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@ARTICLE{Pagliari:298935,
      author       = {F. Pagliari$^*$ and L. Tirinato and E. Di Fabrizio},
      title        = {{R}aman {S}pectroscopies for {C}ancer {R}esearch and
                      {C}linical {A}pplications: a {F}ocus on {C}ancer {S}tem
                      {C}ells.},
      journal      = {Stem cells},
      volume       = {43},
      number       = {4},
      issn         = {1066-5099},
      address      = {Oxford},
      publisher    = {Oxford University Press},
      reportid     = {DKFZ-2025-00369},
      pages        = {sxae084},
      year         = {2025},
      note         = {#EA:E041# / Volume 43, Issue 4, April 2025, sxae084},
      abstract     = {Over the last two decades, research has increasingly
                      focused on Cancer Stem Cells (CSCs), considered responsible
                      for tumor formation, resistance to therapies, and relapse.
                      The traditional 'static' CSC model used to describe tumor
                      heterogeneity has been challenged by the evidence of CSC
                      dynamic nature and plasticity. A comprehensive understanding
                      of the mechanisms underlying this plasticity, and the
                      capacity to unambiguously identify cancer markers to
                      precisely target CSCs are crucial aspects for advancing
                      cancer research and introducing more effective treatment
                      strategies. In this context, Raman spectroscopy (RS) and
                      specific Raman schemes, including CARS, SRS, SERS, have
                      emerged as innovative tools for molecular analyses both in
                      vitro and in vivo. In fact, these techniques have
                      demonstrated considerable potential in the field of cancer
                      detection, as well as in intraoperative settings, thanks to
                      their label-free nature and minimal invasiveness. However,
                      the RS integration in pre-clinical and clinical
                      applications, particularly in the CSC field, remains
                      limited. This review provides a concise overview of the
                      historical development of RS and its advantages. Then, after
                      introducing the CSC features and the challenges in targeting
                      them with traditional methods, we review and discuss the
                      current literature about the application of RS for revealing
                      and characterizing CSCs and their inherent plasticity,
                      including a brief paragraph about the integration of
                      artificial intelligence with RS. By providing the
                      possibility to better characterize the cellular diversity in
                      their microenvironment, RS could revolutionize current
                      diagnostic and therapeutic approaches, enabling early
                      identification of CSCs and facilitating the development of
                      personalized treatment strategies.},
      keywords     = {Cancer Stem Cells (Other) / Cancer detection (Other) /
                      Cancer markers (Other) / Label-free imaging (Other) / Raman
                      spectroscopy (Other)},
      cin          = {E041},
      ddc          = {610},
      cid          = {I:(DE-He78)E041-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39949042},
      doi          = {10.1093/stmcls/sxae084},
      url          = {https://inrepo02.dkfz.de/record/298935},
}