Reto Gerber

Spatial omics is transforming our ability to interrogate local tissue microenvironments by enabling spatially resolved measurement of biomolecules such as transcripts, proteins, and metabolites. However, capturing the full biological complexity of tissues often requires combining multiple modalities, which introduces both experimental as well as computational challenges. To address computational difficulties due to differences in resolution, noise levels, and available channels, we present MIMIC: a reproducible, semi-automated workflow that integrates Mass Spectrometry Imaging and Imaging Mass Cytometry for joint downstream analysis. MIMIC incorporates rigorous quality control, including registration error assessment, and supports pixel-level modeling to delineate analyte-cell type associations. We demonstrate the power of our approach with a proof-of-concept study on artificial tissue and apply it to human liver tissue affected by metabolic dysfunction-associated steatotic liver disease. Despite integration challenges, MIMIC provides a robust framework that successfully recovers known molecular associations and reveals novel spatial relationships across modalities.

Accurate cell type annotation in imaging mass cytometry (IMC) and related technologies critically depends on preprocessing steps such as normalization, segmentation, and marker aggregation. More distinct separation between negative and positive signals enables more precise cell boundary inference and more robust marker assignment to single cells. However, inherent limitations in spatial resolution and uncertain cell boundaries can lead to spillover, where signal leaks from one cell to a neighboring cell, distorting marker intensities and leading to incorrect cell type annotation. To address these challenges, we first systematically investigated the impact of spatial resolution and segmentation variability on per-cell marker aggregation using simulated IMC datasets, establishing upper limits for reliable marker separation in cell type annotation. We further analyzed technical biases in large scale studies, demonstrating that appropriate normalization approaches can significantly reduce batch effects without compromising biological variability. Finally, we benchmarked multiple spillover correction strategies across both (semi-)simulated and real IMC datasets. Our results revealed two simple methods: spatial smoothing of intensity images followed by mean marker aggregation, or resampling of cell masks followed by mean marker aggregation and median calculation, both of which improved annotation performance. Other methods often performed worse than the baseline of simple mean aggregation. Together, these findings underscore the central importance of spatial resolution, normalization, and marker aggregation in IMC data preprocessing for accurate single-cell annotation.

Benchmarking, which involves collecting reference datasets and demonstrating method performance, is a requirement for the development of new computational tools, but also becomes a domain of its own to achieve neutral comparisons of methods. Although a lot has been written about how to design and conduct benchmark studies, this Perspective sheds light on a wish list for a computational platform to orchestrate benchmark studies. We discuss various ideas for organizing reproducible software environments, formally defining benchmarks, orchestrating standardized workflows, and how they interface with computing infrastructure.

Spatial omics is transforming our ability to interrogate local tissue microenvironments by enabling spatially resolved measurement of biomolecules such as transcripts, proteins, and metabolites. However, capturing the full biological complexity of tissues often requires combining multiple modalities, which introduces both experimental as well as computational challenges. To address computational difficulties due to differences in resolution, noise levels, and available channels, we present MIMIC: a reproducible, semi-automated workflow that integrates Mass Spectrometry Imaging and Imaging Mass Cytometry for joint downstream analysis. MIMIC incorporates rigorous quality control, including registration error assessment, and supports pixel-level modeling to delineate analyte–cell type associations. We demonstrate the power of our approach with a proof-of-concept study on artificial tissue and apply it to human liver tissue affected by metabolic dysfunction-associated steatotic liver disease. Despite integration challenges, MIMIC provides a robust framework that successfully recovers known molecular associations and reveals novel spatial relationships across modalities.

BioStudies – one package for all the data supporting a study

The 2024 OME-NGFF Workflows Hackathon, held at the BioVisionCenter at the University of Zurich, brought together an international group of researchers and developers to develop the ecosystem around the open, scalable, and FAIR bioimage file format OME-Zarr. Over five days, participants tackled key challenges in four main areas: (1) advancing the OME-Zarr specification, (2) enabling workflow interoperability by integrating OME-Zarr image processing tasks across multiple open-source frameworks, (3) expanding Java support for Zarr v3 and enhancing the compatibility of OME-Zarr with the popular bioimage analysis software Fiji, and (4) improving the Python resources supporting OME-Zarr. The event led to the release of OME-Zarr 0.5, which formalizes the adoption of Zarr v3 and introduces a sharding strategy to reduce file system overhead. This report provides an overview of the key discussions, outcomes, and future directions emerging from the hackathon, with the goal of fostering continued community engagement in developing OME-Zarr as a robust open bioimaging standard.

Computational biologists are frequently engaged in collaborative data analysis with wet lab researchers. These interdisciplinary projects, as necessary as they are to the scientific endeavor, can be surprisingly challenging due to cultural differences in operations and values. In this Ten Simple Rules guide, we aim to help dry lab researchers identify sources of friction and provide actionable tools to facilitate respectful, open, transparent, and rewarding collaborations.

Computational methods represent the lifeblood of modern molecular biology. Benchmarking is important for all methods, but with a focus here on computational methods, benchmarking is critical to dissect important steps of analysis pipelines, formally assess performance across common situations as well as edge cases, and ultimately guide users on what tools to use. Benchmarking can also be important for community building and advancing methods in a principled way. We conducted a meta-analysis of recent single-cell benchmarks to summarize the scope, extensibility, neutrality, as well as technical features and whether best practices in open data and reproducible research were followed. The results highlight that while benchmarks often make code available and are in principle reproducible, they remain difficult to extend, for example, as new methods and new ways to assess methods emerge. In addition, embracing containerization and workflow systems would enhance reusability of intermediate benchmarking results, thus also driving wider adoption.