Beyond the Granuloma: New Insights into Cardiac Sarcoidosis Using Spatial Proteomics

Cardiac sarcoidosis is poorly understood, challenging to diagnose, and portends a poor prognosis. A lack of animal models necessitates the use of residual human samples to study sarcoidosis, which in turn necessitates the use of analytical tools compatible with archival, fixed tissue. We employed high-plex spatial protein analysis within a large cohort of archival human cardiac sarcoidosis and control tissue samples, studying the immunologic, fibrotic, and metabolic landscape of sarcoidosis at different stages of disease, in different cardiac tissue compartments, and in tissue regions with and without overt inflammation. Utilizing a small set of differentially expressed protein biomarkers, we also report the development of a predictive model capable of accurately discriminating between control cardiac tissue and sarcoidosis tissue, even when no histologic evidence of sarcoidosis is present. This finding has major translational implications, with the potential to markedly improve the diagnostic yield of clinical biopsies obtained from suspected sarcoidosis patients.


Introduction
Sarcoidosis is a multi-system in ammatory disease of uncertain cause and with widely ranging clinical manifestations.[3] The exact prevalence of cardiac sarcoidosis (CS) is di cult to assess due to a signi cant burden of occult disease and missed diagnoses, 2,4,5 with many cases only diagnosed after sudden cardiac death 6 or at the time of heart transplantation/left ventricular assist device (LVAD) implantation. 7,8ough incurable, CS is a treatable disease.The mainstay of therapy is systemic immunosuppression, initially using high-dose glucocorticoids, followed by a transition to systemic steroid-sparing agents. 2,3However, because of the potential risks of systemic immunosuppression, current guidelines only recommend initiating treatment in patients with de nitive, active CS.Owing to the particularly high risk of life-threatening arrhythmias in the CS population, 9 guidelines for primary prevention implantable cardiac de brillator (ICD) make special mention of CS, recommending ICDs for many CS patients even when left ventricular ejection fraction (LVEF) is relatively preserved.However, because ICD therapy has known risks, 10 these recommendations are only applicable to patients with de nitive CS diagnoses.
Given the high morbidity of CS, making a timely and accurate diagnosis is of paramount importance.In general terms, diagnosis relies on clinical characteristics, tissue sampling, and exclusion of alternative causes.More speci cally, the diagnostic reference standard for CS is histologic identi cation of non-caseating granulomas on endomyocardial biopsy (EMB) tissue. 1,3,4Unfortunately, due to the heterogeneous and sparse distribution of these pathognomonic granulomas, the true diagnostic yield of EMB is estimated at 25%. 2,11,12 Advanced imaging techniques such as cardiac MRI and FDG-PET have value in CS as both screening tests and treatment-surveillance tests, but remain most useful as ancillary diagnostics due to limitations of their sensitivity and speci city. 1 The high incidence of occult disease, risky and un-targeted treatment regimens, and insensitive diagnostic gold standard each highlight key areas of unmet need in CS.Without a known cause for sarcoidosis, development of animal models to study this disease has not been possible.Thus, researchers are forced to rely on human biosamples as the primary source for diagnostic, mechanistic, and therapeutic investigation.Until recently, tools for examining these residual or archival human tissue samples have been limited, which in turn has limited breakthroughs in translational CS research.In this manuscript, we describe the application of high-plex spatial protein analysis within a large cohort of archival human CS tissue samples.The present study differs from prior CS research 13 by leveraging the full potential of spatial-omics technology to individually study the myocytes, stromal cells, and vascular cells beyond the pathognomonic granulomas that de ne the disease.This approach is motivated by the fact that although granulomas are the hallmark of CS, they occupy only a small fraction of the total cardiac tissue area.We therefore hypothesize that key patient outcomes like cardiac pump failure and arrhythmia burden are a result of tissue-level biology which occurs far-removed from the regions of overt, active CS in ammation.Using this novel conceptual framework and cutting-edge spatial technology, we describe novel CS biology and uncover a concise set of tissue-level biomarkers which provide high diagnostic accuracy even in tissue without pathognomonic granulomas.

Cohort Description:
The study cohort consisted of n = 48 formalin-xed, para n embedded (FFPE) cardiac tissue samples from the University of Pennsylvania.N = 39 tissue samples were derived from patients with tissue con rmed CS, n = 14 of which were EMBs obtained during diagnostic workup early in CS disease course, and n = 25 were 'advanced-stage' CS tissue obtained at time of cardiac transplant or LVAD implant.Although all CS patients contributing samples had tissue-con rmed CS, among the speci c tissue blocks used for this study, n = 24 had pathologist-documented granulomatous in ammation on the tissue sample used in the study cohort, n = 7 had documented in ammation of uncertain etiology (ranging from "occasional, minimal in ammatory in ltrates" to "signi cant lymphocytic in ammation"), and n = 8 had no identi ed in ammation or granulomas.The remaining n = 9 study tissue samples represented Control cases, consisting of n = 3 'heart failure controls' with known, non-in ammatory, non-ischemic cardiomyopathy (NICM), n = 4 'non-failing (NF) controls' sourced from declined heart donors who had suffered cardiac arrests, and n = 2 'in ammatory controls' sourced from heart transplant recipients with no signi cant acute rejection but with chronic allograft dysfunction (TXP).A diverse, heterogeneous, cohort of 'non-sarcoid' controls was selected speci cally to facilitate an understanding of which protein expression markers are particularly altered in CS, as these would represent the optimal candidate biomarkers for future efforts to improve CS diagnostic accuracy.Cohort details are presented in Table 2. Procurement of human myocardial tissue was performed under protocols and ethical regulations approved by Institutional Review Boards at the University of Pennsylvania and the Gift-of-Life Donor Program (Philadelphia, Pennsylvania, United States), and complies with the Declaration of Helsinki.

Sample Processing and Immuno uorescence Staining:
Unstained sections from FFPE samples were cut (6 µm thickness) and mounted on glass slides.Four sections were mounted on each slide to enable more e cient utilization of study reagents and machine time.In order to permit digital pathology examination and segmentation, study slides underwent immuno uorescence staining for the vascular endothelium marker CD31 (Abcam ab215912, Cambridge UK), the cardiomyocyte marker Troponin I (Abcam ab196384, Cambridge UK), and the stroma/collagen marker Collagen VI (Abcam ab207292, Cambridge UK), along with SYTO for pan-nucleic acid staining (Thermo Fisher S11363, Waltham MA) prior to loading into the GeoMx instrument.Whole-slide scanning was performed by the NanoString GeoMx Digital Spatial Pro ler (DSP) instrument.

Digital-Spatial Protein Expression Analysis:
GeoMx DSP Instrument Spatial protein expression analysis was conducted using the Nanostring GeoMx DSP instrument (Nanostring Technologies, Seattle, WA).Brie y, this instrument relies on photo-cleavable oligonucleotide barcodes which are conjugated to antibodies for the desired protein targets.After incubating the study immuno uorescence slides in the study antibody/oligonucleotide panel reagents, the slides are digitized using the GeoMx DSP instrument to produce 40x magni cation (0.25microns-per-pixel resolution) digital images.Regions of interest (ROIs) from within the digitized slides are then selected for analysis, followed by application of focused UV light to cleave oligonucleotide barcodes.The digital micromirror device of the DSP instrument tunes the UV light with 1-micron resolution, allowing for great exibility and speci city in ROI selection.Released tags are collected by micro-capillary aspiration and stored on nCounter optical barcodes (Nanostring Technologies) for sequencingbased quantitation which is registered to the speci c ROIs of data collection.
Methods for sample preparation prior to running DSP analysis and for cartridge and sequencing after DSP analysis are described in the supplemental methods.

Protein Expression Panel:
The study protein panel for the GeoMx spatial protein expression analysis consisted of n = 79 protein targets designed to provide detailed descriptions of CS stromal cell phenotypes, cell proliferation status, immune cell population, immune cell activation status, immune checkpoint activity, pro-and anti-apoptotic factors, mitogen-activated protein kinase (MAPK) pathway activity, and phosphoinositide-3-kinase-protein kinase (PI3K/AKT) pathway activity (see Supplemental Table S.1 for detailed list).

ROI Selection and Spatial Analysis Work ow:
Tissue ROIs were selected to assess protein expression changes with increasing distance from the CS-de ning granulomas.To that end, ROIs were selected from within granulomas, from peri-granulomatous cardiac parenchymal tissue (< 300µm from granulomas), and from granulomaremote parenchymal tissue (> 500µm away from the edge of any detected granuloma region) in each tissue sample whenever possible.Granuloma-remote ROIs were all ~ 600x600µm squares, and based on the 500µm distance requirement between ROI edge and the nearest granuloma, each granuloma-remote ROI represents the center of a granuloma-free tissue footprint that is ~ 1.3x1.3cm.This granuloma-free footprint is comparable to the size of a standard clinical EMB, and thus simulates a 'false negative' biopsy using conventional histologic methods for diagnosing CS.
The spatial analysis work ow leveraged established methods for digital pathology image analysis, [14][15][16][17] focusing not only on selecting ROIs at different distances from granulomas, but also on conducting a 'tissue-compartment-speci c' analysis within each individual cardiac parenchymal ROI.Speci cally, our approach permitted discrete segmentation of parenchymal ROIs (e.g.non-granuloma ROIs) into distinct areas of illumination (AOIs), with AOIs labeled according to the segmented tissue-types contained within them: cardiomyocyte AOIs, interstitial stroma AOIs (including broblasts, immune cells, capillaries/small vessels etc.), and the vascular compartment AOIs (containing larger vessels along with perivascular stroma).Segmentation was conducted using native GeoMx software by customizing pixel-intensity thresholds in each ROI for the different morphologic immuno uorescence antibodies used (CD-31 for vessels, Troponin-I for myocytes, Collagen-VI for collagenous stroma, SYTO for nuclei).By achieving accurate segmentation of each tissue-compartment, we were able to measure protein expression separately for each tissue compartment AOI within an ROI, allowing more nuanced assessments of tissue content.Figure 1 provides a summary of the study work ow with visual examples of ROIs and AOIs.
It is important to note that image segmentation designations re ect the predominant, but not exclusive, constituents of each compartment.
Myocyte AOIs typically contain not only myocytes, but also small, adjacent areas of stromal tissue which do not stain signi cantly for collagen VI (and therefore were not easily segmented into the collagenous stroma class).Stromal AOIs typically include small venules and capillaries.As a result, the reported protein expression data for myocyte AOIs will include some non-myocyte proteins while stromal AOIs will also contain endothelial cell proteins.Because myocytes dominate in terms of cell count and area within a myocyte AOI, protein expression data for proteins expressed in both myocytes and stromal cell populations will be largely attributable to the myocyte cell contribution.However, for proteins not typically expressed by myocytes, expression within myocyte AOIs will in fact be attributable to stromal cell types.From a data presentation perspective, myocyte AOI results in this manuscript will only include proteins with known expression in myocytes, based on review of published and unpublished single cell RNA 18,19 and proteomics datasets 20,21 (refer to Table 1).However, no data will be discarded through this process, since every study analysis also includes results at the full-ROI level which incorporates all protein expression data from within the ROI.Experimental Design: Our methodologic goal was to leverage the native functions of the GeoMx platform and established digital pathology image analysis methodology to perform a comprehensive and highly nuanced assessment of cohort tissues.We sought to explore several facets of CS biology by executing comparisons within and between tissues in the CS tissue cohort, and executing comparisons between CS tissues and the non-CS control cohort.Speci c pre-speci ed analyses for the cohort of CS patients alone included: 1) analysis of differentially expressed proteins (DEPs) and protein expression variation between granuloma ROIs of CS patients, 2) analysis of DEPs between biopsy and explant samples from CS patients to identify distinctions between earlier vs. later disease, 3) analysis of DEPs between 'peri-granuloma' ROIs and 'granuloma-remote' ROIs to assess whether there is a 'distance gradient' in protein expression, and 4) analysis of DEPs between granuloma-free ROIs which contain in ammatory in ltrates and those without in ltrates.Pre-speci ed analyses for comparing CS tissues to non-CS Controls focused solely on an analysis of granuloma-remote, in ammatory-in ltrate-free ROIs, comparing these histologically bland CS ROIs to the diverse control cohort ROIs.The goal of this analysis was to uncover protein expression patterns that may be speci c to CS, and which are more homogenously distributed than classical granulomatous in ammation.Using the protein expression data from this comparison of CS to Controls, exploratory predictive modeling was performed to assess the diagnostic potential of using in-situ protein biomarkers.

Data analysis
All analyses were conducted with R 4.2.3,Stata IC 15.0, and Python 3.10.13.

Data Visualization
Unbiased data visualizations were generated for each analysis described above.Principal component analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) were utilized for data visualization.

Differential Expression Analysis:
Probe counts were processed, and their quality assessed using GeoMxWork ows (v1.8.0), NanoStringNCTools (v1.10.0), and GeoMxTools (v3.5.0).Brie y, segments (AOIs) were ltered based on nuclei count, binding density, surface area, and background signal.Among all categories, 5 of 571 segments were agged for low nuclei count and discarded.All probes (79) were retained.Inspired by the work of van Hijfte et al. 22 , several normalization strategies were considered and evaluated based on the correlation of mean expression and p-value as calculated from sarcoid vs. control.The lowest correlation and best symmetry detected with signi cant probes (i.e.least bias towards either condition) was observed with quantile normalization.For the ROI-level and AOI-level expression data, differentially expressed probes were identi ed using mixed linear model.Starting from a full parameterization of Where sarcoid is de ned whether the donor was clinically diagnosed with sarcoidosis, in ltrate is the presence of in ltrate, explant is whether the tissue was collected as biopsy or from explant tissue, and distance is an ordinal measure of distance from the edge of the nearest granuloma.Every permutation of each reduced model was t to nd the optimal model based on ANOVA against the full model, and we found the full model to perform best for most probes.
For analysis of myocytes, we noticed improbably high signal of common immune cell markers.We suspected this signal arose from ine cient segmentation resulting in stromal contamination.We used a method like that implemented by AUCell2 to score the signal of immune markers (CD68, CTLA4, CD3, CD4, PD-1, CD8, CD45, and CD20). 23Myocyte segments with a score > 0.2 were dropped from analysis of myocyte AOIs (10/234); meaning, > 20% of the immune markers were found in the top 20%ile of expressed probes.Despite dropping these myocyte segments, data from these AOIs was retained for full-ROI-level analyses.
Ordinal Logistic Regression: To further assess effects of distance from CS in ammation on parenchyma tissue ROI/AOI expression, ordinal logistic regression was performed for all study panel markers.The ordinal scale was designed to capture relative distance, with In ammation(-) peri-granuloma ROIs were assigned a value of '1', in ammation(-) granuloma-remote ROIs assigned a value of '2', and Control ROIs assigned a value of '3'.

Sarcoid classi er models
To predict a diagnosis of sarcoid vs. non-sarcoid, we used either the full-ROI-level data or the individual myocyte and stroma AOI-level data.The primary objective of the modeling experiment was to generate a parsimonious model for predicting CS, because the long-term translational goal is to use this model to inform the development of a multiplex immuno uorescence (IF) platform (which are typically limited to ~ 7 markers). 16ecause of speci c interest in utilizing quantitative multiplex IF in future applications, the feature set was speci cally curated prior to statistical modeling in order to exclude markers which are not well suited to quantitative IF.For example, ubiquitously expressed proteins involved in canonical pathways are generally poorly suited to this type of work, while cell-surface markers and cell type-speci c enzymes and transcription factors are particularly well suited.An additional initial requirement for consideration in the modeling experiment was whether a variable was consistently increased or decreased across the three control subgroups as compared to CS. Due to deliberate and marked heterogeneity of our control population, and due to a desire to identify the proteins with the most convincing and speci c differential expression in CS, this criterion was felt reasonable as a means of ensuring good model t and generalizability.In total, n = 54 variables were considered at the start of modeling work.The potential variables considered during statistical modeling are outlined in Supplemental Table S .For models trained on the segmented myocyte and stroma data, the rst learnable step was a linear regression with the two segments.For the LASSO model, we found optimal performance with α = 0.001.The support vessel classi er model used a RBF kernel with a log softmax output layer.The gradient boosted model was t using 50 trees with a max depth of 3. Performance was overall similar between the three methods prior to further feature reduction to meet the pre-speci ed limit of six or viewer variables.Therefore, LASSO was ultimately utilized due to simplicity and broad acceptance.Starting with LASSO-selected features, the total feature set was then reduced variables via backwards, stepwise, leave-one-out cross validated logistic regression until optimal performance at seven or fewer variables was achieved.For the logistic regression, optimal model classi cation cut-point was determined via the Liu method. 24Model performance was assessed in the held-out test set via accuracy, area under the receiver-operator curve (AUC), sensitivity, speci city, positive predictive value and negative predictive value.

Cohort summary:
The study cohort is summarized in Table 1.Brie y, the baseline characteristics of the CS cohort differed from the Control cohort in the proportion of female patients (13% for CS group vs. 50% for controls, p = 0.012), and in the average LVEF at tissue sampling (26.5% for CS group vs. 39.7% for controls, p = 0.03).There were no signi cant differences among baseline characteristics of CS patients contributing earlier-disease-stage biopsy samples to the cohort vs. those contributing advanced-stage disease samples.

log (quantile (probe
The GeoMx work ow was deployed across the entire 48 sample cohort, generating 305 ROIs which yielded a total of 521 AOIs after segmentation of tissue compartments in cardiac parenchyma ROIs.For nal analyses, there were n = 39 granuloma ROIs, n = 227 non-granulomatous cardiac parenchyma ROIs (from which myocyte and stroma AOIs were segmented), and n = 39 large vascular bed ROIs (from which vascular AOIs were derived -see Fig. 1).Among parenchymal tissue (non-granuloma) ROIs in the CS cohort, there were n = 62 ROIs from EMB tissues (n = 8 perigranuloma parenchymal ROIs, n = 54 granuloma-remote parenchymal ROIs), and n = 161 from advanced-stage explant tissues (n = 19 perigranuloma parenchymal ROIs, and n = 113 granuloma-remote parenchymal ROIs, and n = 29 large vascular bed ROIs).There were n = 35 'in ammation(+)' parenchymal ROIs in the CS cohort which, despite not containing granulomas, had overt histologic immune cell in ltration.There were n = 43 ROIs from Control tissues, including n = 33 parenchymal ROIs and n = 10 large vascular bed ROIs.
Spatial protein expression results demonstrate substantial CS granuloma heterogeneity, both within and between patients: For most study panel proteins (49/79, 62%), the variance in protein expression between granuloma-containing ROIs was higher than the variance between non-granuloma parenchymal ROIs.This is notable, considering parenchymal ROIs consist of stroma and myocytes from hearts with widely varying LVEFs and which, in some cases, contain overt interstitial in ammatory cell in ltrates.The 10 most variably expressed proteins among granuloma ROIs are predominantly immune cell-type markers for macrophages, T-cells, granulocytes and antigen presenting cells: CD68, HLA-DR, CD11c, CD45, CD3, IDO1, CD44, CD40, CD66b, and BCL6.
There were only modest differences in the granuloma protein expression between 'advanced-stage disease' tissues and EMB tissues obtained earlier in the disease course.Immune checkpoint molecule Tim-3 and activated broblast marker FAP-alpha have signi cantly increased expression in advanced-stage CS hearts (p = 0.02 and p = 0.017, respectively).In contrast, immune checkpoint molecule VISTA, nuclear/proliferation marker Histone-H3, and activated MEK1 (part of the RAF/MEK/ERK pathway known to be involved in in ammation and linked to granuloma formation when inhibited) 25,26 each had signi cantly increased expression in earlier/active stage CS (p = 0.007, p = 0.013, and p = 0.017 respectively).
We performed a sub-analysis of the nine CS tissue samples which contributed multiple granuloma ROIs to the dataset.The PCA biplot in Supplemental Figure S.1 suggests substantial intra-sample heterogeneity in CS granulomas, with only modest within-sample groupings.Nearestneighbor analysis of the PCA plot demonstrates that for cases contributing multiple granuloma ROIs, the closest-clustering granuloma ROI is more likely to be from a different tissue sample than from the same sample (13/23, 56.5%).At the individual protein level, the intra-sample coe cient of variation (COV) exceeded inter-sample COV for an average of 11 protein markers in each multi-granuloma sample, with the most substantial intrasample variation seen in T-cell and cytotoxic cell markers (CD27, CD8, GZMB), apoptotic markers (CD95/FAS, Cleaved-Caspase-9), and checkpoint molecules (PDL2) (supplemental Table S.3).
Analysis of the Cardiac Parenchyma Highlights the Protein Expression Pro les of Active and 'Burnt Out' CS: Analysis of intrinsic cardiac parenchymal ROIs (e.g.non-granuloma ROIs) demonstrates substantial differences in the expression of immunologic, cell survival, and cell death pathways between EMB tissue samples obtained during the active workup/management phase of CS and advancedstage disease tissue samples obtained at cardiectomy.These differences are readily apparent via unsupervised data visualization with PCA and t-SNE plots in Fig. 2a.When analyzed as non-compartmentalized ROIs, n = 33 proteins show signi cant differential expression based on disease stage (Fig. 2b and Supplemental Table S.4).When segmented as compartment-speci c AOIs, there are n = 24 signi cant DEPs in the myocyte compartment, and n = 27 DEPs in the stroma compartment.
While substantial overlap exists between DEPs in the ROI-and AOI-level analyses, AOI analyses add important context.We observed signi cantly increased pro-apoptotic factors and decreased MAP-kinase and PI3K/AKT pathway activity in the myocyte compartment AOIs of advanced-stage hearts -a nding consistent with prior research on end-stage cardiomyopathy more generally. 27,28In stroma compartment AOIs of advancedstage hearts, we observed signi cant increases in markers of activated/differentiated broblasts (FAP-alpha and SMA).Again, this is consistent with known advanced-stage cardiomyopathy biology. 29However, we also observed numerous signi cant shifts in expression of immune-related protein markers in the stroma and myocyte AOIs when comparing tissues acquired earlier vs. later in disease which are not as easily explained.
in advanced-stage CS, there was a signi cant decrease in several macrophage and effector T-cell lineage markers (CD3, CD4, CD163), which coincided with signi cant increases in markers of longer-lasting regulatory T-cell (Treg), memory T-cell and B-cell populations (ICOS, FOXP3, CD45RO, CD127, CD20).In addition, the stroma and myocytes of advanced-stage cases manifest a less 'immune primed' state, with decreased expression of major histocompatibility (MHC) molecule HLA-DR, checkpoint molecules PD-L1/PD-L2, and interferon-producing STING.Overall, these ndings are consistent with the theoretical biology of the 'late brous phase' stage of CS (sometimes called 'burnt out' CS), 30 which is thought to involve increased brosis along with a decrease in active in ammatory elements. 30Our ndings support long-standing theories about this process, highlighting numerous key immune cell types and effectors which change as CS progresses.
Examining the 'Distance-Gradient' of Cardiac Parenchymal Biology in CS: As shown in Fig. 3 and Supplemental Table S.5, spatial analysis of the CS parenchyma reveals a previously unreported 'distance-gradient' in protein expression, in which numerous panel proteins were differentially expressed based on a tissue region's relative distance from granulomas.This distance gradient is apparent with unbiased data visualization via PCA and t-SNE (Fig. 3a), and persists even when accounting for confounders like histologic in ammatory in ltrates during differential expression testing with mixed effects models (Fig. 3b).
We also examined 'in ammation(+)' ROIs which do have discrete, non-granulomatous, interstitial in ammatory cell in ltrates, comparing these extreme examples of 'proximity to in ammation' to 'in ammation(-)' ROIs without any discrete in ammation.Unsurprisingly, there were many signi cant DEPs between these groups, including increased expression of numerous immune effector cell markers: CD3, CD4, CD8, CD68, CD163, GZMA, CD14, and CD45 (Supplemental Table S.6).Interestingly, at the ROI-level, 70% (12/17) of the signi cant DEPs which were observed during our in ammation-adjusted comparison of peri-granuloma ROIs to granuloma-remote ROIs are also signi cant DEPs when comparing overt 'in ammation(+)' ROIs to 'in ammation(-)' ROIs.However, these overlapping DEPs are largely not classic immune-effector cell markers, and instead suggest that the distance-gradient observed in this study arises from subtler ndings of immune activity.
Expression of most speci c immune cell-types do not differ between peri-granuloma and granuloma-remote regions.However, peri-granuloma stroma does have a larger population of total immune cells (CD45+).This is primarily due to signi cant increases in long-lasting immune 'sentinels' in the form of CD11c + dendritic cells and CD45RO + memory T-cells, rather than to increases in classic effector cell-types such as those found in overt 'in ammation(+)' parenchyma.2][33][34][35] Peri-granuloma stroma also exhibits increased expression of in ammation-associated pro-brotic mediators such as arginase 1, 36,37 , bronectin, 38 and CD44 39,40 .Interestingly, peri-granuloma stromal cells have decreased expression of immune checkpoint molecule Tim-3, suggesting that that peri-granuloma lymphocytes may be less responsive to immune-checkpoint-mediated inhibition. 41Finally, distance-dependent protein expression also impacts cardiomyocyte biology.Peri-granuloma myocytes manifest a 'stress-activated' state, 42 with increases in in ammation-associated class I/II MHC molecules, CD40, 43 and immune checkpoint PD-L1 44 along with increased injury-repair, brosis, and stiffness-associated CD44 [45][46][47] and bronectin (each of which likely co-localizes with cardiomyocytes rather than being expressed by them). 38e Protein Expression Pro le of 'Granuloma Remote' CS Parenchymal Tissue Differs from that of Controls: A fundamental question at the outset of this research was whether tissue biomarkers of CS exist which can be detected even when no histologic evidence of CS is present.As shown in Fig. 4a and Supplemental Table S.7, we identi ed numerous signi cant DEPs between granuloma-remote, in ammation(-), CS tissue and tissue from non-CS controls.Compared to control samples, granuloma-remote CS parenchyma is characterized by signi cantly increased expression of HLA-DR, Treg markers FOXP3, CD25 and GITR, endothelial/stem-cell marker CD34, and global nuclei/proliferation marker Histone-H3.CS parenchyma manifests decreased expression of CD45RO, PDL2, apoptosis marker CD95/FAS, and inactivated (phosphorylated) GSK3β and GSK3α (from which we infer increased activated GSK3 enzyme activity with resultant NF-κB-mediated pro-in ammatory cytokine production). 48,49 place this nding in the context of the 'distance-gradient' results described in the previous section, we performed ordinal logistic regression, treating peri-granuloma regions, granuloma-remote regions, and Control regions as ordinal classes representing different degrees of distance from granulomas.Interestingly, we observed that 65.3% of study panel proteins (n = 49) demonstrated a signi cant change in expression with increasing distance from CS in ammation (Supplemental Table S.8).Taken together, these results demonstrate both the local impact of granuloma proximity on protein expression as well as the more organ-wide impact of CS on tissue protein expression.Figure 4b provides a further, visual, demonstration of this phenomenon, highlighting the change in expression among several key groups of protein markers when moving from regions of in ammation(+) CS parenchyma to in ammation-free CS parenchyma and nally to control cardiac parenchyma.
A limited, pre-speci ed sub-analysis of ROIs derived from larger vascular beds was performed to assess whether the vasculature in CS differs from controls.Overall, while ROI numbers were limited for this analysis (n = 29 from CS cases, n = 10 from controls), the results suggest an immunologically active environment in CS vascular beds relative to controls, with increased expression of CD3, CD4, CD68, VISTA, CD45, HLA-DR, and CD11c (Supplemental Table S.9).

Spatial Protein Expression Biomarkers Enable Accurate Prediction of Occult CS:
To assess whether the various DEPs between areas of in ammation(-) CS parenchyma and Controls could have diagnostic value, we developed a binary prediction model to classify parenchyma tissue ROIs as originating from CS vs.Control patients.To maximize clinical utility as a tool capable of improving the diagnostic yield of tissue sampling in CS, the model was speci cally developed using only data from CS ROIs which were 'granuloma-remote' and 'in ammation(-).'As shown in Fig. 5, after optimization via leave-one-out cross validation, our nal 7-variable logistic regression model was comprised of MHC molecule HLADR, Treg markers FOXP3, CD25, and GITR, immunomodulatory checkpoint molecule VISTA, natural-killer cell marker CD56, and global nuclei/cell proliferation marker Histone-H3.The model achieved excellent performance in the 'held-out' validation set, with an accuracy of 90.0%, AUROC of 0.92, sensitivity of 89.7% and speci city of 90.9%.Given that it is also possible to sample peri-granuloma regions during a clinical EMB procedure while still 'missing' a granuloma, we also assessed performance of the nal model on in ammation(-), peri-granuloma regions.Performance was excellent on these as well, achieving accuracy of 90% (18/20).

Discussion
In this manuscript, we utilized advanced spatial protein expression pro ling, meticulous digital pathology methods, and a unique experimental design to comprehensively study cardiac tissue from CS patients.Our ndings provide numerous insights into CS biology, not only within characteristic granulomas, but also within the cardiac parenchymal tissue near granulomas and remote from granulomas.Particularly notable is the discovery of a small set of protein markers which are differentially expressed CS tissue as compared to a diverse group of controls, even when no in ammatory process or granuloma is evident in the surrounding area.This nding has translational value that extends beyond descriptive biology, introducing the possibility of improving the diagnostic yield of biopsies in patients with suspected CS.We believe this report represents an important contribution to mechanistic research in CS, lays the groundwork for a novel precision diagnostic tool, identi es potential therapeutic targets and highlights the translational potential of spatial-omics methods.

Heterogeneous Immune Pro les of Granulomas in CS:
Molecular heterogeneity of CS granulomas -both within and between -is a novel nding in our analysis.Enabled by a cohort with six times as many tissues and 23 times as many AOIs as the next-largest spatial pro study, 13 our experiments were better equipped to characterize the substantial variability of the CS immune response than any prior investigation.Potential confounders such as varied disease stage and treatment regimens could have contributed to the granuloma protein expression diversity observed during this study.However, neither confounder would explain the marked intra-sample heterogeneity we observed, which must necessarily arise from an intrinsic biological mechanism.It is conceivable that CS granulomas develop at different times, and that the relative 'age' of a granuloma may dictate the local immune pro le.Granulomas may also go through periods of relative activation and quiescence, and thus may present different pro les at different times.There is precedent for granuloma heterogeneity and granuloma 'aging' in published tuberculosis research, though no prior CS study has been able to investigate this phenomenon. 50In the absence of animal models for CS, prospective cohorts with serial biosampling might be required to provide further mechanistic insights into the causes of granuloma heterogeneity.
The Activated Cardiac Parenchyma in CS: Implications of Disease on Parenchyma Protein Expression: Our analysis of 'disease stage' revealed a large number of DEPs between EMB samples obtained early in the course of symptomatic disease vs.
advanced-disease-stage samples obtained at the time of cardiac explant.Though some of these differences could be attributed to treatment effects, the fact that the EMB and explant populations had similar proportions of patients on active treatment at tissue acquisition (14.2% vs. 15.3%)does not support treatment effects as a major confounder.Additionally, though tissue content differences (e.g.large vascular beds, epicardial tissue, bro-fatty scar, etc.) between endocardial EMB samples and transmural explant samples frequently confound traditional, homogenate-based, 'omics' assays, direct visual selection of ROIs as performed in this experiment ensures that unwanted/extraneous tissue areas do not confound our analyses.Thus, we conclude that the progression from active CS to 'burnt out' disease is the primary cause for the large number of DEPs observed between EMB and advanced-stage tissue samples.While our results demonstrate that advanced-stage CS is indeed less immunologically 'active' than earlier disease, our results also demonstrate that numerous pro brotic which remain quite active in latestage disease.Cell-therapy clinical studies targeting activated cardiac broblasts have received signi cant attention in recent years, 29 and our ndings suggest that CS may be another potential population for these novel therapeutics.
The Distance-Gradient in CS Parenchymal Protein Expression: Numerous study markers differentially expressed based on a tissue region's relative distance from CS in ammation.Unsurprisingly, in ammation(+) cardiac parenchyma, which contains overt histologic cellular in ltrates, has increased expression of nearly 2/3rds of the study panel proteins relative to in ammation(-) regions.More interestingly, when comparing in ammation(-) 'peri-granuloma' tissue regions to 'granuloma-remote' regions, we continued to nd signi cant differential expression of many immune markers and pathways, suggesting that immune activity in CS extends beyond the sites of overt in ammation in a graded, distance-dependent fashion.Whether this observed 'distance gradient' is a consequence of recent in ammatory, of paracrine cytokine effects from nearby granulomas, or both, cannot be ascertained without animal models or serial tissue sampling.Nevertheless, it is clear that relative distance from in ammation has meaningful biological effects on the surrounding parenchyma, even when traditional histologic in ammation is absent.
Adding to this narrative is the discovery that even granuloma-remote, in ammation(-) regions in CS hearts manifest in-situ immune pro les that distinguish them from Control tissue.While this nding has obvious diagnostic utility as demonstrated in our predictive modeling efforts, when interpreted in the context of the 'distance-gradient' experiments, it also highlights the diffuse nature of parenchymal immune activation in CS.As an example, HLADR manifests signi cant, graded, differential expression across each of our parenchymal expression experiments.HLADR is increased in in ammation(+) CS tissue relative to in ammation(-) tissue, is increased in peri-granuloma tissue relative to granuloma-remote tissue, and is increased in granuloma-remote tissue relative to Controls.In fact, more than half of our study markers manifest a statistically signi cant 'distance gradient' when peri-granuloma, granuloma-remote, and Control tissue regions are treated as ordinal classes of 'distance' from overt CS in ammation.Taken together, these observations lead to the conclusion that while parenchymal immune activation in CS is indeed distance-dependent, it is also su ciently widespread to enable differentiation of CS tissue from non-CS tissue.
The expression patterns of Treg markers in this experiment represent a particularly interesting manifestation of both widespread and distancedependent protein expression.Treg and Treg-associated markers FOXP3, CD25, GITR, and VISTA are signi cantly increased relative to controls in DEP testing and are also incorporated into the CS predictive model.Unlike HLADR which is increased broadly in CS (albeit exhibiting a distancegradient), Treg and immune-modulating markers manifest an inverted J-shaped expression pattern in CS tissue, with higher expression in in ammation(-) CS parenchyma than in either in ammation(+) or Control tissue (refer to Fig. 4).Historically, there have been con icting reports on both the abundance and functional abilities of Tregs in sarcoidosis. 51,52It has been speculated that Tregs are functionally de cient in sarcoidosis, 51,52 and given the serious cardiomyopathy suffered by all cohort patients in spite of having elevated Treg-associated protein expression, it is tempting to agree with this theory.However, since our results also show that Treg marker expression is only signi cantly increased in regions without overt in ammation, we cannot rule out the possibility that Tregs are preventing the further spread of in ammation into these locations (and thus, are functioning properly).Indeed, given that nearly every T-cell marker except FOXP3, CD25, and VISTA, and GITR were signi cantly increased in in ammation(+) ROIs relative to in ammation(-) ROIs, it seems more likely that a relative insu ciency in Treg number, rather than a de ciency in Treg function, is contributing to in ammatory injury in CS.4][55] This imbalance between the number of Tregs and the number of effector immune cells in in ammation(+) CS parenchyma is only revealed because of the spatial expression methods employed in this experiment.Non-spatially resolved expression methods which would 'average' the Treg expression across the tissue, would miss such subtleties in regional expression.
From a translational perspective, the CS predictive model developed in this manuscript proves the existence of broadly expressed tissue biomarkers which can be leveraged to discriminate between CS patients and non-CS patients, even in the absence of nearby granulomas.This nding represents an important step towards a new diagnostic paradigm for CS that is relatively robust despite the patchy nature of granulomatous involvement.While clinical translation of the GeoMx technology used in our experiments is impractical due to cost and technical complexity, there are alternative diagnostic avenues worthy of consideration.Quantitative digital pathology using multi-marker immunostaining and whole-slide image analysis has been piloted in oncologic [56][57][58] and cardiovascular research, 14,16 and represents a more clinically viable methodology.A quantitative immunopathology scoring system based on the small set of CS biomarkers discovered in this report could greatly improve the diagnostic yield of clinical biopsies, enabling earlier intervention and better cohort identi cation for future therapeutic trials.
As a retrospective cohort study, there are numerous potential confounders which could have impacted study results.Confounding treatment effects may have distorted some of our ndings.In addition, though our sample size is larger than any similar study ever conducted, is still modest in absolute terms, limiting statistical power for some subgroup analyses.With respect to our analyses involving 'granuloma-remote' tissue regions, it is also conceivable that there are nearby granulomas outside the plane of sectioning which may be impacting our results.However, these occult granulomas hiding in the 'Z-axis' would only serve to reduce the statistical signi cance results, rather than amplify them.Lastly, the nature of the GeoMx assay itself, with a focus on ROIs and digitally segmented AOIs, does not achieve true single-cell-level resolution, limiting interpretation of many ner mechanistic details regarding in-situ CS biology.Follow-up research utilizing prospective sarcoid cohorts, serial tissue sampling (perhaps from other sarcoidosis-affected tissues), complementary single-cell assays, and model-based biological systems would help to address many of these limitations.

Conclusion
This research represents the largest application of high-plex spatial protein pro ling ever performed in human cardiac tissue, and is one of the largest investigations into in-situ CS biology ever conducted.We discovered numerous novel phenomena within CS tissue, providing new insights into granuloma content, disease progression, and the broader parenchymal effects of granulomatous in ammation.Additionally, we leveraged the strengths of ROI-based digital spatial pro ling to perform sophisticated biomarker research which has direct translational implications for improving CS diagnostic accuracy.Beyond the speci c results of this research, we believe that our approach to cohort design, ROI selection, and digital pathology AOI segmentation comprise an important demonstration of how to best utilize these emerging spatial technologies.The data that support the ndings of this study are presented in the Manuscript and Extended Data sections.Unprocessed raw data is available from the corresponding author upon reasonable request.

Abbreviations
Tables Table 1: Cohort details with patient-and tissue-level summary statistics Age (yrs)    Cardiac sarcoidosis (CS) 'distance gradient' analyses, highlighting the impact of proximity to granulomatous in ammation on cardiac parenchyma protein expression.Principal component analysis (PCA, on left) and t-SNE (on right) plots of panel-wide protein expression in the study regions of interest (ROIs).'Peri-granuloma' ROIs located within 300µm of a granuloma (purple circles in PCA and in t-SNE plots) predominantly group together on the left edge of the dataset, while 'granuloma-remote' ROIs located >500 µm from any granuloma largely occupy the rest of the eld (pink circles in PCA and t-SNE plots).3b: Tabular results for differential protein expression analyses comparing samples by relative proximity to granulomatous in ammation.ROI-level protein expression from peri-granuloma cardiac parenchyma is compared to ROI-level expression from granuloma-remote cardiac parenchyma.In addition to 'full-ROI-level' differential expression results, digital segmentation of study ROIs enables tissue-compartment-speci c analysis of protein expression in discrete 'areas of illumination' (AOIs).Via this method, cardiomyocyte-speci c and stromal-tissue-speci c differential protein expression comparisons between peri-granuloma and granuloma-remote parenchymal regions are also presented.Differentially expressed proteins with p-values <0.05 are shown for full-ROI level data and for myocyte-speci c and stroma-speci c data.
Comparison of cardiac sarcoidosis (CS) cardiac parenchyma and non-CS control tissue parenchyma.4a: List of differentially expressed proteins (DEPs) between granuloma-remote, histologic in ammation-free regions-of-interest (ROIs) in cardiac sarcoidosis tissue samples as compared to ROIs from a diverse Control tissue population of failing and non-failing hearts.In addition to 'full-ROI-level' differential expression results, digital CS cardiac sarcoidosis EMB -endomyocardial biopsy MRI -magnetic resonance imaging FDG-PET -Fluorodeoxyglucose positron emission tomography FFPE -formalin-xed para n embedded NICM -Non-ischemic cardiomyopathy control NF -Non-Failing Control DSP -digital spatial pro ling ROI -region of interest AOI -area of illumination Treg -regulatory T cell MAPK -Mitogen-activated protein kinase associated pathway PI3K/AKT -Protein kinase B/phosphoinositide-3-kinase/mTOR pathway DEP -differentially expressed proteins PCA -principal component analysis t-SNE -t-distributed stochastic neighbor embedding Declarations Data Availability:

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