Candace C. Liu

Across human malignancies, the ability of cancer cells to switch states (“plasticity”) drives progression, metastasis and therapy resistance ^1–3 . Here we investigated the transcriptional and epigenetic basis of plasticity in small-cell lung cancer (SCLC), an aggressive, treatment-refractory cancer ^{4, 5} . SCLC tumours display plasticity along a neuroendocrine (NE) to nonNE axis, however the trajectories of lineage transition and the gene regulatory network underlying it, remain poorly defined ⁶ . Using single-cell multi-omics, we resolved distinct transcriptional SCLC cell states, including a previously unrecognised Intermediate state comprised of low-identity immunogenic SCLC cells that serve as an obligatory route in NE to nonNE lineage transition. Using single-cell gene expression and chromatin accessibility data, we next developed a Bo olean Ba yes model for transcription factor networks (BoBa-T) to predict regulators of SCLC cell states. We validated RORB as a novel gatekeeper of NE state that represses the nonNE identity in SCLC. RORB inactivation drives NE cells into the Intermediate state, upregulates immunogenic programs and inhibits tumour growth in immunocompetent hosts. Our work identifies a plastic, immunogenic waypoint for SCLC lineage switching and reveals strategies to target it therapeutically.

Despite its effectiveness in suppressing active HIV-1 replication, antiretroviral therapy (ART) does not eliminate the persistent long-lived pool of HIV-1-infected reservoir cells, preventing the eradication of the infection. Lymphoid tissues are key anatomical sites where these reservoirs persist even in the presence of ART, but the mechanisms that are associated with viral persistence in lymphoid tissues and how tissue networks are reshaped in the setting of viral replication remain incompletely understood. Advances in tissue imaging offer a unique opportunity to characterize immune correlates of viral persistence. Here, we used a spatial proteomic method to map immune microenvironments in HIV-1-infected lymph nodes (LNs) at different stages of infection, including with or without ART. LNs from people with HIV-1 (PWH) were characterized by lower CD4+ T cell counts and higher CD8+ T cell counts in both the whole tissue and within follicles compared to people without HIV (PWOH). CD8+ T cells were more abundant in LN samples with active viral replication, defined by detection of the viral protein p24. Further characterization of p24+ LNs showed that CD8+ T cells located inside of B cell follicles exhibited higher levels of markers associated with immune activation and exhaustion, in addition to the inflammasome protein caspase-1. Using a spatial niche detection method, we found that LNs from PWH with varying levels of viral replication were differentially enriched for CD8+ T cells near antigen-presenting cells, myeloid cells, and fibroblasts. Notably, we found that p24+ cells were less enriched near CD8+ T cells but closer to follicular dendritic cells. Finally, comparing LNs from viremic and aviremic donors, where viremia was defined by detectable plasma viral load, we found low levels of activation markers in CD11c+ cells in aviremic donors, including NLRP3 inflammasome activation. Thus, using spatial proteomics to map the immune landscape in LNs, we identified novel markers characterizing immune cell subsets and tissue microenvironments that were differently enriched in PWH with varying levels of viremia, implying that HIV-1 infection confers long-term changes on the immune landscape in LN tissue. Collectively, these data provide new insights into the complex cell networks associated with viral replication at a key tissue reservoir site, which could be relevant for future HIV-1 cure strategies.

SUMMARY: Despite being heavily infiltrated by immune cells, tuberculosis (TB) granulomas often subvert the host response to Mycobacterium tuberculosis (Mtb) infection and support bacterial persistence. We previously discovered that human TB granulomas are enriched for immunosuppressive factors typically associated with tumor-immune evasion, raising the intriguing possibility that they promote tolerance to infection. In this study, our goal was to identify the prime drivers for establishing this tolerogenic niche and to determine if the magnitude of this response correlates with bacterial persistence. To do this, we conducted a multimodal spatial analysis of 52 granulomas from 16 non-human primates (NHP) who were infected with low dose Mtb for 9-12 weeks. Notably, each granuloma’s bacterial burden was individually quantified allowing us to directly ask how granuloma spatial structure and function relate to infection control. We found that a universal feature of TB granulomas was partitioning of the myeloid core into two distinct metabolic environments, one of which is hypoxic. This hypoxic environment associated with pathologic immune cell states, dysfunctional cellular organization of the granuloma, and a near-complete blockade of lymphocyte infiltration that would be required for a successful host response. The extent of these hypoxia-associated features correlated with worsened bacterial burden. We conclude that hypoxia governs immune cell state and organization within granulomas and is a potent driver of subverted immunity during TB.

Summary: In the United States, approximately one in 1000 children are diagnosed with the autoinflammatory disease, Juvenile Idiopathic Arthritis (JIA). A subset of JIA cases manifests as Systemic JIA (sJIA), which is characterized by joint pain, fevers, rashes, and systemic inflammation. Severe pulmonary complications have not historically been associated with sJIA. Since 2010, inhibitors of interleukin-1 and interleukin 6 (IL-1i/IL-6i) are the recommended course of treatment for sJIA, yet recently studies show evidence of a severe drug hypersensitivity reaction implicating these medications in a subset of those treated. With this reaction, sJIA patients can develop severe lung disease, including pulmonary alveolar proteinosis (PAP). As this drug-associated lung disease has only recently been identified, the etiology of sJIA drug-associated PAP (sJIA-daPAP) is poorly understood. We used multiplexed ion beam imaging by time-of-flight (MIBI-TOF) to define the cellular immune infiltrate and describe pathological features of PAP in sJIA-daPAP patients. We found an enrichment of eosinophils, neutrophils, and M2 macrophages within regions of lipoproteinosis. These enriched subsets all upregulate IFNγ within lipoproteinosis, a signature specific to sJIA-daPAP samples compared to non-sJIA-PAP samples. In a cellular neighborhood analysis, we identified that eosinophils, neutrophils and M2 macrophages frequently co-localize within the same cellular microenvironment, especially in lipoproteinosis regions. Therefore, this spatial coordination may be involved in clearance or persistence of lipoproteinosis in sJIA-daPAP. This study provides a comprehensive overview of sJIA-daPAP immune pathology and suggests cellular mechanisms that drive inflammation in sJIA patients experiencing pulmonary complications associated with delayed drug hypersensitivity during IL-1i/IL-6i treatment.

Multiplexed imaging offers a powerful approach to characterize the spatial topography of tissues in both health and disease. To analyze such data, the specific combination of markers that are present in each cell must be enumerated to enable accurate phenotyping, a process that often relies on unsupervised clustering. We constructed the Pan-Multiplex (Pan-M) dataset containing 197 million distinct annotations of marker expression across 15 different cell types. We used Pan-M to create Nimbus, a deep learning model to predict marker positivity from multiplexed image data. Nimbus is a pre-trained model that uses the underlying images to classify marker expression across distinct cell types, from different tissues, acquired using different microscope platforms, without requiring any retraining. We demonstrate that Nimbus predictions capture the underlying staining patterns of the full diversity of markers present in Pan-M. We then show how Nimbus predictions can be integrated with downstream clustering algorithms to robustly identify cell subtypes in image data. We have open-sourced Nimbus and Pan-M to enable community use at https://github.com/angelolab/Nimbus-Inference .

While technologies for multiplexed imaging have provided an unprecedented understanding of tissue composition in health and disease, interpreting this data remains a significant computational challenge. To understand the spatial organization of tissue and how it relates to disease processes, imaging studies typically focus on cell-level phenotypes. However, images can capture biologically important objects that are outside of cells, such as the extracellular matrix. Here, we developed a pipeline, Pixie, that achieves robust and quantitative annotation of pixel-level features using unsupervised clustering and show its application across a variety of biological contexts and multiplexed imaging platforms. Furthermore, current cell phenotyping strategies that rely on unsupervised clustering can be labor intensive and require large amounts of manual cluster adjustments. We demonstrate how pixel clusters that lie within cells can be used to improve cell annotations. We comprehensively evaluate pre-processing steps and parameter choices to optimize clustering performance and quantify the reproducibility of our method. Importantly, Pixie is open source and easily customizable through a user-friendly interface.

Multiplex single-synapse events from multiple regions of Alzheimer’s disease brain revealed new alterations unique to humans.

Multiplexed ion beam imaging by time-of-flight (MIBI-TOF) is a form of mass spectrometry imaging that uses metal labeled antibodies and secondary ion mass spectrometry to image dozens of proteins simultaneously in the same tissue section. Working with the National Cancer Institute’s (NCI) Cancer Immune Monitoring and Analysis Centers (CIMAC), we undertook a validation study, assessing concordance across a dozen serial sections of a tissue microarray of 21 samples that were independently processed and imaged by MIBI-TOF or single-plex immunohistochemistry (IHC) over 12 days. Pixel-level features were highly concordant across all 16 targets assessed in both staining intensity ( R ² = 0.94 ± 0.04) and frequency ( R ² = 0.95 ± 0.04). Comparison to digitized, single-plex IHC on adjacent serial sections revealed similar concordance ( R ² = 0.85 ± 0.08) as well. Lastly, automated segmentation and clustering of eight cell populations found that cell frequencies between replicates yielded an average correlation of R ² = 0.92 ± 0.06. Taken together, we demonstrate that MIBI-TOF, with well-vetted reagents and automated analysis, can generate consistent and quantitative annotations of clinically relevant cell states in archival human tissue, and more broadly, present a scalable framework for benchmarking multiplexed IHC approaches.