Single-cell analysis of bronchoalveolar cells in inflammatory and fibrotic post-COVID lung disease.

Background: Persistent radiological lung abnormalities are evident in many survivors of acute coronavirus disease 2019 (COVID-19). Consolidation and ground glass opacities are interpreted to indicate subacute inflammation whereas reticulation is thought to reflect fibrosis. We sought to identify differences at molecular and cellular level, in the local immunopathology of post-COVID inflammation and fibrosis. Methods: We compared single-cell transcriptomic profiles and T cell receptor (TCR) repertoires of bronchoalveolar cells obtained from convalescent individuals with each radiological pattern, targeting lung segments affected by the predominant abnormality. Results: CD4 central memory T cells and CD8 effector memory T cells were significantly more abundant in those with inflammatory radiology. Clustering of similar TCRs from multiple donors was a striking feature of both phenotypes, consistent with tissue localised antigen-specific immune responses. There was no enrichment for known SARS-CoV-2-reactive TCRs, raising the possibility of T cell-mediated immunopathology driven by failure in immune self-tolerance. Conclusions: Post-COVID radiological inflammation and fibrosis show evidence of shared antigen-specific T cell responses, suggesting a role for therapies targeting T cells in limiting post-COVID lung damage.

Deep Learning Enables Spatial Mapping of the Mosaic Microenvironment of Myeloma Bone Marrow Trephine Biopsies.

Bone marrow trephine biopsy is crucial for the diagnosis of multiple myeloma. However, the complexity of bone marrow cellular, morphologic, and spatial architecture preserved in trephine samples hinders comprehensive evaluation. To dissect the diverse cellular communities and mosaic tissue habitats, we developed a superpixel-inspired deep learning method (MoSaicNet) that adapts to complex tissue architectures and a cell imbalance aware deep learning pipeline (AwareNet) to enable accurate detection and classification of rare cell types in multiplex immunohistochemistry images. MoSaicNet and AwareNet achieved an AUC of >0.98 for tissue and cellular classification on separate test datasets. Application of MoSaicNet and AwareNet enabled investigation of bone heterogeneity and thickness as well as spatial histology analysis of bone marrow trephine samples from monoclonal gammopathies of undetermined significance (MGUS) and from paired newly diagnosed and posttreatment multiple myeloma. The most significant difference between MGUS and newly diagnosed multiple myeloma (NDMM) samples was not related to cell density but to spatial heterogeneity, with reduced spatial proximity of BLIMP1+ tumor cells to CD8+ cells in MGUS compared with NDMM samples. Following treatment of patients with multiple myeloma, there was a reduction in the density of BLIMP1+ tumor cells, effector CD8+ T cells, and regulatory T cells, indicative of an altered immune microenvironment. Finally, bone heterogeneity decreased following treatment of patients with multiple myeloma. In summary, deep learning-based spatial mapping of bone marrow trephine biopsies can provide insights into the cellular topography of the myeloma marrow microenvironment and complement aspirate-based techniques. SIGNIFICANCE: Spatial analysis of bone marrow trephine biopsies using histology, deep learning, and tailored algorithms reveals the bone marrow architectural heterogeneity and evolution during myeloma progression and treatment.

Uptake of long-chain fatty acids from the bone marrow suppresses CD8+ T-cell metabolism and function in multiple myeloma.

T cells demonstrate impaired function in multiple myeloma (MM) but suppressive mechanisms in the bone marrow microenvironment remain poorly defined. We observe that bone marrow CD8+ T-cell function is decreased in MM compared with controls, and is also consistently lower within bone marrow samples than in matched peripheral blood samples. These changes are accompanied by decreased mitochondrial mass and markedly elevated long-chain fatty acid uptake. In vitro modeling confirmed that uptake of bone marrow lipids suppresses CD8+ T function, which is impaired in autologous bone marrow plasma but rescued by lipid removal. Analysis of single-cell RNA-sequencing data identified expression of fatty acid transport protein 1 (FATP1) in bone marrow CD8+ T cells in MM, and FATP1 blockade also rescued CD8+ T-cell function, thereby identifying this as a novel target to augment T-cell activity in MM. Finally, analysis of samples from cohorts of patients who had received treatment identified that CD8+ T-cell metabolic dysfunction resolves in patients with MM who are responsive to treatment but not in patients with relapsed MM, and is associated with substantial T-cell functional restoration.

Tracking down tumor-specific T cells.

Two papers published in this edition of Cancer Cell (Zheng et al., 2022 and Veatch et al., 2022) provide an elegant illustration of how single-cell sequencing can be used to define a molecular phenotype which identifies tumor-specific T cells.