Biology subjects

Liang, Z.

Publications and source records attributed to Liang, Z..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

Systematic discovery of single-cell protein networks in cancer with Shusi

Context-specific protein-protein interaction (PPI) drive heterogeneity of primary tumor, forming a formidable challenge to effective cancer therapy. However, systematically mapping and modeling these interactions at single-cell resolution across diverse cancer types remains an unmet need. Here, we present Shusi, a large language model-enhanced variational graph auto-encoder model trained on over single cells across 23 cancer types, to predict context-specific PPIs. Shusi outperforms existing state-of-the-art methods, as validated through orthogonal experimental evidence. Cancer-specific mutations are significantly enriched in Shusi-predicted networks, offering complementary insights to conventional marker gene-based approaches. Through systematic evaluations, we demonstrate strong associations between Shusi-predicted network topologies, genetic vulnerabilities, and therapeutic sensitivity. Finally, in acute myeloid leukemia (AML), a blood cancer where cell-state heterogeneity drives clinical resistance, Shusi pinpointed potential targets as actionable vulnerabilities of resistant leukemia subpopulations, as validated experimentally in primary AML. Shusi offers a deep-learning tool for implementing precision medicine based on context-specific protein network architecture.

bioinformatics↗

bioRxiv2025

Global and cross-omics feature aggregation improves single-cell multi-omics integration and clustering

Sequencing technology advancements enable the simultaneous measurement of multiple omics within single cells, thereby facilitating single-cell multi-omics analysis. Most existing deep multi-omics integration methods learn shared or omics-specific representations from multiple omics by omics-wise aggregation. However, these approaches often fail to account for the structural relationships among all cells. Here, we introduce scCODI, an innovative cross-omics deep learning model to integrate single-cell multi-omics data for cell clustering. This model processes paired omics datasets, combining single-cell transcriptomics with either epigenomics or proteomics data to facilitate a more comprehensive exploration of cellular heterogeneity. Specifically, the shared representation of two omics is derived through cross-omics feature aggregation, fully leveraging the complementarity of different omics within the same cell. Additionally, we align the omic-specific and shared representations of each cell using a global relationship-guided contrastive learning module, which enhances the similarity between the shared and omic-specific representations of the same cell. Extensive experiments demonstrate that scCODI outperforms previous methods in single-cell multi-omics data integration and cell clustering, as evidenced by its performance across nine paired multi-omics datasets. This makes it a promising tool for decoding complex biological mechanisms. The code is available at https://github.com/SXUI-BDSI/scCODI

bioinformatics↗

bioRxiv2025

MF-PENS: An Efficient and Versatile Nuclear Extraction Method for Plant Single-Cell Sequencing

The widespread adoption of single-cell transcriptomics in plants remains constrained by the challenges of isolating high-quality nuclei from tissues with rigid cell walls and complex metabolomes. Here, we present methanol fixation and Percoll-enriched nuclei sorting (MF-PENS), a robust strategy designed to tackle these limitations. Distinct from conventional protocols, MF-PENS circumvents the need for fluorescence activated cell sorting (FACS) and effectively reduce RNA leakage, consistently yielding high-quality single-nucleus RNA sequencing (snRNA-seq) data. MF-PENS has been validated across various plant tissues, including fresh and frozen rice roots and leaves, as well as recalcitrant samples such as soybean root nodules, soybean seeds, and tomato fruits. Benchmarking results demonstrate that MF-PENS significantly enhances gene capture efficiency, cell clustering performance, and cell type annotation accuracy. With its broad applicability and superior performance, MF-PENS represents a robust and versatile tool for plant single-cell research, facilitating deeper insights into plant development and biology processes.

plant biology↗

bioRxiv2025

Macroscopic cerebral energy efficiency corresponds to neuron reorganization in awake and anesthetized mice

Non-invasive imaging of brain function and energy supply is crucial for diagnosing and treating brain disorders. Conventional imaging struggles to capture altered relationships between energy supply and utilization caused by brain diseases. A novel method, which can be translated to human patients, is to calculate relative power (rPWR) and relative cost (rCST) to assess cerebral energy efficiency. However, whether rPWR/rCST can track individual changes and neural activity remains unproven. Our study compared these non-invasive measures with invasive two-photon microscopy in awake and anesthetized mice. We found that rPWR/rCST distributions were similar between awake mice and humans, but changed in anesthetized mice, indicating a shift in the brains economic balance. Furthermore, changes in rPWR/rCST were linked to the reorganization of microscopic neural networks, observed with two-photon microscopy. Our work highlights the potential of rPWR/rCST for medical applications, and that neural network reorganization is linked to the brains economic balance.

neuroscience↗

bioRxiv2025

Distinct Connectivity Signatures of Emotions Enhance Precision of Network Biomarkers in Mood Disorders

Mood disorders, including Major Depressive (MDD) and Bipolar (BD) Disorder, are highly prevalent and debilitating conditions that contribute significantly to the global disease burden. These disorders are characterized by persistent emotional dysregulations, such as pervasive sadness and anhedonia, resulting in substantial functional impairments. Although neuroimaging studies have identified differences in brain activity and connectivity between individuals with MDD (MDDs) or BD (BDs) and healthy controls (HCs), reliable and reproducible neurofunctional markers for clinical diagnosis and treatment remain elusive. This study seeks to address this gap by introducing a novel approach that utilizes Divergent Emotional Functional Networks (DEFN), derived from functional magnetic resonance imaging (fMRI) during dynamic emotional processing in naturalistic contexts. Using a combination of naturalistic induction of sustained emotional experience with dynamic functional connectivity (dFC) and machine learning techniques, we decoded emotion-specific patterns of happiness and sadness in healthy individuals. Based on the dynamic functional connectivity signatures, we identify the DEFN and applied it to large clinical mood disorder datasets, including MDD (n=63) and BD patients (n=61). The model with DEFN demonstrated significant improvements in classification accuracy compared to conventional baseline models, achieving up to 10.75% and 9.92% performance increases in MDD and BD datasets, respectively. Additionally, DEFN were found to be highly reproducible across age, gender and models from emotion dataset, supporting the robustness of this model in distinguishing mood disorders from healthy controls. In conclusion, the DEFN approach presents a promising, reproducible, and clinically relevant neural marker for diagnosing and understanding emotional dysfunction in mood disorders, offering potential for more effective and timely interventions.

neuroscience↗

bioRxiv2025

Distinct mechanisms are employed by T-cell-instructed myeloid cells for IL-1β production in humans and mice.

Interleukin-1 beta (IL-1{beta}) is known as an inflammasome-dependent pro-inflammatory cytokine that has been implicated in T-cell-driven autoimmune diseases. In mice, T-cells were reported to instruct myeloid cells to produce IL-1{beta} via an inflammasome-independent mechanism, that engages TNFR and Fas-caspase-8-dependent signalling pathways. In this study, we explored T-cell-driven myeloid IL-1{beta} production in humans. Co-culturing of autologous primary T-cells (memory CD4+ and CD8+) with myeloid cells (monocyte, macrophage and dendritic cells) revealed that both memory CD4+ and CD8+ T cells induce IL-1{beta} secretion. Also, caspase-1 rather than caspase-8 cleaves pro-IL-1{beta} in human myeloid cells. This process depends on TNF-, CD40L and IFN-{gamma} together rather than TNF- alone, leading to upregulated pro-IL-1{beta} expression in human myeloid cells. We additionally show that TNF-, CD40L and IFN-{gamma} independently enhance IL-1{beta} secretion. Together, our study highlights that, despite the shared biology in T-cell-instructed IL-1{beta} production between humans and mice, different underlying molecular pathways are implicated. SummaryLike murine cells, human T-cells stimulate IL-1{beta} secretion from myeloid cells. However, the underlying mechanisms differ, with IL-1{beta} production in humans being driven by TNF-, CD40L, IFN-{gamma}, and Caspase-1, whereas in mice, it is primarily regulated by TNF- and Caspase-8.

immunology↗

bioRxiv2025

Integrative Analysis of Single-cell and Bulk RNA Sequencing Reveals Macrophage Heterogeneity in Lyme Arthritis

IntroductionLyme arthritis (LA) is a common late-stage manifestation of Lyme disease caused by Borrelia burgdorferi (Bb) infection. Macrophages play a crucial role in LA pathogenesis, yet their heterogeneity and functional states within the arthritic microenvironment remain poorly understood. MethodsWe integrated single-cell RNA sequencing data from ankle joint tissues of Bb-infected C57BL/6 mice with bulk RNA sequencing data from both infected ankle joints and bone marrow-derived macrophages. We developed a LA mouse model and validated key findings through histopathological examination and RT-qPCR analysis. ResultsWe identified nine distinct cell types in the joint microenvironment with significant compositional shifts during disease progression. Analysis of 16,617 Macrophages/Monocytes revealed seven functionally distinct subpopulations with differential responses to infection. Pseudo-time trajectory analysis demonstrated three principal differentiation pathways from monocyte-derived macrophages toward anti-inflammatory, TNF+, and IL1b+ inflammatory phenotypes. Cell-cell communication analysis revealed significantly altered TGF-{beta} signaling networks in LA, with the Tgfb1-(Tglbr1+TgIbr2) axis playing a critical role. GSVA and pathway analyses showed metabolic reprogramming from oxidative phosphorylation toward glycolysis in macrophages during infection. Through integrated analysis and LASSO regression, we identified four characteristic genes (SIRPB1C, FABP5, MMP14, and EGR1) as potential LA biomarkers and developed a diagnostic nomogram with high predictive accuracy. ConclusionOur integrative analysis provides comprehensive insights into macrophage heterogeneity and functional plasticity in LA, identifying characteristic biomarkers and potential therapeutic targets. The metabolic reprogramming and altered TGF-{beta} signaling networks we identified may contribute to disease pathogenesis and offer new avenues for intervention strategies.

genomics↗

bioRxiv2025

Magnetoencephalography Reveals Neuroprotective Effects of COVID-19 Vaccination in Non-Human Primates

SUMMARY PARAGRAPHCOVID-19, caused by the SARS-CoV-2 virus, can lead to widespread neurological complications, including cognitive deficits and neurodegenerative symptoms, even in the absence of significant structural brain abnormalities. The potential neuroprotective effects of SARS-CoV-2 vaccination remain underexplored. Here, we demonstrate the neuroprotective effects of a psoralen-inactivated SARS-CoV-2 vaccine in a non-human primate model using resting-state magnetoencephalography (MEG), a non-invasive neurophysiological recording technique with sub-millisecond temporal and submillimeter spatial resolution. MEG scans demonstrated substantial preservation of neural activity across multiple brain regions in vaccinated subjects compared to unvaccinated controls following viral challenge. This approach not only underscores the role of vaccination in mitigating severe neurological outcomes but also highlights the capability of MEG to detect subtle yet significant changes in brain function that may be overlooked by other imaging modalities. These findings advance our understanding of vaccine-induced neuroprotection and establish MEG as a powerful tool for monitoring brain function in the context of viral infections.

neuroscience↗

bioRxiv2025

Age-Dependent Patterns of Gut Bifidobacterium Microbiome Diversity across child Cohorts in a Multi-Ethnic population, Western China

High-throughput sequencing has provided unprecedented opportunities to elucidate the role of human gut microbiota. However, current studies have primarily focused on infant and adult cohorts, leaving biomarkers associated with the development of transitional children unclear, particularly in revealing geographic, multi-ethnic group underrepresented. Here, we analyzed fecal samples from 776 infants and children aged 0 to 15 years with amplicon sequencing of the 16S rRNA and groEL gene, representing multi-ethnic groups from different residential locations in the Yining region of Xinjiang. First, we systematically characterized the gut microbiota Spectrum of children in the Ili region. As age increases, the diversity and functional composition of the microbiota become richer and more stable, with Bifidobacterium consistently dominating the community. Utilizing Bayesian regression models, we identified a cluster of microbiota strongly associated with growth and development in children, which phylogenetically corresponds to Bifidobacterium breve. Finally, we included another similar study in the species-level of Bifidobacterium, and metrics of the age-related effect sizes showed heterogeneity across ethnic groups (Uyghur vs Han: {beta}Deming = 4.40 and Kazakh vs Han: {beta}Deming = 4.33). In conclusion, this study provides novel insights into the developmental trajectories of the gut microbiome in children and its relationship with health. IMPORTANCEThe intestinal microbial markers associated with the development of geographically diverse, multi-ethnic children, particularly those represented by Bifidobacterium species, have been elucidated in this study. Through the application of Bayesian regression models, we identified a microbiome profile closely linked to childhood growth and development, specifically phylogenetically aligned with Bifidobacterium breve. When incorporating a comparable study conducted at the species level of another Bifidobacterium, it was observed that age-related effect sizes varied among different ethnic groups, offering novel insights into the developmental trajectory of childrens gut microbiota and its implications for health.

microbiology↗

bioRxiv2025

Investigating the Mechanisms and Potential Therapeutic Targets of Vestibular Migraine

BackgroundVestibular Migraine (VM) is a complex neurological disorder with recurrent headaches and various vestibular symptoms. Although it severely affects patients quality of life, its causes and pathophysiology are still unclear, and effective treatments are scarce. The lack of data emphasizes the need for bioinformatics to find key genes and pathways in VM, which could help develop new diagnostic and treatment methods. MethodThe GSE109558 dataset was acquired from the Gene Expression Omnibus (GEO) database. To identify VM - related differentially expressed genes, screening was carried out through limma and Weighted Correlation Network Analysis (WGCNA).The functional analysis of VM - related differentially expressed genes was conducted using three bioinformatics approaches: Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and Gene Set Enrichment Analysis (GSEA). Feature selection was further refined using lasso regression and random forest. Also, CIBERSORT was utilized to analyze the infiltration of immune cells, and Spearmans correlation analysis was employed to explore the correlations between diagnostic differentially expressed genes and immune cells.Finally, the Comparative Toxicogenomics Database (CTD) was utilized to search for corresponding drugs, and molecular docking was performed to explore potential therapeutic targets. ResultSix key feature genes (CABIN1, IFIT3, HEATR1, ARHGDIA, RAB11FIP4, and ZNF444) were identified as potential diagnostic markers for VM. Among these, CABIN1 demonstrated the highest diagnostic potential based on ROC curve performance, highlighting its promise as a diagnostic biomarker.Functional annotation of DEGs revealed their enrichment in biological processes related to inflammation, calcium ion channel regulation, and other pathways likely involved in VM pathophysiology. Through the CTD, drugs like Acetaminophen, bisphenol A, and Phenylephrine were identified. Molecular docking simulation was used to explore their potential therapeutic mechanisms for VM. ConclusionThis study offers important insights into the molecular mechanisms of VM and identifies six key feature genes, with CABIN1 standing out as a potential diagnostic marker.These findings pave the way for further research to validate the diagnostic and therapeutic implications of these genes and pathways.

genetics↗

bioRxiv2025

Genome-wide Viral Nascent RNA Sequencing Unveils Polymerase Pausing Landscape at Single-nucleotide Precision

Understanding viral replication and transcription mechanisms is critical for developing effective antiviral strategies. The study of viral gene regulation in host cellular environment is an important bridge for translating mechanistic discoveries from in vitro studies to in vivo but it remains stagnated due to the absence of technological advancement. Current methods for studying viral transcription and replication have been limited to capturing only the mature viral RNAs, obscuring the dynamic intermediates and mechanistic details of these crucial processes. Here, we introduce an original technology we called "Total Elongating Nascent VIral Polymerase single-molecule Sequencing (TenVIP-seq)", which isolates and analyses newly synthesized RNA within the viral RNA-dependent RNA polymerase (RdRp) complex, enabling the discovery of mechanisms critical for both viral replication and transcription. Our first characterization of nascent RNA species for an RNA virus showed that RdRP exhibited non-random pausing with profile signatures along the eight influenza A virus (IAV) gene segments. We also revealed genome-wide pausing at known regulatory sites, such as the poly(U) tract polyadenylation site, and new putative regulatory sites at single-nucleotide resolution. Distinct pausing features between the viral genomic (vRNA) and anti-genomic (cRNA/mRNA) templates were observed, suggesting that RdRp processes transcription and replication differentially on positive and negative sense RNA. The NTP analog drug T-705 (favipiravir) intensified RdRp pauses during genome replication and transcription without introducing new pausing sites, while TRIM25 knockout in host cells infected with virus reduced RdRp pausing globally across the viral genome. Strikingly, we observed that terminal nucleotide misincorporations of nascent RNA sequencing of paused RdRP, which were previously undetectable, were as high as an average of 51.6% (vRNA) and 44.0% (cRNA/mRNA), suggesting that the mutational rate of viruses is much higher than previously thought. We demonstrated that TenVIP-seq holds potential in providing insights into the molecular mechanisms that control viral genome replication, gene expression and regulation, mutation, antiviral drug treatment, and virus-host interaction.

genetics↗

bioRxiv2025

Genomic feature of filled regions of telomere-to-telomere genomes in five model species

T2T genome is a telomere-to-telomere high-quality genome with exceptional accuracy, continuity and integrity. It significantly filled some gaps of chromosome. However, the genomic features unique to the filled regions assembled in the T2T genome across species remain unclear. Here, we collected and compared T2T genomes with their older version from human (Homo sapiens), animal (Mus musculus), and plants (Arabidopsis thaliana, Musa acuminata, and Fragaria vesca). We explored the newly filled regions generated by the T2T genome in comparison to the old one, and focused on: (1) the comparison of their locations, (2) the distribution of genomic features, and (3) gene annotation and gene function for filled regions. Research indicated that the filled regions of the T2T genome ordinarily appear in the centromere and telomere regions, contain a great number of repetitive sequences. The T2T genome revealed the identification of a certain number of newly annotated genes, alongside significant variations in common annotated genes. Interestingly, the genomic features of filled regions exhibit distinct patterns between plants and human/animal. T2T genomes feature analysis serves as a crucial resource for studying pattern characteristics across diverse species and advancing the in-depth development of genomics.

genomics↗

bioRxiv2024

Modulating Prestimulus Alpha and Beta Power with tRNS Establishes Their Causal Role in Visual Perception

Variability in visual perception in response to consistent stimuli is a fundamental phenomenon linked to fluctuations in prestimulus low-frequency neural oscillations--particularly in the alpha (8-13 Hz) and beta (13-30 Hz) bands--typically measured by their power in electroencephalography (EEG) signals. However, the causal role of these prestimulus alpha and beta power fluctuations in visual perception remains unestablished. In this study, we investigated whether prestimulus alpha and beta power causally affect visual perception using transcranial random noise stimulation (tRNS). In a sham-controlled, single-blind, within-subject design, 29 participants performed a visual detection task while receiving occipital tRNS. Online functional near-infrared spectroscopy (fNIRS) was used to measure cortical excitability during stimulation, and offline EEG signals were collected after stimulation. Mental fatigue was incorporated as a state-dependent factor influencing tRNS effects. Our findings demonstrate that, primarily under low fatigue states, tRNS increased cortical excitability during stimulation (indicated by increased fNIRS oxyhemoglobin amplitude), decreased subsequent prestimulus EEG alpha and beta power, and consequently reduced the visual contrast threshold (VCT), indicating enhanced visual perception. Sensitivity analysis revealed that alpha oscillations contributed more significantly to visual perception than beta oscillations under low fatigue. Additionally, the state-dependent effects of tRNS may result from different sensitivities of VCT to neural oscillations across fatigue states. These results provide causal evidence linking prestimulus alpha and beta power to visual perception and underscore the importance of considering brain states in neuromodulation research. Our study advances the understanding of the neural mechanisms underlying visual perception and suggests potential therapeutic applications targeting neural oscillations. Significance statementUnderstanding why we perceive identical visual stimuli differently is a fundamental question in neuroscience. This study provides causal evidence that prestimulus alpha and beta neural oscillations directly influence visual perception, particularly under low mental fatigue state. By using tRNS alongside fNIRS and EEG recordings, we demonstrate that modulating neural excitability can alter perceptual outcomes. Our findings highlight the importance of considering brain state--such as fatigue levels--in neuromodulation research. This work advances our understanding of the neural mechanisms underlying visual perception and opens avenues for developing targeted interventions to enhance sensory processing and cognitive functions, potentially benefiting individuals with perceptual or attentional disorders.

neuroscience↗

bioRxiv2024

Genome-enabled stilbenoid gene cluster discovery in Pleuropterus multiflorus

Stilbenoids, a class of compounds with the stilbene skeleton as part of plant secondary metabolism, are renowned for their diverse health benefits. Among them, 2,3,5,4'-Tetrahydroxystilbene-2-O-{beta}-D-glucoside (THSG), specifically abundant in the famous medicinal plant Pleuropterus multiflorus, exhibits significant pharmacological properties. A selfing progeny of P. multiflorus commercial variety Jinwufugui No.1, which contains high THSG content, was used for genome sequencing. Through the combination of weighted correlation network analysis, genome mining, and enzymatic characterization studies, we identified an unpredicted biosynthetic gene cluster responsible for THSG biosynthesis. This cluster includes a stilbene synthase (PmSTS1), a flavin-containing monooxygenase (PmFMO3), and a UDP-glycosyltransferase (UGT72B90). Our findings suggest this stilbenoid gene cluster is formed by transposable element-mediated gene duplications and neofunctionalization, and regulated by MYB and NAC type transcription factors. Notably, PmFMO3, as the first identified stilbenoid-2-hydroxylase, was confirmed that it can modify the other stilbenoids without the A ring modifications. The novel type of gene cluster, in which FMO serves as the key decorating enzyme, will guide the identification of more biosynthetic gene clusters in plants. In addition, the characterization of PmFMO3 extends our understanding of the FMO gene function in plants, and provides an important catalytic bioparts for the heterologous synthesis of polyhydroxylated stilbenoids.

molecular biology↗

bioRxiv2024

CBAM-ResNet34-based classification and evaluation method for developmental processes of greenhouse strawberries

Strawberries, known for their economic significance and rich nutritional value, are cultivated extensively worldwide. However, a host of workers need to be employed every year to identify and categorize the developmental stages of the strawberries in the greenhouses, which is not only time-consuming, inefficient, increasing the cultivation cost, but also difficult to guarantee the classification accuracy. Meanwhile, affected by the complicated background, occlusions, and color interference, the features of strawberries are proven challenging to be extracted via the traditional neural networks due to serious gradient disappearance. Therefore, an improved CBAM-ResNet34- based classification evaluation method for developmental processes of greenhouse strawberries is investigated. The procedure of this method is as follows: firstly, the developmental stages of greenhouse strawberries are classified by experts into four stages: Stage I (initial stage), Stage II (green and white fruit stage), Stage III (early ripening stage), and Stage IV (fully ripe stage). The 627, 640, 604, and 340 strawberry images for these four stages are captured. Subsequently, the images are divided into training, validation, as well as testing sets and then undergo image pre- processing, expansion, and augmentation. Whereafter, the 7x7 convolution kernel in the first layer of the network is replaced by three consecutive 3x3 convolution cores to eliminate the redundant weights and unnecessary model parameters, and the BasicBlocks configuration is adjusted. Finally, the CBAM attention mechanism is added to each BasicBlock so as to pinpoint the spatial position of the strawberries and extract their major features such as shape, size, and color. Comparison experiments with the conventional deep neural networks LeNet5, AlexNet, VGG16, ResNet18, ResNet34, and every improved part of CBAM-ResNet34 demonstrated that when the learning rate is 0.001, the Dropout rate is 0.3, and the Adams weight decay parameter is 0.001, the accuracies for validation and testing sets can reach to 92.36% and 87.56% with F1 scores of 0.92, 0.87, 0.85 and 0.88.

plant biology↗

bioRxiv2024

Language model generates cis-regulatory elements across prokaryotes

Deep learning had succeeded in designing Cis-regulatory elements (CREs) for certain species, but necessitated training data derived from experiments. Here, we present Promoter-Factory, a protocol that leverages language models (LM) to design CREs for prokaryotes without experimental prior. Millions of sequences were drawn from thousands of prokaryotic genomes to train a suite of language models, named PromoGen2, and achieved the highest zero-shot promoter strength prediction accuracy among tested LMs. Artificial CREs designed with Promoter-Factory achieved a 100% success rate to express gene in Escherichia coli, Bacillus subtilis, and Bacillus licheniformis. Furthermore, most of the promoters designed targeting Jejubacter sp. L23, a halophilic bacterium without available CREs, were active and successfully drove lycopene overproduction. The generation of 2 million putative promoters across 1,757 prokaryotic genera, along with the Promoter-Factory protocol, will significantly expand the sequence space and facilitate the development of an extensive repertoire of prokaryotic CREs.

bioinformatics↗

bioRxiv2024

Anti-tumor activity of camptothecin analog conjugate of a RSPO4-based peptibody targeting LGR4/5/6 in preclinical models of colorectal cancer.

Antibody-drug conjugates (ADCs) have emerged as a major modality of targeted cancer therapy, yet no ADC has been approved specifically for colorectal cancer (CRC). LGR4/5/6 (leucine-rich repeat containing, G protein-coupled receptor 4, 5, 6) are three related receptors that are expressed at high levels together or alternately in nearly all cases of CRC. ADCs targeting LGR5 have been shown to have robust anti-tumor potency, but not all CRC cells express LGR5 and LGR5-positive tumor cells may lose LGR5 expression due to cancer cell plasticity. R-spondin 4 (RSPO4) is a natural protein ligand of LGR4/5/6 with high affinity for all three receptors. We fused a mutant form of RSPO4 furin domain that retains high affinity binding to LGR4/5/6 to human IgG1 Fc to create a peptibody designated R462. Conjugation of R462 with a camptothecin analog designated CPT2 at eight drugs per peptibody led to the generation of R462-CPT2 that showed highly potent cytotoxic activity in vitro in CRC cell lines expressing any of LG4/5/6. In cell line xenograft and PDX models of CRC, R462-CPT2 demonstrated robust anti-tumor effect. Importantly, R462-CPT2 showed no major adverse effect at therapeutically effective dose levels. These results strongly support the use of RSPO ligand drug-conjugates that target LGR4/5/6 simultaneously for the treatment of CRC.

pharmacology and toxicology↗

bioRxiv2024

Examination of Lipid Distributions in Hydrogel-Expanded Mouse Brain Tissue Using Imaging Mass Spectrometry

Imaging is an essential tool in biological research, and imaging mass spectrometry uniquely provides a label-free approach with high molecular specificity. However, imaging mass spectrometry is limited in spatial resolution, which in turn limits the biological structures and processes that can be studied at small dimensions. Custom lens setups and altered optical paths have been used to shrink the diameter of the incident laser beam probe in matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry to achieve high spatial resolutions (< 5 m). However, these research-grade instruments are complex and expensive, making high spatial resolution imaging experiments unrealistic for the broader community. An alternative method for improving spatial resolution is through physical magnification of the substrate, which has been well established in the subfield of expansion microscopy (ExM). ExM leverages superabsorbent hydrogels for isotropic expansion of tissues and retention of analytes containing fluorescent tags. While typical ExM involves covalently anchoring the analyte of interest to the hydrogel network, lipid retention without anchoring has been recently demonstrated for imaging mass spectrometry. Herein, we demonstrate expansion imaging mass spectrometry (ExIMS) of expanded, whole brain tissue and examine lipid distributions in both positive and negative ion mode across multiple brain structures. A linear expansion factor of 4.5-fold is achieved and used to obtain high spatial resolution images of mouse brain cerebellum. Approximately 95% of lipids in both positive and negative ion mode are retained in expanded tissue compared to unexpanded tissue. Additionally, the majority of lipid distributions across the brain are maintained post-expansion. Alterations to the hydrogel formulation (e.g., crosslinker density) can significantly affect the ability of ExIMS to maintain accurate lipid distributions in expanded tissue.

biochemistry↗