ABSTRACT
Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated in the context of multiple vignettes, including integrative analysis on a multimodal Xenium and Visium breast cancer study.
ABSTRACT
Together with the molecular knowledge of genes and proteins, biological images promise to significantly enhance the scientific understanding of complex cellular systems and to advance predictive and personalized therapeutic products for human health. For this potential to be realized, quality-assured image data must be shared among labs at a global scale to be compared, pooled, and reanalyzed, thus unleashing untold potential beyond the original purpose for which the data was generated. There are two broad sets of requirements to enable image data sharing in the life sciences. One set of requirements is articulated in the companion White Paper entitled "Enabling Global Image Data Sharing in the Life Sciences," which is published in parallel and addresses the need to build the cyberinfrastructure for sharing the digital array data (arXiv:2401.13023 [q-bio.OT], https://doi.org/10.48550/arXiv.2401.13023). In this White Paper, we detail a broad set of requirements, which involves collecting, managing, presenting, and propagating contextual information essential to assess the quality, understand the content, interpret the scientific implications, and reuse image data in the context of the experimental details. We start by providing an overview of the main lessons learned to date through international community activities, which have recently made considerable progress toward generating community standard practices for imaging Quality Control (QC) and metadata. We then provide a clear set of recommendations for amplifying this work. The driving goal is to address remaining challenges, and democratize access to common practices and tools for a spectrum of biomedical researchers, regardless of their expertise, access to resources, and geographical location.
ABSTRACT
Multiplexed imaging approaches are getting increasingly adopted for imaging of large tissue areas, yielding big imaging datasets both in terms of the number of samples and the size of image data per sample. The processing and analysis of these datasets is complex owing to frequent technical artifacts and heterogeneous profiles from a high number of stained targets To streamline the analysis of multiplexed images, automated pipelines making use of state-of-the-art algorithms have been developed. In these pipelines, the output quality of one processing step is typically dependent on the output of the previous step and errors from each step, even when they appear minor, can propagate and confound the results. Thus, rigorous quality control (QC) at each of these different steps of the image processing pipeline is of paramount importance both for the proper analysis and interpretation of the analysis results and for ensuring the reusability of the data. Ideally, QC should become an integral and easily retrievable part of the imaging datasets and the analysis process. Yet, limitations of the currently available frameworks make integration of interactive QC difficult for large multiplexed imaging data. Given the increasing size and complexity of multiplexed imaging datasets, we present the different challenges for integrating QC in image analysis pipelines as well as suggest possible solutions that build on top of recent advances in bioimage analysis.
ABSTRACT
Inflammatory bowel disease (IBD) patients may bear an increased neuroendocrine tumor (NET) risk. These tumors are mostly reported as coincidental findings during surgery. We aimed to determine the prevalence of colonic NET in a Dutch nationwide IBD cohort and calculate the prevalence rate ratios (PRR) compared with the general Dutch population. Our second aim was to investigate whether a high bowel surgery rate in IBD could result in a high PRR for NET. The Dutch Pathology Registry (PALGA) was searched to identify all IBD patients with colonic NET in The Netherlands between 1991 and 2011. We determined the prevalence and PRR of colonic NET in a 20-year period. For our second aim, we compared NET prevalence in colonic resection specimens between IBD cases and non-IBD controls (diverticulitis and ischemia). We identified 51 IBD patients who developed colonic NET resulting in a prevalence of 60.4-89.3 per 100,000 patients in a 20-year period with a PRR of 2.8-4.1. However, adjusted for resection type, sex and age, a higher NET prevalence was shown in diverticulitis (OR 5.52, 95% CI 3.47-8.78) and ischemia (OR 1.97, 95% CI 1.09-3.58) compared with IBD. Our key finding is that NET are more prevalent in IBD patients compared with the general population (PRR 2.8-4.1). This might be attributed to a high rate of incidental NET as IBD patients frequently undergo intestinal surgery. A lower adjusted NET prevalence in colonic resection specimens for IBD compared to ischemia and diverticulitis supports this hypothesis.
