Segmentation of Dynamic Total-Body [18F]-FDG PET Images Using Unsupervised Clustering.

Clustering time activity curves of PET images have been used to separate clinically relevant areas of the brain or tumours. However, PET image segmentation in multiorgan level is much less studied due to the available total-body data being limited to animal studies. Now, the new PET scanners providing the opportunity to acquire total-body PET scans also from humans are becoming more common, which opens plenty of new clinically interesting opportunities. Therefore, organ-level segmentation of PET images has important applications, yet it lacks sufficient research. In this proof of concept study, we evaluate if the previously used segmentation approaches are suitable for segmenting dynamic human total-body PET images in organ level. Our focus is on general-purpose unsupervised methods that are independent of external data and can be used for all tracers, organisms, and health conditions. Additional anatomical image modalities, such as CT or MRI, are not used, but the segmentation is done purely based on the dynamic PET images. The tested methods are commonly used building blocks of the more sophisticated methods rather than final methods as such, and our goal is to evaluate if these basic tools are suited for the arising human total-body PET image segmentation. First, we excluded methods that were computationally too demanding for the large datasets from human total-body PET scanners. These criteria filtered out most of the commonly used approaches, leaving only two clustering methods, k-means and Gaussian mixture model (GMM), for further analyses. We combined k-means with two different preprocessing approaches, namely, principal component analysis (PCA) and independent component analysis (ICA). Then, we selected a suitable number of clusters using 10 images. Finally, we tested how well the usable approaches segment the remaining PET images in organ level, highlight the best approaches together with their limitations, and discuss how further research could tackle the observed shortcomings. In this study, we utilised 40 total-body [18F] fluorodeoxyglucose PET images of rats to mimic the coming large human PET images and a few actual human total-body images to ensure that our conclusions from the rat data generalise to the human data. Our results show that ICA combined with k-means has weaker performance than the other two computationally usable approaches and that certain organs are easier to segment than others. While GMM performed sufficiently, it was by far the slowest one among the tested approaches, making k-means combined with PCA the most promising candidate for further development. However, even with the best methods, the mean Jaccard index was slightly below 0.5 for the easiest tested organ and below 0.2 for the most challenging organ. Thus, we conclude that there is a lack of accurate and computationally light general-purpose segmentation method that can analyse dynamic total-body PET images.

Quantitative Perfusion Imaging with Total-Body PET.

Recently, PET systems with a long axial field of view have become the current state of the art. Total-body PET scanners enable unique possibilities for scientific research and clinical diagnostics, but this new technology also raises numerous challenges. A key advantage of total-body imaging is that having all the organs in the field of view allows studying biologic interaction of all organs simultaneously. One of the new, promising imaging techniques is total-body quantitative perfusion imaging. Currently, 15O-labeled water provides a feasible option for quantitation of tissue perfusion at the total-body level. This review summarizes the status of the methodology and the analysis and provides examples of preliminary findings on applications of quantitative parametric perfusion images for research and clinical work. We also describe the opportunities and challenges arising from moving from single-organ studies to modeling of a multisystem approach with total-body PET, and we discuss future directions for total-body imaging.

Gene expression signature predicts rate of type 1 diabetes progression.

BACKGROUND: Type 1 diabetes is a complex heterogenous autoimmune disease without therapeutic interventions available to prevent or reverse the disease. This study aimed to identify transcriptional changes associated with the disease progression in patients with recent-onset type 1 diabetes. METHODS: Whole-blood samples were collected as part of the INNODIA study at baseline and 12 months after diagnosis of type 1 diabetes. We used linear mixed-effects modelling on RNA-seq data to identify genes associated with age, sex, or disease progression. Cell-type proportions were estimated from the RNA-seq data using computational deconvolution. Associations to clinical variables were estimated using Pearson's or point-biserial correlation for continuous and dichotomous variables, respectively, using only complete pairs of observations. FINDINGS: We found that genes and pathways related to innate immunity were downregulated during the first year after diagnosis. Significant associations of the gene expression changes were found with ZnT8A autoantibody positivity. Rate of change in the expression of 16 genes between baseline and 12 months was found to predict the decline in C-peptide at 24 months. Interestingly and consistent with earlier reports, increased B cell levels and decreased neutrophil levels were associated with the rapid progression. INTERPRETATION: There is considerable individual variation in the rate of progression from appearance of type 1 diabetes-specific autoantibodies to clinical disease. Patient stratification and prediction of disease progression can help in developing more personalised therapeutic strategies for different disease endotypes. FUNDING: A full list of funding bodies can be found under Acknowledgments.

Pancreas Whole Tissue Transcriptomics Highlights the Role of the Exocrine Pancreas in Patients With Recently Diagnosed Type 1 Diabetes.

Although type 1 diabetes (T1D) is primarily a disease of the pancreatic beta-cells, understanding of the disease-associated alterations in the whole pancreas could be important for the improved treatment or the prevention of the disease. We have characterized the whole-pancreas gene expression of patients with recently diagnosed T1D from the Diabetes Virus Detection (DiViD) study and non-diabetic controls. Furthermore, another parallel dataset of the whole pancreas and an additional dataset from the laser-captured pancreatic islets of the DiViD patients and non-diabetic organ donors were analyzed together with the original dataset to confirm the results and to get further insights into the potential disease-associated differences between the exocrine and the endocrine pancreas. First, higher expression of the core acinar cell genes, encoding for digestive enzymes, was detected in the whole pancreas of the DiViD patients when compared to non-diabetic controls. Second, In the pancreatic islets, upregulation of immune and inflammation related genes was observed in the DiViD patients when compared to non-diabetic controls, in line with earlier publications, while an opposite trend was observed for several immune and inflammation related genes at the whole pancreas tissue level. Third, strong downregulation of the regenerating gene family (REG) genes, linked to pancreatic islet growth and regeneration, was observed in the exocrine acinar cell dominated whole-pancreas data of the DiViD patients when compared with the non-diabetic controls. Fourth, analysis of unique features in the transcriptomes of each DiViD patient compared with the other DiViD patients, revealed elevated expression of central antiviral immune response genes in the whole-pancreas samples, but not in the pancreatic islets, of one DiViD patient. This difference in the extent of antiviral gene expression suggests different statuses of infection in the pancreas at the time of sampling between the DiViD patients, who were all enterovirus VP1+ in the islets by immunohistochemistry based on earlier studies. The observed features, indicating differences in the function, status and interplay between the exocrine and the endocrine pancreas of recent onset T1D patients, highlight the importance of studying both compartments for better understanding of the molecular mechanisms of T1D.

Crowdsourcing digital health measures to predict Parkinson's disease severity: the Parkinson's Disease Digital Biomarker DREAM Challenge.

Consumer wearables and sensors are a rich source of data about patients' daily disease and symptom burden, particularly in the case of movement disorders like Parkinson's disease (PD). However, interpreting these complex data into so-called digital biomarkers requires complicated analytical approaches, and validating these biomarkers requires sufficient data and unbiased evaluation methods. Here we describe the use of crowdsourcing to specifically evaluate and benchmark features derived from accelerometer and gyroscope data in two different datasets to predict the presence of PD and severity of three PD symptoms: tremor, dyskinesia, and bradykinesia. Forty teams from around the world submitted features, and achieved drastically improved predictive performance for PD status (best AUROC = 0.87), as well as tremor- (best AUPR = 0.75), dyskinesia- (best AUPR = 0.48) and bradykinesia-severity (best AUPR = 0.95).

Computational deconvolution to estimate cell type-specific gene expression from bulk data.

Computational deconvolution is a time and cost-efficient approach to obtain cell type-specific information from bulk gene expression of heterogeneous tissues like blood. Deconvolution can aim to either estimate cell type proportions or abundances in samples, or estimate how strongly each present cell type expresses different genes, or both tasks simultaneously. Among the two separate goals, the estimation of cell type proportions/abundances is widely studied, but less attention has been paid on defining the cell type-specific expression profiles. Here, we address this gap by introducing a novel method Rodeo and empirically evaluating it and the other available tools from multiple perspectives utilizing diverse datasets.

PASI: A novel pathway method to identify delicate group effects.

Pathway analysis is a common approach in diverse biomedical studies, yet the currently-available pathway tools do not typically support the increasingly popular personalized analyses. Another weakness of the currently-available pathway methods is their inability to handle challenging data with only modest group-based effects compared to natural individual variation. In an effort to address these issues, this study presents a novel pathway method PASI (Pathway Analysis for Sample-level Information) and demonstrates its performance on complex diseases with different levels of group-based differences in gene expression. PASI is freely available as an R package.

Enterovirus-associated changes in blood transcriptomic profiles of children with genetic susceptibility to type 1 diabetes.

AIMS/HYPOTHESIS: Enterovirus infections have been associated with the development of type 1 diabetes in multiple studies, but little is known about enterovirus-induced responses in children at risk for developing type 1 diabetes. Our aim was to use genome-wide transcriptomics data to characterise enterovirus-associated changes in whole-blood samples from children with genetic susceptibility to type 1 diabetes. METHODS: Longitudinal whole-blood samples (356 samples in total) collected from 28 pairs of children at increased risk for developing type 1 diabetes were screened for the presence of enterovirus RNA. Seven of these samples were detected as enterovirus-positive, each of them collected from a different child, and transcriptomics data from these children were analysed to understand the individual-level responses associated with enterovirus infections. Transcript clusters with peaking or dropping expression at the time of enterovirus positivity were selected as the enterovirus-associated signals. RESULTS: Strong signs of activation of an interferon response were detected in four children at enterovirus positivity, while transcriptomic changes in the other three children indicated activation of adaptive immune responses. Additionally, a large proportion of the enterovirus-associated changes were specific to individuals. An enterovirus-induced signature was built using 339 genes peaking at enterovirus positivity in four of the children, and 77 of these genes were also upregulated in human peripheral blood mononuclear cells infected in vitro with different enteroviruses. These genes separated the four enterovirus-positive samples clearly from the remaining 352 blood samples analysed. CONCLUSIONS/INTERPRETATION: We have, for the first time, identified enterovirus-associated transcriptomic profiles in whole-blood samples from children with genetic susceptibility to type 1 diabetes. Our results provide a starting point for understanding the individual responses to enterovirus infections in blood and their potential connection to the development of type 1 diabetes. DATA AVAILABILITY: The datasets analysed during the current study are included in this published article and its supplementary information files ( www.btk.fi/research/computational-biomedicine/1234-2 ) or are available from the Gene Expression Omnibus (GEO) repository (accession GSE30211).

ROTS: An R package for reproducibility-optimized statistical testing.

Differential expression analysis is one of the most common types of analyses performed on various biological data (e.g. RNA-seq or mass spectrometry proteomics). It is the process that detects features, such as genes or proteins, showing statistically significant differences between the sample groups under comparison. A major challenge in the analysis is the choice of an appropriate test statistic, as different statistics have been shown to perform well in different datasets. To this end, the reproducibility-optimized test statistic (ROTS) adjusts a modified t-statistic according to the inherent properties of the data and provides a ranking of the features based on their statistical evidence for differential expression between two groups. ROTS has already been successfully applied in a range of different studies from transcriptomics to proteomics, showing competitive performance against other state-of-the-art methods. To promote its widespread use, we introduce here a Bioconductor R package for performing ROTS analysis conveniently on different types of omics data. To illustrate the benefits of ROTS in various applications, we present three case studies, involving proteomics and RNA-seq data from public repositories, including both bulk and single cell data. The package is freely available from Bioconductor (https://www.bioconductor.org/packages/ROTS).

Comparison of methods to detect differentially expressed genes between single-cell populations.

We compared five statistical methods to detect differentially expressed genes between two distinct single-cell populations. Currently, it remains unclear whether differential expression methods developed originally for conventional bulk RNA-seq data can also be applied to single-cell RNA-seq data analysis. Our results in three diverse comparison settings showed marked differences between the different methods in terms of the number of detections as well as their sensitivity and specificity. They, however, did not reveal systematic benefits of the currently available single-cell-specific methods. Instead, our previously introduced reproducibility-optimization method showed good performance in all comparison settings without any single-cell-specific modifications.

Empirical comparison of structure-based pathway methods.

Multiple methods have been proposed to estimate pathway activities from expression profiles, and yet, there is not enough information available about the performance of those methods. This makes selection of a suitable tool for pathway analysis difficult. Although methods based on simple gene lists have remained the most common approach, various methods that also consider pathway structure have emerged. To provide practical insight about the performance of both list-based and structure-based methods, we tested six different approaches to estimate pathway activities in two different case study settings of different characteristics. The first case study setting involved six renal cell cancer data sets, and the differences between expression profiles of case and control samples were relatively big. The second case study setting involved four type 1 diabetes data sets, and the profiles of case and control samples were more similar to each other. In general, there were marked differences in the outcomes of the different pathway tools even with the same input data. In the cancer studies, the results of a tested method were typically consistent across the different data sets, yet different between the methods. In the more challenging diabetes studies, almost all the tested methods detected as significant only few pathways if any.