Data availability
All sequencing data are available in the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) under data accession number GSE276072. The LC–MS/MS raw files have been submitted to the Korea BioData Station (https://kbds.re.kr/) under the identifier KAP240719 and Proteomics IDEntification Database under the accession PXD060715. Published scRNA-seq data used in this paper are available under accession number GEO: GSE13772060, GSE261088101, GSE19274274, GSE12951638, GSE14758141, GSE16617842, GSE17190454 and ArrayExpress: E-MTAB-807744. Cis-regulatory element database was downloaded through SCREEN (https://screen.encodeproject.org/). Full genome sequences for Mus musculus were downloaded from BSgenome mm10 genome database (https://doi.org/10.18129/B9.bioc.BSgenome.Mmusculus.UCSC.mm10).
Code availability
The code used in this study is available via Zenodo at https://zenodo.org/records/11480635 (ref. 102).
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Acknowledgements
This study was supported by the National Research Foundation of Korea (NRF), grants RS-2024-00440814 (J.-E.P), RS-2021-NR056588 (J.-E.P), RS-2024-00407383 (J.-E.P), and RS-2023-00279570 (C.K.); Korea Health Industry Development Institute HR21C019803 (J.-E.P.); Basic Science Research Program through the NRF of Korea, number RS-2023-00219399 (J.Y.); NRF grant, funded by the Korean Government (MSIT), NRF-2022R1A2C1092943 (J.Y.); National Research Council of Science & Technology (NST) Aging Convergence Research Center CRC22013-300 (C.K.); Korean Fund for Regenerative Medicine RS-2023-00216047 (J.-E.P.); and KRIBB Research Initiative Program KGM1132511 (C.K.). Funding has been partly provided by a grant from Korea Institute of Science and Technology (KIST) (Grand challenge intramural program to C.L.) and MD-PhD/Medical Scientist Training Program from the Korea Health Industry Development Institute (K.Y.T.). This research was also supported by the Bio & Medical Technology Development Program of the NRF funded by the Korean government (MSIT) (RS-2024-00440883) (C.K.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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The authors declare no competing interests.
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Nature Aging thanks Prakash Ramachandran, Peter Tessarz and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
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Extended data
Extended Data Fig. 1 Aging induces the accumulation of gentleMACS digestion-resistant tissues that contain ECM-producing cells.
a, IF staining of COL1A1 and COL3A1 in the liver of young, old and 6 weeks DDC-treated mice. b, Undigested tissues from the livers of young and old mice that were caught in the strainer after primary digestion with gentleMACS. c-d, Cells in the liver of old mice were released by primary digestion, after which the remnant tissues were subjected to secondary digestion. c, Representative images showing the light-microscopic morphology of the cells after culture on plates for 1 day (after media change) and 15 days. Initially, 1×105 cells were uniformly plated for each sample. Top, Control (primary digestion). Bottom, Secondary digestion. d, Number of cells per liver in the young control, young secondary digestion, old control, and old secondary digestion after 15 days of culture from (c). P-value calculated by two-sided t-test: young control vs young secondary digestion: p < 0.0001; young secondary digestion vs old secondary digestion: p = 0.0001; old control vs old secondary digesion: p = 0.0246. Each group comprised two biological replicates. The data are presented as mean ± s.e.m. e-g, ECM produced by the cells from control and secondary digestion. e, Light-microscopic image of the ECM around the cultured cells of old samples. f, ECM positive regions selected as region of interest (ROI) through ImageJ. g, Bar plot showing percentage of ROI by digestion condition. h-i, Atomic force microscopy (AFM) was conducted to investigate the physical properties of the ECM derived from control and secondary digested cells, following our observation that ECM from primary digested cells was more prone to detach from the plate. The stiffness of the ECM is characterized by Young’s modulus, which measures how much a material deforms under a specific load. The ECMs were pressed with an AFM cantilever and their stiffness was measured. The ECM derived from secondary digested cells was stiffer compared to the ECM derived from control cells. p = 0.0283. h, Bar plot showing Young’s Modulus of ECM by digestion condition. p = 0.0016 g-h, P-value calculated by two-sided t-test. i, Investigation of ECM through AFM. Insertion of AFM probe (left), topography image of ECM (middle), and recorded Young’s Modulus (right). j-l, Proteomics analysis of the ECM from the cultured cells. j, Volcano plot of the quantitative proteomics data, labels are corresponding genes (two-sided t-test). k, ORA showing the proteins that are significantly enriched in the ECM of cells from secondary digestion. P-value calculated by Fisher’s exact test with Benjamini-Hochberg correction. The database was curated from Gene Ontology. l, Fold change in the expression of collagen proteins relative to control (Ctrl) in equal amounts of ECM proteins (left) and in ECM derived from an equal number of cells (right). Fold change values indicate the relative abundance of collagen proteins in secondary digestion compared to control. ECM, Extracellular matrix, ORA, Over-representation analysis. DDC, 3,5-diethoxycarbonyl-1,4-dihydrocollidine. ROI, region of interest. AFM, atomic force microscopy.
Extended Data Fig. 2 Determination with NMF of dissociation-associated artifacts at the cellular and gene level.
a, Scores of gene signatures of each NMF component (n = 20) projected into UMAP. b, Bar plot showing the gene score of each NMF component. The total score is the sum of the scores of all genes in the component. Average score was calculated as a total score divided by the number of genes in the component (Top left, n = 20. Top right, n = 30, bottom, n = 40). Components with high average score (for example, #6, #16 when n_comp = 20) are repeatedly found with different n_comp. c, Gene score distribution of component #6. A cutoff score of 10 was used. The genes with a score >10 (dissociation bias genes) included the stress-response genes Fosb and Nr4a3 and the sample bias gene Hba-a1. NMF, non-negative matrix factorization.
Extended Data Fig. 3 FiNi-seq allows the isolation and characterization of age-related fibrotic niches in the liver.
FiNi-seq and control cells were obtained from young and old livers and sequenced with FiNi-seq and conventional scRNA-seq, respectively. a, Heatmap showing the expression of representative lineage markers of the 13 major cell types in the whole database. b, Comparison of the old and young tissues in terms of the effect of dissociation method on cell-type proportion. c-d, Flow cytometry of T cells. c, Flowchart of flow cytometry analysis of T cells (CD45 + CD3 + ) in each condition. d, Gating strategy of T cells. e, Distribution of the signature scores of the SAMac, SAEc, and SAMes populations in the FiNi-seq and control of old livers f, Dot plot showing the expression frequency and intensity of myeloid-cell markers (indicated along the bottom x-axis) in the indicated cell types (y-axis; defined as indicated by the upper x-axis) in the whole dataset. g, Gating strategy of macrophages. h, IF staining of MdM (F4/80 + VSIG4-) and KC (F4/80 + VSIG4 + ) in young and old liver (left) with bar plot comparison of VSIG4 + KC in young and old liver (right). P-value calculated by two-sided t-test, p = 0.0019. Each group comprised three biological replicates. The data are presented as mean ± s.e. NK, natural killer cell; KC, Kupffer cell; MdM, monocyte-derived macrophage; cDC, conventional dendritic cell; mDC, myeloid dendritic cell; pDC, plasmacytoid dendritic cell. SAMac, scar-associated macrophage. SAEc, scar-associated endothelial cell. SAMes, scar-associated mesenchymal cell.
Extended Data Fig. 4 The aged fibrotic niche contains a large and unique Fibrotic EC which consists of four functionally distinct subtypes.
FiNi-seq and control cells were obtained from young and old livers and sequenced with FiNi-seq and conventional scRNA-seq, respectively. Five EC types were identified, including a unique Fibrotic EC. a, Bar plot showing the absolute cell number (left) and proportions of the five EC types in all liver cells in each sample (right). b, Bar plot showing the proportions of the five EC types in the ECs of each sample. P-value determined with two-sided Wilcoxon rank-sum test. Fibrotic EC: YC vs OF, p = 0.0357; YF vs OF, p = 0.0159; YF vs OC, p = 0.0190; OC vs OF, p = 0.0043; Portal EC: YF vs OC, p = 0.0381; LSEC: YC vs OF, p = 0.0357; YC vs OC, p = 0.0238; YF vs OF, p = 0.0159; YF vs OC, p = 0.0095; OC vs OF, p = 0.0043; Central EC: YC vs OF, p = 0.0357; YF vs OC, p = 0.0095; OC vs OF, p = 0.0043; LyEC: YC vs OF, p = 0.0357; YF vs OF, p = 0.0159; OC vs OF, p = 0.0067. c, Proportions of the EC types in public scRNA-seq databases of liver ECs in nonalcoholic steatohepatitis, carbon tetrachloride (CCl4)-induced, and bile duct ligation (BDL)-induced fibrotic liver disease. d, Expression of endothelial cell lineage markers on all endothelial subtypes including Fibrotic EC. e, Pseudotime trajectory analysis of endothelial differentiation into fibrotic ECs. (Top left) Pseudotime UMAP showing endothelial subtypes with connecting edges. (Bottom left) Scavenger genes exhibiting downregulation along pseudotime progression. (Top right) Branch trajectory guiding toward EC_Sema3g within the pseudotime UMAP. (Bottom right) Branch trajectory guiding toward EC_Col15a1 within the pseudotime UMAP. f, UMAP projection of Fibrotic EC markers within murine endothelial atlas data, highlighting expression in the intestine and muscle/heart tissues. g, Expression of the Cxcl12 ligand Cxcr4 by T cells (left) and myeloid cells (right) in the four samples. h, Projection of the SenMayo score onto the UMAP of the whole database. i, Pathway scores of the Fibrotic ECs in terms of Notch, Semaphorin 3 G, Selectin E (Sele), chemokine, and collagen projected in UMAP. Pathway scoring was conducted with PROGENy. j, Violin plot showing the SenMayo score of each Fibrotic EC subtype. k, Scatter plot showing the distribution of the four Fibrotic EC subtypes according to their relative gene expression of the Notch ligands Jag1 and Jag2 (left) and the inflammatory genes Tnfsf9 and Il6 (right) l, Chord diagrams showing the directions of CXCL signals (left) and Notch signals (right) emanating from the Fibrotic EC subtypes and the other cell types in FiNi-seq. m, IF staining of Fibrotic EC marker (CD34) and endothelial lineage marker (CD31) in young and old liver (left) with bar plot comparison of CD34 + CD31+ cells in young and old liver (two-sided t-test, p = 0.0149). Each group comprised three biological replicates. The data are presented as mean ± s.e. n, Projection of the expression of the SAEc markers Ackr1 and Vwa1 on the UMAP of the Fibrotic and portal-vein ECs. SAEc, scar-associated endothelial cell. EC, endothelial cell.
Extended Data Fig. 5 Senescent vascular smooth muscle cells shape the fibrotic niche microenvironment, potentially via IL-6 signaling.
FiNi-seq and control cells were obtained from young and old livers and sequenced with FiNi-seq and conventional scRNA-seq, respectively. a, Bar plot showing the proportion of vascular smooth muscle cell (VSMC) subtypes in the four samples. b, GSEA was conducted with the VSMCs using the SenMayo gene set (top) and the SenMayo46 scores of the VSMCs were projected onto the UMAP of these cells (bottom). P-value calculated by permutation test with Benjamini-Hochberg correction. c, Violin plots show the SenMayo genes that were particularly highly enriched in VSMC_Il6high cells compared to the VSMC_Il6low cells. d, Ontology terms that are enriched in VSMC_Il6high cells. e, The genes that were upregulated in both Fibrotic ECs and VSMC_Il6high cells were subjected to common gene ontology analysis. The network of upregulated ontology terms is shown here. The red terms reflect the upregulation of vascular activation in FiNi-seq. d-e, P-value calculated by Fisher’s exact test with Benjamini-Hochberg correction. f, Chord diagram showing the Il6 signals traveling from the VSMC_Il6high cells to the cell types that are enriched in FiNi-seq. GSEA, gene set enrichment analysis. VSMC, vascular smooth muscle cell.
Extended Data Fig. 6 FiNi-seq enriches fibroblast clusters that bear distinct characteristics.
FiNi-seq and control cells were obtained from young and old livers and sequenced with FiNi-seq and conventional scRNA-seq, respectively. a, Number of fibroblasts in the indicated organs (left) and subtypes within the liver (right), as determined by the pan-organ fibroblast atlas. b, Dot plot showing clusters that expressed hepatic stellate cell (HSC) and fibroblast markers. c, Bar plot showing the proportions of HSCs and the fibroblast subtypes in the four samples. Number of biological replicates: YC (n = 3), YF (n = 3), OC (n = 6), OF (n = 5). The data are presented as mean ± s.e. P-value is determined with two-sided Wilcoxon rank-sum test. HSC: YC vs OF, p = 0.0357; YC vs OC, p = 0.0219; YF vs OF, p = 0.0357; YF vs OC, p = 0.0219; FB_Wif1: YC vs OF, p = 0.0357; YF vs OC, p = 0.0162; OC vs OF, p = 0.0055; FB_Smoc1: YF vs OF, p = 0.0357; OC vs OF, p = 0.0067; FB_Portal: YC vs OC, p = 0.0431; YF vs OC, p = 0.0219; OC vs OF, p = 0.0116. d, Number of the total and mesenchymal cells that were captured in the Tabula Muris Senis study18 and in our present study. Young and old control tissue counts were combined, as were young and old FiNi-seq counts. e, UMAP projection of major cell-type clusters in the dataset of young, old and CCl4. f, Top, UMAP projection of CCl4 included dataset by condition. Bottom, Barplot showing proportion of mesenchymal cell, comparing control and FiNi-seq by conditions. g, UMAP projection of mesenchymal cells subtypes in CCl4 included dataset. (fix label after confirm) h, IF staining of FB_Wif1 marker (WIF1) and fibroblast lineage marker (PDGFRA) in young and old liver (left) with bar plot comparison of WIF1 + PDGFRA+ cells in young and old liver (right) (two-sided t-test, p = 0.0038). Each group comprised three biological replicates. The data are presented as mean ± s.e. i, RNAscope assay with staining of the FB_Wif1 (top) and FB_Smoc1 (bottom) fibroblasts in the young (left) and old (right) liver without DAPI. The fibroblast-lineage marker Pdgfra were combined with FB_Wif1 marker Phex, and FB_Smoc1 marker Fmod. j, RNAscope assay showing the FB_Wif1, FB_Smoc1, and Fibrotic EC in old female liver. FB, fibroblast.
Extended Data Fig. 7 Immune-interacting fibroblasts cluster into two functionally distinct groups.
FiNi-seq and control cells were obtained from young and old livers and sequenced with FiNi-seq and conventional scRNA-seq, respectively. a, Expression of Wnt ligand genes and Wnt receptor genes by whole dataset. b, Chord diagram showing the Wnt and non-canonical Wnt (ncWnt) signaling pathways between the FiNi-seq enriched cell types and the fibroblast subtypes. c, UMAP projection of the Axin2 expression in the fibroblasts in the whole dataset. d, Volcano plot showing the DEGs between FB_Wif1 and FB_Smoc1 (two-sided t-test). e, Top gene-ontology terms for the FB_Wif1 and FB_Smoc1 cells. P-value calculated by Fisher’s exact test with Benjamini-Hochberg correction. f, The GSE137720 scRNA-seq dataset shows the liver cells at multiple time points after CCl4-mediated induction of liver fibrosis. The figures show UMAP projection of the vascular smooth muscle cells (VSMCs), fibroblasts (FB), and hepatic stellate cells (HSCs) (left) and the liver cells in the untreated and the CCl4-treated mice at 72 hours and 6 weeks (right). g, Liver portal injury was induced in young mice by feeding them 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) for 6 weeks. Sirius-Red staining revealed fibrosis in the portal area at 6 weeks. h, RNAscope assay showing the FB_Smoc1 marker Fmod (left), the Fibrotic EC marker Sema3g (right), and the senescence marker Cdkn1a (left and right) in control samples. i, Scatter plot showing the expression of Wif1 and Smoc1 with human age with regression line. The data are presented as mean ± 95% c.i. The correlation coefficients and p-values were calculated with two-sided Pearson’s correlation test. p = 0.139 (FB_Wif1), p = 9.136×10−27 (FB_Smoc1). j-l, The GSE261088101 scRNA-seq data shows liver cells at multiple time points in MASH model. j, (Left) UMAP of all data with fibroblast population highlighted. (Right) UMAP projection of fibroblast subtype markers. k, (Left) UMAP projection showing fibroblast subtypes (top) and conditions (bottom). (Right) Bar plot showing proportion of fibroblast subtypes by condition. l, Dot plot showing expression of fibroblast-characterizing genes within fibroblast subtypes, categorized by both condition and age. CCl4, carbon tetrachloride. DEG, differentially expressed genes.
Extended Data Fig. 8 T cell stimulation is elevated in FiNi but the response is dulled in the old liver due to exhaustion.
FiNi-seq and control cells were obtained from young and old livers and sequenced with FiNi-seq and conventional scRNA-seq, respectively. a, Ligand-receptor pairs that contribute to Ccl-pathway interactions shown in Fig. 4a. b, UMAP projection of Cd4 and Cd8a expression. c, Comparison of T cell subtypes by age in FiNi-seq (top) and control (bottom) samples. d,e, Expression of T cell-stimulation factors by dendritic cells. d, Chord diagrams showing the Cd80 (left) and Cd86 (right) signaling between the cell types that are enriched in FiNi-seq. e, Violin plot showing the expression of DC-derived T cell stimulatory molecules (Cd80, Cd86, and Cd83) in the four sample types. f, Absolute numbers (left) and proportions in the whole database (right) of the T cell subtypes in the four sample types. g, Markers distinguishing the Cd8 T cell subtypes. Right top, UMAP projection of the Cd8 T cell subtypes. Right bottom, Dot plot showing expression of T cell exhaustion and cytolytic enzyme markers in Cd8 T cells. Left, Purple shows the Cxcr6+ Trm (Sell–S1pr1–Cd44+) while blue indicates the markers of T cell exhaustion and cytotoxicity. Trm, tissue-resident Cd8 T cells.
Extended Data Fig. 9 snATAC-seq of the fibrotic niche reveals regulators of endothelial and T cell aging.
a, Heatmap of cell-type marker peaks, as determined by FiNi-ATACseq on the young and old FiNi-seq. The total dataset is shown. b, Concordance between the cell-type marker expression levels in the FiNi-RNA-seq dataset (left) and the FiNi-ATACseq-determined gene activity of the markers (right). c, Bar plot showing the cell-type proportions in the young and old FiNi-ATACseq. d, FiNi-ATAC-seq-determined gene activity of Fibrotic EC markers in the EC subtypes. e, UMAP projection of the EC subtypes. f, Enriched pathways in the EC subtypes, as determined by PROGENy. g, Differential peaks in the cis-regulatory regions of Cd274 (which expresses PD-L1) and accessibility of the indicated motifs. h, UMAP projection of the FiNi-ATAC-seq-determined T cell subtypes. i, Bar plot showing the effect of age on FiNi-ATACseq-determined Cd4 T cell subtype proportions. j, Peaks of Cd4 T cell subtype lineage markers. Left, Peak of Sell by cluster. Right, Peak of Foxp3 by cluster. k, Peak of Sell by Cd8 T cell cluster. l, Dot plot of exhausted Trm marker gene activity. m, Peaks of T cell exhaustion marker genes by age. Trm, tissue-resident Cd8 T cells.
Extended Data Fig. 10 The portal area in the old liver is a patchwork of normal tissue and inflammatory and strongly fibrotic niches, each of which is inhabited by a specific fibroblast subtype.
a, Schematic depiction of the Visium processing. b, Visium spot annotation. Left, Zonation information of Visium spots obtained from the original data (GSE192742)74. Right, Scores of cell types that are enriched in FiNi-seq. c, Zonation distribution of the indicated EC subtypes (top), mesenchymal cells (middle), and monocytes and macrophages (bottom). The data are presented as mean ± 95% c.i. d, Heatmap hierarchical clustering of the portal-zone spots between the deconvoluted cell types (black letters) and the MsigDB HallMark77 pathway scores (purple letters). e, Classification of portal spots by clusters obtained from (d). f, DEG analysis between portal spots of different classification. Left, Inflammatory versus homeostatic. Right, Fibrotic versus homeostatic. P-value calculated by two-sided t-test. g-i, Stereo-seq data from an old murine liver. g, Schematic depiction of the production and processing steps used to generate the Stereo-seq data. h, Visualization of the cell type-enriched hotspots after trimming with Gaussian filtering. i, Abundance of Trm_Pdcd1high projected onto the spatial UMAP of the old liver. EC, endothelial cell. FB, Fibroblast.
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Tak, K.Y., Kim, J., Park, M. et al. Quasi-spatial single-cell transcriptome based on physical tissue properties defines early aging associated niche in liver. Nat Aging (2025). https://doi.org/10.1038/s43587-025-00857-7
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DOI: https://doi.org/10.1038/s43587-025-00857-7