTrials.gov identifiers: NCT05478538, NCT05520099, NCT06349642) to assess cytokine induction following ICI treatment. LTFs were encapsulated in hydrogel and cultured ex vivo for up to 72 hours. A sequential treatment strategy that applies control and treatment within the same well was used with response to ICI or CD3/CD28 assessed using a multiplex secretome assay. Viability was assessed using established metabolic assays and dynamic optical coherence microscopy. ResultsLTFs maintained viability and retained T cells responsive to stimulation throughout ex vivo culture. Multiplex immunofluorescence and immunohistochemistry showed key components of the tumor microenvironment, including relative proportions of CD4+ and CD8+ immune cell populations, were preserved. Specimens positive for PD-L1 or dMMR/MSI-high were enriched for cytokine upregulation, including T-cell response cytokines IFN{gamma} and CXCL10, after PD-1 treatment. To demonstrate clinical applicability of the sequential treatment strategy, CNBs from patients with lung, gastrointestinal or kidney cancer were profiled and differential cytokine induction in response to ICI treatment was observed. ConclusionsThe novel ex vivo platform presented is capable of detecting T-cell response to ICI treatment by using a sequential treatment strategy. This approach addresses challenges associated with cross-well heterogeneity in tissue composition and requires half as much tissue as a cross-well comparison, mitigating tissue limitations typically associated with non-surgical biopsies. Importantly, the platform is compatible with established functional assays as well as non-destructive spatial imaging, enabling researchers to characterize response to ICI longitudinally. Ongoing trials will enable clinicians to assess platform performance in predicting response to immunotherapy.

cancer biology↗

bioRxiv2024

Fluorogenic Aptamer Optimizations on a Massively Parallel Sequencing Platform

Fluorogenic aptamers (FAPs) have become an increasingly important tool in cellular sensing and pathogen diagnostics. However, fine-tuning FAPs for enhanced performance remains challenging even with the structural details provided by X-ray crystallography. Here we present a novel approach to optimize a DNA-based FAP (D-FAP), Lettuce, on repurposed Illumina next-generation sequencing (NGS) chips. When substituting its cognate chromophore, DFHBI-1T, with TO1-biotin, Lettuce not only shows a red-shifted emission peak by 53 nm (from 505 to 558 nm), but also a 4-fold bulk fluorescence enhancement. After screening 8,821 Lettuce variants complexed with TO1-biotin, the C14T mutation is found to exhibit an improved apparent dissociated constant ([Formula] vs. 0.82 {micro}M), an increased quantum yield (QY: 0.62 vs. 0.59) and an elongated fluorescence lifetime ({tau}: 6.00 vs. 5.77 ns), giving 45% more ensemble fluorescence than the canonical Lettuce/TO1-biotin complex. Molecular dynamic simulations further indicate that the {pi}-{pi} stacking interaction is key to determining the coordination structure of TO1-biotin in Lettuce. Our screening-and-simulation pipeline can effectively optimize FAPs without any prior structural knowledge of the canonical FAP/chromophore complexes, providing not only improved molecular probes for fluorescence sensing but also insights into aptamer-chromophore interactions.

biophysics↗

bioRxiv2024

Ketomimetic Medium Promotes Metastatic Disposition and Chemoresistance in Breast Cancer Cells through Hypersialylation and Lipid Synthesis

While the triggers for the metastatic transformation of breast cancer (BC) cells remain unknown, recent evidence suggests that intrinsic cellular metabolism could be a crucial driver of migratory disposition and chemoresistance. Aiming to decode the molecular mechanisms involved in BC cell metabolic maneuvering, we study how a ketomimetic (ketone body-rich, low glucose) medium affects Doxorubicin (DOX) susceptibility and invasive disposition of BC cells. We quantified glycocalyx sialylation and found an inverse correlation with DOX-induced cytotoxicity and DOX internalization. These measurements were coupled with single-cell metabolic imaging, bulk migration studies, along with transcriptomic and metabolomic analyses. Our findings revealed that a ketomimetic medium enhances chemoresistance and invasive disposition of BC cells via two main oncogenic pathways: hypersialylation and lipid synthesis. We propose that the crosstalk between these pathways, juxtaposed at the synthesis of the glycan precursor UDP-GlcNAc, furthers advancement of a metastatic phenotype in BC cells under ketomimetic conditions.

cancer biology↗

bioRxiv2023

Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations

Neural mechanisms and underlying directionality of signaling among brain regions depend on neural dynamics spanning multiple spatiotemporal scales of population activity. Despite recent advances in multimodal measurements of brain activity, there is no broadly accepted multiscale dynamical models for the collective activity represented in neural signals. Here we introduce a neurobiological-driven deep learning model, termed multiscale neural dynamics neural ordinary differential equation (msDyNODE), to describe multiscale brain communications governing cognition and behavior. We demonstrate that msDyNODE successfully captures multiscale activity using both simulations and electrophysiological experiments. The msDyNODE-derived causal interactions between recording locations and scales not only aligned well with the abstraction of the hierarchical neuroanatomy of the mammalian central nervous system but also exhibited behavioral dependences. This work offers a new approach for mechanistic multiscale studies of neural processes. Author SummaryMulti-modal measurements have become an emerging trend in recent years due to the capability of studying brain dynamics at disparate scales. However, an integrative framework to systematically capture the multi-scale nonlinear dynamics in brain networks is lacking. A major challenge for creating a cohesive model is a mismatch in the timescale and subsequent sampling rate of the dynamics for disparate modalities. In this work, we introduce a deep learning-based approach to characterize brain communications between regions and scales. By modeling the continuous dynamics of hidden states using the neural network-based ordinary differential equations, the requirement of downsampling the faster sampling signals is discarded, thus preventing from losing dynamics information. Another advantageous feature of the proposed method is flexibility. An adaptable framework to bridge the gap between scales is necessary. Depending on the neural recording modalities utilized in the experiment, any suitable pair of well-established models can be plugged into the proposed multi-scale modeling framework. Thus, this method can provide insight into the brain computations of multi-scale brain activity.

neuroscience↗

bioRxiv2021

Massively Parallel Selection of NanoCluster Beacons

NanoCluster Beacons (NCBs) are multicolor silver nanocluster probes whose fluorescence can be activated or tuned by a proximal DNA strand called the activator. While a single-nucleotide difference in a pair of activators can lead to drastically different activation outcomes, termed the polar opposite twins (POTs), it is difficult to discover new POT-NCBs using the conventional low-throughput characterization approaches. Here we report a high-throughput selection method that takes advantage of repurposed next-generation-sequencing (NGS) chips to screen the activation fluorescence of [~]40,000 activator sequences. We find the nucleobases at positions 7-12 of the 18-nucleotide-long activator are critical to creating bright NCBs and positions 4-6 and 2-4 are hotspots to generate yellow and red POTs, respectively. Based on these findings, we propose a "zipper bag model" that explains how these hotspots lead to the creation of distinct silver cluster chromophores and contribute to the difference in chromophore chemical yields. Combining high-throughput screening with machine learning algorithms, we establish a pipeline to rationally design bright and multicolor NCBs.

bioengineering↗

bioRxiv2020

Bi-directional Modeling Between Cross-Scale Neural Activity

Fundamental principles underlying computation in multi-scale brain networks illustrate how multiple brain areas and their coordinated activity give rise to complex cognitive functions. Whereas the population brain activity has been studied in the micro-to meso-scale in building the connections between the dynamical patterns and the behaviors, such studies were often done at a single length scale and lacked an explanatory theory that identifies the neuronal origin across multiple scales. Here we introduce the NeuroBondGraph Network, a dynamical system incorporating both biological-inspired components and deep learning techniques to capture cross-scale dynamics that can infer and map the neural data from multiple scales. We demonstrated our model is not only 3.5 times more accurate than the popular sphere head model but also able to predict synchronous neural activity and extract correlated low-dimensional latent dynamics. We also showed that we can extend our methods to robustly predict held-out data across several weeks. The extracted effective connectivity agreed with the neuroanatomical hierarchy of motor control. Accordingly, the NeuroBondGraph Network opens the door to revealing comprehensive understanding of the brain computation, where network mechanisms of multi-scale communications are critical.

neuroscience↗

bioRxiv2020

Deep learning enables rapid and robust analysis of fluorescence lifetime imaging in photon-starved conditions

Fluorescence lifetime imaging microscopy (FLIM) is a powerful tool to quantify molecular compositions and study the molecular states in the complex cellular environment as the lifetime readings are not biased by the fluorophore concentration or the excitation power. However, the current methods to generate FLIM images are either computationally intensive or unreliable when the number of photons acquired at each pixel is low. Here we introduce a new deep learning-based method termed flimGANE (fluorescence lifetime imaging based on Generative Adversarial Network Estimation) that can rapidly generate accurate and high-quality FLIM images even in the photon-starved conditions. We demonstrated our model is not only 258 times faster than the most popular time-domain least-square estimation (TD_LSE) method but also provide more accurate analysis in barcode identification, cellular structure visualization, Forster resonance energy transfer characterization, and metabolic state analysis. With its advantages in speed and reliability, flimGANE is particularly useful in fundamental biological research and clinical applications, where ultrafast analysis is critical.

cell biology↗