Faculty Bibliography

Microsporidia are single-celled intracellular parasites that cause opportunistic diseases in humans. Encephalitozoon intestinalis is a prevalent human-infecting species that invades the small intestine. Macrophages are potential reservoirs of infection, and dissemination to other organ systems is also observed. The macrophage response to infection and the developmental trajectory of the parasite are not well studied. Here we use single cell RNA sequencing to investigate transcriptional changes in both the parasite and the host during E. intestinalis infection of human macrophages in vitro. The parasite undergoes large transcriptional changes throughout the life cycle, providing a blueprint for parasite development. While a small population of infected macrophages mount a response, most remain transcriptionally unchanged, suggesting that the majority of parasites may avoid host detection. The stealthy microsporidian lifestyle likely allows these parasites to harness macrophages for replication. Together, our data provide insights into the host response in primary human macrophages and the E. intestinalis developmental program.

Here we used a series of CTCF mutations to explore CTCF's relationship with chromatin and its contribution to gene regulation. CTCF's impact depends on the genomic context of bound sites and the unique binding properties of WT and mutant CTCF proteins. Specifically, CTCF's signal strength is linked to changes in accessibility, and the ability to block cohesin is linked to its binding stability. Multivariate modeling reveals that both CTCF and accessibility contribute independently to cohesin binding and insulation, but CTCF signal strength has a stronger effect. CTCF and chromatin have a bidirectional relationship such that at CTCF sites, accessibility is reduced in a cohesin-dependent, mutant-specific fashion. In addition, each mutant alters TF binding and accessibility in an indirect manner, changes which impart the most influence on rewiring transcriptional networks and the cell's ability to differentiate. Collectively, the mutant perturbations provide a rich resource for determining CTCF's site-specific effects.

Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-eosin-stained whole slide images (WSIs). We train an SSL Barlow Twins encoder on 435 colon adenocarcinoma WSIs from The Cancer Genome Atlas to extract features from small image patches (tiles). Leiden community detection groups tiles into histomorphological phenotype clusters (HPCs). HPC reproducibility and predictive ability for overall survival are confirmed in an independent clinical trial (N = 1213 WSIs). This unbiased atlas results in 47 HPCs displaying unique and shared clinically significant histomorphological traits, highlighting tissue type, quantity, and architecture, especially in the context of tumor stroma. Through in-depth analyses of these HPCs, including immune landscape and gene set enrichment analyses, and associations to clinical outcomes, we shine light on the factors influencing survival and responses to treatments of standard adjuvant chemotherapy and experimental therapies. Further exploration of HPCs may unveil additional insights and aid decision-making and personalized treatments for colon cancer patients.

High-dimensional multiplexed imaging can reveal the spatial organization of tumour tissues at the molecular level. However, owing to the scale and information complexity of the imaging data, it is challenging to discover and thoroughly characterize the heterogeneity of tumour microenvironments. Here we show that self-supervised representation learning on data from imaging mass cytometry can be leveraged to distinguish morphological differences in tumour microenvironments and to precisely characterize distinct microenvironment signatures. We used self-supervised masked image modelling to train a vision transformer that directly takes high-dimensional multiplexed mass-cytometry images. In contrast with traditional spatial analyses relying on cellular segmentation, the vision transformer is segmentation-free, uses pixel-level information, and retains information on the local morphology and biomarker distribution. By applying the vision transformer to a lung-tumour dataset, we identified and validated a monocytic signature that is associated with poor prognosis.

BACKGROUND:The bone marrow (BM) niche contains non-hematopoietic elements including mesenchymal stromal cells (MSC) and bone marrow endothelial cells (BMEC) which provide mechanical support, and control hematopoietic cell growth and differentiation. Although it is known that multiple myeloma (MM) cells interact closely with the BM microenvironment, little is known about the impact of MM on non-hematopoietic niche-forming cells. METHODS:To address the role of the niche in MM pathogenesis, we utilized the 5TGM1 murine model. During the asymptomatic precursor stage of the model, we isolated the rare non-hematopoietic cells and performed single cell RNA sequencing. Using in-silico methods we characterized the individual cellular components of the niche, their relative abundance and differentiation state before and after exposure to MM cells as well as their intercellular interactions. RESULTS:MM engraftment increased the abundance of MSC-lineage cells, BMECs and enhanced endothelial to mesenchymal transition. An inflammatory and oxidative stress signal was identified together with polarization of MSC differentiation away from osteocyte formation towards adipocytes which provide growth factors that are known to support MM expansion. BMEC differentiation was polarized towards sinusoidal endothelial cells with a pro-angiogenic/pro-inflammatory phenotype. CONCLUSIONS:MM cells impact the BM niche by generating a pro-inflammatory microenvironment with MSC differentiation being changed to generate cell subsets that favor MM growth and survival. In order to induce remission and improve long-term outcome for MM patients these inflammatory and oxidative stress signals need to be reduced and normal niche differentiation trajectories restored.

Here we used a series of CTCF mutations to explore CTCF's relationship with chromatin and its contribution to gene regulation. CTCF's impact depends on the genomic context of bound sites and the unique binding properties of WT and mutant CTCF proteins. Specifically, CTCF's signal strength is linked to changes in accessibility, and the ability to block cohesin is linked to its binding stability. Multivariate modelling reveals that both CTCF and accessibility contribute independently to cohesin binding and insulation, however CTCF signal strength has a stronger effect. CTCF and chromatin have a bidirectional relationship such that at CTCF sites, accessibility is reduced in a cohesin-dependent, mutant specific fashion. In addition, each mutant alters TF binding and accessibility in an indirect manner, changes which impart the most influence on rewiring transcriptional networks and the cell's ability to differentiate. Collectively, the mutant perturbations provide a rich resource for determining CTCF's site-specific effects.

Primary cutaneous squamous cell carcinoma (cSCC) is responsible for ~10,000 deaths annually in the United States. Stratification of risk of poor outcome at initial biopsy would significantly impact clinical decision-making during the initial post operative period where intervention has been shown to be most effective. Using whole-slide images (WSI) from 163 patients from 3 institutions, we developed a self supervised deep-learning model to predict poor outcomes in cSCC patients from histopathological features at initial diagnosis, and validated it using WSI from 563 patients, collected from two other academic institutions. For disease-free survival prediction, the model attained a concordance index of 0.73 in the development cohort and 0.84 in the Mayo cohort. The model's interpretability revealed that features like poor differentiation and deep invasion were strongly associated with poor prognosis. Furthermore, the model is effective in stratifying risk among BWH T2a and AJCC T2, known for outcome heterogeneity.

The Hhex gene encodes a transcription factor that is important for both embryonic and post-natal development, especially of hematopoietic tissues. Hhex is one of the most common sites of retroviral integration in mouse models. We found the most common integrations in AKXD (recombinant inbred strains) T-ALLs occur 57-61kb 3' of Hhex and activate Hhex gene expression. The genomic region of murine leukemia virus (MLV) integrations has features of a developmental stage-specific cis regulatory element (CRE), as evidenced by ATAC-seq in murine progenitor cells and high H3K27 acetylation at the syntenic CRE in human hematopoietic cell lines. With ChIP-exonuclease, we describe occupancy of LIM domain binding protein 1 (LDB1), the constitutive partner of the LIM Only-2 (LMO2), GATA1, and TAL1 transcription factors at GATA sites and a composite GATA-E box within the CRE. With virtual 4C analysis, we observed looping between this +65kb CRE and the proximal intron 1 enhancer of HHEX in primary human ETP-ALLs and in normal progenitor cells. Our results show that retroviral integrations at intergenic sites can mark and take advantage of CREs. Specifically, in the case of HHEX activation, this newly described +65kb CRE is co-opted in the pathogenesis of ETP-ALL by the LMO2/LDB1 complex.

PURPOSE/OBJECTIVE:Necrosis quantification in the neoadjuvant setting using pathology slide review is the most important validated prognostic marker in conventional osteosarcoma. Herein, we explored three deep learning strategies on histology samples to predict outcome for OSA in the neoadjuvant setting. EXPERIMENTAL DESIGN/METHODS:Our study relies on a training cohort from New York University (New York, NY) and an external cohort from Charles university (Prague, Czechia). We trained and validated the performance of a supervised approach that integrates neural network predictions of necrosis/tumor content, and compared predicted overall survival (OS) using Kaplan-Meier curves. Furthermore, we explored morphology-based supervised and self-supervised approaches to determine whether intrinsic histomorphological features could serve as a potential marker for OS in the setting of neoadjuvant. RESULTS:Excellent correlation between the trained network and the pathologists was obtained for the quantification of necrosis content (R2=0.899, r=0.949, p < 0.0001). OS prediction cutoffs were consistent between pathologists and the neural network (22% and 30% of necrosis, respectively). Morphology-based supervised approach predicted OS with p-value=0.0028, HR=2.43 [1.10-5.38]. The self-supervised approach corroborated the findings with clusters enriched in necrosis, fibroblastic stroma, and osteoblastic morphology associating with better OS (lg2HR; -2.366; -1.164; -1.175; 95% CI=[-2.996; -0.514]). Viable/partially viable tumor and fat necrosis were associated with worse OS (lg2HR;1.287;0.822;0.828; 95% CI=[0.38-1.974]). CONCLUSIONS:Neural networks can be used to automatically estimate the necrosis to tumor ratio, a quantitative metric predictive of survival. Furthermore, we identified alternate histomorphological biomarkers specific to the necrotic and tumor regions themselves which can be used as predictors.