Robust single-cell matching and multimodal analysis using shared and distinct features.

The ability to align individual cellular information from multiple experimental sources is fundamental for a systems-level understanding of biological processes. However, currently available tools are mainly designed for single-cell transcriptomics matching and integration, and generally rely on a large number of shared features across datasets for cell matching. This approach underperforms when applied to single-cell proteomic datasets due to the limited number of parameters simultaneously accessed and lack of shared markers across these experiments. Here, we introduce a cell-matching algorithm, matching with partial overlap (MARIO) that accounts for both shared and distinct features, while consisting of vital filtering steps to avoid suboptimal matching. MARIO accurately matches and integrates data from different single-cell proteomic and multimodal methods, including spatial techniques and has cross-species capabilities. MARIO robustly matched tissue macrophages identified from COVID-19 lung autopsies via codetection by indexing imaging to macrophages recovered from COVID-19 bronchoalveolar lavage fluid by cellular indexing of transcriptomes and epitopes by sequencing, revealing unique immune responses within the lung microenvironment of patients with COVID.

Eliminating METTL1-mediated accumulation of PMN-MDSCs prevents hepatocellular carcinoma recurrence after radiofrequency ablation.

BACKGROUND AND AIMS: Radiofrequency ablation (RFA) is an important curative therapy in hepatocellular carcinoma (HCC), but recurrence rate remains as high as all the other HCC therapeutic modalities. Methyltransferase 1 (METTL1), an enzyme for m 7 G tRNA modification, was reported to promote HCC development. Here, we assessed the role of METTL1 in shaping the immunosuppressive tumor microenvironment after insufficient RFA (iRFA). APPROACH AND RESULTS: By immunohistochemistry and multiplex immunofluorescence (mIF) staining, we showed that METTL1 expression was enhanced in post-RFA recurrent HCC, accompanied by increased CD11b + CD15 + polymorphonuclear-myeloid-derived suppressor cells (PMN-MDSCs) and decreased CD8 + T cells. Mechanistically, heat-mediated METTL1 upregulation enhanced TGF-ß2 translation to form the immunosuppressive environment by induction of myeloid-derived suppressor cell. Liver-specific overexpression or knockdown of Mettl1 significantly affected the accumulation of PMN-MDSCs and subsequently affected CD8 + T cell infiltration. Complete RFA successfully eliminated the tumor, whereas iRFA-treated mice exhibited enhanced tumor growth and metastasis with increased PMN-MDSC accumulation and decreased CD8 + T cells compared to sham surgery. Interrupting METTL1-TGF-ß2-PMN-MDSC axis by anti-Ly6G antibody, or knockdown of hepatoma-intrinsic Mettl1 or Tgfb2 , or TGF-ß signaling blockade significantly mitigated tumor progression induced by iRFA and restored CD8 + T cell population. CONCLUSIONS: Our study sheds light on the pivotal role of METTL1 in modulating an immunosuppressive microenvironment and demonstrated that interrupting METTL1-TGF-ß2-PMN-MDSC axis could be a therapeutic strategy to restore antitumor immunity and prevent HCC recurrence after RFA treatment, meriting further clinical studies.

Distinct single-cell immune ecosystems distinguish true and de novo HBV-related hepatocellular carcinoma recurrences.

OBJECTIVE: Revealing the single-cell immune ecosystems in true versus de novo hepatocellular carcinoma (HCC) recurrences could help the optimal development of immunotherapies. DESIGN: We performed 5'and VDJ single-cell RNA-sequencing on 34 samples from 20 recurrent HCC patients. Bulk RNA-sequencing, flow cytometry, multiplexed immunofluorescence, and in vitro functional analyses were performed on samples from two validation cohorts. RESULTS: Analyses of mutational profiles and evolutionary trajectories in paired primary and recurrent HCC samples using whole-exome sequencing identified de novo versus true recurrences, some of which occurred before clinical diagnosis. The tumour immune microenvironment (TIME) of truly recurrent HCCs was characterised by an increased abundance in KLRB1+CD8+ T cells with memory phenotype and low cytotoxicity. In contrast, we found an enrichment in cytotoxic and exhausted CD8+ T cells in the TIME of de novo recurrent HCCs. Transcriptomic and interaction analyses showed elevated GDF15 expression on HCC cells in proximity to dendritic cells, which may have dampened antigen presentation and inhibited antitumour immunity in truly recurrent lesions. In contrast, myeloid cells' cross talk with T cells-mediated T cell exhaustion and immunosuppression in the TIME of de novo recurrent HCCs. Consistent with these findings, a phase 2 trial of neoadjuvant anti-PD-1 immunotherapy showed more responses in de novo recurrent HCC patients. CONCLUSION: True and de novo HCC recurrences occur early, have distinct TIME and may require different immunotherapy strategies. Our study provides a source for genomic diagnosis and immune profiling for guiding immunotherapy based on the type of HCC recurrence and the specific TIME.

Microvascular Invasion Status and Its Survival Impact in Hepatocellular Carcinoma Depend on Tissue Sampling Protocol.

BACKGROUND: The aim of this work is to explore the impact of the number of sampling sites (NuSS) and sampling location on microvascular invasion (MVI) detection rate and long-term survival of hepatocellular carcinoma (HCC), and determine the minimum NuSS for sufficient MVI detection. PATIENTS AND METHODS: From January 2008 to March 2017, 1144 HCC patients who underwent hepatectomy were retrospectively enrolled. Associations between NuSS and MVI positive rates and overall survival were investigated. NuSS thresholds were determined by Chow test and confirmed prospectively in 305 patients from April 2017 to February 2019. In the prospective cohort, the distribution of MVI in different sampling locations and its prognostic effect was evaluated. RESULTS: MVI positive rates increased as NuSS increased, steadily reaching a plateau when NuSS reached a threshold. A threshold of four, six, eight, and eight sampling sites within paracancerous parenchyma ≤ 1 cm from tumor was required for detecting MVI in solitary tumors measuring 1.0-3.0, 3.1-4.9, and ≥ 5.0 cm and multiple tumors. Patients with adequate NuSS achieved longer survival than those with inadequate NuSS [hazard ratio (HR) = 0.75, P = 0.043]. For all MVI-positive patients, MVI could be detected positive in paracancerous parenchyma ≤ 1 cm from tumor. Patients with MVI positive in paracancerous parenchyma > 1 cm had higher recurrence risk than those with MVI positive only in parenchyma ≤ 1 cm (HR = 6.05, P < 0.001). CONCLUSIONS: Adequate NuSS is associated with higher MVI detection rate and better survival of HCC patients. We recommend four, six, eight, and eight as the cut-points for evaluating MVI sampling quality and patients' prognostic stratification in the subgroups of solitary tumors measuring 1.0-3.0 cm, 3.1-4.9 cm and ≥ 5.0 cm and multiple tumors, respectively.

Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data.

Cell population delineation and identification is an essential step in single-cell and spatial-omics studies. Spatial-omics technologies can simultaneously measure information from three complementary domains related to this task: expression levels of a panel of molecular biomarkers at single-cell resolution, relative positions of cells, and images of tissue sections, but existing computational methods for performing this task on single-cell spatial-omics datasets often relinquish information from one or more domains. The additional reliance on the availability of "atlas" training or reference datasets limits cell type discovery to well-defined but limited cell population labels, thus posing major challenges for using these methods in practice. Successful integration of all three domains presents an opportunity for uncovering cell populations that are functionally stratified by their spatial contexts at cellular and tissue levels: the key motivation for employing spatial-omics technologies in the first place. In this work, we introduce Cell Spatio- and Neighborhood-informed Annotation and Patterning (CellSNAP), a self-supervised computational method that learns a representation vector for each cell in tissue samples measured by spatial-omics technologies at the single-cell or finer resolution. The learned representation vector fuses information about the corresponding cell across all three aforementioned domains. By applying CellSNAP to datasets spanning both spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we show that CellSNAP markedly enhances de novo discovery of biologically relevant cell populations at fine granularity, beyond current approaches, by fully integrating cells' molecular profiles with cellular neighborhood and tissue image information.

Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment.

Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.