Background Visualizing and quantifying cellular heterogeneity is of central importance to study tissue complexity, development, and physiology and has a vital role in understanding pathologies. Mass spectrometry-based methods including imaging mass cytometry (IMC) have in recent years emerged as powerful approaches for assessing cellular heterogeneity in tissues. IMC is an innovative multiplex imaging method that combines imaging using up to 40 metal conjugated antibodies and provides distributions of protein markers in tissues with a resolution of 1 μm2 area. However, resolving the output signals of individual cells within the tissue sample, i.e., single cell segmentation, remains challenging. To address this problem, we developed MATISSE (iMaging mAss cyTometry mIcroscopy Single cell SegmEntation), a method that combines high-resolution fluorescence microscopy with the multiplex capability of IMC into a single workflow to achieve improved segmentation over the current state-of-the-art. Results MATISSE results in improved quality and quantity of segmented cells when compared to IMC-only segmentation in sections of heterogeneous tissues. Additionally, MATISSE enables more complete and accurate identification of epithelial cells, fibroblasts, and infiltrating immune cells in densely packed cellular areas in tissue sections. MATISSE has been designed based on commonly used open-access tools and regular fluorescence microscopy, allowing easy implementation by labs using multiplex IMC into their analysis methods. Conclusion MATISSE allows segmentation of densely packed cellular areas and provides a qualitative and quantitative improvement when compared to IMC-based segmentation. We expect that implementing MATISSE into tissue section analysis pipelines will yield improved cell segmentation and enable more accurate analysis of the tissue microenvironment in epithelial tissue pathologies, such as autoimmunity and cancer.

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Biomedical data science education faces the challenge of preparing students for conducting rigorous research with increasingly complex and large datasets. At the same time, philosophers of science face the challenge of making their expertise accessible for scientists in such a way that it can improve everyday research practice. Here, we investigate the possibility of approaching these challenges together. In current and proposed approaches to biomedical data science education, we identify a dominant focus on only one aspect of conducting scientific research: understanding and using data, research methods, and statistical methods. We argue that this approach cannot solve biomedical data science’s challenge and we propose to shift the focus to four other aspects of conducting research: making and justifying decisions in research design and implementation, explaining their epistemic and non-epistemic effects, balancing varying responsibilities, and reporting scientific research. Attending to these aspects requires learning on different dimensions than solely learning to apply techniques (first dimension). It also requires learning to make choices (second dimension) and to understand the rationale behind choices (third dimension). This could be fostered by integrating philosophical training in biomedical data science education. Furthermore, philosophical training fosters a fourth dimension of learning, namely, understanding the nature of science. In this article, we explain how we identified the five aspects of conducting research and the four dimensions of learning, and why attending to the fourth dimension is essential. We discuss educational approaches to attend to all aspects and dimensions, and present initial design principles to implement these approaches.