Biology subjects

Yang, T.

Publications and source records attributed to Yang, T..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

GeoGAT-site: A Face-Centered Geometric Graph Attention Network for Protein-Protein Interface Prediction

Protein-protein interactions (PPIs) underpin the intricate machinery of cellular life, orchestrating processes from signal transduction to metabolic regulation, yet their precise interface prediction remains a cornerstone challenge in structural biology, demanding scalable computational paradigms that balance accuracy and efficiency. Herein, we present GeoGAT-site, a pioneering geometric graph attention network that leverages face-centered surface fingerprints extracted from three-dimensional protein architectures to forecast interaction sites. Diverging from conventional vertex-centric approaches, our face-centered methodology achieves a 3.64-fold acceleration in patch generation, mitigating computational bottlenecks while preserving granular surface descriptors. At its core, GeoGAT-site incorporates a bespoke attention mechanism that adaptively modulates inter-facial distances and normal vector orientations, synergistically integrating spatial geometries with physicochemical attributes for nuanced interface delineation. Harnessing a meticulously curated dataset of 150 million face-centered fingerprints from over 20,000 structurally diverse proteins, the model attains robust generalization across heterogeneous interaction motifs. Empirical validation on an independent cohort of 167 protein complexes yields a ROC AUC of 0.89, surpassing established benchmarks including MaSIF-site (0.845), SPPIDER (0.65), and PSIVER (0.63). By furnishing high-fidelity interface annotations, GeoGAT-site augments downstream structural modeling paradigms through targeted constraints, thereby providing a versatile scaffold for unraveling PPI dynamics with profound implications for therapeutic discovery, protein redesign, and molecular epistemology.

bioinformatics↗

bioRxiv2025

Human ribomes reveal DNA-embedded ribonucleotides as a new type of epigenetic mark

Ribonucleoside monophosphates (rNMPs) are abundant in DNA, but their distribution and function in human nuclear genomes remain unknown. Here, we mapped nearly one million rNMPs per genome across diverse human cell types, defining a nuclear "ribome" with non-random distribution patterns. rNMPs are enriched in C/G-rich sequences, epigenetically marked regions, and telomeres. Conserved ribonucleotide-enriched zones (REZs) overlap with CpG islands and R-loops. rNMP concentration near transcription start sites (TSSs) correlates positively with gene expression. Wild-type cells display a broader gene-expression range than ribonuclease H2A (RNH2A) knockouts, in which loss of rNMP cleavage causes pronounced retention of embedded rG and strand-biased rC near TSSs, both increasing with gene expression. These findings establish DNA-embedded rNMPs as a novel epigenetic mark that modulates human gene expression.

molecular biology↗

bioRxiv2025

Protein Frustration Reveals Active Sites in Co-Evolved GPCR:G Protein Complexes and in Engineered Targeted Degrader Complexes

The folded structure of a protein is understood to be an optimal energy state. However, previous studies have shown that certain amino acid residue positions that play a critical role in protein function are often in a suboptimal energy state or "frustrated". Here, we leverage over 1200 three-dimensional structures of G protein-coupled receptors (GPCRs) to demonstrate that residues at the interface between GPCR and its ligand or G protein contain a higher density of frustrated residues compared to other structural regions in the receptor. Likewise, the G subunit of the trimeric G proteins shows multiple clusters of highly frustrated residues on its surface that overlap with their effector protein (G{beta}{gamma}, RGS, Adenyl cyclase, Ric8) binding interfaces. Compared to the co-evolved GPCR:G protein complexes, engineered protein complexes, such as those facilitated by molecular degraders, show a much greater density of highly frustrated residues in the degrader interface. Our study highlights the use of protein frustration as an invaluable tool to evaluate both native protein-protein interfaces and design strategies to facilitate engineered protein complexes.

biophysics↗

bioRxiv2025

Neurons maintain sequential order during radial migration by DSCAM-facilitated antagonism of N-Cadherin

Proper radial migration of developing cortical neurons is critical for the "inside-out" laminar organization of the mammalian cerebral cortex. While substantial progress has been made in our understanding of how neurons terminate migration at the cortical plate (CP)/marginal layer (ML) boundary, the mechanisms that control the sequential order of migrating neurons remain poorly understood. Here, we show that radial migration of upper-layer pyramidal neurons is transiently restrained within the upper cortical plate (UCP), where migrating neurons assemble into vertically organized radial queues that maintain inter-neuronal spacing and migratory order. Using time-lapse imaging of embryonic mouse cortex, we found that leading neurons transiently delay nucleokinesis of trailing neurons, thereby synchronizing neuronal movement within the UCP. Loss of Down syndrome cell adhesion molecule (DSCAM) disrupts this coordinated migratory behavior, allowing trailing neurons to bypass preceding neurons, resulting in abnormal neuronal accumulation near the CP/ML boundary. Mechanistically, DSCAM localizes to membrane interfaces between adjacent migrating neurons. At these sites, DSCAM suppresses N-cadherin-mediated intercellular adhesion, limits F-actin assembly, and inhibits expansion of the proximal cytoplasmic dilation required for nucleokinesis. Dscam-deficient neurons exhibit increased cadherin puncta, more neuron-neuron contacts, and accelerated migration through the UCP. Rescue experiments demonstrate that expressing the extracellular domain of Dscam is sufficient to restore ordered migration, suggesting that an extracellular mechanism regulates orderly neuronal migration. In addition, we identify UNC5c-mediated signaling as a counterbalancing force that promotes upward migration within the UCP. Together, our findings identify radial neuronal queues as a previously unrecognized intermediate structure in cortical morphogenesis and establish DSCAM-dependent regulation of inter-neuronal spacing as a mechanism that preserves the migratory order during inside-out corticogenesis.

developmental biology↗

bioRxiv2025

The bivalent epigenetic regulator ULTRAPETALA1 promotes the Arabidopsis floral transition via recruitment of Polycomb histone methyltransferases for H3K27me3 deposition at the FLC locus

Floral induction is a major transition during the plant life cycle that contributes to reproductive fitness in annual and perennial plants. Flowering occurs in response to multiple environmental and endogenous cues. In Arabidopsis thaliana, many of these cues converge on the regulation of the FLOWERING LOCUS C (FLC) gene, which encodes a MADS domain transcription factor that functions as the main floral repressor. Here we demonstrate that the bivalent epigenetic regulator ULTRAPETALA1 (ULT1) is both necessary and sufficient to promote flowering. We identify ULT1 as a novel component of the autonomous pathway (AP) and show that it accelerates flowering by directly repressing FLC transcription. We demonstrate that ULT1 physically associates with the SWINGER (SWN) and CURLY LEAF (CLF) histone methyltransferase components of Polycomb Repressive Complex 2 (PRC2) to regulate FLC transcription by altering the accumulation of H3K27me3 marks. Our data indicate that ULT1 promotes the floral transition by interacting with SWN and CLF to recruit PRC2 to the FLC locus to deposit repressive histone methylation and reduce transcription of this key floral repressor.

plant biology↗

bioRxiv2025

Development of a digital analysis system for a novel 3D culture-based colony formation to detect malignantly transformed cells in human cell-based therapeutic products

The presence of malignantly transformed cells in human cell-based therapeutic products (hCTPs) is a significant safety concern. Although such cellular impurities in hCTPs can be assessed by detecting anchorage-independent growth using conventional soft agar colony formation (SACF) assays, the sensitivity of these assays is often insufficient. To overcome this limitation, we previously developed a novel tumorigenicity-associated testing method, the digital SACF (D-SACF) assay, which combines a partitioned culture of test cells to concentrate target cells with colony detection via image analysis. However, conventional soft agar culture involves complicated operations, such as preparing multilayered culture media and temperature control, and further technical optimization is required for the widespread adoption of the D-SACF assay. In this study, we focused on a new culture system incorporating a three-dimensional (3D) culture method using a liquid medium containing the low-molecular-weight agar polymer LA717 in low-adhesion culture vessels. We initially confirmed conditions for the efficient high-density 3D culture of normal cells using LA717-supplemented medium in low-adhesion 96-well plates. Using human mesenchymal stem/stromal cells (MSCs) as a normal cell model and HeLa cells as a transformed cell model, we demonstrated that the new 3D culture system effectively maintained the dispersion of MSCs and prevented their aggregation, while transformed HeLa cells exhibited robust anchorage independence, thereby establishing the new liquid/low-molecular-weight agar colony formation (LACF) method as an alternative to SACF. Finally, by systematizing the digital analysis system for the LACF assay (D-LACF assay), which streamlines the overall workflow from the performance evaluation of the test method to product testing and result interpretation, the limitations of the conventional soft agar-based D-SACF assay were addressed and its practicality and utility were enhanced. This in vitro evaluation system is expected to provide a promising approach for improving the quality and safety of hCTPs.

bioengineering↗

bioRxiv2025

The ferroptosis inhibitor NUPR1 coordinates the mitochondrial response to oxidative stress and cell metabolism during COPD pathogenesis in the lung

Chronic obstructive pulmonary disease (COPD) is characterized by chronic injury and oxidative stress leading to progressive lung tissue destruction. Emerging evidence suggests that regulated cell death pathways, particularly ferroptosis, contribute to COPD pathology. We previously identified the stress response protein and known ferroptosis inhibitor nuclear protein 1 (NUPR1) as markedly downregulated in lung tissue from COPD patients. Here, we demonstrate that NUPR1 inhibition exacerbates iron accumulation, enhances lipid peroxidation, impairs mitochondrial function, disrupts cellular metabolism, and increases oxidative stress in lung epithelial cells. Furthermore, Nupr1 knockout mice exhibit mitochondrial abnormalities, increased oxidative damage, and lung tissue changes consistent with COPD pathology. Collectively, our findings establish NUPR1 as a critical regulator of ferroptosis, stress responses, mitochondrial integrity, and metabolic balance in the lung, highlighting its potential contribution to COPD pathogenesis.

cell biology↗

bioRxiv2025

Inulin reduces Ralstonia invasion by promoting cooperation between Lysinibacillus sphaericus and Pseudoarthrobacter spp.

In field production, beneficial microbial agents are typically applied along with key resources that beneficial microbes prefer to utilize. This practice aims to help the beneficial microbes colonize the field and exert their disease resistance functions. However, the mechanisms by which these key resources influence the colonization and disease resistance effects of beneficial microbes remain unknown. In this study, we found that inulin can enhance the growth capacity of Lysinibacillus sphaericus HR92 and its ability to inhibit Ralstonia solanacearum. Further investigation revealed that inulin can enhance strain HR92s flagellar assembly, bacterial chemotaxis, fatty acid metabolism, and siderophore synthesis capabilities, ultimately improving strain HR92s effectiveness in controlling bacterial wilt. Moreover, adding inulin to the soil can enrich Pseudarthrobacter strains. This genus enhances the expression of genes involved in the synthesis of the antibacterial substance HR92, and competes for nutrients with Ralstonia solanacearum, thereby further inhibiting the occurrence of bacterial wilt disease. Additionally, the combination of inulin and Pseudarthrobacter strains upregulates the valine and leucine pathway genes in strain HR92. These genes play a crucial role in the synthesis of surfactin and the antimicrobial VOC 3-methyl-1-butanol. We have demonstrated that the addition of key resources not only enhances the control efficacy of beneficial bacteria against soil-borne diseases but also enriches the "helpers" of beneficial bacteria in soil. ImportanceRalstonia solanacearum is a plant pathogen that can cause bacterial wilt in several important crops. Given the limitations of beneficial bacterial inoculation, we propose a strategy to enhance the biocontrol efficacy of beneficial microorganisms by utilizing key resources. In this study, we demonstrate that key resources inulin can improve the biological control effects of beneficial bacteria, thereby maintaining plant health. Furthermore, we found that increasing the concentration of inulin altered the diversity of the rhizosphere microbiota and specifically enriched the abundance of Pseudarthrobacter, which compete with pathogens for nutrients on one hand and, on the other hand, synergize with inulin to enhance the biocontrol efficacy of the beneficial bacterium HR92. Together, our study reveals that key resources can enhance the biocontrol efficacy of beneficial bacteria and enrich potential beneficial bacteria in resident microbial communities, further reducing tomato diseases.

microbiology↗

bioRxiv2025

THLANet: A Deep Learning Framework for Predicting TCR-pHLA Binding in Immunotherapy Applications

Adaptive immunity is a targeted immune response that enables the body to identify and eliminate foreign pathogens, playing a critical role in the anti-tumor immune response. Tumor cell expression of antigens forms the foundation for inducing this adaptive response. However, the human leukocyte antigens (HLA)-restricted recognition of antigens by T-cell receptors (TCR) limits their ability to detect all neoantigens, with only a small subset capable of activating T-cells. Accurately predicting neoantigen binding to TCR is, therefore, crucial for assessing their immunogenic potential in clinical settings. We present THLANet, a deep learning model designed to predict the binding specificity of TCR to neoantigens presented by class I HLAs. THLANet employs evolutionary scale modeling-2 (ESM-2), replacing the traditional embedding methods to enhance sequence feature representation. Using scTCR-seq data, we obtained the TCR immune repertoire and constructed a TCR-pHLA binding database to validate THLANets clinical potential. The models performance was further evaluated using clinical cancer data across various cancer types. Additionally, by analyzing divided complementarity-determining region (CDR3) sequences and simulating alanine scanning of antigen sequences, we unveiled the 3D binding conformations of TCRs and antigens. Predicting TCR-neoantigen pairing remains a significant challenge in immunology, THLANet provides accurate predictions using only the TCR sequence (CDR3{beta}), antigen sequence, and class I HLA, offering novel insights into TCR-antigen interactions. Author summaryT-cell receptor (TCR) recognition of peptide-human leukocyte antigen (pHLA) complexes is fundamental to immune responses. However, predicting their binding poses a significant challenge due to the intricate dynamics of their interactions. We developed THLANet, a novel deep learning model, to address this challenge by integrating the ESM-2 and Transformer-Encoder modules. This approach enhances sequence feature encoding, improving the models generalization capability and enabling accurate predictions of TCR-pHLA binding in clinical datasets. Using data processed from open-source databases, THLANet outperformed existing methods, such as PanPep and pMTnet, in precision-recall metrics across multiple epitopes. Additionally, THLANet demonstrates superior capability in identifying critical binding sites within 3D structures, providing structural insights into TCR-pHLA interactions. THLANet offers a robust framework for advancing immunotherapy research, with potential applications in the development of personalized medicine.

bioinformatics↗

bioRxiv2025

Circular RNA profiling and functional screening in breast cancer identify circNSD1 as a suppressor of tumor autophagy

Circular RNAs (circRNAs) have emerged as critical regulators of cell biology. However, their function in breast cancer remains elusive. Herein, through circRNA profiling of 38 breast tumors and 10 benign breast tissues, we identified 509 of differentially expressed circRNAs. Integration with transcriptome-wide functional screening of approximately 10,000 circRNAs pinpointed circNSD1(6) as a top ranked tumor suppressor in breast cancer. circNSD1(6) is downregulated in tumors and suppression of the circular form, but not the linear form, promotes breast cancer proliferation and tumor growth. Mechanistically, circNSD1(6) interacts with autophagy receptor SQSTM1/p62 and inhibits the oligomerization of p62, thereby suppressing p62 body formation and p62-dependent autophagy. Furthermore, circNSD1(6) ameliorates p62-mediated Keap1 sequestration, hence suppresses the Nrf2 pathway and oxidative stress tolerance. Our study provides a landscape view of the transcription and function of circRNA in breast cancer, and uncovers the crucial role of circNSD1(6) in autophagy and antioxidant stress.

cancer biology↗

bioRxiv2025

Subunit shuffling dynamics in KaiC's central hub reveal the synchronization mechanism of the cyanobacterial circadian clock

Protein complexes are critical for cellular functions, and subunit exchange within these complexes is increasingly recognized as a key regulatory mechanism. In the cyanobacterial circadian clock, subunits shuffling of the core clock protein KaiC is thought to synchronize the clock, though the underlying mechanism remains unclear. We developed a chromatography-based method to monitor the shuffling dynamics of hexamerization domain of KaiC (KaiC-CI) and found that ATPase activity is essential for this process. By analyzing experiment data with quantitative models, we found that KaiC-CI hexamer stochastically disassembles into two oligomers for shuffling after hydrolysis. Further, by assuming a hidden conformation for post-hydrolysis hexamers, we established an ATPase activity-dependent model that quantitatively describes the shuffling dynamics of KaiC-CI hexamers, linking the shuffling rate to ATP hydrolysis and nucleotide exchange rates. Using this model, we estimated the shuffling dynamics of full-length KaiC with indirect experimental data. Our findings suggest that KaiCs phosphorylation states regulate nucleotide exchange rates in the CI domain, thereby modulating ATPase activity and influencing subunit shuffling. This study provides a mechanistic framework for understanding the role of ATPase activity in subunit exchange and its implications for circadian clock regulation.

biophysics↗

bioRxiv2025

Genome-wide ribonucleotide detection in Archaea

Genome integrity is constantly challenged by the incorporation of ribonucleotides (rNMPs) during DNA synthesis. Covalently linked single and several consecutive rNMPs occur in the genome of a number of organisms. They are mainly introduced by DNA polymerases during DNA replication and repair. In general, cells evolved ribonucleases H (RNases H) specialized in the removal of rNMPs from DNA to avoid any detrimental consequences on genome stability. Here, we describe the involvement of types 1 and/or 2 RNases H in processing embedded rNMPs in the genome of two archaeal species Haloferax volcanii and Thermococcus barophilus. Using combined approaches that include alkaline DNA fragmentation, high-throughput ribose-seq DNA sequencing and nucleotide pool quantification, the distribution, identity, level and sequence context of genomic rNMPs are reported and discussed regards to the intracellular balances of dNTPs and rNTPs. Our results confirm the predominant role of type 2 RNase H in the removal of genomic rNMPs. They also reveal rNMP-base compositions, densities, locations, and variations of surrounding bases at rNMP-embedment for each mutant. The cellular roles of the different RNases H in processing rNMPs in the genome of Archaea are discussed.

genomics↗

bioRxiv2025

Multimodal brain cell atlas across the adult macaque lifespan

High-throughput single-cell omics of non-human primate tissues present a remarkable opportunity to study primate brain aging. Here, we introduce a transcriptomic and chromatin accessibility landscape of 1,985,317 cells from eight brain regions of 13 cynomolgus female monkeys spanning adult lifespan including exceptionally old individuals up to 29-years old. This dataset uncovers dynamic molecular changes in critical brain functions such as synaptic communication and axon myelination, exhibiting a high degree of cell type and brain region specificity. We identify the multicellular networks of the pons and medulla as a previously unrecognized hotspot for aging. Furthermore, comparative analyses with human neurodegeneration datasets highlight both shared and distinct mechanisms contributing to aging and disease. In addition, we uncover transcription factors implicated in monkey brain aging and pinpoint aging-regulated loci linked to longevity and neurodegeneration. This spatiotemporal atlas will advance our understanding of primate brain aging and its broader implications for health and disease.

neuroscience↗

bioRxiv2025

China Autism Brain Imaging Consortium: Charting Brain Growth in Chinese Children with Autism

Autism Spectrum Disorder (ASD) is a lifelong neurodevelopmental condition characterized by atypical brain growth. While advances in neuroimaging and openly sharing large-sample datasets such as the Autism Brain Imaging Data Exchange (ABIDE) have improved understanding of ASD, most studies focus on adolescents and adults, with early brain development-critical for diagnosis and intervention-remaining underexplored. Existing research predominantly involves Western samples, offering limited insight and generalizability into non-Caucasian populations. We introduce the China Autism Brain Imaging Consortium (CABIC) (https://php.bdnilab.com/resources/), a grassroots effort by researchers across the country to aggregate previously collected multi-site structural MRI datasets and phenotypic information from 1,451 autistic children and 1,119 typically developing children, covering an age range from early childhood to school age (1.0 - 12.92 years). Here, we present this resource and depict brain growth charts to push forward a more comprehensive understanding of the brain development in Chinese autism children. We constructed brain growth charts that reveal a developmental shift in autistic children, transitioning from early overgrowth to delayed maturation. Regional analyses identified distinct atypical trajectories across specific brain regions. Individual deviation scores quantified inter-subject variability, characterizing the heterogeneity of brain development in ASD. Comparative analyses between CABIC and ABIDE highlighted differences potentially attributable to ethnicity and culture, advancing our understanding of cross-population neurodevelopmental diversity. CABIC MRI datasets will be shared publicly to foster investigation of the potential neural mechanisms underlying ASD in non-Western populations and support efforts toward precision medicine for autistic individuals across diverse backgrounds.

neuroscience↗

bioRxiv2025

Insights From Protein Frustration Analysis Of BRD4-Cereblon Degrader Ternary Complexes Show Separation of Strong from Weak Degraders

PROteolysis TArgeting Chimeras (PROTACs), also known as Ligand-Directed Degraders (LDDs), are an innovative class of small molecules that leverage the ubiquitin-proteasome system to induce the degradation of target proteins. Structure based design methods are not readily applicable for designing LDDs due to the dynamic nature of the ternary complexes. This study investigates the dynamic properties of five LDD-mediated BRD4-Cereblon complexes, focusing on the challenges of evaluating linker efficiency due to the difficulty in identifying suitable computational metrics that correlate well with the cooperativity or degradation propensity of LDDs. We uncovered that protein frustration, a concept originally developed to understand protein folding, calculated for the residues in the protein-protein interface of the LDD-mediated ternary complexes recapitulate the strength of degradation of the LDDs. Our findings indicated that hydrophobic residues in the interface are among the highly frustrated residues pairs, and they are crucial in distinguishing strong degraders from weak ones. By analyzing frustration patterns, we identified key residues and interactions critical to the effectiveness of the ternary complex. These insights provide practical guidelines for designing and prioritizing more efficient degraders, paving the way for the development of next-generation LDDs with improved therapeutic potential.

biophysics↗

bioRxiv2025

Estimation of total mediation effect for a binary trait in a case-control study for high-dimensional omics mediators

Mediation analysis helps uncover how exposures impact outcomes through intermediate variables. Traditional mean-based total mediation effect measures may suffer from the cancellation of opposite component-wise effects, and existing methods often lack the power to capture weak effects in high-dimensional mediators. Additionally, most high-dimensional mediation analysis methods have focused on continuous outcomes, with limited attention to binary outcomes, particularly in case-control studies. To fill this gap, we propose an R2 total mediation effect measure within the liability framework that offers a clear and intuitive causal interpretation, provides additional insights beyond the mean-based measures, and is invariant to disease prevalence. We develop a cross-fitted, modified Haseman-Elston regression-based estimation procedure tailored for mediation analysis in case-control studies, which can also be applied to cohort studies. Our estimator remains consistent in the presence of non-mediators and weak effects, as demonstrated in extensive simulations. Theoretical justification for consistency is provided under mild conditions and without requiring exact mediator selection. In a case-control substudy of the Womens Health Initiative involving 2150 individuals, we found that many metabolites were mediators with weak effects in the path from BMI to coronary heart disease, and we estimated that 89% (95% CI: 57%-100%) of the BMI-explained variation in underlying CHD liability is mediated by the measured metabolomics. The proposed estimation procedure is implemented in the R package "r2MedCausal", available on GitHub.

genomics↗

bioRxiv2025

A Sensitive Soma-localized Red Fluorescent Calcium Indicator for Multi-Modality Imaging of Neuronal Populations In Vivo

Recent advancements in genetically encoded calcium indicators, particularly those based on green fluorescent proteins, have optimized their performance for monitoring neuronal activities in a variety of model organisms. However, progress in developing red-shifted GECIs, despite their advantages over green indicators, has been slower, resulting in fewer options for end-users. In this study, we explored topological inversion and soma-targeting strategies, which are complementary to conventional mutagenesis, to re-engineer a red genetically encoded calcium indicator, FRCaMP, for enhanced in vivo performance. The resulting sensors, FRCaMPi and soma-targeted FRCaMPi (SomaFRCaMPi), exhibit up to 2-fold higher dynamic range and peak {Delta}F/F0 per single AP compared to widely used jRGECO1a in neurons in culture and in vivo. Compared to jRGECO1a and FRCaMPi, SomaFRCaMPi reduces erroneous correlation of neuronal activity in the brains of mice and zebrafish by two- to four-fold due to diminished neuropil contamination without compromising the signal-to-noise ratio. Under wide-field imaging in primary somatosensory and visual cortex in mice with high labeling density (80-90%), SomaFRCaMPi exhibits up to 40% higher SNR and decreased artifactual correlation across neurons. Altogether, SomaFRCaMPi improves the accuracy and scale of neuronal activity imaging at single-neuron resolution in densely labeled brain tissues due to a 2-3-fold enhanced automated neuronal segmentation, 50% higher fraction of responsive cells, up to 2-fold higher SNR compared to jRGECO1a. Our findings highlight the potential of SomaFRCaMPi, comparable to the most sensitive soma-targeted GCaMP, for precise spatial recording of neuronal populations using popular imaging modalities in model organisms such as zebrafish and mice.

neuroscience↗

bioRxiv2025

Single-Cell Genomics Elucidates Molecular Variations and Regulatory Mechanisms in Circulating Immune Cells

The human peripheral blood displays diverse molecular characteristics across populations, understanding the drivers and underlying mechanisms of which remains challenging. Here, we introduce the Chinese Immune Multi-Omics Atlas (CIMA), elucidating sex-, age-, and genetic-related molecular variations by analyzing multi-omics data from 428 adults with over 10 million immune cells. CIMA generated an enhancer-driven gene regulatory network, identifying 237 high-quality regulons and revealing cell type-specific regulatory mechanisms. Additionally, 11,521 lead cis-expression quantitative trait loci (eQTLs) and 46,339 chromatin accessibility QTLs (caQTLs) were identified at cell type level. CIMA also uncovered pleiotropic associations among immune-related disease risk loci, eQTLs, and caQTLs in a cell type-specific manner. Lastly, a novel cell language model, CIMA-CLM, was developed to predict chromatin accessibility and noncoding variant effects using chromatin sequences and gene expressions. This work represents a population-scale multi-omics resource of human immune cells, providing a valuable reference for future investigation of immune-related diseases.

immunology↗