Single-cell Tumor Analysis
Single-cell technologies are used to help scientists profile gene and protein expression levels in a variety of diseases including cancer. Here is the latest research on single-cell tumor analysis.
Multiplex Spatial Bioimaging for Combination Therapy Design
ALDH1A3-mTOR axis as a therapeutic target for anticancer drug-tolerant persister cells in gastric cancer
Point-of-care oral cytology tool for the screening and assessment of potentially malignant oral lesions
Loss of BAP1 expression is associated with an immunosuppressive microenvironment in uveal melanoma, with implications for immunotherapy development
Methods for ChIP-seq analysis: A practical workflow and advanced applications
Tertiary lymphoid structures improve immunotherapy and survival in melanoma
B cells and tertiary lymphoid structures promote immunotherapy response
Single cell correlation analysis of liquid and solid biopsies in metastatic colorectal cancer
Electron Scattering in Conventional Cell Flask Experiments and Dose Distribution Dependency
DENDRO: genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing
Basal-like and Classical cells coexistence in pancreatic cancer revealed by single cell analysis
Transcriptional diversity and bioenergetic shift in human breast cancer metastasis revealed by single-cell RNA sequencing
Transcription phenotypes of pancreatic cancer are driven by genomic events during tumor evolution
Mechanism- and Immune Landscape-Based Ranking of Therapeutic Responsiveness of 22 Major Human Cancers to Next Generation Anti-CTLA-4 Antibodies
Exploration of the personalized immune checkpoint atlas of plasma cell dyscrasias patients using high‑dimensional single‑cell analysis
Development of Automated Microscopy-Assisted High-Content Multiparametric Assays for Cell Cycle Staging and Foci Quantitation
Single-cell analysis reveals new evolutionary complexity in uveal melanoma
Single-cell analysis on stromal fibroblasts in the microenvironment of solid tumours
Multiview learning for understanding functional multiomics
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