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Deep multi-omic integration to understand inter-organ relationships that regulate systemic immunometabolism


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Dr Yinyin Yuan No more applications being accepted Funded PhD Project (Students Worldwide)

About the Project

For a full project proposal and details on how to apply using our online recruitment portal please see icr.ac.uk/phdsPlease note we only accept applications via the online application system apply.icr.ac.uk

One of the fundamental questions in biology is how systemic metabolism affects immune response. In normal human biology, multiple organs act in concert to maintain metabolic homeostasis and protect against the onset of disease.

The functions of immune cells are in turn closely modulated by their metabolic environment, and the metabolism of many immune cells is altered upon their activation. Improved understanding of the interplay that exists between the host immune response and key metabolic tissue, such as the liver, heart and kidney, will provide critical insights into the mechanisms that govern ‘metabolic-immune’ crosstalk and the biological systems in place to defend against metabolic disease. For example, early activation of key immune cell subtypes by metabolic factors that exist within their microenvironment environment may provide early-warning signals that precipitate a host response to perceived metabolic stress.

Establishing a systematic model of the role of metabolic regulation and its cross-talk with the immune system will enable greater understanding of the physiological systems in place to defend and against metabolic stress. The complexity this interaction in immunometabolism, and the interplay between different tissues mean that an integrated approach is required to fully understand how systemic metabolism and the immune system interact.

At AZ, our cutting-edge capabilities enable routine systematic interrogation of tissue biology using spatial resolved transcriptomics, proteomics and metabolomics. However, the scale and complexity of these data mean we are currently limited to testing specific hypotheses and are yet to implement systematic data-driven approaches that use the information available to its full potential.

This gap will be bridged by using deep multi-omic integration methodologies developed at the Yuan Lab at ICR, linking histological signatures, gene expression profiles, cell signalling and metabolic states. For the first time, we will generate a model describing the mechanism and relative contribution and interplay between key organs in response to early metabolic dysregulation and inflammation.

Aims:

1)        Build from existing multimodal integration work of spatially resolved imaging techniques to define multicellular molecular and morphological signatures of tissue associated with metabolic dysregulation in the liver, kidney, and heart and the associated resident and infiltrating immune microenvironment phenotypes.

2)        Interpretation of transcriptomic data in models of metabolic syndrome have been confounded by inability to distinguish transcriptomic shift from cellular remodelling. Use multicellular signatures from IMC for better understanding of the cell types contributing to the transcriptomic changes associated with early changes in metabolism detected in each organ. Deep learning integration will then be used to distinguish data resulting from differential cell infiltration and regulated gene expression.

3)        Matched clinical data for better understanding of systemic immunometabolism and model translation with the potential for novel target identification.

4)        Build a spatiotemporal model of multicellular signalling, inflammation and metabolomic interdependencies to produce a dynamic model of the aetiology and contribution of each organ during normal systemic function and upon perturbation such as in early and established metabolic dysfunction.


Funding Notes

Students receive an annual stipend, currently £21,000 per annum, as well as having tuition fees (both UK/EU and overseas) and project costs paid for the four-year duration. We are open to applications from any eligible candidates and are committed to attracting and developing the best minds in the world. We particularly welcome applicants from British Black and ethnic minority backgrounds, as they are under-represented at PhD level within the ICR and nationwide.

References


1. AbdulJabbar K^, Raza SEA^, Rosenthal R†, Jamal-Hanjani M†, Veeriah S†, Akarca A, Lund T, Moore D, Salgado R, Bakir MA, Zapata L, Hiley CT, Officer L, Sereno M, Smith CR, Loi S, Hackshaw A, Marafioti T, Quezada SA, McGranahan N, Le Quesne J*, Swanton C*, Yuan Y* (2020). Geospatial immune variability illuminates differential evolution of lung adenocarcinoma, Nature Medicine, 26, 1054–1062.
2. Failmezger H^, Muralidhar S^, Rullan A, de Andrea CE, Sahai E, Yuan Y* (2020). Topological Tumor Graphs: a graph-based spatial model to infer stromal recruitment for immunosuppression in melanoma histology, Cancer Research, IF:9, DOI: 10.1158/0008-5472.CAN-19-2268.
3. Heindl A, Khan AM, Rodrigues DN, Eason K, Sadanandam A, Orbegoso C, Punta M, Sottoriva A, Lise S, Banerjee S, Yuan Y* (2018). Microenvironmental niche divergence shapes BRCA1-dysregulated ovarian cancer morphological plasticity, Nature Communications, IF:12, 9:3917.
4. Heindl A, Sestak I, Naidoo R, Cuzick J, Dowsett M, Yuan Y* (2018). Relevance of spatial heterogeneity of immune infiltration for predicting risk of recurrence after endocrine therapy of ER+ breast cancer, JNCI: Journal of the National Cancer Institute, IF:12, 110(2), djx137.
5. Natrajan R, Sailem H, Mardakheh FM, Arias MG, Dowsett M, Bakal C, Yuan Y* (2016). Microenvironmental heterogeneity parallels breast cancer progression: A histology-genomics integration analysis, PLoS Medicine, IF:11, 16;13(2):e1001961.
6. Hagos YB, Narayanan P, Akarca AU, Marafioti T, Yuan Y* (2019), ConCORDe-Net: Cell Count Regularized Convolutional Neural Network for Cell Detection in Multiplex Immunohistochemistry Images, Medical Image Computing and Computer Assisted Intervention (MICCAI) 2019.
7. Raza SEA, AbdulJabbar K, Jamal-Hanjani M, Veeriah S, Le Quesne J, Swanton C, Yuan Y* (2019), Deconvolving convolution neural network for cell detection, IEEE International Symposium on Biomedical Imaging (ISBI) 2019.
8. Narayanan PL*, Dodson A, Gusterson B, Dowsett M, and Yuan Y* (2018), DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide images, Medical Imaging in Deep Learning (MIDL 2018).
9. Zormpas-Petridis K, Failmezger H, Raza SEA, Roxanis I, Jamin Y, Yuan Y* (2019). Superpixel-based Conditional Random Fields (SuperCRF): Incorporating global and local context for enhanced deep learning in melanoma histopathology. Frontiers in Oncology, 9: 1045, doi.org/10.3389/fonc.2019.01045.
10. Zormpas-Petridis K*, Failmezger H, Roxanis I, Blackledge MD, Jamin Y, Yuan Y* (2018). Capturing global spatial context for accurate cell classification in skin cancer histology, Medical Image Computing and Computer Assisted Intervention (MICCAI) COMPAY workshop 2018.
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