Anjali Seth
Papers
3
Total Citations
45
H-Index
3
About
Anjali Seth is an emerging researcher at the cutting edge of proteomics technology development, with a focus on spatial tissue proteomics and single-cell proteomics. Her work addresses one of the most pressing challenges in modern biomedical research: extracting deep, meaningful protein-level information from extremely small biological samples with high reproducibility and throughput. Seth's most impactful contributions center on developing automated, high-sensitivity workflows that bridge advanced imaging, laser microdissection, and ultrasensitive mass spectrometry. Her 2024 paper on automated sample preparation for laser microdissection-guided proteomics (21 citations) demonstrates how spatial proteomics can be scaled to large patient cohorts without sacrificing sensitivity — a critical step toward clinical applicability. Complementing this, her work on automated single-cell proteomics (20 citations) pushes beyond simple cell-type classification, enabling researchers to interrogate complex biological processes at the single-cell level with unprecedented proteome depth. By making these powerful techniques faster, more reproducible, and accessible to researchers studying disease in real tissue contexts, Seth is helping democratize cutting-edge proteomics. Her rapidly accumulating citations signal growing recognition of her methodological innovations within the proteomics and systems biology communities.
Research Focus
Key Achievements
Top Papers
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Key Collaborators
Related papers
- An automated and fast sample preparation workflow for laser microdissection guided ultrasensitive proteomics
- An Automated and Fast Sample Preparation Workflow for Laser Microdissection Guided Ultrasensitive Proteomics
- Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications
- Fully Automated Workflow for Integrated Sample Digestion and Evotip Loading Enabling High-Throughput Clinical Proteomics
- Single Cell Proteome Mapping of Tissue Heterogeneity Using Microfluidic Nanodroplet Sample Processing and Ultrasensitive LC-MS.
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