ArticleCancer research2021
Label-Free Deep Profiling of the Tumor Microenvironment.
Article in Cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- From Label-Free Multiphoton Imaging to Pathological Reports: A Vision-Language Breast Cancer Margin Pathological Diagnosis System.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Colloid-patterned surfaces distinguish malignant mechanophenotypes.Materials today. Bio · 2026Article
- Lightweight CNN efficiently discriminates ovarian cancer cells from a tumor microenvironment via holographic imaging flow cytometry.Biomedical optics express · 2025Article
- On the importance of simultaneous label-free multimodal nonlinear optical imaging for biomedical applications.APL photonics · 2025Article
- Simultaneous Label-Free Autofluorescence Multi-Harmonic Microscopy.Journal of visualized experiments : JoVE · 2025Article
- Multimodal Optical Imaging Platform for Studying Cellular Metabolism.Journal of visualized experiments : JoVE · 2025Article
- Clinical confocal laser endomicroscopy for imaging of autofluorescence signals of human brain tumors and non-tumor brain.Journal of cancer research and clinical oncology · 2024Article
- Deep and dynamic metabolic and structural imaging in living tissues.Science advances · 2024Article
- Predicting DNA damage response in non-small cell lung cancer organoids via simultaneous label-free autofluorescence multiharmonic microscopy.Redox biology · 2024Article
- Large field-of-view metabolic profiling of murine brain tissue following morphine incubation using label-free multiphoton microscopy.Journal of neuroscience methods · 2024Article
- Noninvasive Nonlinear Optical Computational Histology.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Label-free biomedical optical imaging.Nature photonics · 2023Article
- A deep learning-based stripe self-correction method for stitched microscopic images.Nature communications · 2023Article
- Automatic and label-free detection of meningioma in dura mater using the combination of multiphoton microscopy and image analysis.Neurophotonics · 2023Article
- Diffractive interconnects: all-optical permutation operation using diffractive networks.Nanophotonics (Berlin, Germany) · 2023Article
- Deep learning-based high-speed, large-field, and high-resolution multiphoton imaging.Biomedical optics express · 2023Article
- Label-free multimodal nonlinear optical imaging of needle biopsy cores for intraoperative cancer diagnosis.Journal of biomedical optics · 2022Article
- Resection-inspired histopathological diagnosis of cerebral cavernous malformations using quantitative multiphoton microscopy.Theranostics · 2022Article
- Nonlinear Imaging Histopathology: A Pipeline to Correlate Gold-Standard Hematoxylin and Eosin Staining With Modern Nonlinear Microscopy.IEEE journal of selected topics in quantum electronics : a publication of the IEEE Lasers and Electro-optics SocietyArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Label-free nonlinear microscopy enables nonperturbative visualization of structural and metabolic contrast within living cells in their native tissue microenvironment. Here a computational pipeline was developed to provide a quantitative view of the microenvironmental architecture within cancerous tissue from label-free nonlinear microscopy images. To enable single-cell and single-extracellular vesicle (EV) analysis, individual cells, including tumor cells and various types of stromal cells, and EVs were segmented by a multiclass pixelwise segmentation neural network and subsequently analyzed for their metabolic status and molecular structure in the context of the local cellular neighborhood. By comparing cancer tissue with normal tissue, extensive tissue reorganization and formation of a patterned cell-EV neighborhood was observed in the tumor microenvironment. The proposed analytic pipeline is expected to be useful in a wide range of biomedical tasks that benefit from single-cell, single-EV, and cell-to-EV analysis. SIGNIFICANCE: The proposed computational framework allows label-free microscopic analysis that quantifies the complexity and heterogeneity of the tumor microenvironment and opens possibilities for better characterization and utilization of the evolving cancer landscape.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.