Understanding how individual cells differ within complex biological systems is essential for revealing mechanisms of health and disease. Multimodal single-cell technologies provide powerful tools for this task, yet single-cell lipid profiling by mass spectrometry imaging often faces technical challenges such as batch effects that obscure biological insights. In this study, we developed an integrated workflow that combines immunofluorescence-based protein measurements with mass spectrometry imaging-based lipid analysis at single-cell resolution. We applied this approach to circulating human neutrophils and implemented a strategy to reduce batch-related variability, enabling more reliable comparison across clinical samples. Using the integrated data set, we identified distinct signatures marking the emergence of pathogenic neutrophil populations in patients with liver cirrhosis. These findings demonstrate the value of combining multimodal single-cell profiling with batch-effect correction to discover cellular phenotypes and highlight the potential of this technology for translational and clinical research.
Bessler et al. (2026) studied this question.