Biology subjects

Yang, Q.

Publications and source records attributed to Yang, Q..

At least 127 records · Page 7Linked to original sources

bioRxiv2025

Multi-omics analyses reveal the effect of DNA methylation on iridoid glycosides biosynthesis in Rehmannia glutinosa

Rehmannia glutinosa roots produce a group of lipophilic bioactive components known as iridoid glycosides. However, the molecular mechanisms by which DNA methylation regulates the biosynthesis of iridoid glycosides in R. glutinosa remain unknown. Herein, the development of R. glutinosa roots and the content of iridoid glycosides in the Wenxian region were significantly higher than those in Xinxiang. Low methylation level contributed to the accumulation of iridoid glycosides and the expression of related enzyme genes. Demethylation promoted both roots growth and development, as well as the accumulation of iridoid glycosides. Up-regulated RgALDH13, RgHDR1, RgG10H4, RgDXR1, RgG10H3, and RgUPD1, along with transcription factors (TFs), form the regulatory network for the biosynthesis of iridoid glycosides. Furthermore, the primary active region of the RgG10H4 promoter is located in the -164 bp region, where the RgMYB2 protein specifically binds to the TAACCA motif in the RgG10H4 promoter. Collectively, low levels of DNA methylation enhance the expression of core genes, followed by inducing the accumulation of iridoid glycosides, which suggests that RgMYB2-RgG10H4 plays a positive role in this process. These findings will contribute to a deeper understanding of the role of DNA methylation in the accumulation of iridoid glycosides. HighlightLow levels of DNA methylation contribute to the accumulation of iridoid glycosides and the expression of key enzyme genes in R. glutinosa.

molecular biology↗

bioRxiv2025

Changing the initiation unit of nonribosomal peptide synthetases to access underexplored biosynthetic potential

Nonribosomal peptide synthases (NRPSs) are large multimodular enzymes, capable of synthesizing nonribosomal peptides (NRPs) with diverse structures and bioactivities. Genome sequencing revealed a large number of uncharacterized NRPS biosynthetic gene clusters (BGCs) and their products are underexplored. The majority of NRPSs remain silent potentially attributed to factors such as the low activity of the initiation unit or insufficient precursor supply. Exchanging the starter condensation (Cs) domains within initiation unit can change the length of acyl chains of NRPs, hinting at a promising strategy through swapping of a well-studied Cs domain to activate the initiation unit and harness primary metabolites as precursors, which may offer a new option to access silent BGCs. Here, we first pinpointed two highly efficient fusion sites for initiation unit exchanges. Subsequently, they were validated by replacing the initiation region of endopyrrole pathway with a Cs-containing initiation unit for generation of a lipo-endopyrrole derivate. We promptly leveraged this strategy of changing initiation unit to target six previously silent NRPS BGCs, three BGCs were successfully activated and five novel lipopeptides were identified, demonstrating its application to recover silent BGCs. Furthermore, we extended this strategy in more BGCs from different bacteria. Utilizing a heterologous Cs-containing unit to replace the initiation region of chitinimide biosynthetic pathway led to successful incorporation of N-terminal fatty acid chains into chitinimide to create artificial lipo-chitinimides. This study provides a feasible strategy to rationally recover silent BGCs and add fatty acid chains to NRPs, enriching the genome mining and combinatorial biosynthesis approach for bacterial natural products. SignificanceNonribosomal peptide synthetases (NRPSs) represent a valuable yet underexplored reservoir for bioactive natural products, but most of them are silent. This study introduces the concept that changing initiation unit to activate and optimize the functional expression of NRPSs, and successfully access three of six previously silent NRPS pathways, providing a feasible complement to the current genome mining approaches. Moreover, the N-terminal lipid chains are crucial for the activity of lipopeptides. We employed a heterologous Cs (starter condensation domain)-containing initiation unit to replace the original initiation region for creation of artificial nonribosomal lipopeptides, ultimately yielding three novel lipo-derivatives. This work presents a groundbreaking approach for activating and optimizing NRPSs and provides profound insight into the exploration of bacterial NRPS pathways.

synthetic biology↗

bioRxiv2025

Comparison of QuPath and HALO platforms for analysis of the tumor microenvironment in prostate cancer

QuPath, an open-source digital pathology platform, has gained widespread use for image analysis in biomedical research since its release in 2016. However, its reproducibility and reliability compared to commercial software, such as HALO, requires further validation, particularly for multiplex immunofluorescence (mIF) analysis. In this study, we performed a direct comparison of QuPath and HALO using a mIF-stained prostate cancer tissue microarray (TMA) inclusive of 192 unique cores. We evaluated performance across three key analytical modules: immune cell phenotyping, tumor infiltration with immune cells, and nearest neighbor analysis. Furthermore, we integrated QuPath with CytoMap, an open-source spatial analysis tool, to perform unsupervised clustering of immune cell infiltration--a feature not available in HALO. Our results demonstrated high concordance between two platforms, with correlation coefficients exceeding 0.89 for immune cell density, distance and pattern of cell organization in tumor microenvironment (TME). A neighborhood analysis using CytoMap was further performed and provided a more detailed spatial analysis of immune cell distribution across different prostate cancer grades. A significant increase of CD103+ T cell infiltration into TME was observed in prostate cancer, which might be associated with the expression level of its ligand (E-cadherin) in the tumor region. In conclusion, our findings validate QuPath as a robust and reproducible alternative to commercial platforms for fluorescence-based digital pathology. By demonstrating QuPaths capability to perform high-quality quantitative analysis with additional flexibility for integration with external tools, our study underscores its potential for advancing tumor microenvironment research in translational oncology.

pathology↗

bioRxiv2025

eSPred: Explainable scRNA-seq Prediction via Customized Foundation Models and Pathway-Aware Fine-tuning

Single-cell RNA sequencing (scRNA-seq) has been widely used for studying cellular heterogeneity, but its use for subject-level prediction and clinical applications is still limited. We introduce eSPred, a customized foundation model designed for predictive analysis of scRNA-seq. It integrates cell-type information through a grouping strategy during pre-training and leverages pathway information to guide network flow during fine-tuning. Across multiple datasets, eSPred improves prediction accuracy and highlights pathways linked to disease mechanisms. These results suggest that eSPred can help bridge the gap between single-cell data and subject-level clinical insights, supporting more precise diagnosis and better-informed treatment decisions.

bioinformatics↗

bioRxiv2025

Metabolic reprogramming of protists within the microbiome of sinking particles

Protists play a crucial role in the biological carbon pump, yet their metabolic activity and function in sinking particles, which drive the aggregation and degradation of organic carbon, remains poorly characterized. Here, we examined transcriptional dynamics of protists in sinking and suspended particles collected by marine snow catcher in the Oyashio region to explore active lineages and their function during the sinking export. We found taxon-specific metabolic variations, with suspended particles showing up-regulated transcripts from diatoms, haptophytes, and chlorophytes, while sinking particles exhibited those from diatoms, ciliates, and dinoflagellates. Our data further revealed niche-dependent cellular metabolic reprogramming. The small diatoms Minidiscus variabilis and Thalassiosira oceanica upregulated more genes in suspended particles, while the chain-forming Thalassiosira rotula overexpressed more genes in sinking particles. The activity of both phototrophic and heterotrophic protists in sinking particles suggests the export of fresh organic carbon, with heterotrophic protists driving its degradation. Notably, the dinoflagellates Karlodinium veneficum and Karenia brevis exhibited significantly upregulated genes related to feeding and carbon metabolism in sinking particles. Additionally, the activity of RNA viruses was positively correlated with carbon flux. Overall, our study highlights the critical role of large-celled diatoms, heterotrophic dinoflagellate, ciliates and RNA viruses in organic carbon aggregation and degradation.

microbiology↗

bioRxiv2025

Identification of a RIPK2-Regulated Gene Signature as a Candidate Biomarker for RIPK2 Activity and Prognosis in Prostate Cancer

Receptor-interacting protein kinase 2 (RIPK2) has emerged as a promising drug target in various cancers, including prostate cancer (PC). However, the absence of reliable biomarkers to assess RIPK2 activity limits both patient selection for anti-RIPK2 therapies and treatment monitoring. To address this gap, we performed RNA-Seq analysis on PC cell lines (22Rv1, DU145, and PC3) with CRISPR/Cas9-mediated RIPK2 knockout (RIPK2-KO) using two independent guide RNAs. This analysis identified 13 candidate RIPK2-regulated genes, of which eight were validated by reverse transcription quantitative PCR (RT-qPCR). Furthermore, treatment with two distinct RIPK2 inhibitors significantly reduced RIPK2 signature scores in five independent PC cell lines in a dose- and/or time-dependent manner. Clinical association analyses revealed that high RIPK2 signature scores correlate with metastasis and worse biochemical recurrence-free, progression-free, disease-free, and overall survival, outperforming RIPK2 mRNA levels as a prognostic biomarker. This study establishes, for the first time, a RIPK2-regulated gene signature as a potential biomarker for RIPK2 activity and PC prognosis, warranting further validation in clinical specimens to provide a much-needed tool for patient stratification and response monitoring in RIPK2-targeted therapies.

cancer biology↗

bioRxiv2025

How Bacterial Vesicles Trigger Hearing Loss: The Role of Pyroptosis in Cochlear Damage

Withdrawal StatementThe authors have withdrawn this manuscript because [After submitting my article, I discovered some important missing data that is crucial for supporting my viewpoint and research findings. Although I tried to supplement this data through other means, I was unable to complete it before the deadline. Therefore, I believe the best approach is to withdraw the article so that I can further refine and present my research findings in a complete manner.]. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

immunology↗

bioRxiv2025

Unique epigenomic signatures identify biologically significant subtypes of MDS and predict response to azacitidine

Myelodysplastic syndromes (MDS) are characterized by aberrant DNA methylation, and mutations in epigenetic modifiers are frequently found in these patients. Although DNA methyltransferase inhibitors (DNMTi) are used to treat MDS, response variability remains a challenge in the clinic, with limited predictive markers. Through comprehensive genomic, epigenomic, and transcriptomic analyses, we have gained valuable insights into the intricate interplay between genetic and epigenetic alterations in MDS. We describe aberrantly hyper and hypomethylated regions in MDS, extending beyond promoter regions and affecting long-distance regulatory elements. Using these aberrant DNA methylation patterns, we classified MDS patients into epigenetic subtypes correlated with known molecular drivers. This epigenetic classification includes a novel group of patients characterized only by their shared DNA methylation profile and lacking any genetic drivers. Furthermore, we identified a robust DNA methylation signature capable of distinguishing DNMTi responders from non-responders prior to receiving treatment. Leveraging these DMRs, we developed robust classifiers capable of predictive response to DNMTi by integrating DNA methylation, gene expression, mutations, and laboratory parameters. Our findings highlight the potential of epigenetic-based classifiers for personalized treatment approaches for MDS patients. Key PointsDNA methylation patterns define biologically meaningful MDS subtypes and uncover a new group lacking known mutations. A methylation-based signature at diagnosis predicts azacitidine response, supporting its use in guiding personalized MDS therapy.

cancer biology↗

bioRxiv2025

Single-Cell Epigenomics Uncovers Heterochromatin Instability and Transcription Factor Dysfunction during Mouse Brain Aging

The mechanisms regulating transcriptional changes in brain aging remain poorly understood. Here, we use single-cell epigenomics to profile chromatin accessibility and gene expression across eight brain regions in the mouse brain at 2, 9, and 18 months of age. In addition to a significant decline in progenitor cell populations involved in neurogenesis and myelination, we observed widespread and concordant changes of transcription and chromatin accessibility during aging in glial and neuronal cell types. These alterations are accompanied by dysregulation of master transcription factors and a shift toward stress-responsive programs driven by AP-1, indicating a progressive loss of cell identity with aging. We also identify region- and cell-type-specific heterochromatin decay, characterized by increased accessibility at H3K9me3-marked domains, activation of transposable elements, and upregulation of long non-coding RNAs, particularly in glutamatergic neurons. Together, these results reveal age-related disruption of heterochromatin maintenance and transcriptional programs, identify vulnerable brain regions and cell types, and pinpoint key molecular pathways altered in brain aging. HighlightsO_LISingle-cell multimodal profiling across eight brain regions reveals coordinated chromatin and transcriptional shifts during aging C_LIO_LIAge-related depletion of progenitor cells coincides with dysregulation of key developmental transcription factors C_LIO_LICell identity maintenance is compromised with the decline of master transcription factors C_LIO_LIHeterochromatin destabilization accompanied by activation of AP-1, transposable elements, pseudogene families, and long non-coding RNAs C_LI

genomics↗

bioRxiv2025

THLANet: A Deep Learning Framework for Predicting TCR-pHLA Binding in Immunotherapy Applications

Adaptive immunity is a targeted immune response that enables the body to identify and eliminate foreign pathogens, playing a critical role in the anti-tumor immune response. Tumor cell expression of antigens forms the foundation for inducing this adaptive response. However, the human leukocyte antigens (HLA)-restricted recognition of antigens by T-cell receptors (TCR) limits their ability to detect all neoantigens, with only a small subset capable of activating T-cells. Accurately predicting neoantigen binding to TCR is, therefore, crucial for assessing their immunogenic potential in clinical settings. We present THLANet, a deep learning model designed to predict the binding specificity of TCR to neoantigens presented by class I HLAs. THLANet employs evolutionary scale modeling-2 (ESM-2), replacing the traditional embedding methods to enhance sequence feature representation. Using scTCR-seq data, we obtained the TCR immune repertoire and constructed a TCR-pHLA binding database to validate THLANets clinical potential. The models performance was further evaluated using clinical cancer data across various cancer types. Additionally, by analyzing divided complementarity-determining region (CDR3) sequences and simulating alanine scanning of antigen sequences, we unveiled the 3D binding conformations of TCRs and antigens. Predicting TCR-neoantigen pairing remains a significant challenge in immunology, THLANet provides accurate predictions using only the TCR sequence (CDR3{beta}), antigen sequence, and class I HLA, offering novel insights into TCR-antigen interactions. Author summaryT-cell receptor (TCR) recognition of peptide-human leukocyte antigen (pHLA) complexes is fundamental to immune responses. However, predicting their binding poses a significant challenge due to the intricate dynamics of their interactions. We developed THLANet, a novel deep learning model, to address this challenge by integrating the ESM-2 and Transformer-Encoder modules. This approach enhances sequence feature encoding, improving the models generalization capability and enabling accurate predictions of TCR-pHLA binding in clinical datasets. Using data processed from open-source databases, THLANet outperformed existing methods, such as PanPep and pMTnet, in precision-recall metrics across multiple epitopes. Additionally, THLANet demonstrates superior capability in identifying critical binding sites within 3D structures, providing structural insights into TCR-pHLA interactions. THLANet offers a robust framework for advancing immunotherapy research, with potential applications in the development of personalized medicine.

bioinformatics↗

bioRxiv2025

Tissue-wide profiling of human lungs reveals spatial sequestration of macrophages in tuberculosis

The immune response to human tuberculosis (TB), particularly in the context of complex lung pathology, remains incompletely understood. Here, we employed whole-slide spatial proteomics to map immune cell organization in TB-affected human lung tissues. Our analysis revealed pronounced spatial segregation of major immune cell populations in non-necrotizing TB lesions. At the tissue level, macrophages and lymphocytes formed distinct cellular communities associated with specific pathological features. At the lesion level, macrophages and B cells showed an inverse relationship in both abundance and spatial distribution. Proinflammatory T cells preferentially accumulated in macrophage-rich lesions but remained largely separated from macrophages. Interestingly, lesions exhibiting clear segregation between T cells and macrophages were more common in subclinical TB than in active disease. These findings suggest that spatial isolation of macrophages from effector lymphocytes may help temper inflammation and potentially prevent lesion progression to necrosis, while also enabling immune evasion by Mycobacterium tuberculosis. One Sentence SummaryXiao et al. reveal spatial segregation of immune cells in TB-lung tissue and link the microenvironmental dynamics to disease states of tuberculosis.

immunology↗

bioRxiv2025

Gap-free genomes and transcriptomes uncover race-specific effectors in watermelon wilt pathogen Fusarium oxysporum f. sp. niveum

Watermelon (Citrullus lanatus L.) is one of the worlds most economically important fruit crops, yet it is susceptible to devastating diseases such as vascular wilt caused by Fusarium oxysporum f. sp. niveum (Fon), with limited effective control measures. Fon evolves rapidly in the field to overcome host resistance and global watermelon production is constantly threatened by new pathogenic races. High-quality genomic resources are key to understanding the molecular mechanisms underlying Fon virulence evolution for disease management. Here, we de novo assembled and annotated gapless genomes of three physiological races of Fon (race 1, 2, and 3), and dissected the mechanisms behind their distinctive virulence through comparative genomics and transcriptomics. Whole-genome alignments identified core and accessory chromosomes in Fon where each race carried a unique set of accessory chromosomes or regions. Comparative transcriptomics of Fon infection revealed distinctive temporal patterns of gene expression even among core gene families, particularly those related to cell wall degradation enzymes. Effectoromic prediction and comparative analysis in three gap-free genomes identified 13 race-specific effectors (RSEs) in FonR3, one (FonRSE1) of which was a critical virulence factor of FonR3 on watermelon as demonstrated via functional experiments. The gap-free genome assemblies and annotations, and the RSEs are valuable resources for studying Fon pathobiology and genome evolution, adding in the design of improved disease control strategies.

genomics↗

bioRxiv2025

Multi-Omics and Machine Learning-Based Profiling of Severity Signatures in Mycoplasma Pneumoniae Infection in Children

Mycoplasma pneumoniae pneumonia (MPP) is a common respiratory infection in children, yet the mechanisms driving its progression to severe disease remain poorly understood. This study employs a comprehensive proteomic and metabolomic approach to elucidate severity-related pathways and identify potential biomarkers for improved diagnosis and targeted therapy. By analyzing blood proteomes from 57 pediatric patients with varying MPP severities alongside 10 healthy controls, and integrating multi-omics data from bronchoalveolar lavage fluid (BALF), we uncovered key severity-associated proteins and metabolites linked to inflammatory and metabolic dysregulation. Notably, alterations in L-arginine metabolism, influenced by APAF1 and SERPINB5, were found to modulate the efferocytosis pathway, while ERCC1 and MLH1, crucial components of the Fanconi anemia pathway, were associated with changes in plasmid acid metabolites affecting fatty acid elongation. Machine learning analysis further identified three critical biomarkers--TNFRSF10B, SAT1, and 4-nitrophenol-- that accurately distinguished between mild and severe MPP cases with high sensitivity (0.8) and specificity (1). These findings provide novel insights into the molecular mechanisms underlying severe MPP, highlighting efferocytosis and fatty acid metabolism as key pathways. The identification of severity-specific biomarkers offers a foundation for enhanced diagnostic precision, improved disease stratification, and the development of targeted therapeutic strategies to optimize the management of severe MPP in pediatric patients. HighlightsO_LIIdentified severity signatures in Mycoplasma pneumoniae infection in children. C_LIO_LIConducted the multi-omics analysis of bronchoalveolar lavage fluid (BALF) in children with varying severities of Mycoplasma pneumoniae infection. C_LIO_LIDiscovered biomarkers that effectively distinguish mild from severe Mycoplasma pneumoniae infection. C_LIO_LIFindings offer potential targets to improve the diagnosis and treatment of severe Mycoplasma pneumoniae C_LIO_LIinfection in children. C_LI

microbiology↗

bioRxiv2025

All-optical electrophysiology reveals behavior-dependent dynamics of excitation and inhibition in the hippocampus

Understanding how neuronal integration is modulated by behavior is a fundamental goal in neuroscience. We combined voltage imaging with optogenetic depolarization to reveal how changes in excitatory (E) and inhibitory (I) inputs, modulate the spiking output, subthreshold dynamics, and gain of key genetically defined cell types in the CA1 region of the hippocampus. We imaged pyramidal cells (PCs), vasoactive intestinal peptide (VIP), somatostatin (SST), and parvalbumin (PV) interneurons (INs) and found that locomotion reduced firing in PCs and VIP INs while increasing activity in SST and PV cells. Prolonged optical depolarization experiments and simulations revealed that intracellular theta oscillations are predominantly driven by inhibitory inputs in PCs and VIP cells. Firing rate-laser intensity (F-I) curves revealed distinct gain modulation across cell types, with a divisive gain reduction in PC bursting during locomotion, while simple spikes are unaffected. A two-compartment model suggested that this results from a balanced E/I input increase to somatic and dendritic compartments. These findings reveal how behavioral state-dependent coordination of excitation and inhibition governs hippocampal neuronal dynamics and output-specific gain modulation.

neuroscience↗

bioRxiv2025

Integrating Literature Evidence to Enhance CRISPR-based Target Screening for Hand-foot Syndrome

The experimental high-throughput screening (HTS) methods, exemplified by CRISPR- based screening techniques, have revolutionized target identification in drug discovery. However, such screens frequently yield extensive, often unrelated target lists necessitating costly and time-intensive experimental evaluation and validation. To address this challenge, we propose a dual-filter strategy that integrates literature-mined targets with CRISPR/Cas9 screening outputs, systematically prioritizing the most credible candidates and thereby reducing the experimental validation burden and increasing success rate. To validate this strategy, we applied it with hand-foot syndrome (HFS), a clinically challenging side effect induced by fluoropyrimidine treatment. We identified ATF4 as a key regulator of 5-fluorouracil (5-FU) toxicity in the skin and revealed forskolin as a potential therapeutic agent of HFS through the strategy. Mechanistically, forskolin triggers MEK/ERK-dependent ATF4 induction, subsequently driving 5-FU detoxification via the ATF4-mediated eIF2/I{kappa}B signaling pathway. Our findings demonstrate that this dual-filter strategy could notably accelerate drug discovery by reducing experimental validation burden after target screening.

pharmacology and toxicology↗

bioRxiv2025

SEHI-PPI: An End-to-End Sampling-Enhanced Human-Influenza Protein-Protein Interaction Prediction Framework with Double-View Learning

Influenza continues to pose significant global health threats, hijacking host cellular machinery through protein-protein interactions (PPIs), which are fundamental to viral entry, replication, immune evasion, and transmission. Yet, our understanding of these host-virus PPIs remains incomplete due to the vast diversity of viral proteins, their rapid mutation rates, and the limited availability of experimentally validated interaction data. Additionally, existing computational methods often struggle due to the limited availability of high-quality samples and their inability to effectively model the complex nature of host-virus interactions. To address these challenges, we present SEHI-PPI, an end-to-end framework for human-influenza PPI prediction. SEHI-PPI integrates a double-view deep learning architecture that captures both global and local sequence features, coupled with a novel adaptive negative sampling strategy to generate reliable and high-quality negative samples. Our method outperforms multiple benchmarks, including state-of-the-art large language models, achieving a superior performance in sensitivity (0.986) and AUROC (0.987). Notably, in a stringent test involving entirely unseen human and influenza protein families, SEHI-PPI maintains strong performance with an AUROC of 0.837. The model also demonstrates high generalizability across other human-virus PPI datasets, with an average sensitivity of 0.929 and AUROC of 0.928. Furthermore, AlphaFold3-guided case studies reveal that viral proteins predicted to target the same human protein cluster together structurally and functionally, underscoring the biological relevance of our predictions. These discoveries demonstrate the reliability of our SEHI-PPI framework in uncovering biologically meaningful host-virus interactions and potential therapeutic targets.

bioinformatics↗

bioRxiv2025

Elucidating the Characteristics and Clonal Evolutionary Trajectory of Influenza Neuraminidase Broadly Reactive B Cell

Influenza virus neuraminidase (NA) is receiving increasing attention as a target for universal flu vaccines. Several broad NA inhibition monoclonal antibodies (BImAbs) targeting the highly conserved enzymatic pocket have been previously described. However, the molecular characteristics, clonal evolutionary trajectory, and B cell sources of BImAbs remain poorly understood. Here, using NA-mutant probes, we comprehensively profiled the immune signatures of NA-specific memory B cells (MBCs) from a healthy individual with NA cross-inhibition activity. From the NA-specific MBC repertoires, we identified a series of NA BImAbs with molecular features characterized by long HCDR3 regions with an "xxxDRxxx" motif, which exhibited broad inhibition against diverse influenza NAs. Clonal lineage tracing revealed that these BImAbs followed a clonal evolutionary trajectory encompassing classical MBC (cMBC) and atypical MBC (aMBC). Both cMBC- and aMBC-derived NA BImAbs displayed similar inhibitory activity against the influenza NAs. These findings enhance our understanding of the development of NA BImAbs and provide a foundation for the rational design of NA-based universal flu vaccines.

immunology↗

bioRxiv2025

Color-neutral and reversible tissue transparency enables longitudinal deep-tissue imaging in live mice

Light scattering in biological tissue presents a significant challenge for deep in vivo imaging. Our previous work demonstrated the ability to achieve optical transparency in live mice using intensely absorbing dye molecules, which created transparency in the red spectrum while blocking shorter-wavelength photons. In this paper, we extend this capability to achieve optical transparency across the entire visible spectrum by employing molecules with strong absorption in the ultraviolet spectrum and sharp absorption edges that rapidly decline upon entering the visible spectrum. This new color-neutral and reversible tissue transparency method enables optical transparency for imaging commonly used fluorophores in the green and yellow spectra. Notably, this approach facilitates tissue transparency for structural and functional imaging of the live mouse brain labeled with yellow fluorescent protein and GCaMP through the scalp and skull. We show that this method enables longitudinal imaging of the same brain regions in awake mice over multiple days during development. Histological analyses of the skin and systemic toxicology studies indicate minimal acute or chronic damage to the skin or body using this approach. This color-neutral and reversible tissue transparency technique opens new opportunities for noninvasive deep-tissue optical imaging, enabling long-term visualization of cellular structures and dynamic activity with high spatiotemporal resolution and chronic tracking capabilities. Significance StatementTissue scattering represents a major barrier to deep tissue imaging in vivo. We recently showed that tissue can be rendered transparent in the red spectrum using intensely absorbing dye molecules. Here, we introduce a new, color-neutral and reversible tissue transparency approach. We demonstrate longitudinal structural and functional imaging in the deep tissue of awake mice.

bioengineering↗