Methods for detecting macrophage populations

Abstract

There is a need for methods to detect macrophage populations. [Solution] Methods for detecting a population of macrophages in a sample are provided, comprising detecting and / or determining expression of Cdh5 in macrophages in a sample. Also disclosed are kits for detecting and / or isolating and / or depleting a population of macrophages, methods for depleting a population of macrophages, methods for improving the health of an obese and / or overweight subject, methods for determining the risk of obesity and / or obesity-related metabolic disorders in a subject, and animal models thereof.

Description

[Technical field]

[0001] Technical Field The present disclosure relates generally to methods for detecting macrophage populations. [Background technology]

[0002] background Resident tissue macrophages (RTM) are a diverse population of immune cells that are characterized by tissue-specific phenotypes and exhibit a wide range of functions within the tissues they reside in. However, tissues are complex environments, and macrophage heterogeneity within the same organ has been overlooked until now.

[0003] Within the liver, there exists a population of RTM called Kupffer cells (KCs) lining the hepatic sinusoids that are specialized for detoxifying blood flowing from the intestine through the portal vein to the liver, which may contain microbionts, harmful enteric pathogens, or toxic by-products of digestion. Thus, KCs can destroy old or damaged red blood cells, phagocytose the incoming threat, and play a central role during inflammation, especially in the development of liver pathologies such as viral hepatitis, fibrosis, hepatocellular carcinoma, alcohol-related disorders, or nonalcoholic steatohepatitis (NASH) / fatty liver disease (NAFLD). However, even though the involvement of KCs in these pathologies is undisputed, their exact mechanism of action remains largely unknown.

[0004] KCs originate from monocyte precursors in the fetal liver, acquiring their identity early during embryonic development and maintaining themselves throughout life. Early postnatal circulating monocytes contribute a small proportion of KCs immediately after birth, but at steady state, KC regeneration is almost completely independent of bone marrow-derived cells. However, under inflammatory conditions or when native embryonic KCs are depleted, monocyte-derived macrophages can replace dying embryonic KCs. Furthermore, alongside KCs, other minor populations of ontogenetically and functionally unrelated macrophages reside in the liver, including capsular macrophages and even peritoneal macrophages that are recruited after injury. This results in a mosaic of hepatic macrophage populations that are heterogeneous in origin, phenotype, and function, among which KCs are by far the most abundant.

[0005] Although various studies have proposed the existence of distinct subsets of mouse KCs at steady state, it has been difficult to distinguish embryonic KCs from monocyte-derived macrophages, and whether these distinct populations play different roles in the pathophysiology of liver disease remains elusive. Summary of the Invention [Problem to be solved by the invention]

[0006] In human studies, recent single-cell transcriptomic studies have suggested the existence of two major subsets of liver macrophages, but the functions of these distinct subsets remain elusive. Therefore, there is a need to provide methods to detect populations of macrophages. There is also a need to provide methods to detect subpopulations of tissue or liver macrophages. In particular, there is a need to provide methods to detect metabolically active macrophages. [Means for solving the problem]

[0007] overview In one aspect there is provided a method of detecting a population of macrophages in a sample comprising detecting and / or determining expression of Cdh5 in macrophages in the sample.

[0008] In some examples, the method further includes detecting and determining the expression of one or more markers in macrophages in the sample, including CD107a, CD107b, IGFBP7 (insulin-like growth factor binding protein 7), LYVE1, CD36, CD206 and / or ESAM.

[0009] In some examples, the methods further include detecting and determining that cells in the sample are macrophages by detecting expression of a macrophage marker.

[0010] In some examples, the macrophages are Kupffer cells (KC), optionally, embryonic derived Kupffer cells.

[0011] In some examples, the method further includes detecting and determining which cells in the sample are macrophages by detecting expression of Clec4f, Lyz2, Vsig4, Csf1r, Adgre1, F4 / 80, Tim4, Clec4F, and Vsig4.

[0012] In some examples, the methods include detecting and determining a first population of Kupffer cells that express CD206lo and / or ESAM- and a second population of Kupffer cells that express CD206hi and / or ESAM+.

[0013] In some examples, the methods include detecting and determining a first population of Kupffer cells that express CD206lo and ESAM- and a second population of Kupffer cells that express CD206hi and ESAM+.

[0014] In some instances, overexpression of one or more markers including CD107a, CD107b, IGFBP7 (insulin-like growth factor binding protein 7), LYVE1, CD36, CD206 and / or ESAM determines the population that is a second population of Kupffer cells.

[0015] In some examples, the method further includes detecting, screening, and / or determining the presence of one or more markers including CD206, ESAM, CD36, and combinations thereof.

[0016] In some examples, the method further comprises isolating the first and / or second population of macrophages, and optionally, the method further comprises isolating the first and / or second population of Kupffer cells.

[0017] In some examples, the method further comprises removing a population of cells expressing one or more of Cdh5+, CD107b+, CD206hi and / or ESAM+ from the sample.

[0018] In some examples, the method further includes determining expression of one or more markers including CD45, CD64, F4 / 80, TIM4, Clec4F, Adgre1(F4 / 80), Timd4, Csf1r, and Clec4f, and optionally, the method further includes removing and / or eliminating cells expressing one or more of Adgre1+, Cx3cr1+, Timd4-, Clec4f-, and combinations thereof.

[0019] In another aspect there is provided a kit for detecting and / or isolating and / or depleting a population of macrophages comprising providing an agent for detecting a population of macrophages expressing Cdh5, optionally providing an agent capable of isolating a population of macrophages expressing Cdh5, and optionally providing an agent capable of depleting a population of macrophages expressing Cdh5.

[0020] In some examples, the kit further provides an agent for detecting a population of macrophages expressing CD107b+, CD206hi and ESAM+, and optionally provides an agent capable of isolating a population of macrophages expressing CD107b+, CD206hi and ESAM+, and optionally provides an agent capable of depleting a population of macrophages expressing CD107b+, CD206hi and ESAM+.

[0021] In yet another embodiment, a transgenic animal model is provided that comprises a population of macrophages expressing Cdh5 that are genetically engineered to undergo elimination upon exogenous activation.

[0022] In yet another aspect, a method of depleting a population of macrophages is provided, the method comprising detecting and reducing a population of macrophages in a subject, wherein the population of macrophages expresses one or more of Cdh5, CD107b, CD206, and ESAM.

[0023] In yet another aspect, there is provided a method of improving the health of an obese and / or overweight subject, the method comprising reducing a population of macrophages in the subject, wherein the population of macrophages expresses one or more of Cdh5, CD107b, CD206, and ESAM.

[0024] In yet another aspect, there is provided a method of determining the risk of obesity and / or obesity-related metabolic disorders in a subject, the method comprising detecting the expression level of Igfbp7 / Cd36 expression in macrophages.

[0025] In some examples, the method further includes treating the subject identified as being at risk for obesity and / or an obesity-related metabolic disorder in the subject with an agent capable of depleting macrophage cells that express Cdh5.

[0026] In some examples, the method of any of the aspects disclosed herein reduces CD206hi and ESAM+ macrophages, and optionally, the method reduces Cdh5+, CD206hi, and ESAM+ Kupffer cells.

[0027] Detailed Description of the Drawings Exemplary embodiments of the present disclosure will be better understood and readily apparent to those skilled in the art from the following discussion and, where applicable, in conjunction with the drawings. It should be recognized that other modifications with respect to structural, electrical and optical changes may be made without departing from the scope of the present invention. Exemplary embodiments are not necessarily mutually exclusive, as some embodiments may be combined with one or more embodiments to form new exemplary embodiments. Exemplary embodiments should not be construed as limiting the scope of the present disclosure. [Brief description of the drawings]

[0028] [Figure 1A] Plots and heat maps representing gene expression are shown. CD45+Tomato- liver cells were extracted from healthy Ms4a3crexRosaTomato mice, and a library of mRNA was made and sequenced using Chromium technology. Seurat analysis was performed to define nine clusters with distinct gene expression patterns. Each dot corresponds to a single cell and is colored according to the cluster identified. The expression of a few representative genes was overlaid to define each cluster, and a heat map of the most highly differentially expressed genes (DEGs) within the different clusters is displayed. The following genes are most highly expressed in their respective clusters: Cd79a and Igkc in cluster 0, Cd3g and Trac in cluster 1, Id3 and Mrc1 in cluster 2, C1qb and Lyz2 in cluster 3, Cd9 and Dpp4 in cluster 4, Nkg7 and Ccl5 in cluster 5, Ctla4 and Gzmk in cluster 6, Siglech and Runx2 in cluster 7, and Cx3cr1 and Cd14 in cluster 8. [Figure 1B] A zoomed-in plot of the Adgre1+ macrophage population is shown, including Cx3cr1+ capsular macrophages (Caps.) and two clusters of Timd4+Clec4f+KCs (KC-c1 & KC-c2). The violin plots in this figure show the expression of selected genes in the two KC clusters. [Figure 1C] Figure 1 shows plots of tSNE projections of sorted CD45+CD64+F4 / 80+ liver cells sequenced according to the SMARTseq2 protocol. Each dot corresponds to a single cell and is colored according to the identified cluster. The KC cluster corresponds to c3 and c4. [Figure 1D] A plot of tSNE projections of sorted CD45+CD64+F4 / 80+ liver cells sequenced according to the SMARTseq2 protocol is shown, with overlaid expression of the indicated macrophage-specific genes. [Figure 1E] A dot plot of the combined SMARTseq2 (probe) and Chromium (reference) datasets focused on Clec4f+KC for validation of clustering is shown. [Figure 1F] Figure 1 shows a plot of the scenic analysis of the high-resolution SMARTseq2 dataset overlaid with the four clusters identified by the Seurat analysis. Two stable states within the macrophage population can be seen, corresponding to Seurat c3 and c4. The number of genes included in each regulon is provided in brackets. [Figure 1G] Plots of scene analysis of single-cell RNA-seq SMARTseq2 datasets are shown along with violin plots of representative regulons from each Seurat-defined cluster. [Figure 1H]Dot plot representation of the expression of the indicated markers projected from the tSNE analysis showing the different clusters in raw liver CD45+ singlets analyzed with a 37-marker extended CyTOF panel. Unsupervised analysis with the Phenograph algorithm revealed 15 clusters that were manually assigned as the indicated populations by lineage markers. Dot plot representation of the expression levels of the indicated markers projected from the tSNE analysis. Different clusters of raw liver CD45+ singlets analyzed with a 37-marker extended CyTOF panel are shown (13 most represented markers are shown). [Figure 1I] Shown is a dot plot representation of the expression levels of the indicated markers projected by tSNE analysis. Distinct clusters of live CD45+ singlets in the liver analyzed by a 37-marker CyTOF panel are shown (24 additional markers are shown). [Figure 1J] Plots and heatmaps of CyTOF analysis by the OneSENSE algorithm of live liver CD45+ singlets are shown. F4 / 80+Tim4+KC cells are shown within the dashed black box. [Figure 2A] Flow cytometry plots with the gating strategy used to analyze liver cells are shown, where LSECs are defined as CD45lowCD31+ cells, macrophages as CD45+Lin-F4 / 80+CD64+ cells, monocytes as CD45+Lin-F4 / 80-CD64hiLy6Chi, caps.macs as CD45+Lin-F4 / 80+CD64+Tim4-MHCIIhi cells, and KCs as CD45+Lin-F4 / 80+CD64+Tim4hiMHCIIint cells. [Figure 2B]Flow cytometry analysis using the sorting strategy for bulk RNA sequencing and volcano plot of the 200 most highly expressed genes (p-value < 0.001) between CD206loCD107b-KC1 and CD206hiCD107b+KC2 from bulk RNA sequencing analysis. Each dot represents a differentially expressed gene. The nuclear to cytoplasmic area ratio from sorted cells is shown. [Figure 2C] Flow cytometry plots of Tim4hiKC and MHCIIhi enveloped macrophages among total macrophages (left), and CD206loESAM-KC1 and CD206hiESAM+KC2 among KC (right). For quantification, each dot represents an individual and the median is indicated by a line. [Figure 2D] Flow cytometry plots of MHCIIhi capsule macrophages, CD206loESAM-KC1 and CD206hiESAM+KC2 are shown, with expression of each indicated marker indicated. [Figure 2E] Shown is a dot plot representation of the expression levels of the indicated markers projected from tSNE analysis of live liver singlets. Data were analyzed by an 11-marker flow cytometry panel, showing manually defined KC2 (red), KC1 (blue) and LSEC (green). [Figure 2F] Scanning electron and light (cytospin) microscopy images of flow cytometry sorted hepatic KC1, KC2, capsule macrophages and monocytes are shown. Scale bar represents 1 μm. [Figure 2G] A kinetic plot of the relative abundance of different liver cell populations during development from birth (0 d) to 8 weeks of age is shown. [Figure 2H] Flow cytometry measurements of Tomato expression frequency in the indicated populations in 8-week-old Ms4a3crexRosaTomato mice. Each dot represents an individual and the median is indicated by a line. [Figure 2I]Representative flow cytometry plots from the analysis of S100a4CrexRosaEYFP and Csf1rGFP mice are shown, with each dot representing an individual and the median indicated by a line. [Figure 2J] Representative flow cytometry plots from analysis of CD45.2 parabionts from a pair of CD45.1 / CD45.2 parabiotic mice. WTCD45.1 were surgically coupled to WTCD45.2 and analyzed 3 months later. Each dot represents an individual and the median is indicated by a line. [Figure 3A] Representative flow cytometry plots obtained from analysis of livers from C57BL / 6WT mice that were injected intravenously with anti-CD45 (500 ng per mouse) 5 minutes prior to sacrifice. Each dot represents an individual, and the median is indicated by a line. Non-injected controls and injected mice are shown. [Figure 3B] Shown are low magnification immunofluorescence microscopy images of liver vibratome sections from WTC57BL / 6 mice. Sections were labeled for Clec4F and CD206 and stained with DAPI. Scale bar represents 20 μm. [Figure 3C] High magnification immunofluorescence microscopy images of liver vibratome sections from WTC57BL / 6 mice are shown. Sections were labeled for Clec4F and CD206 and stained with DAPI. Scale bar represents 10 μm. [Figure 3D] 1 shows a single z-slice of a single channel fluorescence micrograph (CD206-red and F4 / 80-green) and a merged image with DAPI-blue showing examples of KC1 and KC2 after CD206 labeling in vivo. Scale bar represents 5 μm. [Figure 3E] 1 shows a scheme for generating CD206CrexRosaTomato mice. [Figure 3F]Immunofluorescence microscopy images of liver sections from Mrc1creERT2xRosaTomato mice treated with a single injection of tamoxifen 24 hours prior to analysis are shown. Sections are labeled for F4 / 80 and stained with DAPI. Scale bars represent 20 μm. Quantification of 32 F4 / 80+CD206lo (KC1) and F4 / 80+CD206hi (KC2) cells in independent fields is displayed. [Figure 3G] Shown are images of liver sections labeled for F4 / 80, CD206 and cytokeratin 7. The distance between KC1 or KC2 and the portal triad was measured and plotted. [Figure 4A] Flow cytometry plots of whole liver projected with CD45-CD31+LSEC, CD45+Lin-F4 / 80+CD64+Tim4+ESAM-CD206loKC1, and CD45+Lin-F4 / 80+CD64+Tim4+ESAM+CD206hiKC2 are shown. [Figure 4B] Flow cytometry profiles of the expression intensities of the indicated markers in the three populations are shown. [Figure 4C] Imaging cytometry (ImageStream) analysis of liver cells, KC1 (blue - top) and KC2 (red - bottom) are shown. [Figure 4D] Scanning electron microscopy images of sorted liver LSECs and KC2s are shown. Scale bars represent 1 μm. Windows indicated by white arrows are shown in enlarged images. [Figure 4E] Flow cytometry measurements of YFP expression frequency in the indicated populations in 8-week-old Lyz2crexRosaYFP mice. KC1 (lower squares) and KC2 (upper squares) are overlaid at each step of the gating strategy. Each dot represents an individual and the median is indicated by the red line. [Figure 4F]Plots of immune cell populations manually annotated based on expression of the indicated markers are shown. CD45+ liver cells, KC1 and KC2 were sorted and loaded onto BD Rhapsody cartridges according to the manufacturer's recommendations. The immune response panel Mm (BD) was used to allow monitoring of expression of 397 genes and generation of tSNE. [Figure 4G] Flow cytometry measurements of the frequency of LSEC, KC1, and KC2 populations in C57BL / 6 mice following clodronate liposome (CLL)-mediated KC depletion. Each dot represents an individual, and the median is indicated by the line. [Figure 4H] Flow cytometry measurements of the frequency of KC1 and KC2 populations in Ms4a3crexRosaTomato mice after clodronate liposome-mediated KC depletion. Each dot represents an individual and the median is indicated by a line. [Figure 5A] 13 shows plots of principal component analysis of transcriptomes from bulk RNA sequencing of selected liver KC1 and KC2. [Figure 5B] A dot plot representation of the expression of genes expressed by KC1 and KC2 is shown. Genes known to be highly expressed in macrophages are Clec4f, Lyz2, Csf1r, Timd4, and those described to be primarily expressed in LSECs are Mrc1, Pecam1, Esam, and Cdh5. [Figure 5C] A heatmap of DEGs between the two populations is shown, in which KC2 has 1364 higher expression genes and 51 lower expression genes compared to the KC1 population. [Figure 5D] A heatmap of selected genes representative of macrophages or endothelial cells expressed in sorted KC1, KC2 or LSECs is shown. [Figure 5E] Principal component analysis of bulk RNAseq data generated after sorting of LSECs, KC1 and KC2. [Figure 5F]Venn diagram of the 100 most highly expressed genes in KC1 and KC2. Among the 100 most highly expressed genes, there are 64 common genes, including the canonical macrophage genes shown. [Figure 5G] Volcano plots of differentially expressed genes between sorted LSECs and KC1s or LSECs and KC2s are shown, highlighting conserved endothelial vs. KC (both subsets) signatures. [Figure 5H] Same analysis as in FIG. 5A, but plotted using the translatome obtained from Lyz2crexRpl22HA mice (RiboTag approach). [Figure 5I] Same analysis as in FIG. 5B, but plotted using the translatome obtained from Lyz2crexRpl22HA mice (RiboTag approach). [Figure 5J] The same analysis as in Figure 5C, but using the translatome from Lyz2crexRpl22HA mice (RiboTag approach), shows a heatmap in which KC2 has higher expression of 309 genes and lower expression of 98 genes compared to the KC1 population. [Figure 5K] The same analysis as in FIG. 5A, but plotted using the proteome. [Figure 5L] The same analysis as in FIG. 5B, but plotted using the proteome. [Figure 5M] The same analysis as in Figure 5C, but using the proteome, shows a heat map showing that KC2 has higher expression of 509 proteins and lower expression of 32 proteins compared to the KC1 population. [Figure 5N] A plot from a principal component analysis of the combined transcriptome, translatome and proteome data sets is shown. [Figure 5O] A Venn diagram of the 100 most highly expressed genes / proteins identified from different techniques is shown. [Figure 5P]RNA-seq-based alluvial plots of general pathways (left) or metabolism-related pathways (right) specific to KC1 or KC2 are shown. [Figure 5Q] Figure 1 shows an RNA-seq-based integrative network analysis of KC2 compared to KC1 at steady state. Network-based integration of gene expression datasets was performed as described by other studies known in the art. Briefly, topological tools for integrative network analysis were mapped to KEGG pathways. Metabolic genes that were up- and down-regulated based on false discovery rate (FDR) were mapped to a model that maintained the attributes of all essential KEGG pathways. [Figure 5R] A heatmap of the top 10 metabolic-related DEGs between KC1 and KC2 is shown. [Figure 6A] Shown are hematoxylin and eosin stained images of livers from mice fed a normal diet (ND) or a high fat diet (HFD) after 9 weeks of feeding. [Figure 6B] Flow cytometry plots of KC1 and KC2 frequencies within KCs in mice fed a HFD for the indicated times are shown, with each dot representing an individual and the median indicated by the line. [Figure 6C] Representative flow cytometry plots from analysis of Ms4a3CrexRosaTomato mice on HFD at the indicated time points are shown, with each dot representing an individual and the median indicated by the line. [Figure 6D] Flow cytometry plots similar to those seen in FIG. 6C are shown, but gated on the KC1 and KC2 populations. [Figure 6E] Heatmap of top DEGs between KC1 and KC2 across different diets (ND normal diet, HFD high fat diet, MCD methionine-choline deficient diet). [Figure 6F] 1 shows an RNA-seq-based analysis of KC1 and KC2 focusing on LDL uptake and lipid storage. [Figure 6G] Pathway analysis using all DEGs between KC1 or KC2 selected from ND and HFD-fed mice is shown. [Figure 6H] Heatmap and pathway analysis of DEGs among KC1 selected from ND or HFD mice. Reference DEGs are indicated in boxes. [Figure 6I] Heatmap and pathway analysis of DEGs between KC2 selected from ND or HFD mice are shown. Reference DEGs are indicated in boxes, and integrated network analysis of DEGs between ND and HFD is provided. [Figure 6J] Shown is a plot of single-cell RNAseq data (55,118 cells) extracted from studies known in the art. KCs were identified by high expression of Clec4f. Expression of Cd36 and Mrc1 is overlaid on the KC population. [Figure 6K] Flow cytometry profiles and MFI quantification of CD36 expression intensity in the indicated populations are shown. [Figure 7A] Representative flow cytometry plots from analysis of Cdh5CreERT2xRosaTomato mice after one week of feeding a tamoxifen-enriched diet for induction of recombination are shown. Percentages of positive cells are shown for the indicated liver cell populations. [Figure 7B] Imaging of chimeric Cdh5CreERT2xRosaTomato mice. Livers were processed, sectioned by vibratome (300 μm thick sections), and stained with Iba-1 and Tomato. [Figure 7C] Representative flow cytometry plots from analysis of Cdh5CreERT2xRosaTomato mice after the indicated time points from the end of tamoxifen-diet induction are shown. [Figure 7D] FIG. 1 shows a schematic diagram of the generation of KC2-depleted mice. [Figure 7E] Flow cytometry analysis of hepatic KCs in Cdh5creERT2xRosaDTR mice. Specific clearance of KC2 depletion was monitored at the indicated time points after a single DT injection. [Figure 7F] Images of mice after 6 weeks of HFD are shown. Absolute body weights of control and KC2-depleted mice over the first 6 weeks of HFD. [Figure 7G] Plots of white adipose tissue weight in control and KC2-depleted mice after 6 weeks of HFD are shown. [Figure 7H] 1 shows plots of hydrogen peroxide and malondialdehyde assays in the liver of the indicated mice. [Figure 7I] Plots from glucose tolerance tests performed in overnight fasted mice at 6 weeks on a HFD are shown. [Figure 7J] Shown are images of hematoxylin and eosin staining of liver sections from the indicated mice. [Figure 7K] 1 shows plots of circulating triglycerides measurements in the indicated mice. [Figure 7L] Plots are shown of measurements of energy intake and expenditure in mice housed individually in metabolic cages over the first week of a HFD. [Figure 7M] Plots are shown of measurements of energy intake and expenditure in mice housed individually in metabolic cages over the first week of a HFD. [Figure 7N] Plots are shown of measurements of energy intake and expenditure in mice housed individually in metabolic cages over the first week of a HFD. [Figure 7O] Plots are shown of measurements of energy intake and expenditure in mice housed individually in metabolic cages over the first week of a HFD. [Figure 7P] Plots are shown of measurements of energy intake and expenditure in mice housed individually in metabolic cages over the first week of a HFD. [Figure 7Q] Flow cytometry analysis plots are shown. Unloaded FITC-labeled glucan-encapsulated siRNA particles (GeRPs) were intravenously injected into mice and liver cells were analyzed 24 hours later. [Figure 7R]Plots of Cd36 expression assessed by qPCR at the end of treatment in the indicated sorted cells are shown. Mice were injected with GeRPs containing scrambled RNA (Scr) or siRNA against Cd36 (si-CD36) three times a week for 2 weeks. [Figure 7S] A plot of body weight at the end of GERP treatment is shown. [Figure 7T] Plots of blood glucose and glucose tolerance test (GTT) measured at the end of treatment are shown. [Figure 7U] Pathway analysis comparing data from CD36KD and Scr-treated mice is shown, and regulated pathways are displayed. Bulk RNA-seq was performed in total liver macrophages. [Figure 7V] Plots of measurements of the oxidative stress markers MDA and H2O2 in the liver of the same mice are shown, with each dot representing an individual and the median indicated by a line. [Figure 7W] 1 shows plots of liver concentrations of triglycerides from CD36KD and Scr treated mice. [Figure 8A] UMAP projection obtained by analyzing CD45+ cells (PTPRC expression) from several human liver datasets available in the literature. Unsupervised clustering was performed using the Seurat analysis pipeline. Feature plot representation of normalized expression levels of macrophage markers (Cd14 and Cd68) and KC2 markers (Mrc1 and Lyve1) in the UMAP with the pooled samples. [Figure 8B] UMAP projection obtained by analyzing CD45+ cells (PTPRC expression) from several human liver datasets available in the literature. Unsupervised clustering was performed using the Seurat analysis pipeline. Feature plot representation of normalized expression levels of macrophage markers (Cd14 and Cd68) and KC2 markers (Mrc1 and Lyve1) in the UMAP with the pooled samples. [Figure 9] FIG. 1 shows a schematic diagram including an outline of the present application. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0029] Description of the embodiments In one aspect there is provided a method of detecting a population of macrophages in a sample comprising detecting and / or determining expression of Cdh5 in macrophages in the sample.

[0030] As used herein, the term "expression" is used loosely to refer to nucleic acid expression (eg, gene expression and / or RNA expression) and protein expression.

[0031] In some examples, the method further comprises detecting and determining expression of one or more markers in macrophages in the sample, including CD107a, CD107b, IGFBP7 (insulin-like growth factor binding protein 7), LYVE1, CD36, CD206, and / or ESAM. In some examples, the method comprises detecting and / or determining expression of two or more, or three or more, or four or more, or five or more, or six or more, or all seven markers. In some examples, the method comprises detecting and / or determining expression of two, three, four, five, six, seven, or all of the markers disclosed herein.

[0032] In some examples, the methods further include detecting and determining that cells in the sample are macrophages by detecting expression of a macrophage marker.

[0033] In some instances, the macrophage is a hepatic macrophage. Hepatic macrophages have long been considered to be a homogenous population of tissue scavengers responsible for the defense of the liver against potential invaders entering through the portal vein downstream of the intestine. Although this function is important, it is only one of a much more detailed list of roles of macrophages. There is now increasing recognition of the ability of macrophages to mediate tissue-specific homeostatic functions.

[0034] In some examples, the macrophages are Kupffer cells (KC), optionally, embryonic derived Kupffer cells.

[0035] As is known in the art, Kupffer cells (KCs) represent a heterogeneous immune cell population that is highly adapted to the liver, the tissue in which they reside. This organ is fundamental to the metabolism of the organism, and thus KCs play a major role in many metabolic processes.

[0036] In some instances, the population of macrophages can be a first population of Kupffer cells (i.e., KC1) and / or a second population of Kupffer cells (i.e., KC2). In some instances, KC1 and KC2 can also express typical (or canonical) macrophage gene expression, RNA expression, and / or protein expression.

[0037] In some instances, the macrophage is a metabolically active macrophage.

[0038] As used herein, the terms "reference marker" or "classical marker" or "universal marker" should be used in conjunction with one another and are used interchangeably to refer to markers known in the art to be expressed in most, if not all, subtypes of macrophages.

[0039] In some examples, the macrophage genes can be Clec4f, Lyz2, Vsig4, Csf1r, and Adgre1 (F4 / 80), etc. In some examples, the typical (standard) and / or universal macrophage markers include one or more markers, such as, but not limited to, F4 / 80, Tim4, Clec4F, and Vsig4.

[0040] In some examples, the method further includes detecting and determining which cells in the sample are macrophages by detecting expression of Clec4f, Lyz2, Vsig4, Csf1r, Adgre1, F4 / 80, Tim4, Clec4F, and Vsig4.

[0041] In the specific context of mouse liver, KCs are known to be localized in the sinusoids and are usually described as a homogenous population of cells expressing certain markers such as F4 / 80, CD64 and Tim4. However, the inventors of the present disclosure observed heterogeneity of CD64+F4 / 80+Tim4+KCs, with a subpopulation expressing ESAM, LYVE1, CD206 and CD36, which the inventors of the present disclosure named KC2 as opposed to ESAM-LYVE1-CD206-Cd36-KC1. The inventors of the present disclosure confirmed that both populations were true Kupffer cells by using several fate-mapping systems known to track macrophages according to their origin.

[0042] KC1 and KC2 were labeled at the same level, confirming their macrophage nature. However, the inventors of the present disclosure also observed that KC2 expressed many markers classically assigned to endothelial cells, such as Esam, Lyve1, or Pecam1. Therefore, the inventors of the present disclosure decided to use another available fate mapping system based on one of these endothelial-related genes, Cdh5. In this system, KC2 was efficiently labeled, but KC1 was not, providing a powerful means to distinguish the two populations.

[0043] Thus, in some examples, the methods include detecting and determining a first population of Kupffer cells (i.e., KC1) that express CD206lo and / or ESAM- and a second population of Kupffer cells (i.e., KC2) that express CD206hi and / or ESAM+. In some examples, the methods include detecting and determining a first population of Kupffer cells (i.e., KC1) that express CD206lo and ESAM- and a second population of Kupffer cells (i.e., KC2) that express CD206hi and ESAM+.

[0044] Among the genes specifically expressed in the KC2 population, there is one in particular called insulin-like growth factor binding protein 7 (igfbp7), which has been very recently described as a key gene allowing the control of hepatocyte metabolism by liver macrophages. It was shown that specific silencing of this gene in liver macrophages abolishes obesity symptoms in mice fed a high-fat diet by reprogramming hepatocytes. However, another notable gene among the KC2-specific genes was Cd36, a fatty acid transporter responsible for the import of these lipids into the cell.

[0045] Thus, in some examples, the method further comprises detecting and determining expression of one or more markers, including CD31, CD63, CD81, CD107a, Lyve1, IGFBP7 (insulin-like growth factor binding protein 7), CD36, and combinations thereof, and optionally determining the population as being a second population of Kupffer cells (i.e., KC2) by overexpression of the one or more markers. In some examples, the method further comprises detecting and determining expression of one or more markers, including CD107a, CD107b, IGFBP7, LYVE1, CD36, CD206, and / or ESAM, and combinations thereof, and determining the population as being a second population of Kupffer cells (i.e., KC2) by overexpression of the one or more markers. In some examples, the method comprises detecting and / or determining expression of two or more, or three or more, or four or more, or five or more, or six or more, or all seven markers. In some examples, the methods involve detecting and / or determining the expression of two, three, four, five, six, seven, or all of the markers disclosed herein.

[0046] In some instances, the second population of Kupffer cells (i.e., KC2) morphologically lacks fenestrae on its surface. In some instances, the second population of Kupffer cells (i.e., KC2) expresses LSEC-associated genes. In some instances, the LSEC-associated genes may include, but are not limited to, one or more of Mrc1, Pecam1 (CD31), Esam, Kdr, Lyve1, and combinations thereof.

[0047] In some examples, the method further includes detecting, screening, and / or determining the presence of one or more markers including CD206, ESAM, CD36, and combinations thereof.

[0048] In some examples, the method further comprises isolating the first and / or second population of macrophages, and optionally, the method further comprises isolating the first and / or second population of Kupffer cells.

[0049] In some examples, the method further comprises removing a population of cells expressing one or more of Cdh5, CD107b, CD206hi and ESAM+ (i.e., a second population of Kupffer cells) from the sample.

[0050] In some examples, the method further comprises determining expression of one or more markers, including CD45, CD64, F4 / 80, TIM4, Clec4F, Adgre1(F4 / 80), Timd4, Csf1r, and Clec4f.

[0051] In some examples, the method further includes eliminating (by depleting or gating) capsule macrophages. In some examples, capsule macrophages express one or more genes including Adgre1+, Cx3cr1+, Timd4-, Clec4f-, etc.

[0052] In another aspect there is provided a kit for detecting and / or isolating and / or depleting a population of macrophages comprising providing an agent for detecting a population of macrophages expressing Cdh5, optionally providing an agent capable of isolating a population of macrophages expressing Cdh5, and optionally providing an agent capable of depleting a population of macrophages expressing Cdh5.

[0053] In some examples, the kit further provides an agent for detecting a population of macrophages expressing CD107b, CD206hi and ESAM+, and optionally provides an agent capable of isolating a population of macrophages expressing CD107b, CD206hi and ESAM+, and optionally provides an agent capable of depleting a population of macrophages expressing CD107b, CD206hi and ESAM+. In some examples, the kit may also include an agent for detecting a population of macrophages expressing one or more markers, including CD107a, CD107b, IGFBP7, LYVE1, CD36, CD206 and / or ESAM, and combinations thereof. In some examples, the kit includes instructions for use, and the population is determined to be a second population of Kupffer cells (i.e., KC2) by overexpression of one or more markers. In some examples, the kits may also include agents for detecting two or more, or three or more, or four or more, or five or more, or six or more, or seven or more, or all of the markers disclosed herein. In some examples, the kits may also include agents for detecting two, three, four, five, six, seven, eight, or all of the markers disclosed herein.

[0054] In addition to specific labeling, targeting of KC2 has allowed the inventors of this disclosure to develop a depletion model in which KC2 can be efficiently removed (Cdh5 creERT2 xRosa DTR Thus, in yet another aspect, a transgenic animal model is provided that includes a population of macrophages expressing Cdh5 that are genetically engineered to undergo elimination upon exogenous activation.

[0055] For example, the animal model can be, but is not limited to, a mouse, or a rat, a non-human primate, etc. In some examples, the animal facility is under specific pathogen-free conditions. In some examples, the method can include the use of animal models such as, but not limited to, C57 / BL6, BALB / c, CD-1, SCID, A / J, Sprague Dawley, Wistar, Rhesus, Japanese, Olive baboon, squirrel, capuchin, etc. In some examples, the method includes the use of C57 / BL6 background mice.

[0056] In some examples, the transgenic animal model is a transgenic mouse model. In some examples, the mouse model is a transgenic mouse model. creERT2 xRosa DTR In some examples, the animal model is a mouse. In some examples, the animal model allows for the inducible and specific depletion of a macrophage population. In some examples, the animal model allows for the inducible and specific depletion of a Kupffer cell population, optionally a second population of Kupffer cells (i.e., KC2). In some examples, the animal model allows for the inducible and specific depletion of a Cdh5 creERT2 xRosa DTR Mice are Cdh5 creERT2 Rosa Mouse (MGI:3848982) DTR These animals were obtained by crossing with Cdh5-expressing mice (MGI:3772576). In these animals, tamoxifen treatment induces expression of the diphtheria toxin receptor only in Cdh5-expressing cells. In some cases, Cdh5 creERT2 xRosa DTR Depletion of KC2-containing cells in mice is induced by injecting diphtheria toxin (DT) into mice previously treated with tamoxifen.

[0057] In yet another aspect, a method of depleting a population of macrophages is provided, the method comprising detecting and reducing a population of macrophages in a subject, wherein the population of macrophages expresses one or more of Cdh5, CD107b, CD206, and ESAM.

[0058] In some instances, the macrophage population that is depleted is a second Kupffer cell population (i.e., KC2). In some instances, depletion of the second Kupffer cell population provides amelioration of metabolic disorders in obesity. In some instances, depletion of the second Kupffer cell population provides amelioration of oxidative stress, improved glucose tolerance and / or less pronounced steatosis.

[0059] Remarkably, KC2-depleted mice did not gain weight when fed a high-fat diet. Moreover, these mice had improved glucose tolerance and were prevented from developing fatty liver. Notably, specific silencing of Cd36 in KCs had a comparable, albeit smaller, effect. This indicates that CD36hiKC2 controls obesity-associated hepatic oxidative stress through CD36 expression.

[0060] Obesity is a global disease associated with high morbidity, and its eradication constitutes one of the most important health challenges of the next century. However, the pathogenesis of the disease remains elusive and a deeper understanding is needed to design effective therapeutic strategies. Although the heterogeneity of macrophages is well recognized across tissues, the diversity within the same tissue is often overlooked. This study reveals the coexistence of two subpopulations of Kupffer cells in mouse liver, with a minor CD206hiCD36hi subpopulation (KC2) having metabolic functions and participating in the control of hepatic oxidative stress associated with obesity. In particular, the inventors of this disclosure identified a subpopulation of KCs (KC2) specifically involved in the development of obesity and developed a system to specifically label and deplete them, paving the way for new strategies to defeat obesity.

[0061] Thus, in another aspect, there is provided a method of improving the health of an obese and / or overweight subject, the method comprising reducing a population of macrophages in the subject, wherein the population of macrophages expresses one or more of Cdh5, CD107b, CD206, and ESAM.

[0062] The present inventors also target the expression of CD36 in KC2, and show that the CD36 pathway in KC2 controls hepatic oxidative stress associated with obesity.Therefore, in yet another aspect, a method for determining the risk of obesity and / or obesity-related metabolic disorders in a subject is provided, comprising detecting the expression level of Igfbp7 and / or Cd36 expression in macrophages.

[0063] In some examples, the method further includes treating the subject identified as being at risk for obesity and / or an obesity-related metabolic disorder in the subject with an agent capable of depleting macrophage cells that express Cdh5.

[0064] In some examples, the method reduces CD206hi and ESAM+ macrophages, and optionally, the method reduces Cdh5+, CD206hi, and ESAM+ Kupffer cells. In some examples, the method reduces a subpopulation of macrophages by a statistically significant amount. In some examples, the subpopulation of macrophages can be KC1 and / or KC2. In some examples, the reduction in the population of Kupffer cells is sufficient to improve a condition (e.g., a metabolic disorder) in a subject.

[0065] In some examples, the method may further include obtaining a biological sample. In some examples, the biological sample may include a solid sample or a liquid sample. In some examples, the biological sample includes a solid sample (e.g., a liver sample).

[0066] In some examples, the method includes determining the biological content (e.g., total triglyceride (TG) content) using a kit (e.g., a colorimetric kit). In some examples, the biological content (e.g., TG content) is normalized to a concentration (e.g., protein concentration) determined by a kit (e.g., a Pierce BCA Protein Assay Kit).

[0067] In some examples, the method includes measuring the intracellular amount of a compound (e.g., HO, or malondialdehyde) using a kit (e.g., the Amplex™ Red hydrogen peroxide / peroxidase assay kit, or a lipid peroxidation (MDA) assay kit (colorimetric / fluorimetric)). In some examples, the method includes performing profiling of a liquid sample (e.g., plasma multi-analyte) using an analyzer (e.g., a clinical chemistry analyzer) with a labeled kit (e.g., a colorimetric kit).

[0068] In some examples, the method includes preparing a solid sample (e.g., hepatocytes) for analysis (e.g., flow cytometry analysis) by isolating cells by centrifugation. In some examples, the solid sample (e.g., hepatocytes) is digested with an enzyme (e.g., collagenase or DNase I) to isolate cells (e.g., macrophages). In some examples, the enzyme digestion is performed for, for example, but not limited to, 5 minutes, or 10 minutes, or 15 minutes, or 20 minutes, or 25 minutes, or 30 minutes, or 35 minutes, or 40 minutes, etc. In some examples, the enzyme digestion is performed at a temperature, for example, but not limited to, 34°C, 35°C, 36°C, 37°C, 38°C, 39°C, 40°C, etc. In some examples, the enzyme digestion is performed at 37°C for 30 minutes by passing through a needle (e.g., an 18G needle).

[0069] In some examples, isolated cells (e.g., macrophages) were directly stained with antibodies for analysis (e.g., flow cytometry) after lysis of cells (e.g., red blood cells). Data was generated using equipment known in the art (e.g., LSRII or Imagestream Amnis) and analyzed by software known in the art (e.g., Flow Jo). Cells were sorted using equipment known in the art (e.g., FACS Aria II or III).

[0070] In some examples, the method includes preparing a biological sample (e.g., cells) for analysis (e.g., flow cytometry), labeling, and recording the data. In some examples, the biological sample (e.g., cells) is stained with an antibody (e.g., cisplatin to determine cell viability). The analysis is performed using a kit (e.g., Cytofkit) and algorithm (e.g., One-sense).

[0071] In some examples, the method includes processing the samples for genomic analysis (e.g., 10X genomic analysis) using a platform (e.g., the Chromium Single Cell 3' platform). In some examples, the method includes pooling and sequencing cells (e.g., CD45+Tomato-, or CD45+Tomato+ cells) in a lane (e.g., a Novaseq lane) by a commercial company (e.g., Novagene AIT).

[0072] In some examples, the method includes preparing a sample for translation analysis (e.g., RiboTag analysis). In some examples, the method includes sorting the cells and resuspending them in a buffer (e.g., lysis buffer) and a compound (e.g., cycloheximide). In some examples, the cell homogenate is centrifuged (e.g., 10,000g, 4°C for 10 minutes) to remove cell debris. In some examples, the supernatant is transferred onto ice and an antibody (e.g., anti-HA, or mouse monoclonal IgC1 antibody) is added to the supernatant. In some examples, the incubation can include, but is not limited to, 1 hour, or 2 hours, or 3 hours, or 4 hours, or 5 hours, or 6 hours, or 7 hours, or 8 hours, or 9 hours, or 10 hours, or 11 hours, or 12 hours, etc. in a cold room. In some examples, the sample with the supernatant and the antibody added is incubated in a cold room (e.g., 4°C) for 4 hours with slow rotation.

[0073] In some examples, the method includes equilibrating magnetic beads (e.g., Dynabeads Protein G) to the sample by washing with a buffer (e.g., homogenization buffer). In some examples, after incubating the sample with the antibody (e.g., for 4 hours), magnetic beads (e.g., Dynabeads Protein G) are added to the sample. In some examples, the sample is further incubated overnight in a cold room (e.g., 4° C.). In some examples, the sample is washed once, or twice, or three times with a buffer (e.g., high salt buffer). In some examples, the sample is washed three times with a buffer (e.g., high salt buffer) (e.g., 5 minutes per wash) on a rotating plate in a cold room.

[0074] In some examples, the method includes removing excess buffer from the magnetic beads and using the purified nucleic acid (e.g., RNA) as a sample (e.g., a single-cell sample). In some examples, the method includes generating a nucleic acid library (e.g., a cDNA library) using a sequencing method (e.g., single-cell RNA sequencing).

[0075] In some examples, the method includes capturing cells in a single run with barcoded samples (e.g., 12 samples) pooled together for an experiment (e.g., a Rhapsody experiment). In some examples, the method includes processing the samples according to the targeted nucleic acid (e.g., mRNA) and creating a sample tag library using a kit (e.g., Rhapsody targeted mRNA and Abseq amplification kit). In some examples, the samples are then subjected to a cycle (e.g., 2x151 cycles) run (e.g., an indexed paired-end sequencing run) on an instrument (e.g., an Illumina HiSeq 4000 system) with a quality control spike-in (e.g., 20% PhiX).

[0076] In some examples, the method includes labeling cells (e.g., KC1, KC2 populations) for imaging (e.g., confocal immunofluorescence) by injecting an animal (e.g., wild-type C57BL / 6 mice) with an antibody (e.g., F4 / 80 Alexa Fluor 488, CD206-APC) prior to sacrificing the animal.

[0077] In some examples, the method includes fixing the sample (e.g., liver lobe) in a fixative (e.g., paraformaldehyde overnight) and then incubating with a sugar solution (e.g., 30% sucrose for 24 hours). In some examples, the method includes embedding the fixed sample (e.g., liver lobe) in an embedding compound (e.g., optimal cutting temperature (OCT)) and cutting into sections using an instrument or embedding in a low melting point gel (e.g., 4% agarose) for instrumental sections.

[0078] In some examples, the method includes fixing the sample in a fixative (e.g., glutaraldehyde, 1 hour at room temperature) and compound (e.g., osmium tetroxide, 1 hour at room temperature). In some examples, the method includes dehydrating the sample through a graded series of alcohols (e.g., 25% to 100% ethanol) and drying using a dryer (e.g., CPD030 critical point dryer). In some examples, the method includes coating the surface on which the cells are grown with a metal (e.g., 5 nm gold) by coating (e.g., sputter coating) using an instrument (e.g., SCD005 high vacuum sputter coater). In some examples, the method includes examining the coated sample using a microscope (e.g., a field emission JSM-6701F scanning electron microscope) at a voltage (e.g., an accelerating voltage of 8 kV) using a detector (e.g., an in-lens secondary electron detector).

[0079] The term "associated" as used herein when referring to two elements refers to a broad relationship between the two elements. The relationship includes, but is not limited to, a physical relationship, a chemical relationship, or a biological relationship. For example, if element A is associated with element B, elements A and B may be directly or indirectly bound to each other, or element A may contain element B, or vice versa.

[0080] The term "adjacent" as used herein when referring to two elements refers to close proximity of one element to another element, which may include, but is not limited to, that the elements are in contact with one another, or may further include that the elements are separated by one or more additional elements disposed between them.

[0081] The term "and / or," e.g., "X and / or Y," should be understood to mean either "X and Y" or "X or Y," and should be interpreted as providing clear support for both meanings or either meaning.

[0082] Further, in the description herein, the term "substantially", whenever used, is understood to include, but is not limited to, "entirely" or "completely". In addition, terms such as "comprising", "comprise", and the like, whenever used, are intended to be open-ended descriptive language in that they broadly include the elements / ingredients described after such terms, in addition to other ingredients not expressly described. For example, when "comprising" is used, a reference to "a" feature is also intended to be a reference to "at least one" of that feature. Terms such as "consisting", "consist", and the like, may be considered as subsets of terms such as "comprising", "comprise", and the like, in the appropriate context. Thus, in embodiments disclosed herein that use terms such as "comprising", "comprise", and the like, it will be recognized that these embodiments provide teachings for corresponding embodiments that use terms such as "consisting", "consist", and the like. Additionally, terms such as "about," "approximately," and the like, whenever used, typically refer to a reasonable variation, such as a + / - 5% variation of the disclosed value, or a 4% variation of the disclosed value, or a 3% variation of the disclosed value, or a 2% variation of the disclosed value, or a 1% variation of the disclosed value.

[0083] Furthermore, in the description herein, certain values ​​may be disclosed in ranges. The values ​​indicating the endpoints of the range are intended to indicate preferred ranges. Whenever a range is described, the range is intended to encompass and teach all possible subranges and individual numerical values ​​within the range. That is, the endpoints of the range should not be interpreted as inflexible limitations. For example, description of a range of 1% to 5% is intended to specifically disclose subranges such as 1% to 2%, 1% to 3%, 1% to 4%, 2% to 3%, and individual values ​​within the range such as 1%, 2%, 3%, 4%, and 5%. It should be recognized that the individual numerical values ​​within the range also include integers, fractions, and decimals. Furthermore, whenever a range is described, the range is also intended to encompass and teach values ​​from the indicated numerical endpoints to two decimal places or two significant digits (where appropriate). For example, description of a range of 1% to 5% is intended to specifically disclose ranges of 1.00% to 5.00% and 1.0% to 5.0%, as well as all intermediate values ​​therein (e.g., 1.01%, 1.02%...4.98%, 4.99%, 5.00% and 1.1%, 1.2%...4.8%, 4.9%, 5.0%, etc.). The above specific disclosure intent is applicable to any depth / breadth of range.

[0084] Furthermore, in describing some embodiments, the present disclosure may disclose a method and / or process in a particular sequence of steps. However, unless otherwise required, it will be recognized that the method or process should not be limited to the particular sequence of steps disclosed. Other sequences of steps may be possible. The particular order of steps disclosed herein should not be construed as an undue limitation. Unless otherwise required, the method and / or process disclosed herein should not be limited to steps performed in the order described. The sequence of steps may be altered and still be within the scope of the present disclosure.

[0085] Furthermore, it will be recognized that while the present disclosure provides embodiments having one or more of the features / characteristics discussed herein, one or more of these features / characteristics may be waived in other alternative embodiments, and the present disclosure provides support for such waiver and these related alternative embodiments. EXAMPLES

[0086] Experimental Section Materials & Methods Animal models mouse All mouse experiments and procedures were approved by the Institutional Animal Care and Use Committee of the Biological Resource Centre (Agency for Science, Technology and Research, Singapore) in accordance with the guidelines of the Agri-Food and Veterinary Authority and the National Advisory Committee for Laboratory Animal Research (ICUAC No. 181402).

[0087] C57 / BL6 mice were obtained from the Jackson Laboratory. All mice were bred and housed under specific pathogen-free conditions in the Animal Facility of the Biological Resource Center and maintained on a C57BL / 6 background. All mice used in in vivo experiments were 7–12 weeks old unless stated. Mice were fed a high-fat diet (60% fat - #D12492 Research Diets Inc.) ad libitum or, where indicated, a tamoxifen-enriched diet (Teklad TD.130855 EnVigo). For glucose tolerance testing, mice were fasted overnight and then fed 2 g.kg-1 glucose by oral gavage according to (Andrikopoulos et al., 2008). Blood glucose was then measured at the indicated time points.

[0088] Indirect calorimetry (metabolic chamber) For metabolic cage studies, mice were individually housed in metabolic chambers and maintained on a 12-h light / dark cycle with lights on from 6 am to 6 pm at 22° C. Oxygen consumption, CO2 output, food consumption, locomotion and energy expenditure were measured using TSA metabolic chambers (TSA System, Germany) in an open-circuit indirect calorimetry system.

[0089] Administration of glucan-encapsulated siRNA particles (GERPs) GERP was prepared as previously described by other studies known in the art. Mice fed HFD for 8 weeks were first randomized according to their body weight and glucose tolerance. Mice were then treated with siRNA against Cd36 (5'-GCAAAUGCAAAGAAGGAAA-3') (SEQ ID NO: 1), or negative control (Scr: 5'-CAGUCGCGUUUGCGACUGG-3') (SEQ ID NO: 2) (Dharmacon) (80μg), and a total dose of 2mg of GERP loaded with Endoporter (0.1mM). Fluorescently labeled (FITC) GERP was administered to mice six times over a period of 15 days by intravenous injection.

[0090] Biochemical parameters in mice Liver samples were taken and immediately flash frozen. From these, total triglyceride (TG) content was determined by a commercial colorimetric kit (Roche; TG12016648). TG concentrations were normalized to protein concentration determined by Pierce BCA Protein Assay Kit (Thermofisher; 23227) according to the manufacturer's instructions. Intracellular H2O2 content was measured using the Amplex™ Red Hydrogen Peroxide / Peroxidase Assay Kit (Life Technologies; A22188). Malondialdehyde content was measured using the Lipid Peroxidation (MDA) Assay Kit (Colorimetric / Fluorometric) (Abcam; ab118970). Plasma multi-analyte profiles were performed using a clinical chemistry analyzer (Mindray BS-240 Pro, BioSentec) with the indicated colorimetric kits (all Biosentec). All assays were performed according to the manufacturer's instructions.

[0091] 1. Preparation of Cells Cell isolation, flow cytometry and sorting of macrophages Standard labeling procedures were used to prepare cells for flow cytometry analysis. Hepatocytes were isolated by centrifugation through density gradients as previously described by other studies known in the art. For macrophages, liver lobules were digested in collagenase / DNase I (0.2 mg.ml-1 collagenase, 5 units.ml-1 DNase I and 10% FBS in RPMI) for 30 min at 37°C and dissociation was completed by several passes through an 18G needle. No concentration was performed to avoid cell loss, and isolated cells were directly stained for flow cytometry after red blood cell lysis. Antibodies used are listed in the Key Resources table. Data were acquired by LSRII (BD Bioscience) or Imagestream Amnis (Merck) and analyzed with Flow Jo (Tree Star, Inc.). Cells were sorted using FACS Aria II or III (BD Bioscience).

[0092] Time-of-Flight Cytometry (CyTOF) Cells were prepared, labeled, and data recorded as previously described by other studies known in the art. Briefly, cells were prepared as in conventional flow cytometry and stained with cisplatin to determine cell viability. The antibodies used are listed in Table 1. The antibodies used were commercially available and were functionally complexed with metals (further details can be found in PMID: 32607886 / Methods Mol Biol. 2020;2164:87-99. / doi: 10.1007 / 978-1-0716-0704-6_10., the contents of which are incorporated herein by reference). Results were analyzed using CytofKit and the One-sense algorithm.

[0093] mass spectrometry Sorted cell populations were lysed in urea lysis buffer (8 M urea / Tris-HCl 50 mM, pH 8), reduced in the presence of 20 mM TCEP at room temperature for 20 min, and further alkylated with 55 mM chloroacetamide. After dilution with 100 mM triethylammonium bicarbonate (TEAB, pH 8.5; Sigma-Aldrich #T7408), samples were digested with a ratio (1:100) of lysyl endopeptidase (LysC, Wako #129-02541) and trypsin (Promega, #V5117) for 4 and 18 h, respectively.

[0094] Samples were further acidified with trifluoroacetic acid (TFA Sigma-Aldrich #T6508; 1% v / v), spun down at 14,000 RPM for 10 minutes at room temperature and desalted using HLB 96-well plates (Waters, #WAT058951). After high pH reversed-phase fractionation of the crude step (4 fractions, Reposil-Pur Basic C18 10 μm, Dr Maisch Gmbh#r10.b9.0025), each fraction was separated on a 50 cm (75 μm i.d.) EASY-Spray RP-C18 LC column (Thermo Scientific) with a 75 min gradient of solvent A (0.1% formic acid in water) and solvent B (99.9% acetonitrile in water, 0.1% formic acid) in an Easy LC 1000 (Thermo Fisher Scientific) coupled to an Obritrap Fusion Lumos mass spectrometer (Thermo Fisher Scientific).

[0095] Peak lists were generated using MaxQuant software version 1.6.7.0. Spectra were searched against the target-decoy Mouse Uniprot database with the following fixed modifications: carbamidomethyl (C) and variable modifications: oxidized (M), deamidated (NQ) acetyl (N-terminal protein). A maximum of two missed cleavages was allowed, and mass tolerance was 4.5 ppm mass deviation (after recalibration) for OT-MS survey scans and 0.5 Da for IT-MS / MS ion fragments. FDR was set to 1%. Label-free quantification (LFQ) was performed.

[0096] Transcriptomics Library construction For bulk RNAseq experiments, 20,000–50,000 cells were FACS sorted and total RNA was extracted using the Arcturus PicoPure® RNA isolation kit (Arcturus® Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer's protocol. All mouse RNA was analyzed on an Agilent bioanalyzer (Agilent, Santa Clara, CA, USA) for quality assessment with an RNA Integrity Number (RIN) range of 5.8–6.7 and a median RIN of 6.4. 2ng of total RNA and 1ul of a 1:50,000 dilution of ERCC RNA Spike in Controls (Ambion® Thermo Fisher Scientific, Waltham, MA, USA) were used to generate cDNA libraries using the Smart-Seq v2 protocol with the following modifications: 1. Addition of 20μM template switch oligo (TSO); 2. Use of 200pg of cDNA from 1 / 5 reaction of the Illumina Nextera XT kit (Illumina, San Diego, CA, USA).

[0097] The length distribution of the cDNA libraries was monitored using a DNA High Sensitivity Reagent Kit on a Perkin Elmer Labchip (Perkin Elmer, Waltham, MA, USA). All samples underwent 2x151 cycles of indexed paired-end sequencing runs (25 samples / lane) on an Illumina HiSeq 4000 system (Illumina).

[0098] For Smart-seq2 single-cell analysis, 288 single cells were sorted in a 96-well plate and cDNA libraries were generated using the Smart-seq v2 protocol with the following modifications: 1. 1 mg / ml BSA lysis buffer (Ambion® Thermo Fisher Scientific, Waltham, MA, USA); 2. Use of 200 pg cDNA from 1 / 5 reaction of the Illumina Nextera XT kit (Illumina, San Diego, CA, USA).

[0099] The length distribution of the cDNA libraries was monitored using a DNA High Sensitivity Reagent Kit on a Perkin Elmer Labchip (Perkin 41 Elmer, Waltham, MA, USA). All samples underwent 2x151 cycles of indexed paired-end sequencing runs (298 samples / lane) on an Illumina HiSeq 4000 system (Illumina, San Diego, CA, USA). After QC filtering, 169 cells were used for analysis.

[0100] For 10X analysis, samples were processed using the Chromium Single Cell 3' (v3 Chemistry) platform (10x Genomics, Pleasanton, CA). Briefly, 100,000 cells CD45+Tomato- containing KCs and 100,000 cells CD45+Tomato+ were pooled and sequenced on a Novaseq lane with Novagene AIT. After QC filtering, 78,944 cells were used for analysis.

[0101] For RiboTag analysis, samples were prepared as described by other studies known in the art. Briefly, 50,000 cells were sorted and resuspended in 1 ml of lysis buffer (50 mM Tris, pH 7.4, 100 mM KCl, 12 mM MgCl2, 1% NP-40, 1 mM DTT, 1:100 protease inhibitor (Sigma Aldrich), 200 units / ml RNasin (Promega) and 0.1 mg / ml cycloheximide (Sigma Aldrich) in RNase-free water) on ice. To remove cell debris, the homogenate was transferred to an Eppendorf tube and centrifuged at 10,000 g and 4° C. for 10 minutes. The supernatant was transferred to a new Eppendorf tube on ice, and 5 μl (=125 μg) of anti-HA antibody (H9658, Sigma Aldrich) or 5 μl (=1 μg) of mouse monoclonal IgG1 antibody (Sigma, Cat#M5284) was added to the supernatant, followed by incubation for 4 h with slow rotation in a cold room at 4°C. Meanwhile, Dynabeads Protein G (Thermo Fisher Scientific) were equilibrated against homogenization buffer by washing three times with 100 μl / sample.

[0102] At the end of the 4-hour incubation with the antibodies, beads were added to each sample, followed by overnight incubation in a cold room at 4°C. Samples were washed three times with high salt buffer (50 mM Tris, 300 mM KCl, 12 mM MgCl2, 1% NP-40, 1 mM DTT, 1:200 protease inhibitors, 100 units / ml RNasin, and 0.1 mg / ml cycloheximide in RNase-free water) for 5 minutes per wash on a rotator in the cold room. At the end of the wash, the beads were magnetized and excess buffer was removed. The purified RNA was then processed as a single-cell sample, given the small amount of RNA recovered. The single-cell RNA sequencing method described above was used to generate cDNA libraries, except that 300 pg of cDNA was used for the Illumina Nextera XT kit.

[0103] For Rhapsody experiments, all processes were performed according to the manufacturer's (BD Biosciences) protocol. 16,775 cells were captured in a single run using 12 barcoded samples pooled together. Samples were processed according to BD mRNA targeting and sample tag library generation using the BD Rhapsody™ Targeted mRNA and Abseq Amplification Kit (Doc ID: 210969 Rev 3.0). Samples then underwent 2x151 cycles of indexed paired-end sequencing runs with 20% PhiX spike-in on an Illumina HiSeq 4000 system (Illumina, San Diego, CA, USA).

[0104] Analysis of transcriptome data Raw read data were aligned to the mouse reference genome GRCm38_M13 from GENCODE using STAR 2.5.3a. Gene expression values ​​(transcripts per million (TPM)) were calculated using the same RSEM program. Dimensionality reduction (PCA, tSNE and UMAP), clustering and differentially expressed gene (DEG) analysis were performed using Seurat version 2.4.3. Wilcoxon rank sum test was performed to calculate p-values ​​of differentially expressed genes. After obtaining p-values, p-value adjustment was performed using Bonferroni correction based on the total number of genes in the dataset, and an adjusted p-value <0.05 was used as the threshold for statistical significance. Integration of 10X and SMARTSeq2 data was performed using Seurat version 3.0.1 with a standard integration pipeline.

[0105] Imaging Confocal imaging KC1 and KC2 were labeled for confocal immunofluorescence imaging by intravenously injecting 2 μg of F4 / 80 Alexa flour 488 (Biolegend #123120) and 2 μg of CD206-APC (Biolegend #141708) into WT C57BL / 6 mice 10 min before sacrificing the animals. Liver lobes were fixed overnight in 4% paraformaldehyde in PBS and then incubated in 30% sucrose in PBS for 24 h. Liver lobes were then embedded in OCT (Killik Bio-Optica #05-9801) and cut into 60 μm thick sections using a cryostat at -14°C or embedded in 4% low melting agarose (Sigma-Aldrich) for 200 μM thick vibratome sections.

[0106] For OCT-embedded tissues, sections were blocked in blocking buffer (PBS, 0.5% BSA, 0.3% Triton) for 15 min, then stained with CD38 Alexa flour 594 (Biolegend #102725) in wash / stain buffer (PBS, 0.2% BSA, 0.1% Triton) for 60 min at room temperature, washed twice for 5 min, stained with DAPI (Sigma #28718-90-3) for 5 min, washed again, and mounted for imaging with Fluorosave™ reagent (Millipore #345789). Images were acquired using an SP5 confocal microscope (Leica) with a 63x oil immersion objective. For visualization purposes and to compensate for uneven slide illumination, the fluorescence intensity of the layers was normalized using the Imaris Normalize Layers tool. Images were subsequently filtered for autofluorescence by channel 42 subtraction of the deep red autofluorescence channel from the APC signal using the Imaris Channel Arithmetics tool.

[0107] For agarose-embedded tissues, sections were permeabilized for 1 h in PBS supplemented with 0.4% Triton X-100 (Sigma-Aldrich) and 3% BSA (Sigma-Aldrich) and preincubated for 1 h in blocking buffer (PBS supplemented with 3% BSA). Tissues were then labeled with appropriate primary and secondary antibodies for 2 h at room temperature.

[0108] Scanning electron microscopy Cells were fixed in 2.5% glutaraldehyde in 0.1 M phosphate buffer (pH 7.4) for 1 h at room temperature, treated with 1% osmium tetroxide (Ted Pella Inc) for 1 h at room temperature, then dehydrated through a graded ethanol series from 25% to 100% and dried using a CPD030 critical point dryer (Bal-Tec AG, Liechtenstein). The cell growth and attachment surfaces were coated with 5 nm of gold by sputter coating using a SCD005 high vacuum sputter coater (Bal-Tec AG). The coated samples were examined using a field emission JSM-6701F scanning electron microscope (JEOL Ltd., United States) at an accelerating voltage of 8 kV using an in-lens secondary electron detector.

[0109] statistical analysis DEG analysis was performed using the Seurat v3 package. All DEGs obtained from the tpm / count matrix were calculated in normalized values ​​using a logFC threshold of 0.25. Wilcoxon rank sum test was performed to calculate p-values ​​of differentially expressed genes. After obtaining p-values, adjustment of p-values ​​was performed using Bonferroni correction based on the total number of genes in the dataset, and an adjusted p-value < 0.05 was used as the threshold for statistical significance.

[0110] [Table 1]

[0111] [Table 2]

[0112] [Table 3]

[0113] [Table 4]

[0114] [Table 5]

[0115] result Unbiased approaches reveal KC transcriptome heterogeneity To assess KC heterogeneity in an unbiased manner, we first used single-cell RNA-seq technology. Figures 1A-1G show an unbiased approach to reveal KC heterogeneity. We purified hepatic CD45+ leukocytes from mouse liver at steady state and profiled thousands of individual cells by using Chromium single-cell gene expression technology (10X). Seurat analysis of this dataset using uniform manifold approximation and projection (UMAP) dimensionality reduction analysis and automated clustering identified nine major clusters (#0-#8) in the CD45+ cell population (Figure 1A). These clusters were manually annotated by using standard population markers: #0 represents B cells, #1 represents T cells, #4 represents monocytes and cDCs, #5 represents NK cells, #6 represents NKT cells, #7 represents pDCs, and #8 represents encapsulated macrophages.

[0116] For clusters #2 and #3, these cells co-expressed a number of genes classically highly expressed by Kupffer cells (KCs), such as Adgre1 (encoding F4 / 80), Timd4, Csf1r, and Clec4f (Figure 1A). Focusing on total Adgre1+ cells, a population considered to be hepatic macrophages, the inventors of the present disclosure identified a subcluster of Cx3cr1+Timd4-Clec4f- cells corresponding to capsular macrophages, and two clusters of Cx3cr1-Timd4+Clec4f+KCs that differentially expressed genes such as Mrc1 (CD206) and Lamp2 (CD107b) (Figure 1B).

[0117] In addition, using the high-resolution SMARTseq2 platform, the inventors of the present disclosure observed that sorted liver CD64+F4 / 80+ cells could be divided into four clusters (Figure 1C). Of these clusters, only two major clusters, c3 and c4, had signatures of canonical KCs, while c1 and c2 showed unrelated signatures (Figure 1D), indicating that they were due to contamination during cell sorting. Next, by integrating the low-resolution 10X data generated with thousands of cells with the deeper but limited number of SMARTseq2 data, the inventors of the present disclosure verified the presence of two clusters of KCs observed with two different single-cell RNA-seq techniques (Figure 1E).

[0118] Next, we used a scene analysis pipeline to uncover regulatory networks in the most sensitive SMARTseq2 single-cell RNA-seq dataset, which confirmed that the total KC population could be divided into two distinct states that harbored a regulon activity pattern consistent with hepatic macrophages, with high expression of canonical KC transcription factors such as Nr1h3 (liver X receptor alpha) and Spic (Figure 1F).

[0119] Furthermore, each cluster of KCs showed a specific regulon activity profile, e.g., Runx3 was more active in c3 and Klf6 was more active in c4 (Figure 1G). Thus, our unbiased single-cell RNA-seq approach revealed two distinct subsets of KCs present at steady state. To validate these findings at the protein level, we used mass cytometry techniques to monitor the expression of an extended panel of common myeloid markers and putative markers such as CD206 and CD107b identified by our unbiased single-cell transcriptome approach (Figures 1H and 1I). By integrating the expression of these markers in live CD45+ cells from mouse liver at steady state, the inventors of the present disclosure identified major immune cell subsets present in the liver: CD19+ B cells (#3), CD90+ T cells (#1, #4, #8 and #13), CD49b+ NK cells (#12), SiglecH+BST2+ pDCs (#7), Ly6C+ monocytes (#14), CD11c+ cDCs (#5), SiglecF+ eosinophils (#10), Ly6G+ neutrophils (#11), and a large population of F4 / 80+Tim4+ KCs (#6 and #15) that were composed of two clusters (Figures 1H and 1I).

[0120] Focusing on this KC population, One-SENSE (one-dimensional soli-expression by nonlinear stochastic embedding) analysis, which allows manual definition of lineage and marker dimensions, revealed several markers differentially expressed by two clusters within this population, notably CD206, but also CD107a&b, CD81 and Lyve1 (Figure 1J).

[0121] Thus, by combining unbiased transcriptomic and proteomic approaches, the inventors of the present disclosure revealed that KCs can be further divided into a CD206loCD107b- population (=KC1) and a CD206hiCD107b+ population (=KC2).

[0122] KCs are divided into two subpopulations that share a common embryonic origin Based on this unsupervised approach, the inventors of the present disclosure designed a panel of markers for use in conventional flow cytometry to analyze KC populations. KCs are classically defined as CD45+CD64+F4 / 80+TIM4+Clec4F+ cells with low expression of CD11b and Ly6C (Figure 2A). The inventors of the present disclosure identified and sorted KC1 and KC2 using CD206 and CD107b, and generated transcriptomic signatures of these two populations (Figure 2B).

[0123] Among the top differentially expressed genes (DEGs) is Esam, a previously reported marker of splenic dendritic cells. Thus, the inventors of the present disclosure retained CD206 and Esam as two reliable markers and used these markers to later define CD206loESAM-KC1 and CD206hiESAM+KC2 by conventional flow cytometry (Figure 2C). The inventors of the present disclosure also measured the expression of other markers highlighted as overexpressed by KC2 by the high-throughput approach, including CD63, CD81, CD107a, and Lyve1, in the two populations (Figure 2D).

[0124] All of these markers were expressed by KC2, and the inventors of this disclosure were unable to identify specific markers for KC1. However, KC1 and KC2 could be clearly distinguished by either manual gating based on CD206 and ESAM expression or by using algorithm-based dimensionality reduction (Figure 2E). Sorted KC1 and KC2 had comparable morphology and were indistinguishable by electron microscopy and cytospin (Figure 2F).

[0125] The present inventors and other authors of studies known in the art have shown that KCs originate from fetal liver monocytes, with a minimal contribution of bone marrow monocytes to maintain the adult KC population. However, some reports have shown that monocyte-derived cells can acquire KC identity under non-homeostatic conditions. Therefore, to clarify the origin of the two subpopulations and accurately classify them as true KCs, the present inventors first investigated when heterogeneity within the KC population emerged and analyzed the presence of KC1 and KC2 from birth to adulthood. While the ratio of monocytes to macrophages was dynamic during the first few weeks after birth, the ratio of KC1 and KC2 populations was very stable and already established at birth, suggesting that they both arise from prenatal precursors, even if the present inventors could not formally exclude that each subpopulation emerged during a separate wave of embryogenesis (Figure 2G).

[0126] Next, the inventors of the present disclosure performed monocyte fate mapping Ms4a3 cre xRosa Tomato A mouse model was used to evaluate the possible contribution of monocytes. As expected, monocytes and monocyte-derived encapsulated macrophages were highly tagged in adult mice, whereas KC1 and KC2 showed comparable but very low tagging (Figure 2H). In addition, S100a4 CRE xRosa YFP and Csf1R GFP In mice, high and comparable reporter expression was observed in KC1 and KC2, confirming the macrophage nature of both subpopulations ( Fig. 2I ).

[0127] The inventors of the present invention also confirmed this by analyzing CD45.1 / CD45.2 parabiotic mice, in which non-host chimerism was very low after 3 months of sharing the systemic circulation in both KC1 and KC2, in contrast to monocyte-derived capsular macrophages, as previously shown by other studies known in the art (Figure 2J).

[0128] Taken together, these results indicate that both KC subsets are true embryonic-derived KCs and therefore do not represent ontogenetically distinct populations but rather two distinct states of KCs.

[0129] KC1 and KC2 have overlapping distribution patterns in situ To evaluate the potential distinct sublocalization of the two subsets in the liver, we first examined their proximity to an intravenously injected anti-CD45 antibody. KC1 and KC2 were labeled with comparable efficiency, confirming their sinusoidal localization (Figure 3A). Next, we viewed these cells in situ using two-photon microscopy, coupled with immunofluorescence microscopy of liver sections from WT mice for more detailed analysis. Clec4F+KCs were easily distinguished from Clec4F-CD206+LSECs, and KC2 were detected as Clec4F+ or F4 / 80+KCs that also express CD206 (Figure 3B-D).

[0130] To fully validate this, the inventors of the present disclosure used Mrc1cre to establish a model that allows the inventors of the present disclosure to track cells expressing CD206 after tamoxifen induction. ERT2 We generated mice (Figure 3E) and named them Rosa Tomato By immunostaining, the inventors of the present disclosure observed that approximately 15% of KCs in these mice were tagged with tomato, in accordance with the proportion observed by using the CD206 marker in conventional flow cytometry analysis ( FIG. 3F ).

[0131] We also measured the distance between F4 / 80+CD206loKC1 and F4 / 80+CD206hiKC2 and their nearest portal triads to evaluate whether the zonation of liver metabolism affects the distribution of KC1 and KC2. No significant differences were observed regarding the zonation of the two populations (Figure 3G). Taken together, these observations suggest that KC1 and KC2 have overlapping distribution patterns in the liver.

[0132] KC2 express LSEC-associated / endothelial markers but are distinct from LSECs KC1 and KC2 are macrophages and express canonical macrophage markers such as F4 / 80, Tim4, Clec4F and Vsig4. However, KC2 also displayed markers known to be expressed by endothelial cells such as CD206, CD31, ESAM and even Lyve1, which was recently identified by the inventors of the present disclosure as a highly ubiquitous macrophage marker (FIGS. 4A and 4B).

[0133] To understand this expression pattern and exclude the possibility that its detection results from phagocytosis of hepatic sinusoidal endothelial cells (LSECs) by KCs, the inventors of the present disclosure performed image stream analysis, combining flow cytometry with microscopy, allowing imaging of individual cells. The inventors of the present disclosure observed that ESAM and CD206 labeling was distributed uniformly throughout KC2 cells, indicating surface expression of these markers (Figure 4C). Furthermore, LSECs possessed fenestrae on their surface, which were absent in KC2 cells (Figure 4D).

[0134] Furthermore, the inventors of the present disclosure have demonstrated that cells expressing the canonical myeloid gene Lyz2 are tagged with Lyz2 cre xRosa YFP We analyzed mice and observed that both KC1 and KC2 were highly labeled, but endothelial cells were not tagged (Figure 4E). The inventors of the present disclosure also confirmed the macrophage nature of KC1 and KC2 by using Rhapsody technology, which allows for rapid parallel sequencing of 400 markers in thousands of single cells. Comparing this lower resolution single cell transcriptome profile of sorted KC1 and KC2 with the entire liver leukocyte population, the inventors of the present disclosure found that both KC1 and KC2 were clearly clustered within the macrophage population, further confirming their macrophage identity (Figure 4F).

[0135] At the functional level, we used a clodronate liposome approach to test the phagocytic capacity of both populations: liposome injection similarly depleted KC1 and KC2, but not LSECs, displaying comparable phagocytic activity (Figure 4G).

[0136] Moreover, KC depletion is known to induce the recruitment of monocytes that rapidly acquire a KC-like phenotype, even if a few key KC genes, such as Timd4, are not re-expressed for several weeks prior. cre xRosa Tomato When performed in mice, the inventors of the present disclosure observed that CD64+Tomato+Tim4- monocyte-derived macrophages rapidly gave rise to both CD206loESAM-KC1-like and CD206hiESAM+KC2-like populations at ratios comparable to steady state (Figure 4H). This highlights the fact that recruited naive mature monocytes have the capacity to acquire both KC1- and KC2-like profiles, suggesting that the identity of both KC1 and KC2 is strongly determined by the liver microenvironment.

[0137] KC1 and KC2 exhibit distinct gene and protein expression signatures Next, we performed deeper transcriptome analysis using the SMARTseq2 protocol for bulk RNA-seq to sort CD206loESAM-KC1 and CD206hiESAM+KC2 to better understand their functions. At this high resolution, the two populations were clearly separated from each other (Figure 5A-C) and also distinct from sorted CD45-CD31+ LSECs (Figure 5D and 5E). KC1 and KC2 expressed similar canonical macrophage genes, including Clec4f, Lyz2, Vsig4, Csf1r, and Adgre1 (F4 / 80) (Figure 5F and 5G).

[0138] In addition to this macrophage signature, as the present inventors previously found, KC2 highly expressed a number of LSEC-associated genes, in particular Mrc1, Pecam1 (CD31), Esam, Kdr and Lyve1. To confirm that these LSEC-associated genes were indeed expressed by KC2 and were not detected due to phagocytosis of LSECs, the present inventors used the RiboTag strategy to evaluate the actively transcribed RNA (translatome) of the two populations. The present inventors found that Lyz2, which the present inventors were able to purify ribosomes from both KC1 and KC2, was highly expressed in KC2. cre XRpl22 HA We generated mice and sequenced their associated RNA. Using this method, despite the amount of initial material, and correspondingly the overall expression detected being reduced, there were still significant differences between KC1 and KC2, including KC2 expression of the "LSEC-associated genes" Mrc1, Esam, and Lyve1 (Figure 5H-J).

[0139] At the protein level, we also measured the expression of approximately 4,500 proteins by mass spectrometry in sorted KC1 and KC2 and found comparable differentially expressed proteins, especially ESAM and CD31, which were more abundant in KC2 (Figure 5K-M). Finally, we integrated transcriptome, translatome and proteome data to generate a robust KC1 and KC2 pan-omics identity (Figure 5N): the core KC program was conserved across both populations (Figure 5O, Figure 14B), but pathway analysis identified a stronger immune signature in KC1, while genes related to cell adhesion and metabolic pathways, especially Cd36, were upregulated in KC2 (Figure 5P-R).

[0140] KC2 exhibits metabolic functions The present inventors further explored the significance of this pan-omics distinct metabolic signature highlighted in KC2 population.Since the present inventors observed the upregulation of genes involved in carbohydrate and lipid metabolism in KC2, the present inventors chose to use a model of high-fat diet (HFD) feeding to induce obesity and its associated metabolic disorders, including glucose intolerance and fatty liver in mice (Figure 6A).The present inventors noticed an increase in the relative frequency of KC2 population compared to mice fed a normal diet (Figure 6B).

[0141] As previously reported in other studies known in the art, the inventors of the present disclosure found that the monocyte fate-mapper Ms4a3 was upregulated in the first 9 weeks of HFD. cre xRosa Tomato We did not notice significant monocyte recruitment to the liver of Ms4a3 mice under HFD (Figure 6C). cre xRosa Tomato KC2 labeling in mouse liver increased after 18 weeks, suggesting that expansion of the KC2 population was partially dependent on monocyte recruitment only at later time points on HFD (Figures 6C and 6D).

[0142] Next, the inventors of the present disclosure compared the transcriptome profiles of two populations of KCs sorted from mice fed a HFD for 2 months. The major differences between the two KC subpopulations were preserved (Figure 6E), even though they both acquired a more pronounced metabolic tropism signature on the HFD (Figure 6F-I). Notably, in KC2, genes involved in fatty acid processing were upregulated, while genes related to amino acid catabolism were downregulated. Among the genes upregulated on the HFD, the inventors of the present disclosure found Cd36, a gene that has been widely described in the literature for its role in regulating lipid uptake and oxidative stress in macrophages.

[0143] Therefore, the inventors of the present disclosure investigated the expression of Cd36 by KC2 in a recently and independently published mouse liver single-cell RNA-seq dataset. By focusing on the Clec4f+KC population, the inventors of the present disclosure observed that Cd36 was more expressed in Mrc1+KC (Figure 6J). The inventors of the present disclosure then verified these transcriptomic results using conventional flow cytometry and observed that the CD36 marker signal was indeed strongly elevated in KC2 compared to KC1 at steady state and upregulated on HFD (Figure 6K). These data highlighted KC2 as a metabolically responsive macrophage population whose lipid handling is regulated in diet-induced obesity.

[0144] CD36-specific targeting in KCs regulates hepatic metabolism To further study the role of KC2 in liver metabolism, the inventors of the present disclosure adopted an interventional approach by targeting KC2 in the context of metabolic challenges induced by HFD. Because all macrophage-directed fate mapping models were unable to distinguish between KC1 and KC2, the inventors of the present disclosure did not specifically target KC2 with these tools. Therefore, the inventors of the present disclosure used a pan-omics signature (Figure 5) to identify KC2-specific markers, which could be used to target KC2 and investigate its functional significance in HFD-fed mice.

[0145] Cdh5 is considered to be a core KC gene, but in parallel, Cdh5cre ERT2 xRosa Tomato The model was successfully used to label endothelial cells. Cdh5 was more expressed in KC2 at both the RNA and protein levels, and Cdh5 was significantly increased in KC2 cells treated with tamoxifen for 7 days. creERT2 xRosa TomatoIn mice, KC2, but not KC1, was specifically tagged (Figures 7A and 7B). Notably, labeling of the two subsets was highly stable over time, with no changes observed 13 weeks after tamoxifen treatment, except for a shift from one subset to the other at steady state (Figure 7C). Thus, the inventors of the present disclosure conclude that Cdh5 creERT2 xRosa DTR Generate mice and generate chimeras (Cdh5cre to avoid targeting radioresistant LSECs that also express Cdh5). ERT2 xRosa DTR After tamoxifen induction, the inventors of the present disclosure administered diphtheria toxin (DT) to the tamoxifen-treated chimeric Cdh5cre mice (Fig. 7D). ERT2 xRosa DTR We validated this system by injecting ESAM-TKC2 into mice and followed the kinetics of KC2 depletion. Tim4+CD206hiESAM+KC2, but not Tim4+CD206loESAM-KC1, were efficiently depleted after a single DT injection (Figure 7E), whereas, for example, adipose tissue macrophages were unaffected, supporting the specificity of such a strategy to target KC2.

[0146] Next, the inventors of the present disclosure investigated tamoxifen and DT-treated Cdh5 creERT2 xRosa DTRThe chimeras (KC2-deficient group) were fed a HFD for several weeks to evaluate the role of KC2 in the metabolic changes caused by the HFD. HFD did not induce weight gain in KC2-deficient animals, whereas KC2-sufficient animals gained weight over the period of HFD (Figures 7F and 7G). Given the remarkable regulation of genes involved in lipid metabolism and oxidative stress in KC2 upon HFD feeding, the inventors of the present disclosure monitored the amounts of both reactive oxygen species (ROS) H2O2, a well-known marker of oxidative stress, and the lipid peroxidation by-product malondialdehyde (MDA) after KC2 depletion. As expected, HFD induced an increase in ROS and MDA (Figure 7H). However, depletion of KC2 led to a decrease in both ROS and MDA, suggesting that KC2 may contribute to hepatic lipid peroxidation and oxidative stress associated with obesity. This was accompanied by improved glucose tolerance and less pronounced steatosis in KC2-deficient animals (Figures 7I and 7J). However, even though metabolic disorders including obesity, oxidative stress, steatosis, and glucose intolerance were reduced, serum triglyceride (TG) concentrations were increased (Figure 7K).

[0147] Consistent with these results, indirect calorimetry revealed that during the early phase of HFD, KC2-depleted animals had lower food intake, higher energy expenditure in terms of VO2 and VCO2 respiratory exchange ratios, and increased locomotor activity than KC2-sufficient animals (Figure 7L-P). In particular, given the intertwining of metabolic functions among various organs, including the liver, pancreas, adipose tissue, and brain, these observations could reflect a thorough metabolic rewiring following KC2 depletion and will merit future detailed studies.

[0148] However, to avoid such possible systemic effects, the inventors of the present disclosure used a more targeted strategy based on glucan-encapsulated siRNA particles (GeRPs) for specific silencing of Cd36 in KCs. Although the inventors of the present disclosure previously showed that GeRPs are specifically delivered to macrophages in the liver, but not to macrophages or other cells in other organs, the inventors of the present disclosure first used FITC-GeRPs to verify the specificity of targeting within liver cell populations. When injected intravenously, FITC-GeRPs were only recovered in KC1 and KC2, but not in other liver cells such as endothelial cells and monocytes, confirming the equal phagocytic capacity of both KC subpopulations (Figure 7Q). Next, mice were fed a HFD for 7 weeks and treated with GeRPs loaded with either control scrambled siRNA or Cd36-targeted siRNA for 2 weeks. This treatment allowed short-term specific silencing of Cd36 in KCs (Figure 7R). During the 2-week treatment period, both blood glucose and glucose tolerance were improved in Cd36 siRNA-treated animals (FIG. 7T), even though the inventors of the present disclosure did not observe a significant effect on weight gain (FIG. 7S).

[0149] To further investigate the effect of Cd36 on KC phenotype, we next analyzed the transcriptome profile after Cd36 silencing and observed the regulation of genes involved in lipid metabolism (Figure 7U). Finally, we tested the effect of Cd36 silencing on hepatic ROS and MDA concentrations. Consistent with our hypothesis, Cd36 silencing reduced the accumulation of both MDA and ROS (Figure 7V). This occurred independently of hepatic triglyceride content (Figure 7W). Taken together, these data highlight the important role of CD36, which is specifically expressed by KC2 in obese mice, for the control of hepatic metabolic homeostasis.

[0150] Herein, we report the existence of two phenotypically and functionally distinct KC subsets in healthy mouse liver. Although the liver is now recognized as a common niche for several macrophage populations, these two populations of KC share a common embryonic origin that clearly distinguishes them from other monocyte-derived liver macrophages. KC1 and KC2 are present at steady state and are not the result of monocyte recruitment that occurs during the neonatal period or during inflammation. These two populations share a common core macrophage signature and have high expression of well-known KC-core markers, including Clec4f, Lyz2, or Csf1r, but KC2 also express a set of genes previously thought to be restricted to LSECs.

[0151] These results are consistent with previous studies known in the art showing that cultured primary KCs share some functional antigens with LSECs; however, the comprehensive approach used in the aforementioned study failed to detect a specific subpopulation of KCs. Recent studies known in the art have detected potential contamination of KC populations with LSECs using conventional flow cytometry, but the inventors of the present disclosure were able to identify and exclude this trace contamination in single-cell RNA-seq datasets. Thus, the KC2 population identified by the inventors of the present disclosure is composed of true KCs as evidenced by fate mapping models and high expression of macrophage-core genes. Consistent with the present study of the present disclosure, the presence of a CD206+ population of KCs has already been reported in humans in recently published studies known in the art and was validated by preliminary data generated by the inventors (Figure 8). Furthermore, Lyve1+CD206+ populations of macrophages with similarity to KC2 are present in atherosclerotic lesions, suggesting that such "metabolic" macrophages may exist in niches other than the liver. Of note, studies known in the art have proposed a role for CD206+ macrophages in active phagocytosis of blood-derived cellular material. Taken together, these data from studies known in the art indicate a central role for this receptor in macrophage biology, and CD206 should be systematically included in future analyses of these cells.

[0152] This also parallels recent findings of two populations of lung interstitial macrophages and throughout the tissue, which are distinguished by differential Lyve1 and CD206 expression. Another important biomarker identified here by the inventors of this disclosure is ESAM. This molecule, whose expression was thought to be restricted to LSECs, is also expressed by a subset of splenic DCs, but was not previously thought to be expressed by macrophages. Lyve1, Cd206 and Esam have long been thought of as endothelial-restricted genes, but the results of this disclosure in the liver and previous results in other organs revisit this concept and clearly show that these genes can also be expressed by RTMs, even if the full functional role of these markers in RTMs needs to be further investigated.

[0153] The major differences between CD206- and CD206+ populations are completely independent of the origin of macrophages, i.e., monocytic or embryonic origin, and should be defined only by the resident niche, reinforcing the concept that the niche is one dominant factor driving macrophage identity. However, here, the inventors of the present disclosure were not able to identify separate sub-liver niches for each subset. Although metabolic zonation in the liver is important and shapes the transcriptome patterns of hepatocytes and LSECs, KC1 and KC2 appear to be randomly distributed within the acini, and thus their polarization is likely associated with unknown factors other than oxygen or nutrient availability. Identification of these determinants should broaden our understanding of what the inventors of the present disclosure can call the distinct "niches" present in the liver. This also highlights the need to investigate the heterogeneity of other different cell types in the liver, including stellate cells and LSECs, which may contribute to KC1 vs. KC2 identity. Indeed, a recent study showed that in an embryonic KC-depleted mouse model, liver-infiltrating monocytes are reprogrammed into KC-like cells by crosstalk with hepatic stellate cells and LSECs. These two studies identified a critical hepatic triptych composed of hepatic stellate cells, LSECs, and macrophages. Thus, it remains a subject of further research to decipher the crosstalk between these three different cell types and their potential subpopulations, and in particular, how it is regulated in the context of various liver pathologies.

[0154] In the context of the KC subsets identified here, the inventors of this disclosure wish to emphasize that KC2 is already present at steady state and poised to respond to metabolic challenges. Thus, determining how this cellular identity arises will be useful for understanding liver pathology, given that nearly a quarter of the human population suffers from nonalcoholic fatty liver disease. In accordance with this, it appears that the cellular liver tryptic is also present in humans, as evidenced by the identification of key "stellakines" secreted by hepatic stellate cells during NASH and cirrhosis that influence macrophage biology. Recent advances in single-cell transcriptomics, as exemplified in studies known in the art, and the subsequent publication of several atlases in both mice and humans, will improve our knowledge of hepatology and liver disease, even if the existence of corresponding functionally distinct KC1 and KC2 populations in humans has not yet been formally established.

[0155] Functionally, the inventors of the present disclosure identified a specific metabolic role for KC2. However, it would be misleading to claim that only KC2 is involved in the control of liver metabolism. The data of the present disclosure rather highlight that both KC subpopulations are involved in this function, even if the KC2 population seems to be more involved in this function. Furthermore, it is noteworthy that KC2 is present at steady state and does not depend on monocyte recruitment, which occurs in the later stages of obesity. In accordance with this, previous studies known in the art have shown the central role of hepatic macrophages in metabolism, considering KCs to be a homogenous population. The authors of studies known in the art have shown that the non-inflammatory factor insulin-like growth factor binding protein 7 (IGFBP7) is important in metabolic control by KCs. Importantly, in addition to Cd36, the inventors of the present disclosure observed that Igfbp7 was one of the top differentially expressed genes between KC1 and KC2 and was highly overexpressed in KC2. Therefore, it is tempting to assume that the effects reported in this study can be mostly caused by the KC2 population. In addition, the inventors of this disclosure provide direct evidence for the role of KC2-expressed receptor CD36 in liver metabolic control. These results are fully consistent with the emerging central role of CD36 in the development of NASH and obesity-related disorders. Although further investigations are required to better understand the systemic effects of KC2 depletion, our targeted approach to silence lipid transporter CD36 reveals the important role of these cells in controlling glucose homeostasis and oxidative stress. It has been previously reported that reducing oxidative stress of KCs in obesity can improve liver metabolism and reduce ROS concentrations. Here, the inventors of this disclosure further refine this observation by showing that KC2 plays a major role in this process by processing lipids via CD36.

[0156] However, it should be recognized that a precise understanding of the pathogenesis of NASH has yet to be achieved, and humans will likely develop a series of convergent diseases. Therefore, it cannot be excluded that KCs may show different involvement in other experimental models of NASH. For example, in a NASH model induced by a methionine-choline deficient (MCD) diet, inflammatory monocytes have been shown to invade the liver and induce a transient change in liver macrophage homeostasis. Furthermore, a very recent study using a different diet known in the art reported that embryo-derived KCs disappear after a few weeks and are replaced by a population of monocyte-derived lipid-associated macrophages (LAMs) that are associated with the development of pathology and are comparable to those already reported in adipose tissue from obese individuals. However, these studies essentially used rodent-based models and, by definition, may be limited in their ability to mimic human disease. As an example, MCD-induced NASH is associated with severe and rapid weight loss, liver inflammation, and subsequent fibrosis. Even if the latter may be closer to the human situation, the former is not observed clinically and may be responsible for model-specific side effects. Conversely, HFD-induced NASH may be more reflective of human obesity-induced disease, but is less efficient at inducing liver fibrosis. Therefore, it seems fundamental to recall that there is still no widely accepted model that fully recapitulates human NAFLD / NASH. That being said, the HFD model used by the inventors of the present disclosure herein does not induce significant recruitment of monocytes in the liver at an early stage. Thus, while most of the studies have focused on monocyte-derived LAMs recruited to the metabolically challenged liver, the study of the present disclosure shows that a portion of embryonic KCs is already destined to assume metabolic functions. Further studies should further refine its exact role in this or even other situations, allowing the design of innovative therapies targeting the metabolic functions of KC2 for the regulation of liver metabolic diseases.

[0157] Finally, in parallel with this study in which the inventors of the present disclosure described two distinct KC1 and KC2 populations, as well as the metabolic function of KC2, the inventors of the present disclosure investigated its antigen-presenting capacity in a mouse model of Hepatitis B virus pathogenesis. This revealed important differences between the two populations, with KC2 responding prominently to IL-2 signaling and involved in the initiation of an efficient T cell-mediated response to hepatocyte antigens. This study reports the specific effect of the KC2 subpopulation in the initiation of immune responses and validates our approach to investigate the heterogeneity of tissue-resident macrophage populations. Indeed, even if the literature often assumes that macrophages from one common tissue constitute a homogeneous population, the data reported in this disclosure and previous studies known in the art showed that sub-tissue niches inhabited by different macrophage populations exist. Thus, the development of macrophage-based therapeutic strategies will have to take this heterogeneity into account in order to improve the specificity and efficiency of innovative therapies.

[0158] In summary, the present inventors aimed to reveal the heterogeneity of KCs by combining single-cell transcriptomics with a specific monocyte fate mapping model and functional validation, and used a high-dimensional approach to characterize macrophage populations in mouse liver. The present inventors identified two distinct populations of embryo-derived Kupffer cells (KCs) in steady-state mouse liver that share a core signature but differentially express a number of genes and proteins: a major CD206loESAM- population (KC1) and a minor CD206hiESAM+ population (KC2). The present inventors confirmed the common embryonic origin of these populations and their independence from inflammatory monocytes and Tim4-encapsulated macrophages.

[0159] Functionally, the inventors of the present disclosure found that KC1 and KC2 have specific transcriptomic and proteomic signatures. KC2 expressed genes involved in metabolic processes, including fatty acid metabolism, in both steady-state and diet-induced obesity and hepatic steatosis. CD206hiESAM+KC2 participates in the control of liver metabolism in obese mouse models by high expression of fatty acid transporter CD36. Functional characterization by depletion of KC2 or targeted silencing of fatty acid transporter Cd36 highlighted the critical contribution of KC2 in hepatic oxidative stress associated with obesity (Figure 9). This study revealed that KCs are more heterogeneous than expected, and in particular described subpopulations related to metabolic functions.

[0160] Applicable The method embodiments disclosed herein provide accurate markers for detecting macrophage subpopulations.The disclosed method embodiments also seek to provide a method for determining the risk of obesity and / or obesity-related metabolic disorders.

[0161] Advantageously, the present disclosure provides methods of regulating obesity that involve active depletion / targeting of subpopulations of Kupffer cells that exhibit metabolic function.

[0162] Even more advantageously, the present disclosure provides a method for the evaluation of the role of hepatic macrophages in controlling obesity using Cdh5. creERT2 -Rosa Tomato A mouse model is provided.

[0163] The present disclosure also provides the diagnostic use of Igfbp7 / Cd36 expression levels in liver macrophages as a sensitive marker for the development of obesity.

[0164] The present disclosure also provides therapeutic uses of the methods and / or mixtures disclosed herein through the development of specific drugs that inhibit the expression of Igfbp7 / Cd36 or associated downstream pathways.

[0165] The present disclosure also provides an unbiased, high-throughput approach to characterize two subsets of mouse Kupffer cells (KCs).

[0166] The present disclosure also provides methods for characterizing distinct Kupffer cell populations, with CD206hiESAM+KC2 cells exhibiting distinct metabolic signatures.

[0167] The present disclosure also advantageously provides that methods of depleting metabolically relevant KC2 subsets prevent diet-induced obesity.

[0168] The present disclosure also provides insight that CD36hiKC2 controls obesity-related hepatic oxidative stress through expression of CD36. Thus, the present disclosure provides a method for improving subjects suffering from obesity-related hepatic oxidative stress.

[0169] It will be appreciated by those skilled in the art that other variations and / or modifications may be made to the embodiments disclosed herein without departing from the spirit or scope of the present disclosure as broadly described. For example, in the description herein, features of different exemplary embodiments may be, for example, mixed, combined, interchanged, incorporated, adopted, modified, or otherwise incorporated across different exemplary embodiments. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive.

Claims

1. 1. A method for detecting a population of macrophages in a sample, comprising: detecting and / or determining the expression of Cdh5 in macrophages in said sample.

2. 2. The method of claim 1, wherein the method further comprises detecting and determining the expression of one or more markers in macrophages in the sample, including CD107a, CD107b, IGFBP7 (insulin-like growth factor binding protein 7), LYVE1, CD36, CD206 and / or ESAM.

3. 10. The method of claim 1, wherein the method further comprises detecting and determining cells in the sample that are macrophages by detecting expression of macrophage markers.

4. 10. The method of claim 1, wherein the macrophages are Kupffer cells (KCs), optionally Kupffer cells of embryonic origin.

5. 2. The method of claim 1, wherein the method further comprises detecting and determining cells in the sample that are macrophages by detecting expression of Clec4f, Lyz2, Vsig4, Csf1r, Adgre1, F4 / 80, Tim4, Clec4F, and Vsig4.

6. 2. The method of claim 1, wherein the method comprises detecting and determining a first population of Kupffer cells that express CD206lo and / or ESAM- and a second population of Kupffer cells that express CD206hi and / or ESAM+.

7. 2. The method of claim 1, wherein the method comprises detecting and determining a first population of Kupffer cells that express CD206lo and ESAM- and a second population of Kupffer cells that express CD206hi and ESAM+.

8. 2. The method of claim 1, wherein overexpression of one or more markers including CD107a, CD107b, IGFBP7 (insulin-like growth factor binding protein 7), LYVE1, CD36, CD206 and / or ESAM determines the population, which is a second population of Kupffer cells.

9. 10. The method of claim 1, wherein the method further comprises detecting, screening, and / or determining the presence of one or more markers including CD206, ESAM, CD36, and combinations thereof.

10. 10. The method of claim 1, wherein the method further comprises isolating a first population and / or a second population of macrophages, and optionally, the method further comprises isolating the first population and / or the second population of Kupffer cells.

11. 2. The method of claim 1, further comprising removing a population of cells expressing one or more of Cdh5+, CD107b+, CD206hi and / or ESAM+ from the sample.

12. 2. The method of claim 1, wherein the method further comprises determining expression of one or more markers including CD45, CD64, F4 / 80, TIM4, Clec4F, Adgre1 (F4 / 80), Timd4, Csf1r, and Clec4f, and optionally, the method further comprises removing and / or eliminating cells expressing one or more of Adgre1+, Cx3cr1+, Timd4-, Clec4f-, and combinations thereof.

13. 1. A kit for detecting and / or isolating and / or depleting a population of macrophages, comprising: providing an agent for detecting a population of macrophages that express Cdh5; Optionally, providing an agent capable of isolating said population of Cdh5-expressing macrophages; Optionally, providing an agent capable of depleting said population of Cdh5-expressing macrophages. Kit including:

14. The kit further provides an agent for detecting a population of macrophages expressing CD107b+, CD206hi, and ESAM+; Optionally, providing an agent capable of isolating said population of CD107b+, CD206hi and ESAM+ expressing macrophages; Optionally, providing an agent capable of depleting said population of CD107b+, CD206hi and ESAM+ expressing macrophages. The kit of claim 13.

15. A transgenic animal model comprising a population of macrophages expressing Cdh5 that are genetically engineered to undergo elimination upon exogenous activation.

16. 1. A method for depleting a population of macrophages, comprising: detecting and reducing a population of macrophages in a subject, wherein the population of macrophages express one or more of Cdh5, CD107b, CD206, and ESAM, and optionally, the method reduces CD206hi and ESAM+ macrophages, and optionally, the method reduces Cdh5+, CD206hi and ESAM+ Kupffer cells.

17. 1. A method of improving the health of an obese and / or overweight subject, comprising: reducing the population of macrophages in the subject; The method, wherein the population of macrophages express one or more of Cdh5, CD107b, CD206, and ESAM, and optionally, the method depletes CD206hi and ESAM+ macrophages, and optionally, the method depletes Cdh5+, CD206hi, and ESAM+ Kupffer cells.

18. 1. A method for determining the risk of obesity and / or obesity-related metabolic disorders in a subject, comprising: Detecting the expression level of Igfbp7 / Cd36 expression in macrophages A method comprising:

19. 20. The method of claim 18, wherein the method further comprises treating the subject identified as being at risk of obesity and / or obesity-related metabolic disorders in the subject with an agent capable of depleting macrophage cells that express Cdh5.

20. 20. The method of claim 18, wherein the method reduces CD206hi and ESAM+ macrophages, and optionally the method reduces Cdh5+, CD206hi, and ESAM+ Kupffer cells.

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