Journal: bioRxiv

Article Title: Non-Invasive classification of macrophage polarisation by 2P-FLIM and machine learning

doi: 10.1101/2022.01.22.477332

Figure Lengend Snippet: 2P-FLIM Metabolimaging analysis. (A) Time-course imaging of representative (same field of view throughout) IFNy-M1 macrophages, scale bar:100μm (B,C) Average fluorescence lifetime (tavg) and optical redox ratio (ORR) values for IFNy-M1 and IL-4-M2 when treated sequentially with olygomycin, FCCP, rotenone+antimycin A and 2-DG of a representative donor. (D) z-score heatmap of 2P-FLIM acquired data for 6 donors separated by macrophage polarisation and metabolic inhibitor. (E) UMAP plot of 2P-FLIM variables after each treatment. (F) UMAP plot of 2P-FLIM variables after FCCP treatment. (G) Receiver-operator curve and area under curve values of random forest machine learning model applied to 2P-FLIM data after FCCP treatment. (H) 2P-FLIM weight features determined by mean decrease accuracy and mean decrease Gini of random forest model used to classify macrophages.

Article Snippet: Uniform Manifold Approximate and Projection (UMAP) was used for dimension reduction, and z-score heatmaps were used to visualise the clustering of 2P-FLIM imaging datasets in global- and single-cell analysis( ) UMAP was implemented in Python, and visualization in Graphpad).

Techniques: Imaging, Fluorescence

Journal: bioRxiv

Article Title: Non-Invasive classification of macrophage polarisation by 2P-FLIM and machine learning

doi: 10.1101/2022.01.22.477332

Figure Lengend Snippet: Single-cell 2P-FLIM imaging analysis. (A) Single-cell analysis using a custom-built Cell Profiler® script, scale bar=100μm. (B) Single-cell UMAP clustering of a representative donor after FCCP treatment using 2P-FLIM variables. (C) Receiver-operating curves (ROC) of random forest model for classification of macrophages of all human donors used in this study. (D) Area under the curve values for ROCs (ROC-AUC) and Out-of-bag error (OOB) calculated using random forest model for each individual donor. (E) Mean decrease in accuracy and mean decrease in Gini of each 2P-FLIM variable returned by the random forest model.

Article Snippet: Uniform Manifold Approximate and Projection (UMAP) was used for dimension reduction, and z-score heatmaps were used to visualise the clustering of 2P-FLIM imaging datasets in global- and single-cell analysis( ) UMAP was implemented in Python, and visualization in Graphpad).

Techniques: Imaging, Single-cell Analysis

Journal: Cell reports

Article Title: linc-ADAIN , a human adipose lincRNA, regulates adipogenesis by modulating KLF5 and IL-8 mRNA stability

doi: 10.1016/j.celrep.2024.114240

Figure Lengend Snippet: (A) Regulatory features at the linc-ADAIN locus, RNA-seq coverage (human adipocytes), transcription factor binding, and active histone modification markers. (B) Tissue expression of linc-ADAIN from GTEx (gene mean transcripts per million [TPM]). (C and D) Induction of linc-ADAIN by the PPARγ agonist rosiglitazone (10 μM) (C) and during adipocyte differentiation in vitro (D) ( N = 3 in triplicate). (E) Cellular fractionation of primary ASC adipocytes. qPCR of MALAT1, GAPDH, and linc-ADAIN of nuclear and cytoplasmic fractions. Data were normalized by averaging of loading controls GAPDH, β-ACTIN, MALAT1, U6, and HPRT and then subtracting the nucleus value and getting a fold change of gene expression compared to nucleus ( N = 3). (F) RNA scope assay showing the spatial expression of linc-ADAIN (red) and nuclei (DAPI/blue) in scramble and linc-ADAIN shRNA hTERT ASC adipocytes (scale bar, 20 μm). (G) UMAP (uniform manifold approximation and projection) projection of linc-ADAIN (linc01230) expression in single-cell RNA-seq of human subcutaneous WAT (Broad Institute ). Data are presented as the mean ± SEM.

Article Snippet: Single cell UMAP data for linc01230 was taken from the Single Cell Portal – Broad Institute ( https://singlecell.broadinstitute.org ) using single cell sequencing data from human white adipose tissue by Emont et al., 2022 .

Techniques: RNA Sequencing, Binding Assay, Modification, Expressing, In Vitro, Cell Fractionation, Gene Expression, RNAscope, shRNA