Biology subjects

Guo, T.

Publications and source records attributed to Guo, T..

At least 37 records · Page 2Linked to original sources

bioRxiv2025

Population-specific brain charts reveal Chinese-Western differences in neurodevelopmental trajectories

Human brain charts provide unprecedented opportunities for decoding neurodevelopmental milestones and establishing clinical benchmarks for precision brain medicine 1-7. However, current lifespan brain charts are primarily derived from European and North American cohorts, with Asian populations severely underrepresented. Here, we present the first population-specific brain charts for China, developed through the Chinese Lifespan Brain Mapping Consortium (Phase I) using neuroimaging data from 43,037 participants (aged 0-100 years) across 384 sites nationwide. We establish the lifespan normative trajectories for 296 structural brain phenotypes, encompassing global, subcortical, and cortical measures. Cross-population comparisons with Western brain charts (based on data from 56,339 participants aged 0-100 years) reveal distinct neurodevelopmental patterns in the Chinese population, including prolonged cortical and subcortical maturation, accelerated cerebellar growth, and earlier development of sensorimotor regions relative to paralimbic regions. Crucially, these Chinese-specific charts outperform Western-derived models in predicting healthy brain phenotypes and detecting pathological deviations in Chinese clinical cohorts. These findings highlight the urgent need for diverse, population-representative brain charts to advance equitable precision neuroscience and improve clinical validity across populations.

neuroscience↗

bioRxiv2025

Experimental evolution of promiscuous kin recognition from a homotypic specific cell surface receptor

Recognizing the difference between self and nonself is a crucial step in the development of multicellularity. Myxococcus xanthus is a model organism for studying these processes during the transition from single cell to multicellular life. The polymorphic cell surface receptor TraA helps to mediate these transitions by directing cooperative behavior toward kin or clonemates. TraA is a highly specific receptor, capable of recognizing other TraA proteins with identical or nearly identical sequences by homotypic binding, but the molecular basis of recognition is poorly understood. In this study, we designed a targeted TraA library, consisting of thousands of variants, which changed 10 predicted specificity residues. By screening this library, we identified TraA variants with different combinations of substitutions that resulted in altered recognition, often leading to promiscuous TraA-TraA binding. Additionally, we identified key residues that dictate specificity between distant TraA groups and showed that changing these residues altered the recognition specificity. Furthermore, we propose a model to explain how TraA recognition specificity evolved through the generation of intermediate promiscuous variants driven by reward/punishment interactions. Our results highlight the malleable nature of the TraA variable domain involved in specificity, shedding light on the molecular and evolutionary basis of social recognition in M. xanthus.

microbiology↗

bioRxiv2025

Systematic evaluation of blood contamination in nanoparticle-based plasma proteomics

Circulating blood proteomics enables minimally invasive biomarker discovery. Nanoparticle (NP)-based circulating plasma proteomics studies have reported varying number of proteins, ranging from ca 2000 to 7000 but its not clear whether higher protein number is more informative. Here, we first develop OmniProt - a silica-NP workflow for plasma proteomics which is optimized through systematic evaluation of NP types and protein corona formation parameters. Next, we present an Astral spectral library for 10,109 protein groups expressed in human plasma. Using Astral with a throughput of 60 sample-per-day, OmniProt identifies ca 3000 to 6000 protein groups from human plasma. Notably, we found that platelet/erythrocyte/coagulation-related contamination artificially elevates protein identifications and compromises quantification accuracy for circulating plasma proteins in NP-enriched samples. Through controlled contamination experiments, we identified and validated protein biomarkers for contaminations of platelet, erythrocyte, and coagulation, respectively, in NP-based plasma proteomics experiments. We further developed an open-access software Baize for accessing plasma proteomics contamination. Finally, we validated our OmniProt workflow and Baize software in a clinical cohort of 193 patients with CT-indistinct benign nodules or early lung cancers. On average, we identified ca 4400 protein groups for each plasma sample without significant contamination. Five samples were flagged out as contaminated. In conclusion, this study reveals that contamination of plasma samples significantly alters the protein identification and quantification in NP-based plasma proteomics experiments, and presents an open-access software Baize to evaluate plasma proteome contamination.

biochemistry↗

bioRxiv2025

Antigenic cancer persister cells survive direct T cell attack

Drug-tolerant persister cancer cells were first reported fifteen years ago as a quiescent, reversible cell state which tolerates unattenuated cytotoxic drug stress. It remains unknown whether a similar phenomenon contributes to immune evasion. Here we report a persister state which survives weeks of direct cytotoxic T lymphocyte (CTL) attack. In contrast to previously known immune evasion mechanisms that avoid immune attack, antigenic persister cells robustly activate CTLs which deliver Granzyme B, secrete IFN{gamma}, and induce tryptophan starvation resulting in apoptosis initiation. Instead of dying, persister cells paradoxically leverage apoptotic caspase activity to avoid inflammatory death. Furthermore, persister cells acquire mutations and epigenetic changes which enable outgrowth of CTL-resistant cells. Persister cell features are enriched in inflamed tumors which regressed during immunotherapy in vivo and in surgically resected human melanoma tissue under immune stress ex vivo. These findings reveal a persister cell state which is a barrier to immune-mediated tumor clearance. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/643359v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@1ca8905org.highwire.dtl.DTLVardef@1e9b584org.highwire.dtl.DTLVardef@8bf43eorg.highwire.dtl.DTLVardef@504918_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2025

Identification of chemical features that influence mycomembrane permeation and antitubercular activity

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), is the deadliest single-agent infection worldwide. Current antibiotic treatment for TB takes a minimum of four months, underscoring the need for better therapeutics. The unique mycobacterial cell envelope, particularly the outermost mycomembrane, has long been thought to promote intrinsic antibiotic resistance by limiting compound entry into Mtb. Understanding chemical features that influence permeation across the mycomembrane may enable more accurate predictions of whole cell anti-Mtb activity, leading to development of more effective antibacterials. Here we query the mycomembrane permeation of over 1500 azide-tagged compounds in live Mtb with the bioorthogonal click chemistry-based assay PAC-MAN. We use cheminformatics and machine learning to identify chemical features associated with mycomembrane permeation and show that they have predictive value via systematic modification of two small molecule series. Additionally, we find that chemical features that influence mycomembrane permeation correlate with anti-Mtb activity in large compound libraries. These findings suggest that the mycomembrane is indeed a significant barrier to whole cell activity in Mtb and provide a rational framework for designing or modifying compounds to overcome this barrier.

microbiology↗

bioRxiv2025

ProteoAutoNet: high-throughput co-eluted protein analysis with robotics and a multi-database search strategy

Co-fractionation mass spectrometry (CF-MS) enables large-scale profiling of endogenous protein-protein interactions. Protein complexes identified by CF-MS using different databases are typically integrated. However, this integration uses varying cutoffs, leading to inconsistencies in protein complex identification. Here, we present ProteoAutoNet, a robotic experimental platform integrating multi-database search strategy into machine learning models, for high-throughput CF-MS analysis. This workflow increases the throughput of sample processing from protein complex to peptide by about two times. We then applied this workflow to map protein interaction networks in thyroid cancer cell lines, identifying significantly upregulated proteasome and prefoldin complexes in lung metastatic follicular thyroid carcinoma cell line FTC238 compared to normal thyroid cell line Nthy-ori 3-1. Notably, we identified a novel protein interaction network comprising PFAS, TGM2, and HK1 that was significantly upregulated in the papillary thyroid carcinoma cell line TPC-1. ProteoAutoNet provides an improved approach for investigating protein-protein interactions and uncovering novel networks, driving advancements in high-throughput proteomics research.

biochemistry↗

bioRxiv2025

Spatial distribution of the proteome in human body and cancers

A comprehensive spatial distribution of the proteome in human body and cancers is fundamental for understanding human biology and diseases including cancers. Here, we present an anatomically resolved human proteome derived from 1781 benign and malignant samples from 58 major tissue types encompassing 251 specific tissues and 25 carcinomas. Based on a spectral library covering over 75% of the human protein-coding genes with 208 understudied and 82 missing proteins characterized, we quantified over 13,000 proteins in these samples using data-independent acquisition proteomics. This data resource presents the so far most comprehensive quantitative proteomic landscape of human tissues and common carcinomas. It allows systematic evaluation of tissue-specific drug responses, identification of drug candidates that may be repurposed as antineoplastics, and discovery of novel targets for anticancer therapy. This resource, available as an online knowledgebase, refines our knowledge of spatial distribution of the human proteome and tumor-specific protein modulation.

systems biology↗

bioRxiv2025

A perturbation proteomics-based foundation model for virtual cell construction

Building a virtual cell requires comprehensive understanding of protein network dynamics of a cell which necessitates large-scale perturbation proteome data and intelligent computational models learned from the proteome data corpus. Here, we generate a large-scale dataset of over 38 million perturbed protein measurements in breast cancer cell lines and develop a neural ordinary differential equation-based foundation model, namely ProteinTalks. During pretraining, ProteinTalks gains a fundamental understanding of cellular protein network dynamics. Our model encodes protein networks and exhibits consistently improved predictive accuracy across various downstream tasks, highlighting its generalization capabilities and adaptability. In cancer cells, ProteinTalks robustly predicts drug efficacy and synergy, identifies novel drug combinations, and, through its interpretability, uncovers resistance-associated proteins. When applied to more complex system, patient-derived tumor xenografts, ProteinTalks predicts potential responses to drugs. Its integration with clinical patient data enhances the prognosis prediction of breast cancer patients. Collectively, we present a foundational model based on proteome dynamics, offering potential for various downstream applications, including drug discovery, and providing a basis for developing virtual cells.

systems biology↗

bioRxiv2024

Population-based metaproteomics reveals functional associations between gut microbiota and phenotypes

The association between gut microbiota and physiological disorders underscores the crucial need to understand its largely unexplored biological functions. In this study, we analyzed 86,876 microbial proteins and 1,277 human proteins from 1,967 Chinese stool samples. Our results indicate that metaproteomic data captures the microbial biodiversity of a population more effectively than metagenomic data, displaying greater variability in microbial functions and providing reliable assessments of microbiota stability. We identified crucial functions of core microbes and discovered 10,714 associations between metaproteomic taxon, microbial function, or human proteins with 39 phenotypes, including 1,381 features related to Type 2 Diabetes (T2D). Importantly, Megasphaera elsdeniis conversion of lactate to butyric acid appears to lower blood glucose levels, suggesting a protective mechanism against T2D. Our study underscores the power of population-based metaproteomics to offer unique functional insights into the gut microbiota. Highlights- The first large-scale population-based metaproteomics landscape. - Metaproteomics efficiently maps microbial functions, exposing individual functional variation. - 10,714 associations between metaproteomic taxa, microbial functions, or human proteins with 39 phenotypes. - Megasphaera elsdenii protects against elevated blood glucose levels in the host by degrading lactate to produce butyric acid.

microbiology↗

bioRxiv2024

Heat Acclimation in Mice Requires Preoptic BDNF Neurons and Postsynaptic Potentiation

Heat acclimation (HA) is a key adaptive response in mammals to repeated heat exposure, essential for fitness and survival1-3. HA improves cardiovascular function, thermal comfort, and exercise capacity4, 5. However, the lack of a genetically tractable model has hindered understanding of the molecular and neural mechanisms underlying HA. Here, we show that 10 days of daily 38{degrees}C exposure lowers core body temperature (Tcore) and reduces anxiety during subsequent heat exposures in mice. HA increases brain-derived neurotrophic factor (BDNF) expression in the medial preoptic area (MPO). BDNF-expressing MPO neurons (MPOBDNF) show increased intrinsic heat sensitivity after HA. These neurons orchestrate downstream targets in the dorsomedial hypothalamus (DMH) and rostral raphe pallidus (rRPa) to mediate HA effects. BDNF, acting through its receptor tropomyosin-related kinase B (TrkB) in the DMH, facilitates the anxiolytic effect of HA by enhancing excitatory synaptic connections between MPOBDNF and DMH neurons. This study provides new insights into HA mechanisms, setting the stage for future research on heat stress reduction and exercise optimization.

neuroscience↗

bioRxiv2024

DDA-BERT: leveraging transformer architecture pre-training for data-dependent acquisition mass spectrometry-based proteomics

In data-dependent acquisition mass spectrometry (DDA-MS)-based proteomics, machine learning based rescoring methods are often employed to integrate multiple scores measuring quality of peptide-spectrum matches (PSMs) from different aspects. Existing rescoring tools face limitations incurred by manual feature extraction, shallow machine learning models, and limited training data. Here, we introduce DDA-BERT, a transformer-based end-to-end deep learning model trained with 95 million human spectra for PSM rescoring. DDA-BERT demonstrates superior performance across various sample types, mass spectrometer platforms, trace sample proteomics, and multiple species proteome data. It consistently outperforms state-of-the-art methods of its kind for DDA-MS spectra analysis with up to 103.5% increased protein group identifications. For single cell proteomics, DDA-BERT identifies up to 85.6% more protein groups compared to existing tools. In addition, DDA-BERT can be effectively extended to proteome of non-human species. DDA-BERT offers a robust and scalable solution for enhancing PSM rescoring in proteomics.

bioinformatics↗

bioRxiv2024

DIA-BERT: pre-trained end-to-end transformer models for enhanced DIA proteomics data analysis

Data-independent acquisition mass spectrometry (DIA-MS) plays an increasingly important role in quantitative proteomics. Here, we introduce DIA-BERT, a software tool that leverages a transformer-based pre-trained artificial intelligence (AI) model for the analysis of DIA proteomics data. Over 276 million of high-quality peptide precursors extracted from existing DIA-MS files were used for training the identification model, while 34 million peptide precursors from synthetic DIA-MS files were used for training the quantification model. Compared to DIA-NN, DIA-BERT led to on average 54% more protein identifications and 37% more peptide precursors in five different human cancer (cervical cancer, pancreatic adenocarcinoma, myosarcoma, gallbladder cancer, and gastric carcinoma) sample sets with a high degree of quantitative accuracy. This study highlights the potential of utilizing pre-trained models and synthetic datasets to advance DIA proteomics analysis.

systems biology↗

bioRxiv2024

Orthogonal spectral and temporal envelope representation in auditory cortex

Speech perception relies on two fundamental acoustic components: spectral and temporal. While spectral information is known to be represented in the auditory cortex through tonotopy, how temporal features are organized has remained unclear. Here, by varying rise-ramp steepness and frequencies, we reveal that the steepness of the temporal envelope--a critical cue for phonemes discrimination and sound source perception--is systematically mapped in the mouse auditory cortex. Using widefield calcium imaging, we discovered that the envelope steepness is represented orthogonally to the tonotopic axis, forming a two-dimensional cortical map that mirrors the dual structure of sounds. This organization was observed in primary-like auditory regions but not in higher-order-like areas, indicating distinct auditory processing streams. These findings uncover a principle of cortical organization, suggesting that the auditory cortex encodes sound along two independent axes and thereby provides a neural basis for parallel processing for complex sounds such as speech and natural acoustic environments.

neuroscience↗

bioRxiv2024

A novel approach for the quantification of single-cell adhesion dynamics from microscopy images

BackgroundCell adhesion, that is the ability to attach to a given substrate, is a key property of cancer cells, as it relates to their potential for dissemination and metastasis. The in vitro assays used to measure it, however, are characterized by several drawbacks, including low temporal resolution and limited procedural standardisation which reduce their usefulness and accuracy. ResultsIn this work, we propose an alternative analytical approach, based on live-cell imaging data, that yields comprehensive information on cell adhesion dynamics at the single-cell level. It relies on a segmentation routine, to identify the pixels belonging to each cell from time-lapse microscopy images acquired during the adhesion process. A tracking algorithm then enables the study of individual cell adhesion dynamics over time. The increased resolution afforded by this method was instrumental for the identification of cell division prior to attachment and the co-existence of markedly different proliferation rates across the culture, previously unidentified patterns of behaviour in the adhesion process. Finally, we generalize our method by substituting the segmentation algorithm of the instrument used to acquire the images, with a custom-made one, showing that this approach can be integrated within routine laboratory analytical procedures and does not necessarily require high-performance microscopy and imaging setups. ConclusionsOur new analytical approach improves the in vitro quantification of cell adhesion, enabling the study of this process with high temporal resolution and increased level of detail. The extension of the analysis to the single-cell level, additionally, uncovered the role of population variability and proliferation in this process. The simple and cost-effective procedure here described enables the accurate characterisation of cell adhesion. Beside improving our understanding of adhesion dynamics, its results could support the development of treatments targeting the ability of cancer cells to adhere to surrounding tissues.

cell biology↗

bioRxiv2024

Benchmarking copy number aberrations inference tools using single-cell multi-omics datasets

Copy number aberrations (CNAs) are an important type of genomic variation which play a crucial role in the initiation and progression of cancer. With the explosion of single-cell RNA sequencing (scRNA-seq), several computational methods have been developed to infer CNAs from scRNA-seq studies. However, to date, no independent studies have comprehensively benchmarked their performance. Herein, we evaluated five state-of-the-art methods based on their performance in tumor vs normal cell classification, CNAs profile accuracy, tumor subclone inference and aneuploidy identification in non-malignant cells. Our results showed that Numbat outperformed others across most evaluation criteria, while CopyKAT excelled in scenarios when expression matrix alone was used as input. In specific tasks, SCEVAN showed the best performance in clonal breakpoint detection and Numbat showed high sensitivity in copy number neutral LOH (cnLOH) detection. Additionally, we investigated how referencing settings, inclusion of tumor microenvironment cells, tumor type, and tumor purity impact the performance of these tools. This study provides a valuable guideline for researchers in selecting the appropriate methods for their datasets.

bioinformatics↗

bioRxiv2024

Integral-Omics: serial extraction and profiling of metabolome, lipidome, genome, transcriptome, whole proteome and phosphoproteome using biopsy tissue

The integrative multi-omics characterization of minute amount of clinical tissue specimens has become increasingly important. Here, we present an approach called Integral-Omics, which enables sequential extraction of metabolites, lipids, genomic DNA, total RNA, proteins, and phosphopeptides from a single biopsy-level tissue specimen. We benchmarked this method with various samples and applied the workflow to perform multi-omics profiling of tissues from six patients with colorectal cancer and found that tumor tissues exhibited suppressed ferroptosis pathway at multi-omics levels. Together, this study presents a methodology that enables sequential extraction and profiling of metabolomics, lipidomics, genomics, transcriptomics, proteomics and phosphoproteomics using biopsy tissue specimens.

biochemistry↗

bioRxiv2024

Fat body-derived cytokine Upd2 controls disciplined migration of tracheal stem cells in Drosophila

Coordinated activation and directional migration of adult stem cells are essential for maintaining tissue homeostasis. Drosophila tracheal progenitors are adult stem cells that migrate posteriorly along the dorsal trunk to replenish degenerating branches that disperse the FGF mitogen. However, it is currently unknown how the overall anterior-to-posterior directionality of such migration is controlled. Here we show that individual progenitor cells migrate together in a concerted, disciplined manner, a behavior that is dependent on the neighboring fat body. We identify the fat body-derived cytokine, Upd2, in targeting and inducing JAK/STAT signaling in tracheal progenitors to maintain their directional migration. Perturbation of either Upd2 production in fat body or JAK/STAT signaling in trachea causes aberrant bidirectional migration of tracheal progenitors. We show that JAK/STAT signaling promotes the expression of genes involved in planar cell polarity leading to asymmetric localization of Fat in progenitor cells. We provide evidence that Upd2 transport requires Rab5- and Rab7-mediated endocytic sorting and Lbm-dependent vesicle trafficking. Our study thus uncovers an inter-organ communication in the control of disciplined migration of tracheal progenitor cells, a process that requires vesicular trafficking of fat body-derived cytokine Upd2 and JAK/STAT signaling-mediated activation of PCP genes.

developmental biology↗

bioRxiv2024

Identification of Multi-landscape and Cell Interactions in the Tumor Microenvironment through High-Coverage Single-Cell Sequencing

Single-cell RNA sequencing (scRNA-seq) is a widely used method for classifying cell types and states and revealing disease mechanisms. However, most contemporary scRNA-seq platforms fail to explore the multilandscape of RNA. Here, we designed a microfluidic chip that combines oligo-dT primers and Random Bridging Co-labeling (RBCL) RNA sequencing to develop an innovative Chigene scRNA-seq technology that can identify gene expression, mutations, and RNA splicing landscapes at the single-cell level. The Chigene scRNA-seq platform demonstrated exceptional performance, with minimal doublet rates of 0.94% (Chigene V1) and 1.93% (Chigene V2). Both versions exhibit high sensitivity, with Chigene V2 achieving nearly 100% RNA coverage and detecting over 1800 genes per cell on average. Targeted capture of single-cell gene mutations enhances mutation detection sensitivity. Moreover, this Chigene V2 platform has been validated in clinical samples for its ability to detect mutations, gene fusions and alternative splicing. The reliability of the platform was further corroborated via known functional gene mutation (CDKN1A) and fusion (FGFR3-TACC). To validate this methods potential for discovering novel gene mutations in clinical samples, our investigation revealed an intriguing cell subpopulation carrying an ARHGAP5 mutation in urothelial carcinoma. These cells exhibited high-frequency mRNA splicing and exhibited specific crosstalk with T cells, distinguishing them from the subpopulation with the ARHGAP5 wild-type phenotype. Overall, this method provides a robust scRNA-seq platform suitable for comprehensive analyses of clinical specimens at different genetic information levels, thereby offering significant potential in the discovery of novel genes and interactions at the single-cell level.

genomics↗