Biology subjects

Sheng, H.

Publications and source records attributed to Sheng, H..

At least 19 recordsLinked to original sources

bioRxiv2026

CHAIR an antigen-resolved antibody-reactome resource for healthy adult immune aging

Antigen-recognition histories accumulate across adult life, but existing human aging resources largely emphasize host-state measurements rather than antigen-resolved humoral memory. We present CHAIR, the Chinese Healthy Aging Immune Resource, which integrates self-antigen, human virome and toxin/virulence-factor PhIP-seq with routine clinical measurements and T/B/NK phenotyping in clinically screened adults aged 20-94 years. The resource framework includes 1,008 unique PhIP-seq samples, 995 clinical records, 977 T/B/NK records and 853 participants with all three layers. Across modeled spaces comprising 23,584 self-antigen targets, 210 virome targets and 1,258 virulence-factor source targets, most measured antibody-reactivity architecture was consistent with broad preservation within domains, whereas age- and sex-associated signals were concentrated in focal targets and modules. Together, CHAIR provides a traceable, antigen-resolved reference framework based on clinically screened adults for investigating focal humoral remodeling across healthy adult aging.

immunology↗

bioRxiv2026

Salt stress reverses root circumnutation, -skewing and -growth direction in Arabidopsis

Plant roots exhibit remarkable developmental plasticity, resulting in the adaptation of growth direction and architecture upon environmental changes. Previously, we demonstrated that inorganic phosphate (Pi) triggers Arabidopsis roots to skew to the left when grown on tilted agar plates. This so-called 'phosphate-dependent skewing' (PDS) is caused by a right-handed (clockwise, CW) circumnutation of the root tip, which is driven by a left-handed (counterclockwise, CCW) cell file rotation (CFR) of epidermal cells in the root elongation zone, and involves the cortical microtubule cytoskeleton (Sheng et al., 2024). In the present study, we demonstrate that NaCl triggers a skewing response in the opposite direction and that all other helical movements are also reversed. Thus, 'Salt-Induced Rightward Skewing' (SIRS) is accompanied by a right-handed (CW) epidermal CFR, a left-handed (CCW) circumnutation of the root tip, and hence, a left-handed (CCW) helical root growth. Comparing different Na+- and Cl- salts revealed that SIRS is predominantly caused by cations, and can be induced by K+ and osmotic stress as well, although Na+ is most efficient. To get further insight into the mechanism underlying this response, we tested candidate genes from an earlier GWAS on root responses to salt stress (Deolu-Ajayi et al., 2019) for their potential involvement. This identified GLT1 and DOB1 as being involved in the root skewing response to Pi and NaCl, respectively. Our findings reveal that Pi and salinity elicit opposing effects on root circumnutation, and hence root skewing and growth direction. Understanding the molecular machinery driving this helical behaviour may help explain adaptive mechanisms, including changes in the spatial architecture of roots, and may facilitate the optimization of crop yield under abiotic stress conditions through breeding or crop management strategies. Our results also shed new light on halotropism, which is typically measured as a change in root growth direction to the right, which in the present study has been identified to represent SIRS.

plant biology↗

bioRxiv2026

3D-PAULM: Integrated Photoacoustic Tomography and Ultrasound Localization Microscopy for Multiparametric Brain and Tumor Imaging

Understanding processes such as blood-brain barrier (BBB) disruption and tumor progression can greatly benefit from simultaneous molecular, functional, and hemodynamic imaging in deep tissue, yet few existing imaging modalities can provide all three in a single system. Here, we present an integrated imaging platform that combines 3D photoacoustic tomography with ultrasound localization microscopy (3D-PAULM) to enable intrinsically co-registered, multiparametric imaging. 3D-PAULM unifies multispectral photoacoustic molecular imaging, ultrasound B-mode imaging, microbubble-enhanced power Doppler, and ultrasound localization microscopy, and concurrently measures blood oxygenation, blood perfusion, microvascular flow dynamics, and molecular probes from near-infrared dyes and photoswitchable phytochromes. We apply 3D-PAULM to quantify BBB leakage in focal ischemia and systemic inflammation, and to perform high-sensitivity molecular imaging of solid tumors alongside functional mapping of tumor hypoxia and super-resolved vascular remodeling. Together, these results establish 3D-PAULM as a versatile platform for integrated functional and molecular imaging in deep tissue.

bioengineering↗

bioRxiv2026

Ecological diversification without genomic reorganization: repeat dynamics and regulatory evolution in Metarhizium robertsii

How can closely related organisms occupy distinct ecological niches without extensive genomic reorganization? We addressed this question by comparing eight strains of the entomopathogenic fungus Metarhizium robertsii that span a shallow phylogenetic gradient yet exhibit striking differences in virulence, plant associations, and metabolic capabilities. Despite nucleotide identity exceeding 98.5% and extensive macrosynteny, these strains showed pronounced variation in repeat-rich genomic regions: synteny gaps were twofold enriched near chromosome ends, transposable element loads varied from less than 3% to over 13% across strains diverging within [~]0.3 million years, and repeat-induced point mutation signatures tracked TE activity rather than phylogenetic distance. Notably, repeat expansion occurs through diverse spatial mechanisms rather than being uniformly concentrated in chromosome-terminal regions. In contrast, core functional repertoires, proteases, carbohydrate-active enzymes, developmental regulators, and most secondary metabolite biosynthetic genes, were highly conserved, with phenotypic differences in virulence and secondary metabolism arising primarily from regulatory divergence and local structural variation rather than gene presence or absence. Metabolic specialization similarly reflected functional repurposing within a conserved enzymatic framework: loss of processive cellulases alongside enrichment of oxidative auxiliary activity enzymes produced a profile convergent with brown-rot fungi but adapted for insect-associated and rhizosphere niches. These results support a hierarchical model of ecological diversification in which diverse repeat expansion mechanisms, TE dynamics, and regulatory innovation operate as coupled axes of localized genomic change within an otherwise constrained genomic framework, reconciling rapid niche differentiation with strong structural conservation and suggesting a general mechanism for intraspecific adaptation in complex eukaryotic microbes.

evolutionary biology↗

bioRxiv2026

Growth-adaptive spring electronics for long-term, same-neuron mapping in the developing rat brain

Neural activity reorganizes profoundly after birth, transitioning from highly synchronous population events to sparse, decorrelated firing in the mature brain. Although inhibitory maturation and shifts in excitation-inhibition balance have been implicated in this process, how individual neurons implement the transition remains unclear because rapid brain growth has prevented long-term, same-neuron mapping. Here, we introduce growth-adaptive spring electronics that provide depth-wise compliance during tissue expansion, maintaining a stable electrode-tissue interface over weeks of neonatal development. We developed a vision-language model-assisted spike processing pipeline for the developing brain that probabilistically matches units across days using high-density waveform spatial footprints, despite developmental changes in the neonatal brain. Together, these innovations enable spike-resolved mapping of the same neurons in rat visual cortex and medial prefrontal cortex from postnatal day 10 to 45. Using population coupling to quantify each neurons coordination with local population activity, we show that developmental decorrelation is driven primarily by a distinct subset of neurons that progressively shifts from strong to weak coupling during postnatal weeks 3 to 5, whereas other neurons remain stably weakly or strongly coupled throughout development. These results resolve population-level desynchronization into identifiable neuron-specific trajectories. This framework enables direct tests in neurodevelopmental disorder models, including schizophrenia and autism, of whether altered maturation reflects global circuit imbalance or selective disruption and mistiming of specific developmental programs.

bioengineering↗

bioRxiv2026

Origins and metabolic evolution of multifunctionality in Metarhizium robertsii: linking phenotypic diversity to ecological niche plasticity

This study investigates the evolution of fungi with complex multifunctional ecological roles using early and recently diverged lineages of Metarhizium robertsii as a model. The early diverged strains are characterized by slower insect killing, extensive pre-mortem fungal proliferation within hosts, and prolific sporulation, whereas recently diverged strains show rapid growth, a "kill and consume" strategy linked to destruxin toxin production, and enhanced plant root colonization. Metabolic assays demonstrated that recently diverged strains utilize a broader range of carbon sources, supporting their ecological versatility as pathogens, endophytes, and saprophytes. Germination rates on insect cuticles and plant roots strongly correlate with each other, and with virulence to Drosophila and beetles (Tenebrio molitor, Popillia japonica), highlighting nutritional flexibility as a key driver of ecological adaptation. Immune response assays in Drosophila revealed that virulence differences among strains are also partly mediated by host immune activation. These findings suggest that nutritional mode shifts underpin the evolutionary trajectory from specialist insect pathogen to plant associations and enhanced insect virulence within M. robertsii, providing a valuable model for studying fungal ecological plasticity and informing the development of fungal biofertilizers and biopesticides.

evolutionary biology↗

bioRxiv2025

PlantCV v4: Image analysis software for high-throughput plant phenotyping

PlantCV is an open-source Python project aimed at developing tools to address a range of image-based, plant phenotyping questions. PlantCV has been used for more than 10 years to automate trait collection from image data and the newest release, PlantCV version 4, continues to lower the barrier to entry for users without substantial coding experience through extensive example use-case tutorials and simplified installation. In addition to usability, we document added functionality since the release of PlantCV v2, including support for more image types such as fluorescence, thermal, and hyperspectral data. Finally, we describe the development of a new subpackage focused on morphological trait measurements like leaf angle, and demonstrate its utility as compared to more manual methods of data collection. CORE IDEASO_LIPlantCV is an open-source, open-development, Python-based software package that has a new release for improved functionality and usability to make image analysis flexible and easier for researchers without a coding background. C_LIO_LIPlantCV is now capable of handling new data types that are relevant to researchers, such as thermal and hyperspectral, and has built in functionality for extracting information from these image types. C_LIO_LIThe software project aims to lower the barrier to entry into image analysis for researchers by providing numerous, versioned, interactive tutorials that cover most common use cases, particularly in plant science. C_LI

plant biology↗

bioRxiv2025

Remote sensing of endogenous pigmentation by inducible synthetic circuits in grasses

Plant synthetic biology holds great promise for engineering plants to meet future demands. Genetic circuits are being designed, built, and tested in plants to demonstrate proof of concept. However, developing these components in monocots, which the world relies on for grain, lags behind dicot models, such as Arabidopsis thaliana and Nicotiana benthamiana. Here, we show the successful adaptation of a ligand-inducible sensor to activate an endogenous anthocyanin pathway in the C4 monocot model Setaria viridis. We identify two transcription factors sufficient to induce endogenous anthocyanin production in S. viridis protoplasts and whole plants in a constitutive or ligand-inducible manner. We also test multiple ligands to overcome physical barriers to ligand uptake, identifying triamcinolone acetonide (TA) as a highly potent inducer of this system. Using hyperspectral imaging and a discriminative target characterization method in a near-remote configuration, we can non-destructively detect anthocyanin production in leaves in response to ligands. This work demonstrates the use of inducible expression systems in monocots to manipulate endogenous pathways, stimulating plants to overproduce secondary metabolites with value to human health. Applying inducible pigmentation coupled with sensitive detection algorithms could enable crop plants to report on the status of field contamination or detect undesirable chemicals impacting agriculture, ushering in an era of agriculture-based sensor systems. SummaryO_ST_ABSSynbio tools for C4 grass modelC_ST_ABS[bullet] Advantage of synthetic switches as tools for biopharming and functional genomics [bullet]Our workflow to optimize the gene circuits from a transient system to a stable transgenic [bullet]Testing and taming golden gate elements in monocot system, S. viridis

plant biology↗

bioRxiv2025

Aird-MSI: a high compression rate and decompression speed format for mass spectrometry imaging data

Mass spectrometry imaging has emerged as a pivotal tool in spatial metabolomics, yet its reliance on the imzML format poses critical challenges in data storage, transmission, and computational efficiency. While imzML ensures cross-platform compatibility, its lower compressed binary architecture results in large file sizes and high parsing overhead, hindering cloud-based analysis and real-time visualization. This study introduces an enhanced Aird compression format optimized for spatial metabolomics through two innovations: (1) a dynamic combinatorial compression algorithm for integer-based encoding of m/z and intensity data; (2) a coordinate-separation storage strategy for rapid spatial indexing. Experimental validation on 47 public datasets demonstrated significant performance gains. Compared to imzML, Aird achieved a 70% reduction in storage footprint (mean compression ratio: 30.03%) while maintaining near-lossless data precision (F1-score = 99.26% at 0.1 ppm m/z tolerance). For high-precision-controlled datasets, Aird accelerated loading speeds by 15-fold in MZmine. The Aird format overcomes crucial bottlenecks in spatial metabolomics by harmonizing storage efficiency, computational speed, and analytical precision, reducing I/O latency for large cohorts. By achieving near-native feature detection accuracy, Aird establishes a robust infrastructure for translational applications, including disease biomarker discovery and pharmacokinetic imaging.

bioinformatics↗

bioRxiv2025

Spatial transcriptomics AI agent charts hPSC-pancreas maturation in vivo

Spatial transcriptomics has revolutionized our understanding of tissue organization by simultaneously capturing gene expression and spatial localization within intact tissues. However, analyzing these increasingly complex datasets requires specialized expertise across computational biology, statistics, and biological context. To address this challenge, we introduce the Spatial Transcriptomics AI Agent (STAgent), an autonomous multimodal agentic AI that integrates multimodal large language models (LLMs) with specialized computational tools to transform weeks-long analysis tasks into minutes of automated processing. Unlike conventional machine learning approaches that are limited to narrow, predefined tasks, STAgent leverages the emergent capabilities of multimodal LLMs - such as flexible reasoning, contextual understanding, and cross-modal integration - which allow it to adapt to novel data, execute multi-step analyses, and generate biologically meaningful insights with minimal human input. STAgent enables autonomous deep research through integrated capabilities, including dynamic code generation for complex analytical workflows, visual reasoning for interpreting spatial patterns, real-time retrieval of relevant peer-reviewd scientific literature, and synthesis of comprehensive, actionable reports. We applied STAgent to investigate the in vivo maturation of human stem cell-derived pancreatic cells (SC-pancreas) transplanted into immunodeficient mice. We generated single-cell spatial transcriptomics data spanning multiple developmental timepoints. STAgent autonomously (1) identified the maturation of initially scattered endocrine cells into well-defined islet-like structures, with predominantly peripheral -cells surrounding {beta}-cell cores supported by an expanding mesenchymal network; (2) revealed strengthening endocrine-endocrine cell interactions over time and, through context-aware gene set analysis, uncovered spatially resolved biological processes driving maturation; (3) unlike traditional analytical approaches, STAgent offers mechanistic explanations of spatial patterns, contextualizing findings with relevant literatures and developing cohesive insights into human pancreatic development. This agentic approach establishes a new paradigm in spatial transcriptomics analysis by substantially lowering the expertise barrier and reducing analysis time, accelerating biological and biomedical discovery.

genomics↗

bioRxiv2025

Silicon regulation of pectin methyl-esterification and cellulose microfibril organization modulates FERONIA-mediated cell wall integrity signaling for rice adaption to salt stress

The beneficial element silicon (Si) plays a crucial role in mitigating salt stress in plants and can activate intracellular signal transduction pathways that reprogram the transcriptome of salt-stressed plants. However, the mechanisms by which plants perceive Si nutrition, induce the expression of Si-responsive genes, and regulate the activity of Si-responsive proteins remain inadequately understood. In this study, we found that Si enhances cell wall integrity by increasing the degree of pectin methyl-esterification and changing the cellulose microfibril arrangement and functions as a regulatory switch for the activities of cell wall integrity sensors (FER homologs OsFLR1 and OsFLR2) during rice plant adaptation to high salinity. Furthermore, the inhibition of OsFLR1 and OsFLR2 through FER-specific inhibitors could negate the beneficial effects of Si and affect the uptake and accumulation of Si in rice seedlings. In conclusion, these findings suggest that Si signaling initiation involves FER-mediated cell wall integrity signaling pathways during salt stress adaptation in rice.

plant biology↗

bioRxiv2025

TaWUS2D regulates the number of grains per spikelet by enhancing the number of fertile ovaries in Multi-Ovary Wheat

Innovative genetic improvements in the staple crop Triticum aestivum (bread wheat) are urgently needed to address the growing global food security crisis. Here, we report the map-based cloning of TaWUS2D, the gene responsible for the dominant multi-ovary phenotype in wheat. Multi-ovary lines are characterized by the development of three fertile ovaries per floret that results in three grains, as opposed to wildtype single ovary wheat. We used HiFi long-reads to assemble a 14.48 Gbp genome scaffold assembly in the background of mutli-ovary wheat line MOV. Using high-resolution genetic mapping, combined with additional genomic resources, we defined the Mov-1 locus to a 135 Kbp region containing two genes. Using five independent deletion mutants and eight TILLING mutants, we demonstrate that a functional WUSCHEL-like protein, TaWUS2D, is required for the multi-ovary phenotype. TaWUS2D is upregulated in the MOV genetic background. This research lays the groundwork for developing new approaches to improve wheat production potential and sustainability in the face of current and future global food challenges.

plant biology↗

bioRxiv2024

Advanced deep-tissue imaging and manipulation enabled by biliverdin reductase knockout.

We developed near-infrared (NIR) photoacoustic and fluorescence probes, as well as optogenetic tools from bacteriophytochromes, and enhanced their performance using biliverdin reductase-A knock-out model (Blvra-/-). Blvra-/- elevates endogenous heme-derived biliverdin chromophore for bacteriophytochrome-derived NIR constructs. Consequently, light-controlled transcription with IsPadC-based optogenetic tool improved up to 25-fold compared to wild-type cells, with 100-fold activation in Blvra-/- neurons. In vivo, light-induced insulin production in Blvra-/- reduced blood glucose in diabetes by [~]60%, indicating high potential for optogenetic therapy. Using 3D photoacoustic, ultrasound, and two-photon fluorescence imaging, we overcame depth limitations of recording NIR probes. We achieved simultaneous photoacoustic imaging of DrBphP in neurons and super-resolution ultrasound localization microscopy of blood vessels [~]7 mm deep in the brain, with intact scalp and skull. Two-photon microscopy provided cell-level resolution of miRFP720-expressing neurons [~]2.2 mm deep. Blvra-/- significantly enhances efficacy of biliverdin-dependent NIR systems, making it promising platform for interrogation and manipulation of biological processes.

cell biology↗

bioRxiv2024

End-to-end multimodal deep learning for real-time decoding of months-long neural activity from the same cells

Long-term, stable, and real-time decoding of behavior-dependent neural dynamics from the same cells is critical for brain-computer interfaces (BCIs) and for understanding neural evolution during learning, and memory, and disease progression. Recent advances in flexible and high-density electrodes have enabled the stability required for long-term tracking but generate vast datasets that challenge existing analysis methods. Current spike sorting approaches rely heavily on manual curation and lack scalability for large-scale, real-time processing. Here, we introduce AutoSort, an end-to-end multimodal deep neural network-based method that enables real-time tracking and decoding of the same neurons over months. AutoSort uses a scalable strategy by learning deep representations from initial recordings and applying the trained model in real-time. It integrates multimodal features, including waveform features, distribution patterns, and inferred neuron spatial locations, to ensure robustness and accuracy. AutoSort outperforms existing methods in both simulated and long-term recordings, reducing computational demands by using only 10% of the time and 25% of the memory compared to conventional methods. By combining AutoSort with high-density flexible probes, we track neural dynamics in real-time during motor learning and skill acquisition over 2 months, capturing intrinsic neural manifold drift, stabilization, and post-learning representational drift. AutoSort offers a promising solution for studying long-term neural intrinsic dynamics and enabling real-time BCI decoding.

bioengineering↗

bioRxiv2024

Brain implantation of tissue-level-soft bioelectronics 1 via embryonic development

The design of bioelectronics capable of stably tracking brain-wide, single-cell, and millisecond-resolved neural activities in the developing brain is critical to the study of neuroscience and neurodevelopmental disorders. During development, the three-dimensional (3D) structure of the vertebrate brain arises from a 2D neural plate1,2. These large morphological changes previously posed a challenge for implantable bioelectronics to track neural activity throughout brain development3-9. Here, we present a tissue-level-soft, sub-micrometer-thick, stretchable mesh microelectrode array capable of integrating into the embryonic neural plate of vertebrates by leveraging the 2D-to-3D reconfiguration process of the tissue itself. Driven by the expansion and folding processes of organogenesis, the stretchable mesh electrode array deforms, stretches, and distributes throughout the entire brain, fully integrating into the 3D tissue structure. Immunostaining, gene expression analysis, and behavioral testing show no discernible impact on brain development or function. The embedded electrode array enables long-term, stable, brain-wide, single-unit-single-spike-resolved electrical mapping throughout brain development, illustrating how neural electrical activities and population dynamics emerge and evolve during brain development.

bioengineering↗

bioRxiv2024

Towards a universal spatial molecular atlas of the mouse brain

Spatially mapping a comprehensive brain at the single-cell level with molecular resolution is crucial for understanding its biological structure, mechanisms, and functions. Recent scalable spatial transcriptomics technologies1-7 have revolutionized our ability to examine millions of cells within the complex brain network, allowing for the generation of multiple mouse brain atlases that illuminate diverse transcriptomic and spatial cell types. However, despite enormous efforts, these spatial transcriptomics atlases still face challenges in providing a comprehensive brain representation: each atlas only captures a fraction of the vast cellular population of the brain and is limited in one or more aspects of spatial resolution, gene detection sensitivity, and gene throughput8. Here, we introduce FuseMap, a deep-learning-based framework for spatial transcriptomics that bridges single-cell or single-spot gene expression with spatial contexts and consolidates various gene panels across spatial transcriptomics atlases. By training on an extensive spatial transcriptomics atlas corpus of the mouse brain1-7, comprising over 18.6 million cells or spots and 26,665 genes from 434 tissue sections across seven datasets in a self-supervised way, FuseMap gains a fundamental understanding of cell identities and gene characteristics. We thus create a comprehensive molecular spatial reference atlas of the whole mouse brain with transcriptome-wide information, achieving multiple tasks including nomenclature harmonization of molecular cell types and tissue regions, identification of novel molecular brain regions, spatial gene imputation, targeted gene-panel selection, and region-specific cell-type interactions inference. Furthermore, the pretrained brain FuseMap model enables mapping new query data to the molecular spatial reference brain through transfer learning, allowing for automated cell and tissue annotations and replacing laborious and suboptimal manual annotations. Overall, we offer FuseMap as a novel computational framework for integration across various spatial transcriptomics platforms and provide a molecular common coordinate framework (molCCF) with pretrained FuseMap brain model for exploration of the brain molecular and cellular landscape with harmonized annotations and coordinates.

genomics↗

bioRxiv2022

A Deep Learning Approach for Histology-Based Nuclei Segmentation and Tumor Microenvironment Characterization

Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research; however, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedure and produces massive data that capture tumor histological details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists. Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nuclei segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nuclei segmentation and TME quantification within image patches; however, existing algorithms are computationally intensive and time-consuming for WSI analysis. In this study, we present Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nuclei segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing methods for WSI analysis in nuclei detection and classification accuracy, as well as computation time.

pathology↗

bioRxiv2022

Multi-Molecular Hyperspectral PRM-SRS Imaging

Lipids play crucial roles in many biological processes under physiological and pathological conditions. Mapping spatial distribution and examining metabolic dynamics of different lipids in cells and tissues in situ are critical for understanding aging and diseases. Commonly used imaging methods, including mass spectrometry-based technologies or labeled imaging techniques, tend to disrupt the native environment of cells/tissues and have limited spatial or spectral resolution, while traditional optical imaging techniques still lack the capacity to distinguish chemical differences between lipid subtypes. To overcome these limitations, we developed a new hyperspectral imaging platform that integrates a Penalized Reference Matching algorithm with Stimulated Raman Scattering (PRM-SRS) microscopy. With this new approach, we directly visualized and identified multiple lipid species in cells and tissues in situ with high chemical specificity and subcellular resolution. High density lipoprotein (HDL) particles containing non-esterified cholesterol was observed in the kidney, indicating that these pools of cholesterol are ectopic deposits, or have yet to be enriched. We detected a higher Cholesterol to phosphatidylethanolamine (PE) ratio inside the granule cells of hippocampal samples in old mice, suggesting altered membrane lipid synthesis and metabolism in aging brains. PRM-SRS imaging also revealed subcellular distributions of sphingosine and cardiolipin in the human brain sample. Compared with other techniques, PRM-SRS demonstrates unique advantages, including faster data processing and direct user-defined visualization with enhanced chemical specificity for distinguishing clinically relevant lipid subtypes in different organs and species. Our method has broad applications in multiplexed cell and tissue imaging.

bioengineering↗