Research Opportunities

Tissue multiplexing is a new imaging method that allows to visualise a large number of protein targets in tissues. This exciting new technology allows for new approaches to phenotyping cells and to decode more complex patters of communication between different tissue compartments. The goal of this project is to develop the required image analysis and inference methods using advanced machine learning and AI in 2D and 3D. As a result, you will be advancing our understanding of the tumour environment and find novel ways of identifying sub-populations of cells that play a critical role in disease progression.

You will be working side by side with world leading cancer researchers at NIH NCI and the University of Oxford. At both sites you will have access to unique patient cohorts. Together with David Wink and Stephen Lockett (both NCI) you will be working on aspects if breast cancer. In Oxford, Richard Bryant and Ian Mills will lead on work in prostate cancer, which is the commonest non-cutaneous cancer in men, and often progresses to incurable metastatic disease. Your work will also be supported by expert pathologists and you will be working towards improving current practice in cellular pathology.

The broader group has already established a very active collaboration and you will be expected to work in both locations. In Oxford, you will be embedded in the Quantitative Biomedical Image Analysis group led by Prof. Rittscher. Part of your role will be to accelerate the exchange of technology and software between the two locations. This project provides a unique opportunity to study mechanisms that are common to different cancer types.

In order to maintain homeostasis in response to environmental changes such as nutrient availability, eukaryotic cells have evolved intricate mechanisms to quickly increase or decrease the activity of fundamental processes such as gene expression, protein expression and degradation. Indeed, several metabolites act as cofactors for important cellular enzymes that regulate e.g. chromatin state and serve as templates for posttranslational modifications flagging proteins for proteolysis via the ubiquitin-proteasome system. Consequently, the identification of metabolites and complementary binding domains has broadened our understanding of human physiology and contributed to the development of new medicines to treat malignant and inflammatory disease. The aim of this project is to systematically map protein-metabolite interactions on a proteome-wide scale by combining the development of specific metabolite-inspired affinity reagents with unbiased approaches such as thermal profiling to dissect metabolite signalling in the context of protein degradation pathways in various cell types. Applicants will have the opportunity to take advantage of a unique combination of synthetic organic chemistry and cell biology techniques to identify new potential drug targets and develop first-in-class ligands for key regulators of protein homeostasis.

T cell recognition of a cognate peptide-MHC (pMHC) complex presented by infected/malignant antigen-presenting cells are of utmost importance for mediating a robust and long-term immune response. The recognition is mediated by specific molecular interactions between heterodimeric T Cell Receptors (TCRs) and pMHC ligands and instructs the nature of ensuing adaptive immune response. A better understanding of TCR:pMHC interaction would allow further harnessing of the adaptive T cell immunity and may lead to the development of  vaccines and therapeutics  both in the context of personalized cancer immunotherapies and infectious diseases such as COVID19. The research interests in the Koohy group are focused on the development of machine-learning and Bayesian statistical models to help us better understand two key components of this interaction: A) architecture of the immune repertoire and its dynamics upon exposure to antigens, B) processing and presentation of antigens by MHC molecules to their cognate T cells.

Cancer is usually characterized by accumulation of genetic alterations. Tumour-specific somatic mutations may generate small mutated proteins known as neoantigens that are presented on the surface of cancer cell as ‘cancerous flags’ in association with class I and II HLA molecules.  Neoantigens can be recognized by autologous T cells as foreign and therefore are considered as targets for improved cancer vaccines and adaptive T cell therapies. Almost similar mechanisms are applied to infectious diseases with the difference that the immunogenic epitopes on the surface of infected cells originate from invasive pathogens. Prediction of both immunogenic viral epitopes and cancer neoantigens has been at the centre of extensive research around the globe over the past couple of decades but remains unsolved.   We have been developing various statistical models such as Bayesian Hidden Markov Models to predict immunogenic epitopes that can be used as targets for vaccines for personalized cancer immunotherapy as well as infectious diseases such as COVID1,2.

Over the past decade we have witnessed unprecedented achievements on various cancer immunotherapies in which patients’ own immune system is modulated to find and kill cancer cells. This is evident by the 2018 Nobel Prize for development of Immune Checkpoint Blocked ICB that has greatly improved patients care. However, not all patients respond the same way, besides, some patients develop immune related adverse events such as checkpoint colitis. 
Multiple factors affect immune response to treatment including mutation burden rate, cytotoxic T cell infiltration, antigen processing and presentation defects, mutation-driven clonal signature and the composition of intestinal microbiota. Owing to advances in high throughput sequencing technologies, in particular recent single cell advancements, these features can now be measured from patients’ samples at single cell level at multiple time points including before, during and after the treatment.  We take readouts of these experiments in the form of high throughput sequencing data including genomics, transcriptomics, T cell receptor repertoire, and epigenomics data to train  statistical and machine learning models to study the mechanisms underlying heterogeneity of the response.

Malaria is a major burden on global health with about 200 million cases worldwide, and 600,000 deaths per year. Inadequate diagnostics is a major barrier to effective management of cases and elimination of the disease. The current gold standard method for malaria diagnosis is light microscopy of blood films. About 170 million blood films are examined every year for malaria, which involves manually identifying and counting parasites. However, microscopic diagnostics are not standardized and depend heavily on the experience and skill of the microscopist, many of whom work in isolation, with no rigorous system in place for maintenance of their skills. For false negative cases this leads to incorrect diagnosis with unnecessary use of antibiotics, a second consultation, lost days of work, and in some cases progression into severe malaria. For false positive cases, this results in unnecessary use of antimalarial drugs and side effects.

To improve malaria diagnostics, the Lister Hill National Center for Biomedical Communications, an R&D division of the U.S. National Library of Medicine, NIH and Mahidol-Oxford Tropical Medicine Research Unit, University of Oxford, in Bangkok, Thailand are developing a fully automated low-cost system that uses a mobile phone and standard light microscope for parasite detection and counting on blood films. Compared to manual counting, automatic parasite counting is more reliable and standardized, reduces the workload of the malaria field workers and reduces diagnostic costs. To count parasites automatically, the system uses image processing methods to find cells infected with parasites in digitized images of blood films. The system is trained on manually annotated images and machine learning methods then discriminate between infected and uninfected cells, detect the type of parasites that are present, and perform the counting. The system uses a regular smartphone and digital images acquired on standard light microscopy equipment making it ideal for resource-poor settings.

This PhD project will develop and test this system for real-world use for malaria diagnosis. It will include optimisation of the system at NIH and testing of the system in the field at MORU including the smartphone application interface and performance, the system for connecting the smartphone to standard light microscopes, development of a core set of performance metrics for the application, field testing of the entire system for malaria diagnosis together with government healthcare workers and National Malaria Control Programme staff, structured interviews to gather feedback on the system and its potential role in malaria diagnosis in different settings, a formal field trial of the system performance and development of a system implementation guidance document for National Malaria Control Programmes.

The student will join a dynamic team of image analysis specialists at NLM and epidemiologists, modellers and clinicians at the MORU offices in Bangkok. They will spend time at field sites in malaria-endemic areas and will interact with government staff. Training will be provided at NIH on basic image analysis and smartphone application development and at MORU on malaria miscroscopy, clinical study methodology, data analysis and research ethics.

Our lab seeks to explore the neural mechanisms underlying cognitive function by exploiting the unique investigative opportunities provided by intracranial electrical recordings during neurosurgical procedures. Using recordings captured from epilepsy patients implanted with subdural and depth electrodes, we investigate the activation of cortical networks during memory encoding and recall. And using recordings captured during implantation of deep brain stimulators, we investigate the role of the basal ganglia in learning and decision-making.

Retroviral sequences remain dormant in the human genome and occupy nearly 7-8% of the genomic sequence. We have shown that one of these viruses termed HERV-K (HML-2) is activated in patients with amyotrophic lateral sclerosis (ALS), and transgenic animals that express the envelope protein of HERV-K develop ALS like symptoms. Hence, we are now using a wide variety of structural biology and virology tools to determine the mechanism by which its expression is regulated and causes neurotoxicity to motor neurons. 

Understand the disease mechanisms and potential treatments for hereditary motor neuron diseases such as spinal muscular atrophy and polyglutamine expansion diseases such as Huntington's disease.

Humans display robust age-dependent sex differences in diverse domains of motor, language and social development, as well as in risk for developmentally-emergent disorders. There is a robust male-bias in risk for early-emerging impairments of attention, motor control, language and social functioning, vs. a female-bias for adolescent-emergent disorders of mood and eating behaviors.  The stereotyped pattern of these sex biases suggests a role for sex differences in brain development, and further implies that these differences unfold in a spatiotemporally-specific manner. In support of this notion - in vivo structural neuroimaging studies find focal sex differences in brain anatomy that vary over development. However, the mechanisms driving these neurodevelopmental differences remain poorly understood in humans. In particular, we do not know how specific spatial and temporal instances of sex-biased brain development in humans relate to the two foundational biological differences between males and females: gonadal sex-steroid profile (henceforth “gonadal”) and X/Y-chromosome count [henceforth “sex chromosome dosage” (SCD)]. In our prior cross-sectional neuroimaging studies, we have however provided extensive evidence that gonads and SCD can both shape regional anatomy of the human brain, and that similar effects can be observed in mice. However, to date there are no available data on the temporal unfolding of gonadal and SCD effects on regional brain anatomy, and no quantitative frameworks for comparing these effects between observational humans studies and experimental work in mice.

This project will build on a longstanding productive collaboration between Drs. Lerch and Raznahan, with rich existing datasets, to better-specify sex as a neurobiological variable in health and disease. Key questions for the project relate to (i) fine-grained spatiotemporal mapping of sex, SCD and gonadal effects using neuroimaging in transgenic mice and rare patient groups, (ii) computational solutions for comparison of these maps between species, and (iii) “decoding” of imaging data using measures of gene expression in brain tissue and integrative functional genomics. The resulting anatomical, and genomic signatures for sex-biased development will be probed for association with biological bases of sex-biased brain disorders.

*This project is available for the 2021 Oxford-NIH Pilot Programme*

Use experimental medicine and neuroimaging approaches to uncover the mechanisms mood disorders in adolescents and adults. Depression is a leading cause of burden of disease worldwide yet we know little about its pathogenesis. The student is going to work across the NIMH and Oxford laboratories and use neuroimaging (fMRI, EEG and MEG) in patients and controls who undergo experimental treatments.

Midbrain dopamine neurons have fundamental roles in reward learning and movement control, and their dysfunction is associated with various disorders in particular Parkinson’s disease. Recent studies have shown substantial diversity in the activity of these neurons depending on where in the striatum their axons project. In our recent experiments we recorded the activity of dopamine axonal terminals while systematically manipulating stimuli, actions and rewards in a precise behavioural task. While the activity of dopamine projections to ventral regions of striatum mainly reflected rewards, dopamine axonal projections to dorsal striatum encoded contralateral stimuli and actions with negligible representation of reward value. These findings raise the questions of whether dopamine signals across striatum encode specific aspects of associations between stimuli, actions and rewards during learning, and whether these anatomically-specific dopamine signals are impaired during Parkinson’s disease. This project will address these questions using a combination of imaging, computational and behavioural experiments in healthy mice as well as mouse models of Parkinson’s disease. In Oxford University (Lak lab), we will use recent genetically–encoded dopamine sensors in combination with fiber photometry to monitor the dynamics of dopamine signals across the striatum while healthy mice perform a learning task guided by sensory stimuli and rewards. These results will provide a foundation for examining these dopamine signals during Parkinson’s disease, which will be performed at NIH (Cui lab). Using MitoPark mouse line (with progressive and robust phynotype of Parkinson’s disease), we will examine the dynamics of striatal dopamine signals using photometry during learning tasks established in healthy mice in Oxford. In analysing the data, we will use learning models to relate dopamine signals with normative computational models of decision making and learning. The project is primarily experimental in nature but will provide an opportunity to develop computational skills. The project will provide fundamental insights into behaviourally-relevant computations that dopamine signals across the striatum encode, and will uncover how these neuronal computations change during Parkinson’s disease. For further information visit: https://www.niehs.nih.gov/research/atniehs/labs/ln/pi/iv/index.cfm and www.laklab.org

*This project is available for the 2021 Oxford-NIH Pilot Programme*

Pluripotent stem cells, such as embryonic stem cells (ESCs), can be used as a model system to study the molecular basis of fate-specification during early mammalian development. They can also be used to derive various types of cells for disease modeling, drug discovery, regenerative medicine, and environmental health sciences. To fully realize these potentials of pluripotent stem cells, it is important to understand the molecular mechanisms that regulate the pluripotent state. We have previously carried out a genome-wide RNAi screen in mouse ESCs and identified a list of novel factors that are important for pluripotency maintenance. Among them, we are currently investigating the function of the Ccr4-Not mRNA deadenylase complex and the INO80 chromatin remodeling complex in ESCs, somatic cell reprogramming, and mouse development using biochemical, genetic, genomic and single cell analysis approaches. In addition, we are developing new genetic and genomic methods to identify and probe genes involved in stem cell fate specification. We are applying these methods in pluripotent and germline stem cells to better understand the maintenance, transition, resolution, and re-establishment of the pluripotent state.