Biology subjects

Min, W.

Publications and source records attributed to Min, W..

17 recordsLinked to original sources

bioRxiv2026

PLANCK: super-multiplex optical imaging without labeling

Molecular information is vital for imaging technology. Optical imaging acquires molecular specificity almost exclusively via labeling strategy, which is fundamentally constrained by limited multiplexing capacity, high running costs, and experimental complexity. Conversely, label-free optical imaging offers substantial technical simplicity but is believed to have little true molecular specificity. Contrary to common belief, here we introduce super-multiplex optical imaging without labeling. By systematically studying paired vibrational spectroscopic imaging and mass spectrometry imaging, we discovered a surprisingly strong (more than 0.9) correlation between their latent space representations, supported by both experiments and theory. This insight prompts us to build supervised learning models to successfully predict spatial distribution of 100 molecular species directly from label-free vibrational images across diverse tissue systems. We developed this technology, named Prediction through Learning with AdvaNced Chemical Kaleidoscope (PLANCK), and demonstrated it with both infrared-based vibrational imaging of organ-scale tissues and Raman-based vibrational imaging of live tissues. Powered by AI, PLANCK decodes the exquisitely rich but otherwise hidden vibrational information into a surprisingly large number of ( [≥] 100) specific molecular species, providing a cost-effective and scalable solution for basic research and translation, including applications in live imaging.

biophysics↗

bioRxiv2025

VIP-OT: Dissecting Single-Cell Biochemical State Dynamics under Perturbation via Vibrational Painting and Optimal Transport

Dissecting the heterogeneous response of individual cells towards genetic and chemical perturbations is central to understanding the dynamic functions of cells and multicellular systems. However, characterizing and modeling how individual cells transition between different states remains a major challenge. Vibrational imaging provides high-content, biochemically informative, label-free molecular fingerprints of single cells but it remains at its infancy for dynamic or predictive analysis of cell state transition. Here, we introduce Vibrational Painting-Optimal Transport (VIP-OT), an integrated experimental-computational framework that overcomes this fundamental limitation. VIP-OT couples multiplexed infrared (IR) and Raman imaging with optimal transport to computationally reconstruct single-cell perturbation trajectories from unpaired population snapshots. When applying to over 22,000 single-cell spectra profiles from human breast adenocarcinoma cells under 16 drug treatments, this framework can retrospectively trace drug response heterogeneity back to baseline metabolic states. We leverage the inferred cell pairings to develop a machine learning model that accurately predicts the full post-treatment metabolic state of individual cells from their pre-treatment spectra. Furthermore, by modeling transitions across dose gradients, we introduce the concept of Spectral Velocity to map dynamic response trajectories and resolve drug combination effects into distinct, path-dependent molecular routes. Together, VIP-OT opens a new direction for dissecting heterogeneous perturbation responses at single cell resolution and serves as a foundation for building virtual simulators of cells under perturbations through high-throughput, high-content, and low-cost vibrational imaging, as well as interpretable in silico modeling.

bioengineering↗

bioRxiv2025

Direct Optical Quantification of Chain Collapse, Reduced Dielectric, and Water Release Driving Protein Phase Separation

Biomolecular condensates represent unique microenvironments that organize intracellular biology and promote biochemical reactions. However, the biomolecular interactions driving condensate phase separation are often weak, transient, and heterogeneous. Investigating the structural biology and chemical properties of condensate interiors has therefore proven experimentally challenging, often requiring the use of perturbative probes. To overcome this challenge, we combine label-free optical scattering and vibrational spectroscopy approaches spanning ultraviolet, visible, mid-infrared, and terahertz wavelengths with deep-learning-based ensemble prediction of intrinsically disordered protein conformations. Our experimental and computational results reveal that the intrinsically disordered N-terminal domain of the RNA Deadbox helicase 4 (DDX4) deviates from random coil behavior and undergoes chain collapse that correlates with phase separation, which leads to lower dielectric and reduced water content inside condensates. Our data support a model of DDX4 phase separation whereby chain collapse, reduced dielectric, and water release enhance the strength of multivalent protein-protein interactions within condensates, driving condensate growth and phase separation through positive feedback. Our study addresses the critical driving forces of biomolecular phase separation across a range of length scales, providing quantitative insights into protein-protein/protein-solvent interactions and the chemical properties of condensate interiors.

biophysics↗

bioRxiv2025

SpaBatch: Batch Alignment of Spatial Transcriptomics Data using Graph Deep Learning

Spatial transcriptomics (ST) has emerged as a transformative technology, enabling the simultaneous capture of gene expression and spatial context within tissues. However, batch effects arising from technical variability remain a significant barrier to integrating datasets generated from different experiments. To this end, we introduce a novel graph deep learning framework (SpaBatch) tailored for the batch alignment of multi-slice spatial transcriptomics data. SpaBatch is an innovative computational framework leveraging Variational Graph Autoencoders, self-supervised learning, and triplet learning with readout aggregation to enhance multi-slice spatial transcriptomics data integration. We validated our framework on multiple datasets encompassing various tissue types and experimental conditions. Through the adjustment of batch effects, SpaBatch facilitates the analysis of spatial transcriptome domains across multiple slices, showcasing its capability to reveal new biological insights in multi-slice spatial transcriptome studies.

bioinformatics↗

bioRxiv2024

Inferring single-cell resolution spatial gene expression via fusing spot-based spatial transcriptomics, location and histology using GCN

Spatial transcriptomics technology allows for the detection of cellular transcriptome information while preserving the spatial location of cells. This capability enables researchers to better understand the cellular heterogeneity, spatial organization and functional interactions in complex biological systems. However, current technological methods are limited by low resolution, which reduces the accuracy of gene expression levels. Here, we propose scstGCN, a multimodal information fusion method based on Vision Transformer (ViT) and Graph Convolutional Network (GCN) that integrates histological images, spot-based spatial transcriptomics data and spatial location information to infer super-resolution gene expression profiles at single-cell level. We evaluated the accuracy of the super-resolution gene expression profiles generated on diverse tissue ST datasets with disease and healthy by scstGCN along with their performance in identifying spatial patterns, conducting functional enrichment analysis, and tissue annotation. The results show that scstGCN can predict super-resolution gene expression accurately, aid researchers in discovering biologically meaningful differentially expressed genes and pathways. Additionally, scstGCN can segment and annotate tissues at a finer granularity, with results demonstrating strong consistency with coarse manual annotations. Key PointsO_LIscstGCN combines multi-modal information including histology image, spot-based spatial transcriptomics (ST) data, and physical spatial location through deep learning methods to achieve single-cell resolution of spot-based ST data without requiring single-cell references. C_LIO_LIscstGCN employs GCN to capture complex relationships between neighboring cells, facilitating the integration of multimodal feature information based on single-cell level, and then accurately infers single-cell resolution spatial gene expression. C_LIO_LIscstGCN can infer single-cell resolution gene expression across the entire tissue region. Through transfer learning, gene expression in three-dimensional tissues can be characterized efficiently. Furthermore, it demonstrates outstanding performance in spatial patterns enhancement, functional enrichment analysis, and annotate tissues at the high-resolution. C_LI

bioinformatics↗

bioRxiv2024

Circular RNA AEBP2: A novel regulator in dendritic cell development

Dendritic cells (DCs) are the main antigen-presenting cells and play a central role in activating adaptive immunity. How DCs is regulated during their development remains to be further elucidated. Circular RNA (circRNA) is a new type of non-coding RNA playing a critical in various cell events. However, little is known about the role of circRNA in DCs. Using in situ RNA hybridization, we found that DCs expressed exonic circular RNA AEBP2 (circAEBP2). circAEBP2 was upregulated in mature DCs compared to immature DCs. Knockdown of circAEBP2 in DCs using small interference RNA (siRNA) arrested DCs at an immature state with lower expression of MHC class II, CD40, CD80, and CD83. circAEBP2 knockdown in DCs also decreased the phosphorylation of nuclear factor-kappa B p65 (P-p65) but increased CD200R1. CircAEBP2-silenced DCs had a lower capacity to activate allogeneic CD4+ and CD8+ T cells in vitro and induced differentiation of regulatory T cells (Tregs). RNA immunoprecipitation assays revealed that circAEBP2 interacts with heterogeneous nuclear ribonucleoprotein F (hnRNP F) which is highly expressed in DCs and subsequently prevented hnRNP F nuclear translocation. In conclusion, we demonstrate that circAEBP2 regulates the development and function of DCs and knockdown of circAEBP2 induces tolerogenic DCs (Tol-DCs) through interacting with the hnRNP F/CD200R1/P-p65 signaling pathway. circAEBP2 is a new regulator in DCs and modulating it provides a novel strategy for inducing Tol-DCs.

immunology↗

bioRxiv2024

Biomolecular condensates can function as inherent catalysts

We report the discovery that chemical reactions can be catalyzed by condensates formed by intrinsically disordered proteins (IDPs). The proteins themselves lack any catalytic activities. Catalytic functions of condensates emerge as a consequence of sequence-dependent mesoscale electrochemical microenvironments created by phase separation. Stimulated Raman spectroscopy suggests that the catalytic behaviors of condensates are attributable to the spatial variations of water activities across condensate interiors and interfaces. We show that condensates are capable of catalyzing diverse cellularly relevant hydrolysis reactions. Through sequence design, the electrochemical properties of condensates can be programmed to exert control over catalytic behaviors. Incorporation of synthetic condensates into live cells alters transcription profiles and enables the activation of gene circuits that depend on products of hydrolysis reactions catalyzed by condensates. Our discovery of suggests that condensates, depending on their composition-dependent electrochemical properties, can be "Condenzymes", which contribute unexpected emergent chemical functions in cells.

biophysics↗

bioRxiv2024

Dimensionality Reduction and Denoising of Spatial Transcriptomics Data Using Dual-Channel Masked Graph Autoencoder

Recent advances in spatial transcriptomics (ST) technology allow researchers to comprehensively measure gene expression patterns at the level of individual cells or even subcellular compartments while preserving the spatial context of their tissue. Spatial domain identification is a critical task in analyzing the ST data. However, effectively capturing distinctive gene expression features and relationships between genes poses a significant challenge. We develop a graph self-supervised learning method STMask for the analysis and exploration of the ST data. STMask combines the masking mechanism with a graph autoencoder, compelling the gene representation learning channel to acquire more expressive representations. Simultaneously, it combines the masking mechanism with graph self-supervised contrastive learning methods, pulling together the embedding distances between spatially adjacent points and pushing apart the representations of different clusters, allowing the gene relationship learning channel to learn more comprehensive relationships. The applications of STMask to four ST datasets demonstrate that STMask outperforms state-of-the-art methods in various tasks, including spatial clustering and trajectory inference. Source code is available at https://github.com/donghaifang/STMask. Author summarySpatial Transcriptomics (ST) is an emerging transcriptomic sequencing technology aimed at revealing the spatial distribution of gene expression and cell types within tissues. This method enables the acquisition of gene expression profiles at the level of individual cells or spots within the tissue, uncovering the spatial expression patterns of genes. However, accurately identifying spatial domains in ST data remains challenging. In our study, we introduce STMask, a self-supervised learning method that combines a dual-channel masked graph autoencoder with masking and contrastive learning. Our work contributes primarily in two aspects: (1) We propose a novel graph self-supervised learning method (STMask) specifically tailored for the analysis and research of ST data, which enhances the ability to capture the unique features of gene expression and spatial relationships within tissues. (2) Through comprehensive experiments, STMask provides valuable insights into biological processes, particularly in the context of breast cancer. It identifies enrichment of various differentially expressed genes in tumor regions, such as IGHG1, which can serve as effective targets for cancer therapy.

genomics↗

bioRxiv2024

Targeting protein tyrosine phosphatase non-receptor type 2 with a novel inhibitor for AML therapy

Acute myeloid leukemia (AML) is a fatal disease characterized by a bleak prognosis. For over four decades, treatment options for AML have been constrained to administering high-dose cytotoxic chemotherapy. However, the emergence of drug resistance and the resultant toxic side effects have created an urgent necessity for identifying novel therapeutic targets. In this study, non-receptor protein tyrosine phosphatase type 2 (PTPN2) is highly expressed in AML. Remarkably, knock down of PTPN2 expression markedly mitigated AML burden both in vitro and in vivo. Additionally, we unveiled the direct interaction between PTPN2 and C-MYC, establishing C-MYC as a direct acting substrate of PTPN2. Then, we performed a small-molecule compound screening to identify K73, a selective inhibitor of PTPN2. At the same time, K73 showed a good safety profile and exhibited strong activity against AML in vivo. In conclusion, we have pinpointed a considerable therapeutic potential in targeting PTPN2 for AML treatment and discovered a novel class of selective PTPN2 inhibitors suitable for AML therapy.

cancer biology↗

bioRxiv2023

Cell arrangement impacts metabolic activity and antibiotic tolerance in Pseudomonas aeruginosa biofilms

Cells must access resources to survive, and the anatomy of multicellular structures influences this access. In diverse multicellular eukaryotes, resources are provided by internal conduits that allow substances to travel more readily through tissue than they would via diffusion. Microbes growing in multicellular structures, called biofilms, are also affected by differential access to resources and we hypothesized that this is influenced by the physical arrangement of the cells. In this study, we examined the microanatomy of biofilms formed by the pathogenic bacterium Pseudomonas aeruginosa and discovered that clonal cells form striations that are packed lengthwise across most of a mature biofilms depth. We identified mutants, including those defective in pilus function and in O-antigen attachment, that show alterations to this lengthwise packing phenotype. Consistent with the notion that cellular arrangement affects access to resources within the biofilm, we found that while the wild type shows even distribution of tested substrates across depth, the mutants show accumulation of substrates at the biofilm boundaries. Furthermore, we found that altered cellular arrangement within biofilms affects the localization of metabolic activity, the survival of resident cells, and the susceptibility of subpopulations to antibiotic treatment. Our observations provide insight into cellular features that determine biofilm microanatomy, with consequences for physiological differentiation and drug sensitivity.

microbiology↗

bioRxiv2023

Spatial heterogeneity in biofilm metabolism elicited by local control of phenazinemethylation

Within biofilms, gradients of electron acceptors such as oxygen stimulate the formation of physiological subpopulations. This heterogeneity can enable cross-feeding and promote drug resilience, features of the multicellular lifestyle that make biofilm-based infections difficult to treat. The pathogenic bacterium Pseudomonas aeruginosa produces pigments called phenazines that can support metabolic activity in hypoxic/anoxic biofilm subzones, but these compounds also include methylated derivatives that are toxic to their producer under some conditions. Here, we uncover roles for the global regulators RpoS and Hfq/Crc in controlling the beneficial and detrimental effects of methylated phenazines in biofilms. Our results indicate that RpoS controls phenazine methylation by modulating activity of the carbon catabolite repression pathway, in which the Hfq/Crc complex inhibits translation of the phenazine methyltransferase PhzM. We find that RpoS indirectly inhibits expression of CrcZ, a small RNA that binds to and sequesters Hfq/Crc, specifically in the oxic subzone of P. aeruginosa biofilms. Deletion of rpoS or crc therefore leads to overproduction of methylated phenazines, which we show leads to increased metabolic activity--an apparent beneficial effect--in hypoxic/anoxic subpopulations within biofilms. However, we also find that biofilms lacking Crc show increased sensitivity to an exogenously added methylated phenazine, indicating that the increased metabolic activity in this mutant comes at a cost. Together, these results suggest that complex regulation of PhzM allows P. aeruginosa to simultaneously exploit the benefits and limit the toxic effects of methylated phenazines. Significance StatementP. aeruginosa causes biofilm-based infections and is known for its production of colorful phenazine derivatives. Among these the methylated phenazines are the most toxic and can cause condition-dependent damage to their producer. In this study, we show that methylated phenazines also have a beneficial effect in that they specifically support metabolic activity at depth in P. aeruginosa biofilms, where oxygen limitation would otherwise stall metabolism. We describe a new link between P. aeruginosa global regulators that control methylated phenazine production in a manner that limits their toxicity while simultaneously enabling their contribution to metabolism. These results expand our understanding of the strategies that enable P. aeruginosa survival in multicellular structures, which is key to its success during chronic host colonization.

microbiology↗

bioRxiv2022

Rhythmic Cilium in SCN Neuron is a Gatekeeper for the Intrinsic Circadian Clock

The internal circadian rhythm is controlled by the central pacemaker in the hypothalamic suprachiasmatic nucleus (SCN). SCN drives coherent and synchronized circadian oscillations via intercellular coupling, which are resistant to environmental perturbations. Here we report that primary cilium is a critical device for intercellular coupling among SCN neurons and acts as a gatekeeper to maintain the internal clock in mice. A subset of SCN neurons, namely neuromedin S-producing (NMS) neurons, exhibit cilia dynamics with a pronounced circadian rhythmicity. Genetic ablation of ciliogenesis in NMS neurons enables a rapid phase shift of the internal clock under experimental jet lag conditions. The circadian rhythms of individual neurons in cilia-deficient SCN slices lose their coherence following external perturbations. Rhythmic cilia dynamics drive oscillations of Sonic Hedgehog (Shh) signaling and oscillated expressions of multiple circadian genes in SCN neurons. Genetic and chemical inactivation of Shh signaling in NMS neurons phenocopies the effect of cilia ablation. Our findings establish ciliary signaling as a novel interneuronal coupling mechanism in the SCN and may lead to novel therapy of circadian disruption-linked diseases. One-Sentence SummaryRhythmic cilium is a critical device for intercellular coupling among SCN neurons and acts as gatekeeper for the internal clock.

neuroscience↗

bioRxiv2021

Super-resolution vibrational imaging using expansion stimulated Raman scattering microscopy

Stimulated Raman scattering (SRS) microscopy is an emerging technology that provides high chemical specificity for endogenous biomolecules and can circumvent common constraints of fluorescence microscopy including limited capabilities to probe small biomolecules and difficulty resolving many colors simultaneously due to spectral overlap. However, the resolution of SRS microscopy remains governed by the diffraction limit. To overcome this, we describe a new technique called Molecule Anchorable Gel-enabled Nanoscale Imaging of Fluorescence and stImulatEd Raman Scattering microscopy (MAGNIFIERS), that integrates SRS microscopy with expansion microscopy (ExM). ExM is a powerful strategy providing significant improvement in imaging resolution by physical magnification of hydrogel-embedded preserved biological specimens. MAGNIFIERS offers chemical-specific nanoscale imaging with sub-50 nm resolution and has scalable multiplexity when combined with multiplex Raman probes and fluorescent labels. We used MAGNIFIERS to visualize nanoscale features in a label-free manner with C-H vibration of proteins, lipids and DNA in a broad range of biological specimens, from mouse brain, liver and kidney to human lung organoid. In addition, we applied MAGNIFIERS to track nanoscale features of protein synthesis in protein aggregates using metabolic labeling of small metabolites. Finally, we used MAGNIFIERS to demonstrate 8-color nanoscale imaging in an expanded mouse brain section. Overall, MAGNIFIERS is a valuable platform for super-resolution label-free chemical imaging, high-resolution metabolic imaging, and highly multiplexed nanoscale imaging, thus bringing SRS to nanoscopy.

bioengineering↗

bioRxiv2020

Upregulated functionality of mitochondria-associated ER membranes in a mouse model of traumatic brain injury

Traumatic brain injury (TBI) can lead to neurodegenerative diseases such as Alzheimers disease (AD) through mechanisms that remain incompletely characterized. Similar to AD, TBI models present with cellular metabolic alterations and modulated cleavage of amyloid precursor protein (APP). Specifically, AD and TBI tissues display increases in amyloid-{beta} as well as its precursor, APP C-terminal fragment of 99 a.a. (C99). Our recent data in cell models of AD indicate that C99, due to its affinity for cholesterol, induces the formation of transient lipid raft domains in the ER known as mitochondria-associated ER membranes ("MAM" domains). The formation of these domains recruits and activates specific lipid metabolic enzymes that regulate cellular cholesterol trafficking and sphingolipid turnover. Increased C99 levels in AD cell models promote MAM formation and significantly modulate cellular lipid homeostasis. Here, these phenotypes were recapitulated in the controlled cortical impact (CCI) model of TBI in adult mice. Specifically, the injured cortex and hippocampus displayed significant increases in C99 and MAM activity, as measured by phospholipid synthesis, sphingomyelinase activity and cholesterol turnover. In addition, our cell type-specific lipidomics analyses revealed significant changes in microglial lipid composition that are consistent with the observed alterations in MAM-resident enzymes. Altogether, we propose that alterations in the regulation of MAM and relevant lipid metabolic pathways could contribute to the epidemiological connection between TBI and AD. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=128 SRC="FIGDIR/small/381756v2_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@12d0989org.highwire.dtl.DTLVardef@1cd1eb2org.highwire.dtl.DTLVardef@19a56caorg.highwire.dtl.DTLVardef@1b7f3e1_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2020

Fibroblasts as an Alternative to Mesenchymal Stem Cells with Successful Treatment and Immune Modulation in EAE Model of Multiple Sclerosis

The immune modulatory potential of mesenchymal stem cells (MSCs) is well known and is the basis for multiple clinical trials in treatment of autoimmune conditions. Unfortunately, MSCs are relatively rare, difficult to expand in culture, and methods of obtaining MSCs are complicated and expensive. In contrast, fibroblasts are found in copious amounts in various tissues, are a robust cellular population, and can be cultured without need for costs associated with culture media. Previous studies by our group and others have demonstrated fibroblasts possess regenerative activities. In the current study we demonstrated: a) fibroblasts inhibit mixed lymphocyte reaction; b) suppress T cell activation; c) inhibit DC maturation; and d) stimulate T regulatory (Treg) cell formation. Importantly, administration of fibroblasts in the experimental autoimmune encephalomyelitis (EAE) model of multiple sclerosis resulted in disease inhibition, which was abrogated upon depletion of Treg cells. This data, combined with existing clinical safety data on fibroblast administration, supports the clinical translation of fibroblast-based therapies for multiple sclerosis.

immunology↗

bioRxiv2019

Live-cell Imaging Analysis of Antimycin-Type Depsipeptides via Bioorthogonal Stimulated Raman Scattering Microscopy

Small-molecule natural products have been an essential source of pharmaceuticals to treat human diseases, but very little is known about their behavior inside dynamic, living human cells. Here, we demonstrate the first structure-activity-distribution study of complex natural products, the anti-cancer antimycin-type depsipeptides, using the emerging bioorthogonal Stimulated Raman Scattering (SRS) Microscopy. Our results show that the intracellular enrichment and distribution of these compounds are driven by their potency and specific protein targets, as well as the lipophilic nature of compounds.

bioengineering↗

bioRxiv2018

Optical Imaging of Metabolic Dynamics in Animals

Direct visualization of metabolic dynamics in living tissues with high spatial and temporal resolution is essential to understanding many biological processes. Here we introduce a platform that combines deuterium oxide (D2O) probing with stimulated Raman scattering microscopy (DO-SRS) to image in situ metabolic activities. Enzymatic incorporation of D2O-derived deuterium into macromolecules generates carbon-deuterium (C-D) bonds, which track biosynthesis in tissues and can be imaged by SRS in situ. Within the broad vibrational spectra of C-D bonds, we discovered lipid-, protein-, and DNA-specific Raman shifts and developed spectral unmixing methods to obtain C-D signals with macromolecular selectivity. DO-SRS enabled us to probe de novo lipogenesis in animals, image protein biosynthesis without tissue bias, and simultaneously visualize lipid and protein metabolism and reveal their different dynamics. DO-SRS, being noninvasive, universally applicable, and cost-effective, can be adapted to a broad range of biological systems to study development, tissue homeostasis, aging, and tumor heterogeneity.

biophysics↗