Dataset Information

Label-Free Deep Profiling of the Tumor Microenvironment.

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.

SUBMITTER: You S

PROVIDER: S-EPMC8137645 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Cancer cells metastasize through the bloodstream either as single migratory circulating tumor cells (CTCs) or as multicellular groupings (CTC-clusters). Existing technologies for CTC enrichment are designed primarily to isolate single CTCs, and while CTC-clusters are detectable in some cases, their true prevalence and significance remain to be determined. Here, we developed a microchip technology (Cluster-Chip) specifically designed to capture CTC-clusters independent of tumor-specific markers from unprocessed blood. CTC-clusters are isolated through specialized bifurcating traps under low shear-stress conditions that preserve their integrity and even two-cell clusters are captured efficiently. Highly parallel architecture of the chip allows deterministic screening of clinically relevant volumes of blood samples at slow, and hence, non-damaging flow rates. Using the Cluster-Chip, we identify CTC-clusters in 30-40% of patients with metastatic cancers of the breast, prostate and melanoma. RNA sequencing of CTC-clusters confirms their tumor origin and identifies leukocytes within the clusters as being tissue-derived macrophages. Together, the development of a device for efficient capture of CTC-clusters will enable detailed characterization of their biological properties and role in cancer metastasis. We used the Cluster-Chip to capture CTC-clusters from the blood of a breast cancer patient with high CTC counts, released CTC-clusters in solution, stained them with TexasRed-conjugated antibodies against the leukocyte cell surface markers CD45, CD14 and CD16, and then isolated intact CTC-clusters individually using a micromanipulator. From a single time point, we retrieved 15 CTC-clusters, and each of those clusters was individually subjected to RNA-sequencing analysis using a next generation platform (SOLiD 5500). In addition, two leukocytes were isolated from the blood of a healthy donor were individually subjected to RNA-sequencing analysis using the same platform.

Project description:Although not yet ready for clinical application, methods based on Raman spectroscopy have shown significant potential in identifying, characterizing, and discriminating between noncancerous and cancerous specimens. Real-time and accurate medical diagnosis achievable through this vibrational optical method largely benefits from improvements in current technological and software capabilities. Not only is the acquisition of spectral information now possible in milliseconds and analysis of hundreds of thousands of data points achieved in minutes, but Raman spectroscopy also allows simultaneous detection and monitoring of several biological components. Besides demonstrating a significant Raman signature distinction between nontumorigenic (MCF-10A) and tumorigenic (MCF-7) breast epithelial cells, our study demonstrates that Raman can be used as a label-free method to evaluate epidermal growth factor activity in tumor cells. Comparative Raman profiles and images of specimens in the presence or absence of epidermal growth factor show important differences in regions attributed to lipid, protein, and nucleic acid vibrations. The occurrence, which is dependent on the presence of epidermal growth factor, of new Raman features associated with the appearance of phosphothreonine and phosphoserine residues reflects a signal transduction from the membrane to the nucleus, with concomitant modification of DNA/RNA structural characteristics. Parallel Western blotting analysis reveals an epidermal growth factor induction of phosphorylated Akt protein, corroborating the Raman results. The analysis presented in this work is an important step toward Raman-based evaluation of biological activity of epidermal growth factor receptors on the surfaces of breast cancer cells. With the ultimate future goal of clinically implementing Raman-guided techniques for the diagnosis of breast tumors (e.g., with regard to specific receptor activity), the current results just lay the foundation for further label-free optical tools to diagnose the disease.

Dataset Information

Label-Free Deep Profiling of the Tumor Microenvironment.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets