Biology subjects

Xiao, G.

Publications and source records attributed to Xiao, G..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Organism-wide spatiotemporal profiling of gene expression utilizing a X-CreERT2/Ai9 tracing system

This study utilizes a novel inducible X-CreERT2/Ai9 system to comprehensively delineate the spatial expression landscape of Fermt2, which encodes a key focal adhesion protein Kindlin-2, across organs in mice. We can confirm all known Kindlin-2-expressing cells and identify any previously unknown Kindlin-2-expressing cells and those that do not express Kindlin-2 in all tissues/organs in mice. We have developed this cost-effective, rapid, and high-fidelity system and implemented its first whole-organism application for comprehensive gene expression profiling. This work not only provides unprecedented insights into roles of Kindlin-2 in diverse physiologies and pathologies, but also establishes the inducible X-CreERT2/Ai9 system as a powerful paradigm-shifting platform for organism-wide pan-organ spatiotemporal expression profiling of any target genes encoding any proteins and micro-RNAs. By enabling high-resolution mapping of drug target expression, this system may lay critical groundwork for elucidating previously unknown adverse and beneficial effects of targeted therapies, offering transformative potential for precision medicine.

biochemistry↗

bioRxiv2025

BiGER: Bayesian Rank Aggregation in Genomics with Extended Ranking Schemes

With the rise of large-scale genomic studies, large gene lists targeting important diseases are increasingly common. While evaluating each study individually gives valuable insights on specific samples and study designs, the wealth of available evidence in the literature calls for robust and efficient meta-analytic methods. Crucially, the diverse assumptions and experimental protocols underlying different studies require a flexible but rigorous method for aggregation. To address these issues, we propose BiGER, a fast and accurate Bayesian rank aggregation method for the inference of latent global rankings. Unlike existing methods in the field, BiGER accommodates mixed gene lists with top-ranked and top-unranked genes as well as bottom-tied and missing genes, by design. Using a Bayesian hierarchical framework combined with variational inference, BiGER efficiently aggregates large-scale gene lists with high accuracy, while providing valuable insights into source-specific reliability for researchers. Through both simulated and real datasets, we show that BiGER is a useful tool for reliable meta-analysis in genomic studies.

genomics↗

bioRxiv2025

Sialin2 Functions as a Mammalian Nitrate Sensor to Sustain Mitochondrial Homeostasis

Nitrogen homeostasis is fundamental for cellular physiology, yet mammalian nitrate (NO3-) sensing mechanisms remain elusive. Here, we identify Sialin2--a proteolytic fragment of the nitrate transporter Sialin generated by cathepsin B (CTSB) cleavage-- as the first mammalian nitrate sensor. Microscale thermophoresis (MST) reveals Sialin2 as a high-affinity nitrate sensor, while Cryo-Electron Microscopy (Cryo-EM) uncovers its structural basis for signaling. We show that Sialin2 localizes to mitochondria and scaffolds liver kinase B1 (LKB1)-AMP-activated protein kinase (AMPK) complexes to drive organelle-specific metabolic adaptation via spatiotemporally controlled AMPK activation, enhancing mitochondrial biogenesis, ATP production, and cell survival. Real-time tracking using the sCiSiNiS biosensor demonstrates nitrate signaling dynamics at physiological levels. This signaling axis redefines nitrate as a direct ligand activating receptor-like cascades, independent of classical nitric oxide synthesis. Our findings establish a paradigm of "inorganic salt signaling biology", wherein anions co-opt trafficking systems to achieve signaling specificity, offering therapeutic avenues for mitochondrial disorders.

cell biology↗

bioRxiv2025

Attention Guided Mechanism Interpretable Drug-Gene Interaction (MIDI) Modeling for Cancer Drug Response Prediction and Target Effect Explanation

Cancer drug discovery using genetic information is still poorly developed. Precisely locating drug atoms and explaining the targeting effect is crucial in precision medicine since it helps understand the drugs mechanism of action. Much data has been collected regarding drug response against cancer cell lines, and many models predict the drug response based on genomic information. However, to our knowledge, none of the data-driven techniques propose to detect the targeting mechanism of small drug molecules against genetic targets. In this work, we propose MIDI (Mechanism Interpretable Drug-Gene Interaction) model to delve deep into the targeting relation between drug molecules against genetic patterns. We show that purely based on a data-driven approach, the attention mechanism in our model could capture the important binding effect of small molecules towards gene targets. We provide both theoretical derivation and experiment results to show the information flow regarding the attention mechanism. In the meantime, we demonstrate that our model presents much higher prediction performance with the interpretation mechanism than the other state-of-the-art drug response prediction models.

cancer biology↗

bioRxiv2025

SpaFun: Discovering Domain-specific Spatial Expression Patterns and New Disease-Relevant Genes using Functional Principal Component Analysis

SpaFun is a novel, non-model-based method developed to address limitations in existing spatially variable gene (SVG) detection techniques, particularly for large-scale spatially resolved transcriptomics (SRT) datasets. These limitations include computational inefficiency, limited statistical power with increasing data size, and the inability to capture spatial heterogeneity and co-expression patterns among genes. Built on functional principal component analysis (fPCA), SpaFun identifies domain-representative genes (DRGs) with significantly better computational efficiency and greater statistical power while accounting for spatial heterogeneity and co-expression patterns among genes. We applied SpaFun to three SRT datasets and demonstrated that SpaFun outperformed state-of-the-art algorithms for identifying representative genes for tumor regions (e.g., DESeq, edgeR, and limma), as well as recently developed novel algorithms designed for spatial omics to identify the representative genes (e.g., SPARK and CSIDE). This highlights SpaFuns ability to accurately identify genes most representative of each spatial domain (e.g., tumor, immune, or stroma regions). By uncovering novel disease-relevant genes overlooked by existing algorithms, SpaFun could provide insights into new molecular mechanisms and propose innovative therapeutic strategies to improve patient outcomes. Key PointsO_LISpaFun is the first method dedicated to identifying DRGs, capturing spatially representative expression patterns within annotated tissue regions, setting it apart from all traditional SVG detection methods. C_LIO_LIIt leverages fPCA to model gene expression as a function of spatial location, avoiding reliance on predefined spatial distribution assumptions. C_LIO_LIThe non-model-based framework ensures compatibility with different SRT platforms and experimental designs, making it a scalable and widely applicable tool for SRT research. C_LI

genomics↗

bioRxiv2025

N-Power AI: A Specialized Agent Framework for Automated Sample Size and Power Analysis in Clinical Trial Design

BackgroundSample size and power analysis are essential in biomedical research and investigations, particularly in clinical trial design, as they ensure sufficient statistical power to detect meaningful effects. However, the complexity of these calculations often requires specialized statistical expertise, making the process inconvenient and limiting accessibility for researchers during early-stage study planning. MethodsWe developed N-Power AI, an agentic framework leveraging large language models (LLMs) to perform sample size and power calculations across diverse study designs. The framework consists of three specialized agents: the Function Agent, a perception and reasoning module that identifies appropriate statistical tests and corresponding R functions; the Calculation Agent, an action module that extracts parameters and executes precise computations; and the Reporting Agent, a presentation module that generates comprehensive, downloadable reports. N-Power AI and advanced LLMs (e.g., GPT-o1, Claude 3.5, Gemini 1.5 Pro) were evaluated against ground truths from statistical software (R) across six common clinical trial scenarios. ResultsDirect LLM outputs showed significant deviations from ground-truth values, particularly in complex scenarios like the Chi-Square Test and Cox Proportional Hazards Model. In contrast, N-Power AI achieved 100% agreement with ground truths across all scenarios. This accuracy is attributed to the Function Agents correct selection of statistical methods, the Calculation Agents accurate computations, and the Reporting Agents ability to produce clear and comprehensive summaries. ConclusionN-Power AI automates sample size and power analysis, offering an effective, efficient, and accessible solution for early-stage study planning. While human expertise remains crucial for high-level statistical planning, N-Power AI enhances accessibility and efficiency, streamlining the analysis process to generate reliable and reproducible results for a wide range of research scenarios.

bioinformatics↗

bioRxiv2025

circAβ-a RNA encoded Aβ175--the hidden driver of β-amyloid plaque formation and deposition in sporadic Alzheimer's disease.

Mechanisms that trigger A{beta} production in sporadic Alzheimers disease are still obscure. We recently reported the expression of a human circular RNA (circA{beta}-a) encoded A{beta} peptide precursor variant (A{beta}175). Presently, we demonstrated that AAV9 virus-expressed circA{beta}-a gave rise to extensive extracellular A{beta} plaque depositions and microglial activation in mouse brain; this recapitulates critical pathogenic hallmarks within a sporadic AD mouse model. Specifically developed antibodies detected robust endogenous A{beta}175 expression in HEK293 cells and hNSC-derived human neurons, underscoring the potential of A{beta}175 as a salient A{beta} precursor. Furthermore, we detected high levels of A{beta}175 oligomers in young-adult human brains. In intermediate and old-age human brain samples, accumulation of soluble A{beta}175 pentamers was reduced and A{beta}175 oligomers were components of most insoluble A{beta} plaques in older human brain. We propose a causal relationship between human circA{beta}-a RNA expression, dysregulation of A{beta}175 oligomer processing/aggregation and A{beta} plaque accumulation in sporadic AD.

neuroscience↗

bioRxiv2024

Absorbing molecules make both abdomen and back transparent in live mice

The field of tissue clearing has experienced rapid advancements in the past decades. Tissue clearing techniques primarily rely on physical or chemical approaches to match the refractive indices of cells and their surrounding medium, thereby reducing light scattering and rendering the tissue optically transparent. By doing so, it becomes possible to utilize fluorescence imaging and 3D reconstruction techniques to observe and analyze the contents and structures of tissues at a cellular level. However, effective research on in vivo tissue clearing remains limited due to the involvement of toxic substances and the removal of water or lipids. A recent article in Science reports a technique that achieves tissue transparency in live animals with absorbing molecules, marking a significant advancement in the field of in vivo tissue clearing. Although this work has great potential for imaging and light-based therapeutics, some labs found it difficult to reproduce the reported results. In this study, we report the successful reproduction of the findings, provide experimental details that we identified as key to reproducibility, present a new transparency effect observed in the back of live mice, which was not shown in the original paper, and offer insights into the future development and applications of in vivo clearing reagents.

bioengineering↗

bioRxiv2024

Computational Identification of Migrating T cells in Spatial Transcriptomics Data

T cells are the central players in antitumor immunity, and effective tumor killing depends on their ability to infiltrate into the tumor microenvironment (TME) while maintaining normal cytotoxicity. However, late-stage tumors develop immunosuppressive mechanisms that impede T cell movement and induce exhaustion. Investigating T cell migration in human tumors in vivo could provide novel insights into tumor immune escape, although it remains a challenging task. In this study, we developed ReMiTT, a computational method that leverages spatial transcriptomics data to track T cell migration patterns within tumor tissue. Applying ReMiTT to multiple tumor samples, we identified potential migration trails. On these trails, chemokines that promote T-cell trafficking display an increasing trend. Additionally, we identified key genes and pathways enriched on these migration trails, including those involved in cytoskeleton rearrangement, leukocyte chemotaxis, cell adhesion, leukocyte migration, and extracellular matrix (ECM) remodeling. Furthermore, we characterized the phenotypes of T cells along these trails, showing that the migrating T cells are highly proliferative. Our findings introduce a novel approach for studying T cell migration and interactions within the tumor microenvironment (TME), offering valuable insights into tumor-immune dynamics.

bioinformatics↗

bioRxiv2024

Proteolysis-targeting vaccines (PROTAVs) for robust tumor combination immunotherapy

Protein/peptide subunit vaccines are promising to promote the tumor therapeutic efficacy of immune checkpoint blockade (ICB). However, current protein/peptide vaccines elicit limited antitumor T cell responses, leading to suboptimal therapeutic efficacy. Here, we present proteolysis-targeting vaccines (PROTAVs) that facilitate antigen proteolytic processing and cross-presentation to potentiate T cell responses for robust ICB combination immunotherapy of melanoma. PROTAVs are modular conjugates of protein/peptide antigens, E3 ligase-binding ligands, and linkers. In antigen-presenting cells (APCs), PROTAVs bind to E3 ligases to rapidly ubiquitinate PROTAV antigens, facilitating antigen proteolytic processing by proteasome, and thereby promoting antigen cross-presentation to T cells and potentiating CD8+ T cell responses. We developed a melanoma PROTAV using a tandem peptide of trivalent melanoma-associated antigens. Co-delivered by lipid nanoparticles (LNPs) with bivalent immunostimulant adjuvants, this PROTAV promotes the quantity and quality of melanoma-specific CD8+ T cells in mice. Further, combining PROTAV and ICB ameliorates the immunosuppressive melanoma microenvironment. As a result, PROTAV and ICB combination enhances melanoma complete regression rates and eradicated 100% large BrafV600E melanoma without recurrence in syngeneic mice. PROTAVs hold the potential for robust tumor combination immunotherapy.

bioengineering↗

bioRxiv2024

BayeSMART: Bayesian Clustering of Multi-sample Spatially Resolved Transcriptomics Data

The field of spatially resolved transcriptomics (SRT) has greatly advanced our understanding of cellular microenvironments by integrating spatial information with molecular data collected from multiple tissue sections or individuals. However, methods for multi-sample spatial clustering are lacking, and existing methods primarily rely on molecular information alone. This paper introduces BayeSMART, a Bayesian statistical method designed to identify spatial domains across multiple samples. BayeSMART leverages artificial intelligence (AI)-reconstructed single-cell level information from the paired histology images of multi-sample SRT datasets while simultaneously considering the spatial context of gene expression. The AI integration enables BayeSMART to effectively interpret the spatial domains. We conducted case studies using four datasets from various tissue types and SRT platforms and compared BayeSMART with alternative multi-sample spatial clustering approaches and a number of state-of-the-art methods for single-sample SRT analysis, demonstrating that it surpasses existing methods in terms of clustering accuracy, interpretability, and computational efficiency. BayeSMART offers new insights into the spatial organization of cells in multi-sample SRT data.

bioinformatics↗

bioRxiv2024

A Lung Cancer Mouse Model Database

Lung cancer, the leading cause of cancer mortality, exhibits diverse histological subtypes and genetic complexities. Numerous preclinical mouse models have been developed to study lung cancer, but data from these models are disparate, siloed, and difficult to compare in a centralized fashion. Here we established the Lung Cancer Mouse Model Database (LCMMDB), an extensive repository of 1,354 samples from 77 transcriptomic datasets covering 974 samples from genetically engineered mouse models (GEMMs), 368 samples from carcinogen-induced models, and 12 samples from a spontaneous model. Meticulous curation and collaboration with data depositors have produced a robust and comprehensive database, enhancing the fidelity of the genetic landscape it depicts. The LCMMDB aligns 859 tumors from GEMMs with human lung cancer mutations, enabling comparative analysis and revealing a pressing need to broaden the diversity of genetic aberrations modeled in GEMMs. Accompanying this resource, we developed a web application that offers researchers intuitive tools for in-depth gene expression analysis. With standardized reprocessing of gene expression data, the LCMMDB serves as a powerful platform for cross-study comparison and lays the groundwork for future research, aiming to bridge the gap between mouse models and human lung cancer for improved translational relevance.

cancer biology↗

bioRxiv2024

A Systematically Optimized Miniaturized Mesoscope (SOMM) for large-scale calcium imaging in freely moving mice

Understanding how neuronal dynamics gives rise to ethologically relevant behavior requires recording of neuronal population activity via technologies that are compatible with unconstrained animal behavior. However, realizations of cellular resolution head-mounted microscopes for mice have been based on conventional microscope designs that feature various forms of ad-hoc miniaturization and weight reduction measures necessary for compatibility with the weight-limits for free animal behavior. As a result, they have typically remained limited to a small field of view (FOV) or low resolution, a shallow depth range and often remain susceptible to motion-induced artifacts. Here, we present a systematically optimized miniaturized mesoscope (SOMM), a widefield, head-mounted fluorescent mesoscope based on a principled optimization approach that allows for mesoscale, cellular resolution imaging of neuroactivity while offering robustness against motion-induced artifacts. This is achieved by co-optimization of a compact diffractive optical element and the associated computational algorithm under form-factor and weight constraints while maximizing the obtainable FOV, depth of field (DOF), and resolution. SOMM enables recordings of neuronal population activity at up to 16 Hz within a FOV of 3.6 x 3.6 mm2 in the cortex of freely moving mice while featuring 4-{micro}m resolution, a DOF of 300 {micro}m at a weight of less than 2.5 g. We show SOMMs performance of recording large-scale neuronal population activity during social interactions, during conditioning-type experiments and by investigating neurovascular coupling using dual-color imaging.

neuroscience↗

bioRxiv2024

Liver cancer initiation is dependent on metabolic zonation but decoupled from premalignant clonal expansion

The origin of cancer is poorly understood because cells that obtain truncal mutations are rarely fate mapped in their native environments. A defining feature of the liver is zonation, or the compartmentalization of metabolic functions in hepatocytes located in distinct regions of the lobule 1. However, it is unknown if cancers develop in some zones but not others, and if there are metabolic determinants of cancer risk that track with cellular position. To study cancer initiation, we examined the effect of activating mutations in Ctnnb1 and loss of function mutations in Arid2, two of the most commonly co-mutated genes in hepatocellular carcinoma (HCC) 2. We exploited glutamine synthetase (GS) as a faithful fate mapping marker of Ctnnb1 mutant hepatocytes. By introducing mutations in distinct zones in a mosaic fashion, we showed that position and metabolic context regulate clone expansion. Mutant clones were maintained in zone 1 but largely outcompeted in zone 3. Paradoxically, clonal maintenance was anti-correlated with cancer initiation, as zone 3 mutant livers showed increased tumorigenesis. To define mechanisms, we individually deleted eleven zone specific genes in HCC mouse models, revealing that Gstm2 and Gstm3 were required for efficient HCC initiation in zone 3. These data indicate that liver cancer initiation is dependent on zonation but independent of clonal expansion.

cancer biology↗

bioRxiv2023

STIE: Single-cell level deconvolution, convolution, and clustering in spatial transcriptomics by aligning spot level transcriptome to nuclear morphology

In spot-based spatial transcriptomics, spots that are of the same size and printed at the fixed location cannot precisely capture the actual randomly located single cells, therefore failing to profile the transcriptome at the single-cell level. The current studies primarily focused on enhancing the spot resolution in size via computational imputation or technical improvement, however, they largely overlooked that single-cell resolution, i.e., resolution in cellular or even smaller size, does not equal single-cell level. Using both real and simulated spatial transcriptomics data, we demonstrated that even the high-resolution spatial transcriptomics still has a large number of spots partially covering multiple cells simultaneously, revealing the intrinsic non-single-cell level of spot-based spatial transcriptomics regardless of spot size. To this end, we present STIE, an EM algorithm that aligns the spatial transcriptome to its matched histology image-based nuclear morphology and recovers missing cells from up to [~]70% gap area between spots via the nuclear morphological similarity and neighborhood information, thereby achieving the real single-cell level and whole-slide scale deconvolution/convolution and clustering for both low- and high-resolution spots. On both real and simulation spatial transcriptomics data, STIE characterizes the cell-type specific gene expression variation and demonstrates the outperforming concordance with the single-cell RNAseq-derived cell type transcriptomic signatures compared to the other spot- and subspot-level methods. Furthermore, STIE enabled us to gain novel insights that failed to be revealed by the existing methods due to the lack of single-cell level, for instance, lower actual spot resolution than its reported spot size, the additional contribution of cellular morphology to cell typing beyond transcriptome, unbiased evaluation of cell type colocalization, superior power of high-resolution spot in distinguishing nuanced cell types, and spatially resolved cell-cell interactions at the single-cell level other than spot level. The STIE code is publicly available as an R package at https://github.com/zhushijia/STIE.

bioinformatics↗

bioRxiv2023

Reconstructing Spatial Transcriptomics at the Single-cell Resolution with BayesDeep

Spatially resolved transcriptomics (SRT) techniques have revolutionized the characterization of molecular profiles while preserving spatial and morphological context. However, most next-generation sequencing-based SRT techniques are limited to measuring gene expression in a confined array of spots, capturing only a fraction of the spatial domain. Typically, these spots encompass gene expression from a few to hundreds of cells, underscoring a critical need for more detailed, single-cell resolution SRT data to enhance our understanding of biological functions within the tissue context. Addressing this challenge, we introduce BayesDeep, a novel Bayesian hierarchical model that leverages cellular morphological data from histology images, commonly paired with SRT data, to reconstruct SRT data at the single-cell resolution. BayesDeep effectively model count data from SRT studies via a negative binomial regression model. This model incorporates explanatory variables such as cell types and nuclei-shape information for each cell extracted from the paired histology image. A feature selection scheme is integrated to examine the association between the morphological and molecular profiles, thereby improving the model robustness. We applied BayesDeep to two real SRT datasets, successfully demonstrating its capability to reconstruct SRT data at the single-cell resolution. This advancement not only yields new biological insights but also significantly enhances various downstream analyses, such as pseudotime and cell-cell communication.

bioinformatics↗

bioRxiv2023

MetaNorm: Incorporating Meta-analytic Priors into Normalization of NanoString nCounter Data

Non-informative or diffuse prior distributions are widely employed in Bayesian data analysis to maintain objectivity. However, when meaningful prior information exists and can be identified, using an informative prior distribution to accurately reflect current knowledge may lead to superior outcomes and great efficiency. We propose MetaNorm, a Bayesian algorithm for normalizing NanoString nCounter gene expression data. MetaNorm is based on RCRnorm, a powerful method designed under an integrated series of hierarchical models that allow various sources of error to be explained by different types of probes in the nCounter system. However, a lack of accurate prior information, weak computational efficiency, and instability of estimates that sometimes occur weakens the approach despite its impressive performance. MetaNorm employs priors carefully constructed from a rigorous meta-analysis to leverage information from large public data. Combined with additional algorithmic enhancements, MetaNorm improves RCRnorm by yielding more stable estimation of normalized values, better convergence diagnostics and superior computational efficiency. R Code for replicating the meta-analysis and the normalization function can be found at github.com/jbarth216/MetaNorm.

bioinformatics↗

bioRxiv2023

High-sensitivity whole-genome recovery of single viral species in environmental samples

Characterizing unknown viruses is essential for understanding viral ecology and preparing against viral outbreaks. Recovering complete genome sequences from environmental samples remains computationally challenging using metagenomics, especially for low-abundance species with uneven coverage. This work presents a method for reliably recovering complete viral genomes from complex environmental samples. Individual genomes are encapsulated into droplets and amplified using multiple displacement amplification. A novel gene detection assay, which employs an RNA-based probe and an exonuclease, selectively identifies droplets containing the target viral genome. Labeled droplets are sorted using a microfluidic sorter, and genomes are extracted for sequencing. Validation experiments using a sewage sample spiked with two known viruses demonstrate the methods efficacy. We achieve 100% recovery of the spiked-in SV40 (Simian virus 40, 5243bp) genome sequence with uniform coverage distribution, and approximately 99.4% for the larger HAd5 genome (Human Adenovirus 5, 35938bp). Notably, genome recovery is achieved with as few as one sorted droplet, which enables the recovery of any desired genomes in complex environmental samples, regardless of their abundance. This method enables targeted characterizations of rare viral species and whole-genome amplification of single genomes for accessing the mutational profile in single virus genomes, contributing to an improved understanding of viral ecology.

bioengineering↗