Research ArticleBIOMEDICAL ENGINEERING

Computational integration of nanoscale physical biomarkers and cognitive assessments for Alzheimer’s disease diagnosis and prognosis

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Science Advances  28 Jul 2017:
Vol. 3, no. 7, e1700669
DOI: 10.1126/sciadv.1700669
  • Fig. 1 Using a computational approach to diagnose AD using nanocharacterization and cognitive assessments.

    An atomic force microscope (AFM) and an HMI system were used to characterize nanomechanical and morphological properties of protein components from human CSF and blood samples. These properties were inputted into the algorithm with cognitive assessments to diagnose AD and predict its progression.

  • Fig. 2 Nanocharacterization of CSF protein components.

    (A) The average Young’s modulus of an entire mapping area using AFM. Healthy samples showed lower values than disease cases. However, the comparison between moderate and severe cases showed no significant difference. (B) Representative images of a topography mapping and a Young’s modulus mapping using AFM. (C) Young’s modulus of particles was obtained from the corresponding points that had relatively high value in the topography mapping. Young’s modulus was significantly increased along with the disease progression. (D) Representative images of particles of four disease stages using the HMI system (CytoViva) (scale bar, 100 μm). (E) Representative AFM images of protein particles from four disease stages (scale bar, 1 μm). Height analysis of those particles was performed using a profiling line crossing the particles to determine particle height (z axis). (F) Particle concentration, shown as the numbers in a constant region of 675 × 900 μm2. There were significantly more particles in moderate and severe cases than in the healthy group. (G) Data of particle height. All disease cases were significantly larger than the healthy group. The particle height showed a gradual increase along with the disease progression. All data are presented as box plots. Sample number n = 34. One-way ANOVA test was performed; α = 0.05. **P < 0.01; ***P < 0.001.

  • Fig. 3 Nanocharacterization of serum protein components using functionalized nanoprobes with AFM.

    (A) The thermal frequency of the tip changed when the tip was bound with Aβ. (B) The relative changes of tip thermal frequency decreased along with the disease progression. (C) The serum and anti-Aβ antibody interaction was measured when the targeted binding sites were detected during the mapping of selected areas. (D) The serum and anti-Aβ antibody interaction decreased along with the disease progression. Lower amount of Aβ inside the serum of AD patients was demonstrated. (E) The binding force between Aβ aggregates and anti-Aβ antibody was measured when the Aβ aggregates and the substrate (coated with anti-Aβ antibody) were separated. (F) The binding force has an increasing trend along with the disease progression. (G) The molecule force between Aβ aggregates and anti-Aβ antibody was extracted from the plateau-shaped region of retract force curves. (H) An increasing trend of molecule force was found, reflecting the increasing fibrillization caused by Aβ components associated with disease progression. All data are presented as box plots. Sample number n = 30. One-way ANOVA test was performed; α = 0.05. *P < 0.05; **P < 0.01; ***P < 0.001.

  • Fig. 4 AD diagnosis using a KF-based algorithm.

    (A) Flowchart of the computational integration of physical biomarkers and cognitive assessments for AD diagnosis and prediction. Nanoscale measurements of CSF and serum samples from a new patient were fused with those measurements of the subjects with known AD stages. Then, the fused data were used to train the KF model. Diagnosis, progression prediction, and optimal checkup frequency were ultimately obtained. (B) Diagnosis of AD stages based on various combinations of physical parameters was conducted to evaluate the diagnostic accuracy. The accumulated diagnosis errors for all cases were calculated by comparing with the doctor’s diagnosis results. AD stages were converted into ordinal variables by assigning ranks of 1 to 4. Healthy condition labeled 1 and numeric assignments increased until 4, which represented severe AD. Measured data and medical records from doctors of 34 patients were used as the population database for the KF model training. Four female patients at the “age/AD stage” of 73/healthy, 75/mild, 77/moderate, and 81/severe were chosen to formulate a new virtual patient to test the proposed KF-based algorithm for the following AD analysis.

  • Fig. 5 The stage diagnosis, progression prediction, and optimal checkup frequency of AD patients based on CSF data, blood data, and CSF-blood combined data.

    (A) The early stages of CSF data showed large errors. (C) On the basis of serum data, the large errors were presented in the later disease stages. (E) When CSF and serum data were both applied for the KF-based prediction, smaller error and better fit were obtained, which showed that more variable inputs for the KF-based algorithm would provide better results. (B) Top: Data distribution in terms of age. Black line is clinical data; green, purple, orange, red, and blue lines are simulated data obtained by linear interpolation of clinical data (in terms of AD status). They correspondingly represent data sets of 19, 7, 6, 5, and 4 visits. Bottom: Prediction results of AD status based on the simulated data as shown in (A). On the basis of CSF data, the corresponding prediction errors were 25.7% (blue line, 4 visits), 15.6% (red line, 5 visits), 8.5% (orange line, 6 visits), 4.3% (purple line, 7 visits), and 2.7% (green line, 19 visits) by comparing with the clinical results. The prediction error can be calculated by Embedded Image, where xpre,j is the predicted AD stage, xclin,j is the clinically obtained AD stage, and j indicates the jth AD stage. The CSF case had good fit at early stages, but the later part differed too much. (D) The serum case had good fit for the whole changing trend, but the error was large. (F) The results from serum and CSF combined data fit well with doctor’s diagnosis when the visits reached five times in 8 years.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/3/7/e1700669/DC1

    fig. S1. Comparisons between cognitive assessment evaluations and the KF-based predictions of AD progression.

    fig. S2. Synthesized Aβ was used to show the influence of a freeze-thaw cycle on the peptide and protein particle aggregation.

    fig. S3. Identifying Aβ-embedded protein aggregates within the CSF.

    fig. S4. Nanomorphology of tau-embedded proteins within the CSF during AD pathogenesis.

    fig. S5. Age- and gender-related analysis.

    fig. S6. Particle size, shown as the size of white spots.

    fig. S7. Dynamics of nanocharacterization and cognitive assessment data in AD progression.

    fig. S8. AD stages of a female patient determined by cognitive assessments and KF-based diagnosis.

    fig. S9. Data analysis of nanomorphology using image processing.

    table S1. Human subjects for CSF samples.

    table S2. Human subjects for serum samples.

    table S3. Details of experimental data sets of CSF-based characterization.

    table S4. Details of experimental data sets of serum-based characterization.

    table S5. MMSE and SAGE scores.

  • Supplementary Materials

    This PDF file includes:

    • fig. S1. Comparisons between cognitive assessment evaluations and the KF-based predictions of AD progression.
    • fig. S2. Synthesized Aβ was used to show the influence of a freeze-thaw cycle on the peptide and protein particle aggregation.
    • fig. S3. Identifying Aβ-embedded protein aggregates within the CSF.
    • fig. S4. Nanomorphology of tau-embedded proteins within the CSF during AD pathogenesis.
    • fig. S5. Age- and gender-related analysis.
    • fig. S6. Particle size, shown as the size of white spots.
    • fig. S7. Dynamics of nanocharacterization and cognitive assessment data in AD progression.
    • fig. S8. AD stages of a female patient determined by cognitive assessments and KF-based diagnosis.
    • fig. S9. Data analysis of nanomorphology using image processing.
    • table S1. Human subjects for CSF samples.
    • table S2. Human subjects for serum samples.
    • table S3. Details of experimental data sets of CSF-based characterization.
    • table S4. Details of experimental data sets of serum-based characterization.
    • table S5. MMSE and SAGE scores.

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