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In the development of a multiplex immunofluorescence (IF) platform and the optimization and validation of new multiplex IF panels using a tyramide signal amplification system, several technical requirements are important for high-quality staining, analysis, and results. The aim of this review is to discuss the basic requirements for performing multiplex IF tyramide signal amplification (TSA) in formalin-fixed, paraffin-embedded cancer tissues to support translational oncology research. Our laboratory has stained approximately 4000 formalin-fixed, paraffin-embedded tumor samples using the multiplex IF TSA system for immune profiling of several labeled biomarkers in a single slide to elucidate cancer biology at a protein level and identify therapeutic targets and biomarkers. By analyzing several proteins in thousands of cells on a single slide, this technique provides a systems-level view of various processes in various tumor tissues. Although this technology shows high flexibility in cancer studies, it presents several challenges when applied to study different histology cancers. Our experience shows that adequate antibody validation, staining optimization, analysis strategies, and data generation are important steps for generating quality results. Tissue management, fixation procedures, storage, and cutting can also affect the results of the assay and must be standardized. Overall, this method is reliable for supporting translational research given a precise, step-by-step approach.

Primary biliary cholangitis (PBC) is a chronic autoimmune liver disease that gradually progresses, making early diagnosis and treatment challenging. Reliable biomarkers could enhance diagnostic accuracy and therapeutic development.BACKGROUNDPrimary biliary cholangitis (PBC) is a chronic autoimmune liver disease that gradually progresses, making early diagnosis and treatment challenging. Reliable biomarkers could enhance diagnostic accuracy and therapeutic development.This study analyzed three publicly available gene expression datasets from the GEO database: GSE119600 (90 PBC patients and 47 healthy controls), GSE159676 (12 PBC patients and 6 controls), and GSE61260 (11 PBC patients and 38 controls). To identify genes closely linked to PBC, we applied machine learning techniques, including LASSO, SVM-RFE, and random forest. We subsequently conducted gene set enrichment (GSEA) and immune cell infiltration analyses to investigate their biological significance. Additionally, potential drug interactions were explored via the DGIdb database, and a ceRNA regulatory network was developed to examine gene regulation. Finally, the expression of selected genes was validated through multiplex immunofluorescence staining of liver tissue samples from PBC patients.METHODSThis study analyzed three publicly available gene expression datasets from the GEO database: GSE119600 (90 PBC patients and 47 healthy controls), GSE159676 (12 PBC patients and 6 controls), and GSE61260 (11 PBC patients and 38 controls). To identify genes closely linked to PBC, we applied machine learning techniques, including LASSO, SVM-RFE, and random forest. We subsequently conducted gene set enrichment (GSEA) and immune cell infiltration analyses to investigate their biological significance. Additionally, potential drug interactions were explored via the DGIdb database, and a ceRNA regulatory network was developed to examine gene regulation. Finally, the expression of selected genes was validated through multiplex immunofluorescence staining of liver tissue samples from PBC patients.We identified PSMB7, TBK1, SLC29A1, and CD244 as key genes associated with PBC; these genes were significantly enriched in immune-related pathways and strongly correlated with immune regulation. Drug target prediction indicated that some genes could interact with existing immunomodulators or anticancer drugs. ceRNA network analysis revealed that TBK1, SLC29A1, and CD244 interact with multiple miRNAs and lncRNAs, potentially regulating the immune microenvironment of PBC through noncoding RNA mechanisms. Immunofluorescence staining confirmed that these genes were highly expressed in liver tissues from PBC patients.RESULTSWe identified PSMB7, TBK1, SLC29A1, and CD244 as key genes associated with PBC; these genes were significantly enriched in immune-related pathways and strongly correlated with immune regulation. Drug target prediction indicated that some genes could interact with existing immunomodulators or anticancer drugs. ceRNA network analysis revealed that TBK1, SLC29A1, and CD244 interact with multiple miRNAs and lncRNAs, potentially regulating the immune microenvironment of PBC through noncoding RNA mechanisms. Immunofluorescence staining confirmed that these genes were highly expressed in liver tissues from PBC patients.By integrating machine learning and functional analyses, this study identified four genes that may serve as potential biomarkers for PBC. Their involvement in immune regulation suggests possible applications in both diagnosis and therapy. Further studies are necessary to explore their clinical relevance and therapeutic potential.CONCLUSIONBy integrating machine learning and functional analyses, this study identified four genes that may serve as potential biomarkers for PBC. Their involvement in immune regulation suggests possible applications in both diagnosis and therapy. Further studies are necessary to explore their clinical relevance and therapeutic potential.

Optimisation and validation of a multiplex immunofluorescence (mIF) workflow, from staining to digital image analysis (DIA), ensure assay robustness. Chromogenic immunohistochemistry (IHC) and fluorescent singleplexes are fundamental in this process, particularly when biomarkers are co‐expressed. We describe our experience developing two mIF panels and the various parameters of staining, scanning and DIA to consider when standardising a digital pathology workflow. Multiplex immunofluorescence is a powerful tool for the simultaneous detection of tissue‐based biomarkers, revolutionising traditional immunohistochemistry. The Opal methodology allows up to eight biomarkers to be measured concomitantly without cross‐reactivity, permitting identification of different cell populations within the tumour microenvironment. In this study, we aimed to validate a multiplex immunofluorescence workflow in two complementary multiplex panels and evaluate the tumour immune microenvironment in colorectal cancer (CRC) formalin‐fixed paraffin‐embedded tissue. We stained CRC and tonsil samples using Opal multiplex immunofluorescence on a Leica BOND RX immunostainer. We then acquired images on an Akoya Vectra Polaris and performed multispectral unmixing using inform. Antibody panels were validated on tissue microarray sections containing cores from six normal tissue types, using qupath for image analysis. Comparisons between chromogenic immunohistochemistry and multiplex immunofluorescence on consecutive sections from the same tissue microarray showed significant correlation (rs > 0.9, P‐value < 0.0001), validating both panels. We identified many factors that influenced the quality of the acquired fluorescent images, including biomarker co‐expression, staining order, Opal‐antibody pairing, sample thickness, multispectral unmixing and biomarker detection order during image analysis. Overall, we report the optimisation and validation of a multiplex immunofluorescence process, from staining to image analysis, ensuring assay robustness. Our multiplex immunofluorescence protocols permit the accurate detection of multiple immune markers in various tissue types, using a workflow that enables rapid processing of samples, above and beyond previous workflows.

Background Fibrinogen-like protein 1 (FGL1)—Lymphocyte activating gene 3 (LAG-3) pathway is a promising immunotherapeutic target and has synergistic effect with programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1). However, the prognostic significance of FGL1-LAG-3 pathway and the correlation with PD-L1 in hepatocellular carcinoma (HCC) remain unknown. Methods The levels of LAG-3, FGL1, PD-L1 and cytotoxic T (CD8 + T) cells in 143 HCC patients were assessed by multiplex immunofluorescence. Associations between the marker’s expression and clinical significances were studied. Results We found FGL1 and LAG-3 densities were elevated while PD-L1 and CD8 were decreased in HCC tissues compared to adjacent normal liver tissues. High levels of FGL1 were strongly associated with high densities of LAG-3 + cells but not PD-L1. CD8 + T cells densities had positive correlation with PD-L1 levels and negative association with FGL1 expression. Elevated densities of LAG-3 + cells and low levels of CD8 + T cells were correlated with poor disease outcome. Moreover, LAG-3 + cells deteriorated patient stratification based on the abundance of CD8 + T cells. Patients with positive PD-L1 expression on tumor cells (PD-L1 TC + ) tended to have an improved survival than that with negative PD-L1 expression on tumor cells (PD-L1 TC − ). Furthermore, PD-L1 TC − in combination with high densities of LAG-3 + cells showed the worst prognosis, and PD-L1 TC + patients with low densities of LAG-3 + cells had the best prognosis. Conclusions LAG-3, FGL1, PD-L1 and CD8 have distinct tissue distribution and relationships with each other. High levels of LAG-3 + cells and CD8 + T cells represent unfavorable and favorable prognostic biomarkers for HCC respectively.

[This corrects the article DOI: 10.3389/fimmu.2023.1324618.].

BackgroundNeoadjuvant chemoimmunotherapy treatment (NCIT) has achieved great success for non-small cell lung cancer (NSCLC); however, the intrinsic mechanism underlying this treatment remains unclear.MethodsThirty-two patients with stage IIA–IIIC NSCLC who underwent surgery after NCIT were included in this retrospective study. Multiplex immunofluorescence (mIF) staining and image analysis assays were performed on the samples collected before and after NCIT for each patient. RNA analyses was applied to confirm the mIF results.ResultsAmong the enrolled patients, 14 achieved major pathological response or pathological complete response (pCR) and were defined as the ‘response’ group, whereas 18 patients did not respond well to NCIT and were defined as the ‘nonresponse’ group. The results of the mIF assays revealed an overall increase in tumor immune lymphocytes (TILs) after NCIT in the stroma area (p = 0.03) rather than the tumor area (p = 0.86). The percentage of CD8+ T cells and tertiary lymphoid structure counts in both the response and nonresponse groups increased significantly after NCIT compared with before NCIT. CD3+ T cells and FOXP3+ cells decreased significantly in the response group but remained unchanged or increased in the nonresponse group. A comparison of the response and nonresponse groups showed that CD3, FOXP3+ and CD8+/PD-1+ cells before NCIT may serve as predictors of the response to neoadjuvant immunotherapy. The RNA analyses confirmed the mIF results that TILs were elevated after NCIT.ConclusionsThe infiltration of immune cells before NCIT was correlated with pathologic complete response, which enhanced the TILs as a promising predictor for selecting patients who were more likely to benefit from NCIT.

Background Immune checkpoint inhibitors (ICI) are approved for treatment of recurrent or metastatic oropharyngeal head and neck squamous cell carcinoma in the first- and second-line settings. However, only 15–20% of patients benefit from this treatment, a feature increasingly ascribed to the peculiar characteristics of the tumor immune microenvironment (TIME). Methods Immune-related gene expression profiling (GEP) and multiplex immunofluorescence (mIF) including spatial proximity analysis, were used to characterize the TIME of 39 treatment-naïve oropharyngeal squamous cell carcinomas (OPSCC) and the corresponding lymph node metastases. GEP and mIF results were correlated with disease-free survival (DFS). HPV-positive tumors disclosed a stronger activation of several immune signalling pathways, as well as a higher expression of genes related to total tumor-infiltrating lymphocytes, CD8 T cells, cytotoxic cells and exhausted CD8 cells, than HPV-negative patients. Accordingly, mIF revealed that HPV-positive lesions were heavily infiltrated as compared to HPV-negative counterparts, with a higher density of T cells and checkpoint molecules. CD8+ T cells appeared in closer proximity to tumor cells, CD163+ macrophages and FoxP3+ cells in HPV-positive primary tumors, and related metastases. In HPV-positive lesions, PD-L1 expression was increased as compared to HPV-negative samples, and PD-L1+ tumor cells and macrophages were closer to PD-1+ cytotoxic T lymphocytes. Considering the whole cohort, a positive correlation was observed between DFS and higher levels of activating immune signatures and T cell responses, higher density of PD-1+ T cells and their closer proximity to tumor cells or PD-L1+ macrophages. HPV-positive patients with higher infiltration of T cells and macrophages had a longer DFS, while CD163+ macrophages had a negative role in prognosis of HPV-negative patients. Conclusions Our results suggest that checkpoint expression may reflect an ongoing antitumor immune response. Thus, these observations provide the rationale for the incorporation of ICI in the loco-regional therapy strategies for patients with heavily infiltrated treatment-naïve OPSCC, and for the combination of ICI with tumor-specific T cell response inducers or TAM modulators for the “cold” OPSCC counterparts.

This study attempted to profile the tumor immune microenvironment (TIME) of non-small cell lung cancer (NSCLC) by multiplex immunofluorescence of 681 NSCLC cases. The number, density, and proportion of 26 types of immune cells in tumor nest and tumor stroma were evaluated, revealing some close interactions particularly between intrastromal neutrophils and intratumoral regulatory T cells (Treg) ( r 2 = 0.439, P < 0.001), intrastromal CD4+CD38+ T cells and CD20-positive B cells ( r 2 = 0.539, P < 0.001), and intratumoral CD8-positive T cells and M2 macrophages expressing PD-L1 ( r 2  = 0.339, P < 0.001). Three immune subtypes correlated with distinct immune characteristics were identified using the unsupervised consensus clustering approach. The immune-activated subtype had the longest disease-free survival (DFS) and demonstrated the highest infiltration of CD4-positive T cells, CD8-positive T cells, and CD20-positive B cells. The immune-defected subtype was rich in cancer stem cells and macrophages, and these patients had the worst prognosis. The immune-exempted subtype had the highest levels of neutrophils and Tregs. Intratumoral CD68-positive macrophages, M1 macrophages, and intrastromal CD4+ cells, CD4+FOXP3- cells, CD8+ cells, and PD-L1+ cells were further found to be the most robust prognostic biomarkers for DFS, which were used to construct and validate the immune-related risk score for risk stratification (high vs . median vs . low) and the prediction of 5-year DFS rates (23.2% vs . 37.9% vs . 43.1%, P < 0.001). In conclusion, the intricate and intrinsic structure of TIME in NSCLC was demonstrated, showing potency in subtyping and prognostication.

A workflow has been evaluated that utilizes a single tissue section to obtain spatially co-registered, molecular, and phenotypical information suitable for AI-enabled image analysis. Desorption electrospray ionization mass spectrometry imaging (DESI-MSI) was used to obtain molecular information followed by conventional histological staining and immunolabelling. The impact of varying DESI-MSI conditions (e.g., heated transfer line (HTL) temperature, scan rate, acquisition time) on the detection of small molecules and lipids as well as on tissue integrity crucial for integration into typical clinical pathology workflows was assessed in human kidney. Increasing the heated transfer line temperature from 150 to 450 °C resulted in a 1.8-fold enhancement in lipid signal at a scan rate of 10 scans/s, while preserving histological features. Moreover, increasing the acquisition speed to 30 scans/s yielded superior lipid signal when compared to 10 scans/s at 150 °C. Tissue morphology and protein epitopes remained intact allowing full histological assessment and further multiplex phenotyping by immunofluorescence (mIF) and immunohistochemistry (mIHC) of the same section. The successful integration of the workflow incorporating DESI-MSI, H&E, and immunolabelling on a single tissue section revealed an accumulation of ascorbic acid in regions of focal chronic inflammatory cell infiltrate within non-cancerous kidney tissue. Additionally, a strong positive correlation between PI 38:3 and proliferating cells was observed in clear cell renal cell carcinoma (ccRCC) showing the utility of this approach in uncovering molecular associations in disease pathology.

Background Anti-PD-(L)1 immunotherapy has been recommended for non-small cell lung cancer (NSCLC) patients with lymph node metastases (LNM). However, the exact functional feature and spatial architecture of tumor-infiltrating CD8 + T cells remain unclear in these patients. Methods Tissue microarrays (TMAs) from 279 IA-IIIB NSCLC samples were stained by multiplex immunofluorescence (mIF) for 11 markers (CD8, CD103, PD-1, Tim3, GZMB, CD4, Foxp3, CD31, αSMA, Hif-1α, pan-CK). We evaluated the density of CD8 + T-cell functional subsets, the mean nearest neighbor distance (mNND) between CD8 + T cells and neighboring cells, and the cancer-cell proximity score (CCPS) in invasive margin (IM) as well as tumor center (TC) to investigate their relationships with LNM and prognosis. Results The densities of CD8 + T-cell functional subsets, including predysfunctional CD8 + T cells (T predys ) and dysfunctional CD8 + T cells (T dys ), in IM predominated over those in TC ( P  < 0.001). Multivariate analysis identified that the densities of CD8 + T predys cells in TC and CD8 + T dys cells in IM were significantly associated with LNM [OR = 0.51, 95%CI (0.29–0.88), P  = 0.015; OR = 5.80, 95%CI (3.19–10.54), P  < 0.001; respectively] and recurrence-free survival (RFS) [HR = 0.55, 95%CI (0.34–0.89), P  = 0.014; HR = 2.49, 95%CI (1.60–4.13), P  = 0.012; respectively], independent of clinicopathological factors. Additionally, shorter mNND between CD8 + T cells and their neighboring immunoregulatory cells indicated a stronger interplay network in the microenvironment of NSCLC patients with LNM and was associated with worse prognosis. Furthermore, analysis of CCPS suggested that cancer microvessels (CMVs) and cancer-associated fibroblasts (CAFs) selectively hindered CD8 + T cells from contacting with cancer cells, and were associated with the dysfunction of CD8 + T cells. Conclusion Tumor-infiltrating CD8 + T cells were in a more dysfunctional status and in a more immunosuppressive microenvironment in patients with LNM compared with those without LNM.