John A. Marwick's research while affiliated with Imperial College London and other places

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Publications (55)

Figure 1. ECM and triage assay workflow. (A) Workflow for screening compounds through the ECM and triage assays. (B) Representative images from live cell imaging showing viable cells (top) and cell death (bottom) induced by compound cytotoxicity. (C) Representative high-content images of fibronectin, collagen I+III, and collagen IV staining with composite image. (D) Representative full-plate montage images showing fibronectin staining with or without TGFβ (5 ng/mL) to assess the assay window between minimum and maximum ECM deposition (left) and TGFβ (5 ng/mL)-induced fibronectin staining across a full 384-well plate for coefficient of variance assessment (right).
Figure 2. Primary human lung fibroblast ECM deposition high-content screen. Primary human lung fibroblasts were treated with TGFβ (5 ng/mL) over 7 days in the presence of compounds (1 µM). Plates were denuded of cells and ECM components immunolabeled and imaged on an ImageXpress microscope. (A) Exemplar images of ECM deposition. (B) Principal component (PC) analysis of the quantification of ECM deposition in response to compound treatment. (C) Hit classification using a Euclidean distance from the negative control with a p value cutoff of 0.05 (corrected using the false discovery rate).
Figure 3. Selected hit compound 10-point concentration response in ECM assay. (A) Full-plate montage of fibronectin staining showing 10-point concentration response of 32 compound hits selected from the primary screen. Compound names listed, with the most active compounds highlighted in red. (B) Concentration responses of fibronectin deposition and cytotoxicity for the six compounds that reduced ECM deposition in a concentrated-related manner (see Suppl. Figs. S1-S3 for collagen I+III and IV data). Conc., concentration.
Figure 4. Secondary hit triage assays. (A) Concentration response of the six hit compounds on αSMA expression measured as a percentage of TGFβ (DMSO) positive control (see Suppl. Fig. S4 for representative high-content image). (B) Concentration response of the six hit compounds on fibroblast proliferation as measured by the percentage of the TGFβ (DMSO) positive control. (C) Concentration response of the six hit compounds on fibroblast apoptosis at 48 h postcompound addition as measured by the activation of caspase 3/7 (see Suppl. Fig. S4 for representative image). (D) Concentration response of the six hit compounds on scratch wound healing as measured by wound confluence at 24 h presented as a percentage of DMSO control (see Suppl. Fig. S4 for representative image).
Application of a High-Content Screening Assay Utilizing Primary Human Lung Fibroblasts to Identify Antifibrotic Drugs for Rapid Repurposing in COVID-19 Patients
  • Article
  • Full-text available

June 2021

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84 Reads

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4 Citations

SLAS DISCOVERY Advancing the Science of Drug Discovery

John A. Marwick

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Richard J. R. Elliott

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James Longden

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Lung imaging and autopsy reports among COVID-19 patients show elevated lung scarring (fibrosis). Early data from COVID-19 patients as well as previous studies from severe acute respiratory syndrome, Middle East respiratory syndrome, and other respiratory disorders show that the extent of lung fibrosis is associated with a higher mortality, prolonged ventilator dependence, and poorer long-term health prognosis. Current treatments to halt or reverse lung fibrosis are limited; thus, the rapid development of effective antifibrotic therapies is a major global medical need that will continue far beyond the current COVID-19 pandemic. Reproducible fibrosis screening assays with high signal-to-noise ratios and disease-relevant readouts such as extracellular matrix (ECM) deposition (the hallmark of fibrosis) are integral to any antifibrotic therapeutic development. Therefore, we have established an automated high-throughput and high-content primary screening assay measuring transforming growth factor-β (TGFβ)-induced ECM deposition from primary human lung fibroblasts in a 384-well format. This assay combines longitudinal live cell imaging with multiparametric high-content analysis of ECM deposition. Using this assay, we have screened a library of 2743 small molecules representing approved drugs and late-stage clinical candidates. Confirmed hits were subsequently profiled through a suite of secondary lung fibroblast phenotypic screening assays quantifying cell differentiation, proliferation, migration, and apoptosis. In silico target prediction and pathway network analysis were applied to the confirmed hits. We anticipate this suite of assays and data analysis tools will aid the identification of new treatments to mitigate against lung fibrosis associated with COVID-19 and other fibrotic diseases.

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Fig. 2 Apo-15 binds to apoptotic cells of different origin in multiple environments. a Representative fluorescence confocal microscopy images (from three independent experiments) human apoptotic (yellow arrows) and viable (white arrows) cells from different lineages: BL-2 (lymphoid), neutrophils (myeloid), and primary airway epithelial cells (epithelial). Cells were incubated with Apo-15 (100 nM, green), AF647-Annexin V (5 nM, red), and Hoechst 33342 (7 μM, blue) for 10 min and imaged under a fluorescence confocal microscope (λ exc. : 405, 488, 633 nm: λ em. : 450, 525, 670 nm). Scale bars: 10 µm. b Divalent cation-independent binding of Apo-15 to apoptotic cells of different origin (top plots: lymphoid cells, bottom plots: myeloid cells). Mixtures of apoptotic (highlighted in green dots) and viable cells (highlighted in black dots) were stained with AF647-Annexin V (25 nM) and Apo-15 (100 nM) in the presence of 2 mM CaCl 2 (left) or 2.5 mM EDTA (right). Representative histograms showing Apo-15 binding (x-axis) vs AF647-Annexin V binding (y-axis) acquired on 5L LSR flow cytometer (n = 5). c Cells were sorted on their Apo-15 positivity (green populations in panel b) or negativity (black populations in panel b). Gating strategy in Supplementary Fig. 7. Morphological analysis (four independent images from two independent experiments) of cytocentrifuge preparations of Apo-15-positive and Apo-15-negative cells under brightfield microscopy (scale bar: 10 µm). d Quantification of fluorescence staining of neutrophils (myeloid, n ≥ 5), BL-2 cells (lymphoid, n ≥ 3) and primary airway epithelial cells (epithelial, n = 5) before and after induction of apoptosis and upon treatment with Apo-15 and AF647-Annexin V. Data acquired on 5 L LSR flow cytometer and presented as mean values ± SEM. P values obtained from two-tailed t tests. Source data (in d) are provided as a Source data file.
Fig. 5 In vivo administration of Apo-15 labels drug-induced apoptosis in different mouse models. a Human neutrophils were treated with R-roscovitine (20 µM) in the absence or presence of QVD (10 μM). Apo-15-positive cells were determined by flow cytometry (n = 4). b Experimental protocol for acute lung injury: LPS (20 μg mL −1 ) was instilled intratracheally and mice received CDKi (30 mg kg −1 ) or saline intraperitoneally 18 h later. Apo-15 was administered intratracheally at 36 h. c Double-blind quantification of Apo-15-positive cells in lung tissue from mice that received LPS or LPS and CDKi (n = 8 per group). d Flow cytometric analysis of BALF from mice that received LPS (20 μg mL −1 ) or LPS (20 μg mL −1 ) and CDKi (AT7519, 4 mg mL −1 ). Both groups were administered Apo-15 (5 μM) in vivo and incubated with AF647-Annexin V (25 nM) ex vivo. Data presented as mean percentages ± s.d. of Apo-15-positive CD45 + LY6G+ apoptotic neutrophils (n = 4). Representative dot plots of CD45 + LY6G+ neutrophils with Apo-15 positive cells highlighted in green and Apo-15-negative cells in black. Gating strategy in Supplementary Fig. 15B. e Fluorescence microscope images of sections of lung tissues from mice that received LPS or LPS and CDKi. Yellow arrows identify Apo-15-positive events (green) from images with n = 8 per group. Scale bar: 100 µm. f Low-magnification intravital images of lungs of saline-treated and LPS-treated mice (LPS: 20 μg mL −1 ). Apo-15 was administered in vivo (5 μM, green) and nuclei were counterstained with DAPI (18 μM, blue) (λ exc. : 405, 488 nm; λ em. : 450, 525 nm). Representative pictures from n = 3 per group. Scale bar: 50 μm. g High-magnification fluorescence images (from three independent experiments) of tumors from xenograft breast cancer mouse models after treatment with cisplatin (10 μg g −1 ). Apo-15 was administered i.v. (5 μM, green) and nuclei were counterstained with DAPI (10 μM, blue) (λ exc. : 405, 488 nm; λ em. : 450, 525 nm). Apoptotic cells were stained with anti-active caspase-3 (300 ng mL −1 , yellow). Scale bar: 50 μm. For (a), (c), and (d), p values were obtained from two-tailed t tests. Source data (in a, c, and d) provided as a Source data file.
Comparative analysis of Apo-15 and commercially-available fluorescent probes for the detection of apoptosis.
Design, synthesis, and in vitro screening of apopeptides a Synthetic scheme for the preparation of apopeptides (CTC-PS: 2-chlorotritylchloride polystyrene resin). Stability analysis of Apo-15 (50 μM) under different proteolytic and oxidative environments. b Sequences, physicochemical and binding properties of apopeptides and cLac-BODIPY. In vitro screening of the peptides (100 nM) was performed by real-time flow cytometry in mixtures of apoptotic (AF647-Annexin V positive) and viable (AF647-Annexin V negative) neutrophils (n ≥ 3, biologically independent experiments). c Fluorescence activation of apopeptides in apoptotic (right) over viable cells (left), with Trp-BODIPY as the environmentally-sensitive reporter. Pictograms of Apo-15 under 365 nm light excitation. d Representative flow cytometry plots of Apo-0 and Apo-15 binding to mixtures of apoptotic (highlighted in green dots) and viable cells (highlighted in black dots). Fluorescence emission was recorded for around 4 min after addition of apopeptides (100 nM) without washing. Background fluorescence (Bf) was calculated as the ratio of fluorescence recorded before (x) and 2 min after (y) peptide addition. Fold increase (Ff) in apoptotic cells was calculated as a ratio of fluorescence recorded for apoptotic cells (y′) to that of viable cells (x′) after 2 min. e Representative fluorescence confocal microscopy images (from three independent experiments) of viable (white arrows) and apoptotic cells (yellow arrows) after incubation with Apo-0 (left, green) and Apo-15 (right, green) (both at 100 nM). Cells were co-stained with Hoechst 33342 (7 μM, blue) and AF647-Annexin V (5 nM, red) as nuclei and apoptosis markers, respectively (λexc.: 405, 488, 633 nm: λem.: 450, 525, 670 nm). Scale bars: 10 μm. Source data (in b) are provided as a Source data file.
Apo-15 rapidly binds exposed PS on the surface of apoptotic cells a Fluorescence microscope images (three independent images from two independent experiments) of GUVs with differential lipid content (PC and PC:PS). Vesicles were stained with 0.3 mol% Lissamine-Rho-DOPE (blue), Apo-15 (100 nM, green) and AF647-Annexin V (5 nM, red) (λexc.: 488, 561, 633 nm: λem.: 525, 593, >650 nm). Scale bar: 10 µm. b Binding specificity of Apo-15 upon incubation with different phospholipids (PS: phosphatidylserine, PG: phosphatidylglycine, PE: phosphatidylethanolamine, CL: cardiolipin, PA: phosphatidic acid, PC: phosphatidylcholine). Data acquired on a spectrophotometer (λexc.: 450 nm, λem.: 520 nm, n > 10, except for PA) and presented as mean values ± SEM. c Representative histograms showing Apo-15 (100 nM) labeling of mixed populations of viable and apoptotic neutrophils upon competition with increasing concentrations of AF647-Annexin V, together with quantification of the mean fluorescence intensity of Apo-15-stained apoptotic cells (n ≥ 3) presented as mean values ± SEM. d Representative histograms of BL-2 and PLB-985 cells after treatment with staurosporine (1 µM for 3 h) and staining with AF647-Annexin V and Apo-15 (n = 3). Apo-15-stained cells are highlighted as green dots and Apo-15-negative cells are highlighted as black dots. Gating strategy in Supplementary Fig. 7. e Time-lapse fluorescence snapshots (from three independent experiments) of apoptotic BL-2 cells after UV irradiation (300 mJ cm⁻²) (Supplementary Movies 1 and 2). BL-2 cells were treated with Apo-15 (green) and AF647-Annexin V (red) and imaged every 30 s for 15 min without washing. Arrows point at a cell when viable (white) and when undergoing apoptosis (yellow). Scale bar: 10 µm. f Quantification of Apo-15 fluorescence intensity and presented as mean values ± SEM over time from Supplementary Movie 1 (n = 3 independent measurements). g Time-lapse fluorescence images (from five independent cells) of subcellular apoptotic bodies (white arrow) in apoptotic neutrophils stained with Apo-15 (green). Images were acquired every 1 s for 15 min (Supplementary Movie 3). Scale bar: 2 µm. For (b) and (c), P values were obtained from two-tailed t tests. Source data (in b, c, and f) are provided as a Source data file.

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A fluorogenic cyclic peptide for imaging and quantification of drug-induced apoptosis

August 2020

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577 Reads

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50 Citations

Nature Communications

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Programmed cell death or apoptosis is a central biological process that is dysregulated in many diseases, including inflammatory conditions and cancer. The detection and quantification of apoptotic cells in vivo is hampered by the need for fixatives or washing steps for non-fluorogenic reagents, and by the low levels of free calcium in diseased tissues that restrict the use of annexins. In this manuscript, we report the rational design of a highly stable fluorogenic peptide (termed Apo-15) that selectively stains apoptotic cells in vitro and in vivo in a calcium-independent manner and under wash-free conditions. Furthermore, using a combination of chemical and biophysical methods, we identify phosphatidylserine as a molecular target of Apo-15. We demonstrate that Apo-15 can be used for the quantification and imaging of drug-induced apoptosis in preclinical mouse models, thus creating opportunities for assessing the in vivo efficacy of anti-inflammatory and anti-cancer therapeutics.

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Fig. 2 Gankyrin expression pre-invasive germ cells. a) 1 year old -maturation delay; b) 2 year old -maturation delay; c) 7 year old pre-invasive TGCC; d)17 year old pre-invasive TGCC; and e) 23 year old pre-invasive TGCC. Yellow arrows -GCNIS cells, orange arrows -Sertoli cells. This experiment was performed along with human fetal testis samples, no primary antibody control is the same as on Fig. 1, Scalebars -50 μm.
Fig. 3 Gankyrin expression in invasive TGCC. a-d Gankyrin (red), POU5F1 (green; GCNIS cells), and MAGE-A4 (blue; spermatogonia) expression in GCNIS containing tubules from patients with invasive TGCC, yellow arrows -GCNIS cells, orange arrows -Sertoli cells. e and f Gankyrin (red), POU5F1 (green; seminoma cells), and MAGE-A4 (blue) expression in seminoma cells (e) and a mixed TGCC (e), inset on D -no primary antibody control. Scalebars -50 μm.
Fig. 4 Effects of Gankyrin knock-down on Gankyrin and POU5F1 expression. Relative Gankyrin mRNA(a) and protein (b) expression in NTera2 cells after Gankyrin knock-down. Relative POU5F1 mRNA (c) and protein (d) expression in NTera2 cells after Gankyrin knock-down. e Representative Western blot for Gankyrin and POU5F1 expression in (V) vehicle and Gankyrin siRNA (T) transfected NTera2 cells. Tubulin was used as a loading control. Data analysed by paired t-test ± SEM, ***p < 0.001, **p < 0.01. Each data point represents the mean of an individualexperiment, each with 3 replicates. Paired samples from an individual experiment are represented by the same colour.
Fig. 5 Effect of Gankyrin knock-down on cell number, cell proliferation and cell cycle of NTera2 cells. a Gankyrin knock-down effect on NTera2 cell number. Relative PCNA (b) and ki67 (c) expression after 24 h of Gankyrin siRNA transfection. Distribution of NTera2 cells in GO/G1 phase (d), G2/M phase (e) or S phase (f). Data analysed by paired t-test, means ± SEM, ** p < 0.001, *p < 0.05. Each data point represents the mean of an individual experiment, each with an 3 replicates. Paired samples from an individual experiment are represented by the same colour. (g) Representative image of the flow cytometry plot obtained during cell cycle experiments.
Fig. 6 Gankyrin knock-down effect on TP53 and apoptosis. a TP53 pathway showing genes of interest in this study. Relative (b) TP53 (n = 4), (c) P21 (n = 4), (d) BAX (n = 4), (e) FAS (n = 4), (f) BAI-1 (n = 6), (g) PAI-1 (n = 7) mRNA expression in NTera2 cells after Gankyrin knock-down. Data (b-g) analysed by paired t-test, means ±SEM, *p < 0.05, **; p < 0.001. Each data point represents the mean of an individual experiment, each with 3 replicates. Paired samples from an individual experiment are represented by the same colour. H) Effects of Gankyrin knock-down on apoptosis in NT2 cells using Nucview apoptosis (CC3) probe. Representative images of four separate experiments, each with 3 replicates.

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The oncogene Gankyrin is expressed in testicular cancer and contributes to cisplatin sensitivity in embryonal carcinoma cells

November 2019

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147 Reads

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12 Citations

BMC Cancer

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L. H. J. Looijenga

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Background: Testicular germ cell cancer (TGCC) develops from pre-malignant germ neoplasia in situ (GCNIS) cells. GCNIS originates from fetal gonocytes (POU5F1+/MAGE-A4-), which fail to differentiate to pre-spermatogonia (POU5F1-/MAGE-A4+) and undergo malignant transformation. Gankyrin is an oncogene which has been shown to prevent POU5F1 degradation and specifically interact with MAGE-A4 in hepatocellular carcinoma (HCC) cells. We aimed to investigate the role of Gankyrin in progression from gonocyte to pre-invasive GCNIS and subsequent invasive TGCC. Methods: We determined Gankyrin expression in human fetal testicular tissue (gestational weeks 9-20; n = 38), human adult testicular tissue with active spermatogenesis (n = 9), human testicular tissue with germ cell maturation delay (n = 4), testicular tissue from patients with pre-invasive GCNIS (n = 6), and invasive TGCC including seminoma (n = 6) and teratoma (n = 7). Functional analysis was performed in-vitro by siRNA knock-down of Gankyrin in the NTera2 cells (derived from embryonal carcinoma). Results: Germ cell expression of Gankyrin was restricted to a sub-population of prespermatogonia in human fetal testes. Nuclear Gankyrin was also expressed in GCNIS cells of childhood and adult pre-invasive TGCC patients, and in GCNIS from seminoma and non-seminoma patients. Cytoplasmic expression was observed in seminoma tumour cells and NTera2 cells. Gankyrin knock-down in NTera2 cells resulted in an increase in apoptosis mediated via the TP53 pathway, whilst POU5F1 expression was unaffected. Furthermore, Gankyrin knock-down in NTera2 cells increased cisplatin sensitivity with an increase in cell death (13%, p < 0.05) following Gankyrin knock-down, when compared to cisplatin treatment alone, likely via BAX and FAS. Our results demonstrate that Gankyrin expression changes in germ cells during normal transition from gonocyte to prespermatogonia. In addition, changes in Gankyrin localisation are associated with progression of pre-invasive GCNIS to invasive TGCC. Furthermore, we found that Gankyrin is involved in the regulation of NTera2 cell survival and that a reduction in Gankyrin expression can modulate cisplatin sensitivity. Conclusions: These results suggest that manipulation of Gankyrin expression may reduce the cisplatin dose required for the treatment of TGCC, with benefits in reducing dose-dependent side effects of chemotherapy. Further studies are required in order to assess the effects of modulating Gankyrin on GCNIS/TGCC using in vivo models.

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Strategy for isolation of human liver non-parenchymal cells a, Patient demographics and clinical information. Data are mean ± s.e.m. b, Flow cytometry gating strategy for isolation of leucocytes (CD45⁺) and other non-parenchymal cells (CD45⁻) from human liver; representative plots from ten livers. c, Flow cytometry gating strategy for isolation of PBMCs; representative plots from four patients. d, Clustering 103,568 cells from healthy (n = 5) and cirrhotic (n = 5) livers, healthy PBMCs (n = 1) and cirrhotic PBMCs (n = 4) (left), annotating the source (PBMC versus liver; middle) and cell lineage inferred from known marker gene signatures (right). e, Dot plot annotating PBMC and liver clusters by lineage signatures. Circle size indicates cell fraction expressing signature greater than mean; colour indicates mean signature expression (red, high; blue, low). f, CXCR4 gene expression in single cells derived from blood or liver tissue, divided by cell lineage. Bottom right, representative immunofluorescence image (n ≥ 3) of CXCR4 (green) and DAPI (blue) in human liver; arrows denote CXCR4⁻ cells in the lumen of a blood vessel. Scale bar, 50 μm. g, Violin plots showing the number of unique genes (nGene), number of total unique molecular identifiers (nUMI) and mitochondrial gene fraction expressed in five PBMC samples. Black lines denote the median. h, Pie charts showing the proportion of cell lineages per PBMC sample. i, Box and whisker plots showing the agreement in expression profiles across five PBMC samples. Pearson correlation coefficients between average expression profiles for cells in each lineage, across all pairs of samples. Black bars denote the median; box edges denote the twenty-fifth and seventy-fifth percentiles; whiskers denote the full range.
Characterization of the cellular interactome in the fibrotic niche a, b, Representative immunofluorescence images (n ≥ 3) of fibrotic niche in cirrhotic liver. a, TREM2 (red), PLVAP (white), PDGFRα (green) and DAPI (blue). b, TREM2 (red), ACKR1 (white), PDGFRα (green) and DAPI (blue). c, Proliferation assay. Human HSCs were treated with conditioned medium from primary hepatic macrophage subpopulations SAMac (n = 2), TMs (n = 2), KCs (n = 2) or control medium (n = 2). The AUC of the percentage change in HSC number over time (hours) is shown on the y axis. Data are mean ± s.e.m. d, Circle plot showing potential interaction magnitude from ligands expressed by SAMacs and SAEndos to receptors expressed on SAMes. e, Circle plot showing potential interaction magnitude from ligands expressed by SAMes to receptors expressed on SAMacs and SAEndos. f, Dot plot of ligand–receptor interactions between SAMes (n = 7 human livers), SAMacs (n = 10 human livers) and SAEndos (n = 7 human livers). Ligand (red) and cognate receptor (blue) shown on the x axis; populations that express ligand (red) and receptor (blue) are shown on the y axis; circle size denotes P value (permutation test); colour (red, high; yellow, low) denotes average ligand and receptor expression levels in interacting subpopulations. g, Top, representative immunofluorescence image (n ≥ 3) of CCL2 (red), CCR2 (white), PDGFRα (green) and DAPI (blue) in fibrotic niche in cirrhotic liver; arrows denote CCL2⁺PDGFRα⁺ cells. Bottom, representative immunofluorescence image (n ≥ 3) of ANGPT1 (red), TEK (white), PDGFRα (green) and DAPI (blue) in fibrotic niche in cirrhotic liver; arrows denote ANGPT1⁺PDGFRα⁺ cells. h, Circle plot denotes potential interaction magnitude from ligands expressed by SAMacs to receptors expressed on SAEndos. i, Dot plot of ligand–receptor interactions between SAMacs (n = 10 human livers) and SAEndos (n = 7 human livers) as in f. j, Representative immunofluorescence image (n ≥ 3) of TREM2 (red), FLT1 (white), VEGFA (green) and DAPI (blue) in fibrotic niche in cirrhotic liver; arrows denote TREM2⁺VEGFA⁺ cells. k, Circle plot of the potential interaction magnitude from ligands expressed by SAEndos to receptors expressed on SAMacs. l, Dot plot of ligand–receptor interactions between SAEndo (n = 7 human livers) and SAMacs (n = 10 human livers) as in f. m, Top, representative immunofluorescence image (n ≥ 3) of TREM2 (red), CD200 (white), CD200R (green) and DAPI (blue) in fibrotic niche in cirrhotic liver; arrows denote TREM2⁺CD200R⁺ cells. Bottom, representative immunofluorescence image (n ≥ 3) of TREM2 (red), DLL4 (white), NOTCH2 (green) and DAPI (blue) in fibrotic niche in cirrhotic liver; arrows denote TREM2⁺NOTCH2⁺ cells. All scale bars, 50 μm. Source data
Quality control and annotation of human liver-resident cells a, Lineage signature expression across 66,135 liver-resident cells from healthy (n = 5) and cirrhotic (n = 5) human livers (red, high; blue, low). b, Dot plot annotating liver-resident cell clusters by lineage signature. Circle size indicates cell fraction expressing signature greater than mean; colour indicates mean signature expression (red, high; blue, low). c, Violin plots of the number of unique genes (left), number of total UMIs (middle) and mitochondrial gene fraction (right) across 66,135 liver-resident cells from healthy (n = 5) and cirrhotic (n = 5) livers. Black lines denote the median. d, Pie charts of the proportion of cell lineage per liver sample. e, Box and whisker plots of the agreement in expression profiles across healthy (n = 5) and cirrhotic (n = 5) liver samples, as in Extended Data Fig. 1i. f, t-SNE visualization of liver-resident cells per liver sample, with cirrhotic samples annotated by aetiology of underlying liver disease. ALD, alcohol-related liver disease; PBC, primary biliary cholangitis.
Annotating human liver lymphoid cells a, Clustering of 36,900 T cells and ILCs (left) from healthy (n = 5) and cirrhotic (n = 5) human livers, annotating the injury condition (right). NK, natural killer cell; cNK, cytotoxic NK cell. b, Fractions of T cell and ILC subpopulations in healthy (n = 5) and cirrhotic (n = 5) livers. c, Selected gene expression in 36,900 T cells and ILCs. d, Heat map of T cell and ILC cluster marker genes (colour-coded by cluster and condition), with exemplar genes labelled (right). Columns denote cells; rows denote genes. e, t-SNE visualizations of downsampled T cell and ILC dataset (7,380 cells from healthy (n = 5) and cirrhotic (n = 5) human livers) before and after imputation (scImpute); annotating data used for visualization and clustering, inferred lineage and injury condition. No additional heterogeneity was observed after imputation. f, Clustering 2,746 B cells and plasma cells (left) from healthy (n = 5) and cirrhotic (n = 5) human livers, annotating the injury condition (right). g, Heat map of B cell and plasma cell cluster marker genes (colour-coded by cluster and condition), with exemplar genes labelled (right). Columns denote cells; rows denote genes. h, Fractions of B cell and plasma cell subpopulations in healthy (n = 5) and cirrhotic (n = 5) livers. Data are mean ± s.e.m. P values determined by Wald test (b). Source data
Annotating human liver MPs a, Clustering and selected genes expressed in 10,737 MPs from healthy (n = 5) and cirrhotic (n = 5) human livers. b, Scaled gene expression of KC cluster markers across MP cells from healthy (n = 5) and cirrhotic (n = 5) livers. c, Representative immunofluorescence images (n ≥ 3) of TIMD4 (red), CD163 (white), MARCO (green) and DAPI (blue) in healthy and cirrhotic liver; arrows denote CD163⁺MARCO⁺TIMD4⁻ cells. d, Immunohistochemistry (left) and cell counts (right) of TIMD4 expression in healthy (n = 12) and cirrhotic (n = 9) human liver. e, Immunohistochemistry (left) and cell counts (right) of MARCO expression in healthy (n = 8) and cirrhotic (n = 8) liver. f, Flow cytometry gating strategy for identifying KCs, TMs and SAMacs in healthy (n = 2) and cirrhotic (n = 3) liver. SAMacs are detected as TREM2⁺CD9⁺ cells within the TM and SAMac gate (see Fig. 2f). g, Representative immunofluorescence images (n ≥ 3) of TREM2 (red), MNDA (white), collagen 1 (green) and DAPI (blue) in cirrhotic liver. h, Representative images (n = 2) of TREM2 (smFISH; red), MNDA (immunofluorescence; green) and DAPI (blue) in cirrhotic liver. i, Representative immunofluorescence images (n ≥ 3) of CD9 (red), MNDA (white), collagen 1 (green) and DAPI (blue) in cirrhotic liver. j, Immunohistochemistry (top) and cell counts (bottom) of TREM2 expression in healthy (n = 10) and cirrhotic (n = 9) liver. k, Immunohistochemistry (top) and cell counts (bottom) of CD9 expression in healthy (n = 12) and cirrhotic (n = 10) liver. l, Top, exemplar tissue segmentation of cirrhotic liver section into fibrotic septae (orange) and parenchymal nodules (purple). Bottom, cell counts based on immunohistochemistry analysis of TREM2 (n = 9), CD9 (n = 11), TIMD4 (n = 9) and MARCO (n = 7) in parenchymal nodules and fibrotic septae. m, Top, clustering and annotation of 208 cycling MP cells from healthy (n = 5) and cirrhotic (n = 5) livers, with scaled gene expression of MP subpopulation markers across four clusters of cycling MP cells. Bottom, fractions of cycling MP subpopulations in healthy (n = 5) and cirrhotic (n = 5) livers. All scale bars, 50 μm. Data are mean ± s.e.m. P values determined by two-tailed Mann–Whitney (e, j, k), two-tailed Wilcoxon test (l) or Wald test (m). Source data

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Resolving the fibrotic niche of human liver cirrhosis at single-cell level

November 2019

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2,203 Reads

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987 Citations

Nature

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R. Dobie

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Currently there are no effective antifibrotic therapies for liver cirrhosis, a major killer worldwide. To obtain a cellular resolution of directly relevant pathogenesis and to inform therapeutic design, we profile the transcriptomes of over 100,000 human single cells, yielding molecular definitions for non-parenchymal cell types present in healthy and cirrhotic human liver. We uncover a novel scar-associated TREM2⁺CD9⁺ macrophage subpopulation, which expands in liver fibrosis, differentiates from circulating monocytes and is pro-fibrogenic. We also define novel ACKR1⁺ and PLVAP⁺ endothelial cells that expand in cirrhosis, are topographically scar-restricted and enhance leucocyte transmigration. Multi-lineage ligand-receptor modelling of interactions between the novel scar-associated macrophages, endothelial cells and PDGFRα⁺ collagen-producing mesenchymal cells reveals intra-scar activity of several pro-fibrogenic pathways including TNFRSF12A, PDGFR and NOTCH signalling. Our work dissects unanticipated aspects of the cellular and molecular basis of human organ fibrosis at a single-cell level, and provides the conceptual framework required to discover rational therapeutic targets in liver cirrhosis.

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Augmentation of Human Monocyte Responses to Lipopolysaccharide by the Protein S and Mer/Tyro3 Receptor Tyrosine Kinase Axis

September 2018

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63 Reads

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18 Citations

The Journal of Immunology

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John A. Marwick

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Mary Jo Heeb

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Ian Dransfield

Resolution of the inflammatory response requires coordinated regulation of pro- and anti-inflammatory mediator production, together with clearance of recruited inflammatory cells. Many different receptors have been implicated in phagocytosis of apoptotic cells (efferocytosis), including Mer, a receptor tyrosine kinase that can mediate recognition and subsequent internalization of apoptotic cells. In this manuscript, we examine the expression and function of the Tyro3/Axl/Mer (TAM) family of receptors by human monocytes. We demonstrate that the Mer ligand, protein S, binds to the surface of viable monocytes via phosphatidylserine-dependent and -independent mechanisms. Importantly, we have identified a novel role for receptor tyrosine kinase signaling in the augmentation of monocyte cytokine release in response to LPS. We propose that low-level phosphatidylserine exposure on the plasma membrane of viable monocytes allows protein S binding that leads to TAM-dependent augmentation of proinflammatory cytokine production. Our findings identify a potentially important role for TAM-mediated signaling during the initiation phase of inflammation.

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Citations (36)

... 3 In recent years, the automated high-content screening (HCS) platform has been developed and applied to the screening of apoptosis-related drugs and cell biology research. [4][5][6] A rapid and accurate automatic identi¯cation method for apoptosis can greatly reduce the interaction between the operator and HCS imaging system, improving the e±ciency of largescale cell analysis. 7 Over the years, machine learning and deep learning technologies have exhibited remarkable performance in biomedical image classi¯cation. ...

Reference:

Automated apoptosis identification in fluorescence imaging of nucleus based on histogram of oriented gradients of high-frequency wavelet coefficients
Application of a High-Content Screening Assay Utilizing Primary Human Lung Fibroblasts to Identify Antifibrotic Drugs for Rapid Repurposing in COVID-19 Patients

SLAS DISCOVERY Advancing the Science of Drug Discovery

John A. Marwick

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Richard J. R. Elliott

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James Longden

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... Programmed cell death, or apoptosis, is a key biological process associated with many types of diseases, including autoimmune and inflammatory diseases and cancer [88]. Since peptidomimetics are known to induce apoptosis [89], HeLa cells were treated with conjugates 1-4, followed by flow cytometry analysis. ...

Reference:

Recent Advances in the Field of Amino Acid-Conjugated Aminoferrocenes—A Personal Perspective
A fluorogenic cyclic peptide for imaging and quantification of drug-induced apoptosis

Nature Communications

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... It is encoded by the PSMD10 gene and plays a crucial role in cell cycle progression, apoptosis and tumorigenesis. Initially, gankyrin was identified in human HCC (12); however, several studies have demonstrated that gankyrin expression is upregulated in other types of cancer (for example kidney, breast, testicular cancer and squamous cell carcinoma) as well (13)(14)(15). ...

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Assessment of gankyrin mRNA expression in tissue and liquid biopsy as a sensitive biomarker for hepatocellular carcinoma
The oncogene Gankyrin is expressed in testicular cancer and contributes to cisplatin sensitivity in embryonal carcinoma cells

BMC Cancer

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L. H. J. Looijenga

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... Furthermore, analysis of published single-cell RNA sequencing (scRNAseq) data revealed that uPAR was significantly upregulated in HSCs in cirrhotic human livers (Fig. S1C). 34 As in humans, uPAR expression was also markedly increased in mouse models of liver fibrosis induced by CCl 4 or a DDC diet compared with expression in the livers of control mice (Fig. S1D-G). Overall, we confirmed that uPAR is a cell surface protein that is overexpressed in liver fibrosis and is a potential target for therapeutic intervention. ...

Reference:

Chimeric antigen receptor-modified macrophages ameliorate liver fibrosis in preclinical models
Resolving the fibrotic niche of human liver cirrhosis at single-cell level

Nature

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R. Dobie

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... MerTK is expressed by many cells including endothelial cells and monocytes/macrophages and is also expressed within the kidney by glomerular cells [20][21][22]. MerTK is a key player in apoptotic cell clearance by phagocytosis; its ligand, the anticoagulant vitamin K-dependent factor protein S, bridges PS exposed on the plasma membrane of apoptotic cells with the MerTK receptor on the surface of phagocytes, enabling phagocytosis to take place [23][24][25]. The absence of a functional MerTK protein leads to a defect in phagocytosis that has been associated with several pathologies such as impaired spermatogenesis, photoreceptor degeneration or autoimmune disease [24,[26][27][28][29]. MerTK expressed by macrophages may also act as a negative regulator of inflammation, as impaired MerTK expression leads to an increase in pro-inflammatory mediators such as interleukin-6 (IL-6), monocyte chemoattractant protein-1 (MCP-1), and tumor necrosis factor-α (TNF-α) [30]. ...

Reference:

Study of the Role of the Tyrosine Kinase Receptor MerTK in the Development of Kidney Ischemia-Reperfusion Injury in RCS Rats
Augmentation of Human Monocyte Responses to Lipopolysaccharide by the Protein S and Mer/Tyro3 Receptor Tyrosine Kinase Axis
  • Citing Article

  • September 2018

The Journal of Immunology

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John A. Marwick

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Mary Jo Heeb

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Ian Dransfield

... Neutrophils can be eliminated by macrophages, to return to homeostasis once dangerous molecules are removed [34]. Contemporarily, the apoptotic neutrophils operate to switch the macrophage profile from pro-to anti-inflammatory (M1 to M2), suppressing inflammatory cytokines such as TNF and IL8 [51]. ...

Reference:

Biocompatibility Analysis of Bio-Based and Synthetic Silica Nanoparticles during Early Zebrafish Development
Neutrophils induce macrophage anti-inflammatory reprogramming by suppressing NF-κB activation

Cell Death and Disease

John A. Marwick

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Oliver Kay

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... In mammalian cells, this represents a truly bewildering array of phagocytic receptors with overlapping activities (reviewed in Refs. [142,143]). This redundancy has rendered the investigation of mammalian efferocytic receptors problematic. ...

Reference:

Understanding the diversity and dynamics of in vivo efferocytosis: Insights from the fly embryo
The “Phagocytic Synapse” and Clearance of Apoptotic Cells
Frontiers in Immunology

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John A. Marwick

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Ian Dransfield

... In monocytes, GSK3β inactivation reduced corticosteroid suppression of proinflammatory responses by inhibition of the enzymatic activity of HDAC2. 39 Glycyrrhizic acid inhibited inflammatory factors by mediation of the PI3K/Akt/GSK3β pathway. 40 Thus, GSK3β may provide a link between the PI3K/Akt pathway and HDAC2. ...

Reference:

Cryptotanshinone Reverses Corticosteroid Insensitivity by Inhibition of Phosphoinositide-3-Kinase-δ in Chronic Obstructive Pulmonary Disease
Glycogen synthase kinase-3β modulation of glucocorticoid responsiveness in COPD

AJP Lung Cellular and Molecular Physiology

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... MAPK pathways are involved in the activation of transcription factors such as NF-κB and AP-1, and the activation of p38 MAPK pathway could lead to glucocorticoid insensitivity for SA patients. [67][68][69] Furthermore, p38 MAPK is crucial in the allergic inflammatory cascade because it is needed in the upregulation of adhesion molecules on eosinophils and epithelial cells when allergy happens. 70 Another in-vitro experiment proves that the inhibition of p38 MAPK pathway may lead to additive and synergistic anti-inflammatory effects and reverse the glucocorticoid insensitivity when corticosteroids are used together. ...

Reference:

Molecular Mechanism Underlying Effects of Wumeiwan on Steroid-Dependent Asthma: A Network Pharmacology, Molecular Docking, and Experimental Verification Study
Oxidants Induce a Corticosteroid-Insensitive Phosphorylation of Histone 3 at Serine 10 in Monocytes
PLOS ONE
John A Marwick

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Corina Tudor

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... A large dose of corticosteroids such as a 14-day course of oral prednisolone or an intramuscular dose of triamcinolone is often administered for the treatment of uncontrolled asthma including that of exacerbations (1). Triamcinolone injections are sometimes used for patients with uncontrolled asthma who are not adherent to maintenance treatment with either inhaled and/ or oral corticosteroid therapy. ...

Reference:

Predicting Response to Triamcinolone in Severe Asthma by Machine Learning: Solving the Enigma
Corticosteroids: use and insensitivity in severe asthma
  • Citing Article

  • March 2011

European Respiratory Monograph

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