The lab’s research interests center on integrating multi-omics approaches to uncover the molecular mechanisms underlying neurodegenerative disorders, with a particular emphasis on Alzheimer’s disease (AD). Leveraging advanced systems genetics and state-of-the-art mass spectrometry-based proteomics and metabolomics, we aim to identify critical disease pathways and potential therapeutic targets.
Advancing Computational Tools for Mass Spectrometry-Based Proteomics and Metabolomics

Our lab is dedicated to the development of cutting-edge computational algorithms and software tools that advance mass spectrometry (MS)-based proteomics and metabolomics. We aim to address critical challenges in the analysis of complex biological datasets by designing innovative frameworks that enhance sensitivity, accuracy, and reproducibility by developing the JUMP toolbox (JUMPsuite). A cornerstone of our work is the creation of the JUMP program (Wang et al., 2014), which significantly improves peptide-spectrum matching and has become a valuable tool for high-throughput proteomic studies. We developed SMAP (Li et al., 2022), a powerful software tool for sample identity verification and quality control in large-scale proteomic studies. Expanding the JUMPsuite, we recently introduced JUMPsem (Kong et al., 2025) for inferring kinase activity and JUMPshiny (Zhang et al., 2025; under review) for downstream statistical analyses. Through close collaboration with Dr. Junmin Peng at St. Jude Children’s Research Hospital, we continue to expand our impact by integrating these tools into diverse biological investigations. We are currently extending our work on developing tools for spatial proteomics and single-cell proteomics.
Systems Proteomics and Multi-Omics Approaches to Neurological Disease

Our lab is also deeply engaged in unraveling the molecular mechanisms of neurological diseases, including Alzheimer’s disease (AD), through the lens of systems proteomics and integrative multi-omics. Our long-term goal is to integrate genetic, proteomic, and spatial data to reveal regulatory networks and causal mechanisms driving neurological disorders. Recently, we applied proteome-wide linkage analysis to investigate psychiatric disease mechanisms, identifying key protein quantitative trait loci (pQTLs) and candidate regulatory pathways (Luo et al., 2024). Currently, we are leveraging state-of-the-art technologies—including spatial proteomics and spatial transcriptomics—to map molecular alterations within the anatomical context of the brain, gaining insight into cell-type–specific and regionally distinct pathological processes that underlie complex brain disorders.
Wang Lab@2025
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