Origin of new beta cells during pregnancy using PCLT and TIS microscopy
Lead Research Organisation:
University of Warwick
Department Name: School of Life Sciences
Abstract
During normal human pregnancy, the mother's need for insulin is suddenly doubled. To fulfil this need, her body creates additional insulin-producing cells--these are called beta cells. This project's question is: Where do the additional cells come from in normal pregnancy? Understanding the body's production of new beta cell is an important biological problem in its own right. This understanding is also important for the treatment of diabetes, because a deep understanding of diseased physiology rests on understanding normal physiology.
Experiments cannot be done on human subjects. We use mice, whose physiological response to pregnancy is fortunately similar to that of humans. For example, during pregnancy, the mass of beta cells more than doubles in mice, and the same is true for humans. This remarkable increase takes place over only a few days in a mouse, which is convenient for our study.
This project's question can be recast: During pregnancy, which types of cells transform themselves into beta cells? Until recently, the accepted wisdom on this question was provided by a wonderful experiment carried out by Melton and Dor. They start with transgenic mice, that is, mice whose DNA has been altered in a certain way. This alteration has little effect on the physiology of the mouse until the mouse is injected with a certain hormone (tamoxifen), at which point the altered DNA starts to produce, but only in one particular type of cell--in the Dor-Melton case, only in beta cells--a certain protein (HPAP) visible under the microscope after staining. The cell is said to be "labelled by HPAP". What makes the experiment so informative is that, when an HPAP-labelled cell divides (replicates), then the cells resulting from division are also labelled. A labelled cell must have had labelled ancestors, and an unlabelled cell must have had unlabelled ancestors. This gives a mechanism for tracking the ancestry of a cell, which works whether or not the cell changes to a cell of different type. Melton and Dor showed that, under circumstances that do not occur in nature (surgical removal of part of the pancreas), new beta cells come only from existing beta cells, via the usual process of cell division.
Our group recently repeated the Melton-Dor experiment using normal pregnancy to create a large but normal demand for additional insulin. Under these conditions, the same labelling technique leads to a very different conclusion--about half of the new beta cells do NOT arise through replication of existing beta cells, but come from somewhere else. Our experiment was not designed to show where the new beta cells did come from, even though we could be sure that some came from division of existing beta cells. Our objective now is to determine with precision the various types of ancestors of new beta cells in pregnancy.
This would be a major step towards understanding insulin-deficiency in diabetes, and may lead to effective treatment. It may become possible to cultivate outside the body, in large numbers, ancestor cells discovered during our project and beta cells that the ancestor cells generate. It is already standard treatment in both type 1 and type 2 diabetes to transplant into the body of a diabetic beta cells that are harvested from road traffic accident victims. However, a supply of beta cells from such a source can supply only a tiny fraction of the need.
The University of Warwick's robotically controlled TIS microscope, the only one of its kind outside Germany, allows us to deduce, from the expression of different proteins at different time points, how a cell changes while becoming a beta cell. TIS detects 40 proteins at the same place in the same tissue section, instead of the 2 or 3 detectable through conventional microscopy. This great power of TIS will enable us to identify which of many suggestions are correct as to the identity of ancestors of beta cells.
Experiments cannot be done on human subjects. We use mice, whose physiological response to pregnancy is fortunately similar to that of humans. For example, during pregnancy, the mass of beta cells more than doubles in mice, and the same is true for humans. This remarkable increase takes place over only a few days in a mouse, which is convenient for our study.
This project's question can be recast: During pregnancy, which types of cells transform themselves into beta cells? Until recently, the accepted wisdom on this question was provided by a wonderful experiment carried out by Melton and Dor. They start with transgenic mice, that is, mice whose DNA has been altered in a certain way. This alteration has little effect on the physiology of the mouse until the mouse is injected with a certain hormone (tamoxifen), at which point the altered DNA starts to produce, but only in one particular type of cell--in the Dor-Melton case, only in beta cells--a certain protein (HPAP) visible under the microscope after staining. The cell is said to be "labelled by HPAP". What makes the experiment so informative is that, when an HPAP-labelled cell divides (replicates), then the cells resulting from division are also labelled. A labelled cell must have had labelled ancestors, and an unlabelled cell must have had unlabelled ancestors. This gives a mechanism for tracking the ancestry of a cell, which works whether or not the cell changes to a cell of different type. Melton and Dor showed that, under circumstances that do not occur in nature (surgical removal of part of the pancreas), new beta cells come only from existing beta cells, via the usual process of cell division.
Our group recently repeated the Melton-Dor experiment using normal pregnancy to create a large but normal demand for additional insulin. Under these conditions, the same labelling technique leads to a very different conclusion--about half of the new beta cells do NOT arise through replication of existing beta cells, but come from somewhere else. Our experiment was not designed to show where the new beta cells did come from, even though we could be sure that some came from division of existing beta cells. Our objective now is to determine with precision the various types of ancestors of new beta cells in pregnancy.
This would be a major step towards understanding insulin-deficiency in diabetes, and may lead to effective treatment. It may become possible to cultivate outside the body, in large numbers, ancestor cells discovered during our project and beta cells that the ancestor cells generate. It is already standard treatment in both type 1 and type 2 diabetes to transplant into the body of a diabetic beta cells that are harvested from road traffic accident victims. However, a supply of beta cells from such a source can supply only a tiny fraction of the need.
The University of Warwick's robotically controlled TIS microscope, the only one of its kind outside Germany, allows us to deduce, from the expression of different proteins at different time points, how a cell changes while becoming a beta cell. TIS detects 40 proteins at the same place in the same tissue section, instead of the 2 or 3 detectable through conventional microscopy. This great power of TIS will enable us to identify which of many suggestions are correct as to the identity of ancestors of beta cells.
Technical Summary
Functional beta-cell mass (FBM) doubles in a few weeks of mouse or human pregnancy. The contribution of different sources to FBM expansion in pregnancy is still unclear. Our earlier published work [1] demonstrates that during pregnancy about half of new beta-cells arise from non-beta-cell precursors, the remainder coming from replication of existing beta-cells. The aim of this project is to clarify the identity and role of precursor cells (of which there could be more than one cell type). We will employ Pulse-Chase Lineage Tracing (PCLT) in two different experiments, using mice with transgenes CAIICreERT and ElastaseCreERT to label duct and acinar cells respectively. Our collaborator, Pedro Herrera of Geneva, will be doing PCLT for delta-cells and sending us tissue sections for analysis. We will see whether ductal, acinar or delta precursors are major contributors to FBM expansion during pregnancy. Analytical power is enhanced by coupling PCLT with a new microscopy platform, Toponome Image System (TIS), that can co-locate 40 or more different proteins in the same pixel of the same intact tissue section. The progeny of labelled cells are marked, and tracked from time point to time point, with the percentage contribution to beta-cell neogenesis unambiguously attributed. Imaging of the lineage label alongside detailed cell morphology, plus molecular co-expression patterns, formed using an additional 30 differentiation markers, will reveal the differentiation route for formation of new beta-cells. We will develop novel analytical tools based on mutual information, maximal information coefficient (MIC) and nonlinear manifold learning. Tracking of signatures from molecular co-expression of protein markers will reveal ancestors of beta-cells. This new knowledge will advance understanding of beta-cell formation and demonstrate the role of non-beta-cells in FBM expansion during pregnancy.
Bibliography:
[1] S. Abouna, et al., Organogenesis, vol. 6, pp. 125-33, 2010.
Bibliography:
[1] S. Abouna, et al., Organogenesis, vol. 6, pp. 125-33, 2010.
Planned Impact
Tissue plasticity and adaptive growth are areas of profound relevance to mammalian physiology. Disorders of these processes lie at the heart of some of the most important and prevalent diseases of man and animal, eg diabetes, a serious and widespread disease. Our research is on mice--one cannot research on humans. Even though there are differences, rodent and human systems are close in normal physiology of pregnancy. Recent studies [1] show that in human, as in mouse, pregnancy [2], neogenesis contributes a significant proportion of new beta cells required by the mother's increased insulin requirements.
Almost all types of diabetes are associated with inadequate beta-cell mass. To cure a diabetic requires adding to the patient's supply of functioning beta-cells. A standard modern approach is to implant islets from cadavers. However, this can meet only a tiny proportion of demand. If we understood the cellular origin of new beta-cells in adult life, the same processes could be encouraged in the diabetic patient with drugs, or cells could be manufactured outside the body and implanted.
Currently islet transplants survive rejection for around 18 months, which is acceptable as an alternative to diabetes, and provide effective treatment, the only real limitation being lack of suitable transplant material. Survival times for implants are rapidly increasing under the influence of intensive research.
Pharma would be a major beneficiary, through manufacturing and selling beta-cells. Novo Nordisk has a major facility near Copenhagen, devoted to the creation of beta cells and to beta-cell therapy. All big pharma is similarly interested.
In fundamental research it is impossible to be specific about timelines. However, a mouse beta cell precursor identified in this project would be rapidly validated in man, in view of the worldwide interest and existing infrastructures. Trials to expand these precursors and generate beta cells on a large scale would also follow rapidly. We think the time-lag would be 5 to 10 years. A particular beneficiary in the UK of a successful outcome for our research would be the NHS, currently spending £14 billion per year on diabetes (http://www.diabetes.co.uk/cost-of-diabetes.html).
Training: Both wet and dry PDRAs will make contact with industry at our workshops, and when pharma visit our labs. Their experience at our workshops and the training they receive while on visits to Bielefeld and Geneva will greatly enhance their employment prospects. Weekly attendance at our multidisciplinary meetings will draw their attention to the importance of interactions with other disciplines and they will be taught the relevant details of the other disciplines by face-to-face tuition from the Investigators.
Presentations to our team, to University seminars and to national and international conferences will develop their skills, making them more employable. Our project will have a beneficial effect on academics from Warwick and elsewhere.
Schubert, the inventor of TIS, is working on a benchtop TIS system, making it eminently suitable as a diagnostic and research tool. Our methods of analysis and our software will be provided with such machines. Our ideas will also be useful whenever similar multi-channel studies are undertaken, and are not confined to TIS. The impact would be felt in laboratories worldwide. For example, our computer programs automate segmentation of images of tissue sections into cells, saving hours of intensive and uninteresting labour, simultaneously greatly improving accuracy.
Through WISDEM (see Google), we will organize two meetings in Year 1, one for regional medical professionals and one aimed at the interested public, to explain our plans. In Year 3 we will again have two similar meetings to report on progress.
[1] A. E. Butler, et al., Diabetologia, vol. 53, pp. 2167-76, 2010.
[2] S. Abouna, et al., Organogenesis, vol. 6, pp. 125-33, 2010.
Almost all types of diabetes are associated with inadequate beta-cell mass. To cure a diabetic requires adding to the patient's supply of functioning beta-cells. A standard modern approach is to implant islets from cadavers. However, this can meet only a tiny proportion of demand. If we understood the cellular origin of new beta-cells in adult life, the same processes could be encouraged in the diabetic patient with drugs, or cells could be manufactured outside the body and implanted.
Currently islet transplants survive rejection for around 18 months, which is acceptable as an alternative to diabetes, and provide effective treatment, the only real limitation being lack of suitable transplant material. Survival times for implants are rapidly increasing under the influence of intensive research.
Pharma would be a major beneficiary, through manufacturing and selling beta-cells. Novo Nordisk has a major facility near Copenhagen, devoted to the creation of beta cells and to beta-cell therapy. All big pharma is similarly interested.
In fundamental research it is impossible to be specific about timelines. However, a mouse beta cell precursor identified in this project would be rapidly validated in man, in view of the worldwide interest and existing infrastructures. Trials to expand these precursors and generate beta cells on a large scale would also follow rapidly. We think the time-lag would be 5 to 10 years. A particular beneficiary in the UK of a successful outcome for our research would be the NHS, currently spending £14 billion per year on diabetes (http://www.diabetes.co.uk/cost-of-diabetes.html).
Training: Both wet and dry PDRAs will make contact with industry at our workshops, and when pharma visit our labs. Their experience at our workshops and the training they receive while on visits to Bielefeld and Geneva will greatly enhance their employment prospects. Weekly attendance at our multidisciplinary meetings will draw their attention to the importance of interactions with other disciplines and they will be taught the relevant details of the other disciplines by face-to-face tuition from the Investigators.
Presentations to our team, to University seminars and to national and international conferences will develop their skills, making them more employable. Our project will have a beneficial effect on academics from Warwick and elsewhere.
Schubert, the inventor of TIS, is working on a benchtop TIS system, making it eminently suitable as a diagnostic and research tool. Our methods of analysis and our software will be provided with such machines. Our ideas will also be useful whenever similar multi-channel studies are undertaken, and are not confined to TIS. The impact would be felt in laboratories worldwide. For example, our computer programs automate segmentation of images of tissue sections into cells, saving hours of intensive and uninteresting labour, simultaneously greatly improving accuracy.
Through WISDEM (see Google), we will organize two meetings in Year 1, one for regional medical professionals and one aimed at the interested public, to explain our plans. In Year 3 we will again have two similar meetings to report on progress.
[1] A. E. Butler, et al., Diabetologia, vol. 53, pp. 2167-76, 2010.
[2] S. Abouna, et al., Organogenesis, vol. 6, pp. 125-33, 2010.
Publications
Ahmed Raza SE
(2016)
Robust normalization protocols for multiplexed fluorescence bioimage analysis.
in BioData mining
Raza S
(2019)
Micro-Net: A unified model for segmentation of various objects in microscopy images
in Medical Image Analysis
| Description | 1. Biological outcomes: Our data [manuscript in preparation] show that during pregnancy: (i) a small proportion of pancreatic duct cells do become other cell types, including beta cells, alpha cells, delta cells and acinar cells, and (ii) these are more highly represented within smaller sized pancreatic islets of pregnant mice (day 19). Importantly, this phenomenon also occurs in adult virgin mice, although to a lesser degree, strongly suggesting an existence of both endocrine and exocrine cell turnover in a healthy adult pancreas. This is a key finding as it provides evidence that rather than being confined to embryonic development (as has been stated by various research groups), pancreatic duct cells of a healthy adult can give rise to beta cells, as well as alpha and delta cells (all of which play an important role in maintaining blood glucose levels) and acinar cell of the exocrine pancreas. 2. Image Analysis of TIS (Toponome Image System) Data: To meet the objectives of the project, we have developed necessary new algorithms and software tools in order to perform the following key steps for analysis of images acquired from TIS: 1) Alignment of multi-tag fluorescence microscopy images. 2) Normalization of TIS data. 3) Detection of nuclei. 4) Segmentation of cells. 5) Computing a biomarker-based protein profile for each segmented cell. 6) Clustering of cells into cell types according to protein profiles, etc. 7) Comparison of clusters with "known" cell types, such as alpha, beta, gamma cells, etc. 8) Follow differential changes in profile as mouse pregnancy proceeds. 9) These methods and computer programs can be applied to pancreatic sections from both wild-type and transgenic mice, and the results can be integrated. Under 1) and 2) above, we employed an improved alignment algorithm [CS1] for TIS images developed previously in our group. New algorithms for normalization of the TIS data have been implemented to reduce the effects of data variability [CS2]. Under 3 & 4) above, we implemented and trained deep learning algorithms to detect, segment nuclei and cells within a visual field ([CS7]; [CS4]; [CS5]) using only the DAPI and ECAD channels in the TIS stack. The algorithms are customised to multi-channel image contents with a novel neural network architecture. Our results are better than previous state-of-the-art for automated segmentation. Moreover, the same architecture and algorithms can be used in diverse segmentation problems in the automated study of whole-slide-images of tissue sections. (As one example, we mention the segmentation of glands, where our work on beta cells has led to a unified algorithm that is potentially useful for the diagnosis of colon cancer [CS7].) We have published one paper [CS4] on this work, and a second larger paper [CS6] has recently been accepted for publication. The above software is part of an extensive software suite named PrIDE (ProBe Image Data Explorer), which is the main software tool we use for the analysis of data collected for the project. PrIDE incorporates the alignment, normalization, nuclear detection and cell segmentation algorithms into a user-friendly software tool. The software includes various tools for image analysis and for profiling of individual cells [CS3]. [CS1] Raza, S. E. A., Humayun, A., Abouna, S., Nattkemper, T. W., Epstein, D. B. A., Khan, M., & Rajpoot, N. M. (2012). RAMTaB: robust alignment of multi-tag bioimages. PLoS ONE, 7(2), e30894. http://doi.org/10.1371/journal.pone.0030894 [CS2] Raza, S. E. A., Langenkämper, D., Sirinukunwattana, K., Epstein, D., Nattkemper, T. W., & Rajpoot, N. M. (2016). Robust normalization protocols for multiplexed fluorescence bioimage analysis. BioData Mining, 9(1), 11. http://doi.org/10.1186/s13040-016-0088-2 [CS3] Khan, A. M., Raza, S. E. A., Khan, M., & Rajpoot, N. M. (2014). Cell phenotyping in multi-tag fluorescent bioimages. Neurocomputing, 134, 254-261. http://doi.org/10.1016/j.neucom.2013.08.043 [CS4] S. E. A. Raza, L. Cheung, D. Epstein, S. Pelengaris, M. Khan, N. M. Rajpoot, Mimo-net: A multi-input multi-output convolutional neural network for cell segmentation in fluorescence microscopy images, in: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017, pp. 337-340. doi:10.1109/ISBI.2017.7950532. [CS5] Raza, S. E. A., Cheung, L., Epstein, D. B. A., Pelengaris, S., Khan, M., & Rajpoot, N. M. (2017). MIMO-Net: A Multi-Input Multi-Output Convolutional Neural Network For Cell Segmentation In Fluorescence Microscopy Images. In International Symposium on Biomedical Imaging. Melbourne. [CS6] Shan E Ahmed Raza, Linda Cheung, Muhammad Shaban, Simon Graham, David Epstein, Stella Pelengaris, Michael Khan, Nasir M. Rajpoot. Micro-Net: A unified model for segmentation of various objects in microscopy images. Medical Image Analysis (2019) Vol 52; 160-173. [CS7] Sirinukunwattana, K., Raza, S. E. A., Tsang, Y.-W., Snead, D., Cree, I., & Rajpoot, N. (2016). Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images. IEEE Transactions on Medical Imaging, 1-1. http://doi.org/10.1109/TMI.2016.2525803 |
| Exploitation Route | 1. In the development of software tools for analysing TIS images, our work on Segmentation of individual cells within the pancreas, has led to its potential application in the Grading of human Cancers. We implemented and trained deep learning algorithms to detect, segment nuclei and cells within a visual field ([Refs in 'Key Findings' CS7]; [CS4]; [CS5]) using only the DAPI and ECAD channels in the TIS stack. The algorithms are customised to multi-channel image contents with a novel neural network architecture. Our results are better than previous state-of-the-art for automated segmentation. Moreover, the same architecture and algorithms can be used in diverse segmentation problems in the automated study of whole-slide-images of tissue sections. (As one example, we mention the segmentation of glands, where our work on beta cells has led to a unified algorithm that is potentially useful for the diagnosis of colon cancer [CS7]). We have published one paper [CS4] on this work, and a second larger paper [CS6] has recently been accepted currently for publication in Medical Image Analysis (Feb 2019) Vol 52; 160-173. 2. Our biological findings provide important information to researchers of diabetes, in particular Gestational Diabetes Mellitus (GDM), a potential life threatening condition to both mother and baby affecting around 5% of UK pregnancies (see point 3). The normal compensatory increase in beta cell mass during pregnancy is defective, making it pertinent to unravel the mechanisms involved in beta cell adaptation. Our data show that rather than being confined to embryonic development or following pancreatic injury, duct cells of a healthy adult pancreas can give rise to beta cells, as well as alpha and delta cells (all of which play an important role in maintaining blood glucose levels) and acinar cells of the exocrine pancreas (Paper in preparation). 3. Results arising from our TIS image analyses, have led to a Collaboration with Dr..Sascha Ott (Warwick Computer Sciences) to investigate beta cell heterogeneity (a highly emerging topic) within and between islets during pregnancy. By employing cutting-edge technology, our preliminary single cell RNA-seq (scRNA-seq) data using DropSeq protocol has produced good quality gene expression profiles of individual islet cells (Paper in preparation). An application for a BBSRC project proposal, headed by Dr Sascha Ott, is to be submitted for the April 2019 round, in order to undertake a more in-depth analyses to perform clustering and differential gene expression analysis on the single-cell data during and following pregnancy. By providing a baseline for what is normal, researchers working on gestational diabetes (GDM) can begin to investigate any deviation from the norm that occurs as a result of the condition. |
| Sectors | Pharmaceuticals and Medical Biotechnology |
| URL | http://www.warwick.ac.uk/BIAlab/projects/ProBe/ |
| Description | A 12 month Industrial placement from Silence Therapeutics PLC who are interested in acquiring multi-channel microscopy (TIS) analysis of clinical Trial material. |
| First Year Of Impact | 2014 |
| Sector | Pharmaceuticals and Medical Biotechnology |
| Description | International Partnering Award |
| Amount | £24,950 (GBP) |
| Funding ID | BB/N022564/1 |
| Organisation | Biotechnology and Biological Sciences Research Council (BBSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 05/2016 |
| End | 05/2018 |
| Title | PrIDE (ProBe Image Data Explorer), |
| Description | As part of this project, we have developed an extensive software suite named PrIDE (ProBe Image Data Explorer), which is the main software tool we use for the analysis of data collected for the project. PrIDE incorporates the alignment, normalization, nuclear detection and cell segmentation algorithms into a user-friendly software tool. The software includes various tools for image analysis and for profiling of individual cells. A first paper on Segmentation has recently been published (below) with a further Manuscript on PrIDE currently in preparation. Raza S, Cheung L, Shaban M, Graham S, Epstein D, Pelengaris S..... Rajpoot N, (2019). Micro-Net: A unified model for segmentation of various objects in microscopy images.. Medical image analysis, pp. 160-173. |
| Type Of Material | Technology assay or reagent |
| Year Produced | 2017 |
| Provided To Others? | No |
| Impact | We implemented automated methods to segment islets using fluorescence images corresponding to Insulin antibody marker. This allows us to find the association of cells for various markers corresponding to varying levels of Insulin expression. The software also allows the user to remove noise and segment regions of interest with manual intervention. The PrIDE software can be applied with advantage to our Pulse-Chase-Lineage-Tracing (PCLT) transgenic mice, giving the opportunity to provide more information about protein co-expressions than the standard PCLT that can help us to identify heterogeneous signatures that may be able to identify non-ß cell precursors, if present. |
| Title | MicroNet |
| Description | We have curated a multi-channel fluorescence cell segmentation data set which will be published along with our recently published [Raza S, Cheung L, Shaban M, Graham S, Epstein D, Pelengaris S..... Rajpoot N, (2019). Micro-Net: A unified model for segmentation of various objects in microscopy images.. Medical image analysis, pp. 160-173]. In this paper, we developed a deep learning architecture MicroNet and tested it not only on finding plasma membrane boundary in our fluorescence image data set but also on finding glandular structures in images of H&E stained tumour tissue sections with promising results. The code and data associated with that paper has been made publicly available upon. In addition, we have generated over 60 TIS stacks for PCLT experiments which will be made publicly available upon publication of our biology paper. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2019 |
| Provided To Others? | Yes |
| Impact | The code and data associated with our paper is publicly available. In addition, we have generated over 60 TIS (Toponome Imaging Systems) stacks for PCLT (Pulse clhase lineage tracing) experiments which will be made publicly available upon publication of our Biology paper (Manuscript in preparation).. |
| Description | Single cell NGS using Drop-Seq to identify beta cell heterogeneity |
| Organisation | University of Valencia |
| Department | Department of Computer Sciences |
| Country | Spain |
| Sector | Academic/University |
| PI Contribution | Our collaboration is with Dr Sascha Ott's group in Computer Sciences. We have biological expertise in providing single beta cells from the pancreata of WT and pregnant mice for subsequent analyses using Drop-Seq. Intellectual input on physiological processes that may occur in beta cells. Provision of data already collected from the current grant - TIS images. Also, NGS data of whole pancreas from WT and pregnant mice to compare with single cell. |
| Collaborator Contribution | Expertise in NGS data analyses - they have analysed our NGS data derived from whole pancreas of WT and pregnant mice. Our collaboration with Dr Ott, brought about the successful acquisition of funds to purchase a Drop-Seq system; Dr Ott's group has provided expertise in analyses of single beta cells from the pancreata of WT and pregnant mice. Our preliminary data is of good quality and has formed the basis of a Wellcome Trust Summer Vacation Scolarship. In addition, this preliminary single islet cell data has been further analysed as part of a PhD thesis (to be submitted in Sep 2019). |
| Impact | Multi-Disciplinary: Life Sciences and Computer Sciences (CS). Single cell RNA (scRNA)-seq data obtained from isolated pancreatic islets of WT versus Pregnant mice has been analysed and has formed the basis of a Wellcome Trust Summer Vacation Scolarship - February 2018. Further analysis of this scRNA-seq data forms part of PhD thesis (CS). |
| Start Year | 2016 |