Biology subjects

Liang, D.

Publications and source records attributed to Liang, D..

At least 19 recordsLinked to original sources

bioRxiv2026

Proteomic Characterization of Prostate Cancer Bone Metastasis Subtypes Defines Progressive Remodeling States of the Bone-Tumor Microenvironment

Prostate cancer (PC) bone metastases (PCBM) are a debilitating disease morbidity that primarily affects the axial skeleton. PCBM are predominantly osteosclerotic with mixed osteosclerotic/osteolytic features, including irregular bone deposition, loss of collagen alignment and increased porosity that mimic features of woven bone formed during development and repair. We hypothesize that progression of PCBM is driven by interactions between tumor cells and the bone microenvironment and is associated with changes in the stromal compartment. We performed tandem mass tag mass spectrometry proteomics on lumbar vertebral specimens from 32 cadaveric PCBM specimens and 3 age-matched controls. Unsupervised clustering segregated specimens into three clusters associated with benign, mixed, and osteosclerotic lesion states, characterized by distinct immune-, tumor-, and osteogenic-associated protein signatures. Histological analysis showed tumor infiltration was accompanied by marrow fibrosis and increased collagen III deposition. We identified a marker signature associated with osteosclerotic remodeling and mapped this signature to independent metastatic PC transcriptomic datasets. The proteomics-derived markers reproduced similar molecular groupings at RNA level, and robustly segregated bone PCBMs from soft tissue metastases PC in RNA data. Together, these findings define molecularly distinct states of tumor-driven changes in PCBM that extend beyond histological classification and may inform biomarker development and therapeutic stratification.

cancer biology↗

bioRxiv2026

Stage-specific proteomic signatures of synaptopathy in Alzheimers disease

Synapse loss is the strongest pathological correlate of cognitive decline in Alzheimers disease, yet the molecular basis of synaptic vulnerability across disease progression remains poorly understood. We performed proteomic profiling of isolated synaptoneurosomes from post-mortem middle temporal gyrus tissue from 67 individuals, comprising 40 individuals spanning all Alzheimers disease Braak stages and 27 non-diseased controls. Differential abundance and weighted gene co-expression network analyses were combined with pathway, cell-type and gene set variation analyses to characterise stage-associated changes in the synaptic proteome. Brain-derived candidate proteins were subsequently evaluated using receiver operating characteristic and regression analyses against cognitive examination scores in two independent plasma proteomics datasets. We identified six distinct stage-associated synaptic proteomic signatures. Mitochondrial function and synaptic signalling declined by mid-stage Alzheimers disease, while clathrin-mediated endocytosis increased at mid-stage without further change at later stages. By contrast, proteostasis and perisynaptic astrocytic processes increased progressively throughout the disease course. Global excitatory and inhibitory protein signals also showed a progressive shift in their relative balance (P = 7.80 x 10-3; adjusted R{superscript 2} = 0.072). These findings guided targeted investigation of candidate plasma markers linked to distinct synaptic processes in two independent proteomic datasets. We nominated 42 plasma proteins mapping to synaptic signatures of progression, which showed associations with disease stage and cognitive measures including Mini-Mental State Examination, Montreal Cognitive Assessment and Clinical Dementia Rating. PHGDH was prioritised across analyses through associations with cognitive impairment from early disease stages (Clinical Dementia Rating transition 0 to 0.5, false discovery rate = 8.87 x 10-18), neuropathological progression (P = 6.85 x 10-7) and excitatory-inhibitory protein imbalance (P = 5.56 x 10-3). Together, our findings define the stage-associated molecular changes at the AD synapse and connect distinct brain pathways to accessible plasma candidates, establishing a mechanistically anchored framework for future patient stratification and evaluation of synapse-directed therapies.

neuroscience↗

bioRxiv2026

Adversarial random forests for omics synthesis

Data availability is critical for understanding complex disease pathways and developing robust predictive models. Although high-throughput omics technologies have improved insight into disease mechanisms, data acquisition from inaccessible tissues such as the central nervous system remains a major limitation, causing small sample sizes and complicating early prediction of neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. Generative modeling has emerged as a powerful approach for synthesizing data to support downstream clustering and prediction with small sample size, but existing methods rarely handle high-dimensional tabular omics data effectively. Adversarial random forests (ARFs) provide a well-performing framework for tabular data generation but are not designed for high-dimensional settings. To address this limitation, we introduce high-dimensional ARF (h-ARF), an extension of ARF optimized for integrated clinical and high-dimensional omics data. Using benchmarks across nine datasets and eight performance metrics, we show that h-ARF better preserves both feature distributions, and downstream clustering and prediction utilities compared with ARFs. The method is implemented in the open-source R package harf, available on CRAN.

bioinformatics↗

bioRxiv2026

MSMICA: computational metabolite identification in untargeted metabolomics by integrating MS, retention time, and biological evidence

Mass Spectrometry Metabolomics Identification Connection Algorithm (MSMICA) is an algorithm for automated metabolite identification in untargeted liquid chromatography-high-resolution mass spectrometry (LC-HRMS) analyses. Limitations in metabolite identification can occur due to the availability and cost of standards and prevent recognition of metabolic factors impacting human health and disease. MSMICA performs mass-to-charge-ratio matching with chemical structures and clusters of LC-HRMS features for adduct and isotope forms. A local optimization is then used to integrate retention time prediction, metabolite precursor-product and transporter correlations, and biospecimen-specific abundance information for metabolite identification. Applying MSMICA to various internal and external mammalian datasets, validation results showed a 96.2 +- 5.1% correct rate of metabolite identification. When multiple LC-HRMS datasets were used, MSMICA enabled greater metabolite identifications, expanded metabolic pathway coverage, and data harmonization. Thus, MSMICA applies multiple pieces of evidence to substantially improve metabolite identification coverage and accuracy for known metabolites.

bioinformatics↗

bioRxiv2026

Development of a metabolomics-based index to monitor dietary effects on chronic inflammation: The Dietary Metabolomics Inflammation Index

BackgroundThe Dietary Inflammatory Index (DII) is widely used to assess the inflammatory potential of diet, but it relies on self-reported dietary assessment and does not directly capture individual differences in metabolism as an intermediate connection to inflammation. High-resolution metabolomics provides objective measurements that complement dietary assessment to support precision nutrition to control inflammation. ObjectiveWe developed, tested, and applied a Dietary Metabolite Inflammatory Index (DMII) to assess diet-related chronic inflammation using metabolites measured by liquid chromatography-high-resolution mass spectrometry. MethodsDII was calculated using dietaryindex R package with Block Food Frequency Questionnaire (FFQ) data. To develop the DMII, chronic inflammation-related dietary metabolites corresponding to the DII food parameters were found through a literature review. Dietary metabolites were identified and quantified by authentic standards by our established laboratory procedures. DMII uses the same inflammatory effect scores as the DII. Three DMII versions were developed: concentration-based, median-based, and quintile-based DMII. Mean and standard deviation of 29 dietary metabolites were calculated by using 3025 human plasma samples from 3 studies. DMII was tested in the Center for Health Discovery and Well-Being cohort (CHDWB) and the Atlanta African American Maternal and Child cohort (ATLAA) using chronic inflammation biomarkers, including high-sensitivity C-reactive protein (hs-CRP), CRP, and IL-6. The median-based DMII was further applied to four Alzheimers disease metabolomics datasets as a proof-of-concept application. ResultsIn the CHDWB study, concentration-based DMII had a weak positive correlation with Block FFQ-derived DII and strongly correlated with median-based and quintile-based DMII. In the same study, all three DMII versions had significant positive correlations with hs-CRP and IL-6. In the ATLAA study, only concentration-based DMII was positively associated with CRP and IL-6. Higher median-based DMII was associated with higher odds of Alzheimers disease. ConclusionsDMII provides a metabolomics-based framework for assessing diet-related chronic inflammation using metabolomics data. This metabolomics approach may complement self-reported dietary assessment to use diet and nutrition to help protect against chronic disease linked to inflammation.

biochemistry↗

bioRxiv2026

β-Catenin Drives Apical-Basal Polarization to facilitate TE lineage commitment In Vitro and In Vivo

Embryo polarization is critical for the first cell fate segregation. While mechanisms underlying its initiation have been described, the intrinsic signaling pathways that regulate this process remain poorly understood. Here, we show that mouse embryonic stem cells, when aggregated under defined conditions, recapitulate the first lineage segregation to generate trophectoderm (TE)-like cell populations and undergo self-organized morphogenesis into blastocyst-like structures. In the blastoid-forming medium, we identify CHIR99021 is essential for the generation of blastoids from both ESCs and totipotent-like cells. CHIR99021 promotes cell polarization and TE differentiation by activating the WNT/{beta}-catenin pathway and upregulating associated genes. Consistent with this, genetic ablation of {beta}-catenin abolished the cell polarization and disrupted blastoid formation from ESCs, a defect that was restored by {beta}-catenin overexpression. Moreover, {beta}-catenin depletion compromised cell polarization in natural embryos. Collectively, this study establishes the Wnt/{beta}-catenin as a critical regulator initiating polarization in vitro and in mouse early embryo development.

developmental biology↗

bioRxiv2026

IgG Propels Atherosclerosis by Noncanonically Activating Macrophages

Despite being a central component of adaptive immunity and a highly abundant serum protein, the contribution of IgG to the milieu of atherosclerosis remains unappreciated. Here, we identify a pro-atherogenic role for IgG as it activates an innate immune cascade, independent of its classical antigen-neutralizing function. Analyses of human coronary artery plaques reveal a positive correlation between IgG and cardiovascular and cerebrovascular disease severity. Integrated single-cell plaque analyses localize IgG, coinciding with its recycling receptor FcRn, to pro-inflammatory and foamy macrophages. Genetic ablation of FcRn in myeloid cells prevents IgG from accumulating in mouse atherosclerotic lesions, diminishing plaque size and inflammation. Mechanistically, IgG acts as an endogenous ligand for TLR4, triggering NF-{kappa}B-NLRP3 inflammasome signaling without requiring its antigen-binding domain. Additionally, IgG accelerates macrophage foam cell formation through upregulation of downstream effector LCN2. Our work uncovers a role for previously overlooked adaptive immune molecules in the pathogenesis of atherosclerosis through a noncanonical mechanism linked with innate immunity.

immunology↗

bioRxiv2026

Spatial mapping of RNA turnover kinetics and regulatory landscapes of mRNA stability in the mammalian brain

Spatial and activity-dependent gene regulation in the mammalian brain requires coordinated control of RNA synthesis and degradation 1,2, yet spatially resolved measurement of RNA turnover kinetics in complex tissues remains technically challenging 3. Here, we present spatial NT-seq, an approach that integrates transgenesis-free metabolic RNA labeling with in situ chemical recoding to spatially co-map RNA abundance and turnover kinetics in the mouse brain. By distinguishing newly synthesized from pre-existing RNAs, this method reveals spatially resolved transcriptional and post-transcriptional responses to electroconvulsive stimulation (ECS), a treatment for refractory depression. We uncover pronounced spatial heterogeneity in RNA turnover, with the dentate gyrus (DG) exhibiting elevated basal RNA turnover and robust ECS-induced responses. These findings reveal a "kinetics scaling" mechanism of coordinated regulation of RNA synthesis and decay, by which DG cells can rapidly remodel their transcript pools in responses to external stimuli or differentiation signals 4,5. Machine learning applied to in vivo RNA kinetics landscapes further identifies sequence features and post-transcriptional regulators underlying region-and cell-type-specific control of mRNA stability. Together, this integrated experimental and computational framework, in vivo Timescope, enables transcriptome-wide mapping of RNA turnover kinetics and the regulatory architecture of RNA stability across spatial and cellular contexts, providing new insights into the spatiotemporal regulation of RNA dynamics in brain function and disease.

genomics↗

bioRxiv2025

Integrating Longitudinal Metabolite Profiles Improves Trait Prediction in Pigs in a Trait- and Timepoint-Dependent Manner

BackgroundAccurate prediction of genetic merit is essential for accelerating genetic improvement in pigs, particularly for traits that are costly or difficult to measure directly. This study investigated the potential of integrating individual-level blood serum metabolite profiles sampled at two developmental stages (10-week and 20-week) into genomic prediction models for five economically important traits: average daily feed intake (DFI), feed conversion ratio (FCR), backfat thickness (BF), test daily gain (TDG), and loin depth (LD). Using a BayesC modeling framework, we analyzed 1,637 pigs from a single purebred population with complete phenotype, genotype, and metabolite-profile data. We evaluated seven models, including a genotype-only model (G), metabolite-only models using metabolite-profile data from 10-week (M1), 20-week (M2), or both timepoints (M1+M2), and combined models integrating genotypes with metabolite-profile data (G+M1, G+M2, G+M1+M2). ResultsHeritability estimates were generally low to moderate, ranging from 0.07 to 0.30 for the 10-week metabolite profile and from 0.04 to 0.28 for the 20-week metabolite profile. Prediction accuracy of phenotype consistently improved when metabolite-profile data were integrated into genotype-based models, although the magnitude of improvement varied depending on the trait and the timepoint of metabolite sampling. Prediction accuracy increased from 0.31 (G) to 0.41 (G+M2; G+M1+M2) for DFI, and from 0.27 (G) to 0.33 (G+M2; G+M1+M2) for FCR. The latter two models also delivered the largest gains over G for BF (from 0.41 to 0.45) and TDG (from 0.28 to 0.32). However, LD benefited the most when both 10-week and 20-week metabolite profiles were combined (G+M1+M2: 0.45 compared to G: 0.42). ConclusionsAcross all traits, models combining genotype data with metabolite profiles from one or multiple timepoints achieved the highest or equally high prediction accuracies compared to the genotype-only model, reflecting complementary biological insights captured by metabolite profiles. These findings highlight the potential value of metabolite-profile data as an intermediate omics layer to enhance genomic prediction, particularly when integration strategies are tailored to trait-specific biology and sampling timepoints.

genetics↗

bioRxiv2025

High-throughput functional characterization of enhancers in totipotent-like cells

Zygotic genome activation (ZGA) marks the initial transcription event in embryogenesis, yet the cis-regulatory mechanism remains unclear. Here, utilizing massively parallel reporter assays, we functionally dissect enhancers across mouse genome in DUX-induced 2C-like cells (2CLCs). Integrated analysis with epigenomic and transcriptomic data from 2CLCs and 2-cell (2C) embryos, active enhancers in totipotent cells are depicted. Among them, a notable proportion of promoters exhibit enhancer activities, showing elevated active chromatin features and correlating with enhanced gene expression during ZGA. Furthermore, only half of the MT2_Mm exhibit enhancer activities in 2CLCs. In addition, 2CLC enhancers augment transcription in 2C embryos. Finally, deleting enhancer regions in both 2CLCs and 2C embryos highlighted their crucial role in facilitating transcription of ZGA genes. These findings advance our comprehension of the cis-regulatory mechanism governing ZGA process.

developmental biology↗

bioRxiv2025

Human-specific features of the cerebellum and ZP2-regulated synapse development

Understanding the unique features of the human brain compared to non-human primates has long intrigued humankind. The cerebellum refines motor coordination and cognitive functions, contributing to the evolutionary development of human adaptability and dexterity. To identify shared and divergent features across primates, we conducted single-nucleus transcriptomic and chromatin accessibility profiling of the adult cerebellar cortex in humans, chimpanzees, macaques, and marmosets. We revealed human-specific transcriptomic and regulatory features, particularly those involved in synaptogenesis. Notably, we identified an enrichment of the sperm receptor zona pellucida glycoprotein 2 (ZP2) and its potential interactors, known for their roles in gamete interaction, in human granule cells. Experimental data show that ZP2 expression in human granule cells is induced by pontine mossy fibers, reducing synaptic proteins at pontocerebellar glomerular synapses, and decreasing cerebellar neuron electrophysiological activity. This unexpected co-option of ZP2 in human-specific synapse regulation provides insights into the evolutionary specialization of the human cerebellum.

neuroscience↗

bioRxiv2025

Depletion of extracellular asparagine impairs self-reactive T cells and ameliorates autoimmunity in a murine model of multiple sclerosis

Amino acids play critical roles in the activation and function of lymphocytes. Here we show that the non-essential amino acid, asparagine, is essential for optimal activation and proliferation of CD4+ T cells. We demonstrate that asparagine depletion at different time points after CD4+ T cell activation reduces mitochondrial membrane potential and function. Furthermore, asparagine depletion at specific time points during CD4+ T cell differentiation reduces cytokine production in multiple CD4+ T cell subsets. In an adoptive transfer model of experimental autoimmune encephalomyelitis (EAE), myelin oligodendrocyte-specific pathogenic T helper 17 cells differentiated under Asn-deficient conditions exhibited reduced encephalitogenic potential and attenuated EAE severity. In a model of EAE induced by active immunization, therapeutic depletion of extracellular Asn significantly reduced disease severity. These results identify asparagine as a key metabolic regulator of the pathogenicity of autoreactive CD4+ T cells and suggest that targeting asparagine metabolism may be a novel therapeutic strategy for autoimmunity.

immunology↗

bioRxiv2025

Highly sensitive and scalable time-resolved RNA sequencing in single cells with scNT-seq2

Understanding gene expression dynamics requires resolving newly synthesized RNAs from pre-existing pools at single-cell resolution. Here, we present scNT-seq2, a highly sensitive and scalable method for time-resolved single-cell RNA sequencing. By systematically optimizing the second-strand cDNA synthesis (2nd SS) step, we substantially improved read alignment rates, reduced background mutations, and enhanced library complexity compared to the original scNT-seq 1. Benchmarking in 4sU-labeled K562 cells demonstrated that scNT-seq2 accurately quantifies newly synthesized transcripts and preserves the gene level RNA turnover. The enhanced sensitivity enables robust detection of dynamic, cell-cycle state specific genes, such as S-phase regulators. Together, scNT-seq2 provides an efficient and versatile tool for dissecting transcriptional dynamics across diverse biological systems at single-cell resolution.

genomics↗

bioRxiv2025

A primate model organism for cardiac arrhythmias identifies a magnesium transporter in pacemaker function

Cardiac arrhythmias afflict tens of millions of people, causing one-fifth of all deaths1. Although mouse models have aided understanding of some pacemaker genes and arrhythmias2,3, mice are not known to naturally acquire arrhythmias, and the substantial differences between mouse and human cardiac anatomy and physiology have limited their utility in preclinical studies and pharmacological testing2,4. To establish a primate genetic model organism for arrhythmias, we carried out an electrocardiographic (ECG) screen of over 350 lab and wild mouse lemurs (Microcebus spp.), an emerging model organism that is among the smallest, fastest-reproducing, and most abundant primates5. Twenty-two lemurs (6.2%) were identified with eight different naturally-occurring arrhythmias resembling human ECG pathologies (SSS, PACs, Afib, PVCs, NSVT, STD, iTWs, STE). Pedigree construction showed two were familial, premature atrial contractions (PACs)/atrial fibrillation (Afib) and sick sinus syndrome (SSS), an episodic bradycardia. Genome sequencing of the SSS pedigree mapped the disease locus to a 1.4 Mb interval on chromosome 7 and supported autosomal recessive Mendelian inheritance. The most appealing candidate gene in the interval was SLC41A2, a little studied magnesium transporter6,7. SLC41A2 is expressed in human iPSC-derived sinoatrial node cells (iSANC) and localizes to the sarcoplasmic reticulum. Although mouse SLC41A2 knockouts do not show a cardiac pacemaker phenotype8, CRISPR-mediated SLC41A2 knockout altered human iSANC magnesium dynamics and slowed their calcium transient firing rate. The results suggest SLC41A2 functions cell autonomously and primate-specifically in cardiac pacemaker cells, and that intracellular magnesium dynamics have a crucial but previously unappreciated role in setting pacemaker rate. Thus, mouse lemur is a valuable model for discovering new genes, molecules, and mechanisms of the primate pacemaker, and for identifying novel candidate genes and therapeutic targets for human arrhythmias. The approach can be used to elucidate other primate diseases and traits.

genetics↗

bioRxiv2025

Sludge liquefaction and stratification enable ultra-high digestion rate and resource recovery for waste activated sludge treatment

Most of the energy, nitrogen/N and phosphorus/P entering wastewater treatment plants (WWTPs) accumulates in waste activated sludge (WAS). While these resources are theoretically recoverable through anaerobic digestion (AD), conventional sludge AD faces long-standing challenges of low organic loading rate and limited N/P recovery. Here, building on an innovative sludge liquefaction and stratification technique, we present a novel process entitled SPREAD that boasts 10 times higher organic loading rates and recovers 5 folds of N/P resources, relative to conventional sludge AD. We develop an anaerobic digestion database (ADDB) for multi-omics analyses of digestion microbiomes, and pinpoint the mechanism underlying the exceptionally high organic loading rate and remarkable N/P recovery in SPREAD. Importantly, we identify a negative correlation between the Gibbs free energy ({Delta}G) and the relative abundance of methanogens, enabling identification of bottleneck steps for stimulation and augmentation of methanogenic digestion. Our study provides a game-changing technology for the treatment and resource recovery from WAS and opens a new avenue for sustainable management and carbon/energy-neutrality of WWTPs.

bioengineering↗

bioRxiv2025

Structural and Functional Insights into GGCX-FIX Interaction: Implications for Vitamin K-Dependent Bleeding Disorders

Gamma-carboxylation, catalyzed by {gamma}-glutamyl carboxylase (GGCX), is a critical post-translational modification essential for the biological activity of vitamin K-dependent proteins (VKDPs). Mutations in GGCX, depending on their specific location, result in vitamin K-dependent coagulation factor deficiency type 1 (VKCFD1), which encompasses a broad spectrum of clinical manifestations ranging from mild to severe, including bleeding disorders, osteoporosis, and vascular calcification. The limited knowledge of GGCXs structure and functional regions hinders our understanding of the consequences of GGCX mutations and the treatment for VKCFD1. This study aimed to identify key functional regions of GGCX and their interactions with VKDPs to better elucidate the molecular mechanisms underlying these diverse clinical symptoms. Using AlphaFold 3 and molecular dynamics simulations, we developed a complex binding model of GGCX, FIX, and reduced vitamin K, which revealed critical regions and residues involved in their interaction. Site-directed mutagenesis and cell-based assays further validated the model, confirming that multisite and regional cooperative binding of FIX to GGCX plays a key role in modulating {gamma}-carboxylation efficiency. Additionally, novel residues (I296, M303, M401, M402) were identified as essential for GGCXs dual enzymatic activities: carboxylation and vitamin K epoxidation. We further demonstrated that the spatial proximity of these active sites supports the hypothesis that GGCXs carboxylation and vitamin K epoxidation centers are interconnected, ensuring the efficient coupling of these processes. Our GGCX-FIX binding and carboxylation model aligns with known pathogenic GGCX mutations, providing valuable insights into the molecular basis of coagulation disorders caused by GGCX mutants.

pathology↗

bioRxiv2024

NOLC1 Suppresses Immuno-chemotherapy by Inhibiting p53-mediated Ferroptosis in Gastric Cancer

Gastric cancer (GC) is one of the most malignant cancers, and cisplatin (Cis)-based chemotherapy remains the main clinical treatment for GC. However, Cis resistance often occurs, largely limiting its therapeutic efficacy in tumors. Therefore, a better understanding of the drug resistance mechanism could reveal new approaches for improving GC treatment efficacy. Here, we define the integrative role of nucleolar and coiled-body phosphoprotein 1 (NOLC1), a molecular chaperone that is significantly upregulated in GC tissues and Cis-resistant GC cells. Knocking down NOLC1 increased GC sensitivity to Cis by regulating ferroptosis. Mechanistically, NOLC1 binds to the p53 DNA binding domain (DBD), decreasing p53 nuclear accumulation stimulated by Cis and suppressing p53 transcriptional functions. Then, the p53-mediated ferroptosis is suppressed. Furthermore, the silence of NOLC1 promoted ferroptosis-induced immunogenic cell death (ICD) and reprogrammed the immunosuppressive tumor microenvironment, thereby increasing sensitivity to anti-programmed cell death-1 (PD-1) therapy plus Cis. The combination of anti-PD-1 plus Cis effectively inhibited GC growth without significant side effects. In summary, our findings reveal that targeting NOLC1 may be a novel therapeutic strategy for GC and may increase the efficacy of chemotherapy combined with immune checkpoint inhibitor (ICI) therapy.

cancer biology↗

bioRxiv2024

In vivo genome editing of human hematopoietic stem cells for treatment of blood disorders by mRNA delivery

Ex vivo autologous hematopoietic stem cells (HSCs) gene therapy provides promising new treatments for hematological disorders. However, current methods involve complex processes and chemotherapeutic conditioning, leading to limited accessibility for treatment and significant side effects. Here, we developed an antibody-free targeted lipid nanoparticles (LNPs) for mRNA delivery to HSCs in vivo, enabling efficient base editing of the HBG target in human HSCs to reactivate fetal hemoglobin in derived erythroid cells. Delivery of ABE8e/sgRNA mRNA with optimized structure LNPs achieves efficient in vivo base editing of HBG in transfusion-dependent {beta}-thalassemia (TDT) patients derived HSCs engrafted in immunodeficient NCG-X mice, showing restored globin chain balance in erythroid cells. Our research indicated that utilizing LNPs for delivery of genome editor achieves efficient editing of endogenous genes of human HSCs. Notably, this non-viral delivery system eliminates the need for harvesting or mobilizing HSCs, providing a potent and one-time treatment potential for blood disorders like sickle cell disease (SCD) and TDT. HighlightsO_LIAntibody-free targeted LNPs system achieves efficient mRNA delivery and base editing in human bone marrow cells including HSCs via one-time intravenous injection. C_LIO_LIDelivery of ABE8e/sgRNA mRNA with optimized LNPs achieves efficient in vivo base editing of HBG in TDT patients derived HSCs engrafted in immunodeficient NCG-X mice, showing restored globin chain balance in erythroid cells. C_LIO_LILNPs efficiently deliver gene editing system to the BM for in vivo editing of human HSCs, providing preclinical evidence for the next generation non-viral gene therapy for blood disorders. C_LI

bioengineering↗