cd45 monoclonal antibody (30-f11) apc-efluortm 780  (Thermo Fisher)

Journal: bioRxiv

Article Title: Single-molecule flow cytometry

doi: 10.1101/2025.08.26.672174

Figure Lengend Snippet: (a) Schematic of the smFC system integrating oblique light-sheet illumination/detection enabling volumetric imaging. (b) Conceptual illustration of membrane protein labelling on cells for three conditions: (1) 0 pM unlabelled control, (2) low-concentration labelling (single-molecule regime), and standard labelling (saturating antibody concentration). (c) Left: Flow cytometry histograms of ATTO647N/CD45-labelled fixed Jurkat T cells at three labelling levels: unlabelled (control), low concentration (3 pM), and standard concentration (30 nM). Only the standard-labelled sample shows a distinct fluorescence peak, while the low-labelled population overlaps with the control (indicated by arrow), highlighting the detection limit of conventional FC. Right: Similar for SBB700/CD45. (d) Maximum intensity projection images from a deskewed z-stack of ATTO647N/CD45-labelled Jurkat T cells. Cells were imaged in 3D using OPM by scanning with x-translation, revealing individual fluorophores localised on the membrane. Scale bar = 10 µ m. (e) ATTO647N counts using OPM for the control (0 pM) and single-molecule regime (30 pM). Means reported and error bars represent standard deviation. (f) as for d but with secondary SBB700/CD45 labelling and with the cells in continuous motion to emulate the effects of flow. (g) As for (e) but for SBB700/CD45.

Article Snippet: Alternative probes were also evaluated including Brilliant Violet anti-human CD45 (Clone HI30, 304043, BioLegend) and RPE-Astral616 anti-human CD45 (Clone 2D1, P012-R616-125, Biotium).

Techniques: Imaging, Membrane, Control, Concentration Assay, Flow Cytometry, Fluorescence, Standard Deviation

Journal: bioRxiv

Article Title: Single-molecule flow cytometry

doi: 10.1101/2025.08.26.672174

Figure Lengend Snippet: (a) Schematic of cells labelled with SBB700/CD45 in flow imaged with smFC. (b) Representative montage (t numbers correspond to frame) of a live Jurkat T cell labelled at low SBB700/CD45 primary antibody concentration (3 pM) imaged in flow using smFC. Individual membrane-bound fluorophores are visible. Scale bar = 5 µ m. (c) Maximum intensity projections of cells labelled at different concentrations imaged using smFC. Control (0 pM) shows diffuse background but no single molecules, while 1 pM shows three molecules. Scale bar = 5 µ m. (d) 2 seconds of smFC imaging in the fluorescence (top) and brightfield (bottom) channels, rapidly capturing data for 10 cells. Scale bar = 100 µ m. (e) CD45 distributions at different primary antibody concentrations, demonstrating sensitivity within the 1-10 mol/cell range. Bars are shifted for visual interpretation. (f) Comparison of quantification of cells from the same sample using smFC and conventional FC. Hybrid single-molecule/intensity analysis allows smFC to span the entire detection range (1-10,000 mol/cell), while affording a ∼ 30 reduction in background signal for the control, allowing digital detection capability between 1-50 mol/cell. Data is presented as mean±standard deviation for smFC and geometric mean±robust standard deviation for FC to minimise effects of outliers.

Article Snippet: Alternative probes were also evaluated including Brilliant Violet anti-human CD45 (Clone HI30, 304043, BioLegend) and RPE-Astral616 anti-human CD45 (Clone 2D1, P012-R616-125, Biotium).

Techniques: Concentration Assay, Membrane, Control, Imaging, Fluorescence, Comparison, Standard Deviation

Journal: iScience

Article Title: Machine learning assisted immune profiling of COPD identifies a unique emphysema subtype independent of GOLD stage

doi: 10.1016/j.isci.2025.112966

Figure Lengend Snippet: Unbiased inflammatory profiling in combination with machine-learning reveals a highly divergent immune environment in COPD lungs with strong lymphocytic inflammation (A) Computational flow cytometry was performed on samples obtained from explanted lungs of patients with COPD ( n = 20) and healthy control samples ( n = 23) from downsized donor lungs (flow cytometry cohort), see for gating strategy. %CD45 + cells were taken for further analysis. (B) Stacked histogram showing relative global changes in immune cell distribution for dendritic cells (DC), macrophages, monocytes, lymphocytes, and polymorphonuclear leukocytes (PMNL) on a single patient level, see also A. (C) Principal component analysis (PCA) scores plot with biplot overlay representing the overall inflammatory profile consisting of 24 different cell populations from each lung and represented as one dot (grey-donors, red-COPD). (D) Supervised orthogonal projections to latent structures discriminant analysis (OPLS-DA) was directed toward the maximum difference between donors and COPD ( x axis) and intra-group differences on the y axis. Ellipses mark the 95% confidence interval of each group. (E) Representation of random forest (RF) analysis with 5,000 trees, model accuracy was evaluated with a split into 65% trainings set and 35% test set stratified for diagnosis. The contribution of each cell population to the RF model is illustrated by the distribution of its minimal depth (white boxes), lower value indicates higher importance. The color histograms represent the distribution how frequently and at what depth the cell type was used for splitting the trees. Cells are sorted in descending order of importance. For each population the log2 fold change (LFC) for each population is shown, dark red higher in COPD, gray higher in donor. (F) The multidimensional scaling (MDS) scores represent sample similarity and state the RF accuracy and 95% confidence interval. (G and H) The marked seven cell types occurring in >300 trees at root node were used for the simplified RF model and achieved similar accuracy. The distribution of these six cell types is shown in (H), Quantification via Wilcoxon rank-sum test with FDR multiple correction. %CD45 data was LOG-transformed as shown; ∗∗∗ p adj ≤ 0.001, black horizontal lines represent median values, see also C. (I) Representative immunofluorescence images of Donor and COPD formalin-fixed paraffin-embedded lung sections; nuclei = blue; T-cells = green, macrophages = yellow, B-cells = white, neutrophils = red, ∗indicates airways, see also . Scale bars represent 500 µm in overview panels and 100 µm in the zoom in sections. (J) Schematic summary of the changes in key immune populations. For each analyte, the direction of regulation is shown dark red higher in COPD, gray decreased in COPD.

Article Snippet: CD45 Monoclonal Antibody (HI30), PerCP-Cyanine5.5 , eBioscience , Cat#: 45-0459-42; RRID: AB_10717530.

Techniques: Flow Cytometry, Control, Biomarker Discovery, Transformation Assay, Immunofluorescence, Formalin-fixed Paraffin-Embedded

Journal: iScience

Article Title: Machine learning assisted immune profiling of COPD identifies a unique emphysema subtype independent of GOLD stage

doi: 10.1016/j.isci.2025.112966

Figure Lengend Snippet: Underlying immune signatures defines COPD sub-groups (A) Flow cytometry-based lung immune cell profiles in patients with COPD ( n = 20) were sub-grouped via K-means and Gaussian mixture models creating two subtypes A ( n = 12) and B ( n = 8). (B) Upper panel: Principal component analysis (PCA) scores representing the overall inflammatory profile (%CD45, LOG-transformed) consisting of 22 different cell populations from each lung and represented as one dot (grey-controls, red-COPD). Lower panel: The supervised method OPLS-DA was directed toward the maximum difference between COPD subtypes ( x axis) and intra-cluster differences on the y axis. Ellipses mark the 95% confidence interval of each group. (C) Separation of COPD subtypes based on the combined lung immune cell profiles (22 populations) and plasma cytokine (24 cytokines) levels peripheral blood (plasma) cytokines by PCA (upper panel) and OPLS-DA (lower panel). (D) Heatmap showing the relative changes for each analyte for each sample. Samples were hierarchically clustered, and presented as dendrograms. (E) Visualization of the effect size between the COPD sub-clusters for each cell population and circulating cytokine using Cohen’s-d, which standardizes the differences between two means and provides an estimate of the effect size, dot size reflects the −log 10 P adj value as determined by Wilcoxon rank-sum test with FDR multiple correction. (F and G) Top six regulated cell populations (F) and top five cytokine between COPD (G) subtypes as identified in D, ∗ p ≤ 0.05, ∗∗ p ≤ 0.01, ∗∗∗ p ≤ 0.001, as determined by Wilcoxon-Mann-Whitney-U-test, black horizontal lines represent median values. (H) Schematic summary of differences between the sub-types. For each analyte, the direction of regulation is shown for each analysis, dark red higher in subtype one.

Article Snippet: CD45 Monoclonal Antibody (HI30), PerCP-Cyanine5.5 , eBioscience , Cat#: 45-0459-42; RRID: AB_10717530.

Techniques: Flow Cytometry, Transformation Assay, Clinical Proteomics, MANN-WHITNEY