{"paper_id":"1c8a5e6a-3d68-4946-81e2-1c9aa385a9fe","body_text":"Harshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 1 of 35\nImmunology and Inflammation\nEndocannabinoids and their\nreceptors modulate endometriosis\npathogenesis and immune response\nHarshavardhan Lingegowda, Katherine B Zutautas, Yuhong Wei, Priyanka Yolmo, Danielle J Sisnett,\nAlison McCallion, Madhuri Koti, Chandrakant Tayade\nDepartment of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada • Rosalind and\nMorris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada • Division of Cancer Biology and\nGenetics, Queen’s University, Kingston, ON, Canada\nhttps://en.wikipedia.org/wiki/Open_access\nCopyright information\nAbstract\nEndometriosis (EM), characterized by the presence of endometrial-like tissue outside the\nuterus, is the leading cause of chronic pelvic pain and infertility in females of reproductive\nage. Despite its high prevalence, the molecular mechanisms underlying EM pathogenesis\nremain poorly understood. The endocannabinoid system (ECS) is known to influence several\ncardinal features of this complex disease including pain, vascularization, and overall lesion\nsurvival, but the exact mechanisms are not known. Utilizing CNR1 knockout (k/o), CNR2 k/o\nand wild type (WT) mouse models of EM, we reveal contributions of ECS and these receptors\nin disease initiation, progression, and immune modulation. Particularly, we identified EM-\nspecific T cell dysfunction in the CNR2 k/o mouse model of EM. We also demonstrate the\nimpact of decidualization-induced changes on ECS components, and the unique disease-\nassociated transcriptional landscape of ECS components in EM. Imaging Mass Cytometry\n(IMC) analysis revealed distinct features of the microenvironment between CNR1, CNR2, and\nWT genotypes in the presence or absence of decidualization. This study, for the first time\nprovides an in-depth analysis of the involvement of the ECS in EM pathogenesis and lays the\nfoundation for the development of novel therapeutic interventions to alleviate the burden of\nthis debilitating condition.\neLife assessment\nUsing new cannabinoid receptor (CNR1 and CNR2) knockout mouse models, this\nimportant paper shows how dysregulation of the endocannabinoid system is\ninvolved in endometriosis progression. The transcriptomic evidence is solid, but a\nmajor limitation of the work is the absence of detailed measurements of lesion size\nand burden by histopathology.\nReviewed Preprint\nPublished from the original\npreprint after peer review\nand assessment by eLife.\nAbout eLife's process\nReviewed preprint version 1\nApril 19, 2024 (this version)\nSent for peer review\nFebruary 8, 2024\nPosted to preprint server\nNovember 6, 2023\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 2 of 35\nIntroduction\nEndometriosis (EM) is a chronic gynecological disorder characterized by the presence and growth\nof endometrial like tissue outside the uterus, referred to as ectopic lesions. Despite its global\nimpact on approximately 200 million individuals and the profound reduction in their quality of\nlife, the exact origins of EM remain elusive1     . Accumulating evidence, including our previous\nstudies, highlight that components of the endocannabinoid system (ECS) are dysregulated within\nEM lesion microenvironment as well as in the systemic circulation of EM patients2     –4     . The ECS\nis a complex signaling network comprised of canonical receptors (CNR1 and CNR2) and\nendocannabinoid (EC) ligands, along with non-canonical extended signaling network of ligands\nand enzymes (extensively reviewed elsewhere5     ). CNR1 and CNR2 are primarily expressed in\nnerve tissues, immune cells, and reproductive tissues, where they regulate various physiological\nprocesses, including pain perception, immune responses, and reproductive functions6     .\nConsequently, EM pathogenesis has been postulated as a consequence of EC deficiency7     ,8     .\nEven though the precise etiology of EM is not known, the widely accepted Sampson’s theory of\nretrograde menstruation suggests that EM lesions originate from refluxed, endometrial fragments\ndeposited during menstruation9     . Both pregnancy and menstruation depend on spontaneous\ndecidualization of endometrial stroma that is extensively remodelled under the influence of\nhormones, growth factors, and select cytokines that orchestrate immune cell recruitment and\nvascular adaptions10     . There is clear evidence that EM patients have defects in eutopic\nendometrium, including differential expression of key endometrial receptivity markers such as\nleukemia inhibitory factor (LIF), protein arginine methyltransferase 5 (PRMT5), and homeobox\nprotein hox-A10 (HOXA10), that have been associated with EM and subsequent infertility11     –13     .\nEvidence also suggests that components of the ECS, including CNR1 and CNR2, are important in\nmaintaining tissue integrity during decidualization and successful implantation of the embryo.\nIndeed, several reports indicate that mice lacking cannabinoid receptors, CNR1 and CNR2,\ndisplayed impaired implantation, increased pregnancy failure rates, heightened edema, and\ninadequate primary decidual zone formation, highlighting the crucial role of ECS signaling in\nsuccessful decidualization, implantation, and pregnancy14     –16     .\nIn EM, CNR1 and CNR2 activation aids in controlling lesion proliferation, pain, and\nvascularization17     ,18     . Keeping in view dysregulated ECS signalling and their central role in\ndecidualization and fertility, we hypothesize that altered CNR1 and CNR2 expression will disrupt\nECS signaling dynamics, leading to further lesion development. Furthermore, involvement of ECS\nin modulating immune response and homeostasis, may disrupt the immune dynamics and foster\nlesion establishment.\nWe conducted a comprehensive investigation into the role of the dysregulated ECS in EM\nestablishment and progression by utilizing CNR1 k/o and CNR2 k/o mouse models. To address the\nunderlying causes of ECS dysfunction, we induced artificial decidualization in WT, CNR1 k/o, and\nCNR2 k/o mice and used the endometrial fragments from decidualized (DD) and undecidualized\n(UnD) uterine horns to induce EM in recipient mice of their respective genotypes. Furthermore, we\nexplored the immunomodulatory potential of the ECS in EM, shedding light on how alterations in\nEC signaling may influence immune cell behavior within the localized peritoneal milieu in mice\ninduced with EM. Our study contributes to the foundational knowledge around ECS dysregulation\nin EM and paves way for potential therapeutic strategies targeting ECS for disease management.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 3 of 35\nMethods\nAnimals\nExperiments described in this work were approved by the Queen’s University Institutional Animal\nCare Committee as per the guidelines provided by the Canadian Council of Animal Care. All\nanimals were assigned randomly to the surgical procedures. All studies were performed using\nadult female mice at ages between 7 to 10 weeks. CNR1 k/o (B6.129P2(C)-Cnr1tm1.1Ltz/J) and CNR2\nk/o (B6.129P2-Cnr2tm1Dgen/J) male and female breeder mice were obtained from Jackson\nLaboratory (Bar Harbor, USA) and were housed in the Queen’s University animal facility. CNR1 k/o\nand CNR2 k/o experimental female mice were obtained by trio breeding with their respective\nhomozygous k/o male counterparts. C57BL/6j (WT) control female mice and vasectomized male\nmice at 7 to 10 weeks were obtained from Jackson Laboratory. All breeder mice were housed in\nstandard breeding cages in a barrier facility and the experimental animals were housed in a\nconventional holding area. Animals were housed at constant temperature (22 ± 1 °C) and relative\nhumidity (50%), with a 12:12 h light:dark cycle (light on 07.00–19.00 h). Food and water were\navailable ad libitum. All experimental animals were acclimatized at the conventional housing\nfacility for 1 week before starting the experiments.\nIn vivo decidualization\nIn this study, we have used a modified syngeneic mouse model of EM, where the donor fragments\nwere obtained from artificially decidualized uterine horns. The method of artificial\ndecidualization used in this study has been previously established and utilized by several research\nstudies11     ,19     ,20     . To artificially induce decidualization, female mice were allowed to mate\nwith vasectomized male mice to induce pseudopregnancy. After day 4 of pseudopregnancy, female\nmice were subjected to laparotomy to receive a 30μL injection of sesame seed oil (S3547, Sigma,\nUSA), intra-luminally into one uterine horn to induce DD. The contralateral, uninjected horn\nserved as an UnD control. After sesame seed oil injection, animals were rested for 4 days, after\nwhich the DD was successfully induced in one uterine horn as shown in Figure 1A     . These\nuterine horns were utilized as donor fragments to induce EM in recipient mice of their respective\ngenotype. Figure 1B      shows the representative images of EM lesions 7 days post-surgical\ninduction.\nMouse model of EM\nEM was surgically induced as described previously2     ,17     . Two independent groups (DD and\nUnD) per genotype were used in this study (n = 8-16). Briefly, the DD and UnD uterine horns from\nthe donor mice were harvested, and uterine horns were longitudinally dissected to reveal the\nendometrium. Uterine fragments were obtained using a 3.0 mm epidermal biopsy punch (33-32,\nIntegra™ Miltex®, USA). Recipient mice were anesthetized under 3.5% isoflurane vaporizer\nanesthesia to make a midline incision in the abdomen (n = 8-16) and two 3.0 mm DD or UnD\nuterine fragments were implanted on the right inner peritoneal wall using a veterinary grade\ntissue bonding glue (1469SB, 3M, USA). WT, CNR1 k/o, and CNR2 k/o control groups (n = 4) were\nsham operated with a midline incision in the abdomen without implantation of uterine fragments.\nMice were sacrificed 7 days after EM induction surgery. Blood was harvested through cardiac\npuncture to assess EC ligands. Peritoneal fluid (PF) was collected by injecting 3 ml of ice-cold\nphosphate buffered saline (PBS) into the peritoneal cavity. Spleens were collected in ice-cold RPMI\nmedia (11875093, ThermoFisher, Canada) before processing to obtain single cell suspension. EM\nlesions were either snap frozen in liquid nitrogen and stored at -80°C or processed using 4%\nparaformaldehyde overnight (12–20 h), kept at 4°C in 70% ethanol, and then embedded in paraffin.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 4 of 35Harshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 4 of 35\nFigure 1\nCharacterization of endocannabinoid ligands in a\nmodified syngeneic mouse model of endometriosis\nA Overview of the modified syngeneic mouse model of EM where pseudo-pregnant WT, CNR1 k/o, and CNR2 k/o mice were\ninduced with DD by injecting sesame oil into the lumen of one uterine horn and the contralateral horn served as UnD control.\nTwo, 3mm UnD and DD harvested fragments were implanted into their respective recipient mouse strain to induce EM. B\nRepresentative images of the EM lesions from WT, CNR1 k/o, and CNR2 k/o mice retrieved from the peritoneal cavity at end\npoint (7 days post EM induction surgery). C-J Bar plots (mean ± SD) showing the concentration of EC ligands 2AG, AEA, PEA,\nand OEA identified in the plasma and EM lesions from mice using targeted LC-MS approach. C-F 2AG, AEA, PEA, and OEA were\ndetected in plasma samples without any significant differences between groups. G, J Significantly higher concentration of\n2AG was observed between the DD lesions of CNR2 k/o and CNR1 k/o mice, and significantly higher levels of OEA in the DD\nlesions from CNR1 k/o mice compared to DD lesions from WT mice. H, I AEA and PEA levels in the tissue samples did not\ndiffer significantly between the comparison groups. n = 4-5 individual biological samples per genotype. Statistical analyses\nwere performed using the ordinary one-way ANOVA with Holm-Sidak post hoc test. * p < 0.05 and ** p < 0.01.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 5 of 35\nLipid extraction and targeted mass spectrometry\nPlasma was undiluted and ~ 10mg of tissue per sample were homogenized with RIPA buffer\n(89900, ThermoFisher, Canada) with 1:100 of protease inhibitor cocktail (535140-1ML, Sigma,\nCanada) to obtain tissue lysates. Both plasma and tissue lysates obtained were individually\nsubjected to solid phase extraction (SPE). Internal standards, both deuterated and non-deuterated\nused in the study to assess the ECS ligands were purchased from Cayman Chemicals, USA\n(Supplementary Data 1, Table 1     ). Aliquots of 100μL of plasma and tissue lysates were added to a\nprotein precipitation plate (CE0-7565-R, Phenomenex, USA) along with 200μL of cold acetonitrile\ncontaining the deuterated internal standards. The filtrate was diluted with 500μL of water and\nsubmitted to SPE extraction on an Oasis HLB 96-Well Plate (WAT058951, Waters, Canada). Samples\nwere washed with 60% methanol prior to elution with acetonitrile. The eluate was dried,\nreconstituted in 100μL of mobile phase A and analyzed by liquid chromatography–mass\nspectrometry (LC-MS as described in Supplementary Data 1, Table 2     ). The endogenous\nconcentration for the four compounds in human plasma were calculated by standard addition.\nFlow cytometry\nSingle-cell suspensions were prepared from murine spleens by mechanical dissociation, RBC lysis,\nand centrifugation. Splenic cells and cells from PF were resuspended in staining buffer (PBS with\n2% fetal bovine serum) at a concentration of 0.5 × 10^6 cells/mL. All antibodies used for flow\ncytometry analyses were purchased from BioLegend, USA, unless otherwise mentioned. The\nantibodies included CD45-FITC (103107), CD3-BV510 (100234), CD4-BV785 (100551), CD8-BV605\n(100744), CD11b-AF700 (101222), F4/80-PE/Cy7 (123114), NK1.1-APC/Cy7 (108724), and CD19-\nPE/Dazzle 594 (115554). Staining was performed following the manufacturer’s recommendations.\nFor each sample, 50μL of the antibody cocktail was added to 50μL of cell suspension in 96 well\nplates. The mixture was incubated at 4°C for 20 mins in the dark, along with anti-CD16/32 Fc block\nantibody (101319). After incubation, cells were washed twice with staining buffer and centrifuged\nbefore fixing the cells with fixation buffer (00-8222/49, ThermoFisher, Canada). Flow cytometry\nanalysis was carried out using a Beckman Coulter CytoFlex S flow cytometer. Compensation\ncontrols were established using single antibody-stained cells. Isotype controls provided baseline\nlevels of non-specific staining and cell populations were defined using fluorescence minus one\n(FMO) control. Data analysis employed FlowJo software (v 10.9; FlowJo, USA) as well as SPECTRE (v\n1.0) computational toolkit in R (v 4.2.3) to obtain t-distributed stochastic neighbor embedding (t-\nSNE) plots based on the unsupervised flowSOM clusters generated by marker expression.\nIn-vitro T-cell functional assay\nTotal CD3+ T cells were isolated from splenocytes of naive WT and CNR2 k/o mice using a negative\nselection magnetic kit (19851A, StemCell, Canada) following the manufacturer’s instructions. All\nrecombinant proteins and compounds were purchased from Biolegend, USA, unless otherwise\nmentioned. Subsequently, 250,000 T cells per well were seeded into a 96-well plate coated with\nanti-mouse CD3 [(2 μg/ml), (100340)]. RPMI-1640 media supplemented with rmIL-2 [(10 ng/ml),\n(575404)], anti-mouse CD28 [(5 μg/ml), (102116)], 10% fetal bovine serum, β-mercaptoethanol (50μ\nM), and penicillin/streptomycin (100 U/ml) was used as the growth medium. T cells were then\nactivated non-specifically with or without the cell activation cocktail consisting of Phorbol 12-\nmyristate 13-acetate (PMA) and ionomycin [(50 ng/ml PMA and 1 μM ionomycin), (423301)] in the\npresence and absence of TNFα [(100 ng/ml), (410-MT-010/C, R&D Systems, USA)] to simulate a\nsterile inflammatory challenge. Following a 48-h incubation period, brefeldin A [(10 μg/ml), (11861,\nCayman Chemicals, USA)] was introduced to the cells to measure intracellular interferon-gamma\n(IFNγ) levels at the 42-h time point. Flow cytometry analysis was conducted using a panel of\nmarkers, purchased from Biolegend, USA unless otherwise mentioned, including CD3e-FITC\n(100306), CD4-AF700 (100430), CD8-PE/fire700 (100792), Ki67-PB (151223), FoxP3-PE (126404), IFNγ\n-BV605 (505840), and Live/dead-K0525 (L304966, ThermoFisher, Canada), to assess various T cell\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 6 of 35\nsubsets and viability. Flow cytometry staining and acquisition was carried out as described above\nwith the addition of permeabilization (00-8333-56, ThermoFisher, Canada) buffer to stain for\nintracellular markers (Ki-67 and IFNγ), according to the manufacturer’s instructions. Data analysis\nwas performed using FlowJo software and visualized using GraphPad Prism (v 9.5.1).\nRNA isolation using RNeasy mini kit\nSnap frozen UnD and DD EM lesions from WT, CNR1 k/o, and CNR2 k/o were homogenized, and\nRNA was isolated using the RNeasy Mini Kit (74104, Qiagen, Canada), according to the\nmanufacturer’s instructions. Briefly, ~20mg EM lesion tissues were placed individually in\nPowerBead Ceramic Tubes (13113-50, Qiagen, Canada) along with lysis buffer. Lesions were\nhomogenized using a Bead Ruptor homogenizer (Omni International, USA) and lysates were\nextracted after centrifugation at 10,000 RCF. Lysate was mixed with 70% ethanol, added to the\nRNeasy spin column, and then centrifuged to bind the RNA to the column. Spin column was\nwashed twice, and RNA was isolated using elution buffer. Total RNA quality was measured using\nthe nanodrop spectrophotometer and stored at -80°C before shipping to BGI Global (Boston, USA)\nfor bulk RNA analysis. RNA integrity was determined using the Agilent 4150 TapeStation System\nAgilent, USA) for sample quality control and only samples with RNA quality number ≥ 7 were\nconsidered for library preparation and further sequencing.\nRNA library preparation, sequencing, and analysis\nLibrary preparation began with mRNA enrichment using oligo dT beads, which selectively capture\nmRNA molecules. Next, the enriched mRNA was fragmented, and first-strand cDNA was\nsynthesized using random N6 primers, followed by second-strand cDNA synthesis using\ndeoxyuridine triphosphate (dUTP). After cDNA synthesis, end repair was performed to generate\nblunt ends, and 3′ adenylation was carried out to facilitate adaptor ligation. Adaptors were ligated\nto the 3′ adenylated cDNA fragments. To enrich the cDNA library for sequencing, PCR amplification\nwas conducted. Prior to amplification, the dUTP-marked strand was specifically degraded by\nUracil-DNA-Glycosylase (UDG). The remaining first-strand cDNA was then amplified using PCR\nprimers. Following amplification, single-strand separation was achieved through denaturation by\nheat. The single-stranded DNA was cyclized using a splint oligo and DNA ligase. DNA nanoball\nsynthesis was performed on the cyclized single-stranded DNA templates. This process facilitated\nthe generation of clonal DNA clusters, providing the material necessary for subsequent\nsequencing. Sequencing was executed using the DNBSEQ Technology platform. The prepared DNA\nlibraries were loaded onto the DNBSEQ sequencer and sequenced at an average depth of 30\nmillion paired end reads (2 x 100) per library. The sequencing data was filtered with SOAPnuke by\nremoving reads containing sequencing adapter; removing reads whose low-quality base ratio\n(base quality less than or equal to 15) is more than 20%, and removing reads whose unknown base\n(‘N’ base) ratio is more than 5%. Next, clean reads were obtained and stored in FASTQ format.\nClean reads were mapped to the mouse reference genome (NCBI: GRCm38.p6) using HISAT2\n(v2.0.4). The subsequent analysis and data mining were performed on Dr. Tom Multi-omics Data\nmining system (https://biosys.bgi.com     ).\nImaging Mass Cytometry: labeling\nA comprehensive panel of antibodies identifying innate and adaptive immune cell populations\nand cell types that are integral to EM lesion microenvironment (Supplementary Data 1, Table 3     )\nwas designed and optimized as previously described21     ,22     . The formalin-fixed paraffin-\nembedded (FFPE) tissue sections underwent deparaffinization and heat-mediated antigen\nretrieval on the Ventana Discovery Ultra auto-stainer platform (Roche Diagnostics, Canada),\nfollowing the below instructions. Initially, the slides were exposed to a temperature of 70 °C in a\npre-formulated EZ Prep solution (Roche Diagnostics, Canada), followed by a subsequent incubation\nat 95 °C in pre-formulated Cell Conditioning 1 solution (Roche Diagnostics, Canada). Following this,\nthe slides were washed in 1× PBS and then exposed to Dako Serum-free Protein Block solution\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 7 of 35\n(Agilent, USA) for 45 min at room temperature. An antibody cocktail, containing metal-conjugated\nantibodies, was prepared using Dako Antibody Diluent (Agilent, USA) at specified dilutions. The\nprimary antibodies within this cocktail were applied to the slides and left to react overnight at 4\n°C, after which the slides were washed with 0.2% Triton X-100 and 1× PBS. For the subsequent step,\na secondary antibody cocktail comprising metal-conjugated anti-biotin antibodies was created in\nDako Antibody Diluent, at a predetermined dilution. The slides were treated with this anti-biotin\ncocktail for 1-h at room temperature and then washed with 0.2% Triton X-100 and 1× PBS. For\ncounterstaining, the slides were exposed to Cell-ID Intercalator-Ir (Fluidigm, Canada) diluted at a\nratio of 1:400 in 1× PBS for 30 min at room temperature. After a 5-min rinse with distilled water,\nthe slides were air-dried in preparation for imaging mass cytometry (IMC) acquisition. The\nHyperion Imaging System (Fluidigm, Canada) was employed for the IMC acquisition process.\nImaging Mass Cytometry: Data analysis\nLesions were grouped based on the origin of uterine fragments (i.e., UnD or DD) from three\ndifferent genotypes (WT, CNR1 k/o, and CNR2 k/o). IMC data analysis methods employed in this\nstudy follow established procedures as outlined in Steinbock toolkit for data preprocessing, image\nsegmentation, and object quantification23     . Cell segmentation utilized a deep learning approach\ndescribed by Greenwald et al24     . Briefly, dual-channel images were generated using nuclear and\ncytoplasmic markers, representing respective signals. The DeepCell tool with Mesmer, a pre-\ntrained deep learning segmentation algorithm from TissueNet, was used to automate cell mask\ngeneration, requiring no additional user input. Given the IMC data was acquired in batches, we\nperformed batch effect corrections using the harmony algorithm as described25     . This involved\niterative clustering and correction of cell positions in the principal component analysis (PCA)\nspace. Subsequently, unsupervised PhenoGraph clustering in R was used to categorize cell types.\nFor this, signals including αSMA, B220, CD19, β-catenin, CD3, CD4, CD8, CD11b, CD11c, CD31, CD68,\nE-cadherin, MPO, pan-cytokeratin, and vimentin were utilized, employing a k-value of 60. To\nascertain cell interactions, imcRtools and cytomapper in R were employed for visualization. A\npermutation test evaluated interactions with neighboring cells. Neighboring cells were defined as\nthose within a 5-pixel radius (5 μm), and the buildSpatialGraph function established the number of\none cluster neighbors interacting with another cluster. A default of 1000 permutations was set.\nEach iteration led to interaction score and p-value computation, and the significant outcomes (at\nalpha 1% risk) were depicted in heatmaps. To delineate spatial cellular neighborhoods, neighbor\nwindows were computed, representing the N nearest cells to each cell. This process followed\nprevious protocols. Employing imcRtools, cellular neighborhood grouping was conducted, leading\nto the identification of 8 cellular neighborhoods in the lesions.\nStatistics\nStatistical analyses performed to compare the concentration of EC ligands through targeted LC-MS\nand evaluation of immune cell population via flow cytometry were conducted using Prism\nGraphPad. A one-way analysis of variance (ANOVA) was performed with Holm-Sidak post-hoc test\nto determine the specific pairwise differences between the groups. For in-vitro T cell functional\nassay, two-way ANOVA was performed using Tukey’s post-hoc test to compare within and between\nthe groups. The significance level was set at α = 0.05. Data are presented as mean ± standard\ndeviation (SD) unless otherwise stated.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 8 of 35\nResults\nLigands of the ECS are dysregulated in a mouse\nmodel of EM lacking CNR1 and CNR2 receptors\nBased on our previous work demonstrating dysregulated ligands of the ECS in both patients and\nour mouse model of EM2     , we first evaluated whether absence of CNR1 or CNR2 led to ECS ligand\nalterations. To do this, we performed targeted mass spectrometry on plasma and EM lesions\nobtained from CNR1 k/o, CNR2 k/o, and their WT controls. In these mice, EM was induced using\nUnD and DD tissues obtained from their respective strains into matched recipients (for example,\nUnD and DD from CNR1 k/o donor mice was implanted into CNR1 k/o recipient mice). We detected\nsome of the major EC ligands such as, 2-Arachidonoylglycerol (2-AG), N-arachidonoylethanolamine\n(AEA), Palmitoylethanolamide (PEA) and Oleoylethanolamide (OEA) in plasma and EM lesions\nfrom all genotypes. All identified ECS ligands are predominantly anti-inflammatory and the range\nof 2-AG, AEA, PEA, and OEA in the plasma and lesions were comparable to our previous study2     .\nIn the plasma, we found no significant differences in ECS ligands across all groups (Fig 1C-F     ),\nwhich could be due to the rapid homeostasis achieved in circulation26     . However, in the lesion\nmicroenvironment, we captured higher levels of several EC ligands (Fig 1G-J     ). In CNR1 k/o mice,\nsignificantly higher concentrations of OEA were observed in the DD compared to UnD lesions (Fig\n1J     ), and overall, was on average two-fold higher compared to both lesions from WT and CNR2\nk/o mice. 2-AG, which selectively binds to the CNR2 receptor was significantly higher in both the\nUnD and DD EM lesions from CNR2 k/o mice (Fig 1G     ) compared to the CNR1 k/o counterparts.\nThis could indicate a compensatory response in the absence of CNR2. Together, these findings\nprovide insights into potential dysregulation of ECS ligands in the absence of CNR1 and CNR2 and\ntheir involvement in DD vs UnD scenario during EM lesions establishment.\nImpact on gene expression and pathway alterations in\nEM lesions from mice in the absence of CNR1 and CNR2\nNext, we investigated the effects of CNR1 and CNR2 absence on the transcriptomic profile of both\nUnD and DD EM lesions from their respective genotypes. Bulk RNA sequencing was performed on\nboth UnD and DD lesions from WT, CNR1 k/o, and CNR2 k/o mice as detailed earlier to elucidate the\nmolecular alterations associated with the disruption of these two-receptors signaling. Differential\nexpression analysis revealed changes in gene expression profiles among the different genotypes\nand lesion types (Fig 2A     ). A total of 1100 and 639 differentially expressed genes (DEGs) were\nfound in both UnD and DD lesions of CNR1 k/o and CNR2 k/o mice, respectively, compared to WT\ncontrols (UnD data is provided in Supplementary Data 2     ). To gain insights into the biological\nimplications of the observed gene expression changes, we conducted Kyoto Encyclopedia of Genes\nand Genomes (KEGG) pathway enrichment analysis on the DEGs identified in UnD and DD lesions\nof CNR1 k/o and CNR2 k/o mice compared to WT mice. In the DD lesions from CNR1 k/o mice, KEGG\npathway analysis revealed significant alterations in several pathways (Fig 2B     ). Notably, the cell\nadhesion molecules pathway was prominently affected, indicating a potential role for CNR1 in\nmediating cell-cell interactions and tissue remodeling processes. Additionally, the cyclic adenosine\nmonophosphate (cAMP) signaling emerged as another negatively impacted pathway, implicating\nCNR1 in modulating intracellular signaling cascades. In DD lesions from the CNR2 k/o mice,\nanalysis highlighted distinct pathways affected in the context of inflammation and EM (Fig 2C     )\nincluding the cytokine receptor interactions pathway, pointing to the involvement of CNR2 in\nimmune responses and inflammatory processes associated with EM. Furthermore, we captured\nalterations in the steroid hormone biosynthesis pathway suggesting a role for CNR2 in hormone-\nrelated mechanisms relevant to endometrial tissue development and homeostasis.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 9 of 35Harshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 9 of 35\nFigure 2\nTranscriptomic profiling of endometriosis-like lesions from CNR1 and CNR2\nknockout mice reveals extensive differential gene expression and altered pathways\nA Summary of the differentially expressed genes (DEGs) from bulk RNA sequencing analysis conducted on both UnD and DD\nlesions from WT, CNR1 k/o, and CNR2 k/o mice, revealing extensive changes in gene expression profiles among the different\ngenotypes and lesion types. A total of 1100 and 639 DEGs were identified in both UnD and DD lesions of CNR1 k/o and CNR2\nk/o mice, respectively, compared to WT controls. B KEGG pathway analysis revealed significantly altered cell adhesion\nmolecules and cAMP signaling pathways in DD lesions of CNR1 k/o. C KEGG pathway analysis in DD lesions of CNR2 k/o mice\nshowed changes associated with cytokine receptor interactions and steroid hormone biosynthesis pathways. D, E Venn\ndiagrams showing the DEGs among the 59 genes directly associated with the ECS, where we found limited DEGs in DD lesions\nof CNR1 k/o (3) and CNR2 k/o mice (2), respectively. F A comprehensive gene ontology analysis highlighting the roles of 59\nECS genes across diverse biological processes (blue), cellular (orange), and molecular functions (light blue), accentuating\ntheir broader impact beyond canonical ECS functions. Gene Number indicates the number of DEGs enriched in pathway. Rich\nRatio indicates the ratio of enriched DEGs to background genes and Q-value indicates significance, with a value closer to zero\nbeing more significant and is corrected by Benjamini-Hochberg method.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 10 of 35\nNext, we performed a subset analysis for genes directly involved in ECS signaling. A total of 59 key\ngenes in ECS signaling were selected, as identified in a study by Tanaka et al27     . Surprisingly,\ndespite the central role of CNR1 and CNR2 in ECS signaling, we found a limited number of DEGs\nrelated to this system. Out of 59 genes directly associated with ECS, only 3 (CNR1, PLCH1 and\nPLCH2) and 2 (CNR2 and PLAG2GE) DEGs were identified in the DD lesions of CNR1 k/o (Fig 2D     )\nand CNR2 k/o (Fig 2E     ), respectively, compared to DD lesions from WT controls. This result\nsuggests that CNR1 and CNR2 modulate the EM microenvironment through intricate interactions\nwith other signaling pathways beyond the canonical ECS pathway. A comprehensive gene ontology\n(GO) classification analysis on the 59 identified ECS genes (Fig 2F     ) unveiled their multifaceted\nroles in reproductive functions, immune system regulation, and cellular processes. The genes\nexhibited enrichment in molecular functions such as receptor activity and lipid binding. In terms\nof cellular components, these genes were associated with plasma membrane structures, and\nintracellular compartments, signifying their diverse subcellular localization and potential\ninvolvement in dynamic cellular processes. Furthermore, GO analysis highlighted their\nparticipation in biological processes such as immune response modulation, lipid metabolism, cell\ncommunication, and intracellular signaling pathways, indicating the broader impact of ECS\nbeyond its canonical functions. The results of the UnD comparisons between the genotypes are\nprovided in the supplementary files (Supplementary Data 1, Fig 1     ). While the UnD lesions\nexhibited distinct gene expression patterns compared to DD lesions, common trends in the effects\nof CNR1 k/o and CNR2 k/o on gene expression were observed across both lesion types. Specifically,\nthe cytokine receptor interaction, complement cascade, and inflammatory mediator pathways\nwere significantly altered in the UnD lesions from CNR1 k/o and CNR2 k/o mice compared to UnD\nlesions from WT mice. Overall, our findings highlight the significant impact of CNR1 and CNR2 k/o\non gene expression in EM lesions and the implications for EM pathogenesis.\nDisruption of genes related to adaptive immune\nresponse in EM lesions without CNR1 and CNR2\nBuilding upon our previous investigation into the transcriptomic alterations, we conducted an in-\ndepth analysis of differentially expressed immune-related genes (as per InnateDB version 5.4) in\nboth UnD and DD lesions across all genotypes. Here, our analysis is focused on the immune-related\ngenes within DD lesions of CNR1 k/o and CNR2 k/o mice compared to WT controls. Comparison of\nthe UnD lesions of CNR1 k/o and CNR2 k/o with WT EM mice are included in the supplementary\nfiles (Supplementary Data 1, Fig 2     ) The differential expression bar plot (Fig 3A     ) provides\nrepresentation of the upregulated and downregulated genes in each comparison. The volcano\nplots for DD lesions from CNR1 k/o vs. WT (Fig 3B     ) and CNR2 k/o vs. WT (Fig 3C     ) illustrate the\nvarious DEGs. Notably, CNR1 k/o DD lesions exhibited 39 downregulated (eg., NLRP6 and IL1a,\npivotal regulators of inflammatory response) and 14 upregulated genes (eg., CXCL9 and CXCL10,\nchemokines involved in immune cell recruitment), while CNR2 k/o DD lesions showed 40\ndownregulated (eg., SIGLECG and IL6, involved in immune regulation and pro-inflammatory\nresponse) and 25 upregulated genes (eg., C8a, C9, and MASP2, part of the complement system),\nhighlighting substantial changes in gene expression associated with CNR1 and CNR2 disruption\ncompared to the DD lesions from WT mice. Of particular interest, we observed significant\ndownregulation of T cell-related genes (CD3e, CD3g, GATA3, and CTLA4) in the CNR2 k/o DD lesions\n(Supplementary Data 3     ), aligning with the CD3+ T cell dysfunction observed in the PF and\nspleen and further validations from in-vitro functional assay (mentioned below). However, we did\nnot find the same difference in the UnD lesions of CNR2 k/o mice, which also showed significantly\nlow number of DEGs (11 compared to 65) (Supplementary Data 3     ). This observation clearly\nemphasises a potential link between CNR2 dysfunction with decidualization characterized by T\ncell signaling issues within the EM microenvironment. To understand functional implications of\nthe DEGs, we conducted KEGG pathway analysis on specific differentially expressed immune\ngenes in DD lesions from CNR1 k/o and CNR2 k/o mice. Notably, in CNR1 k/o DD lesions compared\nto WT DD, the chemokine signaling pathway, cytokine-cytokine receptor interaction, and toll-like\nreceptor (TLR) signaling pathways were negatively affected (Fig 3D     ). These findings provide\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 11 of 35\ninsights into the impact of CNR1 disruption on immune cell function within the DD environment.\nConversely, CNR2 k/o DD lesions were associated with significant alterations in cytokine-cytokine\nreceptor interaction, Th17, Th1, and Th2 cell differentiation pathways (Fig 3E     ). These pathways\nare proven to be involved in T cell development, differentiation, and effector functions, aligning\nwith our observed dysregulation of T cell-related genes. Together, these findings further elucidate\nthe roles of CNR1 and CNR2 in modulating immune responses within the context of EM.\nMultispectral flow cytometry revealed altered immune cell\nprofiles in a mouse model of EM lacking CNR1 and CNR2\nImmune dysregulation is recognized as a crucial factor in the pathogenesis of EM28     . To elucidate\nthe impact of CNR1 and CNR2 absence on immune cell populations, we performed multispectral\nimmune profiling in splenocytes and cells from the PF from WT, CNR1 k/o, and CNR2 k/o mice\nharboring UnD and DD lesions. Representative gating panels of PF CD3 (y axis) vs CD11b (x axis)\ncells (Fig 4A-E     ) illustrate distinct profiles among WT, CNR1 k/o, and CNR2 k/o mice with EM, as\nwell as CNR2 k/o naïve, non-operated mice, and CNR2 k/o sham-operated controls. Strikingly, CD3+\nT cell populations were nearly absent in the PF of CNR2 k/o mice with EM, regardless of lesion\ntypes (UnD and DD), when compared to other groups (Fig 4C      and F     ). This trend further\nextended to CD4+ helper T cells and CD8+ cytotoxic T cells (Fig 4G-H     ). CNR1 k/o mice with DD\nlesions exhibited significantly reduced CD3+ (Fig 4F     ), CD4+ (Fig 4G     ), and CD8+ (Fig 4H     ) T\ncell frequencies compared to their UnD counterparts, as well as lower CD19+ B cells and NK1.1+\nNK cells (Fig 4K     ) populations, compared to WT and CNR2 k/o mice. Concomitant with the\nreduction of T cell subsets, an increase in CD11b monocytes was observed in the PF of CNR2 k/o\nmice with UnD and DD lesions compared to WT and CNR1 k/o mice (Fig 4I     ). Similarly, CNR1 k/o\nmice with DD lesions displayed higher monocyte/macrophage populations compared to their UnD\ncounterparts (Fig 4I     ). Furthermore, WT mice with DD lesions demonstrated significantly lower\nCD3+ T cell frequencies compared to their UnD counterparts (Fig 4F     ), suggestive of a\ndecidualization-associated effect. Splenocytes exhibited analogous trends (Fig 4L     ), as depicted\nby tSNE plots (bar plots and contour gating plots in Supplementary Data 1, Fig 3      and 4     ).\nThese alterations in immune cell numbers reinforce the influence of CNR1 and CNR2\ndysregulation and decidualization on immune cell populations, confirmed both locally in PF and\nsystemically in the spleen.\nT cells from CNR2 k/o mice exhibit\nimpaired viability upon TCR activation\nTo validate the functional consequences of CNR2 deficiency on T cell behavior, we conducted a\nseries of in-vitro assays using T cells isolated from splenocytes of naïve WT and CNR2 k/o mice.\nCD3+ T cells were activated non-specifically, with or without PMA/ionomycin cocktail, in the\npresence or absence of tumor necrosis factor alpha (TNFα) to create a sterile inflammatory\nchallenge.\nWe observed a significant reduction in the viability of total CD3+ T cells from CNR2 k/o mice upon\nactivation with PMA/ionomycin (gating strategies in Supplementary Data 1, Fig 5     ) compared to\nmedia controls (Fig 5A      and B     ). In contrast, WT CD3+ T cells activated with PMA/ionomycin,\nwith or without TNFα, exhibited no significant difference in viability when compared to the media\ncontrol (Fig 5A      and B     ). This observation aligns with our in-vivo findings, whereby CD3+ T\ncells from CNR2 k/o mice with EM exhibited a significant reduction in both the splenic and PF\npopulation, but not in the SHAM-operated mice, emphasizing the EM-specific nature of this effect.\nAdditionally, the overall reduced viability of CD3+ T cells of CNR2 k/o mice upon PMA/ionomycin\nactivation led to decrease in proliferation of CD4+ T cells (Fig 5C     ) but not of CD8+ T cells (Fig\n5D     ). However, our results indicated that although CNR2-deficient T cells displayed reduced\nviability upon activation, they exhibited higher levels of IFNγ production compared to CD3+ T cells\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 12 of 35Harshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 12 of 35\nFigure 3\nBulk RNA sequencing revealed alterations in immune-related gene\nexpression and pathway in EM lesions from CNR1 k/o and CNR2 k/o mice\nA Bar plot overview of the differentially expressed (DE) immune-related genes among different genotypes and lesion types.\nB, C The volcano plots for DD lesions of CNR1 k/o vs. WT and CNR2 k/o vs. WT, respectively, revealed 39 downregulated and\n14 upregulated genes in CNR1 k/o DD lesions, while CNR2 k/o DD lesions exhibited 40 downregulated and 25 upregulated\ngenes. Log2 fold change as the x-axis and log10 Q-value (FDR adjusted) as the y-axis. Vertical dotted lines on the x-axis\nindicate ± 1-fold change and vertical dotted line on the y-axis indicate Q-value of 0.05. D, E KEGG pathway analysis of DEGs in\nCNR1 k/o DD lesions show significant alteration in the chemokine signaling pathway, cytokine-cytokine receptor interaction,\nand toll-like receptor signaling pathways, while in CNR2 k/o DD lesions, alterations were observed in pathways related to\ncytokine-cytokine receptor interaction, Th17, Th1 and Th2 cell differentiation.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 13 of 35Harshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 13 of 35\nFigure 4\nFlow cytometry profiling of PF and splenic cells show altered\nimmune cell phenotypes in CNR1 k/o and CNR2 k/o mice with EM\nA-E Gating panels of PF cells showing CD3 KO525-A on the y-axis vs CD11b APC-A700-A on the x-axis among WT, CNR1 k/o,\nand CNR2 k/o mice with DD EM lesions, as well as CNR2 k/o naive and CNR2 k/o sham-operated controls. F CD3+ total T cells\nin the PF of CNR2 k/o mice with EM lesions, regardless of lesion types, were significantly reduced compared to WT and CNR1\nk/o mice with EM, as well as CNR2 k/o naïve and sham operated mice. G, H This extended to the subsets of CD3+ T cells,\nCD4+, and CD8+ T cells, respectively. CNR1 k/o mice with DD lesions also exhibited significantly decreased CD3+ total T cells,\nCD4+ helper T cells, and CD8+ cytotoxic T cell frequencies compared to their UnD counterparts. I CD11b+\nmonocyte/macrophage populations were increased in the PF of CNR2 k/o mice with UnD and DD lesions compared to WT\nand CNR1 k/o mice. CNR1 k/o mice with DD lesions displayed higher monocyte/macrophage populations compared to their\nUnD counterparts. K, J CNR1 k/o mice with DD lesions exhibited lower CD19+ B cells and NK1.1+ NK cell populations\ncompared to WT and CNR2 k/o mice. L Immune cell populations in splenocytes were analogous to findings from PF cells,\ndepicted by tSNE plots. n = 5-7 individual biological samples per genotype. Statistical analyses were performed using the\nordinary one-way ANOVA with Holm-Sidak post hoc test. * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001. Data\npresented as mean ± SD.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 14 of 35\nfrom WT mice, suggesting their functional competence (Supplementary Data 1, Fig 5     ). These\nfindings shed light on the intricate role of CNR2 in modulating T cell responses, with potential\nimplications for immune dysregulation.\nSpatial cell analysis of EM lesions from WT, CNR1 k/o, and\nCNR2 k/o mice reveal altered immune cell populations\nTo gain a comprehensive insight into the spatial distribution of immune cells and stromal cell\ntypes within EM lesion architecture of UnD and DD lesions of WT, CNR1 k/o, and CNR2 k/o mice, we\nemployed IMC analysis. This approach aimed to elucidate the impact of CNR1 and CNR2 absence\non the cellular composition and organization of EM lesions in mice. The schematic representation\nof the IMC procedure (Fig 6A     ) outlines the steps involved in this analysis. Following the\nacquisition of two regions of interest (ROI) per section (based on the H&E stain), single-cell\nsegmentation (Fig 6B     ) and subsequent segmentation quality assessment (Fig 6C     ) were\nperformed. After batch effect correction of samples, non-linear dimensionality reduction of the\nsample type revealed a distinct expression pattern of immune cells and cell state markers between\nUnD and DD lesions, as well as differences between the genotypes (Fig 6D     ). After unsupervised\nphenotyping and labeling of the different cell types, uniform manifold approximation and\nprojection (UMAP) dimensionality reduction further highlighted the key differences in cell\ncomposition between UnD and DD lesions (Fig 6E     ). Overall, combined expression of the cell\ntypes of DD lesions from all three genotypes exhibited increased stromal compartments, decreased\nepithelial cells, and heightened macrophage infiltration compared to the expression of cell types\nfrom UnD lesions. Representative images illustrate the distribution of different cell types based on\nunsupervised clustering and labeling (Fig 7A     ).\nFurther analysis of the cell type distribution (Fig 7B     ) through bar plots unveiled several\ndifferences. Although not significant, T cell expression was increased (CD4+ helper T cells and\nCD8+ cytotoxic T cells) in both UnD and DD lesions of CNR1 k/o and CNR2 k/o mice compared to\nWT (Fig 7C     ). This observation highlights that resident T cells are not impacted in the absence of\nCNR1 and CNR2 within the endometriotic milieu. Intriguingly, in EM lesions from CNR1 k/o and\nCNR2 k/o, we saw significantly elevated expression of monocytes/macrophages (Fig 7D     ), stromal\ncells (Fig 7E     ) and hallmarks of EM such as proliferation and vascularization (Fig 7F     )\ndemonstrating an altered microenvironment in the absence of these receptors. To comprehend\ncell-cell interactions and their implications, we conducted cellular neighborhood (CN) analysis.\nThis approach grouped cells based on information within their direct spatial vicinity and\nidentified intricate spatial relationships among diverse cell types within the lesion\nmicroenvironment. This analysis revealed distinct clustering patterns across different cell types\nwithin the lesion architecture of the DD lesions from WT, CNR1 k/o, and CNR2 k/o mice\n(Supplementary Data 1, Fig 6     ). Immune cells predominantly clustered together in CN 4, while\nother cell types (stroma, epithelial cells, and vasculature) exhibited distinct clustering patterns\nacross CN 6, 3, and 8 in DD lesions (Fig 7G     ). Although most of the immune cell types clustered\ntogether in the UnD lesions, cell types of the lesion architecture clustered distinctly compared to\nthe DD lesions (Supplementary Data 1, Fig 6     ). This clustering emphasizes the interplay\nbetween immune cells and the broader cellular components of the lesions. In summary, our\ncomprehensive investigation has unveiled intricate spatial relationships among immune cells and\ndiverse cell types within EM lesions in mice. The observed alterations in T cell expression, coupled\nwith stromal dynamics, in CNR1 k/o and CNR2 k/o lesions underscore the pivotal roles of these\nreceptors in shaping the endometriotic microenvironment.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 15 of 35Harshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 15 of 35\nFigure 5\nIn-vitro validation of CNR2 deficiency on CD3 T cell viability and\nfunctionality in conditions representative of EM lesion microenvironment\nA Representative gating of percentage live, CD3+ total T cells from WT and CNR2 k/o mice activated with or without\nPMA/Ionomycin cocktail in the presence or absence of TNFα and media control. Graph with live gating shows count on the y\naxis and live/dead-KO525 marker on the x axis. B Bar graphs of percentage live population of CD3+ total T cells from CNR2\nk/o mice show a significant decrease in the viability of cells activated with PMA/ionomycin with or without TNFα. Whereas, no\nsignificant changes were observed in the CD3+ total T cells from WT mice as well as from CNR2 k/o mice in media. C\nActivation of CD3+ T cells of CNR2 k/o mice with PMA/ionomycin affected proliferation of CD4+ helper T cells specifically, with\nor without the presence of TNFα when compared to both their media control and WT controls. D No significant changes were\nobserved in the proliferative CD8+ cytotoxic T cells from CNR2 k/o mice compared to their WT controls across different\nactivation and non-activation groups. Ordinary two-way ANOVA with Tukey’s post hoc test was performed to assess statistical\nsignificance. **p<0.05, **** p < 0.0001. Data presented as mean ± SD.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 16 of 35Harshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 16 of 35\nFigure 6\nImaging mass cytometry spatial profiling of immune cell distribution\nand cellular patterns in EM lesions in CNR1 k/o, CNR2 k/o, and WT mice\nA The IMC data collection and analysis workflow outlines the steps involved in gaining comprehensive insights into the\nspatial distribution of immune cells and relevant cell types within UnD and DD EM-like lesions of WT, CNR1 k/o, and CNR2 k/o\nmice. B, C Representative images showing the single-cell segmentation performed following the acquisition of 2 regions of\ninterest (ROI) per section (3 biological samples per genotype) and segmentation quality of the data after segmentation\nanalysis was conducted, respectively. D Non-linear dimensionality reduction after batch effect correction showed distinct\nexpression patterns of immune cells and cell state markers between UnD and DD lesions. DD lesions from the CNR1 k/o and\nCNR2 k/o mice showed expression pattern that was significantly different from the DD lesions of WT mice, as well as\ncompared to UnD lesions among different genotypes. E UMAP dimensionality reduction highlighted key cell types and\ndifferences in composition between UnD and DD lesions. DD lesions exhibited increased stroma and fibroblasts, decreased\nepithelial cells, and heightened macrophage infiltration compared to UnD lesions.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 17 of 35Harshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 17 of 35\nFigure 7\nImaging mass cytometry revealed altered cellular composition and\nneighborhoods in EM lesions from CNR1 k/o, CNR2 k/o, and WT mice\nA Representative image showing the distribution of different cell types within EM lesions based on unsupervised clustering\nand labeling. B Stacked bar plots reveal the distribution of various cell types within UnD and DD lesions of CNR1 k/o and\nCNR2 k/o mice compared to WT mice. C Increased CD3+ T cells and CD4+ helper T cells expression was found in the DD, EM\nlesions from CNR1 and CNR2 k/o mice compared to WT mice highlighting that T cells residing in the lesions were not affected.\nD CD11b+ monocyte and F4/80+ macrophage expression was significantly increased in the DD, EM lesions from mice lacking\nCNR1 and CNR2 compared to the WT controls. E Vimentin expressing stromal compartments that predominantly make up the\nEM lesions were also significantly increased in the DD lesions from CNR1 k/o and CNR2 k/o mice compared to WT mice. F\nHallmark features of EM lesions, such as proliferation (Ki67+) and vascularization (CD31+) were significantly increased in the\nDD, EM lesions from mice lacking CNR1 and CNR2 compared to the WT controls. Combined, it highlights the effect on early\nlesion development and further progression through sustained proliferation due to dysregulated CNR1 and CNR2. D\nHeatmap representation of the CN analysis show distinct clustering patterns observed in the DD lesions among the different\ngenotypes, where immune cells mainly clustered together in CN 4, while other cell types such as stroma, epithelial cells, and\nvasculature exhibited distinct clustering patterns across CN 6, 3, and 8, respectively.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 18 of 35\nDiscussion\nEmerging evidence from our previously reported findings and others, implicated dysregulation of\nECS, comprising CNR1 and CNR2 canonical receptors along with their EC ligands, in EM\npathophysiology2     ,29     . The ECS is involved in several physiological processes including (but not\nlimited to) pain perception, immune regulation, and reproductive functions30     ; CNR1 and CNR2\nare expressed in immune cells, nerve tissues, and serve as critical regulators of reproductive\nprocesses including decidualization and embryo implantation31     ,32     . The etiology of EM has\nbeen speculated to be routed in defective decidualization and retrograde menstruation of the\nendometrial fragments combined with ECS dysregulation8     ,9     ,33     . While it is plausible that\ndecreased ECS function influences EM lesion initiation, progression, and severe pain experience, it\nis not clear whether ECS dysfunction actively contributes to EM pathogenesis, or whether it\nrepresents a secondary consequence of alterations occurring within the refluxed endometrial\ntissue, leading to establishment of EM lesions.\nKeeping this central dogma in view and to provide insights into early events of EM pathogenesis,\nwe induced EM in both CNR1 k/o and CNR2 k/o mice utilizing syngeneic, DD and UnD uterine\nendometrial fragments. Absence of CNR1 and CNR2 did not influence systemic levels of ECS\nligands but the lesion microenvironment displayed significant changes in the levels of OEA and\nPEA, suggesting a tissue-specific response.\nOne intriguing aspect of ECS involvement in EM is its role in decidualization, a process pivotal for\nuterine receptivity to embryo implantation and successful pregnancy, that may also contribute to\nEM establishment34     . Although both CNR1 and CNR2 are active in decidualization, CNR1 may\nhave a more prominent role. Absence of CNR1 and CNR2 show compromised decidualization in\nmice in a CNR1-dependent manner validated through in-vitro studies35     . Similarly, in our study,\nEM lesions (both UnD and DD) from mice lacking CNR1 showed significantly more DEGs (2088),\ncompared to CNR2 (287) and WT (2). Genes essential for decidualization such as IGFBP2, BMP3,\nPTGDR, WNT7a, and ESR1 were downregulated in the DD, EM lesions from CNR1 k/o mice\ncompared to their UnD counterparts. This further reinforces the role of CNR1 in the uterine and\nEM lesion microenvironment, including their role in decidualization response. Moreover, the\ninterplay between CNR1 and CNR2 are important since CNR2 contribute to immunomodulation,\nwhich is a key process during decidualization36     ,37     . Given the complexity of ECS signalling and\ncompensatory mechanisms, we focused our investigation on the immune dysregulation aspect of\nEM pathophysiology. Our findings suggest that altered ECS dynamics during decidualization\ndisrupt ECS signaling, leading to dysregulation of immune responses and aberrant cellular\nbehavior. Indeed, immune dysfunction is a hallmark of EM28     ,38     , and ECS could play a crucial\nrole in shaping immune responses particularly through its impact on T cell function yet there is no\nclear evidence. Alterations in T cell populations and functions have been associated with EM\nprogression, suggesting their vital role in EM pathogenesis and maintenance39     ,40     . Our flow\ncytometry analysis revealed significant alterations in immune cell populations in mice bearing EM\nlesions, with a notable absence of CD3+ T cells, CD4+ helper T cells, and CD8+ cytotoxic T cells\nspecifically in CNR2 k/o mice. Mechanistic in-vitro studies further confirmed an aberrant T cell\nresponse in CNR2 k/o mice, as with T cell receptor activation and stimulation there was decreased\nviability. Combined, our findings show that CNR2 is critical in T cell survival upon TCR activation\nwith pathogen-associated molecular patterns (PAMPs)/danger-associated molecular patterns\n(DAMPs) or antigen-mediated signals. These findings also shed light on a previously unrecognized\nrole of CNR2 in EM-associated adaptive immune dysfunction given the critical role of T cells in\nimmune surveillance and regulation. Furthermore, speculation of EM being a cause of ECS\ndysfunction could be of importance since CNR2 was found to be reduced in the lesions of EM\npatients, as shown by our previous study2     . In addition, the bulk RNA sequencing strengthens the\nfinding of dysregulation of T cell-related genes in CNR2 k/o EM lesions. The observed\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 19 of 35\ndownregulation of T cell-related genes such as CD3e, CD3g, GATA3, and CTLA4 is consistent with\nthe diminished CD3+ T cell populations and highlights the relevance of CNR2 in T cell-mediated\nimmune responses within the endometriotic microenvironment.\nKEGG pathway analysis of differentially expressed immune-related genes in CNR2 k/o DD lesions\nfurther revealed that Th1, Th2, and Th17 cell differentiation pathways were impacted, and the\nprevious literature confirms dysregulation of these pathways in EM pathophysiology41     ,42     .\nAdditionally, our in-vitro functional assay showing CD4+ helper T cells being affected\n(proliferation) more than the CD8+ cytotoxic T cell subset further adds to the subset specific\nbehaviour of CNR2. Our results provide a missing link between the ECS and immune system\nfunctioning during EM.\nTo gain insights into the early features of lesion initiation and establishment, we conducted IMC\nanalysis of EM lesions across genotypes and different lesion types (DD and UnD). DD lesions from\nCNR1 and CNR2 k/o mice showed higher T cell residing in the lesions with increased stromal\ncompartments and monocytes/macrophages population compared to WT lesions. Stromal cells\ncontribute to the early development of EM lesions by promoting inflammation, angiogenesis,\nfibrosis, and immune modulation43     –45     . Additionally, the interaction between macrophages\nand stromal cells is important in EM, with the NLRP3 inflammasome playing a role in lesion\ndevelopment46     . Studies have implicated macrophages in EM lesion growth where they support\nangiogenesis (formation of blood vessels) by producing pro-angiogenic factors such as vascular\nendothelial growth factor (VEGF)47     –49     . Given the combined increase in proliferation,\nendothelial marker, and monocytes/macrophages in EM lesions from mice potentially indicates\nthat they could modulate the early lesion microenvironment in the event of CNR1 and CNR2\ndysregulation. Based on the proportion of these macrophages to certain phenotypes, such as M1 or\nM2, would dictate lesion development and subsequent progression. Further studies are required\nto tease out molecular interactions of CNR1 and CNR2 with specific immune cell subsets in a\ncomplex EM lesion microenvironment and determine how it contributes to establishment of blood\nsupply and lesion survival.\nSeveral limitations should be acknowledged in our study. Firstly, understanding the homeostatic\naspects of ECS, both with and without the presence of CNR1 and CNR2, remains a complex\nchallenge. While our global k/o mouse models provide valuable insights, further research utilizing\ntargeted k/o specific to the uterus could offer a more precise understanding of their contributions\nto uterine physiology and further implications in EM establishment. Secondly, the use of mouse\nmodels to study EM has inherent limitations due to species differences and the inability to fully\nrecapitulate the human disease. Thirdly, the molecular and cellular complexities associated with\nDD and UnD endometrial tissues, as well as the timing of EM induction in these mice, may not\nperfectly mirror the human condition. These limitations emphasize the need for future\ninvestigations to enhance the translational relevance of our findings and further our\nunderstanding of the complex interplay between ECS, decidualization, and EM pathogenesis.\nIn conclusion, our study offers evidence for the involvement of CNR1 and CNR2 dysregulation in\nEM pathogenesis. Through an integrative analysis of transcriptomic profiles, immune cell\ndynamics, and spatial relationships within EM lesions from mice, we unveil the intricate\ninteractions between ECS, immune responses, and cellular changes in EM. By identifying potential\nmechanisms through which ECS disruption could impact EM, our research provides a foundation\nfor the development of targeted therapies addressing the ECS’s influence on EM.\nThese findings will advance our understanding of EM and lead to innovative therapeutic strategies\nto manage this complex disorder.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 20 of 35\nAcknowledgements\nWe thank Dr Alexandra Furtos and Karine Gilbert from the Regional Mass Spectrometry Centre at\nUniversité de Montréal for designing and performing mass-spectrometry evaluation; Dr Yuhong\nWei and the Single Cell and Imaging Mass Cytometry Platform (SCIMAP) at McGill University\nGoodman Cancer Institute for processing, labeling, and acquiring samples for IMC imaging;\nBrittney Armitage-Brown from the Animal Care Services at Queen’s University for breeding mice\nutilized in this study. This work was supported by funding from the Canadian Institutes of Health\nResearch (CIHR-394340) to C. T and M. K.\nData and Code availability\nBulk mRNA sequencing data and IMC data generated in this study will be available upon\nreasonable request. All the codes used in the current study are from previously published articles,\nas cited in the article. Authors do not report original code.\nAuthor contributions\nH. L., and C. T. designed and conceived experiments; H. L., K. Z., Y. W., P. Y., D. S., and A. M.,\nconducted experiments. H. L., K. Z., and P. Y., analyzed results; H. L., and C. T., wrote the\nmanuscript, generated the figures, and wrote the figure legend. C. T., and M. K., supervised the\nproject and obtained research funding. All authors discussed the results and commented on the\nmanuscript.\nFunding\nThis research was supported with funds from Canadian Institutes of Health Research (CIHR)\nSupplementary Files\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 21 of 35Harshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 21 of 35\nSupplementary Table 1\nDeuterated and non-deuterated standards used for mass\nspectrometry analysis of the ECS ligands in plasma and tissues.\nSupplementary Table 2\nExperimental conditions for ECS ligand identification through LC-MS.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 22 of 35Harshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 22 of 35\nSupplementary Table 3\nIMC antibodies used for the current study with their respective dilutions\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 23 of 35Harshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 23 of 35\nSupplementary Figure 1\nDEGs of bulk RNA sequencing between the UnD lesions\nfrom CNR1 k/o and CNR2 k/o compared to WT controls.\nA, B The volcano plots for DD lesions of CNR1 k/o and CNR2 k/o compared to WT, respectively. Log2 fold change is\nrepresented on the x-axis and log10 Q-value (FDR adjusted) as the y-axis. Vertical dotted lines on the x-axis indicate ± 1-fold\nchange and vertical dotted line on the y-axis indicate Q-value of 0.05. C, D KEGG pathway analysis of DEGs of CNR1 k/o UnD\nlesions and CNR2 k/o UnD lesions compared to WT controls, respectively. Gene Number represents the number of DEGs\nenriched in the pathway. Rich Ratio shows the ratio of enriched DEGs to background genes and Q-value indicates\nsignificance, with a value closer to zero being more significant and is corrected by Benjamini-Hochberg method.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 24 of 35Harshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 24 of 35\nSupplementary Figure 2\nKEGG pathway analysis of immune specific DEGs.\nA, B Immune specific genes that were differentially expressed were subjected to KEGG pathway analysis between CNR1 k/o\nUnD lesions and CNR2 k/o UnD lesions compared to WT UnD controls, respectively.\nSupplementary Figure 3\nGating panel representing T cells from splenocytes.\nA-G, Splenocytes stained for CD3 on the y-axis and CD11b on the x-axis among WT, CNR1 k/o, and CNR2 k/o mice with EM like\ncondition, as well as CNR1 k/o and CNR2 k/o naive and sham-operated controls.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 25 of 35Harshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 25 of 35\nSupplementary Figure 4\nFlow cytometry analysis of immune cell phenotypes in the\nsplenocytes of WT, CNR1 k/o and CNR2 k/o mice with EMS.\nA-F Bar plot (mean ± SD) representation of splenocytes (SP) stained for CD3+ total T cells, CD4+ helper T cells, CD8+ cytotoxic\nT cells, CD19+ B cells, CD11b+ monocytes and F4/80 monocytes/macrophages of all the genotypes with EM lesions,\nrespectively. n = 5-7 individual biological samples per genotype. Statistical analyses were performed using the ordinary one-\nway ANOVA with Holm-Sidak post hoc test. * p < 0.05, ** p < 0.01, *** p < 0.001 and **** p < 0.0001\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 26 of 35Harshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 26 of 35\nSupplementary Figure 5\nIn vitro functional assay and flow cytometry evaluation\nof activated CD3 T cells from naïve WT and CNR2 k/o mice.\nA, B Flow cytometry gating strategies for CD3+ T cells to identifiy the different phenotypes and functional state, respectively.\nC, Bar plots representing the percentage positive live cells that are double positive for proliferation (Ki67) and IFNγ markers.\nD, Bar plots showing the percentage positive live cells that are negative for Ki67 and positive for IFNγ. Statistical analyses\nwere performed using the ordinary one-way ANOVA with Holm-Sidak post hoc test. * p < 0.05, ** p < 0.01, *** p < 0.001 and\n**** p < 0.0001. Data presented as mean ± SD.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 27 of 35Harshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 27 of 35\nSupplementary Figure 6\nIMC and cellular neighbourhoods (CN) in UnD, EMS lesions from CNR1 k/o, CNR2 k/o and WT mice.\nA, B Representative image of 8 distinct cell types from CN analysis of DD and UnD lesions from WT, CNR1 k/o, and CNR2 k/o\nmice, respectively. C Heatmap representation of CN analysis shows distinct clustering patterns observed in the UnD lesions\namong the different genotypes.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 28 of 35\nReferences\nGiudice L. C., Kao L. C. (2004) Endometriosis. 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(1996) Vascular endothelial growth factor is produced by peritoneal fluid\nmacrophages in endometriosis and is regulated by ovarian steroids Journal of Clinical\nInvestigation 98\nArticle and author information\nHarshavardhan Lingegowda\nDepartment of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada\nKatherine B Zutautas\nDepartment of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada\nORCID iD: 0000-0003-4304-1024\nYuhong Wei\nRosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada\nPriyanka Yolmo\nDepartment of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada,\nDivision of Cancer Biology and Genetics, Queen’s University, Kingston, ON, Canada\nDanielle J Sisnett\nDepartment of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada\nORCID iD: 0000-0001-8061-0631\nAlison McCallion\nDepartment of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada\nORCID iD: 0000-0002-7433-9221\nMadhuri Koti\nDepartment of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada,\nDivision of Cancer Biology and Genetics, Queen’s University, Kingston, ON, Canada\nChandrakant Tayade\nDepartment of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada\nFor correspondence: tayadec@queensu.ca\nORCID iD: 0000-0001-9062-050X\nCopyright\nThis is an open-access article, free of all copyright, and may be freely reproduced, distributed,\ntransmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The\nwork is made available under the Creative Commons CC0 public domain dedication.\nEditors\nReviewing Editor\nSang Jun Han\nBaylor College of Medicine, Houston, United States of America\n49\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 32 of 35\nSenior Editor\nTadatsugu Taniguchi\nUniversity of Tokyo, Tokyo, Japan\nReviewer #1 (Public Review):\nSummary:\nThe endocannabinoid system (ECS) components are dysregulated within the lesion\nmicroenvironment and systemic circulation of endometriosis patients. Using endometriosis\nmouse models and genetic loss of function approaches, Lingegowda et al. report that\ncanonical ECS receptors, CNR1 and CNR2, are required for disease initiation, progression, and\nT-cell dysfunction.\nStrengths:\nThe approach uses genetic approaches to establish in vivo causal relationships between\ndysregulated ECS and endometriosis pathogenesis. The experimental design incorporates\nbulk RNAseq approaches, as well as imaging mass spectrometry to characterize the mouse\nlesions. The identification of immune-related and T-cell-specific changes in the lesion\nmicroenvironment of CNR1 and CNR2 knockout (KO) mice represents a significant advance\nWeaknesses:\nAlthough the mouse phenotypic analyses involve a detailed molecular characterization of the\nlesion microenvironment using genomic approaches, detailed measurements of lesion\nsize/burden and histopathology would provide a better understanding of how CNR1 or CNR2\nloss contributes to endometriosis initiation and progression. The cell or tissue-specific effects\nof the CNR1 and CNR2 are not incorporated into the experimental design of the studies.\nAlthough this aspect of the approach is recognized as a major limitation, global CNR1 and\nCNR2 KO may affect normal female reproductive tract function, ovarian steroid hormone\nlevels, decidualization response, or lead to preexisting alterations in host or donor tissues,\nwhich could affect lesion establishment and development in the surgically induced,\nsyngeneic mouse model of endometriosis.\nhttps://doi.org/10.7554/eLife.96523.1.sa1\nReviewer #2 (Public Review):\nSummary:\nThe endocannabinoid system (ECS) regulates many critical functions, including reproductive\nfunction. Recent evidence indicates that dysregulated ECS contributes to endometriosis\npathophysiology and the microenvironment. Therefore, the authors further examined the\ndysregulated ECS and its mechanisms in endometriosis lesion establishment and progression\nusing two different endometrial sources of mouse models of endometriosis with CNR1 and\nCNR2 knockout mice. The authors presented differential gene expressions and altered\npathways, especially those related to the adaptive immune response in CNR1 and CNR2 ko\nlesions. Interstingly, the T-cell population was dramatically reduced in the peritoneal cavity\nlacking CNR2, and the loss of proliferative activity of CD4+ T helper cells. Imaging mass\ncytometry analysis provided spatial profiling of cell populations and potential relationships\namong immune cells and other cell types. This study provided fundamental knowledge of the\nendocannabinoid system in endometriosis pathophysiology.\nStrengths:\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 33 of 35\nDysregulated ECS and its mechanisms in endometriosis pathogenesis were assessed using\ntwo different endometrial sources of mouse models of endometriosis with CNR1 and CNR2\nknockout mice. Not only endometriotic lesions, but also peritoneal exudate (and splenic) cells\nwere analyzed to understand the specific local disease environment under the dysregulated\nECS.\nProviding the results of transcriptional profiles and pathways, immune cell profiles, and\nspatial profiles of cell populations support altered immune cell population and their\ndisrupted functions in endometriosis pathogenesis via dysregulation of ECS.\nIn line 386: Role of CNR2 in T cells. The finding that nearly absent CD3+ T cells in the\nperitoneal cavity of CNR2 ko mice is intriguing.\nThe interpretation of the results is well-described in the Discussion.\nWeaknesses:\nThe study was terminated and characterized 7 days after EM induction surgery without the\ndetails for selecting the time point to perform the experiments.\nThe authors also mentioned that altered eutopic endometrium contributes to the\nestablishment and progression of endometriosis. This reviewer agrees with lines 324-325. If\nso, DEGs are likely identified between eutopic endometrium (with/without endometriosis\nlesion induction) and ectopic lesions. It would be nice to see the data (even though using\npublicly available data sets).\nFigure 7 CDEF. The results of the statistical analyses and analyzed sample numbers should be\nadded. Lines 444-450 cannot be reviewed without them.\nThis reviewer agrees with lines 498-500. In contrast, retrograded menstrual debris is not\ndecidualized. The section could be modified to avoid misunderstanding.\nhttps://doi.org/10.7554/eLife.96523.1.sa0\nAuthor response:\nReviewer #1 (Public Review):\nSummary:\nThe endocannabinoid system (ECS) components are dysregulated within the lesion\nmicroenvironment and systemic circulation of endometriosis patients. Using\nendometriosis mouse models and genetic loss of function approaches, Lingegowda et al.\nreport that canonical ECS receptors, CNR1 and CNR2, are required for disease initiation,\nprogression, and T-cell dysfunction.\nStrengths:\nThe approach uses genetic approaches to establish in vivo causal relationships between\ndysregulated ECS and endometriosis pathogenesis. The experimental design incorporates\nbulk RNAseq approaches, as well as imaging mass spectrometry to characterize the\nmouse lesions. The identification of immune-related and T-cell-specific changes in the\nlesion microenvironment of CNR1 and CNR2 knockout (KO) mice represents a significant\nadvance\nWeaknesses:\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 34 of 35\nAlthough the mouse phenotypic analyses involve a detailed molecular characterization of\nthe lesion microenvironment using genomic approaches, detailed measurements of\nlesion size/burden and histopathology would provide a better understanding of how\nCNR1 or CNR2 loss contributes to endometriosis initiation and progression. The cell or\ntissue-specific effects of the CNR1 and CNR2 are not incorporated into the experimental\ndesign of the studies. Although this aspect of the approach is recognized as a major\nlimitation, global CNR1 and CNR2 KO may affect normal female reproductive tract\nfunction, ovarian steroid hormone levels, decidualization response, or lead to preexisting\nalterations in host or donor tissues, which could affect lesion establishment and\ndevelopment in the surgically induced, syngeneic mouse model of endometriosis.\nWe appreciate the reviewer's thoughtful and constructive feedback. We agree that the\nadditional measurements of lesion size/burden and histopathology would provide valuable\ninsights into the specific contributions of CNR1 and CNR2 to endometriosis progression.\nHowever, the focus of this study was on assessing the alterations in complex immune\nmicroenvironment due to the absence of CNR1 and CNR2, given their close relation in\nregulating immune cell populations. We will plan to incorporate these measurements in\nfuture studies to further strengthen the understanding of the disease pathogenesis. Regarding\nthe potential effects of global knockout, the reviewer raises a valid concern. To address this,\nwe will explore cell and/or tissue-specific knockout models in future experiments to better\nisolate the direct effects of CNR1 and CNR2 on the disease process, while minimizing potential\nconfounding factors from systemic alterations.\nReviewer #2 (Public Review):\nSummary:\nThe endocannabinoid system (ECS) regulates many critical functions, including\nreproductive function. Recent evidence indicates that dysregulated ECS contributes to\nendometriosis pathophysiology and the microenvironment. Therefore, the authors\nfurther examined the dysregulated ECS and its mechanisms in endometriosis lesion\nestablishment and progression using two different endometrial sources of mouse models\nof endometriosis with CNR1 and CNR2 knockout mice. The authors presented differential\ngene expressions and altered pathways, especially those related to the adaptive immune\nresponse in CNR1 and CNR2 ko lesions. Interestingly, the T-cell population was\ndramatically reduced in the peritoneal cavity lacking CNR2, and the loss of proliferative\nactivity of CD4+ T helper cells. Imaging mass cytometry analysis provided spatial\nprofiling of cell populations and potential relationships among immune cells and other\ncell types. This study provided fundamental knowledge of the endocannabinoid system in\nendometriosis pathophysiology.\nStrengths:\nDysregulated ECS and its mechanisms in endometriosis pathogenesis were assessed\nusing two different endometrial sources of mouse models of endometriosis with CNR1\nand CNR2 knockout mice. Not only endometriotic lesions, but also peritoneal exudate\n(and splenic) cells were analyzed to understand the specific local disease environment\nunder the dysregulated ECS.\nProviding the results of transcriptional profiles and pathways, immune cell profiles, and\nspatial profiles of cell populations support altered immune cell population and their\ndisrupted functions in endometriosis pathogenesis via dysregulation of ECS.\nIn line 386: Role of CNR2 in T cells. The finding that nearly absent CD3+ T cells in the\nperitoneal cavity of CNR2 ko mice is intriguing.\n\nHarshavardhan Lingegowda et al., 2024 eLife. https://doi.org/10.7554/eLife.96523.1 35 of 35\nThe interpretation of the results is well-described in the Discussion.\nWeaknesses:\nThe study was terminated and characterized 7 days after EM induction surgery without\nthe details for selecting the time point to perform the experiments.\nThe authors also mentioned that altered eutopic endometrium contributes to the\nestablishment and progression of endometriosis. This reviewer agrees with lines 324-\n325. If so, DEGs are likely identified between eutopic endometrium (with/without\nendometriosis lesion induction) and ectopic lesions. It would be nice to see the data (even\nthough using publicly available data sets).\nFigure 7 CDEF. The results of the statistical analyses and analyzed sample numbers\nshould be added. Lines 444-450 cannot be reviewed without them.\nThis reviewer agrees with lines 498-500. In contrast, retrograded menstrual debris is not\ndecidualized. The section could be modified to avoid misunderstanding.\nWe would like to thank the reviewer for insightful comments, suggestions and acknowledging\nthe importance of the work presented in this manuscript.\nRegarding 7-day time point, we have provided rationale in lines 479-481, but agree that it isn’t\nsufficient and hence we have provided additional details on the selection of the 7-day time\npoint for the experiments in methods section (Mouse model of EM). We have also noted the\nsuggestion on providing comparison of differentially expressed genes in the eutopic\nendometrium vs ectopic lesions. Since there are publications comparing the eutopic vs\nectopic gene expression patterns (PMIDs: 33868805 and 18818281), including a study\nexploring the ECS genes in the endometrium throughout different menstrual cycles (PMID:\n35672435), we believe additional analysis using the same dataset may not yield new\ninformation. However, we see the value in reviewer’s comment, and we will look at the gene\nexpression patterns in the uterine vs endometriosis like lesions in our future studies with\ntissue or cell specific CNR1 and CNR2 knockout models to understand functional relevance of\nECS in endometriosis initiation.\nSince the IMC study was exploratory for proof of concept, we did not have enough biological\nreplicates for meaningful statistical validation (n = 2-3). We have clarified this information in\nthe methods, results, and figure legends for appropriately representing the limitations of the\ncurrent setup.\nFinally, we appreciate the feedback on the section discussing retrograded menstrual debris.\nEven though the menstrual debris may not be decidualized, some endometriotic lesions have\nthe ability to decidualize based on their response to estrogen and progesterone in a cycling\nmanner (PMID: 26450609), similar to the endometrium in the uterine cavity. We have\nclarified this in the revised MS.","source_license":"CC0","license_restricted":false}