Faculty Bibliography

Pathogen encounter can result in epigenetic remodeling that shapes disease caused by heterologous pathogens. Here, we examined innate immune memory in the context of commonly circulating respiratory viruses. Single-cell analyses of airway-resident immune cells in a disease-relevant murine model of SARS-CoV-2 recovery revealed epigenetic reprogramming in alveolar macrophages following infection. Post-COVID-19 human monocytes exhibited similar epigenetic signatures. In airway-resident macrophages, past SARS-CoV-2 infection increased activity of type I interferon (IFN-I)-related transcription factors and epigenetic poising of antiviral genes. Viral pattern recognition and canonical IFN-I signaling were required for the establishment of this innate immune memory and augmented secondary antiviral responses. Antiviral innate immune memory mounted by airway-resident macrophages post-SARS-CoV-2 was necessary and sufficient to ameliorate secondary disease caused by influenza A virus and curtailed hyperinflammatory dysregulation and mortality. Our findings provide insights into antiviral innate immune memory in the airway that may facilitate the development of broadly effective therapeutic strategies.

Diverse cellular insults converge on activation of the heat shock factor 1 (HSF1), which regulates the proteotoxic stress response to maintain protein homoeostasis. HSF1 regulates numerous gene programmes beyond the proteotoxic stress response in a cell-type- and context-specific manner to promote malignancy. However, the role(s) of HSF1 in immune populations of the tumour microenvironment remain elusive. Here, we leverage an in vivo model of HSF1 activation and single-cell transcriptomic tumour profiling to show that augmented HSF1 activity in natural killer (NK) cells impairs cytotoxicity, cytokine production and subsequent anti-tumour immunity. Mechanistically, HSF1 directly binds and regulates the expression of key mediators of NK cell effector function. This work demonstrates that HSF1 regulates the immune response under the stress conditions of the tumour microenvironment. These findings have important implications for enhancing the efficacy of adoptive NK cell therapies and for designing combinatorial strategies including modulators of NK cell-mediated tumour killing.

Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerful, but are inherently limited by the cost and quality of annotations used for training. Therefore, we present Histomorphological Phenotype Learning, a self-supervised methodology requiring no labels and operating via the automatic discovery of discriminatory features in image tiles. Tiles are grouped into morphologically similar clusters which constitute an atlas of histomorphological phenotypes (HP-Atlas), revealing trajectories from benign to malignant tissue via inflammatory and reactive phenotypes. These clusters have distinct features which can be identified using orthogonal methods, linking histologic, molecular and clinical phenotypes. Applied to lung cancer, we show that they align closely with patient survival, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype. These properties are maintained in a multi-cancer study.

Multiplexed Imaging technologies are powerful techniques that enable ultrahigh-plex spatial phenotyping of whole tissue sections at single cell spatial resolution. Co-Detection by Indexing (CODEX) multiplexing can detect up to 100 proteins using cyclic detection of DNA conjugated antibodies applied to tissue sections. However, it is necessary to correlate multiplexed fluorescent (mIF) spatial images with Hematoxylin and Eosin (H&E) stained sections post analysis. To effectively correlate mIF spatial images with H&E morphology, an (H&E) staining protocol was developed that is directly applied to the CODEX Fusion flow-cell slide after analysis allowing for direct H&E correlation and annotation with mIF images.

Approximately 30% of early-stage lung adenocarcinoma patients present with disease progression after successful surgical resection. Despite efforts of mapping the genetic landscape, there has been limited success in discovering predictive biomarkers of disease outcomes. Here we performed a systematic multi-omic assessment of 143 tumors and matched tumor-adjacent, histologically-normal lung tissue with long-term patient follow-up. Through histologic, mutational, and transcriptomic profiling of tumor and adjacent-normal tissue, we identified an inflammatory gene signature in tumor-adjacent tissue as the strongest clinical predictor of disease progression. Single-cell transcriptomic analysis demonstrated the progression-associated inflammatory signature was expressed in both immune and non-immune cells, and cell type-specific profiling in monocytes further improved outcome predictions. Additional analyses of tumor-adjacent transcriptomic data from The Cancer Genome Atlas validated the association of the inflammatory signature with worse outcomes across cancers. Collectively, our study suggests that molecular profiling of tumor-adjacent tissue can identify patients at high risk for disease progression.

With the development of genomic technologies, the isolation of genomic DNA (gDNA) from clinical samples is increasingly required for clinical diagnostics and research studies. In this study, we explored the potential of utilizing various leftover blood samples obtained from routine clinical tests as a viable source of gDNA. Using an automated method with optimized pre-treatments, we obtained gDNA from seven types of clinical leftover blood, with average yields of gDNA ranging from 3.11 ± 0.45 to 22.45 ± 4.83 μg. Additionally, we investigated the impact of storage conditions on gDNA recovery, resulting in yields of 8.62-68.08 μg when extracting gDNA from EDTA leftover blood samples stored at 4 °C for up to 13 weeks or -80 °C for up to 78 weeks. Furthermore, we successfully obtained sequenceable gDNA from both Serum Separator Tube and EDTA Tube using a 96-well format extraction, with yields ranging from 0.61 to 71.29 μg and 3.94-215.98 μg, respectively. Our findings demonstrate the feasibility of using automated high-throughput platforms for gDNA extraction from various clinical leftover blood samples with the proper pre-treatments.

Whereas the cellular and molecular features of human inflammatory skin diseases are well characterized, their tissue context and systemic impact remain poorly understood. We thus profiled human psoriasis (PsO) as a prototypic immune-mediated condition with a high predilection for extracutaneous involvement. Spatial transcriptomics (ST) analyses of 25 healthy, active lesion, and clinically uninvolved skin biopsies and integration with public single-cell transcriptomics data revealed marked differences in immune microniches between healthy and inflamed skin. Tissue-scale cartography further identified core disease features across all active lesions, including the emergence of an inflamed suprabasal epidermal state and the presence of B lymphocytes in lesional skin. Both lesional and distal nonlesional samples were stratified by skin disease severity and not by the presence of systemic disease. This segregation was driven by macrophage-, fibroblast-, and lymphatic-enriched spatial regions with gene signatures associated with metabolic dysfunction. Together, these findings suggest that mild and severe forms of PsO have distinct molecular features and that severe PsO may profoundly alter the cellular and metabolic composition of distal unaffected skin sites. In addition, our study provides a valuable resource for the research community to study spatial gene organization of healthy and inflamed human skin.