{
  "id": 236191,
  "title": "AI and 'Ramanomics' could eliminate a major obstacle to studying living cells",
  "url": "https://urgent.news/2026/08/06/ai-and-ramanomics-could-eliminate-a-major-obstacle-to-studying-living",
  "topic": "ai",
  "section": "AI",
  "published": "2026-08-06T19:40:01.000Z",
  "source": {
    "name": "Phys.org",
    "slug": "phys-org",
    "url": "https://phys.org/news/2026-08-ai-ramanomics-major-obstacle-cells.html"
  },
  "original_language": "en",
  "account": "Scientists have developed a novel technique that merges artificial intelligence and Raman spectroscopy, known as Ramanomics, to revolutionize the study of living cells. By analyzing unique biochemical signatures instead of relying on fluorescent dyes, this method allows researchers to observe cellular structures noninvasively without altering them, a significant improvement over traditional approaches.\n\nTraditionally, fluorescent dyes have been used to identify and visualize structures within cells, but they introduce foreign elements that can disrupt natural cellular behavior, limit the number of structures that can be examined simultaneously, and reduce measurement accuracy. In contrast, the new Ramanomics approach offers a label-free alternative that preserves the natural state of cells, enabling clearer insights into their functioning, disease impacts, and treatment responses.\n\nLed by University at Buffalo researchers, the team combined AI and Ramanomics to identify cellular organelles based on their distinctive biochemical profiles. By capturing the biochemical fingerprints of four organelles using Raman spectroscopy, the researchers trained machine-learning models to recognize each organelle. The AI model achieved approximately 90% accuracy in identifying the organelles from new Raman measurements, demonstrating its effectiveness in analyzing cellular structures without dye labeling.\n\nThis label-free methodology offers several advantages over conventional dye-based imaging. It eliminates the need for preparing and applying fluorescent labels, reduces experimental artifacts, and enhances measurement sensitivity, resulting in cleaner and more reliable data. By eliminating the impact of fluorescent dyes, researchers can obtain a more accurate representation of cellular processes, making it easier to study disease progression, monitor treatment effects, and identify molecular changes associated with serious conditions like cancer and metabolic disorders.\n\nThe team's findings, published in ACS Omega in June 2026, highlight the potential of this approach to transform cellular diagnostics and molecular medicine. By integrating AI models with emerging Raman imaging systems that utilize quantum light sources, the researchers aim to further accelerate data collection and increase resolution, enabling more extensive analysis of cellular states and accelerating the development of personalized medical treatments.",
  "summary": "Fluorescent dyes have long been used in biological research to identify and visualize structures within living cells. Although effective, they have several drawbacks, including altering the cells under study, limiting the number of structures that can be examined at once and reducing measurement accuracy.",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}