High-spatial-resolution in situ mapping of biomolecules within tissue reveals critical insights into the complex molecular landscape and spatial organization of biological systems. Mass spectrometry imaging (MSI) is a powerful tool for spatially resolved molecular analysis of biological samples, with ongoing demand for improved spatial resolution. Tissue expansion combined with MSI (TEMI) is a recently developed approach that enables multiomics molecular mapping across various biological tissues with significantly improved spatial resolution. Unlike conventional methods that depend on instrument-based enhancements in spatial resolution, TEMI physically enlarges tissue samples via harsh-condition-free hydrogel expansion, achieving more than 3.5-fold increase in effective imaging resolution using standard MSI instrumentation. TEMI delivers single-cell spatial resolution in tissue samples and enables detection of biomolecular heterogeneity that remains uncharacterizable in unexpanded tissue using conventional MSI. Notably, TEMI supports high-spatial-resolution mapping of multiple biomolecular classes-including lipids, metabolites, N-glycans, peptides and proteins-within a single tissue sample. Here, we provide a detailed, step-by-step guide for TEMI, including hydrogel-based tissue expansion under mild conditions, cryosectioning of the expanded tissue-hydrogel sample, a comprehensive experimental workflow for multiomics TEMI on a single tissue section, data acquisition and visualization pipelines, as well as troubleshooting tips. Overall, we demonstrate that TEMI overcomes the long-standing spatial limitations of MSI without requiring hardware modifications, ensuring compatibility with existing MSI instruments and promoting broad accessibility and adoption within the research community.
The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.
Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al.· Journal of Systems and Softw...· 111 citations· ⚡8
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al.· Journal of Systems and Softw...· 78 citations· ⚡6
This paper highlights the challenges to conduct proper affect-related studies with psychology, provides a comprehensive literature review in affect theory, and proposes guidelines for conducting psychoempirical software engineering.
D. Graziotin, Xiaofeng Wang, P. Abrahamsson· SSE@SIGSOFT FSE· 56 citations· ⚡4
This study conducts a multiple case study on twenty European software startups and proposes a prototype-centric learning model in early stage software startups, and identifies factors that occur as barriers but also facilitators for prototyping in earlystage software startups.
Anh Nguyen-Duc, Xiaofeng Wang, P. Abrahamsson· International Conference on...· 44 citations· ⚡5
It is demonstrated that linker-free PROTACs can outperform traditional designs, marking a paradigm shift in PROTAC development for targeted protein degradation.
Pinal, a 16-billion-parameter foundation model that produces protein candidates from natural-language functional descriptions, supports natural language as a high-level interface for candidate generation in protein design, enabling programmable exploration with reduced reliance on manually specified structural or sequence constraints.
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.