| First Author | Lovergne L | Year | 2021 |
| Journal | Sci Rep | Volume | 11 |
| Issue | 1 | Pages | 15598 |
| PubMed ID | 34341363 | Mgi Jnum | J:311426 |
| Mgi Id | MGI:6754930 | Doi | 10.1038/s41598-021-93686-8 |
| Citation | Lovergne L, et al. (2021) An infrared spectral biomarker accurately predicts neurodegenerative disease class in the absence of overt symptoms. Sci Rep 11(1):15598 |
| abstractText | Although some neurodegenerative diseases can be identified by behavioral characteristics relatively late in disease progression, we currently lack methods to predict who has developed disease before the onset of symptoms, when onset will occur, or the outcome of therapeutics. New biomarkers are needed. Here we describe spectral phenotyping, a new kind of biomarker that makes disease predictions based on chemical rather than biological endpoints in cells. Spectral phenotyping uses Fourier Transform Infrared (FTIR) spectromicroscopy to produce an absorbance signature as a rapid physiological indicator of disease state. FTIR spectromicroscopy has over the past been used in differential diagnoses of manifest disease. Here, we report that the unique FTIR chemical signature accurately predicts disease class in mouse with high probability in the absence of brain pathology. In human cells, the FTIR biomarker accurately predicts neurodegenerative disease class using fibroblasts as surrogate cells. |
