Biology subjects

Li, G.

Publications and source records attributed to Li, G..

At least 145 records · Page 8Linked to original sources

bioRxiv2025

Powerful one-dimensional scan to detect heterotic QTL

To meet the growing global demand for food, increasing yields through heterosis in agriculture is crucial. A deep understanding of the genetic basis of heterosis has led to the development of a quantitative genetic framework that incorporates both dominance and epistatic effects. However, incorporating all pairwise epistatic interactions is computationally challenging due to the large sequencing depth and population sizes needed to uncover the genes behind complex traits. In this study, we developed hQTL-ODS, a one-dimensional scanning method that directly assesses the net contribution of each locus to heterosis. Simulations show that hQTL-ODS reduces computational time while offering higher power and lower false-positive rate. We applied this method to a population of 5,234 wheat hybrids with whole-genome sequenced profile, revealing key epistatic hubs that play a critical role in determining heterosis. Our findings offered valuable insights for improving breeding strategies to enhance crop yields.

genetics↗

bioRxiv2025

Heart spheroids from primary cells for studying cardiac development

Organoids can be broadly classified based on their source: either from primary cells or from in vitro stem cell differentiation. Organoids derived from primary cells have been developed for various organs such as the liver, intestine, and colon, demonstrating significant value in disease modeling and drug screening. However, primary cell-derived heart organoids have not yet been reported. In this study, we developed heart organoid-like spheroids using murine atrial and ventricular cells. We demonstrated that these spheroids largely preserved the cell lineages present in vivo. We further evaluated their responses to treatment with growth factors. Subsequently, we fused atrial and ventricular spheroids to observe the migration patterns of different cell types. Then, we generated mixed spheroids composed of cells from different heart regions or organs and found that region- or organ-specific cells tended to cluster together. Notably, liver- and intestine-derived cells promoted cardiomyocyte maturation within these mixed organoids. Finally, we performed a fibroblast ablation assay in heart spheroids derived from transgenic mice and observed the effects on other cell lineages. Overall, we successfully generated heart spheroids from murine primary cells and demonstrated their in vivo-like characteristics. Compared to stem cell-derived organoid systems, this primary cell-derived approach holds great promise for translational research due to its potential to better preserve native cellular features.

developmental biology↗

bioRxiv2025

Strain-level metagenomic profiling using pangenome graphs with PanTax

Microbes are omnipresent, thriving in a range of habitats, from oceans to soils, and even within our gastrointestinal tracts. They play a vital role in maintaining ecological equilibrium and promoting the health of their hosts. Consequently, understanding the diversity in terms of strains in microbial communities is crucial, as variations between strains can lead to different phenotypic expressions or diverse biological functions. However, current methods for taxonomic classification from metagenomic sequencing data have several limitations, including their reliance solely on species resolution, support for either short or long reads, or their confinement to a given single species. Most notably, most existing strain-level taxonomic classifiers rely on the sequence representation of multiple linear reference genomes, which fails to capture the sequence correlations among these genomes, potentially introducing ambiguity and biases in metagenomic profiling. Here, we present PanTax, a pangenome graph-based taxonomic profiler that overcomes the short-comings of sequence-based approaches, because pangenome graphs possess the capability to depict the full range of genetic variability present across multiple evolutionarily or environmentally related genomes. PanTax provides a comprehensive solution to taxonomic classification for strain resolution, compatibility with both short and long reads, and compatibility with single or multiple species. Extensive benchmarking results demonstrate that PanTax drastically outperforms state-of-the-art approaches, primarily evidenced by its significantly higher precision or recall at strain level, while maintaining comparable or better performance in other aspects across various datasets. PanTax is an open-source user-friendly tool that is publicly available at https://github.com/LuoGroup2023/PanTax.

bioinformatics↗

bioRxiv2025

SIMIT-seq: a bead-free, scalable microfluidic platform for single cell mRNA sequencing with ultrahigh cell-indexing rate

SIMIT-seq is a bead-free, scalable microfluidic platform designed for high-efficiency single-cell mRNA sequencing. Conventional microfluidic-based single-cell RNA sequencing platforms rely heavily on barcoded beads and intricate co-encapsulation schemes, often constrained by double Poisson limitations and the complexities of bead synthesis. In contrast, SIMIT-seq eliminates the need for beads entirely by employing a deterministic, orthogonal barcoding strategy within a two-dimensional micro-well array. This platform achieves an impressive single-cell indexing rate of 96.6% without the need for complex microfluidic operations. Here, we describe the design and fabrication of the SIMIT-seq platform, outline its workflow for transcript capture and library preparation, and demonstrate its application in profiling K562 cells. Our results validate both the high fidelity of molecular barcode immobilization and the systems capability to support downstream single-cell mRNA sequencing. SIMIT-seq offers a cost-effective and scalable alternative for single-cell transcriptomics, providing a promising foundation for future single-cell omics applications.

bioinformatics↗

bioRxiv2025

Isoform switch of CD47 provokes macrophage-mediated pyroptosis in ovarian cancer

Cancer cells expressing CD47 escape macrophage phagocytosis by binding to the SIRP ligand expressed on macrophages. CD47 targeted therapy offers a promising approach to cancer treatment. Here we report that two major isoforms of CD47 differ in ovarian cancer and normal tissues. The truncated isoform lacking exon 9 and exon 10 (CD47-S) was exclusively expressed in normal tissues, whereas the full-length isoform (CD47-L) was predominantly expressed in ovarian cancer tissues. Interestingly, CD47-S was unable to locate at the cell surface to bind with SIRP as CD47-L did, and thereby inactivated "dont-eat-me" signal. Mechanistic investigations revealed that splicing factor HNRNPA1 promoted splicing switch from CD47-L to CD47-S through exon 9 and exon 10 skipping. We further developed antisense oligonucleotides (ASOs) that effectively switched CD47-L to CD47-S. Importantly, ASOs treatment evoked macrophage-mediated antitumor immune response, thereby triggered pyroptosis of ovarian cancer cells. Moreover, CD47-targeting ASOs significantly reduced tumor growth in patient-derived xenograft. Together, ASO-mediated isoform switch of CD47 has emerged as a promising strategy to improve immune responses against tumors.

cancer biology↗

bioRxiv2025

Design and Biological Activity of a Novel Brain Penetrant Urea Compound Against Glioblastoma

Glioblastoma (GBM) remains the most lethal primary brain tumor, largely due to therapy-resistant glioma stem cells (GSCs) and the ability of non-stem cells to dedifferentiate under therapeutic pressure. We developed MXC-017, a novel urea-based compound that crosses the blood-brain-barrier, directly targets GSCs, and prevents radiation-induced GSC formation. Using click chemistry pull-down and mass spectrometry, we identified vimentin as the target of MXC-017, further validated by in silico docking. Global transcriptomic profiling (bulk RNA-seq) and single-cell RNA-seq analyses revealed MXC-017s efficacy with minimal off-target effects, supported by metabolic and kinome assays. Normal cell toxicity was negligible in fibroblasts, microglia, astrocytes, and murine neural progenitors. Maximum tolerated dose was identified and we observed significantly extended median survival in 17 PDOX GBM models when treated with MXC-017 plus radiation, benchmarked against standard-of-care temozolomide. These findings underscore the therapeutic potential of vimentin-targeting agents to overcome radiation resistance and improve outcomes for GBM patients. Statement of SignificanceGlioblastomas distinctive nature and the blood-brain barrier hamper therapies targeting therapy-resistant GSCs. We developed a novel urea-based agent that crosses the barrier, targets GSCs, and prevents radiation-induced GSC formation. With minimal off-target effects, reduced toxicity, and superior survival in PDOX models, it offers potential to improve outcome in GBM.

neuroscience↗

bioRxiv2025

GLASS: A Graph Learning Algorithm for Screening Splice-Aware Alignments of Long-Read RNA-seq

With the continuous development of third-generation RNA-seq, obtaining accurate splice-aware alignments for the long RNA-seq reads to reference genomes has become one of the major challenges in transcriptomic analysis. To mitigate the erroneous alignments arising from existing long-read RNA-seq aligners, we propose GLASS, a novel splice-aware alignments filtering approach based on third-generation transcriptome data. GLASS introduces a newly designed Read-AS Map model and integrates graph machine learning techniques for detecting and removing falsely spliced aligned reads from alignment files. Experimental results demonstrate that GLASS significantly reduces the error rate of spliced alignmnt and enhance the accuracy of subsequent transcriptome reconstruction. Additionally, GLASS demonstrates strong generalization ability in data from species such as Mus musculus and Arabidopsis thaliana, providing a new approach for the efficient processing of third-generation transcriptome data and offering more reliable data support for subsequent gene expression analysis and functional studies.

bioinformatics↗

bioRxiv2025

Benchmarking single-cell multi-modal data integrations

Single-cell multi-modal omics, remarked as "Method of the Year 2019" by Nature Methods, provides valuable insights into complex tissues. Recent advances have enabled the generation both unpaired (separate profiling) and paired (simultaneous measurement) multi-modal datasets, driving rapid development of single-cell multi-modal integration tools. Nevertheless, there is a pressing need for a comprehensive benchmark to assess algorithms under varying integrated dataset types, integrated modalities, dataset sizes, and data quality. Here, we present a systematic benchmark for 40 single-cell multi-modal integration algorithms involving modalities of DNA, RNA, protein and spatial omics for paired, unpaired and mosaic datasets (a mixture of paired and unpaired datasets). We evaluated usability, accuracy, and robustness to assist researchers in selecting suitable integration methods tailored to their datasets and applications. Our benchmark provides valuable guidance in the ever-evolving field of single-cell multi-omics. The Stage 1 protocol for this Registered Report was accepted in principle on 3rd Jul 2024. The protocol, as accepted by the journal, can be found at https://springernature.figshare.com/articles/journal_contribution/Benchmarking_single-cell_multi-modal_data_integrations/26789572.

bioinformatics↗

bioRxiv2025

Diet-induced phospholipid remodeling dictates ferroptosis sensitivity and tumorigenesis in the pancreas

High-fat diet (HFD) intake has been linked to an increased risk of pancreatic ductal adenocarcinoma (PDAC), a lethal and therapy-resistant cancer. However, whether and how specific dietary fats drive cancer development remains unresolved. Leveraging an oncogenic Kras-driven mouse model that closely mimics human PDAC progression, we screened a dozen isocaloric HFDs differing solely in fat source and representing the diversity of human fat consumption. Unexpectedly, diets rich in oleic acid - a monounsaturated fatty acid (MUFA) typically associated with good health - markedly enhanced tumorigenesis. Conversely, diets high in polyunsaturated fatty acids (PUFAs) suppressed tumor progression. Relative dietary fatty acid saturation levels (PUFA/MUFA) governed pancreatic membrane phospholipid composition, lipid peroxidation, and ferroptosis sensitivity in mice, concordant with circulating PUFA/MUFA levels being linked to altered PDAC risk in humans. These findings directly implicate dietary unsaturated fatty acids in controlling ferroptosis susceptibility and tumorigenesis, supporting potential "precision nutrition" strategies for PDAC prevention.

cancer biology↗

bioRxiv2025

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

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

microbiology↗

bioRxiv2025

The photoreaction process of a large-Stokes-shift red fluorescence protein-LSSmCherry1

Large Stokes shift red fluorescent proteins are highly valued in fluorescence imaging due to their considerable spectral separation and minimal self-absorption. However, a notable gap remains in our understanding of these proteins excited-state dynamics. Unlocking this knowledge could potentially drive significant advancements in cellular and molecular biology. In this study, we systematically examine the excited-state dynamics of LSSmCherry1, a large Stokes shift red fluorescent protein, under varying isotopic compositions and temperatures. Our aim is to elucidate its distinctive spectral properties. Through steady fluorescence spectral experiments at different temperatures, we demonstrate that the large Stokes shift is significantly reduced by approximately 80 nm at low temperatures compared to room temperature. Using transient fluorescence and absorption spectroscopy, we dissect the excited-state dynamics of LSSmCherry1 in neutral environments. We also investigate the kinetic isotopic effect by comparing spectra in water and heavy water. These analyses allow us to delineate plausible photocyclic pathways. Our results reveal that the excited-state dynamics of LSSmCherry1 follow a model similar to that observed in other fluorescent proteins, such as Green Fluorescent Protein (GFP). Notably, we demonstrate that the excited-state proton transfer (ESPT) process is the primary origin of the large Stokes shift in LSSmCherry1. This ESPT process occurs rapidly, within approximately 365 fs after excitation in the neutral environment. This study provides crucial insights into the mechanisms underlying large Stokes shift fluorescent proteins, potentially paving the way for the development of improved fluorescent probes for biological imaging. TOC GraphicSome journals require a graphical entry for the Table of Contents. This should be laid out "print ready" so that the sizing of the text is correct. Inside the tocentry environment, the font used is Helvetica 8 pt, as required by Journal of the American Chemical Society. The surrounding frame is 9 cm by 3.5 cm, which is the maximum permitted for Journal of the American Chemical Society graphical table of content entries. The box will not resize if the content is too big: instead it will overflow the edge of the box. This box and the associated title will always be printed on a separate page at the end of the document.

biochemistry↗

bioRxiv2025

Discovering Polyphosphate and Polyhydroxyalkanoate-Accumulating Organisms Across Ecosystems: Phenotype-targeted Genotyping via FACS-sequencing

Intracellular biopolymers serve versatile functions that allow microbes to adapt to fluctuating environmental conditions. The metabolic interdependence of dual intracellular polymers, namely polyphosphate (polyP) and polyhydroxyalkanoates (PHA), is a defining feature of functionally polyphosphate-accumulating organisms (PAOs), the key agents enabling enhanced biological phosphorus removal (EBPR) for wastewater treatment. However, beyond EBPR systems, the presence and identities of PAOs that possess both polyP and PHA in natural environments such as soil have never been examined due to a lack of available detection tools, despite their potential roles in carbon and phosphorus cycling. This study presents a novel phenotype-targeted approach integrating triple-stained fluorescence-activated cell sorting (FACS) with 16S rRNA gene amplicon sequencing (termed TriFlow-Seq) to simultaneously detect, quantify, and phylogenetically characterize PAOs accumulating both polyP and PHA (referred to as PHA- PAOs). TriFlow-Seq was validated using polymer staining image analysis and Single-Cell Raman micro-spectroscopy. Application to EBPR systems successfully enriched known PHA- PAOs, including Candidatus Accumulibacter, Tetrasphaera, Dechloromonas, Pseudomonas. It also revealed novel candidate PHA-PAOs, particularly within the Rhodobacteraceae family. When applied to soil samples, TriFlow-Seq led to the first discovery of diverse PHA-PAOs dominated by Pseudomonas, Halomonas, and Nannocystis in maize rhizosphere soils. These predominant genera are known rhizosphere inhabitants of essential crops with key plant growth- promoting functions including phosphate solubilization, biofilm formation, and phytohormone production, yet simultaneous polyP and PHA accumulation has not been directly reported in the maize rhizosphere. Our findings revealed unexpectedly high PHA-PAO prevalence and distinct phylogenetic patterns associated with different maize genotypes, suggesting a potentially overlooked role for dual polymer storage in microbial rhizosphere dynamics and function. This study establishes a pioneering approach to investigate dual polyP and PHA-containing PAO identities and their important roles in rhizosphere structure and plant health. SynopsisNovel method reveals dual-polymer bacteria in activated sludge and crop rhizospheres, suggesting new candidates supporting wastewater treatment and agricultural sustainability.

microbiology↗

bioRxiv2025

cfMethDB: a comprehensive cfDNA methylation data resource for cancer biomarkers

Cancer is a major global health threat, and early detection of cancer is crucial for improving patient outcomes. DNA methylation in circulating cell-free DNA (cfDNA) has emerged as a promising biomarker for non-invasive cancer diagnosis. However, the integration and utilization of existing cfDNA methylation data have been limited, hindering comprehensive research efforts, especially in the discovery of cfDNA methylation biomarkers. To address this challenge, we introduce cfMethDB, a comprehensive database dedicated to cfDNA methylation in cancer that encompasses 4828 publicly available datasets. Through standardized analysis, we identified 1,048,770 differentially methylated cytosines (DMCs) as candidate biomarkers across seven cancer types. With cfMethDB, we not only identified known cfDNA methylation biomarkers, but also discovered several genes, such as ZIC4, that could be novel biomarkers. Moreover, cfMethDB offers a suite of user-friendly tools, including biomarker evaluation, pan-cancer search and end motif analysis. We hope that cfMethDB will serve as a valuable platform for the discovery of novel cancer cfDNA methylation biomarkers and will facilitate cancer research and clinical applications. cfMethDB is publicly available at: https://cfmethdb.hzau.edu.cn/home.

genomics↗

bioRxiv2025

Unveiling Gene Modules at Atlas Scale through Hierarchical Clustering of Single-Cell Data

A major challenge in scRNAseq analysis is how to recover the biologically meaningful cell ontology tree and conserved gene modules across datasets. Data integration and batch-effect correction have been the key to effectively analyze multiple datasets, but often fail to disentangle cell states in heterogeneous samples, such as in cancer and the immune system. Here we present super single cell clustering (SuperSCC), a novel algorithm that utilizes machine-learning models to discover cell identities and gene modules from multiple datasets without the need of data integration. Of note, SuperSCC can be implemented both in cell lineage and cell state level, thereby building the hierarchy of cell programs with specific cell identity and gene modules. Such information has the great potential to identify the shared rare populations across datasets regardless of batch effect and benefits label transfer for mapping cell labels from reference to query. We used SuperSCC to perform atlas level data analysis on more than 90 datasets and build a cell state map of complex tissue in healthy and diseased stages, such as human lung. We show that SuperSCC outperforms existing approaches in identifying cellular context, has better annotation accuracy, and outlines gene modules that indicate conserved immune cell status in lung microenvironments.

bioinformatics↗

bioRxiv2025

Comparative Transcriptomic Analysis of Embryonic Stem Cells (ESCs) across Mammalian Species

Pluripotency, the ability of cells to self-renew and differentiate into all the cell types in an animals body, is vital for mammalian early development. This study presented a comprehensive comparative transcriptomic analysis of embryonic stem cells across multiple mammalian species, defining their progression through expanded/extended, naive, formative, and primed pluripotency states. Our findings revealed both conserved and species-specific mechanisms underlying pluripotency regulation. We also emphasized the limitations of existing state-specific markers and their limited cross-species applicability, while identifying de novo pluripotency markers that can inform future research. Despite variability in gene expression dynamics, gene co-expression networks showed remarkable conservation across species. Among pluripotency states, the primed state demonstrated the highest conservation, evidenced by shared markers, preserved gene networks, and stronger selective pressures acting on its genes. These findings provide critical insights into the evolution and regulation of pluripotency, laying a foundation for refining stem cell models to enhance their translational potential in regenerative medicine, agriculture, and conservation biology.

genomics↗

bioRxiv2025

HydraRNA: a hybrid architecture based full-length RNA language model

RNA, an essential component of the central dogma of molecular biology, plays versatile roles in all cellular processes. RNA large language models (LLMs) are emerging as powerful methods in RNA research to decipher its intricate network of function and regulation. However, previous RNA LLMs were based on the transformer model and pre-trained on short segment of non-coding RNAs, which limits their general usability. Here we present the first full-length RNA foundation model, HydraRNA, which is based on a hybrid architecture of bidirectional state space model and multi-head attention mechanism, and is pre-trained on a large amount of both protein-coding and non-coding RNAs. Despite being pre-trained with the fewest parameters and the least GPU resources, HydraRNA learns better RNA representations and outperforms the existing foundation models on a variety of downstream tasks, including RNA classification, prediction of RNA secondary structure, RBP binding sites, mRNA stability and translation efficiency. Furthermore, HydraRNA can accurately predict the effect of mutations and estimate the relative contributions of different mRNA regions to the RNA stability and translation. We anticipate that HydraRNA will enable dissecting the diverse properties of RNA, accelerating the research of RNA regulation and facilitating the optimal design of RNA therapeutics.

bioinformatics↗

bioRxiv2025

Sex-biased Transcriptome in in vitro Produced Bovine Early Embryos

BackgroundMorphologic sex differences between males and females typically emerge after the primordial germ cell migration and gonad formation, although sex is determined at fertilization based on chromosome composition. A key debated sexual difference is the embryonic developmental rate, with in vitro produced male embryos often developing faster. However, the molecular mechanisms driving early embryonic sex differences remain unclear. ResultsTo investigate the transcriptional sex difference during early development, in vitro produced bovine blastocysts were collected and sexed by PCR. A significant male-biased development was observed in expanded blastocysts. Ultra-low input RNA-seq analysis identified 837 DEGs, with 231 upregulated and 606 downregulated in males. Functional enrichment analysis revealed male-biased DEGs were associated with metabolic regulation, whereas female-biased DEGs were related to female gonad development, sex differentiation, inflammatory pathways, and TGF-beta signaling. Comparing X chromosome and autosome expression ratio, we found that female-biased DEGs contributed to the higher X-linked gene dosage, a phenomenon not observed in male embryos. Moreover, we identified the sex-biased transcription factors and RNA-bind proteins, including pluripotent factors such as SOX21 and PRDM14, and splicing factors FMR1 and HNRNPH2. Additionally, we revealed 1,555 significantly sex-biased differential alternative splicing (AS), predominantly skipped exons, mapped to 906 genes, with 59 overlapping with DEGs enriched in metabolic and autophagy pathways. By incorporating novel isoforms from long reads sequencing, we identified 1,151 sex-biased differentially expressed isoforms (DEIs) associated with 1,017 genes. Functional analysis showed that female-biased DEIs were involved in the negative regulation of transcriptional activity, while male-biased DEIs were related to energy metabolism. Furthermore, we identified sex-biased differential exon usage in DENND1B, DIS3L2, DOCK11, IL1RAPL2, and ZRSR2Y, indicating their sex-specific regulation in early embryo development. ConclusionThis study provided a comprehensive analysis of transcriptome differences between male and female bovine blastocysts, integrating sex-biased gene expression, alternative splicing, and isoform dynamics. Our findings indicate that enriched metabolism processes in male embryos may contribute to the faster developmental pace, providing insights into sex-specific regulatory mechanisms during early embryogenesis. Plain English summaryMale and female early embryos develop at different speeds, with male embryos often developing faster than female embryos. However, the reasons behind these early differences remain unclear. In this study, we examined gene activity in bovine embryos to uncover the biological factors regulating these early sex differences. We collected in vitro-produced bovine blastocysts, examined their sex, and confirmed that male embryos develop faster. By analyzing global gene activity, including alternative splicing, which allows one gene to code for multiple RNA isoforms and proteins, we found distinct gene expression profiles between male and female embryos. Male embryos showed higher activity in genes related to metabolism and cellular functions, while female embryos had increased activity in genes associated with female-specific gonad development and gene expression regulation. We also examined differences in how genes on the X chromosome were expressed. Female embryos had higher X-linked gene expression, which may contribute to sex-specific developmental regulation. Additionally, we identified sex-specific transcription factors and RNA-binding proteins that regulate early embryo development, some of which are known to control pluripotency and gene splicing. Overall, our study provides new insights into how gene activity shapes early sex differences, suggesting that enhanced metabolism in male embryos may be a key driver of their faster developmental rate. HighlightsO_LIMale embryos develop faster due to increased gene expression in metabolism pathways C_LIO_LIFemale embryos exhibit higher X-linked gene expression, suggesting X-dosage compensation plays a role in early development C_LIO_LISex-biased alternative splicing events contribute to embryonic metabolism, autophagy, and transcriptional regulation in embryos C_LIO_LISex-biased isoform diversity contributes to distinct developmental regulation in male and female embryos C_LIO_LIKey pluripotency factors (SOX21, PRDM14) and splicing regulators (FMR1, HNRNPH2) drive sex-specific gene expression C_LI

developmental biology↗

bioRxiv2025

Surface Expansion Regionalization of the Hippocampus in Early Brain Development

The hippocampal formation is implicated in a myriad of crucial functions, particularly centered around memory and emotion, with distinct subdivisions fulfilling specific roles. However, there is no consensus on the spatial organization of these subdivisions, given that the functional connectivity and gene expression-based parcellation along its longitudinal axis differs from the histology-based parcellation along its medial-lateral axis. The dynamic nonuniform surface expansion of the hippocampus during early development reflects the underlying changes of microstructure and functional connectivity, providing important clues on hippocampal subdivisions. Moreover, the thin and convoluted properties bring out the hippocampal maturity largely in the form of expanding surface area. We thus unprecedentedly explore the development-based surface area regionalization and patterns of the hippocampus by leveraging 513 high-quality longitudinal MRI scans during the first two postnatal years. Our findings imply two discrete hippocampal developmental patterns, featuring one pattern of subdivisions along the anterior-posterior axis (head, regions 1 and 5; body, regions 2, 4, 6, and 7; tail, region 3) and the other one along the medial-lateral axis (subiculum, regions 4, 5, and 6; CA fields, regions 1, 2, and 7). Most of the resulting 7 subdivisions exhibit region-specific and nonlinear spatiotemporal surface area expansion patterns with an initial high growth, followed by a transition to low increase. Each subregion displays bilaterally symmetric pattern. The medial portion of the hippocampal head experiences the most rapid surface area expansion. These results provide important references for exploring the fine-grained organization and development of the hippocampus and its intricate cognitions.

neuroscience↗