Journal: PLOS One

Article Title: Single-cell transcriptomics and machine learning characterize LTA4H-linked lipid metabolism and inflammatory signatures in the knee osteoarthritis IPFP/synovium compartment

doi: 10.1371/journal.pone.0360615

Figure Lengend Snippet: (A) Boruta algorithm evaluating the importance of the top 20 genes ranked by node degree in the PPI network; (B) Gene selection and regression coefficients in the elastic net model; (C) Cross-validation in LASSO regression for determining the optimal penalty parameter; (D) Out-of-bag error curve of the random forest model; (E) Cross-validation accuracy corresponding to different numbers of variables in SVM-RFE analysis; (F) Intersection analysis of genes selected by five machine-learning algorithms, including LASSO, elastic net, random forest, Boruta, and SVM-RFE; (G) ROC curves and corresponding AUCs of the candidate feature gene model in the training and validation cohorts; (H) Comparison of model-predicted KOA probabilities between the control and KOA groups; (I) Expression differences of the candidate feature genes LTA4H, PLIN2, and PPT1 in the training cohort, validation cohort, and merged dataset; (J) Reactome GSEA pathway enrichment analysis related to the candidate feature genes.

Article Snippet: Paraffin-embedded tissue sections were deparaffinized, rehydrated, subjected to antigen retrieval, and blocked before incubation with a primary antibody against LTA4H (Bioss, bs-3908R, 1:300) at 4°C overnight.

Techniques: Selection, Biomarker Discovery, Analysis, Comparison, Control, Expressing

Journal: PLOS One

Article Title: Single-cell transcriptomics and machine learning characterize LTA4H-linked lipid metabolism and inflammatory signatures in the knee osteoarthritis IPFP/synovium compartment

doi: 10.1371/journal.pone.0360615

Figure Lengend Snippet: (A) UMAP distribution of LTA4H, PLIN2, and PPT1 expression; (B) Expression levels of LTA4H, PLIN2, and PPT1 across different cell types; (C) Expression differences of the three genes between the control and KOA groups in myeloid cells; (D) Expression differences of the three genes between the high and low composite lipid metabolism score groups; (E) Spearman correlation analysis between the expression levels of the three genes and the composite lipid metabolism score; (F) Summary of Spearman’s ρ values for the three candidate genes in KOA myeloid cells (n = 4,823 cells).

Article Snippet: Paraffin-embedded tissue sections were deparaffinized, rehydrated, subjected to antigen retrieval, and blocked before incubation with a primary antibody against LTA4H (Bioss, bs-3908R, 1:300) at 4°C overnight.

Techniques: Expressing, Control, Analysis

Journal: PLOS One

Article Title: Single-cell transcriptomics and machine learning characterize LTA4H-linked lipid metabolism and inflammatory signatures in the knee osteoarthritis IPFP/synovium compartment

doi: 10.1371/journal.pone.0360615

Figure Lengend Snippet: (A) Overall cell–cell communication network between LTA4H⁺ Myeloid and LTA4H − Myeloid cells and other cell types in KOA samples; (B) Heatmap of incoming and outgoing signaling patterns across cell populations; (C) Analysis of signaling sending and receiving strength across cell populations; (D) Ligand–receptor interaction analysis with LTA4H + /LTA4H − Myeloid cells as signaling sources and target cells; (E) State distribution of myeloid cells along the pseudotime trajectory; (F) Pseudotime distribution of myeloid cells along the trajectory; (G) Distribution of myeloid cells from the control and KOA groups along the pseudotime trajectory; (H) Distribution of LTA4H⁺Myeloid and LTA4H − Myeloid cells along the pseudotime trajectory; (I) Distribution of LTA4H expression levels along the myeloid-cell pseudotime trajectory; (J) Proportion of LTA4H-positive cells across different States. CellChat analysis included 11,817 cells from three KOA samples, whereas pseudotime analysis included 9,512 myeloid cells from six biological samples.

Article Snippet: Paraffin-embedded tissue sections were deparaffinized, rehydrated, subjected to antigen retrieval, and blocked before incubation with a primary antibody against LTA4H (Bioss, bs-3908R, 1:300) at 4°C overnight.

Techniques: Analysis, Control, Expressing

Journal: PLOS One

Article Title: Single-cell transcriptomics and machine learning characterize LTA4H-linked lipid metabolism and inflammatory signatures in the knee osteoarthritis IPFP/synovium compartment

doi: 10.1371/journal.pone.0360615

Figure Lengend Snippet: (A) UMAP distribution of myeloid cells by sample origin; (B) UMAP distribution of annotated myeloid-cell subclusters; (C) Dot plot showing the expression of canonical marker genes across myeloid-cell subclusters; (D) UMAP distribution of myeloid-cell subclusters in the control and KOA groups; (E) Compositional proportions of myeloid-cell subclusters in the control and KOA groups; (F) Expression levels of LTA4H across different myeloid-cell subclusters; (G) Pseudotime trajectory of monocytes and inflammatory macrophages; (H) Distribution of monocytes and inflammatory macrophages along the pseudotime trajectory; (I) Expression distributions of the inflammation-related genes CCL3, TNF, IL1B, and OLR1 along the pseudotime trajectory; (J) Distribution of the inflammatory score calculated based on inflammation-related genes along the pseudotime trajectory; (K) Changes in the proportion of LTA4H-positive cells along pseudotime progression. Subclustering included 9,512 myeloid cells, whereas pseudotime analysis included 3,579 monocytes and inflammatory macrophages from six biological samples.

Article Snippet: Paraffin-embedded tissue sections were deparaffinized, rehydrated, subjected to antigen retrieval, and blocked before incubation with a primary antibody against LTA4H (Bioss, bs-3908R, 1:300) at 4°C overnight.

Techniques: Expressing, Marker, Control, Analysis

Journal: PLOS One

Article Title: Single-cell transcriptomics and machine learning characterize LTA4H-linked lipid metabolism and inflammatory signatures in the knee osteoarthritis IPFP/synovium compartment

doi: 10.1371/journal.pone.0360615

Figure Lengend Snippet: (A) LTA4H-centered regulatory network in inflammatory macrophages under the in silico knockout framework; (B) Manifold alignment analysis of gene regulatory networks before and after LTA4H in silico knockout; (C) Relationship between mean gene expression and perturbation strength; (D) Volcano plot of gene perturbations after LTA4H in silico knockout; (E) Bar plot of the top 20 perturbed genes following LTA4H in silico knockout; (F) Perturbation strength and significance analysis of the top 20 perturbed genes; (G) GO functional enrichment analysis of genes perturbed after LTA4H in silico knockout. The analysis included 910 inflammatory macrophages from six biological samples.

Article Snippet: Paraffin-embedded tissue sections were deparaffinized, rehydrated, subjected to antigen retrieval, and blocked before incubation with a primary antibody against LTA4H (Bioss, bs-3908R, 1:300) at 4°C overnight.

Techniques: In Silico, Knock-Out, Analysis, Gene Expression, Functional Assay

Journal: PLOS One

Article Title: Single-cell transcriptomics and machine learning characterize LTA4H-linked lipid metabolism and inflammatory signatures in the knee osteoarthritis IPFP/synovium compartment

doi: 10.1371/journal.pone.0360615

Figure Lengend Snippet: (A) Representative Safranin O/Fast Green staining of knee joints from the Sham and KOA groups. (B) Quantification of cartilage degeneration using the OARSI histopathological score. (C) Representative immunohistochemical staining of LTA4H in knee synovial tissues from the Sham and KOA groups. (D) Quantitative analysis of LTA4H immunohistochemical staining using the H-score. (E) Representative double immunofluorescence staining showing the spatial colocalization of LTA4H with the macrophage marker CD68 in knee synovial tissues. Each point in the quantitative plots represents one animal (n = 4 per group). OARSI scores were compared using the Mann–Whitney U test, whereas H-scores were compared using an independent-samples t-test. *P < 0.05; P < 0.01.

Article Snippet: Paraffin-embedded tissue sections were deparaffinized, rehydrated, subjected to antigen retrieval, and blocked before incubation with a primary antibody against LTA4H (Bioss, bs-3908R, 1:300) at 4°C overnight.

Techniques: Staining, Immunohistochemical staining, Analysis, Double Immunofluorescence Staining, Marker, MANN-WHITNEY

Journal: Advanced Science

Article Title: Aged ZnO Nanoparticles Promote Breast Cancer Lung Metastasis by Remodeling the Pulmonary Microenvironment Through the CXCL10/Wnt/β‐Catenin Axis

doi: 10.1002/advs.78249

Figure Lengend Snippet: CXCL10 silencing abrogates aged ZnO NPs–induced accumulation of pro‐metastatic stromal populations. (A) Schematic of coculture model: NIH‐3T3 or Raw264.7 cells with CXCL10 knockdown were stimulated with PBS, fresh or aged NPs (7.5 µg/mL), co‐cultured with 4T1 cells for 72 h. (B) qPCR analysis confirming CXCL10 knockdown in Raw264.7 cells ( n = 3). (C‐E) CXCL10 knockdown in Raw264.7 macrophages attenuates ZnO NP‐enhanced M2 polarization, as shown by reduced Arg1, Mrc1 expression (C) and CD206 + cell frequency (D, E) ( n = 3). (F) qPCR analysis confirming CXCL10 knockdown in NIH‐3T3 cells ( n = 3). (G, H) Flow cytometric analysis (G) and quantification (H) of the percentage of CD140a‐positive cells among CXCL10‐knockdown NIH‐3T3 cells in the co‐culture system ( n = 3). (I) qPCR analysis of CAF‐related genes (FN1, FSP1, α‐SMA, CD140a) in CXCL10‐knockdown NIH‐3T3 cells from the co‐culture model ( n = 3). (J) Schematic illustration of primary MDSCs preparation: primary mouse bone marrow‐derived macrophages were differentiated into MDSCs by supplement of GM‐CSF (40 ng/mL) and IL‐6 (40 ng/mL). MDSCs with CXCL10 knockdown were stimulated with PBS, fresh ZnO NPs, or aged ZnO NPs (7.5 µg/mL), then co‐cultured with 4T1 cells for 72 h. Cells were collected for further experiments. (K) qPCR analysis of relative CXCL10 mRNA expression in MDSCs with CXCL10 knockdown ( n = 3). (L, M) Flow cytometric analysis (L) and quantification (M) of the proportion of granulocytic CD11b + Ly6G + Ly6C low PMN‐MDSCs in the co‐culture system after CXCL10 knockdown ( n = 3). All data were represented as the mean ± SD. P values were calculated by one‐way ANOVA followed by Tukey's test. * p <0.05; ** p <0.01.

Article Snippet: Sections were then incubated overnight at 4°C with the following primary antibodies: F4/80 (Servicebio, GB11027; 1:750), Ki‐67 (Abcam, ab15580; 1:200), FSP‐1 (Bioss, bs‐1015R; 1:250), FN‐1 (BD Pharmingen, 610077; 1:250), CD206 (Affinity, DF7564; 1:250), αSMA (Affinity, AF1032; 1:500), bFGF (Proteintech, 14726‐1‐AP; 1:200), COX‐2 (Cell Signaling Technology, 12282S; 1:100).

Techniques: Knockdown, Cell Culture, Analysis, Expressing, Co-culture Assay, Derivative Assay