Next-generation pathology detection of T cell-antigen-presenting cell immune synapses in human liver allografts

Abstract

Background and aims: In otherwise near-normal appearing biopsies by routine light microscopy, next-generation pathology (NGP) detected close pairings (immune pairs; iPAIRs) between lymphocytes and antigen-presenting cells (APCs) that predicted immunosuppression weaning failure in pediatric liver transplant (LTx) recipients (Immunosuppression Withdrawal for Stable Pediatric Liver Transplant Recipients [iWITH], NCT01638559). We hypothesized that NGP-detected iPAIRs enrich for true immune synapses, as determined by nuclear shape metrics, intercellular distances, and supramolecular activation complex (SMAC) formation.

Approach and results: Intralobular iPAIRs (CD45 high lymphocyte-major histocompatibility complex II + APC pairs; n = 1167, training set) were identified at low resolution from multiplex immunohistochemistry-stained liver biopsy slides from several multicenter LTx immunosuppression titration clinical trials (iWITH; NCT02474199 (Donor Alloantigen Reactive Tregs (darTregs) for Calcineurin Inhibitor (CNI) Reduction (ARTEMIS); Prospective Longitudinal Study of iWITH Screen Failures Secondary to Histopathology). After excluding complex multicellular aggregates, high-resolution imaging was used to examine immune synapse formation ( n = 998). By enriching for close intranuclear lymphocyte-APC distance (mean: 0.713 μm) and lymphocyte nuclear flattening (mean ferret diameter: 2.1), SMAC formation was detected in 29% of iPAIR-engaged versus 9.5% of unpaired lymphocytes. Integration of these morphometrics enhanced NGP detection of immune synapses (ai-iSYN). Using iWITH preweaning biopsies from eligible patients ( n = 53; 18 tolerant, 35 nontolerant; testing set), ai-iSYN accurately predicted (87.3% accuracy vs. 81.4% for iPAIRs; 100% sensitivity, 75% specificity) immunosuppression weaning failure. This confirmed the presence and importance of intralobular immune synapse formation in liver allografts. Stratification of biopsy mRNA expression data by immune synapse quantity yielded the top 20 genes involved in T cell activation and immune synapse formation and stability.

Conclusions: NGP-detected immune synapses (subpathological rejection) in LTx patients prior to immunosuppression reduction suggests that NGP-detected (allo)immune activity usefulness for titration of immunosuppressive therapy in various settings.

Fig. 1.. Workflow for refinement of ai-iSYN classifier, confirmation of active immune synapse biology, and confirmation of prediction model in a clinical cohort.

A three-step approach was used to define software-assisted identification of immune synapses detected on LoRes imaging, confirm its biological relevance, and incorporation of the method into a larger, clinically-applicable prediction algorithm for operational tolerance. (A) First, images from our repository of clinical trials were used to evaluate the morphological characteristics of the lymphocyte (CD45^high) in computer-assisted identified lobular iPAIRs with an APC (MHCII^only). HiRes imaging of iPAIRs was used to gather cytoplasmic (Fig. S5) and nuclear data (Fig. S6) that enabled refinement of the new classifier, ai-iSYN (Fig. 2A). (B) Biological definitions of immune synapses were queried using cellular polarization (SMAC formation) as a surrogate for stable immune synapse formation (Fig. 2B–C) and gene expression enrichment for pathways related to active immune synapses (Fig. 3, Table 1). Ai-iSYNs (CD34⁻/CD3⁺/CD45⁺/MHCII^± cells paired with MHCII^only cells) were visualized to determine if lymphocytes in these computer-identified pairs displayed SMACs. The ai-iSYN classifier was applied to the existing iWITH dataset of pediatric baseline eligibility biopsies and the number of lobular ai-iSYNs/mm² was used to inform the analysis of the existing gene expression data for signatures related to immune synapse formation and queried for relationships to DSA values. (C) To show clinical application, the refined classifier for lobular ai-iSYN, lobular CD8⁺ cells (Fig. S9), and MAC387⁺ cells, markers of an inflamed liver immune microenvironment, were used as three parameters of a prediction algorithm for determining success after immunosuppression withdrawal using iWITH trial specimens as a confirmation cohort (Fig. 4).

Fig. 2.. Definition of ai-iSYNs and Representative Phenotypes of Actively Bound Immune Synapses.

(A) Biopsies were stained with a mIHC panel that combined CD45 (a pan lymphocyte marker), HLA-DPB1 [major histocompatibility complex (MHC) II expressed strongly on APCs], CD8 (expressed by cytotoxic T cells, natural killer cells, cortical thymocytes, and some dendritic cells), MAC387 (calprotectin expressed by granulocytes, monocytes and recently immigrated tissue macrophages), and CD34 (endothelial cell marker). After LoRes WSI, the refined ai-iSYN classifier: (1) identified CD34⁻/CD45⁻/MHCII^only APCs paired with a CD34⁻/CD45^high/MHCII^any lymphocytes (as in the iPAIR classifier); (2) included only pairs where the distance between the closest points of the paired nuclei is ≤3 μm; (3) confirmed that the long axes of the nuclei of the paired cells are parallel to each other ±20°; and (4) considered only pairs where the lymphocyte nuclear ratio of long axis:short axis is 1.3 to 1.7, indicative of elongation. Nuclei with elongation >1.7 were excluded because the majority of these nuclei were suboptimally segmented with artificially created spurs. (B) Lobular lymphocytes identified (or not) by the ai-iSYN algorithm were evaluated for polarization of CD3 and CD45 from HiRes images, including detection of SMAC formation. The phenotype of the lymphocytes engaged in ai-iSYNs were defined based on binding stability depicted in published literature (, , –38): “Stable,” “Transient,” or “Unbound”. “Stable” lymphocytes have complete polarization of CD3 to one cytoplasmic location with CD45 exclusion from that region, indicative of SMAC formation. “Transient” lymphocytes have multiple clusters of CD3 in which CD45 is excluded giving the cytoplasm a speckled or spotted appearance. “Transient” lymphocytes may also have an inside-out phenotype in which a single patch of CD45 is present with CD3 exclusion. “Unbound” pairings do not have features of “Stable” or “Transient” pairings and show overlapping signal for CD3 and CD45. Lymphocytes not present in ai-iSYNs were also evaluated. Representative examples are shown. (C) Proportions of lobular lymphocyte phenotypes (in ai-iSYNs n=176; no in ai-iSYNs n=148) were plotted: “Stable” (green), “Transient” (yellow), “Unbound” (red). Chi-squared analysis of the contingency table of binding stability shows significant difference between the paired and unpaired lymphocyte population (p<<0.001).

Fig. 3.. Biclustering of mRNA expression data based on number of ai-iSYN/mm² allows for cross-platform data integration.

Evaluation of iWITH mRNA expression data using supervised biclustering based on lobular ai-iSYN/mm² (histopathological data) to rank patients. Each symbol represents a single baseline eligibility biopsy (all eligible and ineligible iWITH patients). The X-axis corresponds to the strength of correlation to the bicluster seeding pattern (0 = strongest). The Y-axis relates to the cumulative average gene expression for the top 20 genes (IL2RG, LCP1, CD74, CD5, LTB, MICB, CD53, IDO1, GZMA, CD97, HLA-DMB, CCL19, LCK, HLA-DRA, TIGIT, PTPRC, CD48, CCR2, CCL21, and ENTPD1) relative to control genes. To query the relationship to DSA, symbols are used to correlate to the mean fluorescent intensity (MFI) value of DSA presence in the patient: open square = <1,000; filled square = 1–20,000K; filled circle = 20,000+.

Fig. 4.. Use of mIHC parameters for prediction of operational tolerance from eligibility biopsies allows for separation of tolerant from non-tolerant subjects.

Shown in this three-dimensional scatter plot of tolerant (squares, n=18) and non-tolerant (circles, n=35) patients according to the number of lobular CD8⁺ cells/mm² (T effector cells, x-axis), MAC387⁺ cells/mm² (infiltrating macrophages, y-axis), and lobular ai-iSYNs/mm² (z-axis). Axes are scaled where 0 is the lowest expression and 1 is the highest expression (see Methods). The inner cube (shaded box) identifies the inclusion thresholds that, simultaneously, maximize the number of tolerant subjects and minimize the number of non-tolerant subjects. Ranges for the inner cube correspond to (1) lobular CD8⁺: 2 – 52 cells/mm²; (2) MAC387⁺: 3 – 66 cells/mm²; (3) Lobular ai-iSYNs: 1 – 17 pairs/mm². Subjects within the inner cube are closed symbols; subjects outside the inner cube are open symbols.