Multivariate statistical analysis of surface enhanced Raman spectra of human serum for Alzheimer's disease diagnosis

Elena Ryzhikova; Nicole M. Ralbovsky; Lenka Halámková; Dzintra Celmins; Paula Malone; Eric Molho; Joseph Quinn; Earl A. Zimmerman; Igor K. Lednev

doi:10.3390/app9163256

Multivariate statistical analysis of surface enhanced Raman spectra of human serum for Alzheimer's disease diagnosis

Elena Ryzhikova, Nicole M. Ralbovsky, Lenka Halámková, Dzintra Celmins, Paula Malone, Eric Molho, Joseph Quinn, Earl A. Zimmerman, Igor K. Lednev

Neurology

Research output: Contribution to journal › Article › peer-review

27 Scopus citations

Abstract

Alzheimer's disease (AD) is the most common form of dementia worldwide and is characterized by progressive cognitive decline. Along with being incurable and lethal, AD is difficult to diagnose with high levels of accuracy. Blood serum from Alzheimer's disease (AD) patients was analyzed by surface-enhanced Raman spectroscopy (SERS) coupled with multivariate statistical analysis. The obtained spectra were compared with spectra from healthy controls (HC) to develop a simple test for AD detection. Serum spectra from AD patients were further compared to spectra from patients with other neurodegenerative dementias (OD). Colloidal silver nanoparticles (AgNPs) were used as the SERS-active substrates. Classification experiments involving serum SERS spectra using artificial neural networks (ANNs) achieved a diagnostic sensitivity around 96% for differentiating AD samples from HC samples in a binary model and 98% for differentiating AD, HC, and OD samples in a tertiary model. The results from this proof-of-concept study demonstrate the great potential of SERS blood serum analysis to be developed further into a novel clinical assay for the effective and accurate diagnosis of AD.

Original language	English (US)
Article number	3256
Journal	Applied Sciences (Switzerland)
Volume	9
Issue number	16
DOIs	https://doi.org/10.3390/app9163256
State	Published - Aug 1 2019

Keywords

Alzheimer's disease
Artificial neural networks
Blood serum
Differential diagnosis
Genetic algorithm
Silver colloidal nanoparticles
Surface enhanced Raman spectroscopy

ASJC Scopus subject areas

Materials Science(all)
Instrumentation
Engineering(all)
Process Chemistry and Technology
Computer Science Applications
Fluid Flow and Transfer Processes

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10.3390/app9163256

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title = "Multivariate statistical analysis of surface enhanced Raman spectra of human serum for Alzheimer's disease diagnosis",

abstract = "Alzheimer's disease (AD) is the most common form of dementia worldwide and is characterized by progressive cognitive decline. Along with being incurable and lethal, AD is difficult to diagnose with high levels of accuracy. Blood serum from Alzheimer's disease (AD) patients was analyzed by surface-enhanced Raman spectroscopy (SERS) coupled with multivariate statistical analysis. The obtained spectra were compared with spectra from healthy controls (HC) to develop a simple test for AD detection. Serum spectra from AD patients were further compared to spectra from patients with other neurodegenerative dementias (OD). Colloidal silver nanoparticles (AgNPs) were used as the SERS-active substrates. Classification experiments involving serum SERS spectra using artificial neural networks (ANNs) achieved a diagnostic sensitivity around 96% for differentiating AD samples from HC samples in a binary model and 98% for differentiating AD, HC, and OD samples in a tertiary model. The results from this proof-of-concept study demonstrate the great potential of SERS blood serum analysis to be developed further into a novel clinical assay for the effective and accurate diagnosis of AD.",

keywords = "Alzheimer's disease, Artificial neural networks, Blood serum, Differential diagnosis, Genetic algorithm, Silver colloidal nanoparticles, Surface enhanced Raman spectroscopy",

author = "Elena Ryzhikova and Ralbovsky, {Nicole M.} and Lenka Hal{\'a}mkov{\'a} and Dzintra Celmins and Paula Malone and Eric Molho and Joseph Quinn and Zimmerman, {Earl A.} and Lednev, {Igor K.}",

note = "Funding Information: Funding: This work was supported by the State University of New York Technology Accelerator Fund. E.M. was supported by the Riley Family Chair in Parkinson{\textquoteright}s Disease. Publisher Copyright: {\textcopyright} 2019 by the authors.",

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doi = "10.3390/app9163256",

language = "English (US)",

volume = "9",

journal = "Applied Sciences (Switzerland)",

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T1 - Multivariate statistical analysis of surface enhanced Raman spectra of human serum for Alzheimer's disease diagnosis

AU - Ryzhikova, Elena

AU - Ralbovsky, Nicole M.

AU - Halámková, Lenka

AU - Celmins, Dzintra

AU - Malone, Paula

AU - Molho, Eric

AU - Quinn, Joseph

AU - Zimmerman, Earl A.

AU - Lednev, Igor K.

N1 - Funding Information: Funding: This work was supported by the State University of New York Technology Accelerator Fund. E.M. was supported by the Riley Family Chair in Parkinson’s Disease. Publisher Copyright: © 2019 by the authors.

PY - 2019/8/1

Y1 - 2019/8/1

N2 - Alzheimer's disease (AD) is the most common form of dementia worldwide and is characterized by progressive cognitive decline. Along with being incurable and lethal, AD is difficult to diagnose with high levels of accuracy. Blood serum from Alzheimer's disease (AD) patients was analyzed by surface-enhanced Raman spectroscopy (SERS) coupled with multivariate statistical analysis. The obtained spectra were compared with spectra from healthy controls (HC) to develop a simple test for AD detection. Serum spectra from AD patients were further compared to spectra from patients with other neurodegenerative dementias (OD). Colloidal silver nanoparticles (AgNPs) were used as the SERS-active substrates. Classification experiments involving serum SERS spectra using artificial neural networks (ANNs) achieved a diagnostic sensitivity around 96% for differentiating AD samples from HC samples in a binary model and 98% for differentiating AD, HC, and OD samples in a tertiary model. The results from this proof-of-concept study demonstrate the great potential of SERS blood serum analysis to be developed further into a novel clinical assay for the effective and accurate diagnosis of AD.

AB - Alzheimer's disease (AD) is the most common form of dementia worldwide and is characterized by progressive cognitive decline. Along with being incurable and lethal, AD is difficult to diagnose with high levels of accuracy. Blood serum from Alzheimer's disease (AD) patients was analyzed by surface-enhanced Raman spectroscopy (SERS) coupled with multivariate statistical analysis. The obtained spectra were compared with spectra from healthy controls (HC) to develop a simple test for AD detection. Serum spectra from AD patients were further compared to spectra from patients with other neurodegenerative dementias (OD). Colloidal silver nanoparticles (AgNPs) were used as the SERS-active substrates. Classification experiments involving serum SERS spectra using artificial neural networks (ANNs) achieved a diagnostic sensitivity around 96% for differentiating AD samples from HC samples in a binary model and 98% for differentiating AD, HC, and OD samples in a tertiary model. The results from this proof-of-concept study demonstrate the great potential of SERS blood serum analysis to be developed further into a novel clinical assay for the effective and accurate diagnosis of AD.

KW - Alzheimer's disease

KW - Artificial neural networks

KW - Blood serum

KW - Differential diagnosis

KW - Genetic algorithm

KW - Silver colloidal nanoparticles

KW - Surface enhanced Raman spectroscopy

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Multivariate statistical analysis of surface enhanced Raman spectra of human serum for Alzheimer's disease diagnosis

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