Biology subjects

Zhao, W.

Publications and source records attributed to Zhao, W..

At least 73 records · Page 4Linked to original sources

bioRxiv2025

Autofluorescence imaging reveals the impact of cryopreservation on T cell metabolism and activation response

Cryopreservation or the process of freezing cells is a cornerstone of most cell therapy protocols. Optimization of cryopreservation protocols and cryoprotectant agents to improve cell viability and functionality is under further investigation. However, the impact of cryopreservation on cellular metabolism and function immediately post-thaw is not fully understood. Here, we used label-free, non-invasive optical metabolic imaging (OMI) of NAD(P)H and FAD to characterize the activation response of frozen T cells from healthy donors and lymphoma patients post-thaw. Using OMI, we identified significant metabolic shift, along with delayed and diminished activation response in healthy donor T cells throughout the first 4.5 hours upon thawing. In cryopreserved peripheral T cells from lymphoma patients in our bispecific CD19/CD20 CAR T therapy clinical trial, OMI could identify early metabolic stress and allowed gating of metabolically-fit cells associated with post-thaw viability. Notably, in our pilot study, only metabolically-fit T cells from complete responders exhibited metabolic responses to activating stimuli within the first 4.5 hours post-thaw. Overall, our findings suggest that 4-5 hours post-thaw is a critical time window to assess the impact of cryopreservation and thawing, and support the potential of OMI to optimize cryopreservation protocols and evaluate patient T cell quality for cell therapy.

immunology↗

bioRxiv2025

CycleGRN: Inferring Gene Regulatory Networks from Cyclic Flow Dynamics in Single-Cell RNA-seq

Oscillatory processes such as the cell cycle play critical roles in cell fate determination and disease development, yet existing gene regulatory network (GRN) inference methods often fail to account for their dynamic nature. We propose CycleGRN, a novel framework that treats cell cycle gene expression observations as an invariant measure of a stochastic differential equation and learns from data a dynamical system that fits cycling biological processes. Using a directed graph constructed along the inferred flow field in the cell space, we estimate Lie derivatives for all genes, enabling velocity inference beyond the cell cycle subspace. To quantify regulatory interactions, we introduce a time-lagged correlation operator between any pair of genes supported on the flow-aligned directed graph, which respects the intrinsic geometry of the data manifold and allows temporal ordering consistent with the underlying oscillatory process. The method requires only raw gene expression data at single-cell resolution and a list of cycle genes, without temporal binning or splicing dynamics. We evaluate our method on four synthetic datasets generated from mechanistic models with known network structures with oscillatory subnetworks, and on a mouse retinal progenitor single-cell RNA-seq dataset spanning three cell types and a knockout condition. Across all settings, our method consistently ranks among the top-performing approaches and demonstrates strong recovery of oscillatory and directional interactions.

bioinformatics↗

bioRxiv2025

Astrocytic FABP5 drives non-cell-autonomous oligodendrocyte injury in multiple system atrophy by promoting TNF signaling and ferroptotic stress

Multiple system atrophy (MSA) is a fatal -synucleinopathy characterized by progressive parkinsonism, cerebellar and autonomic dysfunction. Currently, the mechanisms driving cerebellar white matter neuroinflammation and degeneration in MSA are poorly understood. Here, we identified fatty acid-binding protein 5 (FABP5) as a key factor regulating cerebellar inflammation in MSA pathogenesis in a detailed study of human, mouse and cultured astrocytes. Firstly, transcriptomic profiling of human MSA cerebellar white matter revealed activation of pro-inflammatory and ferroptotic pathways, with FABP5 identified as a key pathway component that is upregulated. We confirmed that FABP5 is upregulated in reactive astrocytes in the PLP--syn transgenic mouse model, also in LPS-treated primary astrocytes. Fabp5 silencing suppressed TNF signaling, mitigated ferroptosis, and restored mitochondrial function. These findings suggest astrocytic FABP5 as a central intracellular regulator linking glial inflammation, ferroptosis, and mitochondrial injury. Overall, this mechanism suggests that FABP5 drives pathology mediated by astrocytes oligodendroglia in MSA, therefore representing a novel and promising therapeutic target.

neuroscience↗

bioRxiv2025

Inferring the regulation dynamics of oscillatorynetworks from scRNA-seq data

Oscillatory processes such as the cell cycle play critical roles in cell fate determination and disease development. Yet, most current gene regulatory network (GRN) inference methods are based on gene-gene correlations or temporal progression, not adequately accounting for the recurrence in cyclic processes. We hypothesize that constraining the continuous ordering of relative positions along the cell cycle can enhance GRN inference accuracy of cell cycle regulation. To test performance, we evaluated eight representative methods and applied three of them to a mouse retinal progenitor single-cell gene expression dataset [1]. Incorporating cell cycle positions inferred by Tricycle [2] led to significant improvements compared against using experimental times, particularly for early progenitor cells that been hypothesized to be more intrinsically driven by cell cycle regulation. These findings highlight the promise of integrating oscillatory processes into causal inference frameworks to advance our understanding of gene regulation.

bioinformatics↗

bioRxiv2025

Fast and interpretable quantification of biological shape heterogeneity via stratified Wasserstein kernel

Modern imaging technologies produce vast collections of cellular and subcellular structures, calling for principled methods that enable shape comparison across individuals and populations. We introduce the stratified Wasserstein framework, which treats each shape as an unstructured point cloud and embeds it into Euclidean space via ranked local distance profiles. This embedding yields an isometry-invariant Euclidean distance and a positive-definite kernel for population analysis, with a consistent sample-based estimator that supports large datasets in near-quadratic time. By leveraging kernel methods, the framework enables statistically rigorous tasks such as nonparametric hypothesis testing, providing theoretical guarantees as well as interpretability. We demonstrate our frameworks applicability to large-scale biological datasets. Analyzing 2D cancer cell contours, we quantify population-level discrepancies and identify representative cells contributing most strongly to the observed differences. Using 3D volumes of cell envelope and nucleus, we reveal progression patterns that capture morphological changes across cell populations both at the level of individual shapes. These results establish a simple and principled tool for population-level biological shape analysis, with potential impact across diverse domains of computational imaging and data science.

bioinformatics↗

bioRxiv2025

A novel allele of Sh1 underlines the conversion of waxy corn to wx-sweet corn

AbstractWaxy corn and sweet corn are two major types of fresh-eating corn. Each has significant market value due to its unique sensory and nutritional profiles. Developing a variety that combines both traits would meet growing consumer demand and create new market opportunities. From a fast neutron-mutagenized population of the waxy corn inbred line HB522, we identified a mutant, wx-sweet, whose kernels exhibit both waxy and sweet properties at the filling stage. Using bulked segregant analysis (BSA) and fine mapping, we localized the causal locus to SHRUNKEN1 (Sh1) on chromosome 9, which was confirmed by an allelic test with a known Mu-insertion mutant. PCR and sequencing indicated a putative large structural variation in the second and third exons of Sh1 in wx-sweet, likely disrupting the reading frame. This novel sh1 allele significantly reduced sucrose synthase activity. The sh1 and waxy1 mutant genes act synergistically to remodel carbohydrate metabolism in wx-sweet endosperms. This remodeling, revealed by integrated transcriptomic and metabolomic analyses, drives transcriptional reprogramming and restructures metabolic flux. These changes thereby enhance sucrose and raffinose accumulation, which underlies the unique waxy-sweet texture in fresh-eating maize endosperm. In summary, our work not provides valuable genetic resources for fresh-eating corn breeding, but also elucidates, for the first time, the molecular mechanism underlying the synergistic formation of waxy and sweet texture. Key messageThe novel allele of Sh1 acts synergistically with waxy1 to regulate carbohydrate metabolism, which elucidates the molecular mechanism for the unique waxy-sweet texture in fresh-eating corn endosperm. Our work provides new strategies for sweet-waxy maize breeding.

genetics↗

bioRxiv2025

An automated image analysis pipeline for wide-field optical redox imaging of patient-derived cancer organoids

Wide-field optical redox imaging provides a fast and accessible method to monitor metabolic changes in cells and has recently been developed for drug screening in patient-derived cancer organoids (PDCOs). However, manual analysis of wide-field optical redox images is inefficient and laborious for large-scale drug screens. Here, we developed an automated pipeline for PDCO segmentation, single-PDCO tracking, and background correction in autofluorescence images. This pipeline was tested on two imaging systems over a 3-day time-course with two drug doses to demonstrate generalizability across imaging systems. Segmentation was performed using a fine-tuned Cellpose model, which when compared to manual masks, achieved mean Dice scores >0.8 across systems, indicating high reproducibility. Automated single-PDCO tracking was compared to manual tracking and the accuracy of the tracking algorithm exceeded 94% by two metrics, recall and Jaccard index. For background correction, the automated pipeline uses the full field-of-view to reduce sampling bias. Compared to the manual analysis pipeline, the automated pipeline resolves single-PDCO responses with comparable sensitivity to drug treatment but with over 127x faster processing time. This novel automated image analysis pipeline improves throughput and robustness in PDCO image analysis, which increases the accessibility and scalability of wide-field optical redox imaging for PDCO drug screening.

bioengineering↗

bioRxiv2025

Benchmarking Perturbation Tools for the Noncoding Genome

Deciphering the functionality of the noncoding genome which includes important cis-regulatory elements (CREs) and transcribed noncoding RNA genes remains technically challenging. Here, using massively parallel genetic screening, we systematically benchmark the performance of five representative loss-of-function perturbation tools, including single guide RNA (gRNA) mediated SpCas9 cleavage or CRISPR interference, and paired gRNA (pgRNA) involved dual-SpCas9, Big Papi (paired SpCas9 and SaCas9) or dual-enAsCas12a fragment deletion methods, in decoding the roles of the noncoding genome. For targeting CREs such as enhancer, dual-SpCas9 outperforms other methods with superior efficiency of destroying functional genomic regions. For perturbing noncoding RNA genes, in addition to dual-SpCas9, other RNA-targeting methods such as RNA interference are recommended to discriminate transcript-dependent or -independent roles. A deep learning model DeepDC with associated web server is built to facilitate optimal dual-SpCas9 pgRNA design for efficiently deleting a genomic fragment. Together, our work provides practical guidance on selecting appropriate loss-of-function tools to resolve the functional complexity of the noncoding genome.

genetics↗

bioRxiv2025

Auditory Stimulus Information Entropy Modulates Inter-Brain Synchronization: Evidence from Wireless EEG Hyperscanning

Inter-brain synchronization (IBS) - reflecting inter-individual correlated neural activity during interaction - marks shared experiences like music listening. The ability of complex auditory stimuli (e.g., music) to induce IBS links to their information dynamics, notably the uncertainty they evoke, which challenges the nervous systems predictive coding. Based on mutual prediction theory (interacting individuals simultaneously process their own behavior and predict their partners; accurate mutual predictions lead to convergent neural representations and thus IBS), this study hypothesized that higher stimulus uncertainty enhances IBS (heightened uncertainty reduces independent predictability, promoting convergent representations and stronger IBS). Using information entropy to quantify uncertainty, the study conducted hyperscanning, manipulated entropy across Resting State and 6 Hz auditory stimuli (ASSR, MMN, AHER, Dream Wedding), and measured IBS via phase-locking values (PLV). Results showed frequency specificity: 6 Hz PLV increased with entropy (DW {approx} AHER > MMN {approx} ASSR > Resting State); Alpha band had highest PLV in Resting State. Critically, PLVs differed significantly between any two conditions, and each experimental conditions PLV was also significantly different from that of the Resting State. Findings confirm a 6 Hz-specific positive association between auditory uncertainty and IBS, suggesting musical elements may facilitate social interaction by modulating entropy, with entropy-IBS relations showing frequency dependence.

neuroscience↗

bioRxiv2025

Joint analysis of expression and variation at single cell resolution by scVar

Tumor heterogeneity represents a critical determinant affecting both cancer diagnosis and therapeutic efficacy. Traditional bulk sequencing approaches are limited in their ability to resolve genomic alterations at the resolution of individual cells. In this study, we present scVar, an integrated analytical framework designed for mutation profiling using single-cell transcriptomic data. scVar enables sensitive and robust detection of single-nucleotide variants, particularly facilitating the identification of low-frequency variants. The framework incorporates customizable filtering parameters including variant allele frequency, and provides comprehensive functional and clinical annotations for identified mutations. Furthermore, scVar includes downstream analytical modules that facilitate the joint investigation of transcriptomic and mutational profiles within single cells. Benchmarking on simulated datasets and matched tumor samples demonstrates that scVar consistently outperforms existing methods, especially in detecting variants of low abundance, and shows strong concordance with whole-exome sequencing data. Application of scVar to non-small cell lung cancer samples effectively characterized ITH across spatially distinct tumor regions and diverse cellular populations. Collectively, scVar offers an integrated platform for concurrent analysis of somatic mutations and transcriptomic data derived from single-cell RNA sequencing, providing a powerful tool for elucidating tumor evolution and the complex interplay between genomic heterogeneity and the tumor microenvironment.

bioinformatics↗

bioRxiv2025

FLIM Playground: An interactive, end-to-end graphical user interface for analyzing single-cell fluorescence lifetime data

Fluorescence lifetime imaging microscopy (FLIM) is a cellular-resolution molecular imaging technique. Yet, the journey from raw photon decays to biological insight remains fragmented by multi-step data extraction and siloed analyses. This work presents FLIM Playground, the first interactive graphical platform that unifies single-cell FLIM workflows, embeds user checks at each stage, and offers diverse user options. Built in Python and available open-source, FLIM Playground runs on major operating systems as a ready-to-run application and is web deployable. Its Data Extraction section collects and checks field-of-view metadata, calibrates via instrument response function shift or fluorescence lifetime standard, and extracts single-cell fluorescence lifetime features, along with morphology and texture features across channels. Multiple datasets can be merged through an interface that assigns categorical labels. The Data Analysis section provides real-time visual analytic modules for outputs from Data Extraction or user-provided datasets. Lifetime extraction by fitting and phasor were validated by comparison with a commercial software and published results, respectively, and both sections were demonstrated on a FLIM dataset of cancer cell lines to obtain biological insights. By adopting best practices and offering interactivity, FLIM Playground accelerates hypothesis-driven discovery and promotes reproducibility, and its modular design can incorporate new imaging modalities, extraction methods, and analysis modules.

bioengineering↗

bioRxiv2025

Mass spectrometry-based proteomic and glycoproteomic drafts of fifteen human body fluids

The study of proteins and N-glycosylation in human body fluids (HBFs) is crucial for understanding both physiological and pathological states, offering valuable insights for disease diagnostics and monitoring. In this study, we present a comprehensive proteomics and glycoproteomics dataset featuring 15 types of precious HBFs (plasma (P), urine (U), cerebrospinal fluid (CSF), pleural fluid (PF), ascitic fluid (AF), synovial fluid (SF), pericardial fluid (PCF), peritoneal dialysis effluent (PDE), bile (B), gastric juice (GJ), seminal plasma (SP), saliva (SA), pancreatic juice (PJ), sweat (SW), and tear (T)). We also provide detailed protocols for protein processing specific to each type of HBF. The resulting peptides and enriched intact N-glycopeptides were analyzed via liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data independent acquisition (DIA) and combined electron transfer/higher-energy collisional dissociation and stepped collision energy/higher-energy collisional dissociation (EThcD-sceHCD) mode, respectively. The resulting spectra and searched files have been uploaded to the online repository iProX (www.iprox.cn) and made easily accessible (PXD068799) to facilitate further research into HBF proteomics and glycoproteomics.

systems biology↗

bioRxiv2025

Memory dysfunction and psychiatric outcomes in anti-NMDA receptor encephalitis are linked to altered structural brain complexity

IntroductionMost patients with anti-N-methyl-D-aspartate receptor encephalitis (NMDARE) experience long-term neuropsychiatric sequelae despite immunotherapy. However, how these residual symptoms relate to structural brain changes in the post-acute phase remains unclear. Recently, fractal dimensionality (FD) has emerged as a sensitive imaging marker of structural brain complexity in related conditions but has not been explored in autoimmune encephalitis. MethodsThis cross-sectional study combined clinical, cognitive, and neuroimaging analyses in 70 patients with post-acute NMDARE (median time from onset: 22 months), and 70 healthy controls matched for age (t=-0.57, p=0.57) and sex ({chi}2=0, p=1). High-resolution T1-weighted magnetic resonance imaging (MRI) data were analyzed with FreeSurfer and computational fractal analysis. Clinical outcomes were assessed with the modified Rankin Scale (mRS) and Clinical Assessment Scale in Autoimmune Encephalitis (CASE). Psychiatric manifestations underwent phenotypical analyses, and memory performance was assessed with standardized neuropsychological tests. ResultsPatients with NMDARE were severely affected at peak illness but showed substantial overall improvement in the post-acute stage (median mRS at peak=5, post-acute=1, z=-7.2, p<0.001; median CASE at peak=11, post-acute=1, z=-7.2, p<0.001). However, 67% of patients showed residual CASE symptoms, with memory dysfunction (61%) and psychiatric symptoms (36%) representing the most prevalent domains. Therein, psychiatric symptoms showed a phenotypical shift from a schizophrenia-like phenotype at peak illness to an affective phenotype in the post-acute stage. Neuroimaging uncovered a characteristic pattern of reduced structural brain complexity, including the hippocampus bilaterally and a fronto-cingulo-temporal cluster in cortical gray matter and cerebral white matter. Importantly, normative modeling revealed that patients with residual symptoms showed stronger alterations of brain complexity than those without (psychiatric: t=-2.65, p=0.010; memory: t=-3.98, p<0.001; both vs. no symptoms: t=-5.46, p<0.001). Similarly, patients with stronger alterations of brain complexity showed lower scores of visuospatial and verbal memory (all pFDR<0.015). DiscussionPost-acute NMDARE is characterized by systematic reductions in structural brain complexity, consistently involving previously implicated regions while identifying changes in the cingulate cortex as a new morphological correlate. These changes are linked to residual psychiatric symptoms and memory dysfunction, highlighting FD as a promising new imaging marker of long-term outcomes. Our findings suggest that current treatment strategies may be insufficient to fully address the residual symptom burden after the acute phase of NMDARE.

neuroscience↗

bioRxiv2025

Sleep deprivation exhibits age-dependent effect on infra-slow global brain activity

Infra-slow (< 0.1 Hz) global brain activity, quantified by the global mean BOLD (gBOLD) signal in resting-state fMRI, is elevated during sleep and closely associated with cerebrospinal fluid (CSF) dynamics, a key mechanism for the brain waste clearance implicated in neurodegenerative disorders such as Alzheimers disease (AD). However, the effect of sleep deprivation on gBOLD activity and its interaction with aging remain poorly understood. Using a rigorously controlled in-laboratory total sleep deprivation (TSD) protocol, we demonstrate that TSD significantly increases both the amplitude of the gBOLD signal and its coupling with CSF flow, suggesting a compensatory mechanism that may enhance glymphatic clearance following acute sleep loss. Notably, these TSD-induced enhancements exhibit robust age dependency, with diminished responses in individuals at midlife (40-50 yrs). The absence of this compensatory mechanism in these midlife participants may exacerbate age-related impairments in neurotoxic clearance and increase dementia susceptibility, thereby offering mechanistic insights into the nexus between sleep disruption, aging, and neurodegeneration.

neuroscience↗

bioRxiv2025

Effects of Nanopore Confinement on the Conformational, Dynamical, and Self-Assembly Properties of an FG-Repeat Peptide

The central channels of nuclear pore complexes (NPCs) in eukaryotic cells are filled with protein chains whose sequences contain characteristic phenylalanine-glycine motifs. Properties of these nanopore-confined FG-repeat sequences are of central importance to NPC function. Here we demonstrate an approach to nuclear magnetic resonance (NMR) studies of FG-repeat sequences (or other polypeptides) that are tethered within pores with diameters similar to those of NPC channels. By attaching alkyl phosphonate groups to the N-terminus of a 30-residue peptide that contains four FG repeats, called FG30, we tether FG30 chains to walls of 20-nm-diameter pores in anodic aluminum oxide (AAO) wafers through phosphonate-surface bonds. Quantitative 13C and 31P NMR measurements indicate 90 mM peptide concentrations (300 mg/ml) within the pores. NMR spectra and spin relaxation measurements show that FG30 chains are dynamically disordered and random-coil-like in buffer-filled pores over a broad temperature range. In contrast, FG30 aggregates in free solution at concentrations above 2 mM, forming structurally ordered fibrils according to electron microscopy and NMR measurements. These results demonstrate the utility of AAO as a scaffold for studies of polypeptides in nanopore-confined environments and show that tethering to nanopore walls can dramatically alter the self-assembly properties of an FG-repeat sequence. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=141 SRC="FIGDIR/small/675621v2_ufig1.gif" ALT="Figure 1"> View larger version (92K): org.highwire.dtl.DTLVardef@7e0287org.highwire.dtl.DTLVardef@d81553org.highwire.dtl.DTLVardef@601639org.highwire.dtl.DTLVardef@52fc7a_HPS_FORMAT_FIGEXP M_FIG C_FIG

biophysics↗

bioRxiv2025

OmniExtract: An automatic data extraction tool based on Large Language Model and Prompt Engineering

Extracting structured information from documents or scientific papers is crucial for data sharing and retrieval. Recently, Large Language Model (LLM) has shown its impressive ability in text understanding and several tools based on LLM has been developed. However, its still difficult to find a universal and user-friendly tool for various practical extraction tasks. To address this challenge, we propose OmniExtract, an automatic data extraction tool with user-friendly configuration files which can adapt to various data extraction tasks. OmniExtract uses a prompt optimized engineering to improve prompt and obtain high performance, and it can support a comprehensive data extraction including text and tables. Evaluation results show that OmniExtract obtains a high accuracy over 80% for 3 datasets. Furthermore, two additional data extraction applications using OmniExtract have been provided, achieving an accuracy of 92.21% and an average F1 score of 0.83 respectively. The data reliability performance shows that OmniExtract is a valuable tool for database updating.

bioinformatics↗

bioRxiv2025

Curing autoimmune diabetes with islet and hematopoietic cell transplantation after CD117 antibody-based conditioning

Mixed hematopoietic chimerism after allogeneic hematopoietic cell transplantation (HCT) promotes tolerance of transplanted donor-matched solid organs, corrects autoimmunity, and could transform therapeutic strategies for autoimmune type 1 diabetes (T1D). However, development of non-toxic bone marrow conditioning protocols is needed to expand clinical use. We developed a chemotherapy-free, non-myeloablative (NMA) conditioning regimen that achieves mixed chimerism and allograft tolerance across MHC barriers in NOD mice. We obtained durable mixed hematopoietic chimerism in prediabetic NOD mice using anti-c-Kit monoclonal antibody, T-cell depleting antibodies, JAK1/2 inhibition, and low-dose total body irradiation prior to transplantation of MHC-mismatched B6 hematopoietic cells, preventing diabetes in 100% of chimeric NOD:B6 mice. In overtly diabetic NOD mice, NMA conditioning followed by combined B6 HCT and islet transplantation durably corrected diabetes in 100% of chimeric mice without chronic immunosuppression or graft-versus-host disease (GVHD). Chimeric mice remained immunocompetent, as assessed by blood count recovery and rejection of 3rd party allogeneic islets. Adoptive transfer studies and analysis of autoreactive T cells confirmed correction of autoimmunity. Analysis of chimeric NOD mice revealed central thymic deletion and peripheral tolerance mechanisms. Thus, with NMA conditioning and cell transplantation, we achieved durable hematopoietic chimerism without GVHD, promoted islet allograft tolerance, and reversed established T1D. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=197 SRC="FIGDIR/small/673576v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@23fcdorg.highwire.dtl.DTLVardef@1aac97borg.highwire.dtl.DTLVardef@191e06forg.highwire.dtl.DTLVardef@25be2b_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2025

Merits and challenges of plasma proteomics on association replicability

Developments in proteomic platforms have enabled the generation of large-scale high-throughput plasma proteomics data [1-3]. With recent breakthroughs in AI modelling, these data have significantly enhanced our understanding of molecular mechanisms underlying human behaviors and diseases [4-6]. However, the replicability of associations between plasma proteomics and phenotypes remains underexplored. Here, we systematically assessed the replicability of associations with recent plasma proteomics data in the UK biobank. Over 75% of cognitive function and mental health traits demonstrated high overall (proteomics-wide) replicability when brain-related traits were considered as phenotypes. Although mean cortical thickness (CT) as phenotype exhibited clearly reduced replicability, total cortical surface area (CSA) and cortical volume (CV) showed high overall replicability across hemispheres and over twenty brain regions. In comparative multi-omics analyses based on the same cohort of participants, proteomics outperformed genomics across all brain-related traits, and exceeded metabolomics for over half of traits where metabolomics also exhibited high overall replicability. Furthermore, we developed a predictive framework to estimate the replicability for potential future proteomics panels based on the crucial influential factors including dilution level, proportion of samples below the limit of detection (LOD), and sample size. Moreover, we constructed an individual replicability index for proteins and identified eleven proteins with highly replicable associations across cognitive function and mental health traits, which was in line with the recent identifications of pleiotropic proteins in large-scale population studies. Collectively, our results revealed the challenges in the association replicability of plasma proteomics under reduced data quality (from "Explore" to "Expansion" assay panels), and we further explored how to sustain high replicability in potential future panels. Fundamentally, our findings affirm the merits of plasma proteomics: this molecular omics platform enables highly replicable associations for mapping biomedical signatures.

bioinformatics↗