Biology subjects

Hulme, G.

Publications and source records attributed to Hulme, G..

3 recordsLinked to original sources

bioRxiv2023

OPTIMAL: An OPTimsed Imaging Mass cytometry AnaLysis framework for Segmentation and Data Exploration

Analysis of Imaging Mass Cytometry (IMC) data and other low-resolution multiplexed tissue imaging technologies is often confounded by poor single cell segmentation and sub-optimal approaches for data visualisation and exploration. This can lead to inaccurate identification of cell phenotypes, states or spatial relationships compared to reference data from single cell suspension technologies. To this end we have developed the "OPTIMAL" framework to benchmark any approaches for cell segmentation, parameter transformation, batch effect correction, data visualisation/clustering and spatial neighbourhood analysis. Using a panel of 27 metal-tagged antibodies recognising well characterised phenotypic and functional markers to stain the same FFPE human tonsil sample Tissue Microarray (TMA) over 12 temporally distinct batches we tested several cell segmentation models, a range of different arcsinh cofactor parameter transformation values, five different dimensionality reduction algorithms and two clustering methods. Finally we assessed the optimal approach for performing neighbourhood analysis. We found that single cell segmentation was improved by the use of an Ilastik-derived probability map but that issues with poor segmentation were only really evident after clustering and cell type/state identification and not always evident when using "classical" bi-variate data display techniques. The optimal arcsinh cofactor for parameter transformation was 1 as it maximised the statistical separation between negative and positive signal distributions and a simple Z-score normalisation step after arcsinh transformation eliminated batch effects. Of the five different dimensionality reduction approaches tested, PacMap gave the best data structure with FLOWSOM clustering out-performing Phenograph in terms of cell type identification. We also found that neighbourhood analysis was influenced by the method used for finding neighbouring cells with a "disc" pixel expansion outperforming a "bounding box" approach combined with the need for filtering objects based on size and image-edge location. Importantly OPTIMAL can be used to assess and integrate with any existing approach to IMC data analysis and, as it creates .FCS files from the segmentation output, allows for single cell exploration to be conducted using a wide variety of accessible software and algorithms familiar to conventional flow cytometrists.

bioinformatics↗

bioRxiv2022

Programmed cell death-1 receptor mediated regulation of Tbet+ NK1.1- Innate Lymphoid Cells within the Tumor Microenvironment

Innate Lymphoid Cells (ILCs) play a key role in tissue mediated immunity and can be controlled by co-receptor signaling. Here we define a subset of ILCs that are Tbet+NK1.1- and are present within the tumor microenvironment (TME). We show programmed death-1 receptor (PD-1) expression on ILCs within TME is found in Tbet+NK1.1-ILCs. PD-1 significantly controlled the proliferation and function of Tbet+NK1.1-ILCs in multiple murine and human tumors. We found tumor derived lactate enhanced PD-1 expression on Tbet+NK1.1-ILCs within the TME, which resulted in dampened mTOR signaling along with increased fatty acid uptake. In line with these metabolic changes, PD-1 deficient Tbet+NK1.1-ILCs expressed significantly increased IFN{gamma}, granzyme B and K. Furthermore, PD1 deficient Tbet+NK1.1- ILCs contributed towards diminished tumor growth in an experimental murine model of melanoma. These data demonstrate that PD-1 can regulate anti-tumor responses of Tbet+NK1.1-ILCs within the tumor microenvironment. HighlightsO_LITbet+NK1.1- ILCs are found in WT and PD1 ko mice C_LIO_LIPD-1 is expressed on Tbet+NK1.1- ILC1s within multiple TME C_LIO_LIPD-1 controls the proliferation and function of Tbet+NK1.1- ILCs within the tumor microenvironment by modulating fatty acid metabolism. C_LIO_LIPD-1 regulates the proliferation of human Tbet+ ILC1s in human cutaneous squamous cell carcinoma (cSCC) and melanoma tumor microenvironment. C_LI

immunology↗

bioRxiv2022

CXCR2 inhibition enables NASH-HCC immunotherapy

ObjectiveHepatocellular carcinoma (HCC) is increasingly associated with non-alcoholic steatohepatitis (NASH). HCC immunotherapy offers great promise; however, recent data suggests NASH-HCC may be less sensitive to conventional immune checkpoint inhibition (ICI). We hypothesised that targeting neutrophils using a CXCR2 small molecule inhibitor may sensitise NASH-HCC to ICI therapy. DesignNeutrophil infiltration was characterised in human HCC and mouse models of HCC. Late-stage intervention with anti-PD1 and/or a CXCR2 inhibitor was performed in murine models of NASH-HCC. The tumour immune microenvironment was characterised by imaging mass cytometry, RNA-seq and flow cytometry. ResultsNeutrophils expressing CXCR2, a receptor crucial to neutrophil recruitment in acute-injury, are highly represented in human NASH-HCC. In models of NASH-HCC lacking response to ICI, the combination of a CXCR2 antagonist with anti-PD1 suppressed tumour burden and extended survival. Combination therapy increased intratumoral XCR1+ dendritic cell activation and CD8+ T cell numbers which are associated with anti-tumoral immunity, this was confirmed by loss of therapeutic effect upon genetic impairment of myeloid cell recruitment, neutralisation of the XCR1-ligand XCL1 or depletion of CD8+ T cells. Therapeutic benefit was accompanied by an unexpected increase in tumour-associated neutrophils (TANs) which switched from a pro-tumor to anti-tumour progenitor-like neutrophil phenotype. Reprogrammed TANs were found in direct contact with CD8+ T cells in clusters that were enriched for the cytotoxic anti-tumoural protease granzyme B. Neutrophil reprogramming was not observed in the circulation indicative of the combination therapy selectively influencing TANs. ConclusionCXCR2-inhibition induces reprogramming of the tumour immune microenvironment that promotes ICI in NASH-HCC. Significance of this studyWhat is already known on this subject? O_LIImmune checkpoint inhibition therapy is emerging as a promising new therapy for the treatment of advanced hepatocellular carcinoma (HCC). C_LIO_LIOnly a minority of HCC patients will respond to immune checkpoint inhibition (ICI) therapy and recent data suggest that HCC on the background of NASH may have reduced sensitivity to this treatment strategy. C_LIO_LINeutrophils are a typical myeloid component of the liver in NASH and are found either within the HCC tumour microenvironment or in a peritumoural location. C_LIO_LINeutrophils have considerable phenotypic plasticity and can exist in both tumour promoting and tumour suppressing states. C_LIO_LINeutrophils may have the ability to influence ICI therapy. C_LI What are the new findings? O_LICXCR2+ neutrophils are found in human NASH and within the tumour of both human and mouse models of NASH-HCC. C_LIO_LIThe resistance of NASH-HCC to anti-PD1 therapy is overcome by co-treatment with a CXCR2 small molecule inhibitor, with evidence of reduced tumour burden and extended survival. C_LIO_LIAnti-PD1 and CXCR2 inhibitor combine to selectively reprogramme tumour-associated neutrophils (TANs) from a pro- to an anti-tumour phenotype. C_LIO_LIReprogrammed TANs proliferate locally within Granzyme B+ immune clusters that contain physically associating CD8+ T cells and antigen presenting cells. C_LIO_LIConventional XCR1+ dendritic cells (cDC1s) are found to be elevated in anti-PD1 and CXCR2 inhibitor treated HCCs and together with CD8+ T cells are required for therapeutic benefit. C_LI How might it impact on clinical practice in the foreseeable future? O_LITANs can be selectively manipulated to adopt an anti-tumour phenotype which unlocks their potential for cancer therapy. The ability of CXCR2 antagonism to combine with ICI therapy to bring about enhanced therapeutic benefit in NASH-HCC (and potentially in HCC of other aetiologies) warrents clinical investigation. C_LI

cancer biology↗