Maturation signatures of conventional dendritic cell subtypes in COVID-19 suggest direct viral sensing.

Growing evidence suggests that conventional dendritic cells (cDCs) undergo aberrant maturation in COVID-19, which negatively affects T-cell activation. The presence of effector T cells in patients with mild disease and dysfunctional T cells in severely ill patients suggests that adequate T-cell responses limit disease severity. Understanding how cDCs cope with SARS-CoV-2 can help elucidate how protective immune responses are generated. Here, we report that cDC2 subtypes exhibit similar infection-induced gene signatures, with the upregulation of IFN-stimulated genes and IL-6 signaling pathways. Furthermore, comparison of cDCs between patients with severe and mild disease showed severely ill patients to exhibit profound downregulation of genes encoding molecules involved in antigen presentation, such as MHCII, TAP, and costimulatory proteins, whereas we observed the opposite for proinflammatory molecules, such as complement and coagulation factors. Thus, as disease severity increases, cDC2s exhibit enhanced inflammatory properties and lose antigen presentation capacity. Moreover, DC3s showed upregulation of anti-apoptotic genes and accumulated during infection. Direct exposure of cDC2s to the virus in vitro recapitulated the activation profile observed in vivo. Our findings suggest that SARS-CoV-2 interacts directly with cDC2s and implements an efficient immune escape mechanism that correlates with disease severity by downregulating crucial molecules required for T-cell activation.

Integrating metabolic profiling of pancreatic juice with transcriptomic analysis of pancreatic cancer tissue identifies distinct clinical subgroups.

Introduction: Metabolic reprogramming is a hallmark feature of pancreatic ductal adenocarcinoma (PDAC). A pancreatic juice (PJ) metabolic signature has been reported to be prognostic of oncological outcome for PDAC. Integration of PJ profiling with transcriptomic and spatial characterization of the tumor microenvironment would help in identifying PDACs with peculiar vulnerabilities. Methods: We performed a transcriptomic analysis of 26 PDAC samples grouped into 3 metabolic clusters (M_CL) according to their PJ metabolic profile. We analyzed molecular subtypes and transcriptional differences. Validation was performed by multidimensional imaging on tumor slides. Results: Pancreatic juice metabolic profiling was associated with PDAC transcriptomic molecular subtypes (p=0.004). Tumors identified as M_CL1 exhibited a non-squamous molecular phenotype and demonstrated longer survival. Enrichment analysis revealed the upregulation of immune genes and pathways in M_CL1 samples compared to M_CL2, the group with worse prognosis, a difference confirmed by immunofluorescence on tissue slides. Enrichment analysis of 39 immune signatures by xCell confirmed decreased immune signatures in M_CL2 compared to M_CL1 and allowed a stratification of patients associated with longer survival. Discussion: PJ metabolic fingerprints reflect PDAC molecular subtypes and the immune microenvironment, confirming PJ as a promising source of biomarkers for personalized therapy.

Integrating AI-Powered Digital Pathology and Imaging Mass Cytometry Identifies Key Classifiers of Tumor Cells, Stroma, and Immune Cells in Non-Small Cell Lung Cancer.

Artificial intelligence (AI)-powered approaches are becoming increasingly used as histopathologic tools to extract subvisual features and improve diagnostic workflows. On the other hand, hi-plex approaches are widely adopted to analyze the immune ecosystem in tumor specimens. Here, we aimed at combining AI-aided histopathology and imaging mass cytometry (IMC) to analyze the ecosystem of non-small cell lung cancer (NSCLC). An AI-based approach was used on hematoxylin and eosin (H&E) sections from 158 NSCLC specimens to accurately identify tumor cells, both adenocarcinoma and squamous carcinoma cells, and to generate a classifier of tumor cell spatial clustering. Consecutive tissue sections were stained with metal-labeled antibodies and processed through the IMC workflow, allowing quantitative detection of 24 markers related to tumor cells, tissue architecture, CD45+ myeloid and lymphoid cells, and immune activation. IMC identified 11 macrophage clusters that mainly localized in the stroma, except for S100A8+ cells, which infiltrated tumor nests. T cells were preferentially localized in peritumor areas or in tumor nests, the latter being associated with better prognosis, and they were more abundant in highly clustered tumors. Integrated tumor and immune classifiers were validated as prognostic on whole slides. In conclusion, integration of AI-powered H&E and multiparametric IMC allows investigation of spatial patterns and reveals tissue relevant features with clinical relevance. SIGNIFICANCE: Leveraging artificial intelligence-powered H&E analysis integrated with hi-plex imaging mass cytometry provides insights into the tumor ecosystem and can translate tumor features into classifiers to predict prognosis, genotype, and therapy response.

High-Resolution Analysis of Mononuclear Phagocytes Reveals GPNMB as a Prognostic Marker in Human Colorectal Liver Metastasis.

Patients with colorectal liver metastasis (CLM) present with heterogenous clinical outcomes and improved classification is needed to ameliorate the therapeutic output. Macrophages (Mϕ) hold promise as prognostic classifiers and therapeutic targets. Here, stemming from a single-cell analysis of mononuclear phagocytes infiltrating human CLM, we identified two Mϕ markers associated with distinct populations with opposite clinical relevance. The invasive margin of CLM was enriched in pro-inflammatory monocyte-derived Mϕ (MoMϕ) expressing the monocytic marker SERPINB2, and a more differentiated population, tumor-associated Mϕ (TAM), expressing glycoprotein nonmetastatic melanoma protein B (GPNMB). SERPINB2+ MoMϕ had an early inflammatory profile, whereas GPNMB+ TAMs were enriched in pathways of matrix degradation, angiogenesis, and lipid metabolism and were found closer to the tumor margin, as confirmed by spatial transcriptomics on CLM specimens. In a cohort of patients, a high infiltration of SERPINB2+ cells independently associated with longer disease-free survival (DFS; P = 0.033), whereas a high density of GPNMB+ cells correlated with shorter DFS (P = 0.012) and overall survival (P = 0.002). Cell-cell interaction analysis defined opposing roles for MoMϕ and TAMs, suggesting that SERPINB2+ and GPNMB+ cells are discrete populations of Mϕ and may be exploited for further translation to an immune-based stratification tool. This study provides evidence of how multi-omics approaches can identify nonredundant, clinically relevant markers for further translation to immune-based patient stratification tools and therapeutic targets. GPNMB has been shown to set Mϕ in an immunosuppressive mode. Our high dimensional analyses provide further evidence that GPNMB is a negative prognostic indicator and a potential player in the protumor function of Mϕ populations.

Integrated longitudinal immunophenotypic, transcriptional and repertoire analyses delineate immune responses in COVID-19 patients.

To understand how a protective immune response against SARS-CoV-2 develops over time, we integrated phenotypic, transcriptional and repertoire analyses on PBMCs from mild and severe COVID-19 patients during and after infection, and compared them to healthy donors (HD). A type I IFN-response signature marked all the immune populations from severe patients during the infection. Humoral immunity was dominated by IgG production primarily against the RBD and N proteins, with neutralizing antibody titers increasing post infection and with disease severity. Memory B cells, including an atypical FCRL5+ T-BET+ memory subset, increased during the infection, especially in patients with mild disease. A significant reduction of effector memory, CD8+ T cells frequency characterized patients with severe disease. Despite such impairment, we observed robust clonal expansion of CD8+ T lymphocytes, while CD4+ T cells were less expanded and skewed toward TCM and TH2-like phenotypes. MAIT cells were also expanded, but only in patients with mild disease. Terminally differentiated CD8+ GZMB+ effector cells were clonally expanded both during the infection and post-infection, while CD8+ GZMK+ lymphocytes were more expanded post-infection and represented bona fide memory precursor effector cells. TCR repertoire analysis revealed that only highly proliferating T cell clonotypes, which included SARS-CoV-2-specific cells, were maintained post-infection and shared between the CD8+ GZMB+ and GZMK+ subsets. Overall, this study describes the development of immunity against SARS-CoV-2 and identifies an effector CD8+ T cell population with memory precursor-like features.