Browsing by Subject "bioinformatics"

The advancement of high-throughput imaging technologies has revolutionized the study of the tumor microenvironment (TME), including high-grade serous ovarian carcinoma (HGSOC), a cancer type characterized by genetic instability and high intra-tumor heterogeneity. HGSOC is often diagnosed at advanced stages and has a high relapse rate following initial treatment, presenting significant clinical challenges. Understanding the dynamic and complex tumor microenvironment in HGSOC is crucial for developing effective therapeutic strategies, as it includes various interacting cells and structures. Currently most methods are focusing on deciphering the TME on a single cell level, but the volume of the data poses a challenge in large scale studies. This thesis focuses on developing a comprehensive pipeline for accurate detection and phenotyping of immune cells within the TME using tissue cyclic immunofluorescence imaging. The proposed pipeline integrates Napari, an advanced visualization tool, and several existing computational methods to handle large-scale imaging datasets efficiently. The primary aim is to create Napari plugins for fast browsing and detailed visualization of these datasets, enabling precise cell phenotyping and quality control. Handling large images was resolved through the implementation of Zarr and Dask methodologies, enabling efficient data management. Key image processing methodologies include the use of the StarDist algorithm for cell segmentation, preprocessing steps for fluorescence intensity normaliza tion, and the Tribus tool for semi-automated cell type classification. In total, we annotated 976,082 single cells on three HGSOC samples originating from pre- or post-neoadjuvant chemotherapy tumor sections. The accurate annotation of immune sub-populations was enhanced by visual evaluation steps, addressing the limitations of the discussed methods. Accurately annotating dense tissue areas is crucial for describing the cellular composition of samples, particularly tumor-infiltrating immune populations. The results indicate that the proposed pipeline not only enhances the understanding of the TME in HGSOC but also provides a robust framework for future studies involving large-scale imaging data.

Triglycerides are a type of lipid that enters our body with fatty food. High triglyceride levels are often caused by an unhealthy diet, poor lifestyle, poorly treated diseases such as diabetes and too little exercise. Other risk factors found in various studies are HIV, menopause, inherited lipid metabolism disorder and South Asian ancestry. Complications of high triglycerides include pancreatitis, carotid artery disease, coronary artery disease, metabolic syndrome, peripheral artery disease, and strokes. Migration has made Singapore diverse, and it contains several subpopulations. One third of the population has genetic ancestry in China. The second largest group has genetic ancestry in Malaysia, and the third largest has genetic ancestry in India. Even though Singapore has one of the highest life expectancies in the world, unhealthy lifestyles such as poor diet, lack of exercise and smoking are still visible in everyday life. The purpose of this thesis was to introduce GWAS-analysis for quantitative traits and apply it to real data, and also to see if there are associations between some variants and triglycerides in three main subpopulations in Singapore and compare the results to previous studies. The research questions that this thesis answered are: what is GWAS analysis and what is it used for, how can GWAS be applied to data containing quantitative traits, and is there associations between some SNPs and triglycerides in three main populations in Singapore. GWAS stands for genome-wide association studies designed to identify statistical association between genetic variants and phenotypes or traits. One reason for developing GWAS was to learn to identify different genetic factors which have an impact on significant phenotypes, for instance susceptibility to certain diseases Such information can eventually be used to predict the phenotypes of individuals. GWAS have been globally used in, for example, anthropology, biomedicine, biotechnology, and forensics. The studies enhance the understanding of human evolution and natural selection and helps forward many areas of biology. The study used several quality control methods, linear models, and Bayesian inference to study associations. The research results were examined, among other things, with the help of various visual methods. The dataset used in this thesis was an open data used by Saw, W., Tantoso, E., Begum, H. et al. in their previous study. This study showed that there are associations between 6 different variants and triglycerides in the three main subpopulations in Singapore. The study results were compared with the results of two previous studies, which differed from the results of this study, suggesting that the results are significant. In addition, the thesis reviewed the ethics of GWAS and the limitations and benefits of GWAS. Most of the studies like this have been done in Europe, so more research is needed in different parts of the world. This research can also be continued with different methods and variables.

ScRNA-seq captures a static picture of a cell's transcriptome including abundances of unspliced and spliced RNA. RNA velocity methods offer the opportunity to infer future RNA abundances and thus future states of a cell based on the temporal change of these unspliced and spliced RNA. Early RNA velocity methods have shed light on transcriptional dynamics in many biological processes. However, due to strict assumptions in the underlying model, these models are not reliable when analysing and inferring velocity for genes with complex expression dynamics such as genes with transcriptional boosts. These genes can for example be observed in erythropoietic and hematopoietic data. Several new RNA velocity methods have been proposed recently. Among these, veloVI and Pyro-Velocity both employ Bayesian methods to estimate the reaction rate and latent parameters. Thus the problem of estimating RNA velocity is turned into a posterior probability inference, that allows for more flexible inference of model parameters and the quantification of uncertainty. The objectives of this thesis were to investigate newly published RNA velocity methods, veloVI and Pyro-Velocity, in comparison to the established tool scVelo. To achieve this, we applied the methods to data obtained from scRNA-seq of healthy and ERCC6L2 disease bone marrow cells. ERCC6L2 disease can cause bone marrow failure with a risk of progression to acute myeloid leukemia with erythroid predominance. Specifically, we evaluated whether RNA velocity results reflect hematopoietic differentiation, if genes with transcriptional boosts affect the velocity results, and if RNA velocity analysis can indicate why erythropoiesis in ERCC6L2 disease is affected. We find that new RNA velocity methods can not produce velocity estimations that are fully in line with what is known of hematopoiesis in our data. Further, the results suggest that velocity estimations by veloVI are affected by genes with transcriptional boosts. Moreover, RNA velocity methods examined in this thesis are not robust and cannot reliably predict cell transitions based on the estimated velocity. Subsequently, velocity estimations for disease data such as ERCC6L2 disease must be evaluated carefully before drawing any conclusion about the differentiation process. In conclusion, this thesis highlights the need for models that can model complex transcription kinetics. Still, as this field is rapidly growing and promising new methods are being developed, improvement of RNA velocity analysis, in general, is possible.