Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

  • Daniel Royston

Funding is provided for up to four years.

The diagnosis of blood cancer requires integration of clinical, pathological, immunophenotypic and genetic / molecular findings. Accurate and consistent interpretation of bone marrow trephines (BMT) is particularly important for distinguishing between important blood cancer subtypes. Such distinction guides patient management and informs risk stratification. In response, our group have developed pioneering artificial intelligence (AI) approaches to accurately capture important tissue features of the bone marrow in blood cancer. This includes the development of automated and quantitative approaches to detect cellular and stromal changes present on routinely prepared diagnostic samples. We are now developing strategies to combine these approaches with newly developed advanced spatial transcriptomic and IHC / IF multiplexing strategies for patient-derived bone marrow biopsies.

About the Research

Our goal is to apply whole-sample single cell annotations for the development and validation of a new generation of computational models of the cellular and stromal architecture of the bone marrow in health and disease, with line of sight to clinical adoption [Figure 1]. These approaches are ideally suited to explore and support additional work defining the bone marrow microenvironment in blood cancer.

This project builds upon our group’s expertise in the computational analysis of tissue biopsies taken from patients investigated for blood cancer. As part of an established collaborative network in Oxford involving Cellular Pathology, Haematology and Biomedical Engineering we have already successfully demonstrated the potential of image analysis / machine learning (ML) to improve the accuracy and prognostic significance of tissue features extracted from bone marrow biopsy specimens. Our collaborative team in Oxford has close links with leading academic groups within the UK, Europe and the US. We have also established close links to several Pharma groups looking to apply our approaches to inform the evaluation of novel therapeutics in the context of Phase I/II clinical trials.

This position is ideal for candidates who either have a background/strong interest in machine learning and have an interest in interdisciplinary work. As a DPhil student within our group, you will gain experience in the assessment and analysis of diagnostic human tissue samples using a range of advanced tissue imaging techniques. In close collaboration with Prof. Rittscher’s group (Institute of Biomedical Engineering [IBME], Oxford) you will develop advanced computational analytical methods and machine learning (ML) approaches to analyse tissue samples. This will be integrated with hypothesis-driven application of spatial feature analysis, including the use of spatial transcriptomics and single-cell omic approaches to interrogate the intimate cell-cell and cell-stromal interactions driving blood cancer initiation and progression (Dr Ros Cooper, Cellular Pathology / NDCLS, Oxford).


Training Opportunities 

In addition to the generic training opportunities offered by the MRC Weatherall Institute of Molecular Medicine (WIMM) and Medical Sciences Division (see below), DPhil students joining our group will be trained in a wide range of tissue diagnostic and analytical techniques including conventional microscopy, immunohistochemistry (IHC) / immunofluorescence (IF) microscopy, polarising light microscopy and spatial transcriptomics. This will involve supervised training in the use of specialist software and incorporate methodologies designed to analyse and integrate multiomic data from patient samples. Training will be supported by collaborators spanning multiple research themes and clinical / academic  departments within the University and NHS. Finally, all DPhil students joining our group will participate fully in Prof. Rittscher’s successful student training programme at the IBME, incorporating weekly lab meetings. The research groups of Prof. Royston and Prof. Rittscher have a strong track record of training students and post-doctoral staff and are experienced and successful collaborators.

Students are encouraged to attend the MRC Weatherall Institute of Molecular Medicine DPhil Course, which takes place in the autumn of their first year. Running over several days, this course helps students to develop basic research and presentation skills, as well as introducing them to a wide range of scientific techniques and principles, ensuring that students have the opportunity to build a broad-based understanding of differing research methodologies.

Generic skills training is offered through the Medical Sciences Division's Skills Training Programme. This programme offers a comprehensive range of courses covering many important areas of researcher development: knowledge and intellectual abilities, personal effectiveness, research governance and organisation, and engagement, influence, and impact. Students are actively encouraged to take advantage of the training opportunities available to them.

 As well as the specific training detailed above, students will have access to a wide range of seminars and training opportunities through the many research institutes and centres based in Oxford.

The Department has a successful mentoring scheme, open to graduate students, which provides an additional possible channel for personal and professional development outside the regular supervisory framework. We hold an Athena SWAN Silver Award in recognition of our efforts to build a happy and rewarding environment where all staff and students are supported to achieve their full potential.




Ryou H, Sirinukunwattana K, Aberdeen A, Grindstaff G, Stolz BJ, Byrne H, Harrington HA, Sousos N, Godfrey AL, Harrison CN, Psaila B, Mead AJ, Rees G, Turner GDH, Rittscher J, Royston D. Continuous Indexing of Fibrosis (CIF): improving the assessment and classification of MPN patients. Leukemia. 2022 Dec 5. doi: 10.1038/s41375-022-01773-0.


Hosuk Ryou, Korsuk Sirinukunwattana, Ruby Wood, Alan Aberdeen, Jens Rittscher, Olga Weinberg, Robert Hasserjian, Olga Pozdnyakova, Frank Peale, Brian Higgins, Pontus Lundberg, Kerstin Trunzer, Claire N Harrison, Daniel Royston; Quantitative Analysis of Bone Marrow Features Highlights Heterogeneity in Myelofibrosis Patients Treated with Zinpentraxin Alfa in a Phase II Clinical Study. Blood 2023; 142 (Supplement 1): 4558. doi:


Ryou H, Lomas O, Theissen H, Thomas E, Rittscher J, Royston D. Quantitative interpretation of bone marrow biopsies in MPN—What's the point in a molecular age? Br J Haematol. 2023; 203(4): 523–535.


AI-Based Morphological Fingerprinting of Megakaryocytes: a New Tool for Assessing Disease in MPN Patients. Korsuk Sirinukunwattana, Alan Aberdeen, Helen Theissen, Nikolaos Sousos, Bethan Psaila, Adam J. Mead, Gareth D.H. Turner, Gabrielle Rees, Jens Rittscher and Daniel Royston. Blood Adv. 2020 Jul 28;4(14):3284-3294. doi:  10.1182/bloodadvances.2020002230.


Royston D, Mead AJ, Psaila B. Application of Single-Cell Approaches to Study Myeloproliferative Neoplasm Biology. Hematol Oncol Clin North Am. 2021 Apr;35(2):279-293. doi: 10.1016/j.hoc.2021.01.002. PMID: 33641869; PMCID: PMC7935666.