Research Highlights

Dr. Mun developed ultra-high-pressure multidimensional liquid chromatography systems, online phosphopeptide enrichment, and methods to analyze phospho- and glycoproteomes from limited amounts of clinical samples. He also contributed to data acquisition and interpretation algorithms that improve the accuracy and sensitivity of mass spectrometry data, and to automated sample preparation workflows that process large clinical sample sets reproducibly with minimal hands-on work. These technologies enable sensitive, in-depth proteome analysis from limited clinical material.

Representative publications

• Mun, D.-G.*, Nam, D.*, Kim, H., Pandey, A., and Lee, S.-W. Accurate precursor mass assignment improves peptide identification in data-independent acquisition mass spectrometry. Analytical Chemistry. 2019, 91, 8453-8460.
• Mun, D.-G., Renuse, S., Saraswat, M., Madugundu, A., Udainiya, S., Kim, H., Park, S. R., Zhao, H., Nirujogi, R. S., Na, C. H., Kannan, N., Yates J. R., Lee, S.-W., and Pandey, A. PASS-DIA: A data-independent acquisition approach for discovery studies. Analytical Chemistry. 2020, 92, 14466-14475.
• Mun, D.-G.*, Joshi, N. S.*, Budhraja, R., Sachdeva, G. S., Kang, T., Bhat, F. A., Ding, H., Madden, B. J., Zhong, J., and Pandey, A. Automated sample preparation workflow for tandem mass tag-based proteomics. Journal of the American Society for Mass Spectrometry. 2023, 34, 2087-2092.

By integrating proteome, phosphoproteome, and glycoproteome data with genomic and transcriptomic data, he led the first proteogenomic study in Korea, a precision medicine study of early-onset gastric cancer that refined patient subtypes and identified the key signaling pathways defining each subtype. Proteogenomic analysis of cholangiocarcinoma, a highly aggressive cancer, revealed phosphoproteome-based subtypes not resolved by transcriptomics, and proteomic analysis of mouse models nominated subtype-specific therapeutic candidates. Advancing these methods further, he built a proteogenomic map of 28 primary human cell types, uncovering many previously undetected non-canonical and modified peptides.

Representative publications

• Mun, D.-G.*, Bhin, J.*, Kim, S.*, Kim, H.*, Jung, J.*, Jang, Y., Park J.-M., Kim, H., Lee, H., Bae, J., Back S., Kim, S.-J., Kim, J., Park, H., Li, H., Hwang, K.-B., Park, Y., Yook, J., Kim, B., Kwon, S.Y., Ryu, S.Y., Park, D.Y., Jeon, T.Y., Kim, D.H., Lee, J.-H., Han, S.-U., Song, K.S., Park, D., Park, J.W., Yu, M.-H., Lee, H., Kim, K.P., Yang, E.K., Kim, H.K., Paek, E., Lee, S., Lee, S.-W., and Hwang, D. Proteogenomic characterization of human early onset gastric cancer. Cancer Cell. 2019, 35, 111-124.
• Mun, D.-G.*, Jessen, E.*, Tomlinson, J. K., Carlson, D., Budhraja, R., Alva-Ruiz, R., Abdelrahman, A., Watkins, R., Gregory, L., McCabe, C., Wang, C., Graham, R. P., Woods, K., Golkowski, M., Baker, M., Gores, G. J., Ilyas, S. I., Conboy, C., Larson, E. L., Sample, J. W., Ozmert, E. H., Kandasamy, R. K., Borad, M. J., Roberts, L., Andersen, J., Pandey, A., and Smoot, R. L. Multiomics combined with machine learning defines unique molecular subtypes of cholangiocarcinoma and identifies TNK1 as a therapeutic target. Hepatology. 2025.
• Mun, D.-G., Madugundu, A. K., Renuse, S., Nirujogi, R. S., Na, C. H., Kim, M.-S., Saraswat, M., Singh, S., Ramarajan, M. G., Tiwary, S., Cox, J., Prakash, A., Halushka, M. K., Burns, K. H., Kandasamy, R. K., and Pandey, A. A transcriptomic and proteomic map of primary human cell types. Nucleic Acids Research. 2026. 54, gkaf1498.

Applying quantitative proteomics to diverse clinical specimens, he characterized the host immune response to SARS-CoV-2 infection in nasopharyngeal swabs, Alzheimer’s disease-related proteome changes in cerebrospinal fluid, and aging-associated protein alterations in FFPE kidney tissue. He is currently developing biomarkers for the diagnosis of cholangiocarcinoma using patient blood and bile samples.

Representative publications

• Mun, D.-G., Vanderboom, P. M., Madugundu, A. K., Garapati, K., Chavan, S., Peterson, J. A., Saraswat, M., and Pandey, A. DIA-based proteome profiling of nasopharyngeal swabs from COVID-19 patients. Journal of Proteome Research. 2021, 20, 4165-4175.
• Mun, D.-G., Budhraja, R., Bhat, F. A., Zenka, R. M., Johnson, K. L., Moghekar, A., and Pandey, A. Four-dimensional proteomics analysis of human cerebrospinal fluid with trapped ion mobility mass spectrometry using PASEF. Proteomics. 2023, 23, e2200507.
• Mun, D.-G., Pujari, G. P., Sachdeva, G., Madden, B. J., Charlesworth, M. C., Johnson, K., Archila, L. R., Alexander, M. P., Denic, A., Mullan, A. F., Sharma, V., Larson, N. B., Luehrs, A. C., Rule, A. D., and Pandey, A. Spatial proteomics to discover aging-associated alterations in the renal tubulointerstitium. Clinical Proteomics. 2025. 22, 37.

To improve proteome coverage and throughput in single cell proteomics, he advanced data acquisition and analysis to identify KRAS mutant peptides in single cancer cells and reported the first proteome and PTM analysis at the single nucleus level. He also developed a multiplexed method that identifies modified peptides across multiple single cells with improved sensitivity, and applied these technologies to single immune cells and salivary gland-derived cells. The same low-input methods extend to spatial proteomics: using laser capture microdissection (LCM), he profiled micro-regions of tissues including bile duct, gallbladder, and brain, generating cell-level proteome information that conventional approaches could not readily obtain.

Representative publications

• Mun, D.-G., Bhat, F. A., Ding, H., Madden, B. J., Natesampillai, S., Badley, A.D., Johnson, K. L., Kelly, R. T., and Pandey, A. Optimizing single cell proteomics using trapped ion mobility spectrometry for label-free experiments. Analyst. 2023, 148, 3466-3475.
• Mun, D.-G., Bhat, F. A., Joshi, N. S., Sandoval, L., Ding, H., Jain, A., Peterson, J. A., Kang, T., Pujari, G. P., Tomlinson, J. L., Budhraja, R., Zenka, R. M., Kannan, N., Kipp, B. R., Dasari, S., Gaspar-Maia, A., Smoot, R. L., Kandasamy, R. K., and Pandey, A. Diversity of PTMs and cell signaling revealed by single cell and single organelle mass spectrometry. Communications Biology. 2024, 7, 884.
• Mun, D.-G.*, Nishida, H.*, Bhat, F. A., Pasupuleti, R. R., Delanghe, B., and Pandey, A. Multiplexed quantitation of post-translationally modified peptides in single cells using triggered MS/MS combined with super heavy tandem mass tags. Journal of Proteome Research. 2026. 25, 1184.