{
  "abstract": "Extracellular vesicles (EVs) provide a rich, minimally invasive source of cancer biomarkers, yet EV surface glycoproteins remain comparatively underexploited compared with EV internal cargo. Here, we report a bio silent, azide-based Fourier transform infrared (FTIR) spectroscopic platform for simultaneous, label-minimal detection of two colorectal cancer (CRC) EV surface biomarkers, carcinoembryonic antigen (CEA) and cadherin-17 (CDH-17), directly from intact vesicles. EVs from five CRC cell lines and a non-cancerous control line were first distinguished by their intrinsic biochemical fingerprints across the lipid, protein, carbohydrate, and nucleic acid spectral regions, achieving up to 97% classification accuracy by multivariate (PCA/SIMCA) and machine-learning (PLS-DA) analysis. Selective azide labeling of CEA and CDH-17 via biotinylated antibodies and streptavidin–azide generated a specific vibrational signal in the 2000–2300 cm⁻¹ bio-silent window without perturbing native EV spectra; peak authenticity was confirmed by Gaussian deconvolution, quantitative signal-to-noise analysis (SNR = 315 ± 99), and buffer/serum-artifact controls. Density-of-states overlap, cosine correlation, and constrained non-negative least-squares regression enabled simultaneous deconvolution and quantification of both biomarkers from mixed-antibody spectra, resolving cell line–specific expression patterns and enabling EV subtyping. Critically, the platform was validated on plasma-derived EVs from CRC patients and a healthy donor, where diagnostic performance exceeded that of the cell-line panel (AUC = 1.0 for metastatic CRC vs. healthy across all six spectral ratios; AUC = 0.58–1.0 for early-stage CRC vs. healthy). These results establish azide-FTIR spectroscopy as a rapid, label-efficient, and artifact-controlled platform for multiplexed EV biomarker detection, with direct evidence of its translatability to clinical liquid biopsy specimens rather than relying on cell-line data alone.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Chemistry, Shiv Nadar Institution of Eminence, Uttar Pradesh 201314, Delhi-NCR, India",
    "Department of Chemistry",
    "Indian Institute of Technology Bhilai",
    "Department of Bioscience and Biomedical Engineering",
    "All India Institute of Medical Sciences, Department of Biophysics, India",
    "Department of Electrical and Computer Engineering",
    "University of Cyprus"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Debashish Paul",
      "orcid": "0000-0002-6764-5052"
    },
    {
      "affs": [
        0
      ],
      "name": "Natasha Natasha"
    },
    {
      "affs": [
        1,
        2
      ],
      "name": "Sunita Saha"
    },
    {
      "affs": [
        1,
        2,
        3
      ],
      "name": "Suchetan Pal"
    },
    {
      "affs": [
        4
      ],
      "name": "Ananya Ananya"
    },
    {
      "affs": [
        4
      ],
      "name": "Saroj Kumar"
    },
    {
      "affs": [
        5,
        6
      ],
      "name": "Chrysafis Andreou"
    },
    {
      "affs": [
        0
      ],
      "name": "Tatini Rakshit"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/b2ejp129psd9w.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15000620/v2",
  "fulltext_url": null,
  "id": "b2ejp129psd9w",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15000620/v2",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1304|Spectroscopy Techniques in Biomedical and Chemical Research"
    ]
  ],
  "openalex": "W7214931617",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15000620/v2",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/b2ejp129psd9w.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "10.26434/chemrxiv.15000620",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15000620/v2"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1304|Spectroscopy Techniques in Biomedical and Chemical Research",
      "subfield": "biophysics"
    }
  ],
  "title": "Simultaneous CEA and CDH-17 Detection from Cancer Extracellular Vesicles Via Machine Learning Enabled Bio Silent Azide-Infrared Spectroscopy",
  "title_text": "Simultaneous CEA and CDH-17 Detection from Cancer Extracellular Vesicles Via Machine Learning Enabled Bio Silent Azide-Infrared Spectroscopy",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/b2ejp129psd9w",
  "venue": "ChemRxiv",
  "versions": []
}