About

Rosalyn Chen is a faculty member associated with the Department of Anesthesiology at the University of Wisconsin School of Medicine and Public Health. The provided page does not include specific details about her research focus, background, or key contributions. Therefore, no further biographical information is available from the given content.

Research topics

• Chemistry
• Cell biology
• Biology
• Biochemistry
• Nanotechnology
• Organic chemistry
• Computational biology
• Combinatorial chemistry
• Materials science
• Chromatography
• Chemical engineering

Selected publications

• Gene-level gut microbiome signatures as predictive biomarkers for response to immune checkpoint inhibitors across multiple cancer types

  Gut Microbes · 2026-04-23 · 1 citations

  articleOpen access

  Targeting programmed cell death protein 1 (PD-1) and cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) with immune checkpoint inhibitors (ICIs) has improved survival across multiple cancer types, but the variability in patient response highlights the need for better predictive biomarkers. Existing studies rely on taxonomic abundance derived from reference genome databases, limiting the discovery and functional interpretation of uncharacterized microbes. Here, we integrated metagenomic data from multiple ICI-treated cohorts spanning diverse cancer types and geographic regions and developed a deep learning model, named BioP-VAE, that incorporates biological prior knowledge via protein sequence embeddings and uses gene-level microbial abundance features as input. Gene-level microbial abundance outperformed taxonomy abundance in predicting both ICI response and 12-month progression-free survival (PFS). In patients receiving combination immune checkpoint blockade (CICB), BioP-VAE achieved a mean AUC of 0.89 in intracohort and 0.88 in cross-cohort evaluation. Notably, in the monotherapy-treated intracohorts, BioP-VAE achieved a mean AUC of 0.97. Feature attribution analysis revealed key microbial genes. Additionally, we identified distinct predictive microbial signatures via age-stratified analysis, suggesting that host age may modulate microbiome‒immune interactions. Importantly, this is the first large-scale study to evaluate gene-level microbial abundance features for ICI response prediction across multiple cancer types by deep learning. Our findings demonstrate that incorporating biological prior knowledge into deep learning models can improve the discovery of microbial biomarkers that can be generalized across cancer types and treatment settings, offering a novel strategy for patient stratification in immunotherapy.

  PublisherDOI

• Evolution in single-cell proteomics drives new frontiers in cancer biology and precision medicine

  Cancer Biology and Medicine · 2026-04-24

  articleOpen accessSenior author

  The inherent heterogeneity of cancer leads to varied responses to treatment and underscores the need for the development of precision medicine. Single-cell proteomics is crucial for deciphering intra- and inter-tumor heterogeneity. Mass cytometry and other antibody-based technologies, which enable simultaneous quantification of >100 proteins per cell, have been widely applied in cancer studies. Concurrently, liquid chromatography-tandem mass spectrometry-based single-cell proteomics has dramatically improved protein coverage from approximately 1,000 to >6,500 proteins per cell, driven by enhanced sample preparation, instrumentation, and throughput. These high-throughput capabilities now empower large-scale, protein-level analyses, which have uncovered detailed maps of tumor heterogeneity. The resulting insights deepen our understanding of tumor biology and provide new opportunities for guiding precision cancer medicine.

  PublisherOA PDFDOI

• Enhanced NH3 monitoring of UV-assisted PANI:PSS/SiNW sensor through hierarchical morphology remodeling

  Sensors and Actuators B Chemical · 2026-01-08

  articleCorresponding
  PublisherDOI

• High-performance affinity peptide sensor for prostate specific antigen detection

  Biomedical Materials · 2025-02-18 · 2 citations

  article

  Abstract Prostate-specific antigen (PSA) is the best serum biomarker for prostate cancer (PCa), and the detection of PSA concentration can be used to assess the risk of malignancy. Given the current lack of effective treatment options for PCa, early detection and intervention are particularly important. To address this, we developed a novel electronic biosensor aimed at highly sensitive detection of PSA. The core materials of this sensor consist of the receptor material, PSA-affinity peptides, and the support material, single-walled carbon nanotubes. These materials are cost-effective, can operate at room temperature, and exhibit good stability, which aids in optimizing the sensor’s performance and stability. We attached the carbon nanotubes to a gold fork electrode and successfully fabricated the device by chemically linking the peptides to the carbon nanotubes. PSA was subsequently detected through the binding of PSA molecules to specific peptide sequences in standard solution. The sensor achieved a detection limit as low as 10 −13 μg μL −1 , which is lower than that of currently used detection methods, demonstrating exceptional sensitivity. This biosensor offers advantages such as low cost, high efficiency, and strong specificity, indicating its broad application prospects in medical diagnostics, particularly in PCa screening.

  PublisherDOI

• Characterization of dsRNA binding proteins through solubility analysis identifies ZNF385A as a dsRNA homeostasis regulator

  Nature Communications · 2025-04-11 · 3 citations

  articleOpen accessSenior author

  Double-stranded RNA (dsRNA) binding proteins (dsRBPs) play crucial roles in various cellular processes, especially in the innate immune response. Comprehensive characterization of dsRBPs is essential to understand the intricate mechanisms for dsRNA sensing and response. Traditional methods have predominantly relied on affinity purification, favoring the isolation of strong dsRNA binders. Here, we adopt the proteome integral solubility alteration (PISA) workflow for characterizing dsRBPs, resulting in the observation of 18 known dsRBPs and the identification of 200 potential dsRBPs. Next, we focus on zinc finger protein 385 A (ZNF385A) and discover that its knockout activates the transcription of interferon-β in the absence of immunogenic stimuli. The knockout of ZNF385A elevates the level of endogenous dsRNAs, especially transcripts associated with retroelements, such as short interspersed nuclear element (SINE), long interspersed nuclear element (LINE), and long terminal repeat (LTR). Moreover, loss of ZNF385A enhances the bioactivity of 5-Aza-2’-deoxycytidine (5-AZA-CdR) and tumor-killing effect of NK cells. Our findings greatly expand the dsRBP reservoir and contribute to the understanding of cellular dsRNA homeostasis. Double-stranded RNA (dsRNA) is key in immunity and cellular regulation. Here, using the PISA workflow, the authors find 218 proteins with altered solubility upon dsRNA binding. ZNF385A knockout activates interferon-β transcription and increases endogenous dsRNA levels, enhancing the bioactivity of 5-Aza-2’-deoxycytidine and NK cell-mediated cytotoxicity.

  PublisherOA PDFDOI

• High-Performance Olfactory Receptor-Derived Peptide Biosensor for Acetic Acid Gas Detection Based on Polystyrene Microsphere Templates

  ACS Sensors · 2025-10-31

  articleCorresponding

  Acetic acid gas, as an organic pollutant, poses serious hazards to both human health and the environment. Consequently, the real-time detection of low concentrations of acetic acid gas has become a matter of considerable concern. In this study, a bioelectronic sensor was synthesized by directly coupling N-terminal cysteine-modified olfactory receptor-derived peptide with thioester-functionalized single-walled carbon nanotubes through chemical linkage, and its performance was experimentally tested. More importantly, to further enhance its performance, polystyrene microsphere templating was employed during synthesis, this improvement significantly improved the overall performance of the sensor compared to the original process, and then characterization and gas sensitivity tests were carried out. The results revealed that the sensor exhibited extremely high sensitivity, with a detection limit 1 order of magnitude lower than that of the sensor fabricated without the template, reaching 1 ppt at room temperature. This detection limit is 2-3 orders of magnitude lower than that of conventional devices. Moreover, the sensor demonstrated excellent selectivity, stability, and rapid response characteristics.

  PublisherDOI

• ABCG1 promotes the proliferation and migration of clear cell renal cell carcinoma and reduces its apoptosis

  International Journal of Medical Sciences · 2025-05-30 · 1 citations

  articleOpen access

  In summary, these findings highlight the critical role of ABCG1 in ccRCC progression and suggest its potential as a biomarker for diagnosis and prognosis.

  PublisherOA PDFDOI

• Differentiation of high risk prostate cancer with a facile urinary exosome detection workflow

  iScience · 2025-01-27 · 2 citations

  articleOpen access

  for quality checking, trained on 271 patients, achieved an area under the receiver operating characteristic curve (AUROC) of 0.76 in the test set of 351 patients. Combination of PURE-AID with prostate-specific antigen (PSA) and age increases AUROC to 0.80 and reduces 54.3% of unnecessary biopsies with 86.8% sensitivity. Our study provides a new classification system for differentiating high-grade PCa in a workflow- and patient-friendly manner.

  PublisherDOI

• Sbmyb111 act as a transcriptional activator of flavonoid synthesis in Scutellaria baicalensis

  Plant Molecular Biology · 2025-07-10 · 1 citations

  article
  PublisherDOI

• STBD1 mediates the crosstalk between glycogen and lipid droplets in clear cell renal cell carcinoma

  Cell Reports · 2025-10-01 · 3 citations

  articleOpen access

  The accumulation of lipid droplets (LDs) and glycogen is a major hallmark of clear cell renal cell carcinoma (ccRCC), yet their interplay remains unclear. By proteomic profiling of 50 ccRCC tumors, we observe activation of glycogen- and LD-related pathways. Using proximity labeling of the LD proteome, we identify starch-binding domain-containing protein 1 (STBD1), a glycogen-binding protein involved in glycophagy, as a novel LD component. Further mechanistic investigation shows that STBD1 targets LDs via N-terminal myristoylation and mediates glycogen-LD colocalization. Its depletion decreases LD abundance and impairs both glycophagy and lipophagy, suggesting a critical role of STBD1 in both the biogenesis and autophagic degradation of LDs. Furthermore, STBD1 knockdown alters lipid composition, enhances ferroptosis sensitivity, and suppresses tumor growth both in vitro and in vivo. Collectively, our findings establish STBD1 as a critical mediator of glycogen-LD crosstalk and highlight its potential as a therapeutic target in ccRCC.

  PublisherDOI

Frequent coauthors

• Ning Zhang

  First Affiliated Hospital of Harbin Medical University

  40 shared

• Lingjun Li

  University of Wisconsin–Madison

  39 shared

• Hao Zhuang

  Zhengzhou University

  32 shared

• Mingming Xiao

  Shanghai Cancer Institute

  25 shared

• Guoxuan Qin

  Tianjin University

  21 shared

• Xinran Zhang

  Hainan University

  20 shared

• Huajun Gao

  Tianjin Medical University

  19 shared

• Baicai Yang

  19 shared

Labs

• Alaa Abd-Elsayed, MD, MPH LaboratoryPI