NEAT1 regulates BMSCs aging through disruption of FGF2 nuclear transport
Aging is a complex biological process marked by the progressive decline in physiological functions and regenerative capacity . At the cellular level, aging manifests as widespread alterations in gene expression, protein interactions, and subcellular organization, ultimately leading to impaired cell cycle regulation and homeostasis . During the aging process, aged bone marrow mesenchymal stem cells (BMSCs) is characterized by cell cycle arrest and a notable reduction in their capacity for osteoblast differentiation, making the replacement and repair of bone tissue increasingly difficult . The diminished regenerative potential of aged BMSCs contributes directly to the onset of skeletal disorders, such as osteoporosis and impaired fracture healing . These age-related dysfunctions in BMSC activity underscore the need for a deeper understanding of the molecular drivers of cellular senescence and tissue regeneration. Long non-coding RNAs (lncRNAs), a class of RNA molecules exceeding 200 nucleotides in length, have recently emerged as pivotal regulators of gene expression and cellular functions . LncRNAs exert their influence through various mechanisms, including chromatin remodeling, transcriptional regulation, and post-transcriptional modifications . Among them, Nuclear Paraspeckle Assembly Transcript 1 (NEAT1) has received significant attention due to its role in the formation and maintenance of paraspeckle nuclear bodies, which is involved in gene regulation, RNA processing, and cellular stress responses . NEAT1 exists in two isoforms: the shorter NEAT1_1 (3.7 kb) and the longer NEAT1_2 (23 kb) . While extensive research has highlighted NEAT1’s contribution to nuclear architecture, paraspeckle assembly, and DNA repair pathways under oxidative stress, its role in the aging of BMSCs and their ability in bone regeneration remains insufficiently explored [ – ]. Fibroblast Growth Factor 2 (FGF2), a critical mitogen for BMSCs, plays a key role in several physiological processes, including angiogenesis, wound healing, and osteogenesis [ – ]. FGF2’s mitogenic properties are particularly relevant in the context of BMSC function, where it supports proliferation and differentiation [ – ]. Additionally, Karyopherin Subunit Beta 1 (KPNB1), a nuclear transport protein, is essential for the translocation of proteins and ribonucleoproteins across the nuclear envelope . The precise coordination between FGF2, KPNB1, and other mitogenic proteins is essential for maintaining cellular function and supporting tissue regeneration . Disruption in these protein interactions could lead to compromised cell function and contribute to the age-related decline in regenerative potential seen in BMSCs . In this study, we propose that NEAT1 plays a critical role in regulating BMSC aging and bone regeneration by modulating the interactions between key mitogenic proteins, particularly FGF2 and KPNB1. We hypothesize that NEAT1 exerts its regulatory influence by altering itself subcellular localization and attenuating the interactions of these proteins, thereby affecting the regenerative capacity of BMSCs. Using a multifaceted approach, including NEAT1 fluorescence in situ hybridization (FISH), NEAT1 pulldown, proteomics analysis, and single-cell analysis, we aim to elucidate how NEAT1 shapes the mitogenic protein network and contributes to the aging of BMSCs. By understanding how NEAT1 orchestrates the interaction between mitogenic proteins such as FGF2 and KPNB1 may open new avenues for the treatment of age-associated bone diseases, thereby contributing to the broader field of regenerative medicine and aging research. 

Animals The work has been reported in line with the ARRIVE guidelines 2.0. All animal experimental procedures were approved by the Laboratory Animal Care and Use Committee at Anhui Medical University (Approval No. LLSC20220738). Three-month-old and eighteen-month-old C57BL/6 mice were representing young and aged mice, respectively. After anesthetized with 2% sodium pentobarbital (50 mg/kg, intraperitoneal), mice were immobilized and subjected to the alveolar bone healing model (extract the first molar). Sample sizes and inclusion criteria were determined to ensure adequate statistical power, guided by existing literature and prior experience . Mice were euthanized via rapid CO2 asphyxiation followed by cervical dislocation, and alveolar bone tissues were harvested for histological analyses. Primary cell culture BMSCs were isolated from human alveolar bone specimens obtained from volunteers aged 26–33 (young) and 66–78 (aged), all of whom provided informed consent. Inclusion criteria and cell culture protocols adhered to established methods as described previously . All procedures involving human MSCs were approved by the Ethical Committee at the College & Hospital of Stomatology of Anhui Medical University (Approval No. T2021014). BMSCs were cultured in Gibco™ BASIC MEM α medium (Thermo Fisher Scientific) supplemented with 10% fetal bovine serum (ScienCell) and 1% penicillin-streptomycin, maintained at 37 °C in a humidified atmosphere with 5% CO2. To evaluate the distinct characteristics of BMSCs from human alveolar bone specimens, flow cytometry analysis showed that the isolated cells were negative for CD45 (0.24%), CD34 (1.11%), and CD11b (0.25%), surface markers of BMSCs, and positive for CD44 (99.97%), CD105 (91.25%), CD29 (99.96%), and CD73 (99.83%) (Figure a-A). The BMSCs exhibited a typical spindle-shaped morphology, with cells growing in a spindle-like or irregular triangular shape on the surface of the culture substrate (Figure a-B). We next assessed differentiation potential of the BMSCs, we induced osteogenic and adipogenic differentiation of BMSCs in vitro. After 21 days, staining with alizarin red and oil red O revealed clear mineralized nodules in the osteogenic group and distinct lipid droplets in the adipogenic group, thereby confirming the multilineage differentiation potential of the isolated BMSCs (Figure a-C). Transfection of small interfering RNAs (siRNAs) and plasmids siRNAs targeting NEAT1 was designed and synthesized by RiboBio (Guangzhou, China). An adenoviral vector GV315 containing NEAT1 (NR_028272) were purchased from GeneChem Co. (Shanghai, China). Transfections were performed using Lipofectamine 2000 (Invitrogen) according to the manufacturer’s instructions. All sequence details are provided in Supplementary Table . Cell senescence-associated β-galactosidase (SA-β-gal) staining BMSCs were seeded in 12-well plates and incubated with staining solution according to the SA-β-gal kit (GENMED, Shanghai, China). After 16 h of coculture, five randomly selected fields were chosen to quantify SA β-gal positive cells under inverted microscopy. Fluorescence in situ hybridization (FISH) Fluorescence in situ hybridization probes were custom-designed and synthesized by RiboBio. The subcellular localization of NEAT1in BMSCs was assessed using a FISH kit (RiboBio). Briefly, BMSCs seeded on coverslips and frozen femur sections were fixed with 4% paraformaldehyde for 15 min at 4 °C and permeabilized with 1% Triton X-100 for 20 min. Coverslips were incubated in prehybridization buffer at 37 °C for 30 min, followed by overnight hybridization at 37 °C in the dark with probes. Post-hybridization washes were conducted with decreasing concentrations of SSC buffer at 42 °C. Nuclei were counterstained with DAPI for 10 min, and samples were visualized using confocal fluorescence microscopy. RNA pull-down LncRNA NEAT1 pull-down assays were performed using the Pierce™ Magnetic RNA-Protein Pull-Down Kit (#20164, Thermo Fisher Scientific) per the manufacturer’s protocol. Biotin-labeled oligonucleotide probes targeting junction sites of sense and antisense NEAT1 (NR_028272) were synthesized by GenePharma. Undifferentiated BMSCs (1 × 10⁷ cells) were lysed in IP lysis buffer (#87787, Thermo Fisher Scientific) supplemented with protease and RNase inhibitors. Probes were incubated with streptavidin magnetic beads, and RNA–protein complexes were isolated. Eluted proteins were analyzed via silver staining, mass spectrometry, and Western blotting. RNA was extracted using TRIzol reagent for further analysis. Detailed probe sequences and antibody information are listed in Supplementary Table . LC-MS/MS analysis Protein digestion was carried out using the Filter-Aided Sample Preparation (FASP) protocol. Briefly, detergents, DTT, and low-molecular-weight contaminants were removed by ultrafiltration with 200 μL of UA buffer (8 M urea, 150 mM Tris-HCl, pH 8.0) using 10 kDa Microcon filters under centrifugal concentration. For cysteine residue blocking, 100 μL of 0.05 M iodoacetamide in UA buffer was added, and the samples were incubated in the dark for 20 min. The filters were washed with 100 μL of UA buffer three times, followed by two washes with 100 μL of 25 mM NH4HCO3. Protein digestion occurred overnight at 37 °C with 3 μg trypsin (Promega) in 40 μL of 25 mM NH4HCO3, and the peptides were collected in the filtrate. LC-MS/MS analysis was performed on a Q Exactive HF-X mass spectrometer coupled with an Easy nLC system (Thermo Fisher Scientific). The peptide mixture was loaded onto an in-house packed C18 reversed-phase column (15 cm × 75 μm, 5 μm resin) equilibrated with buffer A (0.1% formic acid in HPLC-grade water). Peptides were separated using a linear gradient of buffer B (0.1% formic acid in 84% acetonitrile) at 250 nL/min for 60 min, controlled by IntelliFlow technology. Data were acquired in a data-dependent top-10 mode, dynamically selecting the most abundant precursor ions (300–1800 m/z) for HCD fragmentation. Target values were set by predictive Automatic Gain Control (pAGC), and dynamic exclusion was applied for 20 s. Survey scans were obtained at a resolution of 70,000 at m/z 200, while HCD spectra were recorded at a resolution of 17,500 at m/z 200. Normalized collision energy was set to 27 eV, and the underfill ratio (the minimal percentage of the target value likely to be reached at maximum fill time) was set to 0.1%. MS data were processed with MaxQuant software (version 1.3.0.5). The data were searched against the UniProtKB Homo sapiens database (194,237 entries, downloaded 2020-12-10). The search included a precursor mass window of 6 ppm and allowed up to two missed cleavages by trypsin, with a fragment ion mass tolerance of 20 ppm. Carbamidomethylation of cysteine (C) was defined as a fixed modification, and oxidation of methionine (M) and phosphorylation of serine, threonine, and tyrosine (S/T/Y) were defined as variable modifications. A false discovery rate (FDR) of 0.01 was applied for peptide and protein identification. Analysis of single-cell RNA-Seq and RNA-Seq Single-cell RNA-seq data from 2,221 telomerase-immortalized human BMSCs were obtained from NCBI GEO (GSM3439738). Quality control and normalization were conducted using Seurat. Highly variable genes were subjected to principal component analysis, and significant components were selected for UMAP clustering. Differentially expressed genes within clusters were identified and validated using ROC curves. Bulk RNA-seq data (GSE139073) from young (18–44 years) and aged (> 65 years) healthy individuals were analyzed. Raw reads were processed using FastQC and aligned with HISAT2. Differential expression was assessed with DESeq, and genes were clustered using k-means. Read counts were normalized to transcripts per million (TPM) for downstream analyses. Immunofluorescence Coverslips were placed in six-well plates with a drop of complete medium. A 500 μL cell suspension was added onto each coverslip and incubated for 4 h. After adding 2 mL of medium, incubation continued overnight. Cells were fixed with 4% paraformaldehyde for 20 min and permeabilized with 0.3% Triton X-100. Following blocking with 5% bovine serum albumin (BSA) at 37 °C for 30 min, cells were incubated overnight at 4 °C with primary antibodies. Secondary antibodies were applied for 1 h at room temperature in the dark. Nuclei were stained with DAPI, and coverslips were mounted using Antifade Mounting Medium (LEAGENE). Fluorescent images were captured via microscopy. RNA immunoprecipitation (RIP) RNA immunoprecipitation was conducted using the EZ-Magna RIP Kit (#17–701, Merck Millipore) according to the manufacturer’s guidelines. Undifferentiated BMSCs (1 × 10⁷ cells) were lysed and incubated with magnetic beads conjugated to specific antibodies or control IgG overnight at 4 °C. RNA was extracted from immunoprecipitate to assess NEAT1 expression via quantitative real-time PCR (qRT-PCR). Western blotting Cells were lysed in RIPA buffer (P0013, Beyotime Biotechnology) containing protease and phosphatase inhibitors. Proteins were separated on 10–15% SDS-PAGE gels and transferred to polyvinylidene difluoride (PVDF) membranes (Merck Millipore). Membranes were blocked with 5% non-fat milk and incubated overnight with primary antibodies (Supplementary Table ). After washing, membranes were incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies. Bands were visualized using an enhanced chemiluminescence (ECL) kit (Merck Millipore) and quantified with ImageJ software. Quantitative real-time PCR Total RNA was extracted using TRIzol reagent. Reverse transcription was performed with the PrimeScript RT reagent kit (Takara Bio). qRT-PCR was carried out using SYBR Green PCR Master Mix (Vazyme) on an ABI Prism 7900 system (Applied Biosystems). Gene expression was normalized to ACTIN, GAPDH, or U1 levels. Experiments were conducted in triplicate, and primer sequences are listed in Supplementary Table . The 2^–ΔΔCT method was used for quantification. Cytoplasmic/Nuclear separation Cytoplasmic/Nuclear Separation was performed using the Cytoplasmic/Nuclear Separation Kit (BB-36021, Bestbio) according to the manufacturer’s instructions. Briefly, take 5–10 × 10⁶ cells and centrifuge at 4 °C, 500×g for 3 min. Carefully aspirate the medium and collect the cells. Wash the cells twice with cold PBS, draining the supernatant as thoroughly as possible after each wash. Add 400 μl of cold Extract Solution A to the cell pellet. Swirl or pipette to mix thoroughly, then incubate on ice for 20–30 min with occasional shaking. Centrifuge at 4 °C, 1200×g for 5 min. Transfer the supernatant to a pre-chilled clean centrifuge tube to collect the cytoplasmic fraction. Store at 4 °C or use directly for downstream experiments. Wash the pellet with PBS, then centrifuge at 4 °C, 2000×g for 5 min. Discard the supernatant. Resuspend the pellet in 200 μl of Preservation Solution B to isolate the nuclear components. Store for later use or proceed directly with downstream experiments. Co-immunoprecipitation (Co-IP) BMSCs were lysed in IP lysis buffer (P0013, Beyotime Biotechnology) with protease and phosphatase inhibitors. Protein lysates (500 μg) were incubated with 5 μg of specific antibodies and protein A/G agarose beads (Roche) overnight at 4 °C. Beads were washed, and proteins were eluted for SDS-PAGE analysis. Western blotting was performed using standard protocols and antibodies listed in Supplementary Table . Statistical analysis Data are presented as mean ± standard deviation (SD). Experiments were independently replicated at least three times. Statistical significance between two groups was assessed using the student’s t-test. One-way ANOVA was employed for multiple comparisons, Significance levels were set at * P < 0.05, ** P < 0.01, and *** P < 0.001. 

The work has been reported in line with the ARRIVE guidelines 2.0. All animal experimental procedures were approved by the Laboratory Animal Care and Use Committee at Anhui Medical University (Approval No. LLSC20220738). Three-month-old and eighteen-month-old C57BL/6 mice were representing young and aged mice, respectively. After anesthetized with 2% sodium pentobarbital (50 mg/kg, intraperitoneal), mice were immobilized and subjected to the alveolar bone healing model (extract the first molar). Sample sizes and inclusion criteria were determined to ensure adequate statistical power, guided by existing literature and prior experience . Mice were euthanized via rapid CO2 asphyxiation followed by cervical dislocation, and alveolar bone tissues were harvested for histological analyses. 

BMSCs were isolated from human alveolar bone specimens obtained from volunteers aged 26–33 (young) and 66–78 (aged), all of whom provided informed consent. Inclusion criteria and cell culture protocols adhered to established methods as described previously . All procedures involving human MSCs were approved by the Ethical Committee at the College & Hospital of Stomatology of Anhui Medical University (Approval No. T2021014). BMSCs were cultured in Gibco™ BASIC MEM α medium (Thermo Fisher Scientific) supplemented with 10% fetal bovine serum (ScienCell) and 1% penicillin-streptomycin, maintained at 37 °C in a humidified atmosphere with 5% CO2. To evaluate the distinct characteristics of BMSCs from human alveolar bone specimens, flow cytometry analysis showed that the isolated cells were negative for CD45 (0.24%), CD34 (1.11%), and CD11b (0.25%), surface markers of BMSCs, and positive for CD44 (99.97%), CD105 (91.25%), CD29 (99.96%), and CD73 (99.83%) (Figure a-A). The BMSCs exhibited a typical spindle-shaped morphology, with cells growing in a spindle-like or irregular triangular shape on the surface of the culture substrate (Figure a-B). We next assessed differentiation potential of the BMSCs, we induced osteogenic and adipogenic differentiation of BMSCs in vitro. After 21 days, staining with alizarin red and oil red O revealed clear mineralized nodules in the osteogenic group and distinct lipid droplets in the adipogenic group, thereby confirming the multilineage differentiation potential of the isolated BMSCs (Figure a-C). 

siRNAs targeting NEAT1 was designed and synthesized by RiboBio (Guangzhou, China). An adenoviral vector GV315 containing NEAT1 (NR_028272) were purchased from GeneChem Co. (Shanghai, China). Transfections were performed using Lipofectamine 2000 (Invitrogen) according to the manufacturer’s instructions. All sequence details are provided in Supplementary Table . 

BMSCs were seeded in 12-well plates and incubated with staining solution according to the SA-β-gal kit (GENMED, Shanghai, China). After 16 h of coculture, five randomly selected fields were chosen to quantify SA β-gal positive cells under inverted microscopy. 

Fluorescence in situ hybridization probes were custom-designed and synthesized by RiboBio. The subcellular localization of NEAT1in BMSCs was assessed using a FISH kit (RiboBio). Briefly, BMSCs seeded on coverslips and frozen femur sections were fixed with 4% paraformaldehyde for 15 min at 4 °C and permeabilized with 1% Triton X-100 for 20 min. Coverslips were incubated in prehybridization buffer at 37 °C for 30 min, followed by overnight hybridization at 37 °C in the dark with probes. Post-hybridization washes were conducted with decreasing concentrations of SSC buffer at 42 °C. Nuclei were counterstained with DAPI for 10 min, and samples were visualized using confocal fluorescence microscopy. 

LncRNA NEAT1 pull-down assays were performed using the Pierce™ Magnetic RNA-Protein Pull-Down Kit (#20164, Thermo Fisher Scientific) per the manufacturer’s protocol. Biotin-labeled oligonucleotide probes targeting junction sites of sense and antisense NEAT1 (NR_028272) were synthesized by GenePharma. Undifferentiated BMSCs (1 × 10⁷ cells) were lysed in IP lysis buffer (#87787, Thermo Fisher Scientific) supplemented with protease and RNase inhibitors. Probes were incubated with streptavidin magnetic beads, and RNA–protein complexes were isolated. Eluted proteins were analyzed via silver staining, mass spectrometry, and Western blotting. RNA was extracted using TRIzol reagent for further analysis. Detailed probe sequences and antibody information are listed in Supplementary Table . 

Protein digestion was carried out using the Filter-Aided Sample Preparation (FASP) protocol. Briefly, detergents, DTT, and low-molecular-weight contaminants were removed by ultrafiltration with 200 μL of UA buffer (8 M urea, 150 mM Tris-HCl, pH 8.0) using 10 kDa Microcon filters under centrifugal concentration. For cysteine residue blocking, 100 μL of 0.05 M iodoacetamide in UA buffer was added, and the samples were incubated in the dark for 20 min. The filters were washed with 100 μL of UA buffer three times, followed by two washes with 100 μL of 25 mM NH4HCO3. Protein digestion occurred overnight at 37 °C with 3 μg trypsin (Promega) in 40 μL of 25 mM NH4HCO3, and the peptides were collected in the filtrate. LC-MS/MS analysis was performed on a Q Exactive HF-X mass spectrometer coupled with an Easy nLC system (Thermo Fisher Scientific). The peptide mixture was loaded onto an in-house packed C18 reversed-phase column (15 cm × 75 μm, 5 μm resin) equilibrated with buffer A (0.1% formic acid in HPLC-grade water). Peptides were separated using a linear gradient of buffer B (0.1% formic acid in 84% acetonitrile) at 250 nL/min for 60 min, controlled by IntelliFlow technology. Data were acquired in a data-dependent top-10 mode, dynamically selecting the most abundant precursor ions (300–1800 m/z) for HCD fragmentation. Target values were set by predictive Automatic Gain Control (pAGC), and dynamic exclusion was applied for 20 s. Survey scans were obtained at a resolution of 70,000 at m/z 200, while HCD spectra were recorded at a resolution of 17,500 at m/z 200. Normalized collision energy was set to 27 eV, and the underfill ratio (the minimal percentage of the target value likely to be reached at maximum fill time) was set to 0.1%. MS data were processed with MaxQuant software (version 1.3.0.5). The data were searched against the UniProtKB Homo sapiens database (194,237 entries, downloaded 2020-12-10). The search included a precursor mass window of 6 ppm and allowed up to two missed cleavages by trypsin, with a fragment ion mass tolerance of 20 ppm. Carbamidomethylation of cysteine (C) was defined as a fixed modification, and oxidation of methionine (M) and phosphorylation of serine, threonine, and tyrosine (S/T/Y) were defined as variable modifications. A false discovery rate (FDR) of 0.01 was applied for peptide and protein identification. 

Single-cell RNA-seq data from 2,221 telomerase-immortalized human BMSCs were obtained from NCBI GEO (GSM3439738). Quality control and normalization were conducted using Seurat. Highly variable genes were subjected to principal component analysis, and significant components were selected for UMAP clustering. Differentially expressed genes within clusters were identified and validated using ROC curves. Bulk RNA-seq data (GSE139073) from young (18–44 years) and aged (> 65 years) healthy individuals were analyzed. Raw reads were processed using FastQC and aligned with HISAT2. Differential expression was assessed with DESeq, and genes were clustered using k-means. Read counts were normalized to transcripts per million (TPM) for downstream analyses. 

Coverslips were placed in six-well plates with a drop of complete medium. A 500 μL cell suspension was added onto each coverslip and incubated for 4 h. After adding 2 mL of medium, incubation continued overnight. Cells were fixed with 4% paraformaldehyde for 20 min and permeabilized with 0.3% Triton X-100. Following blocking with 5% bovine serum albumin (BSA) at 37 °C for 30 min, cells were incubated overnight at 4 °C with primary antibodies. Secondary antibodies were applied for 1 h at room temperature in the dark. Nuclei were stained with DAPI, and coverslips were mounted using Antifade Mounting Medium (LEAGENE). Fluorescent images were captured via microscopy. 

RNA immunoprecipitation was conducted using the EZ-Magna RIP Kit (#17–701, Merck Millipore) according to the manufacturer’s guidelines. Undifferentiated BMSCs (1 × 10⁷ cells) were lysed and incubated with magnetic beads conjugated to specific antibodies or control IgG overnight at 4 °C. RNA was extracted from immunoprecipitate to assess NEAT1 expression via quantitative real-time PCR (qRT-PCR). 

Cells were lysed in RIPA buffer (P0013, Beyotime Biotechnology) containing protease and phosphatase inhibitors. Proteins were separated on 10–15% SDS-PAGE gels and transferred to polyvinylidene difluoride (PVDF) membranes (Merck Millipore). Membranes were blocked with 5% non-fat milk and incubated overnight with primary antibodies (Supplementary Table ). After washing, membranes were incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies. Bands were visualized using an enhanced chemiluminescence (ECL) kit (Merck Millipore) and quantified with ImageJ software. 

Total RNA was extracted using TRIzol reagent. Reverse transcription was performed with the PrimeScript RT reagent kit (Takara Bio). qRT-PCR was carried out using SYBR Green PCR Master Mix (Vazyme) on an ABI Prism 7900 system (Applied Biosystems). Gene expression was normalized to ACTIN, GAPDH, or U1 levels. Experiments were conducted in triplicate, and primer sequences are listed in Supplementary Table . The 2^–ΔΔCT method was used for quantification. 

Cytoplasmic/Nuclear Separation was performed using the Cytoplasmic/Nuclear Separation Kit (BB-36021, Bestbio) according to the manufacturer’s instructions. Briefly, take 5–10 × 10⁶ cells and centrifuge at 4 °C, 500×g for 3 min. Carefully aspirate the medium and collect the cells. Wash the cells twice with cold PBS, draining the supernatant as thoroughly as possible after each wash. Add 400 μl of cold Extract Solution A to the cell pellet. Swirl or pipette to mix thoroughly, then incubate on ice for 20–30 min with occasional shaking. Centrifuge at 4 °C, 1200×g for 5 min. Transfer the supernatant to a pre-chilled clean centrifuge tube to collect the cytoplasmic fraction. Store at 4 °C or use directly for downstream experiments. Wash the pellet with PBS, then centrifuge at 4 °C, 2000×g for 5 min. Discard the supernatant. Resuspend the pellet in 200 μl of Preservation Solution B to isolate the nuclear components. Store for later use or proceed directly with downstream experiments. 

BMSCs were lysed in IP lysis buffer (P0013, Beyotime Biotechnology) with protease and phosphatase inhibitors. Protein lysates (500 μg) were incubated with 5 μg of specific antibodies and protein A/G agarose beads (Roche) overnight at 4 °C. Beads were washed, and proteins were eluted for SDS-PAGE analysis. Western blotting was performed using standard protocols and antibodies listed in Supplementary Table . 

Data are presented as mean ± standard deviation (SD). Experiments were independently replicated at least three times. Statistical significance between two groups was assessed using the student’s t-test. One-way ANOVA was employed for multiple comparisons, Significance levels were set at * P < 0.05, ** P < 0.01, and *** P < 0.001. 

Age-dependent redistribution of NEAT1 from nucleus to cytoplasm To investigate the role of lncRNA NEAT1 in BMSC aging. Significant age-associated alterations of NEAT1 subcellular localization in bone marrow and BMSCs were observed. Compared with young mice, the number of Nestin + cells were decreased in bone marrow of aged mice, accompanied by significant cytoplasmic localization of fluorescence in situ hybridization (FISH) of NEAT1 (Fig. A, Figure b-A). Meanwhile, NEAT1 is predominantly confined to the nucleus in young BMSCs (Fig. B). In contrast, aged BMSCs exhibited substantial cytoplasmic accumulation of NEAT1, indicating a pronounced shift in subcellular distribution with advancing age (Fig. B). Quantitative analysis corroborated this redistribution and we observed that total NEAT1 expression increased significantly with the aging of BMSCs, demonstrating a significant increase in cytoplasmic NEAT1 expression in aged BMSCs (Fig. C). Moreover, we used serial passaging as an additional method to induce BMSC aging. Our findings were consistent with previous observations. After 25 generations of continuous passage, BMSCs exhibited distinct morphological signs of senescence (Figure b-B). Additionally, the expression level of NEAT1 was significantly increased (Figure b-C). Karyoplasmic separation experiments, utilizing actin as a cytoplasmic marker and U1 as a nuclear marker, revealed that NEAT1 progressively translocated from the nucleus to the cytoplasm as BMSCs underwent continuous passage and senescence (Figure b-D). Upon further examination of nuclear division and the post-division stages during BMSCs osteoblasts differentiation, we observed that NEAT1 tightly encircles the nucleus and exhibits a spindle-like polar distribution (Fig. D). Additionally, Fluorescence signal analysis suggests that DNA content and NEAT1 expression levels display a positive correlation during karyokinesis, supporting the involvement of NEAT1 in mitosis or cell proliferation (Fig. E). These findings suggest that with BMSCs aging, NEAT1 distribution undergoes significant changes, and importantly, its localization is closely associated with nuclear division. Collectively, the results provide valuable insights into NEAT1 potential functional role in cellular aging processes. NEAT1 related to BMSCs osteoblast differentiation, mitotic cell cycle, and ribosome biogenesis Given the cytoplasmic localization of NEAT1 associated with aging, NEAT1 knockdown in aged BMSCs led to a reduction in β-glucosidase activity (Fig. A). Additionally, we overexpressed NEAT1 in young BMSCs and knocked down NEAT1 in aged BMSCs. We then measured the protein and mRNA levels of senescence-associated secretory phenotype (SASP), including MMP3, IL-6, IL-1β, and VEGF, as well as classical senescence markers such as p21 and p16. Our results showed that NEAT1 knockdown in aged BMSCs significantly decreased various aging indicators (Fig. B and C), while NEAT1 overexpression in young BMSCs led to an increase in these markers (Figure A and B). To further elucidate the functional role of NEAT1, RNA pulldown and proteomic analyses were performed to identify its interacting partners in BMSCs. The Venn diagram (Fig. D) illustrated the overlap of NEAT1-interacting proteins revealing a core set of 346 conserved proteins. This finding suggests a robust and conserved network of NEAT1-associated proteins indicative of multifaceted functional roles. Proteomic profiling (Fig. E) uncovered key proteins enriched within NEAT1 complexes, notably FGF2, which plays a critical role in karyomitosis and cellular proliferation. Other significant interactors included proteins involved in protein nuclear transport, such as Karyopherin Subunit Beta 1 (KPNB1), indicating its broad involvement in cellular processes and protein complex. Functional enrichment analysis (Fig. F) further underscored the biological processes and molecular functions associated with NEAT1-interacting proteins. Top enriched Gene Ontology (GO) terms included protein stabilization, osteoblast differentiation, mitotic cell cycle, and ribosomal biogenesis. The overrepresentation of these proteins emphasizes the essential role of NEAT1 in BMSCs growth, proliferation, and differentiation. These findings suggest that NEAT1 associates with a diverse array of proteins integral to vital cellular processes, particularly within osteogenic pathways. FGF2 and KPNB1 involves BMSCs proliferation and bone regeneration Next, the expression patterns of FGF2, KPNB1 during BMSCs proliferation and aging were investigated. Single-cell RNA sequencing data of BMSCs were analyzed using Uniform Manifold Approximation and Projection (UMAP) clustering (Fig. A). By delineating the distinct cell populations and their respective cell cycle phases (Fig. B) and illustrating the differentiation trajectory of BMSCs (Fig. C), potential interactions and transitions between specific cellular states among BMSC clusters were indicated. Gene expression mapping (Fig. D and E) revealed the spatial distribution of FGF2, KPNB1, CCND1, and PCNA across cell clusters, with FGF2, KPNB1, and PCNA exhibiting higher expression in proliferative clusters, whereas CCND1 was more uniformly expressed. Quantitative PCR analysis (Fig. F) demonstrated that the mRNA levels of FGF2 and KPNB1 were decreased in aged BMSCs compared to young cells. Immunofluorescence staining (Fig. G) confirmed the colocalization of FGF2 and KPNB1 within the nuclear compartment of BMSCs. We next validated our hypothesis in an animal model. Histological analysis of regenerated alveolar bone (Fig. I) demonstrated the presence of FGF2 and KPNB1 within the newly formed bone matrix, suggesting their involvement in bone regeneration processes. Moreover, a significant increase in femoral bone thickness was observed following NEAT1 knockdown (Figure A). key bone parameters such as bone volume fraction (BV/TV), trabecular number (Tb.N), and trabecular thickness (Tb.Th) were all notably improved (Figure B). Additionally, H&E and Masson staining revealed that NEAT1 knockdown led to a significant increase in femoral bone content (see Figure C and D), suggesting that the NEAT1-FGF2 network plays a critical role in regulating bone regeneration. The region of FGF2 that binds to NEAT1 is enriched with nuclear localization motifs To explore the molecular mechanisms underlying NEAT1 related regulatory effects, its interaction with FGF2 and KPNB1 was examined. The NEAT1-FGF2 interaction matrix identified specific residues within FGF2, that strongly associate with particular NEAT1 nucleotide regions, suggesting sequence-specific binding (Fig. A). The NLS Mapper ( http://nls-mapper.iab.keio.ac.jp/ ) and NetworKIN ( http://networkin.info/ ) further indicated the region of FGF2 that binds to NEAT1 is enriched with nuclear localization signal (NLS) motif (Fig. B). This suggests that the specific binding of NEAT1 may play a potential role in influencing FGF2 nuclear translocation. Consistent with the mass spectrometry, RNA pulldown assays demonstrated that NEAT1 in BMSCs resulted in specific enrichment of the 18-kDa FGF2 isoform and KPNB1, confirming the specificity of the NEAT1/FGF2/KPNB1 interactions (Fig. C). RNA immunoprecipitation (RIP) assays (Fig. D) further confirmed significant enrichment of NEAT1 bound to FGF2 in BMSCs, reinforcing the specificity and strength of their interaction. Moreover, a significant increase in FGF2 nuclear accumulation in aged mouse BMSCs were observed after NEAT1 knockdown (Fig. E). These findings reveal that NEAT1 exerts a critical regulatory influence on FGF2, likely modulating its transport and function through molecular complex, and potentially impacting cellular processes such as nuclear import and BMSCs proliferation. NEAT1 reduces interaction between FGF2 and KPNB1 To assess the regulatory effects of NEAT1 on FGF2 & KPNB1 interactions and FGF2 nuclear transport, knockdown and overexpression experiments were performed in aged BMSCs and young BMSCs, respectively. Compared to the effects of NEAT1 knockdown (Fig. A) and overexpression (Fig. B) on the global expression levels of FGF2, co-immunoprecipitation indicates that after NEAT1 knockdown, KPNB1 binds more FGF2, promoting the nuclear transport of FGF2 (Fig. C). Conversely, when NEAT1 is overexpressed, it binds to the nuclear localization motif of FGF2, affecting FGF2 binding to KPNB1 and FGF2 nuclear transport (Fig. D). Co-immunoprecipitation and mass spectrum analyses (Fig. E) further highlighted key biological processes and pathways influenced by FGF2. Top enriched GO terms included positive regulation of myoblast differentiation, mitotic spindle assembly checkpoint signaling, regulation of microtubule nucleation, translation, and RNA binding, while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways revealed significant enrichment in nucleocytoplasmic transport, DNA replication, and signaling pathways regulating stem cell pluripotency. These results suggest that NEAT1/FGF2-associated protein complexes participate in critical cellular processes linked to cell differentiation and proliferation. 

To investigate the role of lncRNA NEAT1 in BMSC aging. Significant age-associated alterations of NEAT1 subcellular localization in bone marrow and BMSCs were observed. Compared with young mice, the number of Nestin + cells were decreased in bone marrow of aged mice, accompanied by significant cytoplasmic localization of fluorescence in situ hybridization (FISH) of NEAT1 (Fig. A, Figure b-A). Meanwhile, NEAT1 is predominantly confined to the nucleus in young BMSCs (Fig. B). In contrast, aged BMSCs exhibited substantial cytoplasmic accumulation of NEAT1, indicating a pronounced shift in subcellular distribution with advancing age (Fig. B). Quantitative analysis corroborated this redistribution and we observed that total NEAT1 expression increased significantly with the aging of BMSCs, demonstrating a significant increase in cytoplasmic NEAT1 expression in aged BMSCs (Fig. C). Moreover, we used serial passaging as an additional method to induce BMSC aging. Our findings were consistent with previous observations. After 25 generations of continuous passage, BMSCs exhibited distinct morphological signs of senescence (Figure b-B). Additionally, the expression level of NEAT1 was significantly increased (Figure b-C). Karyoplasmic separation experiments, utilizing actin as a cytoplasmic marker and U1 as a nuclear marker, revealed that NEAT1 progressively translocated from the nucleus to the cytoplasm as BMSCs underwent continuous passage and senescence (Figure b-D). Upon further examination of nuclear division and the post-division stages during BMSCs osteoblasts differentiation, we observed that NEAT1 tightly encircles the nucleus and exhibits a spindle-like polar distribution (Fig. D). Additionally, Fluorescence signal analysis suggests that DNA content and NEAT1 expression levels display a positive correlation during karyokinesis, supporting the involvement of NEAT1 in mitosis or cell proliferation (Fig. E). These findings suggest that with BMSCs aging, NEAT1 distribution undergoes significant changes, and importantly, its localization is closely associated with nuclear division. Collectively, the results provide valuable insights into NEAT1 potential functional role in cellular aging processes. 

Given the cytoplasmic localization of NEAT1 associated with aging, NEAT1 knockdown in aged BMSCs led to a reduction in β-glucosidase activity (Fig. A). Additionally, we overexpressed NEAT1 in young BMSCs and knocked down NEAT1 in aged BMSCs. We then measured the protein and mRNA levels of senescence-associated secretory phenotype (SASP), including MMP3, IL-6, IL-1β, and VEGF, as well as classical senescence markers such as p21 and p16. Our results showed that NEAT1 knockdown in aged BMSCs significantly decreased various aging indicators (Fig. B and C), while NEAT1 overexpression in young BMSCs led to an increase in these markers (Figure A and B). To further elucidate the functional role of NEAT1, RNA pulldown and proteomic analyses were performed to identify its interacting partners in BMSCs. The Venn diagram (Fig. D) illustrated the overlap of NEAT1-interacting proteins revealing a core set of 346 conserved proteins. This finding suggests a robust and conserved network of NEAT1-associated proteins indicative of multifaceted functional roles. Proteomic profiling (Fig. E) uncovered key proteins enriched within NEAT1 complexes, notably FGF2, which plays a critical role in karyomitosis and cellular proliferation. Other significant interactors included proteins involved in protein nuclear transport, such as Karyopherin Subunit Beta 1 (KPNB1), indicating its broad involvement in cellular processes and protein complex. Functional enrichment analysis (Fig. F) further underscored the biological processes and molecular functions associated with NEAT1-interacting proteins. Top enriched Gene Ontology (GO) terms included protein stabilization, osteoblast differentiation, mitotic cell cycle, and ribosomal biogenesis. The overrepresentation of these proteins emphasizes the essential role of NEAT1 in BMSCs growth, proliferation, and differentiation. These findings suggest that NEAT1 associates with a diverse array of proteins integral to vital cellular processes, particularly within osteogenic pathways. 

Next, the expression patterns of FGF2, KPNB1 during BMSCs proliferation and aging were investigated. Single-cell RNA sequencing data of BMSCs were analyzed using Uniform Manifold Approximation and Projection (UMAP) clustering (Fig. A). By delineating the distinct cell populations and their respective cell cycle phases (Fig. B) and illustrating the differentiation trajectory of BMSCs (Fig. C), potential interactions and transitions between specific cellular states among BMSC clusters were indicated. Gene expression mapping (Fig. D and E) revealed the spatial distribution of FGF2, KPNB1, CCND1, and PCNA across cell clusters, with FGF2, KPNB1, and PCNA exhibiting higher expression in proliferative clusters, whereas CCND1 was more uniformly expressed. Quantitative PCR analysis (Fig. F) demonstrated that the mRNA levels of FGF2 and KPNB1 were decreased in aged BMSCs compared to young cells. Immunofluorescence staining (Fig. G) confirmed the colocalization of FGF2 and KPNB1 within the nuclear compartment of BMSCs. We next validated our hypothesis in an animal model. Histological analysis of regenerated alveolar bone (Fig. I) demonstrated the presence of FGF2 and KPNB1 within the newly formed bone matrix, suggesting their involvement in bone regeneration processes. Moreover, a significant increase in femoral bone thickness was observed following NEAT1 knockdown (Figure A). key bone parameters such as bone volume fraction (BV/TV), trabecular number (Tb.N), and trabecular thickness (Tb.Th) were all notably improved (Figure B). Additionally, H&E and Masson staining revealed that NEAT1 knockdown led to a significant increase in femoral bone content (see Figure C and D), suggesting that the NEAT1-FGF2 network plays a critical role in regulating bone regeneration. 

To explore the molecular mechanisms underlying NEAT1 related regulatory effects, its interaction with FGF2 and KPNB1 was examined. The NEAT1-FGF2 interaction matrix identified specific residues within FGF2, that strongly associate with particular NEAT1 nucleotide regions, suggesting sequence-specific binding (Fig. A). The NLS Mapper ( http://nls-mapper.iab.keio.ac.jp/ ) and NetworKIN ( http://networkin.info/ ) further indicated the region of FGF2 that binds to NEAT1 is enriched with nuclear localization signal (NLS) motif (Fig. B). This suggests that the specific binding of NEAT1 may play a potential role in influencing FGF2 nuclear translocation. Consistent with the mass spectrometry, RNA pulldown assays demonstrated that NEAT1 in BMSCs resulted in specific enrichment of the 18-kDa FGF2 isoform and KPNB1, confirming the specificity of the NEAT1/FGF2/KPNB1 interactions (Fig. C). RNA immunoprecipitation (RIP) assays (Fig. D) further confirmed significant enrichment of NEAT1 bound to FGF2 in BMSCs, reinforcing the specificity and strength of their interaction. Moreover, a significant increase in FGF2 nuclear accumulation in aged mouse BMSCs were observed after NEAT1 knockdown (Fig. E). These findings reveal that NEAT1 exerts a critical regulatory influence on FGF2, likely modulating its transport and function through molecular complex, and potentially impacting cellular processes such as nuclear import and BMSCs proliferation. 

To assess the regulatory effects of NEAT1 on FGF2 & KPNB1 interactions and FGF2 nuclear transport, knockdown and overexpression experiments were performed in aged BMSCs and young BMSCs, respectively. Compared to the effects of NEAT1 knockdown (Fig. A) and overexpression (Fig. B) on the global expression levels of FGF2, co-immunoprecipitation indicates that after NEAT1 knockdown, KPNB1 binds more FGF2, promoting the nuclear transport of FGF2 (Fig. C). Conversely, when NEAT1 is overexpressed, it binds to the nuclear localization motif of FGF2, affecting FGF2 binding to KPNB1 and FGF2 nuclear transport (Fig. D). Co-immunoprecipitation and mass spectrum analyses (Fig. E) further highlighted key biological processes and pathways influenced by FGF2. Top enriched GO terms included positive regulation of myoblast differentiation, mitotic spindle assembly checkpoint signaling, regulation of microtubule nucleation, translation, and RNA binding, while Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways revealed significant enrichment in nucleocytoplasmic transport, DNA replication, and signaling pathways regulating stem cell pluripotency. These results suggest that NEAT1/FGF2-associated protein complexes participate in critical cellular processes linked to cell differentiation and proliferation. 

Aging significantly impairs the proliferative and regenerative capacity of BMSCs, which is essential for maintaining bone homeostasis and facilitating bone repair . As a critical nuclear-paraspeckle component, NEAT1 has emerged involved in gene expression regulation, cellular proliferation and senescence [ – ]. Concurrently, as the growth factor of BMSCs, fibroblast growth factors are well-documented for the mitogenic roles in involving cellular growth, proliferation, and differentiation . Despite their individual importance, the interplay between NEAT1 and FGF2 in the context of BMSCs aging and differentiation remains insufficiently explored. In this study, we discovered that NEAT1 undergoes age-dependent subcellular redistribution in BMSCs, interacts with FGF2 and KPNB1, and modulates FGF2 nuclear localization, thereby influencing BMSCs proliferation and osteogenic differentiation. NEAT1 is a nuclear-enriched abundant transcript, has been extensively studied for its role in paraspeckle formation and regulation of gene expression during the cell cycle . Previous research implicated NEAT1 in maintaining nuclear integrity, modulating transcriptional activity, and influencing cellular senescence and differentiation . Importantly, the increased cytoplasmic accumulation of NEAT1 involved mRNA stability and related signaling pathways . Meanwhile, aligns with previous research  the correlation between elevated expression levels and increased nuclear DNA content implies the relationship between NEAT1 and cell proliferation. In this study, the observed age-dependent redistribution of NEAT1 from the nucleus to the cytoplasm in BMSCs correlates with diminished proliferative capacity, underscoring the functional significance of its subcellular localization. Moreover, during BMSCs differentiation, NEAT1 binds with abundant mitogenic FGF2, encircles the nucleus, and exhibits spindle-like distribution of polarity, suggesting its involvement in cell proliferation regulation. This interaction reveals that NEAT1 influences BMSCs homeostasis by modulating FGF2 activity, integrating previous insights on NEAT1 regulatory roles with its impact on BMSC aging processes. Nuclear localization signals (NLSs) are specific short amino acid sequences, which directed proteins to the nucleus by facilitating their recognition by karyopherin . These specific motifs are essential for the protein nuclear import and performing critical functions. In this study, as well as binding to FGF2 and KPNB1, NEAT1 specifically binds to the NLS region of FGF2, which suggests a novel competitive mechanism wherein NEAT1 may inhibit the interaction between FGF2 and KPNB1 by occupying FGF2 NLS region. Consequently, NEAT1 overexpression reduces FGF2-KPNB1 binding, hindering FGF2 nuclear translocation and impairing BMSC proliferation and osteogenic differentiation. Conversely, NEAT1 knockdown enhances FGF2-KPNB1 interaction and promotes nuclear import of FGF2, thereby facilitating proliferation and differentiation. Meanwhile, NEAT1 pulldown analyses validated that NEAT1 interacts with a diverse array of proteins involved in mitotic cell cycle, ribosome biogenesis, cytoskeletal organization, and osteoblast differentiation. The enrichment of ribosomal proteins and cyclins among NEAT1 interactors underscores its potential role in cell growth and differentiation . This mechanism resembles that NEAT1 acting as molecular scaffolds, particularly relevant in aging, where the maintenance of efficient signaling networks is crucial for cellular function . 

Our findings provide significant insights into the control of FGF2 nuclear transport processes by NEAT1, which underscores a pivotal regulatory axis that influences BMSCs aging, offering potential avenues for therapeutic intervention in age-related bone disorders. 

Below is the link to the electronic supplementary material. Supplementary Material 1 Supplementary Material 2 Supplementary Material 3 

