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recombinant mouse spp1 protein  (MedChemExpress)


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    Structured Review

    MedChemExpress recombinant mouse spp1 protein
    Dynamic cell portion changes and intercellular communication analysis revealed <t>SPP1</t> signaling pathway was critical in microglia after SCI. A , B Stacked bar plots depicting changes in the relative abundance of major cell types in spinal cord ( A ) and peripheral immune cell populations ( B ) across various time points. Astrocytes, microglia, OPCs, and MDMs show marked shifts, particularly in the acute (1~3 dpi) and subacute phases of SCI, the absence of 42 dpi stems directly from the source data ( GSE172167 ), where immune cell clusters were not identified or annotated at this specific time point in the original study. C Intercellular communication networks illustrate increased signaling complexity at 1 and 3 dpi compared to the sham condition. D Quantitative result of the total number of interactions in sham, 1 dpi, and 3 dpi samples, showing a significant increase in cell–cell interactions post-injury. E Heatmaps displaying the changes in signaling patterns for key cell types. SPP1 became prominent at 1 dpi. F Information flow of microglia indicated the SPP1 signal was significant
    Recombinant Mouse Spp1 Protein, supplied by MedChemExpress, used in various techniques. Bioz Stars score: 94/100, based on 3 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/recombinant mouse spp1 protein/product/MedChemExpress
    Average 94 stars, based on 3 article reviews
    recombinant mouse spp1 protein - by Bioz Stars, 2026-02
    94/100 stars

    Images

    1) Product Images from "Integrative analysis and experimental validation identify the role of CD44 and Nucleolin in regulating gliogenesis following spinal cord injury"

    Article Title: Integrative analysis and experimental validation identify the role of CD44 and Nucleolin in regulating gliogenesis following spinal cord injury

    Journal: Cell Regeneration

    doi: 10.1186/s13619-025-00253-x

    Dynamic cell portion changes and intercellular communication analysis revealed SPP1 signaling pathway was critical in microglia after SCI. A , B Stacked bar plots depicting changes in the relative abundance of major cell types in spinal cord ( A ) and peripheral immune cell populations ( B ) across various time points. Astrocytes, microglia, OPCs, and MDMs show marked shifts, particularly in the acute (1~3 dpi) and subacute phases of SCI, the absence of 42 dpi stems directly from the source data ( GSE172167 ), where immune cell clusters were not identified or annotated at this specific time point in the original study. C Intercellular communication networks illustrate increased signaling complexity at 1 and 3 dpi compared to the sham condition. D Quantitative result of the total number of interactions in sham, 1 dpi, and 3 dpi samples, showing a significant increase in cell–cell interactions post-injury. E Heatmaps displaying the changes in signaling patterns for key cell types. SPP1 became prominent at 1 dpi. F Information flow of microglia indicated the SPP1 signal was significant
    Figure Legend Snippet: Dynamic cell portion changes and intercellular communication analysis revealed SPP1 signaling pathway was critical in microglia after SCI. A , B Stacked bar plots depicting changes in the relative abundance of major cell types in spinal cord ( A ) and peripheral immune cell populations ( B ) across various time points. Astrocytes, microglia, OPCs, and MDMs show marked shifts, particularly in the acute (1~3 dpi) and subacute phases of SCI, the absence of 42 dpi stems directly from the source data ( GSE172167 ), where immune cell clusters were not identified or annotated at this specific time point in the original study. C Intercellular communication networks illustrate increased signaling complexity at 1 and 3 dpi compared to the sham condition. D Quantitative result of the total number of interactions in sham, 1 dpi, and 3 dpi samples, showing a significant increase in cell–cell interactions post-injury. E Heatmaps displaying the changes in signaling patterns for key cell types. SPP1 became prominent at 1 dpi. F Information flow of microglia indicated the SPP1 signal was significant

    Techniques Used:

    SPP1-CD44 signaling promotes microglial activation and inflammatory response. A SPP1 signaling pathway network showing interactions between microglia and other cell types. B A circular plot illustrating the interaction network of microglia with other cells in various ligand -receptor pairs, including Spp1 - Cd44 . C , D Violin plots showing expression levels of Spp1 and Cd44 across different cell types in sham (blue) and 1 dpi (red). Both genes show elevated expression in microglia following injury. E Violin plot of Cd44 expression in microglia subclusters, showing the upregulation in the wound healing and inflammatory response2 cluster at 1 dpi. F The microglia were sorted by flow cytometry and ( G ) Cd44 gene expression was detected by qRCR. H Flow cytometry analysis of CD44 positive microglia after SCI, showing a marked increase in CD44 + microglia during 7 dpi. I Immunofluorescence images of spinal cord lesion site stained for Iba1, CD44, SPP1, and merged with DAPI. White arrows indicate the co-stained CD44 + and SPP1 + signals in Iba1 positive microglia ( J ) Quantification results of CD44 + and SPP1 + in Iba1 positive microglia cells. K Using PLA to detect specific SPP1-CD44 interactions of spinal cord lesion site in situ. L Quantification of PLA results, the PLA signal is quantified and plotted as the area of PLA signal per Iba1 positive cell. M , N qRT-PCR showing dose- and time-dependent increases of Cd44 expression in BV2 microglia after recombinant SPP1 stimulation. O Representative images of PLA assay specific SPP1-CD44 interactions of BV2 microglia cells in vitro. P PLA signal was quantified and plotted as the area of PLA signal per cell. Q ELISA quantification of IL-6 levels in cell supernatant after SPP1 stimulation. R Western blot showing the time course of CD44 and p-NF-κB p65 protein expression in BV2 cells after SPP1 treatment. ( S – T ) Quantification of CD44 and p-NF-κB p65 protein levels. Data are presented as mean ± SEM. ( n = 3, * P < 0.05, ** P < 0.01, *** P < 0.001)
    Figure Legend Snippet: SPP1-CD44 signaling promotes microglial activation and inflammatory response. A SPP1 signaling pathway network showing interactions between microglia and other cell types. B A circular plot illustrating the interaction network of microglia with other cells in various ligand -receptor pairs, including Spp1 - Cd44 . C , D Violin plots showing expression levels of Spp1 and Cd44 across different cell types in sham (blue) and 1 dpi (red). Both genes show elevated expression in microglia following injury. E Violin plot of Cd44 expression in microglia subclusters, showing the upregulation in the wound healing and inflammatory response2 cluster at 1 dpi. F The microglia were sorted by flow cytometry and ( G ) Cd44 gene expression was detected by qRCR. H Flow cytometry analysis of CD44 positive microglia after SCI, showing a marked increase in CD44 + microglia during 7 dpi. I Immunofluorescence images of spinal cord lesion site stained for Iba1, CD44, SPP1, and merged with DAPI. White arrows indicate the co-stained CD44 + and SPP1 + signals in Iba1 positive microglia ( J ) Quantification results of CD44 + and SPP1 + in Iba1 positive microglia cells. K Using PLA to detect specific SPP1-CD44 interactions of spinal cord lesion site in situ. L Quantification of PLA results, the PLA signal is quantified and plotted as the area of PLA signal per Iba1 positive cell. M , N qRT-PCR showing dose- and time-dependent increases of Cd44 expression in BV2 microglia after recombinant SPP1 stimulation. O Representative images of PLA assay specific SPP1-CD44 interactions of BV2 microglia cells in vitro. P PLA signal was quantified and plotted as the area of PLA signal per cell. Q ELISA quantification of IL-6 levels in cell supernatant after SPP1 stimulation. R Western blot showing the time course of CD44 and p-NF-κB p65 protein expression in BV2 cells after SPP1 treatment. ( S – T ) Quantification of CD44 and p-NF-κB p65 protein levels. Data are presented as mean ± SEM. ( n = 3, * P < 0.05, ** P < 0.01, *** P < 0.001)

    Techniques Used: Activation Assay, Expressing, Flow Cytometry, Gene Expression, Immunofluorescence, Staining, In Situ, Quantitative RT-PCR, Recombinant, In Vitro, Enzyme-linked Immunosorbent Assay, Western Blot



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    Dynamic cell portion changes and intercellular communication analysis revealed SPP1 signaling pathway was critical in microglia after SCI. A , B Stacked bar plots depicting changes in the relative abundance of major cell types in spinal cord ( A ) and peripheral immune cell populations ( B ) across various time points. Astrocytes, microglia, OPCs, and MDMs show marked shifts, particularly in the acute (1~3 dpi) and subacute phases of SCI, the absence of 42 dpi stems directly from the source data ( GSE172167 ), where immune cell clusters were not identified or annotated at this specific time point in the original study. C Intercellular communication networks illustrate increased signaling complexity at 1 and 3 dpi compared to the sham condition. D Quantitative result of the total number of interactions in sham, 1 dpi, and 3 dpi samples, showing a significant increase in cell–cell interactions post-injury. E Heatmaps displaying the changes in signaling patterns for key cell types. SPP1 became prominent at 1 dpi. F Information flow of microglia indicated the SPP1 signal was significant

    Journal: Cell Regeneration

    Article Title: Integrative analysis and experimental validation identify the role of CD44 and Nucleolin in regulating gliogenesis following spinal cord injury

    doi: 10.1186/s13619-025-00253-x

    Figure Lengend Snippet: Dynamic cell portion changes and intercellular communication analysis revealed SPP1 signaling pathway was critical in microglia after SCI. A , B Stacked bar plots depicting changes in the relative abundance of major cell types in spinal cord ( A ) and peripheral immune cell populations ( B ) across various time points. Astrocytes, microglia, OPCs, and MDMs show marked shifts, particularly in the acute (1~3 dpi) and subacute phases of SCI, the absence of 42 dpi stems directly from the source data ( GSE172167 ), where immune cell clusters were not identified or annotated at this specific time point in the original study. C Intercellular communication networks illustrate increased signaling complexity at 1 and 3 dpi compared to the sham condition. D Quantitative result of the total number of interactions in sham, 1 dpi, and 3 dpi samples, showing a significant increase in cell–cell interactions post-injury. E Heatmaps displaying the changes in signaling patterns for key cell types. SPP1 became prominent at 1 dpi. F Information flow of microglia indicated the SPP1 signal was significant

    Article Snippet: After adhesion, recombinant mouse SPP1 protein (MCE, Cat No. HY- P71786 ) and recombinant mouse PTN protein (MCE, Cat No. HY- P71213 ) were separately administered to the microglia and astrocytes at concentrations of 0, 0.1, 0.5, and 1 μg/mL for a duration of 24 h. Subsequent to the stimulation period, the culture medium was carefully removed, and the cells were gently washed twice with PBS.

    Techniques:

    SPP1-CD44 signaling promotes microglial activation and inflammatory response. A SPP1 signaling pathway network showing interactions between microglia and other cell types. B A circular plot illustrating the interaction network of microglia with other cells in various ligand -receptor pairs, including Spp1 - Cd44 . C , D Violin plots showing expression levels of Spp1 and Cd44 across different cell types in sham (blue) and 1 dpi (red). Both genes show elevated expression in microglia following injury. E Violin plot of Cd44 expression in microglia subclusters, showing the upregulation in the wound healing and inflammatory response2 cluster at 1 dpi. F The microglia were sorted by flow cytometry and ( G ) Cd44 gene expression was detected by qRCR. H Flow cytometry analysis of CD44 positive microglia after SCI, showing a marked increase in CD44 + microglia during 7 dpi. I Immunofluorescence images of spinal cord lesion site stained for Iba1, CD44, SPP1, and merged with DAPI. White arrows indicate the co-stained CD44 + and SPP1 + signals in Iba1 positive microglia ( J ) Quantification results of CD44 + and SPP1 + in Iba1 positive microglia cells. K Using PLA to detect specific SPP1-CD44 interactions of spinal cord lesion site in situ. L Quantification of PLA results, the PLA signal is quantified and plotted as the area of PLA signal per Iba1 positive cell. M , N qRT-PCR showing dose- and time-dependent increases of Cd44 expression in BV2 microglia after recombinant SPP1 stimulation. O Representative images of PLA assay specific SPP1-CD44 interactions of BV2 microglia cells in vitro. P PLA signal was quantified and plotted as the area of PLA signal per cell. Q ELISA quantification of IL-6 levels in cell supernatant after SPP1 stimulation. R Western blot showing the time course of CD44 and p-NF-κB p65 protein expression in BV2 cells after SPP1 treatment. ( S – T ) Quantification of CD44 and p-NF-κB p65 protein levels. Data are presented as mean ± SEM. ( n = 3, * P < 0.05, ** P < 0.01, *** P < 0.001)

    Journal: Cell Regeneration

    Article Title: Integrative analysis and experimental validation identify the role of CD44 and Nucleolin in regulating gliogenesis following spinal cord injury

    doi: 10.1186/s13619-025-00253-x

    Figure Lengend Snippet: SPP1-CD44 signaling promotes microglial activation and inflammatory response. A SPP1 signaling pathway network showing interactions between microglia and other cell types. B A circular plot illustrating the interaction network of microglia with other cells in various ligand -receptor pairs, including Spp1 - Cd44 . C , D Violin plots showing expression levels of Spp1 and Cd44 across different cell types in sham (blue) and 1 dpi (red). Both genes show elevated expression in microglia following injury. E Violin plot of Cd44 expression in microglia subclusters, showing the upregulation in the wound healing and inflammatory response2 cluster at 1 dpi. F The microglia were sorted by flow cytometry and ( G ) Cd44 gene expression was detected by qRCR. H Flow cytometry analysis of CD44 positive microglia after SCI, showing a marked increase in CD44 + microglia during 7 dpi. I Immunofluorescence images of spinal cord lesion site stained for Iba1, CD44, SPP1, and merged with DAPI. White arrows indicate the co-stained CD44 + and SPP1 + signals in Iba1 positive microglia ( J ) Quantification results of CD44 + and SPP1 + in Iba1 positive microglia cells. K Using PLA to detect specific SPP1-CD44 interactions of spinal cord lesion site in situ. L Quantification of PLA results, the PLA signal is quantified and plotted as the area of PLA signal per Iba1 positive cell. M , N qRT-PCR showing dose- and time-dependent increases of Cd44 expression in BV2 microglia after recombinant SPP1 stimulation. O Representative images of PLA assay specific SPP1-CD44 interactions of BV2 microglia cells in vitro. P PLA signal was quantified and plotted as the area of PLA signal per cell. Q ELISA quantification of IL-6 levels in cell supernatant after SPP1 stimulation. R Western blot showing the time course of CD44 and p-NF-κB p65 protein expression in BV2 cells after SPP1 treatment. ( S – T ) Quantification of CD44 and p-NF-κB p65 protein levels. Data are presented as mean ± SEM. ( n = 3, * P < 0.05, ** P < 0.01, *** P < 0.001)

    Article Snippet: After adhesion, recombinant mouse SPP1 protein (MCE, Cat No. HY- P71786 ) and recombinant mouse PTN protein (MCE, Cat No. HY- P71213 ) were separately administered to the microglia and astrocytes at concentrations of 0, 0.1, 0.5, and 1 μg/mL for a duration of 24 h. Subsequent to the stimulation period, the culture medium was carefully removed, and the cells were gently washed twice with PBS.

    Techniques: Activation Assay, Expressing, Flow Cytometry, Gene Expression, Immunofluorescence, Staining, In Situ, Quantitative RT-PCR, Recombinant, In Vitro, Enzyme-linked Immunosorbent Assay, Western Blot

    A: RNA-seq analysis of HSCs (LX2) treated with GDF15 (1μg/ml, 24 hr) identifies increased expression of genes encoding multiple ECM proteins ( COL7A1 ), and molecules involved in angiogenesis ( VCAM1, ANGPLT4, PDGFB, FLT1 ). B: Pathway analysis identifies GDF15 to increase expression of genes involved in inflammatory signaling ( TNFA, IFNγ, IL6 ), angiogenesis (hypoxia, angiogenesis) and metabolism. C: Human primary HSCs (HHStec) were stimulated with either GDF15 (1μg/ml) or SPP1 (osteopontin, (1μg/ml)) for 24 hr and Western Blotting used to determine expression of IL-8 and phospho-STAT3. D: HSC (HHStec cells) were grown in monoculture, MP41 cells were grown in monoculture, HSC+MP41 cells were grown in co-culture, HSCs were treated with conditioned media (CM) from MP41 cells or GDF15 (1μg/ml) for 24 hr before probing for the expression of COL1A1 and PDGFRB by Western Blot. E: Immunofluorescence staining of COL1A1 in HSCs (HHStec) cells treated with basal media or conditioned media from MP41 or UM066 cells. F: CellChat analysis of human UM samples identifies HSC-derived collagens as being a potential outgoing signal to multiple immune subtypes and UM cells.

    Journal: bioRxiv

    Article Title: GDF15 reprograms the microenvironment to drive the development of uveal melanoma liver metastases

    doi: 10.1101/2025.05.07.652654

    Figure Lengend Snippet: A: RNA-seq analysis of HSCs (LX2) treated with GDF15 (1μg/ml, 24 hr) identifies increased expression of genes encoding multiple ECM proteins ( COL7A1 ), and molecules involved in angiogenesis ( VCAM1, ANGPLT4, PDGFB, FLT1 ). B: Pathway analysis identifies GDF15 to increase expression of genes involved in inflammatory signaling ( TNFA, IFNγ, IL6 ), angiogenesis (hypoxia, angiogenesis) and metabolism. C: Human primary HSCs (HHStec) were stimulated with either GDF15 (1μg/ml) or SPP1 (osteopontin, (1μg/ml)) for 24 hr and Western Blotting used to determine expression of IL-8 and phospho-STAT3. D: HSC (HHStec cells) were grown in monoculture, MP41 cells were grown in monoculture, HSC+MP41 cells were grown in co-culture, HSCs were treated with conditioned media (CM) from MP41 cells or GDF15 (1μg/ml) for 24 hr before probing for the expression of COL1A1 and PDGFRB by Western Blot. E: Immunofluorescence staining of COL1A1 in HSCs (HHStec) cells treated with basal media or conditioned media from MP41 or UM066 cells. F: CellChat analysis of human UM samples identifies HSC-derived collagens as being a potential outgoing signal to multiple immune subtypes and UM cells.

    Article Snippet: For recombinant proteins: human osteopontin (SPP1) (Sino biological #10352-H08H), GDF-15 (#957-GD, R&D Systems) were used at 1μg/ml to stimulate serum-starved HSCs.

    Techniques: RNA Sequencing, Expressing, Western Blot, Co-Culture Assay, Immunofluorescence, Staining, Derivative Assay

    (A) Scheme of human cancer types included in the integrated analysis. (B) Numbers of samples in each normal tissue or cancer type. (C) Marker genes of the major cell types in the pan-cancer scRNA-seq atlas. (D) UMAPs showing LUM , MCAM , CSPG4 , MYH11 , ACTA2 , FAP , MMP11 , PIEZO2 , C1QA , C1QB , C1QC , CD34 expression in CAFs. (E) Signature genes of apCAF subclusters. (F) Expression of CD24 and CD37 in the two apCAF lineages. (G) Combined overall survival of the 14 types of cancer with SPP1 expression. (H) Regulon of SPI1 in each CAF subcluster revealed by SCENIC algorithm. (I) Regulon of POU5F1 in each CAF subcluster revealed by SCENIC algorithm. (J) ChIP-seq binding peaks of OCT4 at the SPP1 promoter in fibroblasts visualized using the WashU Epigenome Browser from the Cistrome project.

    Journal: bioRxiv

    Article Title: Single-cell resolution spatial analysis of antigen-presenting cancer-associated fibroblast niches

    doi: 10.1101/2024.11.15.623232

    Figure Lengend Snippet: (A) Scheme of human cancer types included in the integrated analysis. (B) Numbers of samples in each normal tissue or cancer type. (C) Marker genes of the major cell types in the pan-cancer scRNA-seq atlas. (D) UMAPs showing LUM , MCAM , CSPG4 , MYH11 , ACTA2 , FAP , MMP11 , PIEZO2 , C1QA , C1QB , C1QC , CD34 expression in CAFs. (E) Signature genes of apCAF subclusters. (F) Expression of CD24 and CD37 in the two apCAF lineages. (G) Combined overall survival of the 14 types of cancer with SPP1 expression. (H) Regulon of SPI1 in each CAF subcluster revealed by SCENIC algorithm. (I) Regulon of POU5F1 in each CAF subcluster revealed by SCENIC algorithm. (J) ChIP-seq binding peaks of OCT4 at the SPP1 promoter in fibroblasts visualized using the WashU Epigenome Browser from the Cistrome project.

    Article Snippet: For the wound healing assay, cells were plated onto 6-well tissue culture plates coated with 50 μg/ml Matrigel (BD Biosciences) with or without 100 ng/ml recombinant mouse SPP1 protein (R&D Systems) or 1 μg/ml SPP1 monoclonal antibody (Bio X Cell).

    Techniques: Marker, Expressing, ChIP-sequencing, Binding Assay

    (A) All apCAFs marked by MHC II molecule expression are extracted from and re-clustered, revealing four apCAF subclusters. (B) UMAPs of signature genes of apCAF subclusters including CD74 , MSLN , UPK3B , KRT19 , PTPRC , CD52 (C) Pseudotime analysis reveals two distinct trajectories of apCAFs. Expression of CD74 , HLA-DRA , MSLN , PTPRC and SPP1 along the trajectories are shown. (D) Up-regulated genes in the F-apCAF lineage (subcluster 2 vs 1) are used to perform GSEA pathway analysis. Significant pathways are shown. (E) Up-regulated genes in the M-apCAF lineage (subcluster 3 vs 0) are used to perform GSEA pathway analysis. Significant pathways are shown. (F) Differentially expressed genes in apCAFs in cancer compared to normal tissues. Six most up-regulated and robustly expressed genes are identified: NDUFA4L2 , SPP1 , PLOD2 , EGLN3 , ANGPTL4 , HILPDA . (G) Expression of NDUFA4L2 , SPP1 , PLOD2 , EGLN3 , ANGPTL4 , HILPDA in each CAF subcluster. (H) Combined overall survival of the 14 types of cancer with the six-gene signature ( NDUFA4L2 , SPP1 , PLOD2 , EGLN3 , ANGPTL4 , HILPDA ). (I) Regulatory network of transcription factors in each CAF subcluster revealed by SCENIC algorithm. (J) Regulatory network of genes by SPI1 in F-apCAFs. (K) Regulatory network of genes by POU5F1 in M-apCAFs. (L) Abundance of F-apCAFs in different cancer types. (M) Abundance of M-apCAFs in different cancer types.

    Journal: bioRxiv

    Article Title: Single-cell resolution spatial analysis of antigen-presenting cancer-associated fibroblast niches

    doi: 10.1101/2024.11.15.623232

    Figure Lengend Snippet: (A) All apCAFs marked by MHC II molecule expression are extracted from and re-clustered, revealing four apCAF subclusters. (B) UMAPs of signature genes of apCAF subclusters including CD74 , MSLN , UPK3B , KRT19 , PTPRC , CD52 (C) Pseudotime analysis reveals two distinct trajectories of apCAFs. Expression of CD74 , HLA-DRA , MSLN , PTPRC and SPP1 along the trajectories are shown. (D) Up-regulated genes in the F-apCAF lineage (subcluster 2 vs 1) are used to perform GSEA pathway analysis. Significant pathways are shown. (E) Up-regulated genes in the M-apCAF lineage (subcluster 3 vs 0) are used to perform GSEA pathway analysis. Significant pathways are shown. (F) Differentially expressed genes in apCAFs in cancer compared to normal tissues. Six most up-regulated and robustly expressed genes are identified: NDUFA4L2 , SPP1 , PLOD2 , EGLN3 , ANGPTL4 , HILPDA . (G) Expression of NDUFA4L2 , SPP1 , PLOD2 , EGLN3 , ANGPTL4 , HILPDA in each CAF subcluster. (H) Combined overall survival of the 14 types of cancer with the six-gene signature ( NDUFA4L2 , SPP1 , PLOD2 , EGLN3 , ANGPTL4 , HILPDA ). (I) Regulatory network of transcription factors in each CAF subcluster revealed by SCENIC algorithm. (J) Regulatory network of genes by SPI1 in F-apCAFs. (K) Regulatory network of genes by POU5F1 in M-apCAFs. (L) Abundance of F-apCAFs in different cancer types. (M) Abundance of M-apCAFs in different cancer types.

    Article Snippet: For the wound healing assay, cells were plated onto 6-well tissue culture plates coated with 50 μg/ml Matrigel (BD Biosciences) with or without 100 ng/ml recombinant mouse SPP1 protein (R&D Systems) or 1 μg/ml SPP1 monoclonal antibody (Bio X Cell).

    Techniques: Expressing

    (A) IHC staining for pan-cytokeratin (PanCK) in human PM samples. Scale bars, 250 μm. (red arrow, normal mesothelium; blue arrow, cytokeratin + CAFs). (B) Multiplex IHC staining for PanCK, SPINK4 and DAPI in human PM samples. Scale bars, 10 μm. (C) Visualization of normal mesothelium adjacent to M-apCAF-enriched areas from Xenium assay. M-apCAFs, cancer cells and the expression of normal mesothelial cell genes MSLN and UPK3B are shown. (D) Ligand-receptor interaction analysis between M-apCAFs (ligands) and different populations of immune cells (receptors). (E) Ligand-receptor interaction analysis between M-apCAFs (ligands) and cancer cells (receptors). (F) SPP1 expression in the iCMS2 CAFs (patient 2, 3, 5, 8 (P2, P3, P5, P8)) and iCMS3 CAFs (patient 1, 4, 6, 7 (P1, P4, P6, P7)) from the GeoMx assay.

    Journal: bioRxiv

    Article Title: Single-cell resolution spatial analysis of antigen-presenting cancer-associated fibroblast niches

    doi: 10.1101/2024.11.15.623232

    Figure Lengend Snippet: (A) IHC staining for pan-cytokeratin (PanCK) in human PM samples. Scale bars, 250 μm. (red arrow, normal mesothelium; blue arrow, cytokeratin + CAFs). (B) Multiplex IHC staining for PanCK, SPINK4 and DAPI in human PM samples. Scale bars, 10 μm. (C) Visualization of normal mesothelium adjacent to M-apCAF-enriched areas from Xenium assay. M-apCAFs, cancer cells and the expression of normal mesothelial cell genes MSLN and UPK3B are shown. (D) Ligand-receptor interaction analysis between M-apCAFs (ligands) and different populations of immune cells (receptors). (E) Ligand-receptor interaction analysis between M-apCAFs (ligands) and cancer cells (receptors). (F) SPP1 expression in the iCMS2 CAFs (patient 2, 3, 5, 8 (P2, P3, P5, P8)) and iCMS3 CAFs (patient 1, 4, 6, 7 (P1, P4, P6, P7)) from the GeoMx assay.

    Article Snippet: For the wound healing assay, cells were plated onto 6-well tissue culture plates coated with 50 μg/ml Matrigel (BD Biosciences) with or without 100 ng/ml recombinant mouse SPP1 protein (R&D Systems) or 1 μg/ml SPP1 monoclonal antibody (Bio X Cell).

    Techniques: Immunohistochemistry, Multiplex Assay, Expressing

    (A) Robust cell type decomposition is performed in human PM sample with robust cytokeratin + CAF formation to deconvolve the Xenium data into cell types using our pan-cancer scRNA-seq atlas as reference. (B) Four spatial niches are identified. Percentages of cell types within each niche are shown. (C) Visualization of the spatial distribution of different cell types in four M-apCAF-enriched regions. (D) Expression of T cell immunosuppressive genes across four spatial niches. (E) RT-PCR (n=3/group) and western blots measuring the expression of SPP1 in OmMeso cells after tumor conditioned medium treatment. (F) Wound healing assays are performed to measure the migration capability of MC38 colon cancer cells in the presence of mouse recombinant protein or anti-SPP1 mAb. Representative pictures of cell migration at 0h, 24h, 48h are shown. n=3/group. (G) Matrigel transwell assays in the presence of mouse recombinant protein or anti-SPP1 mAb for 24 hours are performed. Representative pictures for each group are shown. n=3/group. (H) MC38 cancer cells are injected intraperitoneally into wildtype (WT) or Spp1 knockout (KO) mice on a C57BL/6 background (WT, n=8; KO, n=10). Mice are sacrificed 4 weeks after cancer cell injection. Peritoneal cancer index (PCI) scores and ascites formation are measured. (I) MC38 cancer cells are injected intraperitoneally into wildtype C57BL/6 mice. Mice are treated with control Ab or anti-SPP1 mAb (n=5/group) one week after cancer cell injection and maintained at two doses/week. Mice are sacrificed 4 weeks after cancer cell injection. PCI scores and ascites formation are measured.

    Journal: bioRxiv

    Article Title: Single-cell resolution spatial analysis of antigen-presenting cancer-associated fibroblast niches

    doi: 10.1101/2024.11.15.623232

    Figure Lengend Snippet: (A) Robust cell type decomposition is performed in human PM sample with robust cytokeratin + CAF formation to deconvolve the Xenium data into cell types using our pan-cancer scRNA-seq atlas as reference. (B) Four spatial niches are identified. Percentages of cell types within each niche are shown. (C) Visualization of the spatial distribution of different cell types in four M-apCAF-enriched regions. (D) Expression of T cell immunosuppressive genes across four spatial niches. (E) RT-PCR (n=3/group) and western blots measuring the expression of SPP1 in OmMeso cells after tumor conditioned medium treatment. (F) Wound healing assays are performed to measure the migration capability of MC38 colon cancer cells in the presence of mouse recombinant protein or anti-SPP1 mAb. Representative pictures of cell migration at 0h, 24h, 48h are shown. n=3/group. (G) Matrigel transwell assays in the presence of mouse recombinant protein or anti-SPP1 mAb for 24 hours are performed. Representative pictures for each group are shown. n=3/group. (H) MC38 cancer cells are injected intraperitoneally into wildtype (WT) or Spp1 knockout (KO) mice on a C57BL/6 background (WT, n=8; KO, n=10). Mice are sacrificed 4 weeks after cancer cell injection. Peritoneal cancer index (PCI) scores and ascites formation are measured. (I) MC38 cancer cells are injected intraperitoneally into wildtype C57BL/6 mice. Mice are treated with control Ab or anti-SPP1 mAb (n=5/group) one week after cancer cell injection and maintained at two doses/week. Mice are sacrificed 4 weeks after cancer cell injection. PCI scores and ascites formation are measured.

    Article Snippet: For the wound healing assay, cells were plated onto 6-well tissue culture plates coated with 50 μg/ml Matrigel (BD Biosciences) with or without 100 ng/ml recombinant mouse SPP1 protein (R&D Systems) or 1 μg/ml SPP1 monoclonal antibody (Bio X Cell).

    Techniques: Expressing, Reverse Transcription Polymerase Chain Reaction, Western Blot, Migration, Recombinant, Injection, Knock-Out, Control

    (A) UMAP of major cell types across the 6 human PDAC samples identified from the Xenium assays. (B) Marker genes of the major cell types in PDAC. (C) Proportions of the major cell types in the treatment naïve and chemoradiotherapy (chemo-RT)-treated PDAC samples. (D) Expression of SPP1 in cancer cell 1 and cancer cell 2 populations. (E) Robust cell type decomposition is performed in PDAC to deconvolve the Xenium data into cell types using our pan-cancer scRNA-seq atlas as reference. (F) Spp1 WT or KO tumors are digested into single cell suspension and subjected to scRNA-seq. Major cell types are identified in the merged data from Spp1 WT and KO groups. (G) Marker genes of major cell types in Spp1 WT and KO tumors. (H) IHC staining and quantification for CD3 and CD8 in Spp1 WT and KO tumors (n=3/group). (I) Expression of Spp1 , Ptprc , Cd24a and Msln in CAFs of Spp1 WT and KO tumors.

    Journal: bioRxiv

    Article Title: Single-cell resolution spatial analysis of antigen-presenting cancer-associated fibroblast niches

    doi: 10.1101/2024.11.15.623232

    Figure Lengend Snippet: (A) UMAP of major cell types across the 6 human PDAC samples identified from the Xenium assays. (B) Marker genes of the major cell types in PDAC. (C) Proportions of the major cell types in the treatment naïve and chemoradiotherapy (chemo-RT)-treated PDAC samples. (D) Expression of SPP1 in cancer cell 1 and cancer cell 2 populations. (E) Robust cell type decomposition is performed in PDAC to deconvolve the Xenium data into cell types using our pan-cancer scRNA-seq atlas as reference. (F) Spp1 WT or KO tumors are digested into single cell suspension and subjected to scRNA-seq. Major cell types are identified in the merged data from Spp1 WT and KO groups. (G) Marker genes of major cell types in Spp1 WT and KO tumors. (H) IHC staining and quantification for CD3 and CD8 in Spp1 WT and KO tumors (n=3/group). (I) Expression of Spp1 , Ptprc , Cd24a and Msln in CAFs of Spp1 WT and KO tumors.

    Article Snippet: For the wound healing assay, cells were plated onto 6-well tissue culture plates coated with 50 μg/ml Matrigel (BD Biosciences) with or without 100 ng/ml recombinant mouse SPP1 protein (R&D Systems) or 1 μg/ml SPP1 monoclonal antibody (Bio X Cell).

    Techniques: Marker, Expressing, Suspension, Immunohistochemistry

    (A) Spatial niches are identified in human PDAC sample with TLS formation. (B) Based on the spatial niches and expression of SPP1 in cancer cells, PDAC sample is classified into four areas: stroma, TLS, SPP1 - and SPP1 + cancer. Deconvolved cell types are shown in each area. (C) Expression of SPP1 is visualized in SPP1 - and SPP1 + cancer areas. (D) Proportions of F-apCAFs and M-apCAFs are quantified in stromal, TLS, SPP1 - and SPP1 + cancer areas (n=3 for each area). (E) Western blots measuring the expression of SPP1 in PanMeso cells after tumor conditioned medium treatment. (F) GFP + PanMeso cells are co-injected with a murine PDAC cell line (BMFA3: In Vivo 1 or CT1BA5: In Vivo 2) at a 1:1 ratio. Tumors are harvested 1 month after injection and digested into single-cell suspension. GFP + cells are collected by flow sorting and subjected to RNA-seq analysis in comparison to parental PanMeso cells to evaluate the Spp1 expression. (G) Syngeneic PDAC cancer cells (6620c1) are injected orthotopically into wildtype (WT) or Spp1 knockout (KO) C57BL/6 mice (n=6/group). Tumors are harvested 1 month after injection. (H) Spp1 WT or KO tumors are digested into single cell suspension and subjected to scRNA-seq (6 tumors/group, every two tumors are pooled together for library construction). Ratio of each cell type between WT and KO group is compared and quantified. (I) CAFs from both Spp1 WT or KO tumors are extracted from the scRNA-seq data. iCAF, myCAF and apCAF clusters are identified. (J) Signature genes of each CAF subtype. (K) Proportional changes of CAF subtypes between Spp1 WT and KO group. (L) UMAPs showing sslCAF marker Pi16 and Dpt expression between Spp1 WT and KO tumors. (M) CytoTRACE analysis determining the progenitor and differentiation status among iCAFs, myCAFs and apCAFs, with higher score indicating more stem-like and less differentiated status. (N) Quantification of the expression of T cell chemoattractant genes in CAFs between Spp1 WT and KO tumors. (O) Syngeneic PDAC cancer cells (6620c1) are injected orthotopically into wildtype C57BL/6 mice. Mice are treated with control Ab (n=5) or anti-SPP1 mAb (n=7) one week after cancer cell injection and maintained at two doses/week. Mice are sacrificed 4 weeks after cancer cell injection.

    Journal: bioRxiv

    Article Title: Single-cell resolution spatial analysis of antigen-presenting cancer-associated fibroblast niches

    doi: 10.1101/2024.11.15.623232

    Figure Lengend Snippet: (A) Spatial niches are identified in human PDAC sample with TLS formation. (B) Based on the spatial niches and expression of SPP1 in cancer cells, PDAC sample is classified into four areas: stroma, TLS, SPP1 - and SPP1 + cancer. Deconvolved cell types are shown in each area. (C) Expression of SPP1 is visualized in SPP1 - and SPP1 + cancer areas. (D) Proportions of F-apCAFs and M-apCAFs are quantified in stromal, TLS, SPP1 - and SPP1 + cancer areas (n=3 for each area). (E) Western blots measuring the expression of SPP1 in PanMeso cells after tumor conditioned medium treatment. (F) GFP + PanMeso cells are co-injected with a murine PDAC cell line (BMFA3: In Vivo 1 or CT1BA5: In Vivo 2) at a 1:1 ratio. Tumors are harvested 1 month after injection and digested into single-cell suspension. GFP + cells are collected by flow sorting and subjected to RNA-seq analysis in comparison to parental PanMeso cells to evaluate the Spp1 expression. (G) Syngeneic PDAC cancer cells (6620c1) are injected orthotopically into wildtype (WT) or Spp1 knockout (KO) C57BL/6 mice (n=6/group). Tumors are harvested 1 month after injection. (H) Spp1 WT or KO tumors are digested into single cell suspension and subjected to scRNA-seq (6 tumors/group, every two tumors are pooled together for library construction). Ratio of each cell type between WT and KO group is compared and quantified. (I) CAFs from both Spp1 WT or KO tumors are extracted from the scRNA-seq data. iCAF, myCAF and apCAF clusters are identified. (J) Signature genes of each CAF subtype. (K) Proportional changes of CAF subtypes between Spp1 WT and KO group. (L) UMAPs showing sslCAF marker Pi16 and Dpt expression between Spp1 WT and KO tumors. (M) CytoTRACE analysis determining the progenitor and differentiation status among iCAFs, myCAFs and apCAFs, with higher score indicating more stem-like and less differentiated status. (N) Quantification of the expression of T cell chemoattractant genes in CAFs between Spp1 WT and KO tumors. (O) Syngeneic PDAC cancer cells (6620c1) are injected orthotopically into wildtype C57BL/6 mice. Mice are treated with control Ab (n=5) or anti-SPP1 mAb (n=7) one week after cancer cell injection and maintained at two doses/week. Mice are sacrificed 4 weeks after cancer cell injection.

    Article Snippet: For the wound healing assay, cells were plated onto 6-well tissue culture plates coated with 50 μg/ml Matrigel (BD Biosciences) with or without 100 ng/ml recombinant mouse SPP1 protein (R&D Systems) or 1 μg/ml SPP1 monoclonal antibody (Bio X Cell).

    Techniques: Expressing, Western Blot, Injection, In Vivo, Suspension, RNA Sequencing Assay, Comparison, Knock-Out, Marker, Control

    Characterization of ligament immune compartment and possible pathogenic role of SPP1 + macrophages. (a) UMAP plot of immune cells in spinal ligament tissues. (b) Inflammation scores of the main immune cell lineages in ligament. (c) UMAP plot of monocytes and macrophages in spinal ligament tissues. (d) Hierarchical clustering of DEGs in monocytes and macrophages. (e) Pseudo‐time analysis of monocyte and macrophage subpopulations. (f) Pathway enrichment analysis for DEGs of macrophages from ligament tissues. (g) Ligand–receptor interactions between macrophages and other cell types. (h–j) Quantitative PCR assays (h) and western blot analysis (i, j) of control cultured primary ligament cells and those treated with short interfering RNA targeting ATF3 and recombinant SPP1. *, p ‐value < 0.05; **, p ‐value < 0.01; ***, p ‐value < 0.001.

    Journal: Aging Cell

    Article Title: Single‐cell RNA sequencing reveals the CRTAC1 + population actively contributes to the pathogenesis of spinal ligament degeneration by SPP1 + macrophage

    doi: 10.1111/acel.14320

    Figure Lengend Snippet: Characterization of ligament immune compartment and possible pathogenic role of SPP1 + macrophages. (a) UMAP plot of immune cells in spinal ligament tissues. (b) Inflammation scores of the main immune cell lineages in ligament. (c) UMAP plot of monocytes and macrophages in spinal ligament tissues. (d) Hierarchical clustering of DEGs in monocytes and macrophages. (e) Pseudo‐time analysis of monocyte and macrophage subpopulations. (f) Pathway enrichment analysis for DEGs of macrophages from ligament tissues. (g) Ligand–receptor interactions between macrophages and other cell types. (h–j) Quantitative PCR assays (h) and western blot analysis (i, j) of control cultured primary ligament cells and those treated with short interfering RNA targeting ATF3 and recombinant SPP1. *, p ‐value < 0.05; **, p ‐value < 0.01; ***, p ‐value < 0.001.

    Article Snippet: For recombinant protein stimulation experiments, primary human ligament cells from patients with traumatic injury were incubated with 200 ng/mL recombinant SPP1 protein (1433‐OP‐050, R&D Systems, USA).

    Techniques: Real-time Polymerase Chain Reaction, Western Blot, Control, Cell Culture, Small Interfering RNA, Recombinant