ArticleAnalytical chemistry2021
A Single-Organelle Optical Omics Platform for Cell Science and Biomarker Discovery.
Article in Analytical chemistry, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 21 citations in OpenAlex.
- Article
- RamEx: an R package for high-throughput microbial ramanome analyses with accurate quality assessment.Microbiome · 2026Article
- Adaptive Raman spectral unmixing method based on Voigt peak compensation for quantitative analysis of cellular biochemical components.Biomedical optics express · 2025Article
- Lipidomics and temporal-spatial distribution of organelle lipid.Journal of biological methods · 2025Review
- Raman-based machine-learning platform reveals unique metabolic differences between IDHmut and IDHwt glioma.Neuro-oncology · 2024Article
- Identification of extracellular vesicles from their Raman spectra via self-supervised learning.Scientific reports · 2024Article
- Mitochondrial Dysfunction and Apoptosis in Brain Microvascular Endothelial Cells Following Blast Traumatic Brain Injury.Cellular and molecular neurobiology · 2023Article
- Rapid, label-free histopathological diagnosis of liver cancer based on Raman spectroscopy and deep learning.Nature communications · 2023Article
- Single cell metabolism: current and future trends.Metabolomics : Official journal of the Metabolomic Society · 2022Review
- Mitochondrial Dysfunction: A Prelude to Neuropathogenesis of SARS-CoV-2.ACS chemical neuroscience · 2022Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
Research in fundamental cell biology and pathology could be revolutionized by developing the capacity for quantitative molecular analysis of subcellular structures. To that end, we introduce the Ramanomics platform, based on confocal Raman microspectrometry coupled to a biomolecular component analysis algorithm, which together enable us to molecularly profile single organelles in a live-cell environment. This emerging omics approach categorizes the entire molecular makeup of a sample into about a dozen of general classes and subclasses of biomolecules and quantifies their amounts in submicrometer volumes. A major contribution of our study is an attempt to bridge Raman spectrometry with big-data analysis in order to identify complex patterns of biomolecules in a single cellular organelle and leverage discovery of disease biomarkers. Our data reveal significant variations in organellar composition between different cell lines. We also demonstrate the merits of Ramanomics for identifying diseased cells by using prostate cancer as an example. We report large-scale molecular transformations in the mitochondria, Golgi apparatus, and endoplasmic reticulum that accompany the development of prostate cancer. Based on these findings, we propose that Ramanomics datasets in distinct organelles constitute signatures of cellular metabolism in healthy and diseased states.
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Registered trials
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.