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Direct identification of the proteins targeted by small molecules can provide clues for disease diagnosis, prevention, and drug development. Despite concentrated attempts, there are still technical limitations associated with the elucidation of direct interactors. Herein, we report a target-ID system called proximity-based compound-binding protein identification (PROCID), which combines our direct analysis workflow of proximity-labeled proteins (Spot-ID) with the HaloTag system to efficiently identify the dynamic proteomic landscape of drug-binding proteins. We successfully identified well-known dasatinib-binding proteins (ABL1, ABL2) and confirmed the unapproved dasatinib-binding kinases (e.g., BTK and CSK) in a live chronic myeloid leukemia cell line. PROCID also identified the DNA helicase protein SMARCA2 as a dasatinib-binding protein, and the ATPase domain was confirmed to be the binding site of dasatinib using a proximity ligation assay (PLA) and in cellulo biotinylation assay. PROCID thus provides a robust method to identify unknown drug-interacting proteins in live cells that expedites the mode of action of the drug.
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http://dx.doi.org/10.1016/j.chembiol.2022.10.001 | DOI Listing |
Cell Rep Methods
September 2025
Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Institute of Molecular Health Sciences, ETH Zurich, Zurich, Switzerland. Electronic address:
In cancer research, multiplexed imaging allows detailed characterization of the tumor microenvironment (TME) and its link to patient prognosis. The integrated immunoprofiling of large adaptive cancer patient cohorts (IMMUcan) consortium collects multi-modal imaging data from thousands of patients with cancer to perform broad molecular and cellular spatial profiling. Here, we describe and compare two workflows for multiplexed immunofluorescence (mIF) and imaging mass cytometry (IMC) developed within IMMUcan to enable the generation of standardized data for cancer tissue analysis.
View Article and Find Full Text PDFTransl Oncol
September 2025
Pharmacy of Jiangxi cancer hospital&institute, Nanchang, Jiangxi, China. Electronic address:
Background: Renal cell carcinoma (RCC) is a common malignant tumor with metabolic reprogramming and immune evasion features. δ-Aminolevulinic acid dehydratase (ALAD), a key enzyme in heme biosynthesis, has been implicated in cancer progression and treatment outcomes, but its role in RCC remains unclear.
Methods: This study integrated multi-omics datasets from TCGA, CPTAC, and GEO to analyze ALAD's expression, prognostic value, and functional implications in RCC.
Allergy
September 2025
School of Biomedical Sciences, Faculty of Medicine, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China.
Research (Wash D C)
September 2025
Department of Urology, Fudan University Shanghai Cancer Center, State Key Laboratory of Genetics and Development of Complex Phenotypes, School of Life Sciences, Human Phenome Institute, Fudan University, Shanghai 200433, China.
Collecting duct carcinoma (CDC) is a rare but aggressive form of renal cell carcinoma (RCC) that has limited understanding and an undefined systemic therapeutic regimen. Herein, we conducted a comprehensive proteogenomic analysis of CDC tumors and normal adjacent tissues to elucidate the biology of the disease. CDC exhibited high heterogeneity in tumor mutational burden, and enhanced ribosome biogenesis was the most striking malignant feature of CDC, even compared with other common kidney carcinomas.
View Article and Find Full Text PDFAdv Sci (Weinh)
September 2025
School of Artificial Intelligence, Jilin University, Changchun, 130012, China.
Single-cell multi-omics technologies are pivotal for deciphering the complexities of biological systems, with Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) emerging as a particularly valuable approach. The dual-modality capability makes CITE-seq particularly advantageous for dissecting cellular heterogeneity and understanding the dynamic interplay between transcriptomic and proteomic landscapes. However, existing computational models for integrating these two modalities often struggle to capture the complex, non-linear interactions between RNA and antibody-derived tags (ADTs), and are computationally intensive.
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