OLR1-positive mononuclear phagocytes as a marker of liver inflammation

Abstract

The present disclosure relates to a novel biomarker of a liver condition. Specifically, the present disclosure provides a method of identifying subjects with or at risk of developing liver inflammation and / or liver disease, and uses of a marker indicative of liver inflammation and / or liver disease. There is also provided a modulator for use in the treatment and / or prevention of liver inflammation and / or liver disease.

Description

OLR1 -POSITIVE MONONUCLEAR PHAGOCYTES ASA MARKER OF LIVER INFLAMMATIONFIELDThe present disclosure relates to a novel biomarker of a liver condition. Specifically, the present disclosure provides a method of identifying subjects with or at risk of developing liver inflammation and / or liver disease, and uses of a marker indicative of liver inflammation and / or liver disease. There is also provided a modulator for use in the treatment and / or prevention of liver inflammation and / or liver disease.BACKGROUNDChronic liver disease (CLD) is a major global healthcare problem, affecting 480 million people worldwide and resulting in 2 million deaths per year (1). Irrespective of the cause, chronic damage to the liver results in scarring (called fibrosis) which ultimately can progress to cirrhosis, leading to clinical complications and increased mortality. Higher levels of fibrosis in the liver are associated with poor clinical outcomes (2), whilst improvements in fibrosis are associated with a better clinical prognosis (3). Due to this close relationship between fibrosis and CLD outcomes, there is an interest in developing new anti-fibrotic therapies for patients with liver diseases. However, such therapies remain very limited, with only one recently FDA approved drug (Resmiterom) licenced for the treatment of metabolic dysfunction-associated steatotic liver disease (MASLD) (4), the commonest cause of CLD in the developed world. Hence, there is still an unmet need in the art for novel therapeutic options for patients with CLD.CLD of different aetiologies is characterised by hepatic inflammation and the accumulation of immune cells in the liver, which are key regulators of injury responses and the development of fibrosis. Specifically, cells of the monocyte-macrophage lineage have been shown to play a crucial role in regulating fibrosis in the liver (5). Previous work employing single-cell RNA-seq (scRNAseq) to identify a specific subpopulation of macrophages which expand in fibrotic human fibrotic liver, has found these macrophages are localised in areas of scarring (termed the fibrotic niche) and are distinguished by expression of markers including TREM2 and CD9 (6). These TREM2+ CD9+ scar-associated macrophages (SAMacs), have been shown to promote the activation of the scar-producing hepatic myofibroblasts both in the liver (6,7) and in other fibrotic tissues (8), making them a potential target for antifibrotic therapies. However, macrophages in the liver also play a crucial role in the regression of fibrosis and restoration of normalliver architecture (9 - 14). It therefore currently remains unclear how to selectively target the pro-inflammatory and pro-fibrotic functions of SAMacs, without disrupting their regulatory and restorative properties.It is amongst one of the objectives of the present disclosure to overcome some of the drawbacks associated with existing approaches used to detect, prevent and / or treat liver inflammation and / or liver disease.SUMMARYThe present disclosure is based in part on the unexpected finding of OLR1 expression in myeloid cells being associated with liver inflammation, liver disease and / or increased rate of liver disease progression. In particular, the present inventors have surprisingly identified OLR1 as a marker of pro-inflammatory and / or pro-fibrotic population of mononuclear phagocytes. Without wishing to be bound by theory, these OLR1-positive mononuclear phagocytes regulate and / or exacerbate inflammation and / or fibrosis in the liver, and may be used as a marker indicative of subjects at risk of developing or subjects who have liver inflammation and / or liver disease. Further, these OLR1 -positive mononuclear phagocytes may be targeted to reduce, treat and / or prevent liverinflammation and / or liver disease.Accordingly, in a first aspect of the present disclosure, there is provided a method of identifying subjects with or at risk of developing liver inflammation and / or liver disease, said method comprising detecting oxidised low density lipoprotein receptor 1 (OLR1)-positive mononuclear phagocytes in a sample obtained from a subject, wherein the detection of OLR1 -positive mononuclear phagocytes is indicative of liver inflammation and / or liver disease.In another aspect of the present disclosure, there is provided a use of oxidised low density lipoprotein receptor 1 (OLR1) expressed by mononuclear phagocytes as a marker of liver inflammation and / or liver disease.As used herein, the terms “comprise”, “comprising” and / or “comprises” are used to denote aspects and embodiments of this invention that “comprise” a particular feature or features. It should be understood that these terms may also encompass aspects and / orembodiments which “consist essentially of” or “consist of” the relevant feature or features.In any aspects of the present disclosure, a “liver disease” or “liver condition” may comprise or consist of one or more conditions selected from the group comprising or consisting of: non-alcoholic fatty liver disease (NAFLD), metabolic-dysfunction associated steatotic liver disease (MASLD), alcohol-related liver disease (ARLD), alcoholic hepatitis, autoimmune hepatitis, primary biliary cholangitis (PBC), primary sclerosing cholanigitis (PSC), hereditary haemochromatosis, viral hepatitis, liver fibrosis, cirrhosis, chronic liver disease and / or liver cancer. In some examples, a liver disease or condition may comprise or consist of non-alcoholic fatty liver disease, hepatitis, liver fibrosis, cirrhosis, chronic liver disease and / or liver cancer. In some examples, a liver disease or condition may consist of one or more conditions selected from the group consisting of: non-alcoholic fatty liver disease, metabolic-dysfunction associated steatotic liver disease (MASLD), alcohol-related liver disease (ARLD), alcoholic hepatitis, autoimmune hepatitis, primary biliary cholangitis (PBC), primary sclerosing cholanigitis (PSC), hereditary haemochromatosis, viral hepatitis, liver fibrosis, cirrhosis, chronic liver disease and / or liver cancer.In any of the aspect of the present disclosure, the sample may be obtained from (i) a healthy subject, (ii) a subject suspected of having a liver condition or disease, (iii) a subject at risk of developing liver inflammation and / or a liver condition / disease or (iv) a subject with liver inflammation and / or a liver condition / disease, for example.The sample of any aspects of the present disclosure may be a tissue sample. In some examples, the sample may be obtained from the liver of the subject, such as by performing a biopsy, for example. As it would be appreciated by the skilled person in the art, obtaining a sample directly from the liver may be one approach of increasing the accuracy of detecting and / or measuring OLR1 expression in mononuclear phagocytes in the liver.In some examples, the sample of any aspects of the present disclosure may comprise or consist of a blood sample. A blood sample may comprise or consist of a whole blood sample or a portion thereof. In some examples, a blood sample may be obtained from a subject and cells of interest may be isolated to detect OLR1 expression. In someexamples, mononuclear phagocytes may be isolated from a blood sample from a subject to detect OLR1 expression. By way of example only, various techniques may be used to detect or assess OLR1 expression, such as by using flow cytometry, FACS, ELISA, immunohistochemistry, western blotting, qPCR and other suitable sequencing methods (e.g. single cell sequencing) known in the art, for example.In some examples, the sample of any aspects of the present disclosure may comprise or consist of a blood sample and / or a tissue sample obtained from the liver of a subject. By way of example, detecting OLR1 expression in mononuclear phagocytes in the blood and tissue sample obtained from the liver of a subject may increase specificity and / or sensitivity of detecting OLR1 expression in mononuclear phagocytes. In some examples, OLR1 expression in mononuclear phagocytes in the blood and in a tissue sample obtained from the liver of the same subject may be used (e.g. in combination) to determine or assess OLR1 expression compared to a control, such as a control sample or a threshold value.In other examples, OLR1 expression in the blood may be compared to OLR1 expression in mononuclear phagocytes in the liver to determine or assess the relative OLR1 expression level in the liver. In this context, determining or assessing the relative OLR1 expression level may refer to determining or assessing the relative increase or decrease of OLR1 expression level in the liver as compared to the blood.The term “detection” as used herein may comprise detecting the presence of OLR1 expression, such as detecting the presence of mononuclear phagocytes that express OLR1. The term detection may also encompass detecting and / or quantifying a level of OLR1 expression, a (total) number of OLR1 -positive cells or a proportion of cells that express OLR1 in a sample relative to a control and / or a threshold value. As used herein, detecting a level of OLR1 expression may encompass detecting a level of OLR1 gene and / or protein expression. In some examples, detecting OLR1 expression may comprise or consist of detecting a level of OLR1 gene expression. In some examples, detecting OLR1 expression may comprise or consist of detecting a level of OLR1 protein expression. In some examples, detecting OLR1 expression may comprise or consist of detecting a level of OLR1 gene and protein expression. In any of these examples, the OLR1 -positive cells may be OLR1 -positive mononuclear phagocytes, such as scar-associated macrophages (SAMacs). In one example, the OLR1-positive cells may beOLR1 -positive mononuclear phagocytes, such as scar-associated macrophages (SAMacs), in the liver or in a sample obtained from the liver of a subject.In some examples, the level of OLR1 expression may be indicative of and / or correlate with the severity of liver inflammation and / or liver disease. In some examples, the level of OLR1 expression may be indicative of and / or correlate with the likelihood of developing liver disease. In some examples, the level of OLR1 expression may be indicative of and / or correlate with the rate of liver disease progression.In some examples, the level of OLR1 gene and / or protein expression may increase with severity of a liver disease. In addition, or alternatively, the level of OLR1 gene and / or protein expression may increase with severity of liver fibrosis. Various techniques or methods of determining the severity of liver fibrosis would be known to the skilled person in the art, such as by histological staging of liver biopsy (40) and / or the application of validated non-invasive fibrosis assays, such as liver stiffness or serum markers (41).Accordingly, in any aspects or examples detailed herein, detecting a level of OLR1 expression may encompass detecting a level of OLR1 gene and / or protein expression, wherein the detection of an increased level of OLR1 gene and / or protein expression is indicative of a liver disease and / or a liver condition. In some examples, the increased level of OLR1 gene and / or protein expression may be relative to a control. In some examples, the increased level of OLR1 gene and / or protein expression may be 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 200%, 300%, 400% or 500% greater than a control. In a preferred example, the increased level of OLR1 gene and / or protein expression may be two-fold, three-fold, four-fold, five-fold, six-fold, seven-fold, eight-fold, nine-fold, ten-fold, 11-fold, 12-fold or 13-fold compared to a control.As used herein, a control may be a reference level or a reference value, such as OLR1 mRNA or protein expression level obtained from healthy controls. In some examples, a control may be a range, a median or a mean obtained from healthy controls. As used herein, health controls may refer to normal subjects or a population of normal subjects not suffering from liver inflammation and / or a liver disease or condition.In any aspects of the present disclosure, OLR1 expression may be detected using an in vitro or ex vivo assay. By way of example only, various techniques may be used to detector assess OLR1 expression, such as by using flow cytometry, FACS, ELISA, immunohistochemistry, western blotting, qPCR and other suitable sequencing methods (e.g. single cell sequencing) known in the art, for example.As detailed herein, the detection of OLR1 expression typically refers to detection of OLR1 expression in mononuclear phagocytes. By way of example, mononuclear phagocytes may encompass, comprise or consist of: Kupffer cells, circulating monocytes trafficking to the liver, monocytes migrating to the liver and / or tissue-resident monocytes. In some examples, mononuclear phagocytes may comprise or consist of monocytes, macrophages, hepatic macrophages and / or scar-associated macrophages. In some examples, OLR1 -positive mononuclear phagocytes may comprise or consist of macrophages, hepatic macrophages and / or scar-associated macrophages. In some examples, OLR1 -positive mononuclear phagocytes may comprise or consist of hepatic macrophages and / or scar-associated macrophages. In some examples, OLR1-positive mononuclear phagocytes may comprise or consist of scar-associated macrophages.The term “scar-associated macrophage” typically refers to macrophages that may be found in tissues at or near a site of inflammation, scars and / or injury. Without wishing to be bound by theory, scar-associated macrophages of the liver typically refer to macrophages found at or near a site of inflammation, scars and / or injury in the liver. Within the context of the present disclosure, these scar-associated macrophages are typically localised in or near areas of scarring, which may be referred to as the fibrotic niche. Again, without wishing to be bound by theory, the OLR1-positive scar-associated macrophages of the present disclosure typically exhibit a pro-inflammatory and / or pro-fibrotic phenotype.In some examples, pro-inflammatory and / or pro-fibrotic scar-associated macrophages may express one or more markers selected from the group comprising or consisting of: TREM2, CD9, CD63, SPP1, or a combination thereof. These scar-associated macrophages may be referred to as scar-associated macrophages subpopulation 2 (SAMacs2) throughout the present disclosure. In some examples, scar-associated macrophages express the markers TREM2, CD9, CD63 and SPP1. More specifically, the scar-associated macrophages of the present disclosure, such as those associated with or indicative of liver inflammation and / or liver disease, may express the markers OLR1, TREM2, CD9, CD63 and SPP1.In any aspects of the present disclosure, the mononuclear phagocytes that express OLR1 may also express or may be identified by other markers, such as by expression of higher levels of pro-inflammatory and / or pro-fibrotic markers. In some examples, the OLR1-positive mononuclear phagocytes may express I L-1 p, TNF, IL18, NLRP3, AREG, PDGF, or a combination thereof. In some examples, the OLR1-positive hepatic macrophages of the present disclosure may express IL-1 p, TNF, IL18, NLRP3, AREG, PDGF, or a combination thereof. In some examples, the OLR1-positive scar-associated macrophages of the present disclosure may express IL-1 p, TNF, IL18, NLRP3, AREG, PDGF, or a combination thereof. In some examples, the OLR1-positive scar-associated macrophages in the liver may express IL-1 p, TNF, IL18, NLRP3, AREG and PDGF. The expression of IL-1 p, TNF, IL18, NLRP3, AREG and / or PDGF in any of the examples provided herein may be relative to a control sample, such as a sample obtained from a healthy subject, a healthy tissue, OLR1 -negative cells and / or OLR1 -negative mononuclear phagocytes, for example.Without wishing to be bound by theory, the present application provides for the first time that the identification of OLR1 -positive mononuclear phagocytes may contribute to and / or exacerbate inflammation and / or fibrosis in the liver. Accordingly, in addition to the identification of OLR1 -positive mononuclear phagocytes as a marker of liver inflammation and / or liver disease, OLR1 -positive mononuclear phagocytes may be targeted for the treatment and / or prevention of liver inflammation and / or liver disease.Again, without wishing to be bound by theory, targeting OLR1 -positive mononuclear phagocytes may reduce inflammation and / or fibrosis in the liver by reducing or inhibiting pro-inflammatory and / or pro-fibrotic activity of OLR1 -positive mononuclear phagocytes. By way of example only, OLR1 -positive mononuclear phagocytes may be targeted by targeted killing of OLR1-positive mononuclear phagocytes. In addition, or alternatively, OLR1 -positive mononuclear phagocytes may be targeted by inhibiting or reducing gene and / or protein expression of OLR1 in the mononuclear phagocytes. In some examples, OLR1 -positive mononuclear phagocytes may be targeted using an OLR1 blocking antibody that reduces or inhibits the function of OLR1 and / or an siRNA that reduces or inhibits OLR1 expression. In some examples, targeting of OLR1-positive mononuclear phagocytes in the liver may result in a reduction in pro-inflammatory and / or pro-fibrotic mediators, such as IL-1 p, TNF, IL18, NLRP3, AREG and / or PDGF. In some examples,targeting of OLR1 -positive mononuclear phagocytes in the liver may reduce or inhibit activation of myofibroblasts and thereby reduce scar production or formation in the liver. In some examples, targeting of OLR1-positive mononuclear phagocytes in the liver may reduce or inhibit fibrillar collagen gene expression in the liver, such as by reducing or inhibiting the expression of Type 1 collagen and Type 3 collagen genes (e.g. COL1A1 and / or COL3A1).In a further aspect of the present disclosure, there is provided a modulator for use in the treatment and / or prevention of liver inflammation and / or liver disease, wherein the modulator reduces or inhibits oxidised low density lipoprotein receptor 1 (OLR1) expression and / or function in mononuclear phagocytes.As stated, a “liver disease” or “condition” may comprise or consist of one or more conditions selected from the group comprising or consisting of: non-alcoholic fatty liver disease, metabolic-dysfunction associated steatotic liver disease (MASLD), alcohol-related liver disease (ARLD), alcoholic hepatitis, autoimmune hepatitis, primary biliary cholangitis (PBC), primary sclerosing cholanigitis (PSC), hereditary haemochromatosis, viral hepatitis, liver fibrosis, cirrhosis, chronic liver disease and / or liver cancer. In some examples, a liver disease or condition may comprise or consist of non-alcoholic fatty liver disease, hepatitis, liver fibrosis, cirrhosis, chronic liver disease and / or liver cancer. In some examples, a liver disease or condition may consist of one or more conditions selected from the group consisting of: non-alcoholic fatty liver disease, metabolic-dysfunction associated steatotic liver disease (MASLD), alcohol-related liver disease (ARLD), autoimmune hepatitis, primary biliary cholangitis (PBC), primary sclerosing cholanigitis (PSC), hereditary haemochromatosis, viral hepatitis, liver fibrosis, cirrhosis, chronic liver disease and / or liver cancer. In some examples, the liver disease or condition may comprise or consist of metabolic-dysfunction associated steatotic liver disease (MASLD), alcohol-related liver disease (ARLD), alcoholic hepatitis, primary biliary cholangitis (PBC), primary sclerosing cholanigitis (PSC) and autoimmune hepatis (AIH). In one example, the liver disease or condition may comprise or consist of alcohol-related liver disease (ARLD). In some examples, the liver disease or condition may comprise or consist of alcoholic hepatitis.As stated, the modulator detailed herein typically reduces or inhibits oxidised low density lipoprotein receptor 1 (OLR1) expression and / or function in mononuclear phagocytes. Insome examples, the modulator may reduce or inhibit OLR1 gene and / or protein expression in mononuclear phagocytes. In some examples, the modulator may reduce or inhibit OLR1 protein function or activity in mononuclear phagocytes, such as by reducing or inhibiting OLR1 gene expression and / or by binding to OLR1 and thereby inhibiting or reducing OLR1 function and / or activity.Accordingly, the term “modulator” as used herein may encompass various means suitable for targeting OLR1-positive cells. In particular, the modulator may be used to specifically target, reduce or inhibit gene and / or protein expression of OLR1 in mononuclear phagocytes, such as hepatic macrophages or scar-associated macrophages. In some examples, the modulator may be used to specifically target, reduce or inhibit function and / or activity of the OLR1 protein in mononuclear phagocytes, such as hepatic macrophages or scar-associated macrophages. The modulator may be used to specifically target, reduce or inhibit pro-inflammatory and / or pro-fibrotic activity of OLR1 -positive mononuclear phagocytes, such as OLR1 -positive hepatic macrophages and / or OLR1 -positive scar-associated macrophages.By way of example, a modulator as used herein may comprise or consist of: a small interfering RNA (siRNA), a short hairpin RNA (shRNA), a vector or nanoparticles adapted to deliver an siRNA or a shRNA, or OLR1 inhibitors / blockers to a site of interest, CRISPR / Cas-mediated modulation of OLR1 expression, a blocking / inhibitory antibody targeting OLR1, a small molecule inhibitor, a chemical inhibitor, a peptide inhibitor,, or a combination thereof. In some examples, the modulator of the present disclosure may comprise or consist of a modulator that is adapted to induce targeted killing of OLR1-positive mononuclear phagocytes, such as OLR1 -positive hepatic macrophages or scar-associated macrophages. In some examples, the modulator of the present disclosure may comprise or consist of a modulator that is adapted to induce targeted killing of OLR1 -positive cells in the liver, such as OLR1 -positive macrophages or scar-associated macrophages in the liver. In some examples, the modulator of the present disclosure may comprise or consist of a vector or a nanoparticle adapted to deliver siRNA, shRNA, OLR1 inhibitor and / or OLR1 blocker to a site of interest to induce targeted killing of OLR1 -positive mononuclear phagocytes, such as OLR1 -positive hepatic macrophages or scar-associated macrophages. In one example, the modulator detailed herein may be an anti-OLR1 antibody. In one example, the modulator detailed herein may be a nucleicacid construct capable of downregulating or inhibiting OLR1 expression, such as an siRNA and / or shRNA.As stated, the modulator of the present disclosure may preferentially and / or specifically target OLR1 -positive mononuclear phagocytes. The term “target” may encompass the modulator reducing or inhibiting (i) OLR1 gene expression, (ii) OLR1 protein expression, and / or (iii) function or activity of OLR1 protein in a cell of interest. Accordingly, the modulator of the present disclosure may reduce or inhibit OLR1 expression and / or function in OLR1-positive mononuclear phagocytes. In some examples, the modulator of the present disclosure may reduce or inhibit OLR1 expression and / or function in OLR1-positive mononuclear phagocytes. In some examples, the modulator of the present disclosure may reduce or inhibit OLR1 expression and / or function in OLR1-positive macrophages. In some examples, the modulator of the present disclosure may reduce or inhibit OLR1 expression and / or function in OLR1 -positive hepatic macrophages. In some examples, the modulator of the present disclosure may reduce or inhibit OLR1 expression and / or function in OLR1 -positive scar-associated macrophages. In some examples, the modulator of the present disclosure may reduce or inhibit OLR1 expression and / or function in OLR1 -positive scar-associated macrophages in the liver. As stated, the OLR1 -positive scar-associated macrophages in the liver may typically express TREM2, CD9, CD63 and SPP1.Without wishing to be bound by theory, the modulator of the present disclosure targets OLR1 -positive pro-inflammatory and / or pro-fibrotic mononuclear phagocytes and thereby reduces pro-inflammatory and / or pro-fibrotic cytokine production in the liver. In some examples, the modulator of the present disclosure may specifically target, modulate, inhibit or reduce OLR1 expression and / or function in mononuclear phagocytes, and thereby reduce pro-inflammatory and / or pro-fibrotic cytokines produced by the mononuclear phagocytes. In some examples, the modulator of the present disclosure may specifically target, modulate, inhibit or reduce OLR1 expression and / or function in hepatic macrophages, and thereby reduce pro-inflammatory and / or pro-fibrotic cytokines produced by the hepatic macrophages. In some examples, the modulator of the present disclosure may specifically target, modulate, inhibit or reduce OLR1 expression and / or function in scar-associated macrophages, and thereby reduce pro-inflammatory and / or pro-fibrotic cytokines produced by the scar-associated macrophages. In some examples, the modulator detailed herein may prevent, reduce orinhibit activation of myofibroblasts, such as scar-producing myofibroblasts in the liver. Advantageously, the modulator detailed herein may be useful in reducing scar production or formation in the liver by myofibroblasts, such as by attenuating fibrillar collagen gene and / or protein expression in the liver.By way of example only, the modulator (e.g. anti-OLR1 antibody) may specifically bind OLR1 (protein) expressed by mononuclear phagocytes and thereby reduce production of pro-inflammatory and / or pro-fibrotic mediators (e.g. cytokines). In some examples, the modulator (e.g. shRNA or siRNA) may reduce or inhibit OLR1 gene expression and thereby reduce production of pro-inflammatory and / or pro-fibrotic mediators (e.g. cytokines) by the mononuclear phagocytes.In some examples, the modulator may also reduce or inhibit OLR1 gene and / or protein expression in peripheral cells and / or in tissues in which OLR1 -positive mononuclear phagocytes may be found. In some examples, the modulator may have a systemic effect, and thereby modulate or reduce OLR1 gene and / or protein expression in OLR1 -positive mononuclear phagocytes. In some examples, the modulator may have a systemic effect, and thereby modulate or reduce OLR1 protein function or activity in OLR1 -positive mononuclear phagocytes. Without wishing to be bound by theory, systemic exposure to a modulator adapted to reduce or inhibit OLR1 gene and / or protein expression in OLR1-positive mononuclear phagocytes may be sufficient to prevent, reduce or inhibit inflammation and / or fibrosis in the liver. In some examples, systemic exposure to a modulator adapted to reduce or inhibit OLR1 protein function or activity in OLR1 -positive mononuclear phagocytes may be sufficient to prevent, reduce or inhibit inflammation and / or fibrosis in the liver.In another aspect of the present disclosure, there is provided a kit for detection of OLR1-positive mononuclear phagocytes, wherein the kit may be used for the detection or diagnosis of liver inflammation and / or liver disease.The kit of the present disclosure may comprise or consist of:(i) a probe for the detection of OLR1 expression; and(ii) one or more probes for the detection of markers selected from the group consisting of: TREM2, CD9, CD63 and / or SPP1.The kit of the present disclosure may comprise or consist of:(i) a probe for the detection of OLR1 expression; and(ii) one or more probes for the detection of markers selected from the group consisting of: TREM2, CD9, CD63, SPP1, CD88, CD68 and / or IBA1.In some examples, the probe(s) may be an antibody against a target of interest. The probe(s) may or may not be conjugated to a detectable moiety, such as a fluorophore. The probe(s) may be provided with a secondary probe, wherein the secondary probe may be for the detection of a primary probe that binds a target of interest.In some examples, the probe for the detection of OLR1 expression may be an antibody against OLR1. In some examples, the anti-OLR1 antibody may be an antibody conjugated to a fluorophore.In some examples, the anti-OLR1 antibody may be immobilised on a substrate, such as a multi-well plate or a lateral flow device, for example.In some examples, mononuclear phagocytes may be isolated from a sample of interest prior to contacting the mononuclear phagocytes with one or more probe(s) of the kit detailed herein.In some examples, the kit may be provided with a capture agent, such as an antibody immobilised on a substrate adapted to capture cells of interest (e.g. mononuclear phagocytes). The capture agent may comprise or consist of an antibody against a marker of mononuclear phagocytes or scar-associated macrophages. For example, the capture agent for scar-associated macrophages may comprise or consist of an antibody against TREM2, CD9, CD63 or SPP1. In some examples, the capture agent for scar-associated macrophages may comprise or consist of a plurality of antibodies, wherein each type of antibody is against TREM2, CD9, CD63 or SPP1.In some examples, the cells captured by the capture agent as described herein may be contacted with any suitable probe detailed above, such as an anti-OLR1 antibody conjugated to a detectable moiety (e.g. a fluorophore).In other examples, the kit may be suitable for an in-tube detection, wherein the probe may be provided in a buffer or a solution. Accordingly, the kit of the present disclosure may further comprise or further consist of a buffer or a solution.Optionally, the kit may further comprise or consist of:(iii) a positive control;(iv) a negative control;(v) a receptacle for receiving a tissue or a blood sample.DETAILED DESCRIPTIONThe present disclosure will now be described in more detail, by way of example only, with reference to the figures, which show:Figure 1: OLR1+ scar-associated macrophages accumulate in fibrotic liver and have a pro-inflammatory phenotype. Analysis of liver scRNAseq data from 65 patient samples (39 healthy and 26 cirrhotic). (A) Clustering of 81,577 mononuclear phagocytes identifies 2 subpopulations of SAMacs. (B) Both SAMac subpopulations express high levels of SAMac markers TREM2 and CD9. (C) Differential composition analysis demonstrates significant expansion of both SAMac subpopulations in diseased vs healthy liver tissue. (D) Differential expression analysis between SAMad and SAMac2 show significant transcriptional differences between these populations. (E) SAMac2 macrophage subpopulation is significantly enriched for OLR1 expression and SAMad is significantly enriched for GPNMB expression. (F) OLR1+ SAMac2 is enriched for expression of a number of pro-inflammatory mediators, whilst GPNMB SAMad is enriched for anti-inflammatory / pro-resolution mediators.Figure 2: OLR1+ scar-associated macrophages accumulate in the fibrotic niche of fibrotic human liver. Multiplex immunofluorescence of fibrotic human liver for markers OLR1 (red), CD68 (green) and type 1 collagen (grey). Low and high magnification images shown. Co-localisation of CD68 and OLR1 demonstrates the accumulation of OLR1+ macrophages (white arrows) in areas or fibrosis marked by collagen 1 (Coll) staining.Figure 3: High liver OLR1 expression is predictive of a worse prognosis in patients with liver disease due to MASLD. Analysis of data from SteatoSITE cohort. (A) LiverOLR1 gene expression (log2 counts per million) increases as fibrosis stage increases in patients with MASLD. (B) Biopsy cases from the SteatoSITE dataset (n=447) with available RNAseq and no hepatic decompensation-related coding event before the time of the biopsy were used for time-to-event analysis. Normalized gene expression counts for OLR1 was used to divide cases into low and high risk of hepatic decompensation (Kaplan-Meier estimator curves with log-rank test p-value shown). Patients with high OLR1 were at increased risk of liver decompensation. (C) Biopsy cases from the SteatoSITE dataset (n=508) with available RNAseq were used for time-to-event analysis. Normalized gene expression counts for OLR1 was used to divide cases into low and high risk of death (Kaplan-Meier estimator curves with log-rank test p-value shown). Patients with high OLR1 expression had lower survival rates.Figure 4: OLR1 is specifically expressed in myeloid cells in the liver. Analysis of scRNAseq data from 81 patients (42 healthy and 39 with chronic liver disease) totalling 843,457 cells. (A) LIMAP plot showing annotation of different cell types from the human liver. Mononuclear phagocyte (MP) population contains liver monocytes and macrophages. (B) Dot plot showing normalised OLR1 gene expression across all cells from human scRNAseq analysis. OLR1 is specifically expressed by MP cells.Figure 5: Serum OLR1 levels do not correlate with liver disease severity. To assess whether serum OLR1 could serve as a potential biomarker of liver disease severity serum OLR1 in patient samples was measured using an OLR1 ELISA. No differences in serum OLR1 were detected between healthy volunteers (n=9), patients with chronic liver disease and no evidence of cirrhosis (pre-cirrhotic; n= 7) and patients with advanced cirrhosis (n=9). These data indicate that serum OLR1 is not a reliable indicator of liver fibrosis severity.Figure 6: OLR1+ scar-associated macrophages expand in mouse liver fibrosis and have a pro-inflammatory phenotype. Mouse liver fibrosis was induced by 4 weeks of chronic CCl4 administration, with harvest at stated timepoints after the final CCl4 injection, representing active fibrosis (24 and 96hrs) and fibrosis regression (1 week and 4 weeks). Control animals were uninjured. (A-D) ScRNAseq analysis of mouse blood and liver MPs from control, 24hr, 96hr and 4 week mice. (A) Annotated clustering of mouse MPs identifies 2 populations of SAMacs. (B) Olr1 normalised gene expression is enriched in SAMac2 cluster. (C) Differential abundance analysis of macrophage compositionbetween timepoints shows expansion of SAMac1 and SAMac2 during active mouse fibrosis. (D) Differential expression analysis between SAMad and SAMac2 highlights significantly upregulated Olr1 and Il1b expression in the SAMac2 subpopulation. (E-F) Flow cytometry analysis of hepatic macrophages from mouse chronic CCl4 model of liver fibrosis. OLR1 + SAMac2 and OLR1-CD319+ SAMac identified by flow cytometry analysis. (E) Quantitation of OLR1+ SAMac per gram of liver tissue at different timepoints in CCl4 model, demonstrates expansion of this population during active fibrosis. (F) Intracellular flow cytometry analysis of IL-1p expression on OLR1+ SAMac2 vs CD319+OLR1-SAMad, shows increased IL-1β protein production in OLR1+ SAMacs. Data expressed as AGMFI (delta geometric mean fluorescent intensity) relative to control samples incubated at 4°C.Figure 7: OLR1 promotes pro-inflammatory cytokine production in human monocyte-derived macrophages. (A-C) Human THP-1 monocyte cell line was treated with OLR1 targeting (OLR1-) or control non-targeting (NC) siRNA to inhibit OLR1 gene expression. (A) Gene expression of OLR1 is significantly reduced in OLR1- vs control (NC) THP-1 cells. (B) Pro-inflammatory cytokine IL1B gene expression is reduced in OLR1- (purple; bar on the right hand side) vs NC (grey; bar on the left hand side) cells.(C) Secretion of IL-1β protein measured by ELISA is significantly reduced in OLR1- vs NC THP-1 cells. (D-E) Primary human monocyte-derived macrophages (MDM) were generated from patients with cirrhosis and treated with OLR1 (triangles and purple bars) or control (circles and grey bars) siRNA. (D) OLR1 gene expression is significantly reduced following treatment with OLR1 siRNA compared to control (NC) siRNA. (E) OLR1 siRNA shows trends to reduced IL1B and TNFA gene expression in human MDMs compared to control siRNA.Figure 8: Liver OLR1 expression increases with fibrosis severity in different causes of human liver disease. Analysis of OLR1 expression in human liver bulk transcriptomic data from different causes of liver disease. Patients are stratified by histological fibrosis stage; mild (F0-F1) and severe (F2-F4). (A) Analysis of OLR1 expression in bulk RNA-seq data from patients with MASLD published by Govaere et al.

[0037] (GEO accession GSE135251). Significant upregulation of OLR1 expression is observed in patients with severe (n=121) vs mild (n=85) fibrosis. (B) Analysis of OLR1 expression in bulk RNA-seq data from patients with PBC published by Laschtowitz et al.

[0039] (doi: https: / / doi. Org / 10.5281 / zenodo.13990103). Significant upregulation of OLR1expression is observed in patients with severe (n=7) vs mild (n=7) fibrosis. (C) Analysis of OLR1 expression in bulk RNA-seq data from patients with PSC published by Laschtowitz et al.

[0039] (doi: https: / / doi. Org / 10.5281 / zenodo.13990103). Upregulation of OLR1 expression is observed in patients with severe (n=9) vs mild (n=6) fibrosis. (D) Analysis of OLR1 expression in bulk RNA-seq data from patients with alcohol-related liver disease (ARLD) and alcoholic hepatitis (AH) in collaboration with the InTeam consortium

[0038] (dbGaP Study Accession: phs001807.v1.p1). Significant upregulation of OLR1 expression is observed in patients with ARLD and AH with severe (n=38) vs mild (n=21) fibrosis. Statistical comparisons between groups performed via Student’s t-test.Figure 9: OLR1+ macrophages expand in different causes of human chronic liver diseaseHuman liver tissue was stained with a multiplex immunofluorescence panel on the Akoya PhenoCycler Fusion. (A) Identification of OLR1+ CD68+ scar-associated macrophages in multiplex immunofluorescence data. Example positive cells indicated by white arrows. Green=OLR1, Magenta=CD68, Blue=DAPI. (B) Quantitation of number of OLR1 + CD68+ SAMacs per mm2liver tissue in patients with histologically normal liver (n=13) and patients with chronic liver disease secondary to autoimmune hepatitis (AIH) (n=14), alcohol-related liver disease (ARLD) (n=10), MASLD (n=20), PBC (n=9) and PSC (n=11). Statistical comparisons are made between normal (healthy) liver samples and samples from each aetiology of liver disease (One-way ANOVA with Dunnett’s post test; ***P<0.001). Box and whiskers plot with all boxes showing median (centre line), first and third quartiles (lower and upper box limits), minimum / maximum values (whiskers).Figure 10: Targeting OLR1 in human monocyte-derived macrophages (MDMs) reduces activation of scar-producing myofibroblasts(A) Relative COL1A1 and COL3A1 mRNA expression of LX2 hepatic stellate cells following incubation with conditioned media from primary MDMs pre-treated with OLR1 targeting or control siRNA. Bars are min to max expression with line at mean. Dots connected by lines represent data from MDMs of 6 different CLD patients. Ratio paired t-test, *p<0.05. (B) Relative COL1A1 and COL3A1 mRNA expression of LX2 hepatic stellate cells following incubation with conditioned media from primary MDMs pre-treated with OLR1 blocking antibody (MEDI6570) or isotype control antibody. Bars are min to max expression with line at mean. Dots connected by lines represent data from MDMs of 4 different CLD patients. Ratio paired t-test, *p<0.05.EXAMPLESAn OLR1+ Scar-associated Macrophage subpopulation expands in human liver fibrosisTo study macrophage heterogeneity and identify disease-associated subpopulations, scRNAseq data from a total of 65 patient samples (39 healthy and 26 with cirrhosis) was analysed. Following quality control, a total of 81,577 mononuclear phagocytes were studied in detail. Clustering analysis revealed 2 subpopulations of scar-associated macrophages (SAMac; termed SAMad and SAMac2), expressing the previously described markers TREM2 and CD9 (6) (Fig 1A-B). Using differential composition analysis, both SAMac subpopulations were seen to expand in diseased liver tissue (Fig 1C).OLR1+ Scar-associated macrophages have a pro-inflammatory phenotypeIt was hypothesised that these 2 SAMac subpopulations may adopt different functions in liver fibrosis. To study the transcriptional differences between these populations, differential expression analysis was performed (Fig 1D). SAMad expressed higher levels of the previously described SAMac marker GPNMB (10), whilst SAMac2 expressed higher levels of scavenger receptors including OLR1 (Fig 1E). Focussing on inflammatory mediators, the OLR1+ SAMac2 population expressed higher levels of a number of pro-inflammatory and pro-fibrotic mediators (IL1B, TNF, IL18, NLRP3, AREG, PDGF), whilst the GPNMB+ SAMad population expressed higher levels of antiinflammatory mediators (GPNMB, MMP9, IL10) (Fig 1F). It was confirmed that OLR1 + SAMacs accumulated in fibrotic human liver and are spatially located in areas of fibrosis using immunofluorescent staining (Fig 2). Overall, these data demonstrate that the OLR1+ SAMac subpopulation has a pro-inflammatory phenotype in fibrotic human liver and is located in areas of injury, suggesting it may be a key mediator of inflammation and fibrosis in human liver disease.High hepatic OLR1 expression is associated with a poor prognosis in patients with MASLDTo study the links between liver OLR1 expression and liver fibrosis in human MASLD, additional analyses was performed on RNA-seq data from liver biopsy samples across the full MASLD spectrum in the SteatoSITE cohort (2). This demonstrated a progressiveincrease in OLR1 expression as fibrosis increases in patients with MASLD (Fig. 3A). The SteatoSITE cohort also includes linked clinical outcome data (2). Strikingly, high expression of OLR1 in the liver was associated with increased rates of liver disease progression (Fig. 3B) and higher overall mortality (Fig. 3C) in patients with MASLD. In summary, these data indicate that increased OLR1 expression is associated with worsening fibrosis and adverse clinical outcomes in patients with MASLD.OLR1 is specifically expressed by myeloid cells in the liverA previous in vitro study has suggested that OLR1 expression can be induced on liver endothelial cells (27). To confirm that OLR1 is specifically expressed in macrophages in the human liver, expression of OLR1 was measured in all liver cell types from our human scRNAseq data, comprising 843,457 cells from 81 individuals (42 healthy and 39 with advanced chronic liver disease) (Fig. 4A). These data highlight that OLR1 is only expressed in myeloid cells and not in other hepatic cell types such as endothelial cells (Fig. 4B). Therefore, interventions aimed at targeting OLR1 in the liver should be specific to myeloid cells, potentially minimising off target effects.Serum OLR1 levels are not correlated with liver disease severityOur data demonstrate that high levels of liver OLR1 expression is associated with poor patient prognosis in MASLD (Fig. 3B-C). Serum levels of OLR1 have been used as a biomarker of cardiovascular disease (28). Serum OLR1 has also been suggested in one study as a potential biomarker for the presence of liver disease (MASLD) (29). However, no literature exists on the potential role of serum OLR1 as a biomarker of fibrosis in the liver. Soluble OLR1 was measured in the serum of healthy individuals (n=9), patients with early liver disease (n=7) and patients with cirrhosis (n=9). No difference in serum OLR1 was observed between groups (Fig. 5). Therefore, serum OLR1 does not appear to be a useful biomarker of liver disease severity, but rather the hepatic levels of OLR1 are associated with liver disease severity and poor prognosis.OLR1+ scar-associated macrophages expand in murine liver fibrosisRodent models of liver fibrosis are often used for mechanistic understanding and in vivo testing of therapeutic interventions. To determine whether a similar pro-inflammatory OLR1+ SAMac subpopulation exist in mouse liver fibrosis, the well characterised chronic carbon tetrachloride (CCl4) mouse model of liver fibrosis progression and regression (10) was employed. scRNAseq was performed on blood and liver immune cells fromhealthy control mice and mice during liver fibrosis progression (24 and 96hrs) and regression (4 weeks). Analysis of monocytes and macrophages identified 2 clusters of liver-derived SAMacs (Fig 6A). Olr1 expression was specifically enriched in SAMac2 subpopulation (Fig 6B) and the SAMac2 subpopulation was noted to expand in the active fibrosis disease stage (24 and 96hrs after the last CCl4 injection) (Fig. 6C). Similar to human data, differential expression analysis between mouse SAMac subpopulations demonstrated increased expression of Olr1 and pro-inflammatory mediators (such as 111 b) in the mouse SAMac2 subpopulation (Fig. 6D).Expansion of OLR1 + SAMacs in mouse fibrotic liver tissue was confirmed using flow cytometry, demonstrating that they are significantly expanded during active liver injury but have declined in number 4 weeks after the cessation of injury (Fig. 6E). Hence, similar to humans, OLR1 + SAMacs also accumulate in fibrotic mouse liver.Murine OLR1+ scar-associated macrophages have a pro-inflammatory phenotype Our gene expression data in human and mouse indicates that OLR1+ SAMacs have a more pro-inflammatory phenotype, expressing higher levels of cytokines such as IL-1β which is important in promoting liver inflammation and fibrosis (30). To confirm this finding at protein level flow cytometry in the mouse CCl4 model was performed. OLR1 + SAMacs from fibrotic mouse liver tissue produced higher levels of IL-1β than OLR1-SAMacs (Fig. 6F), confirming that OLR1 expression distinguishes a more pro-inflammatory population of SAMacs.Targeting OLR1 in human monocyte-derived macrophages reduces pro-inflammatory mediator productionOLR1 (LOX-1) is a class E scavenger receptor, which has been described to have a role in the pathogenesis of atherosclerosis (28), cardiac fibrosis (31) and various cancers (32). Indeed, therapeutic targeting of OLR1 has been suggested as a treatment for atherosclerosis (28) and candidate compounds are currently being explored in clinical trials in patients with cardiovascular disease (33). However, OLR1 expression has not previously been described in SAMacs and its role in the pathogenesis of liver disease remains unknown. OLR1 has been shown to activate pro-inflammatory signalling in endothelial cells and macrophages in the context of atherosclerosis (34-36). Given that OLR1 expression is significantly upregulated in the pro-inflammatory SAMac2 subpopulation in human and murine liver fibrosis, it was hypothesised that OLR1 mightbe regulate pro-inflammatory functions in this SAMac subpopulation and may represent a novel therapeutic target to abrogate liver inflammation and fibrosis. Utilising the THP-1 monocyte cell line, it was demonstrated that reduction of OLR1 expression using siRNA (Fig. 7 A), results in reduced gene expression (Fig. 7B) and protein production of IL-1β (Fig. 7C). Knockdown of OLR1 expression in primary human monocyte-derived macrophages obtained from patients with cirrhosis (Fig 7D), also demonstrated a trend to reduced expression of pro-inflammatory cytokines IL1B and TNF (Fig. 7E). These data indicate that targeting of OLR1 expression and activity can reduce pro-inflammatory production in monocyte-derived macrophages. These findings suggest that therapeutic inhibition of OLR1 on liver SAMac in vivo has the potential to attenuate hepatic inflammation and inhibit liver fibrosis.Liver OLR1 expression increases with fibrosis severity in different causes of human liver diseaseWe have analysed publicly available human liver bulk RNA-seq data from patients with different causes of chronic liver disease (Fig. 8). These data demonstrate that hepatic OLR1 expression increases with the degree of fibrosis in patients with liver disease due to MASLD, alcohol-related liver disease (ARLD) and alcoholic hepatitis (AH), primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC) (Fig 8A-D). Hence hepatic OLR1 expression represents a potential biomarker and therapeutic target across a range of liver diseases.OLR1+ macrophages expand in different causes of human chronic liver disease To assess whether OLR1+ macrophages accumulate in different forms of human chronic liver disease, we have performed high dimensional antibody staining using the Akoya PhenoCycler Fusion system. OLR1+ CD68+ macrophages were annotated (Fig 9A) and quantitated in liver tissue from patients with no underlying liver disease and those with chronic liver disease from a range of different causes (Fig 9B). These data demonstrate an accumulation of OLR1+ SAMacs in fibrotic livers of patients with MASLD, ARLD, PBC, PSC and autoimmune hepatis (AIH).Targeting OLR1 in human monocyte-derived macrophages reduces activation of scar-producing myofibroblastsTo determine the effect of macrophages OLR1 inhibition on the function of myofibroblasts (the main scar-producing cells in the fibrotic liver), we performedconditioned media transfer assays. Primary human monocyte-derived macrophages from patients with cirrhosis were cultured and treated with OLR1 siRNA (or control siRNA) or an OLR1 blocking antibody (or isotype control antibody). Conditioned media from treated macrophages was then applied to the LX2 hepatic stellate cell line. Both knockdown of macrophage OLR1 via siRNA (Fig 10A) or blockade of macrophage OLR1 via blocking antibody MEDI6570 (Fig 10B) attenuated fibrillar collagen gene expression in LX2 cells, indicating that macrophage OLR1 modulation can reduce scar-production by liver myofibroblasts.ConclusionsThe findings detailed herein identify OLR1 as a marker of a pro-inflammatory subpopulation of SAMacs in the liver, which regulates inflammatory activity in macrophages. OLR1 therefore represents a novel therapeutic target for liver inflammation and fibrosis, whilst hepatic OLR1 levels could serve as a biomarker to improve risk stratification of patients with CLD.MATERIALSAND METHODOLOGYHuman Liver scRNAseqHuman single-cell RNA-seq data was obtained from 8 published studies (6, 13-19). All datasets were obtainable from the European Nucleotide Archive (ENA) in the form of paired fastq files, derived from raw sequencing data using the 10X Genomics sequencing protocol. Cell Ranger software (version 7.0.0) was executed on the University of Edinburgh computing server Eddie for each sample. In subsequent processing and analysis conducted in R (version 4.3.1), the Seurat package (version 4.3.0) was employed to store and merge individual datasets as Seurat Objects (20). Quality control (QC) filtering procedures were performed on each sample before merging. DecontX (version 1.14.2), was employed to eliminate ambient RNA contamination (21). Cells failing quality thresholds (mitochondrial gene < 15% & nFeature < 100) were removed from each sample. Seurat's merge function consolidated data sets from multiple studies into single merged objects.The Seurat 5.0.0 (Seurat5) package was used to convert the merged object into a Seurat5 (S5) Object, which was written with BPCells matrices, which employ bit-packing compression to store count matrices on disk, enabling memory-efficient single-cellanalysis. Subsampled cells (1,000 from each patient) were selected using 'Sketching' methods from the stored on-disk dataset, optimizing for rare populations.Normalization, scaling, variable feature identification, and principal component analysis (PCA) (npcs=50) were conducted on the S5 object's sketch assay, followed by streamlined Harmony integrative analysis (24) using the Integrate Layers function of Seurat5. Post-harmony analysis included FindNeighbors (dims=30), FindClusters, and runUMAP (dims=30) for the sketch assay. DimPlots were generated to visualize UMAP reduction. Subsequently, the Projectintegration function was employed to individually load the whole datasets into the S5 object and integrate all cells. Cluster labels derived from the sketched cells were projected onto the whole dataset using ProjectData. FindAllMarkers (test.use = wilcox) function of Seurat5 was used to identify top marker genes per cluster, assisting in manual cell type annotation based on previous literature (6). 11 cell lineages were identified and appended as metadata to the S5 object.Mononuclear phagocytes (MP) was subsetted and saved as an independent S5 object. Subsequent analysis for this S5 object involved FindVariableFeatures, ScaleData, RunPCA, RunUMAP (dims=1:30), FindNeighbors (dims=1:30), FindClusters (resolution^.4), and annotation for the DecontXcounts assay. Doublet clusters and low-quality clusters (median nFeature < 800) were labelled and removed iteratively, to ensure that only high-quality cells remained. Final clustering was performed at resolution^.6. Manual annotation was performed using defined marker genes.Differential composition analysis (DCA) and differential gene expression analysis (DGEA) were performed for the cleaned and annotated MPs. DCA per cell type was performed for the patients between experiment group (healthy vs. fibrotic). FindMarkers (test.use = MAST) was used to perform DGEA between SAMad and SAMac2. DotPlot, VlnPlot, pheatmap, and EnhancedVolcano were utilized for the visualization of the results.Human Liver bulk RNAseqUnified transparent approval for the pan-Scotland SteatoSITE project (2) was provided by the West of Scotland Research Ethics Committee 4 (Reference: 20 / WS / 0002). RNAseq and histology data was generated as described in detail in the original manuscript (2). Plots comparing OLR1 expression at different fibrosis stages were generated usingthe SteatoSITE online data browser (https: / / shiny.igc.ed.ac.uk / SteatoSITE_gene_explorer / ). For correlation between 0LR1 gene expression and time-to-event analysis, biopsy cases with available RNAseq from the SteatoSITE multimodal database (2) were used (R 4.3.3). Normalized counts for 0LR1 was available as part of the dataset (2). Decompensation events in the clinical data extract were defined by a combination of ICD codes and UK OPCS-4 codes identifying procedures relating to cirrhosis- related hospital admissions activity. Decompensation event analysis was only undertaken on biopsy cases for which the first decompensation-related coding was present in the clinical data extract after the biopsy date and analysis was undertaken using death as a competing risk. The cutpoint for 0LR1 gene activity was calculated using surv_cutpoint() from the ‘survminer’ package, applying maximally selected rank statistics of the ‘maxstat’ package with a minimum proportion of 0.25. Kaplan-Meier estimator curves of all-cause mortality or decompensation events for assigned high / low risk groups were compared by log-rank testing in ‘survminer’.Analysis of other stated bulk RNA-seq datasets were performed in a similar manner, stratifying patients by reported fibrosis stage, and assessing changes in normalised OLR1 expression.MiceAdult male C57BL / 6JCrl mice aged 8-10 weeks were purchased from Charles River. Mice were housed under specific pathogen-free conditions at the University of Edinburgh. All experimental protocols were approved by the University of Edinburgh Animal Welfare and Ethics Board in accordance with UK Home Office Legislation, following ethical guidelines. Liver fibrosis was induced in C57BL / 6JCrl wildtype mice with 4-weeks (nine injections) of twice-weekly intraperitoneal CCL at a dose of 0.4 pl / g body weight, diluted 1:3 in olive oil as previously described (10). Mice were randomly assigned to receive CCL or to serve as healthy controls. No sample size calculation or blinding was performed. From the C57BL / 6JCrl WT mice, blood and liver tissue was obtained 24-hours, 96-hours and 4-weeks after the final CCl4 injection for single-cell RNA sequencing and liver tissue only 24-hours, 96-hours, 168-hours and 4-weeks for flow cytometry analysis. Comparison was made to liver and blood obtained from age-matched uninjured mice. Animals were humanely killed by CO2 induction at the stated timepoints after CCI4 administration.Mouse liver cell isolation for scRNAseq and flow cytometryFor mouse liver flow cytometry and scRNA-seq, single-cell suspensions were prepared as previously described, with minor modifications (9). In brief, mouse livers were perfused in situ with ice cold PBS via the Inferior Vena Cava (IVC), portal vein was cut to allow the release of perfusion PBS, until the liver was blanched. The liver was excised and weighed. The right lobe and the caudate lobes were dissected and weighed and immediately placed in ice cold PBS for cell isolation. The livers were mechanically chopped into small fragments with a razor blade, then digested in an enzyme cocktail containing of 0.625 mgml-1 collagenase D (Roche, 11088882001), 0.85mgml-1 collagenase V (Sigma-Aldrich, C9263-1G), 1 mgml-1 dispase (Gibco, Invitrogen, 17105-041) and 30 Uml-1 DNase (Roche, 10104159001) in Roswell Park Memorial Institute (RPMI, Gibco, 21875034) 1640 medium, at 37°C for 20 min with agitation (240 r.p.m). Following digestion, the cell suspension was strained through a 100pm filter (EASYstrainer Greiner Bio-One, 542000), along with RPMI, and centrifuged. The resultant supernatant was removed, and cell pellet was resuspended in RPMI and centrifuged again. Following this, resultant supernatant was removed, and the cell pellet was lysed for erythrocytes by 3 minutes incubation with 10% RBC lysis buffer (Biolegend, 420301) in dH2O. Cells were washed in PEB buffer (containing PBS (Thermo Fisher Scientific, 14190-094), 2% Foetal Bovine Serum (FBS) (Thermo Fisher Scientific, 10500-064), and 2mM EDTA (Sigma-Aldrich, E5134) and centrifuged. After the supernatant was discarded, the resultant cell pellet was resuspended in PEB buffer, filtered through a 35pm nylon mesh (Corning, 352235). A 6pL aliquot was taken for cell counting, cells / mL count was obtained on a TC-20 cell counter (Bio-Rad), using trypan blue as a live dead marker, and the rest of the sample was centrifuged to obtain a pellet. Based on the cell count, the sample was resuspended in appropriate volume of PEB buffer to plate 2 million cells in a 96-well plate. For flow cytometry analysis and scRNA-seq prior to antibody staining, samples were blocked in anti-mouse CD16 / 32 antibody (1:100; BioLegend, 101302) and 10% normal mouse serum (Sigma, M5905) for 10 minutes at 4°C.Mouse blood cell isolation for scRNAseqFor mouse peripheral blood scRNA-seq, at the time of harvest, whole blood (100-200pL) was collected from the IVC, into 0.5mM EDTA solution, on ice. Erythrocytes were lysed by incubation with 10% Red Blood Cell (RBC) lysis buffer (Biolegend, 420301) in dH2O for 5 minutes, resuspended in PEB buffer (same composition as for hepatic NPCisolation), followed by centrifugation at 4°C, 300g for 5 minutes. The supernatant was removed, and cell pellet was lysed again by resuspending in 10% RBC lysis buffer in dH2O for 5 minutes, followed by centrifugation at 4°C, 300g for 5 minutes, before resuspending in PEB buffer and centrifuging the cell suspension. After the resulting supernatant was removed, the cell pellets were resuspended in 1mL of PEB buffer and filtered through 35pm nylon mesh (Corning, 352235). Followed by centrifugation at4°C, 300g for 5 minutes, the residual supernatant was removed, and cell pellet was lysed again by resuspending in 10% RBC lysis buffer in dH2O for 5 minutes, followed by centrifugation at 4°C, 300g for 5 minutes, before resuspending in PEB buffer. Before the addition of antibodies, blood samples were blocked in anti-mouse CD16 / 32 antibody (1:100; BioLegend, 101302) and 10% normal mouse serum (Sigma, M5905) for 10 min at4°C.Mouse Flow Cytometry and FACS sortingFluorescence-activated cell sorting (FACS) on mouse cells isolated was performed as described previously (6). Following blocking, the isolated cells were incubated with primary antibodies for 20 minutes at4°C. All human and mouse antibodies, conjugates, dilutions, and their catalogue numbers used for FACS of hepatic NPCs and systemic leukocytes are presented in Supplementary Table 19 from previous work (6). After antibody staining, cells were washed with PEB buffer. For mouse cell sorting (FACS) analysis, cell viability staining (DAPI; 1:1,000) was then performed, immediately before acquiring the samples. Mouse macrophage cell sorting for scRNA-seq was performed on a BD FACSAriall (Becton Dickinson). For scRNA-seq, viable hepatic CD45+ cells and peripheral blood CD45+ Ly-6G- leukocytes were sorted from healthy (n=6) and CCk-treated (24-hours, 96-hours, 4-weeks; n=6) mice and processed for droplet-based scRNA-seq. To account for mouse-to-mouse variability, 3 mice were pooled for each scRNA-seq sample.For flow cytometry cells were isolated and following blocking, cells were incubated with primary antibodies for 20 minutes at 4°C. Mouse antibodies used for flow cytometry analysis of livers from healthy and CCk-treated C57BL / 6JCrl WT-mice, their fluorophore conjugates, dilutions and catalogue numbers are presented in T able 1.1. For mouse flow cytometry analysis cells were then incubated with streptavidin-BV650 (BioLegend 405232; 1:100) for 20 min at 4°C. Flow cytometry compensations were set up using single stained beads (UltraComp eBeads, Thermo Fisher Scientific, 01-2222-42).Controls for gating included ‘fluorescence-minus-one’ (FMO) samples. Cell viability was assessed using the following live / dead stain DAPI (1:1000 dilution, Invitrogen, D3571), added immediately before acquiring the samples. Counting beads were added immediately prior to running the sample (Precision Count Beads, Biolegend, 424902; 50ul, Con: 1.01x106). Flow cytometry analysis was performed on the 6-laser LSRFortessa™ Cell Analyzer (BD Biosciences), using the gating strategy described in the section below. All flow cytometry data were analysed using FlowJo™ v10.8 Software (BD Life Sciences).Table 1.1 Antibodies used for liver flow cytometry of C57BL / 6JCrl WT-mice.Leucocytes were identified as viable CD45+ cells. For the further identification of specific leukocytes, neutrophils were first defined based on their positive expression of Cd11b and Ly-6G in both liver and blood. In the not neutrophil gate, a lineage gate was used to remove NK Cells (NK1.1), eosinophils (Siglec F), plasmacytoid dendritic cells (Siglec H), B cells (CD19) and T cells (CD3). Conventional dendritic cells (eDCs) were gated as Cd11chi, F4 / 80lo and excluded. Monocytes and hepatic macrophages were gated as CD11b+ F4 / 80+. These cells were further subdivided into Kupffer cells (Timd4+, F4 / 80hi), Ly-6Chi monocytes (Ly-6Chi MHCHIo) and SAMacs (MHCII+ CD9+ F4 / 80+ TIMD4-). Finally, within the SAMac population OLR1+CD319- SAMac2 and OLR1-CD319+ SAMad subpopulations were identified and enumerated. The total number cells in the digested portion of the liver was counted and represented as cells / g of liver based on the number of cells in the whole sample prior to staining and the known weight of the liver obtained for flow cytometric analysis.For assessment of IL-ip protein expression in macrophage subpopulations by intracellular flow cytometry (ICFC), 5x106 isolated liver cells were incubated in a 5ml non adherent (polypropylene) FACS tube with 1 ul / ml Momensin (BD Bioscience, 554724) in complete RPMI (Gibco, 21875034) with 10% FBS (Thermo Fisher Scientific, 10500-064), for 3 hours at 37°C. Control samples were incubated for 3 hours at 4°C. Following incubation, cells were centrifuged at 4°C, 500g for 5 minutes and supernatant was removed. Pelleted samples were then stained with Zombie NIR (Biolegend, 423105) for cell viability (concentration 1:1000) in PBS and incubated with cells for 10 minutes at room temperature, in the dark. Then cells were washed with PEB buffer spin at 500G for 5 minutes. Pelleted cells were resuspended in PEB and 5ul of FC Block and incubated for 10 minutes, followed by the surface staining antibodies for 20 minutes at 4°C. After washing off the antibody mix, cells were stained with streptavidin-BV421 (BioLegend 405225; 1:100) for 20 minutes at 4°C. Then the cells were washed twice in PEB buffer before overnight fixation in 90ul fixation buffer (3:1 diluent to concentrate) (Invitrogen DO-5521-00) at 4°C, protected from light. After fixation, cells were washed initially with 100ul permeabilisation buffer (Dilute x10 in dH20) (Invitrogen, 00-8333-56) at 1000G for 3 minutes and then in 150ul permeabilization buffer at 1000G for another 3 minutes. Next, cells were resuspended and incubated with the intracellular antibody mix in permeabilisation buffer for 60 minutes at 4°C. After intracellular staining, wash initially with 100pL of permeabilisation buffer, then with 150pL permeabilisation buffer and spin at 600G for 3 minutes each time. Following these washes, cells were resuspended in PEB buffer and acquired on the flow cytometer. All mouse antibodies, fluorophore conjugates, dilutions and catalogue numbers are presented in Table 1.2. Flow cytometry analysis was performed on the 6-laser LSRFortessa (BD Biosciences). The experiment was repeated three times, and data were collected from a total of 8 mice. In the ICFC experiment, gating was performed to identify SAMac2 and SAMad as above, followed by calculation of the geometric mean fluorescence intensity (GMFI) of I L-1 (3 within these populations gates using FlowJo v10.8.2.Table 1.2 Antibodies used for liver ICFC of C57BL / 6JCrl WT-mice.Mouse scRNAseqSingle cells were processed through the ChromiumTM Single Cell Platform using the Chromium Single Cell 3' Library and Gel Bead Kit v2 (10X Genomics, PN-120237) and the ChromiumTM Single Cell A Chip Kit (10X Genomics, PN-120236) as per the manufacturer’s protocol. Single cells were sorted into PBS plus 0.1% BSA, washed twice and counted using a Bio-Rad TC20. Then, 10,800 cells were added to each lane of the 10X microfluidics chip, 10X gel beads into another and Oil into the third lane. The cells were partitioned into Gel Beads in Emulsion in the ChromiumTM instrument, in which cell lysis and bar-coded reverse transcription of RNA occurred followed by amplification, fragmentation and 3' adaptor and sample index attachment. Libraries were sequenced on an Illumina HiSeq 4000.Mouse liver scRNAseq analysisFASTQ files were aligned to pre-built mouse (mm 10-3.0.0) reference transcriptomes, which were downloaded from 10X Genomics. Read alignment was performed using the Cell Ranger v3.0.2 Single-Cell Software Suite from 10X Genomics for each dataset separately. A Seurat Object was created for each individual dataset using the ‘CreatSeuratObject’ function with default parameters and included features detected in at least three cells and cells where at least included 200 features. The Seurat objects of each condition (24-hours, 96-hours, 4-weeks and uninjured) and each source (liver and blood) were merged into a single object which contained the combined count matrices of all the datasets. For QC thresholds, cells with fewer than 300 genes and with mitochondrial gene content greater than 30% were excluded from downstream analysis. Data was log normalised and Highly Variable Genes (HVGs) selected. Linear dimensional reduction was applied using principal component analysis (PCA) and the top 20 principal components (PCs) used for unsupervised (SNN graph-based) clustering. Analyses were performed using the Seurat R package. All visualisations were produced using Seurat functions in conjunction with the ggplot2 (version 3.2.1; (25)) and grid (version 3.6.3; R Core Team

[2020] ) R packages.Initial clustering was conducted on all 12 mouse scRNA-seq datasets. Upon cell lineage annotation followed by signature analysis, the mononuclear phagocyte lineage (MP) was isolated and re-analysed as above to identify MP heterogeneity. Differential gene expression analysis (DGE) for each of the identity classes was conducted in Seurat using the Wilcoxon rank sum test to assess significance. Genes were considered as marker genes of MP subpopulations only if their log-transformed fold change was greater than 0.2 and if they were expressed in at least 10% of cells in the respective cluster. The results of the DGE analysis was used to annotate the cell lineages and the MP clusters.DGE analysis between the SAMad and SAMac2 in mouse datasets was conducted using a ‘Model-based Analysis of Single-cell Transcriptomics’ MAST approach (26). Genes identified through MAST DGE analysis with log-transformed fold change greater than 0.2 and adjusted P-value <0.05 were considered as marker genes.Human Liver IHCFor histological assessment of MASLD biopsies, anonymized unstained formalin-fixed paraffin-embedded liver biopsy sections were provided by the Lothian NRS Human Annotated Bioresource (study number SR848) under authority from the East of ScotlandResearch Ethics Service REC 1, reference 15 / ES / 0094. Formalin-Fixed Paraffin-Embedded (FFPE) tissue sections were dewaxed in xylene twice for 5 minutes) and then rehydrated in decreasing concentrations of ethanol (100%, 75%, 65%) for two minutes each. Following a wash in dH20, heat mediated AR was carried out (if required) for 15 minutes, using either sodium citrate (pH6) or Tris-EDTA (pH9) buffers. Slides were washed in PBS and then incubated in 3% hydrogen peroxide for 10 minutes, to inhibit endogenous peroxidase activity. Following further washes in PBS, slides were blocked with protein block (Abeam, ab64226) for 30 minutes at room temperature. Primary antibody was diluted to a specified concentration in antibody diluent (Abeam, ab64211), and incubated at 4°C, overnight (see table 1.3 for full antibody list and conditions). Slides were then washed in PBS / T (PBS, plus 0.1% Tween 20, Sigma-Aldrich P1379) and incubated with ImmPress HRP Polymer Detection Reagents (Vector Laboratories, MP-7402) - depending on the primary antibody host species - for 30 minutes at room temperature. Slides were washed with PBST and staining was detected with either Cyanine3 (Cy3; 1:1000), Cyanine5 (Cy5; 1:2000) or Fluorescein (Fl; 1:500) conjugated tyramide (Perkin Elmer, NEL744001KT), for 10 minutes at room temperature. Slides were washed in PBST before a further heat mediated antigen retrieval, protein block and primary antibody incubation, ImmPress Polymer and detection with tyramide as before. This sequence was repeated once again for the 3rd primary antibody, then sections were incubated with DAPI (Merck, D3571; 1:1000 in PBS) and mounted with Prolong Gold. Stained slides were stored at 4oC in the dark until imaged. Fluorescent images of whole tissue sections were captured using the slide scanner AxioScan. ZI (Carl Zeiss, Oberkochen, Germany) at 20X magnification. Thresholds for all the fluorescent channels were set against the negative controls, were kept constant throughout the same experiment. Images were processed and scale bars were added on Zen Blue (Zeiss) software.Table 1.3 Conditions for Human OLR1 / CD68 / COL1 staining.The Akoya Phenocycler Fusion staining (65 antibodies) and imaging was conducted on human liver tissue according to the manufacturers protocol. Patients with different aetiologies of cirrhosis and healthy controls were included. Cell segmentation, clustering, annotation and quantitation of changes in cellular abundance was performed in R.THP1 monocyte cell cultureTHP1 monocytes were cultured in RPMI (Gibco, 31870074) containing 1% Pen-strep (Gibco, 11548876), 1% L-glutamine (Gibco, 25030081) and 10% fetal bovine serum (FBS) (Gibco, A5256701) at 37 °C, 5% CO2 humidified conditions. For assessing the gene and cytokine expression of THP1 macrophages, THP1 monocytes were seeded at a density of 1.14 x 105 cells / cm2 and treated with 100 nM PMA (Merck, P1585) on day 1. On day 3, 25 nM OLR1 Silencer™ Select Validated siRNA (Ambion, 4390824) or Silencer™ Select Negative Control No. 1 siRNA (Ambion, 4390843) was delivered to THP1 cells via TranslT-X2® Dynamic Delivery System (Mirus, MIR 6003) according to the manufacturer’s manual. THP1 macrophages media was changed and RNA / supernatant were collected 6 days later.Primary monocytes isolation and cell cultureLocal approval for procuring blood samples from CLD patients for in vitro analyses was obtained from the Lothian NRS BioResource and Tissue Governance Unit (study number SR574), following review at the East of Scotland Research Ethics Service (reference 15 / ES / 0094). All subjects provided written informed consent. For PBMC isolation, SepMate™-50 (IVD) kit was used, following manufacturer’s instructions. In brief, 15 mL of Lymphoprep™ (STEMCELL Technologies, 07801) was added to a SepMate™ tube. A maximum of 15 ml patients’ blood were diluted in 2% FBS (Gibco, A5256701) phosphate buffered saline (PBS) (Gibco, 10010023) in 1:1 dilution factor before adding to the SepMate™ tube. It was centrifuged at 1200 g for 15 min at 22 °C. The supernatant was collected and top up to 45 mL with 2% FBS PBS. It was subsequently centrifuged at 300g for 8 min at 4 °C. The supernatant was discarded and the PBMC pellet was resuspend in 10 mL 2% FBS PBS.Monocytes were enriched using EasySep™ Human Monocyte Enrichment Kit (STEMCELL Technologies, 19059), following manufacturer’s instructions. In short, to target unwanted cells, 50 µL of Tetrameric Antibody Complexes Enrichment Cocktailwere added to every mL of PBMC suspension in a collection tube. The mix was then incubated at room temperature (RT) for 5 min. 50 µL of magnetic particles were mixed with every mL of sample and incubated in RT for 5 min. The tube was inserted into EasyEights™ EasySep™ magnet (STEMCELL Technologies, 18103) and incubated for 2.5 min in RT. Primary monocytes were then cryopreserved using CryoStor CS10 (STEMCELL Technologies, 100-1061) and stored in liquid nitrogen for further use.For assessing the gene expression of human primary macrophages, monocytes were thawed and cultured in RPMI (Gibco, 31870074) with 1% Pen-strep (Gibco, 11548876), 1% L-glutamine (Gibco, 25030081), 10 mM HEPES (Gibco, 15630080), 50 ng / mL human M-CSF (PETROTECH, 300-25), and 10% FBS (Gibco, A5256701) at a density of 1.14 x 105cells / cm2or 3.13x 105cells / cm2in 37 °C, 5% CO2 humidified conditions. On day 4, 25 nM OLR1 Silencer™ Select Validated siRNA (Ambion, 4390824) or Silencer™ Select Negative Control No. 1 siRNA (Ambion, 4390843) was delivered to cells via TransIT-X2® Dynamic Delivery System (Mirus, MIR 6003) according to the manufacturer’s manual. On day 6, media was changed. RNA and supernatant were collected 24 hours later. OLR1 inhibition in patient-derived MDMs was performed by adding the human OLR1 Blocking Antibody Golocdacimab / MEDI6570 (MedChemExpress, HY-P99646) or human IgG1 lambda1 Isotype Antibody (MedChemExpress, HY-P99992) for 48 hours before supernatant collection. All supernatants were centrifuged (1000 g, 10 min) to remove debris and used subsequently for ELISA or conditioned media stimulation assays.LX2 human hepatic stellate cells were cultured in DMEM high-glucose with 1% L-glutamine (Sigma-Aldrich, D5796) containing 1% Pen-strep (Gibco, 11548876), and 10% fetal bovine serum (FBS) (Sigma-Aldrich, F9665). For conditioned media experiments, LX2 cells were seeded at a density of 3.13 x 104cells / cm2. The following day, cells were serum-starved for 24 hours. On day 3, the supernatant from primary macrophages was mixed with serum-free media at a ratio of 1 / 4 and used to treat LX2 cells. On day 4, RNA extraction or proliferation assays were performed.qPCRCells were homogenized using a QIAshredder (QIAGEN, 79654) and subsequently RNA was extracted using PureLinkTM RNA Mini Kit (Invitrogen, 12183018A) according to manufacturer’s instructions. 0.05 - 1 pg RNA was used for cDNA synthesis usingSuperScript™ IV Reverse Transcriptase (Invitrogen, 1809005) following manufacturer’s manual.Quantitative PCR was conducted using LightCycler® 480 SYBR Green I Master (Roche, 04707516001) on QuantStudio™ 5 Real-Time PCR System (Applied Biosystems™). The following thermal cycling conditions were used: 1) 95 °C for 5 min. 2) 40 cycles of 95 °C for 10s, 60 °C for 20s, and 72 °C for 30s. Ct values of the genes tested were analyzed using 2-ΔΔCtmethod, with BACTIN as the housekeeping control.ELISAFor OLR1 quantification, ELISA assay (Ray Biotech, ELH-LOX1-1) was performed on human blood serum samples diluted 1:5 according to manufacturer’s instructions. For IL-ip quantification, ELISA assay (R& D systems, DY201) was performed on the supernatant of primary MDMs diluted 1:2 or 1:4 according to manufacturer’s instructions. A microplate reader (Agilent BioTek) was used to measure the absorbance of the samples at 450nm. Standard curves were created using absorbances of OLR1 and IL-1P standards with known concentration.Statistical AnalysisAll non-bioinformatics data was analysed using GraphPad Prism 9 for macOS (version 9.5.1). All graphs are shown as the mean±standard error of the mean (SEM). Statistical analysis was done using GraphPad Prism 9 for macOS (version 9.5.1). Significance values are highlighted: *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001.REFERENCES1. Devarbhavi H, Asrani SK, Arab JP, Nartey YA, Pose E, Kamath PS. Global burden of liver disease: 2023 update. J Hepatol. 2023;79:516-537.2. Kendall TJ, Jimenez-Ramos M, Turner F, Ramachandran P, Minnier J, McColgan MD, et al. An integrated gene-to-outcome multimodal database for metabolic dysfunction-associated steatotic liver disease. Nat Med. 2023;29:2939-2953.3. Sanyal AJ, Anstee QM, Trauner M, Lawitz EJ, Abdelmalek MF, Ding D, et al. Cirrhosis regression is associated with improved clinical outcomes in patients with nonalcoholic steatohepatitis. Hepatology. 2022;75:1235-1246.4. Harrison SA, Bedossa P, Guy CD, Schattenberg JM, Loomba R, Taub R, et al. A Phase 3, Randomized, Controlled Trial of Resmetirom in NASH with Liver Fibrosis. N Engl J Med. 2024;390:497-509.5. Hammerich L, Tacke F. Hepatic inflammatory responses in liver fibrosis. Nat Rev Gastroenterol Hepatol. 2023;20:633-646.6. Ramachandran P, Dobie R, Wilson-Kanamori JR, Dora EF, Henderson BEP, Luu NT, et al. Resolving the fibrotic niche of human liver cirrhosis at single-cell level. Nature [Internet], 2019;575:512-518. Available from: http: / / www.nature.com / articles / s41586-019-1631-37. Fabre T, Barron AMS, Christensen SM, Asano S, Bound K, Lech MP, et al. Identification of a broadly fibrogenic macrophage subset induced by type 3 inflammation. Sci Immunol. 2023;8:eadd8945.8. Bhattacharya M, Ramachandran P. Immunology of human fibrosis. Nat Immunol.2023;24:1423-1433.9. Tacke F, Trautwein C. Mechanisms of liver fibrosis resolution. J Hepatol.2015;63:1038-1039.10. Ramachandran P, Pellicoro A, Vernon MA, Boulter L, Aucott RL, Ali A, et al. Differential Ly-6C expression identifies the recruited macrophage phenotype, which orchestrates the regression of murine liver fibrosis. Proceedings of the National Academy of Sciences [Internet], 2012;109: E3186-E3195. Available from: http: / / www. pnas.org / cgi / doi / 10.1073 / pnas.111996410911. Hendrikx T, Porsch F, Kiss MG, Rajcic D, Papac-Milicevic N, Hoebinger C, et al. Soluble TREM2 levels reflect the recruitment and expansion of TREM2(+) macrophages that localize to fibrotic areas and limit NASH. J Hepatol. 2022;77:1373-1385.12. Coelho I, Duarte N, Barros A, Macedo MP, Penha-Gongalves C. Trem-2 Promotes Emergence of Restorative Macrophages and Endothelial Cells During Recovery From Hepatic Tissue Damage. Front Immunol. 2020;11:616044.13. Andrews TS, Nakib D, Perciani CT, Ma XZ, Liu L, Winter E, et al. Single-cell and spatial transcriptomics characterisation of the immunological landscape in the healthy and PSC human liver. J Hepatol [Internet], 2024; Available from: https: / / doi. Org / 10.1016 / j.jhep.2023.12.02314. Guilliams M, Bonnardel J, Haest B, Vanderborght B, Wagner C, Remmerie A, et al. Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches. Cell [Internet], 2022; Available from: https: / / www.sciencedirect.com / science / article / pii / S009286742101481115. MacParland SA, Liu JC, Ma X-Z, Innes BT, BartczakAM, Gage BK, et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat Commun [Internet], 2018;9:4383. Available from: http: / / www.nature.com / articles / s41467-018-06318-716. Poch T, Krause J, Casar C, Liwinski T, Glau L, Kaufmann M, et al. Single-cell atlas of hepatic T cells reveals expansion of liver-resident naive-like CD4(+) T cells in primary sclerosing cholangitis. J Hepatol. 2021;75:414-423.17. Tamburini BAJ, Finlon JM, Gillen AE, Kriss MS, Riemondy KA, Fu R, et al. Chronic Liver Disease in Humans Causes Expansion and Differentiation of Liver Lymphatic Endothelial Cells. Front Immunol [Internet], 2019;10. Available from: https: / / www.frontiersin.org / article / 10.3389 / fimmu.2019.01036 / full18. Zhang H, MaY, Cheng X, Wu D, Huang X, Chen B, etal. Targeting epigenetically maladapted vascular niche alleviates liver fibrosis in nonalcoholic steatohepatitis. Sci Transl Med. 2021;13:eabd1206.19. Buonomo EL, Mei S, Guinn SR, Leo IR, Peluso MJ, Nolan M-A, et al. Liver stromal cells restrict macrophage maturation and stromal IL-6 limits the differentiation of cirrhosis-linked macrophages. J Hepatol [Internet], 2022;76:1127-1137. Available from: https: / / www.sciencedirect.com / science / article / pii / S016882782200007120. Satija R, Farrell JA, Gennert D, Schier AF, Regev A. Spatial reconstruction of single-cell gene expression data. Nat Biotechnol. 2015;33:495-502.21. Yang S, Corbett SE, Koga Y, Wang Z, Johnson WE, Yajima M, et al. Decontamination of ambient RNA in single-cell RNA-seq with DecontX. Genome Biol.2020;21:57.22. Hao Y, Stuart T, Kowalski MH, Choudhary S, Hoffman P, Hartman A, et al. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat Biotechnol [Internet], 2024;42:293-304. Available from: https: / / doi.org / 10.1038 / s41587-023-01767-y23. Parks B. BPCells: Single Cell Counts Matrices to PCA. R package version 0.2.0 [Internet], 2024; Available from: https: / / github.com / bnprks / BPCells24. Korsunsky I, Millard N, Fan J, Slowikowski K, Zhang F, Wei K, et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat Methods [Internet], 2019;16:1289-1296. Available from: http: / / www.nature.com / articles / s41592-019-0619-025. Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York; 2016.26. Finak G, McDavid A, Yajima M, Deng J, Gersuk V, Shalek AK, et al. MAST: A flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol [Internet], 2015 [cited 2024 Jan 21];16:1-13. Available from: https: / / genomebiology.biomedcentral.eom / articles / 10.1186 / s13059-015-0844-5 27. Zhang Q, Liu J, Liu J, Huang W, Tian L, Quan J, et al. oxLDL induces injury and defenestration of human liver sinusoidal endothelial cells via LOX1. J Mol Endocrinol.2014;53:281-293.28. Barreto J, Karathanasis SK, Remaley A, Sposito AC. Role of LOX-1 (Lectin-Like Oxidized Low-Density Lipoprotein Receptor 1) as a Cardiovascular Risk Predictor. Arterioscler Thromb Vase Biol [Internet], 2021;41:153-166. Available from: https: / / doi.org / 10.1161 / ATVBAHA.120.31542129. Ozturk O, Colak Y, Senates E, Yilmaz Y, Ulasoglu C, Doganay L, et al. Increased serum soluble lectin-like oxidized low-density lipoprotein receptor-1 levels in patients with biopsy-proven nonalcoholic fatty liver disease. World J Gastroenterol.2015;21:8096-8102.30. Barbier L, Ferhat M, Salame E, Robin A, Herbelin A, Gombert J-M, et al. Interleukin-1 Family Cytokines: Keystones in Liver Inflammatory Diseases. Front Immunol. 2019;10:2014.31. Villa M, Cerda-Opazo P, Jimenez-Gallegos D, Garrido-Moreno V, Chiong M, Quest AF, et al. Pro-fibrotic effect of oxidized LDL in cardiac myofibroblasts. Biochem Biophys Res Commun. 2020;524:696-701.32. Murdocca M, De Masi C, Pucci S, Mango R, Novelli G, Di Natale C, et al. LOX-1 and cancer: an indissoluble liaison. Cancer Gene Ther [Internet], 2021;28:1088-1098. Available from: https: / / doi.org / 10.1038 / s41417-020-00279-033. Vavere AL, Sinsakul M, Ongstad EL, Yang Y, Varma V, Jones C, et al. Lectin-Like Oxidized Low-Density Lipoprotein Receptor 1 Inhibition in Type 2 Diabetes: Phase 1 Results. J Am Heart Assoc. 2023;12:e027540.34. Cominacini L, Pasini AF, Garbin U, Davoli A, Tosetti ML, Campagnola M, et al. Oxidized Low Density Lipoprotein (ox-LDL) Binding to ox-LDL Receptor-1 in Endothelial Cells Induces the Activation of NF-κ B through an Increased Production of Intracellular Reactive Oxygen Species *. Journal of Biological Chemistry [Internet], 2000;275:12633-12638. Available from: https: / / doi.org / 10.1074 / jbc.275.17.12633 35. Yang H-Y, Bian Y-F, Zhang H-P, Gao F, Xiao C-S, Liang B, et al. LOX 1 is implicated in oxidized low density lipoprotein induced oxidative stress of macrophages in atherosclerosis. Mol Med Rep. 2015;12:5335-5341.36. Ding Z, Liu S, Wang X, Dai Y, Khaidakov M, Deng X, et al. LOX-1, mtDNA damage, and NLRP3 inflammasome activation in macrophages: implications in atherogenesis. Cardiovasc Res. 2014;103:619-628.37. Govaere O, Cockell S, Tiniakos D, Queen R, Younes R, Vacca M, et al. Transcriptomic profiling across the nonalcoholic fatty liver disease spectrum reveals gene signatures for steatohepatitis and fibrosis. Sci Transl Med 2020;12:eaba4448. https: / / doi.org / 10.1126 / scitranslmed.aba4448.38. Argemi J, Latasa MU, Atkinson SR, Blokhin IO, Massey V, Gue JP, et al. Defective HNF4alpha-dependent gene expression as a driver of hepatocellular failure in alcoholic hepatitis. Nat Commun 2019;10:3126. https: / / doi.org / 10.1038 / s41467-019-11004-3.39. Laschtowitz A, Lindberg EL, Liebhoff A-M, Liebig LA, Casar C, Steinmann S, et al. Liver transcriptome analysis reveals PSC-attributed gene set associated with fibrosis progression. JHEP Reports 2025;7. https: / / doi. Org / 10.1016 / i.ihepr.2024.101267.40. Matsukuma K, Yeh MM. Practical Guide, Challenges, and Pitfalls in Liver Fibrosis Staging. Surg Pathol Clin. 2023;16(3):457-472. doi:10.1016 / j.path.2023.04.002.41. Lai JC, Liang LY, Wong GL. Noninvasive tests for liver fibrosis in 2024: are there different scales for different diseases? Gastroenterol Rep (Oxf). 2024 Apr 11;12:goae024. doi: 10.1093 / gastro / goae024. PMID: 38605932; PMCID: PMC11009030.

Claims

CLAIMS:

1. A method of identifying subjects with or at risk of developing liver inflammation and / or liver disease, said method comprising detecting oxidised low density lipoprotein receptor 1 (OLRI)-positive mononuclear phagocytes in a sample obtained from a subject, wherein the detection of OLR1 -positive mononuclear phagocyte is indicative of liver inflammation and / or a liver disease.

2. The method according to claim 1, wherein the sample is obtained from the liver of the subject.

3. The method according to claims 1 or 2, wherein the method comprises detecting OLR1 gene and / or protein expression.

4. The method according to any of claims 1 to 3, wherein the OLR1 -positive mononuclear phagocytes are hepatic macrophages.

5. The method according to any of claims 1 to 3, wherein the OLR1 -positive mononuclear phagocytes are scar-associated macrophages.

6. The method according to claim 5, wherein the scar-associated macrophages express TREM2, CD9, CD63 and SPP1.

7. The method according to any of claims 1 to 6, wherein the detection comprises detection of a level of OLR1 expression relative to a control.

8. The method according to any of claims 1 to 7, wherein the liver disease is selected from the group comprising or consisting of: non-alcoholic fatty liver disease, metabolic-dysfunction associated steatotic liver disease (MASLD), alcohol-related liver disease (ARLD), alcoholic hepatitis, autoimmune hepatitis, primary biliary cholangitis (PBC), primary sclerosing cholanigitis (PSC), hereditary haemochromatosis, viral hepatitis, liver fibrosis, cirrhosis, chronic liver disease and / or liver cancer.

9. A modulator for use in the treatment and / or prevention of liver inflammation and / or liver disease, wherein the modulator reduces or inhibits oxidised low density lipoprotein receptor 1 (OLR1) expression and / or function in mononuclear phagocytes.

10. The modulator for use according to claim 9, wherein the modulator reduces or inhibits OLR1 expression and / or function in mononuclear phagocytes in the liver.

11. The modulator for use according to claims 9 or 10, wherein the mononuclear phagocytes are hepatic macrophages.

12. The modulator for use according to any of claims 9 to 11, wherein the mononuclear phagocytes are scar-associated macrophages.

13. The modulator for use according to any of claims 9 to 12, wherein the modulator reduces or inhibits:(i) OLR1 gene expression;(ii) OLR1 protein expression; and / or(iii) OLR1 protein function and / or activity.

14. The modulator for use according to any of claims 9 to 13, wherein the reduction or inhibition of OLR1 expression modulates inflammation and / or fibrosis in the liver.

15. The modulator for use according to any of claims 9 to 14, wherein the reduction or inhibition of OLR1 expression modulates pro-inflammatory and / or pro-fibrotic activity of mononuclear phagocytes.

16. The modulator for use according to any of claims 9 to 15, the modulator reduces pro-inflammatory and / or pro-fibrotic cytokine production in the liver.

17. The modulator for use according to claims 9 to 16, wherein the modulator is an siRNA, an shRNA and / or an anti-OLR1 antibody.

18. Use of oxidised low density lipoprotein receptor 1 (OLRI)-positive mononuclear phagocytes as a marker of liver inflammation and / or liver disease.

19. The use according to claim 18, wherein the OLR1 -positive mononuclear phagocytes are hepatic macrophages.

20. The use according to claims 18 or 19, wherein the OLR1 -positive mononuclear phagocytes are scar-associated macrophages.

21. The use according to claim 20, wherein the scar-associated macrophages express TREM2, CD9, CD63 and SPP1.

22. The use according to any of claims 18 to 21, wherein the level of the marker is indicative of:(i) the severity of liver inflammation and / or liver disease; and / or(ii) the likelihood of developing liver inflammation and / or liver disease; and / or (iii) increased rate of liver disease progression.

23. The use according to any one of the claims 18 to 22, wherein the liver disease is selected from the group comprising or consisting of: non-alcoholic fatty liver disease, metabolic-dysfunction associated steatotic liver disease (MASLD), alcohol-related liver disease (ARLD), alcoholic hepatitis, autoimmune hepatitis, primary biliary cholangitis (PBC), primary sclerosing cholanigitis (PSC), hereditary haemochromatosis, viral hepatitis, liver fibrosis, cirrhosis, chronic liver disease and / or liver cancer.

24. The use according to any one of claims 18 to 23, wherein the marker is detected using an in vitro or ex vivo assay.

25. The method according to any of claims 1 to 8, the modulator for use according to any of claims 9 to 17 or the use according to any of claims 18 to 24, wherein the liver disease is alcohol-related liver disease; optionally, wherein the alcohol-related liver disease is alcoholic hepatitis.

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