Biology subjects

Ma, R.

Publications and source records attributed to Ma, R..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Distinct Programs Drive Organ Development and Regeneration

A prevailing paradigm in stem cell and developmental biology posits that regeneration is a recapitulation of embryonic programs, yet the molecular logic underlying these processes remains unclear. Here, using the mammary gland as a model, we identified a novel intermediate cell state essential for basal-to-luminal differentiation during regeneration. The transcription factor Pou2f3 is a master regulator of this state, critical for regeneration but dispensable for organ physiology. Mechanistically, we found that POU2F3 functions as a transcriptional repressor that drives lineage conversion by restricting chromatin accessibility of basal genes rather than by directly activating luminal programs. Furthermore, we uncovered a POU2F3-TNF feedback loop that maintains epithelial homeostasis, where TNF signaling suppresses Pou2f3 to inhibit differentiation, and loss of luminal cells relieves this repression to trigger regeneration. Strikingly, cell fate is governed by signaling pathways essential for cell-cell communication during development, but by Pou2f3-dependent chromatin remodeling for rapid response and injury repair. We validate the regeneration-specific requirement for Pou2f3 in mammary gland and further demonstrate analogous regeneration defects in prostate and pancreas. These findings challenge the long-held assumption that regeneration recapitulates development and establish a framework for understanding context-specific mechanisms of tissue plasticity, with implications for regenerative medicine and epithelial biology.

cell biology↗

bioRxiv2025

Integrating Millions of Years of Evolutionary Information into Protein Structure Models for Function Prediction

BackgroundUnderstanding life processes relies on accurate protein function prediction, fundamentally requiring the integration of evolutionary information encoded in sequences with spatial characteristics from 3D structures. Existing approaches often face limitations, however, by either over-relying on sequence, using simplified structural representations instead of fine-grained spatial details, or failing to capture the synergistic relationship between sequence and structure, compounded by challenges in acquiring annotated data. ResultsTo address these issues, we propose a novel contrast-aware pre-training framework, ESMSCOP. ESMSCOP leverages a state-of-the-art protein language model to harness evolutionary insights embedded in sequences, and introduces a new encoder to fuse topological and fine-grained spatial structural features. By employing a contrastive pre-training strategy with auxiliary supervision, ESMSCOP effectively bridges the sequence-structure gap, yielding rich and informative representations. ConclusionsExtensive experiments conducted on multiple benchmark datasets demonstrate that ESMSCOP achieves superior performance in protein function prediction tasks compared to existing methods. Furthermore, it shows strong performance even when utilizing relatively less pre-training data compared to some large-scale models.

bioinformatics↗

bioRxiv2025

Transfer RNA modifications during rhizome development in Oryza longistaminata

The rhizome organ endows many clonal plants with the ability to reproduce under stressful climates. Such rhizome-mediated perenniality also underlies the recent success of perennial rice breeding. Despite the importance of this organ, its developmental regulation is poorly understood. Here, using Oryza longistaminata, an emerging model for rhizome biology, we explored the involvement of tRNA modification in rhizome development. Through comparative profiling of modified nucleotides in rhizome samples at different developmental stages and/or subjected to various treatments, we identified a number of key rhizome-related tRNA modifications and revealed that, akin to the observations in many non-plant organisms, tRNA modifications are associated with hormonal signalling to regulate rhizomes responses to environmental cues. Furthermore, genome-wide analysis of tRNA-modifying genes was performed to facilitate future functional studies. These results and analyses not only deepen our understanding of rhizome development but also underscore the regulatory roles of tRNA modification in plant organ development.

plant biology↗

bioRxiv2025

Developmental Connectomics of the Mouse Cerebellum

To uncover the developmental processes that establish the precise patterns of synaptic connectivity in the CNS, we employed a connectomic approach in the mouse cerebellar cortex between birth and 2 weeks of age. There were dramatic quantitative and qualitative changes in the structure and connectivity of cerebellar cells. Parallel fiber synapses onto Purkinje cells increased [~]500-fold, with the most rapid growth taking place a week after birth. To support this profound synaptogenesis, Purkinje cells generated thousands of transient parallel fiber-oriented filopodia that received nascent synapses from parallel fibers. Importantly, we find that granule cells initiate synaptic output onto Purkinje cells only after receiving mossy fiber input, revealing a sequential, input-dependent logic for circuit assembly. In sharp contrast to the concurrent pruning of climbing fiber inputs, parallel fiber connectivity expanded and became highly individualized during development. Despite anatomical overlap, neighboring Purkinje cells share significantly fewer parallel fiber inputs than expected by chance. Moreover, parallel fibers themselves diverged spatially, further enforcing selective input allocation and resulting in highly specific parallel fiber cohorts for each Purkinje cell. Our findings uncover a mechanistic sequence in which early afferent activity and transient cellular structures guide the selective wiring and expansion of parallel fiber input to Purkinje cells, establishing developmental principles that ensure functional specificity in the mature brain.

neuroscience↗

bioRxiv2025

Lipid Bilayer Membranes with Asymmetrically Distributed LPC and DAG

The complex chemical and biophysical characteristics of biomembranes are influenced by the asymmetric distribution of specific lipids. In vitro, the introduction of lysophosphatidylcholine (LPC) into one leaflet of lipid bilayers is frequently utilized to regulate membrane protein activity. In vivo, the conversion of phosphatidylinositol 4,5-bisphosphate (PIP2) to diacylglycerol (DAG) in one leaflet can also modulate membrane protein activities. However, the effects of such variations in lipid composition, lipid quantity, and particularly the asymmetry of lipid distribution on the properties and morphology of biomembranes remain to be fully elucidated. Through molecular dynamics simulations, we demonstrate that the asymmetric distribution of LPC and the asymmetric conversion of PIP2 induce asymmetric alterations in membrane structures and lipid dynamics. Such alterations can generate an imbalance in the lateral pressure distribution between the two leaflets, potentially leading to membrane curvature. The extent of membrane curvature is also influenced by the length and degree of unsaturation of the lipid acyl tail chain. Our findings underscore the critical role of lipid asymmetry in shaping biomembrane structure and dynamics, providing new insights into the regulation of membrane proteins and cellular functions mediated by these specific lipids.

biophysics↗

bioRxiv2025

Optimized large-scale longitudinal biorepository of gastroesophageal adenocarcinoma patient-derived organoids: High-fidelity models for personalized treatment to overcome resistance.

A major limitation in studying gastroesophageal adenocarcinoma (GEA) has been the lack of reliable models that represent the diseases complexity. We present lessons learned from a comprehensive large-scale biobanking effort combining traditional sample collection with several in vitro models including 3-dimensional patient-derived organoids (PDOs), 2-dimensional cancer-associated fibroblasts (CAFs), tumor-infiltrating lymphocytes (TILs) and/or in vivo xenografts. This initiative started in 2018, integrating multiple advanced ex-vivo models such as PDOs, patient-derived xenografts (PDXs) and organoids (PDXOs). This unique resource now includes tumor avatars from over 380 consented patients, making it the largest living GEA biobank in the world. We achieved > 90% success rate in creating per-patient models, including 227 tumor-derived and 203 neighboring normal PDOs. These organoids accurately mirror key features of the original tumors, such as their histology (e.g. microsatellite instability), mutations, and drug response, across treatment points. Notably, PDOs can predict individual patient responses to chemotherapy within five weeks, underscoring their clinical relevance. Furthermore, high-throughput drug screening on PDO subsets generates personalized chemosensitivity profiles for 22 drugs. Through a process of continued refinement of culture techniques and tumor sampling approach, our large-scale comprehensive collection of GEA avatars represents a unique and valuable preclinical experimental resource for precision oncology. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=76 SRC="FIGDIR/small/663874v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@f8a032org.highwire.dtl.DTLVardef@dd6fa3org.highwire.dtl.DTLVardef@1cc558corg.highwire.dtl.DTLVardef@492a5_HPS_FORMAT_FIGEXP M_FIG Schematic depiction of GEA live-banking workflow C_FIG

cancer biology↗

bioRxiv2025

Uncovering smooth structures in single-cell data with PCS-guided neighbor embeddings

Single-cell sequencing is revolutionizing biology by enabling detailed investigations of cell-state transitions. Many biological processes unfold along continuous trajectories, yet it remains challenging to extract smooth, low-dimensional representations from inherently noisy, highdimensional single-cell data. Neighbor embedding (NE) algorithms, such as t-SNE and UMAP, are widely used to embed high-dimensional single-cell data into low dimensions. But they often introduce undesirable distortions, resulting in misleading interpretations. Existing evaluation methods for NE algorithms primarily focus on separating discrete cell types rather than capturing continuous cell-state transitions, while dynamic modeling approaches rely on strong assumptions about cellular processes and specialized data. To address these challenges, we build on the Predictability-Computability-Stability (PCS) framework for reliable and reproducible data-driven discoveries. First, we systematically evaluate popular NE algorithms through empirical analysis, simulation, and theory, and reveal their key shortcomings such as artifacts and instability. We then introduce NESS, a principled and interpretable machine learning approach to improve NE representations by leveraging algorithmic stability and to enable robust inference of smooth biological structures. NESS offers useful concepts, quantitative stability metrics, and efficient computational workflows to uncover developmental trajectories and cell-state transitions in single-cell data. Finally, we apply NESS to six single-cell datasets, including those about pluripotent stem cell differentiation, organoid development, and multiple tissue-specific lineage trajectories. Across these diverse contexts, NESS consistently yields useful and verifiable biological insights, such as identification of transitional and stable cell states and quantification of transcriptional dynamics during development. Notably, NESS resolves distinct neuronal subpopulations during embryoid formation and provides a deeper understanding of their cell-state dynamics.

bioinformatics↗

bioRxiv2025

Sulindac Sulfide Suppresses Oncogenic Transformation Through let-7b-Mediated Repression of K-Ras Signaling

Cell transformation is a key early event in tumorigenesis, yet the molecular mechanisms underlying its chemoprevention remain poorly defined. Here, we show that sulindac sulfide (SS), the active metabolite of the NSAID sulindac, inhibits chemically induced transformation of NIH/3T3 cells through a COX-independent mechanism. SS treatment upregulates the tumor-suppressive microRNA let-7b, which directly targets K-Ras and suppresses downstream ERK signaling. Notably, K-Ras negatively regulates let-7b via activation of ERK and LIN28B, forming a reciprocal feedback loop that drives transformation. SS disrupts this loop by downregulating p-ERK and LIN28B, thereby restoring let-7b expression. Functional analyses confirmed that let-7b, but not let-7g, is required for SS-mediated inhibition of transformation. In human colon cancer tissues and cell lines, let-7b is downregulated, but restored upon SS treatment. These findings identify a novel let-7b/K-Ras/LIN28B/ERK regulatory axis targeted by SS and provide mechanistic insight into its role in early-stage cancer chemoprevention.

cell biology↗

bioRxiv2025

Sulindac modulates the response of triple negative breast cancer to anti-PD-L1 immunotherapy

Teriple-negative breast cancer (TNBC) is a heterogeneous tumor, and there is a lack of effective therapies. Immune checkpoint inhibitors (ICIs) therapy has been widely used to treat a variety of human cancers including TNBC. Despite the FDAs approval of therapies like Atezolizumab (anti- PD-L1) and Pembrolizumab (anti-PD-1) for certain TNBC cases, more than half of all TNBC patients, especially those with low PD-L1 levels and in advanced stages, remain unresponsive. Therefore, there is urgent clinical need to find other innovative combination therapy strategy for these TNBC patients. In this study, we examined the efficacy of sulindac to enhance the response of TNBC to anti-PD-L1 immunotherapy. We utilized a 4T1 syngeneic mouse tumor model to compare the inhibitory effects of PD-L1 antibody, sulindac, and their combination on 4T1 tumor growth. We found that mice treated with combination therapy showed a significant reduction in tumor volume, along with increased infiltration of activated T lymphocytes (Granzyme B+/CD8+ T cells) in the tumor tissues. We also established a PBMC humanized mouse model to further confirmed that combination therapy could significantly reduce the tumor size of TNBC patient-derived xenografts (56S) and the combination group were found to have the most infiltrating activated human T lymphocytes (Granzyme B+/CD8+ T cells) in the tumor tissues. Immunofluorescent staining of organoids also confirmed that organoids from combination group have more CD8+ T cell and the results of organoid cell viability assay in vitro indicated that combination of sulindac with PD-L1 antibody together with activated human PBMC significantly reduce 56S organoid cell viability, which is consistent with in vivo study. When investigating the mechanism of action, first we found sulindac could downregulate exosomal PD-L1 by decreasing expression of nsMase2 which is a major regulator in production of exosomal PD-L1. Second, we demonstrated that sulindac could downregulate PD-L1 by blocking Stat3 signaling and enhancing the expression of miR-570-3p which can potentially target PD-L1, which in turn led to a further decrease in exosomal PD-L1. Previous study showed that PD-L1 antibody could be bound and consumed by exosomal PD-L1 in the blood circulation. Therefore, in combination therapy, sulindac downregulating exosomal PD-L1 leads to increased availability of PD-L1 Ab, which potentially improves the overall efficacy of anti-PD-L1 therapy. In conclusion, our findings provide unique insights into the mechanism of action and efficacy for sulindac as an immunomodulatory agent in combination with anti-PD-L1 therapy for the treatment of TNBC.

cancer biology↗

bioRxiv2025

OriGene: A Self-Evolving Virtual Disease Biologist Automating Therapeutic Target Discovery

Here, we present OriGene, a self-evolving multi-agent system that functions as a virtual disease biologist, systematically identifying original and mechanistically grounded therapeutic targets at scale. OriGenes architecture integrates over 600 specialized tools through a Model Context Protocol (MCP), enabling it to reason across diverse data modalities including genomics, protein networks, pharmacology, clinical records and literature evidence, to generate and prioritize target discovery hypotheses. We implemented a strategy combining a knowledge graph-based Tool RAG with an advanced agent selection mechanism to enable dynamic, context-aware tool deployment. Through a self-evolving framework, OriGene continuously integrates human and experimental feedback to iteratively refine its core thinking templates, tool composition, and analytical protocols, thereby enhancing both accuracy and adaptability over time. To comprehensively evaluate its performance, we established TRQA, an original benchmark comprising over 1,900 expert-level question-answer pairs spanning a wide range of diseases and target classes. OriGene consistently outperforms human experts, leading research agents, and state-of-the-art large language models in accuracy, recall, and robustness, particularly under conditions of data sparsity or noise. Critically, OriGene nominated previously underexplored therapeutic targets for liver (GPR160) and colorectal cancer (ARG2), which demonstrated significant anti-tumor activity in patient-derived organoid and tumor fragment models mirroring human clinical exposures. These findings demonstrate OriGenes potential as a scalable and adaptive platform for AI-driven discovery of mechanistically grounded therapeutic targets, offering a new paradigm to accelerate drug development.

bioinformatics↗

bioRxiv2025

Longitudinal deep phenotyping in a mouse model of West syndrome reveals temporal dynamics of synapse remodeling, gliosis, and proteomic and lipidomic changes during seizure evolution

Neurodevelopmental disorders can have long-lasting effects, causing not only early pediatric symptoms but also a range of neurological issues throughout adulthood. West syndrome is a severe neurodevelopmental disorder marked by infantile spasms, an early symptom that typically subsides with age. However, many patients progress to other seizure forms, known as seizure evolution, which is closely linked to poor long-term outcomes. Despite its clinical significance, the neurobiological mechanisms behind seizure evolution in West syndrome remain poorly understood. Recent genetic studies have consistently identified the CYFIP2 p.Arg87Cys variant in West syndrome patients, and the Cyfip2+/R87C mouse model carrying this mutation has been shown to recapitulate key symptoms of the disorder, including infantile spasms. In this study, we aimed to gain deeper insight into seizure evolution by conducting longitudinal deep phenotyping of the Cyfip2+/R87C mouse model from the neonatal stage to seven months of age. We tracked seizure activity through behavioral and EEG recordings and employed multi-omic analyses, including tissue and single-cell level transcriptomics, ultrastructural analysis, proteomics, and lipidomics, to capture a comprehensive view of molecular and cellular changes. Our results showed that after an initial period of neonatal spasms, Cyfip2+/R87C mice entered a seizure-free phase, followed by spontaneous recurrent seizures in adulthood, ultimately leading to premature death. This progression was associated with synaptic remodeling, sequential activation of different glial cell types, lipid droplet accumulation in astrocytes, and significant proteomic and lipidomic changes in the brain. These findings suggest that seizure evolution in West syndrome involves complex, time-dependent interactions between neurons and glial cells, along with alterations in lipid metabolism. Our study highlights the potential of longitudinal multi-omic approaches to uncover underlying mechanisms of seizure evolution and suggests that targeting these changes could offer novel therapeutic strategies. Additionally, the dataset generated here may provide valuable insights for other epilepsy and neurodevelopmental disorder models.

neuroscience↗

bioRxiv2025

Modular Engineering of Escherichia coli for Enhanced Nickel Uptake, Survival, Biomineralization, and Hydrogen-Supported Bioremediation

Nickel contamination poses increasing environmental and industrial challenges, demanding effective and sustainable remediation strategies. Here, we present a modular synthetic biology approach to engineer Escherichia coli for efficient nickel enrichment, resilience in toxic environments, intracellular biomineralization, and hydrogen-based co-culture stability. Our system comprises four functional modules: (1) a nickel enrichment module, in which the fusion protein NixA-F1v outperformed existing uptake systems, and a mutant repressor RcnRC35L enhanced retention by suppressing nickel efflux; (2) a survival module where heterologous expression of Helicobacter pylori Hpn conferred tolerance to high nickel concentrations, and YejM increased phage resistance; (3) a nickel microparticle module enabling intracellular bioconversion of nickel into detectable microparticles even without engineered uptake proteins; and (4) a hydrogen supply module supporting co-culture stability through E. coli-cyanobacteria adhesion for sustained hydrogen production. These modules were experimentally validated through comparative uptake assays, survival profiling, and microscopy-based particle detection. Our findings demonstrate a customizable and integrative microbial platform for metal bioremediation and biosensing, with potential applications in environmental engineering and industrial waste treatment.

synthetic biology↗

bioRxiv2025

Capturing Natural Evolution in Function-guided RNA Design via Genomic Foundation Models

RNAs play critical roles in gene expression regulation, catalysis, genetic information transmission, and immune response. However, current RNA engineering approaches face limitations in designing RNA for efficient in vivo folding and functional execution. Here, we present a novel, zero-shot strategy that integrates genomic large language models (LLMs) with inverse folding models (IFMs) to generate RNA sequences that mirror natural evolutionary patterns while preserving the structural integrity of critical functional regions. Benchmarking 9 state-of-the-art genomic foundation models, we show that their unsupervised log-likelihood scores correlate strongly with RNA fitness, enabling zero-shot sequence design. Applied to fluorescent RNA aptamers, our approach yielded 20 Broccoli variants, over half of which achieved up to 155% in fluorescence and a twofold increase in binding affinity, with robust performance both in vitro and in vivo. For Pepper, two rounds of optimization produced 40 variants, 16 of which demonstrated up to 2.6 fold in fluorescence and a threefold boost in binding affinity, including five with superior in vivo brightness.This approach offers a scalable, efficient, and resource-light method for RNA design, with significant implications for RNA-based therapeutics and diagnostics. By leveraging general foundation models to facilitate RNA evolution in a zero-shot fashion, we believe our work represents a major advancement in the field of RNA engineering.

evolutionary biology↗

bioRxiv2025

Hepatic Arterial Flow-Induced Portal Tract Fibrosis in Portal Hypertension: The Role of VCAM-1 and Osteopontin-Expressing Macrophages

BackgroundThe liver undergoes significant hemodynamic changes during surgery, transplantation, or cirrhosis with portal hypertension(PH). The hepatic artery buffer response(HABR), which compensates for reduced portal venous flow by increasing hepatic artery(HA) flow, is hypothesized to induce pathological portal tract remodeling. This study investigates the molecular mechanisms underlying this process. MethodsPH was induced in Sprague-Dawley rats via partial portal vein ligation(PPVL). Structural evaluation(microCT), immune cell profiling, hemodynamic measurements, and transcriptomic analysis in macrophages(M{phi}) from sham or PPVL rats were conducted. ResultsMicroCT revealed decreased portal vein flow and increased HA flow correlated with portal pressure(r=0.799, p<0.01). A 2-fold increase in portal tract fibrosis(p<0.001) was observed with increased -SMA+ myofibroblasts in PPVL rats. CD68+ M{phi} peaked at 10 days post-PPVL, and their depletion significantly reduced fibrosis(p<0.001), indicating critical roles of M{phi} in portal tract remodeling. VCAM-1 was elevated in HA endothelium and portal fibroblasts (PFs); VCAM-1 neutralization reduced collagen accumulation(p<0.05), CD68+ M{phi}(46.3%, p<0.01), and CD3+ T cells(18%, p<0.05). M{phi}-conditioned medium increased VCAM-1 in PFs(8-fold, p<0.001) and enhanced PF migration, while VCAM-1 knockdown reduced this effect (p<0.01). Single-cell RNA sequencing data(GSE171904) and RNA-FISH revealed increased interactions between osteopontin (Spp1)+ M{phi} and PFs, with Spp1+ M{phi} driving fibrosis. Spp1 knockdown in M{phi} co-culture reduced PF fibrogenic markers, while recombinant Spp1 upregulated Col1a1, Fn1, and Acta2 expression in PFs. ConclusionIncreased VCAM-1 in arterial endothelial cells and PFs facilitates the recruitment of Spp1+ M{phi}, which drive HA flow-mediated vascular remodeling and portal tract fibrosis. These findings highlight arterial flow-induced fibrosis as a key mechanism in PH, potentially contributing to disease progression and decompensation. SynopsisLiver hemodynamic changes in portal hypertension drive extracellular matrix accumulation and portal tract remodeling via Spp1+ macrophages. This study highlights how altered blood flow induces fibrosis, and its potential role in decompensation, and identifies therapeutic targets for advanced liver disease.

pathology↗

bioRxiv2025

Single cell resolved spatial immune repertoire unveils spatial heterogeneity of lymphoid aggregates in human immune disorders

Adaptive immunity, mediated by T and B cell responses, is essential for defending against infections and cancers while also being implicated in autoimmune diseases. Tracking T and B cell repertoires in situ at single-cell resolution is essential for understanding adaptive immune responses. To address the lack of tools for in situ single-cell T/BCR (XCR) sequencing, we developed Stereo-XCR-seq, an efficient strategy for retrieving and sequencing TCR and BCR from Stereo-seq cDNA libraries at subcellular resolution. Stereo-XCR-seq provides unbiased full-length XCR reads alongside spatial transcriptomics, enabling the identification of heterogeneous lymphoid aggregates with distinct clonal activities in cancers and inflammatory bowel disease (IBD). We identified plasma cell aggregates that differ from tertiary lymphoid structures (TLSs) in both transcriptomic profiles and clonal activities, with spatial positioning potentially mediating unique immune responses. Collectively, Stereo-XCR-seq enables in situ single-cell profiling of T and B cell clonal activities within tissue microenvironments, providing insights into lymphocyte adaption to environmental stimuli. This technology provides potential for advancing our understanding of tissue immunity and the development of therapeutic strategies for immune disorders.

immunology↗

bioRxiv2024

Determination of Gaussian curvature modulus and spontaneous curvature via membrane buckling

The elastic properties of membranes are typically characterized by a few phenomenological parameters, including bending and Gaussian curvature moduli measuring the membrane rigidity against its deformation and topological change, as well as spontaneous curvature arising from the asymmetry between the two leaflets in the lipid bilayers. Though tether-based and fluctuation-based experiments are commonly utilized to measure the bending modulus, measuring the Gaussian curvature modulus and the spontaneous curvature of the membrane is considered to be much more difficult. In this paper, we study the buckling process of a circular membrane with nonzero spontaneous curvature under compressive stresses. It is found that when the stress exceeds a critical value, the circular membrane will transform from a spherical cap to a buckled shape, with its buckling degree enhanced with the increase of stress until its base is constricted to almost zero. As the stress-strain relationship of the buckled membrane strongly depends on the Gaussian curvature modulus and the spontaneous curvature,we therefore propose a method to determine the Gaussian curvature modulus and the spontaneous curvature simultaneously by measuring its stress-strain relationship during a buckling process.

biophysics↗

bioRxiv2024

Identifying spatially variable genes by projecting to morphologically relevant curves

Spatial transcriptomics enables high-resolution gene expression measurements while preserving the two-dimensional spatial organization of the biological sample. A common objective in spatial transcriptomics data analysis is to identify spatially variable genes within predefined cell types or regions within the tissue. However, these regions are often implicitly one-dimensional, making standard two-dimensional coordinate-based methods less effective as they overlook the underlying tissue organization. Here we introduce a methodology grounded in spectral graph theory to elucidate a one-dimensional curve that effectively approximates the spatial coordinates of the examined sample. This curve is then used to establish a new coordinate system that reflects tissue morphology. We then develop a generalized additive model (GAM) to estimate spatial patterns which permits the detection of genes with variable expression in the new morphologically relevant coordinate system. Our approach directly models gene counts, thereby eliminating the need for normalization or transformations to satisfy normality assumptions. A second important advantage over existing hypothesis-testing approaches is that our method not only improves performance but also accurately estimates gene expression patterns and precisely pinpoints spatial loci where deviations from constant expression occur. We validate our approach through extensive simulation and by analyzing experimental data from multiple platforms such as Slide-seq and MERFISH. As an example of its ability to enable biological discovery, we demonstrate how our methodology enables the identification of novel interferon-related subpopulations in the mouse mucosa, as well as markers of inflammation-associated fibroblasts in a multi-sample spatial transcriptomic dataset.

bioinformatics↗

bioRxiv2024

Basal EFNA3 Facilitates Luminal-Driving Mammary Epithelial Migration and Cancer Metastasis by Promoting OXPHOS

Collective migration is essential for tissue development, repair, and cancer metastasis. However, the mechanisms by which different cell types coordinate to facilitate cohesive movement remain poorly understood. Here, we report distinct migratory behaviors in luminal and basal cells during collective migration of the mammary epithelium. We show that luminal cells are leaders of directional migration. However, basal cells also play a central role in promoting collective migration by producing EFNA3, which targets luminal cells and enhances oxidative phosphorylation and energy production. This promotion of epithelial migration and branching morphogenesis is confirmed in vivo. Importantly, EFNA3 is a prognostic marker for breast cancer metastasis, with high expression correlating with increased metastasis. Conversely, inhibition of EFNA3 significantly slows metastasis. These findings reveal a novel mechanism of epithelial migration that is critical for organogenesis and identify EFNA3 as a potential therapeutic target for cancer metastasis.

developmental biology↗