FTIR Spectroscopy of Cerebrospinal Fluid Reveals Variations in the Lipid: Protein Ratio at Different Stages of Alzheimer’s Disease

Abstract

Alzheimer’s disease (AD) is a disease of advanced civilization and a common form of dementia in people over 65 years of age. We used Fourier transform infrared (FTIR) spectroscopy combined with principal component analysis (PCA) to determine changes in the quantity and quality of the cerebrospinal fluid from AD patients at three different stages of the disease (ADI, ADII, and ADIII), as well as from patients with mild cognitive impairment (MCI). Moreover, based on the FTIR spectra, we calculated the ratio of α-helix and β-sheet secondary protein structures as well as the lipid-protein balance as potential AD markers. The FTIR spectra of cerebrospinal fluid obtained from MCI, ADI, ADII, and ADIII patients showed that peaks corresponding to protein and deoxyribonucleic acid (DNA), and phospholipid and lipid vibrations were shifted in comparison with those of control subjects. Furthermore, the levels of these chemical compounds were lower in the patients than in the control subjects. The β-sheet secondary protein structure levels were increased in the MCI and AD patients compared with the control subjects. In addition, significant changes in the lipid-protein balance were observed. Interestingly, as the disease progressed, the lipid-protein balance became further disrupted, that is, the lipid amount decreased with disease progression. PCA analysis of lipid-protein FTIR regions revealed that the spectra could be used to distinguish between controls and patients with MCI, ADI, ADII, and ADIII.

Keywords

Alzheimer’s disease cerebrospinal fluid Fourier transform infrared spectroscopy lipid-protein balance PCA-LDA

INTRODUCTION

Due to the phenomenon of aging societies worldwide, the percentage of elderly persons has significantly increased, and as a result, the number of people with dementia is growing. Alzheimer’s disease (AD) is a form of dementia. Approximately 44 million people in the world are estimated to have AD or a related form of dementia. The disease affects more women than men [1]. The presentation of AD differs between patients [2]. Early symptoms are often mistakenly associated with age or stress [3]. In the early stage of the disease, also known as mild cognitive impairment (MCI), the most common symptom is difficulty recalling recent events. People with MCI are at risk for developing AD [4, 5].

The diagnosis of AD is based on patient history, supplemented by a family interview and clinical observations of the presence of characteristic neurologic and neuropsychologic features, and the results of the differential diagnosis (diagnosis of exclusion) [6, 7]. Advanced imaging methods, such as computed tomography, magnetic resonance imaging (MRI), positron emission tomography (PET), and single-photon emission tomography, help exclude other brain pathologies or other types of dementia [8]. Moreover, these methods can be used to predict the transition from prodromal stages, such as MCI, to AD [9]. Unfortunately, however, these diagnostic techniques are expensive and full width at half maximum (FWHM) identify new AD markers, such as the lipid-protein balance in the cerebrospinal fluid (CSF), which circulates through the inner ventricular system across the blood-brain barrier and is absorbed into the bloodstream [10]. In addition, new and cheaper diagnostic methods are sought to recognize the disease at an early stage of development and provide the results efficiently. One possibility is Fourier transform infrared spectroscopy (FTIR), which is a repetitive method that provides important information about the chemical structure of measurement samples, as well as structural changes caused by disease. With FTIR spectroscopy, several biomarkers can be detected and analyzed simultaneously in a relatively short time [11]. Previous studies indicate that FTIR spectroscopy can be used for the diagnosis of breast [12 –14], lung [15], liver [16], cervix [17], and brain [18] cancers, and leukemia [19], and for the early detection of bladder cancer recurrence [20].

The causes and progression of AD are still poorly understood. The disease is associated with brain plaques and tangles [21]. AD is an amyloidosis, a neurodegenerative process in which abnormal levels of amyloid protein (amyloid-β) are observed in the brain [22]. In the process of abnormal transformations involving β- and γ-secretases (enzymes that dissolve amyloid-β protein precursor into fragments) [23], amyloid-β is fragmented into insoluble amyloid-β forms that are deposited intracellularly and then extracellularly in the form of senile plaques. The presence of insoluble forms of amyloid-β leads to hyperphosphorylation of the tau protein, which is involved in binding and stabilizing microtubules. This results in impaired biochemical communication between neurons and cell death [24]. FTIR spectroscopy with highly specific antibody sensors may be useful for detecting amyloid-β [25]. In addition, protein microarrays comprising hundreds of microspots can be analyzed by infrared imaging. This could be helpful for monitoring changes in protein structures that are visible in the FTIR spectra [26]. Due to the complexity of FTIR spectra of biologic samples, multivariate analysis of the measurements is necessary. Previous analyses demonstrated that changes in the materials collected from people with AD differ significantly from those in healthy people [27]. Changes in the lipid distribution and metabolism observed in postmortem and in vitro assays suggest that lipid perturbations are involved in AD pathology [28]. The fact that a genetic variant of apolipoprotein E (APOE), APOE ɛ 4, is the highest genetic risk factor for late-onset AD is an important piece of evidence implicating lipids, especially cholesterol, in AD pathogenesis [29]. Furthermore, in AD mouse models, cholesterol and its transporter, apoE, are colocalized with amyloid-β immunoreactivity in fibrillary plaques [30], suggesting the involvement of cholesterol in amyloid plaque formation. These findings are also observed in AD human patients [31]. The amount and metabolism of phospholipids are also changed in AD patients. Reductions of 10% to 40% in phosphatidylcholines, phosphatidylinositols, phosphatidylethanolamines, and plasmalogens in AD brains compared with controls are reported [32, 33]. Moreover, Panchal et al. demonstrated that the blood serum of AD patients shows changes in the vibrations corresponding to lipid and nucleic acid structures involved in oxidative stress-dependent processes of AD [30]. Salman and Morderchai also noticed spectral differences in the FTIR region of lipid vibrations in the blood serum of AD patients and reported changes in the protein region [34]. Furthermore, using FTIR spectroscopy, Correia et al. showed that plasma samples obtained from cognitively impaired individuals contain a higher content of saturated lipids, carboxylic acids, reactive carbonyls, and other molecules related to oxidative stress (reactive oxygen species and reactive nitrogen species), as well as protein modifications [30]. Kiskis et al. showed vibrational signatures of lipids that are co-localized with the β-sheet vibrations, which provide information about the effects of lipids on amyloid-β levels [35]. Several studies suggest that depression is the first step in AD development. Interestingly, depressed patients showing lipid-protein balance disturbances on the basis of FTIR spectra of the CSF also have lipid-protein imbalances in the blood serum [36, 37], as well as in brain tissues such as the prefrontal cortex and hypothalamus [38]. In the present study, we performed FTIR spectroscopy combined with hierarchical cluster analysis (HCA), principal component analysis (PCA), and mathematical analyses to determine secondary protein structures, as well as the ratios of α-helix and β-sheet protein structures. The lipid-protein balance in patients with MCI and AD at different stages was also calculated. Qualitative and quantitative chemical changes in the CSF of patients with AD and MCI are also described.

MATERIALS AND METHODS

Materials

The study was conducted under Institutional Review Board Protocol No. KBET/6/06/2014) beginning in June 2014, at the University of Rzeszow. The experimental protocols used in this study were approved by the institutional ethics committees of the University of Rzeszow, and carried out in accordance with the approved guidelines. We obtained written informed consent from all subjects who participated in the study (including subject’s guardians), and the methods were performed in accordance with relevant regulations and guidelines. The subjects were recruited from the Department of Neurology, State Hospital No. 2, Rzeszow, Poland. We included 18 patients with MCI and 47 with AD diagnosed according to the National Institute on Aging and Alzheimer’s Association criteria. Patients were assessed by a medical doctor specializing in dementia disorders and a neuropsychologist. We used the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCa), and the clock-drawing test as measures of general cognition. Moreover, the Global Deterioration Scale (GDS), the 7-minute screen, and Alzheimer’s Disease Assessment Scale Cognitive Subscale were used to assess the subjects for dementia. Patients with MCI had an MMSE score≥24 and MoCA score≥26, objective memory loss, a GDS score of 3, indicating normal daily activity function, and absence of dementia. Patients with AD had an MMSE score≤23, MoCa score≤25, and a GDS score of 4–6. According to the GDS score, AD patients were classified as having mild, moderate, and severe stages of the disease. All patients were free of any significant neurologic disease other than AD. The Control group comprised patients with an MMSE score≥27 and a GDS score of 1. Cognitive deterioration was ruled out by neuropsychologic screening (MMSE, MoCa, and the clock-drawing test), and subjects had no neurologic central nervous system disorders or inflammatory syndromes. Characteristics of the study group, including sex, age, and AD stage, are summarized in Table 1. CSF samples were collected lumbar puncture for diagnostic purposes. CSF was examined for standard parameters, such as cell count, glucose, proteins, and immunoglobulins to exclude neuroinflammatory disease. None of the patients had an elevated cell count or intrathecal IgG. Other test samples were centrifuged on acquisition at 2000×g, collected in polypropylene tubes, and stored in 50 mL aliquots at –80°C until use.

Table 1

Characteristics of the study group including sex, age, and disease stage

	Controls	MCI	Mild (ADI)	Moderate (ADII)	Severe (ADIII)	Whole group
Cases (n)	6	18	23	20	4	71
Age (mean±SD)	61.7 (4.2)	65.5 (10,8)	75.2 (8.4)	73.7 (8.3)	76.3 (4.3)	70 (10.8)
Range	57–71	47–91	50–88	55–85	71–83	47–91
Sex (male/female)	3/3	8/10	9/14	7/13	3/1	30/41

FTIR spectroscopy

We acquired FTIR spectra using a Vertex 70 (Bruker) spectrometer, applying the Attenuated Total Reflectance (ATR) technique. The selected infrared radiation level was in the middle range (400–4000 cm^-1). We performed 64 scans with 4 cm^-1 spectral resolution. For each sample, we identified the same absorption bands corresponding to nucleic acids, proteins, polysaccharides, lipids, and water. Each sample was measured in triplicate. To determine the changes in protein secondary structure, we calculated the second derivative of the spectra. All spectra were analyzed with OPUS software. Furthermore, baseline correction was performed using the concave rubberband method with 64 baseline points. Spectrum normalization using the min-max normalization method was performed for each spectrum to exclude the influence of sample position of the ATR crystal. In this paper, the presented spectrum underwent baseline correction and normalization.

To obtain information about secondary protein structure, the second derivative of the FTIR spectra with 17 smoothing points was calculated using OPUS software. Moreover, the second derivative of the FTIR spectra allows us to increase the resolution of the obtained FTIR spectrum using a mathematic algorithm.

Deconvolution of the amide I region (1600–1700 cm^-1)

The secondary protein structure, and the percentage of the α and β components were analyzed by curve-fitting using MagicPlot 2.1. software. First, the secondary derivative spectra were determined based on the ATR-FTIR spectra to determine the initial peak positions for curve-fitting, and the peaks were fitted using a Gaussian function. The area under the curve was considered 100% and each component was expressed as its percentage after fitting.

Lipid-protein balance

To determine the lipid-protein balance, the values of the peak area corresponding to lipid and protein vibrations were calculated using ORIGIN 8.0. software. This procedure was carried out using the normalized spectra. The separately obtained results for protein and lipid vibrations were then added to each other. Next, we calculated the ratio of lipid and protein vibrations according to the following formula: $R_{l - p} = \frac{S_{l}}{S_{p}},$ (1)where: R_l _- p = calculated lipid-protein ratio, S_l = sum of the areas of maximum absorbance of lipids fraction, Sp = sum of the area of maximum absorbance of the protein fraction.

Computational analysis

To determine the sensitivity and specificity of principal component analysis-linear discriminant analysis (PCA-LDA) analysis, the leave-one-sample-out cross-validation method was performed. The PCA-LDA analysis was performed for FTIR regions between 800 cm^- ¹ and 1800 cm^- ¹. PCA-LDA results were checked by the leave-one-sample-out cross-validation method, which involves leaving all spectra from a single sample out of the model once before assessing performance. This analysis was performed using OriginLab software.

Statistical analysis

We used one-way analysis of variance followed by the Tukey’s test to detect any differences in the mean values of maximum absorbance, peak position, FWHM, and peak area for each individual wavenumber corresponding to the main absorption bands. The statistical hypotheses were tested and considered significant at an α level≤0.05. To identify sample groups with the highest similarity with respect to the entire absorbance spectra, we applied hierarchical cluster analysis with Euclidean distance and Ward’s algorithms. Statistical and multidimensional analyses were performed using STATISTICA ver. 9 and PAST software.

RESULTS AND DISCUSSION

In this study, FTIR spectroscopy combined with PCA-LDA was used to investigate qualitative and quantitative chemical changes in the CSF of people with AD and MCI. Moreover, secondary protein structures, the ratio of α and β structures, and the lipid-protein balance in patients with MCI and AD at different stages were calculated from the FTIR spectra.

The FTIR spectra of the Control CSF (Fig. 1, red spectrum) displayed three important regions. The first region (3100 cm^-1 – 2700 cm^-1) shows C-H stretching vibrations from lipids. The second region (1700 cm^-1 – 1250 cm^-1) represents protein functional group vibrations, and the third region (1300 cm^-1 – 900 cm^-1) exhibits C-O, C-O-P, and P = O vibrations from DNA and phospholipids. The absorption bands result from the FTIR active vibrations of different functional groups in these molecules. In the lipid region, we observed symmetric vibrations of CH₂ at 2956 cm^-1 and asymmetric vibrations of CH₂ at 2847 cm^-1. The protein functional group vibrations are in the second FTIR spectral region, showing a peak at 1740 cm^-1 corresponding to the C = O stretching vibration of ester carbonyls in lipids. Next, the protein region corresponds with amide vibrations. The peaks at 1650 cm^-1, 1560 cm^-1, 1376 cm^-1, and 1290 cm^-1 correspond with amide I, N-H bending vibration/C-N stretching vibration of amide II, C = O, CH₂ wagging, and amide III vibrations, respectively. Moreover, the region between 1300 cm^-1 and 1000 cm^-1 corresponds with the asymmetric and symmetric vibrations of PO₃ ² ^- groups that make up the nucleic acid and phospholipid structures. Furthermore, out-of-plane CH bending vibrations [39 –45] at 888 cm^-1 were observed in the FTIR spectra of Control CSF samples (Fig. 1a).

Fig.1

Mean FTIR spectra of CSF samples: (a) Control subject; (b) MCI patient; (c) ADI patient; (d) ADII patient; (e) ADIII patient.

Differences in the maximum absorbance values were observed in FTIR spectra of MCI and AD patients compared with those of the Control group (Fig. 1). The height of peaks corresponding to DNA, phospholipids, amide III, and lipid vibrations decreased in the MCI, ADI, ADII, and ADIII FTIR spectra compared with Control spectrum. Interestingly, the decrease was greater with more advanced disease. Consequently, the lowest maximum absorbance values of these peaks were observed in the ADIII CSF FTIR spectrum. Except for differences in the maximum absorbance values, shifts of peaks compared with the Control FTIR spectrum were observed in the MCI and AD FTIR spectra. Amide I vibrations in the ADIII FTIR spectrum located at 1644 cm^-1, while amide II vibrations were shifted by 1 cm^-1, 5 cm^-1, 8 cm^-1, and 9 cm^-1 toward lower wavenumbers in the CSF FTIR spectra of the MCI, ADI, ADII, and ADIII groups, respectively. Moreover, amide III vibrations were also shifted in MCI and AD FTIR spectra. This shift was approximately 4 cm^-1, 9 cm^-1, and 10 cm^-1 toward higher wavenumbers in the CSF FTIR spectra of the ADI, ADII, and ADIII groups, respectively. Some postmortem studies of AD patients using FTIR spectroscopy reported results similar to ours, with respect to tau and amyloid-β levels in the CSF based on enzyme-linked immunosorbent assay [46, 47]. Moreover, the protein products of amyloid-β protein precursor digestion, i.e., fibrillating Aβ peptides, are a hallmark of AD development. One very interesting finding obtained by FTIR spectroscopy was the shift of the amide vibrations in the blood serum samples of AD patients. This shift reflects the formation of amyloid protein deposits [48]. In our study, we also observed peaks in the characteristic region of the amide I vibration (Fig. 1). Moreover, Schaeberie et al. showed that amyloid-β deposits have a specific signal in FTIR spectroscopy, like an amide I band shift in the direction of the lower frequencies [49]. We also observed a shift of amide I vibrations toward lower frequencies in the FTIR spectrum of the CSF from ADIII patients.

The peaks corresponding to DNA, phospholipids, and lipids vibrations were also shifted in the MCI and AD spectra. In the FTIR spectra of CSF samples from MCI (Fig. 1, pink spectrum), ADI (Fig. 1, green spectrum), ADII (Fig. 1, blue spectrum), and ADIII (Fig. 1, black spectrum) patients, we observed a shift of peaks corresponding to protein as well as DNA, phospholipids, and lipid vibrations, compared with the Control CSF. The peak at 2956 cm^-1 (CH₂ group of lipids) was shifted –6 cm^-1 in the FTIR spectra of MCI and ADI CSF and 10 cm^-1 toward lower wavenumbers in the FTIR spectra of ADII and ADIII CSF. Moreover, the peak at 2847 cm^-1 in the ADI CSF FTIR spectrum was shifted 6 cm^-1, and in ADII, ADIII FTIR spectra, 11 cm^-1 toward a higher wavenumber. The peak at wavenumber 1078 cm^-1 was shifted by 2 cm^-1 in the MCI and ADI FTIR spectra, and 4 cm^-1 and 6 cm^-1 in ADII and ADIII FTIR spectra, respectively. Moreover, the ADIII FTIR spectrum also showed a second peak due to DNA and phospholipid vibrations that was shifted by 4 cm^-1 toward lower wavenumbers. Only a few studies have reported changes in CSF lipid levels of patients with AD. The lipid fraction of the CSF from patients with dementia is reduced by 40% compared with controls [50]. Kosicek et al. [51] observed changes in the ratio of sphingomyelin and phospholipids in AD patients. The phospholipid fraction of the CSF from AD patients is decreased [52, 53]. Interestingly, sphingolipid levels are correlated with the amounts of amyloid and tau [54]. An 18-carbon acyl chain ceramide correlates with all amyloid-β species in an older age bracket (54 years) and APOE ɛ 4 carriers.

All vibrations, which are assigned to a wavenumber and their locations in the FTIR spectra, compared with the Control group, are summarized in Table 2. The average peak position, maximum absorbance, peak area, and FWHM for all analyzed groups, are provided in Table 3. The positions of the peaks corresponding to the CH₂ lipid vibrations in the MCI FTIR spectrum were significantly different compared to those of the Control group. The positions of the peaks corresponding to CH₂ lipid vibrations and amide II in the ADI FTIR spectrum were significantly different from those of the Control group. Moreover, the positions of the peaks for lipids, amide II, and amide III, and one peak corresponding to PO₃ ^2- vibrations in DNA and lipids in the ADII FTIR spectrum were significantly different compared with those of the Control group. The positions of these peaks and those corresponding to amide I and the second peak of PO₃ ^2- vibrations in the ADIII FTIR spectrum were significantly different from those in the Control group. The maximum absorbance values for the peaks at 2847 cm^-1, 1376 cm^-1, 1290 cm^-1, 1078 cm^-1, and 962 cm^-1 in MCI, ADI, ADII, and ADIII FTIR spectra were significantly different compared with those of the Control group. Moreover, the maximum absorbance value of the peak at 2956 cm^-1 in all of the AD FTIR spectra was significantly different from that of the Control group. Furthermore, the maximum absorbance values of the peak corresponding to amide II vibrations in the ADII and ADIII FTIR spectra were significantly different compared with that of the Control group. Thus, the maximum absorbance values of the peaks at 2956 cm^-1, 2847 cm^-1, 1290 cm^-1, and 1078 cm^-1 were significantly different from those of the Control group. The area of the peaks corresponding to the CH₂ wagging vibration (1376 cm^-1) and at one of the PO₃ ^2- stretching vibration (962 cm^-1) wavenumber in the MCI and all AD FTIR spectra were significantly different compared with those of the Control group. Furthermore, the peak area corresponding to amide I in the ADI FTIR spectrum was significantly different compared with the Control group. The FWHM of the peaks at 2847 cm^-1, 1290 cm^-1, and 1078 cm^-1 - in the MCI and all AD FTIR spectra were significantly different compared with those of the Control group. The FWHM value of peaks corresponding to CH₂ wagging vibrations (1376 cm^-1) and at 1078 cm^-1 in the MCI FTIR spectrum were significantly different compared with those of the Control group. Furthermore, the FWHM of the second peaks corresponding to CH₂ lipid vibrations (2847 cm^-1) and PO₃ ^2- (962 cm^-1) in the ADII and ADIII FTIR spectra were significantly different compared with those of the Control group.

Table 2

FTIR analysis of CSF samples 35–41. Con., Control group; MCI, mild cognitive impairment; ADI, ADII, and ADIII refer to Braak’s staging of Alzheimer’s disease-associated neurofibrillary pathology, Δ indicates differences in the peak position between the measured sample and the Control group

Wavenumber (cm^-1)
Con.	MCI	Δ	ADI	Δ	ADII	Δ	ADIII	Δ	Vibrations
3277	3277		3277		3277		3277		O-H stretching (water) [34]
2956	2950	–6	2950	–6	2946	–10	2946	–10	CH₂ stretching symmetric (lipids) [27 , 38]
2847	2847		2853	6	2858	11	2858	11	CH₂ stretching asymmetric (lipids) [27 , 38]
1740	1740		1740		1740		1740		ester C = O stretching [38]
1650	1650		1650		1650		1644	–6	Amide I [37, 38]
1560	1559	–1	1555	–5	1552	–8	1551	–9	Amide II - - N-H bending vibration/C-N stretching vibration [37, 38]
1376	1376		1376		1376		1376		CH₂ wagging vibration [37]
1290	1290		1294	4	1297	9	1300	10	Amide III (proteins) [37]
1078	1080	2	1080	2	1082	4	1084	6	PO₃ ^2- stretching asymmetric (DNA, RNA, phospholipids) [33]
962	962		962		962		958	–4	PO₃ ^2-stretching (DNA, RNA) [33]
888	888		888		888		888		CH out of plain bending vibrations [35]

Table 3

Average peak position, maximum absorbance value, peak area, and FWHM. *p < 0.05, versus Control group

Mean ± SEM
Con.				MCI				ADI				ADII				ADIII
Position (cm^-1)	Max abs. (a.u.)	Area	FWHM	Position (cm^-1)	Max abs. (a.u.)	Area	FWHM	Position (cm^-1)	Max abs. (a.u.)	Area	FWHM	Position (cm^-1)	Max abs. (a.u.)	Area	FWHM	Position (cm^-1)	Max abs. (a.u.)	Area	FWHM
3277±2.00	0.17±0.02	11.04±1.22	53.87±3.56	3277±3.00	0.17±0.01	10.49±3.22	48.49±4.32	3277±4.00	0.17±0.01	10.92±2.98	52.97±3.21	3277±2.00	0.17±0.01	10.87±3.21	52.71±3.34	3277±2.00	0.17±0.01	10.72±3.21	51.75±3.67
2956±3.00	1.27±0.01	130.87±3.21	79.75±7.21	2950±2.00*	1.25±0.02*	96.46±4.56*	85.52±2.21	2950±3.00*	0.99±0.02*	81.09±7.21*	82.06±1.12	946±4.00*	0.71±0.02*	33.17±4.21*	91.35±5.43*	2946±2.00*	0.70±0.02*	27.72±9.72*	85.22±5.21*
2847±2.00	1.15±0.01	164.55±4.32	372.57±18.98	2847±3.00*	1.11±0.01*	106.47±12.21*	185.93±10.21*	2853±2.00*	0.94±0.02*	103.26±10.76*	259.33±12.38*	2858±4.00*	0.70±0.02*	100.15±8.98*	287.13±18.94*	2858±4.00*	0.70±0.03*	92.34±9.42*	282.37±14.21*
1740±4.00	0.12±0.02	2.83±0.87	24.50±9.91	1740±3.00	0.11±0.01	2.79±1.01	20.01±3.21	1740±2.00	0.11±0.01	1.78±0.98	20.19±2.34	1740±5.00	0.10±0.01	1.50±0.54	23.63±4.32	1740±3.00	0.10±0.02	1.27±0.97	21.69±3.28
1650±2.00	1.97±0.02	123.01±4.98	56.27±7.21	1650±2.00	1.97±0.02	125.34±2.26	55.18±5.32	1650±3.00	1.97±0.02	123.41±7.32	54.62±99.45	1650±4.00	1.97±0.03	121.29±4.32	57.75±8.09	1644±3.00*	1.97±0.01	124.48±11.75	56.68±7.32
1560±3.00	1.95±0.03	113.09±9.67	59.29±7.54	1559±2.00	1.94±0.02	105.61±3.32	51.38±5.32	1555±2.00*	1.92±0.03	104.09±7.21	51.72±12.34	1552±3.00*	1.87±0.02*	97.30±5.98	56.80±11.32	1551±3.00*	1.82±0.01*	91.09±8.26	54.34±7.29
1376±2.00	0.79±0.02	40.25±2.23	56.30±5.32	1376±3.00	0.57±0.02*	38.31±6.12	62.73±8.43*	1376±3.00	0.57±0.02*	33.17±3.67*	65.27±6.43*	1376±4.00	0.54±0.03*	25.93±3.43*	54.54±21.37	1376±2.00	0.51±0.02*	20.59±4.32*	44.81±7.21
1290±3.00	0.52±0.01	36.98±2.12	148.84±15.78	1290±3.00	0.32±0.02*	12.75±5.23*	114.93±12.32*	1294±2.00	0.30±0.03*	10.87±3.21*	111.63±7.89*	1297±3.00*	0.30±0.01*	9.91±3.47*	55.98±7.65*	1300±3.00*	0.29±0.01*	8.99±1.87*	46.39±16.32*
1078±2.00	0.15±0.01	77.03±4.56	146.47±9.92	1080±1.00	0.12±0.01*	61.14±4.11*	64.87±12.21*	1080±3.00	0.06±0.02*	49.76±3.67*	63.99±9,87*	1082±1.00*	0.05±0.01*	26.95±3.32*	96.64±8.32*	1084±2.00*	0.05±0.01*	19.38±3.21*	89.63±8.92*
962±1.00	0.96±0.02	5.54±2.11	49.16±7.65	962±2.00	0.81±0.02*	3.16±1.03	42.56±5.21	962±2.00	0.66±0.01*	2.68±1.12*	46.92±3.46	962±3.00	0.30±0.02*	2.46±0.87*	35.56±2.26*	958±2.00*	0.19±0.02*	1.91±0.61*	39.99±2.72*
888±4.00	0.07±0.01	3.71±1.09	10.91±2.29	888±2.00	0.07±0.00	2.21±1.87	10.92±0.32	888±3.00	0.07±0.00	1.87±0.89	10.81±0.56	888±4.00	0.01±0.02	2.71±0.89	11.71±0.87	888±1.00	0.01±0.02	2.66±1.23	10.04±4.32

Peak shifts as well as differences in the maximum absorbance value suggest structural changes in the molecules. Therefore, to obtain information about changes in the protein structure, we calculated the second derivative of the FTIR region corresponding to amide I vibrations (1700 cm^-1 - 1600 cm^-1), because they are very sensitive to changes in secondary protein structures (Fig. 2). In the second derivative of CSF Control FTIR spectra between 1700 cm^-1 and 1600 cm^-1 (Fig. 3a), we observed peaks corresponding to the secondary structure of protein vibrations of β-sheets (1600 cm^-1 to 1620 cm^-1), β-turns (1670 cm^-1 to 1690 cm^-1), and α-helices (1650 cm^-1) [27]. In the second derivative of the FTIR spectra for MCI and AD CSF (Fig. 2), we observed a shift of peaks corresponding to secondary protein structures between 1700 cm^-1 and 1600 cm^-1, compared with the Control CSF spectrum. Second derivatives of the FTIR spectrum of the Control group (Fig. 2, red spectrum) showed vibrations at 1629 cm^-1, 1678 cm^-1, and 1692 cm^-1 corresponding to the β-harmonic secondary protein structure and a peak at 1651 cm^-1 corresponding to the α-helix structure. In the case of MCI (Fig. 2, pink spectrum), the peaks at 1651 cm^-1 and 1678 cm^-1 were shifted approximately 2 cm^-1 and 6 cm^-1, respectively, compared with the Control group. Furthermore, in the second derivative of the MCI FTIR spectrum (Fig. 2, pink spectrum), the peak at 1692 cm^-1 was absent. In comparison with the Control group, the second derivative of FITR spectrum of the ADI group (Fig. 2, green spectrum) showed shifted peaks at 1651 cm^-1 and 1678 cm^-1 toward higher wavenumbers, while the peak at 1692 cm^-1 was shifted toward a lower wavenumber. In the case of ADII (Fig. 2, blue spectrum), the peaks at 1629 cm^-1, 1651 cm^-1, 1678 cm^-1, and 1692 cm^-1 were shifted approximately 2 cm^-1, 1 cm^-1, 2 cm^-1, and -2 cm^-1, respectively, compared with the Control group. The second derivative of the ADIII FTIR spectrum (Fig. 2, black spectrum) showed that the peaks at 1629 cm^-1, 1651 cm^-1, and 1678 cm^-1 had shifted approximately 10 cm^-1, 2 cm^-1, and 6 cm^-1, respectively, compared with the Control group. In the second derivative of the ADIII FTIR spectrum, the peak at 1692 cm^-1 was absent.

Fig.2

Second derivative of CSF FTIR spectral region between 1700 cm^-1 and 1600 cm^-1: (a) Control; (b) MCI; (c) ADI; (d) ADII; (e) ADIII groups.

Fig.3

Deconvolution of amide I FTIR region (1700, 1600 cm^-1) for samples: (a) Control; (b) MCI; (c) ADI; (d) ADII; (e) ADIII groups.

To determine the percentage of secondary α and β structures and the α – β ratio, we performed deconvolution of the amide I FTIR region (Fig. 3). In Fig. 3, a different number of band components, as well as a different shape of these components for each of the samples were observed. In the Control group (Fig. 3a) and ADII group (Fig. 3d), seven components were observed, while in the MCI group (Fig. 3b), there were only six components. Deconvolution of the amide I FTIR region in the ADI group (Fig. 3c) showed eight components, and for ADIII (Fig. 3e) only five components. Moreover, in each sample, the shapes of the obtained components were different; therefore, the area of components corresponding to the α and β structures, respectively, were calculated and the α – β ratio was calculated (Table 4).

Table 4

Peak area, percentage value, and ratio between α and β structures (Mean±SEM). *p < 0.05, versus Control group

	Peak area		Percentage %		α-helix - β-sheet ratio
Sample	α-helix	β-harmonica	α-helix	β-harmonica
Con.	41.50±7.51	21.68±3.45	65.69±18.09	34.31±15.91	1.91±0.31
MCI	35.33±5.67	21.64±5.76	62.02±15.73	37.98±26.92	1.63±0.72
ADI	22.25±5.5*	19.75±4.7	52.98±24.98	47.02±24.10	1.13±0.61
ADII*	23.59±7.98*	45.99±6.34*	33.90±24.48*	66.10±13.79*	0.51±0.17*
ADIII*	8.80±1.21*	30.97±3.34*	22.13±13.75*	77.87±10.78*	0.28±0.07*

Table 4 shows a significantly higher value of the α-helix structures for the ADI, ADII, and ADIII groups compared with the Control group. Moreover, the α-helix - β-sheet ratio of the Control, MCI, and ADI groups were not statistically different, while the α – β ratios for the ADII and ADIII groups were significantly different compared with the Control group. Miller et al. [55] calculated the second derivative of the FTIR spectra for AD patients and analyzed the amide I FTIR region, which showed differences between Control subjects and AD patients. Our results are very similar (Figs. 2, 3); the changes in the amide I region could be responsible for the structural changes in proteins that are caused by AD or MCI. During AD progression, different types of secretases from a variety of peptides, including Aβ peptides, degrade amyloid protein precursor protein. These peptides are formed not only outside the neuron, but also within the neuron (i.e., intracellularly). The peptides are formed by degradation of the AβT peptide (Aβ total) and the increase in β protein structures [56 –58]. We noticed an increase of the β protein structure along with worsening of the disease stage, e.g., in ADI the amount of the β structure was smaller than that in ADII and ADIII.

Previous findings suggest that proteins and/or lipids may be useful biomarkers of AD. To our knowledge, however, there are no published data regarding the protein-lipid balance. Therefore, we calculated the average protein and lipid fraction in MCI and AD patients and then calculated the lipid-protein balance (Fig. 4), and found that the protein balance and structure strongly depends on the lipid balance.

Fig.4

Mean values of lipids (a), proteins (b), and the ratio between lipid and protein values (c) in the analyzed groups. *p < 0.05, versus Control group.

Figure 4 shows the mean values of lipids (a), proteins (b), and lipid-protein balance (c) in the analyzed groups. We observed a significant decrease in the lipid fraction in the CSF from MCI and AD patients compared with the Control group, while proteins were significantly decreased only in the CSF collected from ADII and ADIII patients. Moreover, all analyzed groups showed significant changes in the lipid-protein balance in comparison with the Control group. Interestingly, the lipid-protein balance decreases with progression of AD, that is, the amount of lipids decreased with progression of the disease (Fig. 4a). Changes in the quantity of phospholipids negatively affect the fluidity of the cell membrane, consequently inducing structural changes in the membrane and transport proteins [59]. Membrane lipids are also involved in cell signaling and determine the localization or function of the protein within the membrane, thus affecting synaptic throughput [60]. The consequence of these changes is impaired protein function. Therefore, we suggest that the lipid-protein balance, and not just the protein or lipid fraction, can be a biologic marker of AD.

Cluster analysis obtained for all analyzed infrared ranges (Fig. 5a) showed that the Control group was a homogeneous group. Moreover, the HCA analysis revealed that the patients with MCI, ADI, ADII, and ADIII were not homogenous. One cluster of these patients contained between 3 and 5 samples from the MCI and AD groups. Moreover, samples from the MCI and ADI groups showed higher similarity than those from MCI and ADII or ADIII groups. Unfortunately, based on the HCA analysis obtained for all of the analyzed infrared ranges, it was not possible to distinguish between patients with AD at different stages. Moreover, Griebe et al., using FTIR to diagnose AD from the CSF of patients, showed that it is possible to distinguish between AD and Control patients with 88.5% sensitivity and 80% specificity [61]. Panchal et al. performed HCA analysis for AD and Control patients in all infrared ranges and in a selected range (1480–910 cm^-1). They showed better ability to distinguish between Control and AD patients using the selected range (98.4%) compared to the entire range (82%) [30]. Therefore, we performed HCA analysis for the lipid-protein balance (Fig. 5b), which revealed five homogeneous groups. Importantly, HCA analysis obtained from the calculated lipid-protein balance grouped all Control, MCI, ADI, ADII, ADIII patients into their respective groups. Moreover, MCI patients were more similar to ADI patients, and ADII patients were more similar to ADIII patients. The Control group was clearly different from the other groups.

Fig.5

HCA of CSF from Control, MCI, ADI, ADII, and ADIII groups. Results obtained from all analyzed infrared ranges (a) and the calculated lipid-protein balance (b).

The sensitivity and specificity values of PCA-LDA analysis for all analyzed groups (Control, MCI, and AD) are shown in Table 5. These results suggest that FTIR spectroscopy can be used to distinguish CSF collected from healthy and non-healthy people. Our results revealed that the sensitivity of our model was between 72% and 95%, and the specificity was between 61% and 92%.

Table 5

Discriminant analysis results using PCA-LDA and leave-one-out cross validation

Sensitivity Specificity	Control	MCI	ADI	ADII	ADIII
Control	100%	75%	82%	81%	95%
	100%	92%	90%	91%	80%
MCI	75%	100%	85%	93%	98%
	92%	100%	75%	99%	78%
ADI	82%	85%	100%	83%	81%
	90%	75%	100%	74%	71%
ADII	81%	93%	83%	100%	72%
	91%	99%	74%	100%	61%
ADIII	95%	98%	81%	72%	100%
	80%	78%	71%	61%	100%

Based on the results of the present study and the literature regarding the roles of proteins and lipids in AD development, changes in the lipid-protein balance and an increase in the amount of β secondary protein structures may be useful biomarkers of AD. The quantitative and qualitative changes in these chemical compounds are clearly discernable in FTIR spectra, making this technique a potentially useful diagnostic tool for AD.

Footnotes

ACKNOWLEDGMENTS

Funding for this study was provided by the University of Rzeszow. The study was performed as part of the ‘Centre for Innovative Research in Medical and Natural Sciences’ project realized by University of Rzeszow, co-financed within the Regional Operational Programme for the Podkarpackie Province for 2007–2013, contract number UDA-RPPK.01.03.00-18-004/12-00.

Authors’ disclosures available online ().

References

Alzheimer’s Association (2017) Alzheimer’s disease facts and figures. Alzheimers Dement 13, 325–373.

What is Alzheimer’s disease?, https://www.alzheimers.org.uk/about-dementia/types-dementia/alzheimers-disease

Waldemar

, Dubois

, Emre

, Georges

, McKeith

, Rossor

, Scheltens

, Tariska

, Winblad

(2007) Recommendations for the diagnosis and management of Alzheimer’s disease and other disorders associated with dementia: EFNS guideline. Eur J Neurol 14, E1–E26.

Tabert

, Liu

, Doty

, Serby

, Zamora

, Pelton

, Marder

, Albers

, Stern

, Devanand

(2005) A 10-item smell identification scale related to risk for Alzheimer’s disease. Ann Neurol 58, 155–160.

McKhann

, Drachman

, Folstein

, Katzman

, Price

, Stadlan

(1984) Clinical diagnosis of Alzheimer’s disease: Report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34, 939–944.

Mendez

(2006) The accurate diagnosis of early-onset dementia. Int J Psychiatry Med 36, 401–412.

Klafki

, Staufenbiel

, Kornhuber

, Wiltfang

(2006) Therapeutic approaches to Alzheimer’s disease. Brain 129, 2840–2855.

(2006) Dementia: Supporting people with dementia and their carers in health and social care. Quick reference guide. National Institute for Health and Clinical Excellence, London.

Schroeter

, Stein

, Maslowski

, Neumann

(2009) Neural correlates of Alzheimer’s disease and mild cognitive impairment: A systematic and quantitative meta-analysis involving 1351 patients. Neuroimage 47, 1196–1206.

10.

Berchtold

, Cotman

(1998) Evolution in the conceptualization of dementia and Alzheimer’s disease: Greco-Roman period to the 1960s. Neurobiol Aging 19, 173–189.

11.

Matas

, Glehn

, Fernandes

, Soares

(2013) Cerebrospinal fluid analysis in the context of CNS demyelinating diseases. Arq Neuropsiquiatr 71, 685–688.

12.

Depciuch

, Kaznowska

, Zawlik

, Wojnarowska

, Cholewa

, Heraud

, Cebulski

(2016) Application of Raman spectroscopy and infrared spectroscopy in the identification of breast cancer. Appl Spectrosc 70, 251–263.

13.

Depciuch

, Kaznowska

, Golowski

, Koziorowska

, Zawlik

, Cholewa

, Szmuc

, Cebulski

(2017) Monitoring breast cancer treatment using a Fourier transform infrared spectroscopy-based computational model. J Pharm Biomed Anal 143, 261–268.

14.

Zawlik

, Kaznowska

, Cebulski

, Kolodziej

, Depciuch

, Vongsvivut

, Cholewa

(2016) FPA-FTIR microspectroscopy for monitoring chemotherapy efficacy in triple-negative breast cancer. Sci Rep 6, 37333.

15.

Lewis

, Lewis

, Ghosal

, Bayliss

, Lloyd

, Wills

, Godfrey

, Kloer

, Mur

(2010) Evaluation of FTIR spectroscopy as a diagnostic tool for lung cancer using sputum. BMC Cancer 10, 640.

16.

Diem

, Chiriboga

, Yee

(2000) Infrared spectroscopy of human cells and tissue. VIII. Strategies for analysis of infrared tissue mapping data and applications to liver tissue. Biopolymers 57, 282–290.

17.

El-Tawil

, Adnan

, Muhamed

, Othman

(2008) Comparative study between Pap smear cytology and FTIR spectroscopy: A new tool for screening for cervical cancer. Pathology 40, 600–603.

18.

Wehbe

, Pineau

, Eimer

, Vital

, Loiseau

, Deleris

(2010) Differentiation between normal and tumor vasculature of animal and human glioma by FTIR imaging. Analyst 135, 3052–3059.

19.

Khanmohammadi

, Ansari

, Garmarudi

, Hassanzadeh

, Garoosi

(2007) Cancer diagnosis by discrimination between normal and malignant human blood samples using attenuated total reflectance-Fourier transform infrared spectroscopy. Cancer Invest 25, 397–404.

20.

Gok

, Aydin

, Sural

, Zorlu

, Bayol

, Severcan

(2016) Bladder cancer diagnosis from bladder wash by Fourier transform infrared spectroscopy as a novel test for tumor recurrence. J Biophotonics 9, 967–975.

21.

Tiraboschi

, Hansen

, Thal

, Corey-Bloom

(2004) The importance of neuritic plaques and tangles to the development and evolution of AD. Neurology 62, 1984–1989.

22.

Nussbaum

, Ellis

(2003) Alzheimer’s disease and Parkinson’s disease. N Engl J Med 348, 1356–1364.

23.

Pesz

, Misiak

, Sçsiadek

(2011) Genetyczne podstawy zespołów otępiennych. In Choroby otępienne. Teoria i praktyka, Leszek

, ed. Continuo, Wrocław, pp. 45–48.

24.

Chun

, Johnson

GVW

(2007) The role of tau phosphorylation and cleavage in neuronal cell death. Front Biosci 12, 733–756.

25.

Nabers

, Ollesch

, Schartner

, Kötting

, Genius

, Haußmann

, Klafki

, Wiltfang

, Gerwert

(2015) An infrared sensor analysing label-free the secondary structure of the Abeta peptide in presence of complex fluids. J Biophotonics 8, 584–596.

26.

Meutter

, Derfoufi

, Goormaghtigh

(2016) Analysis of protein microarrays by FTIR imaging. Biomed Spectrosc Imaging 5, 145–154.

27.

Correia

, Lopes

, Silva

, Rosa

, Henriques

, IvonneDelgadillo , Silva

, Nunes

(2016) FTIR spectroscopy - a potential tool to identify metabolic changes in dementia patients. J Alzheimers Neurodegener Dis 2, 007.

28.

Wong

, Braidy

, Poljak

, Pickford

, Thambisetty

, Sachdev

(2017) Dysregulation of lipids in Alzheimer’s disease and their role as potential biomarkers. Alzheimers Dement 13, 810–827.

29.

Corder

, Saunders

, Strittmatter

, Schmechel

, Gaskell

, Small

, Roses

, Haines

, Pericak-Vance

(1993) Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer’s disease in late onset families. Science 261, 921–923.

30.

Panchal

, Loeper

, Cossec

, Perruchini

, Lazar

, Pompon

, Duyckaerts

(2010) Enrichment of cholesterol in microdissected Alzheimer’s disease senile plaques as assessed by mass spectrometry. J Lipid Res 51, 598–605.

31.

Lazar

, Bich

, Panchal

, Desbenoit

, Petit

, Touboul

, Dauphinot

, Marquer

, Laprevote

, Brunelle

, Duyckaerts

(2013) Time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging reveals cholesterol overload in the cerebral cortex of Alzheimer disease patients. Acta Neuropathol 125, 133–144.

32.

Kosicek

, Hecimovic

(2013) Phospholipids and Alzheimer’s disease: Alterations, mechanisms and potential biomarkers. Int J Mol Sci 14, 1310–1322.

33.

Gonzalez-Dominguez

, Garcia-Barrera

, Luis Gomez-Ariza

(2014) Combination of metabolomic and phospholipid-profiling approaches for the study of Alzheimer’s disease. J Proteomics 104, 37–47.

34.

Salman

, Mordechai

(2017) The potential of infrared spectroscopy and multivariate analysis of peripheral blood components as a validated clinical test for early diagnosis of Alzheimer’s disease. Insighs Med Phys 2, 1–4.

35.

Kiskis

, Fink

, Nyberg

, Thyr

, Li

, Enejder

(2015) Plaque-associated lipids in Alzheimer’s diseased brain tissue visualized by nonlinear microscopy. Sci Rep 5, 13489.

36.

Depciuch

, Sowa-Kucma

, Nowak

, Dudek

, Siwek

, Styczen

, Parlinska-Wojtan

(2016) Phospholipid-protein balance in affective disorders: Analysis of human blood serum using Raman and FTIR spectroscopy. A pilot study. J Pharm Biomed Anal 131, 287–296.

37.

Depciuch

, Sowa-Kucma

, Nowak

, Szewczyk

, Doboszewska

, Parlinska-Wojtan

(2017) The role of zinc deficiency-induced changes in the phospholipid-protein balance of blood serum in animal depression model by Raman, FTIR and UV-vis spectroscopy. Biomed Pharmacother 89, 549–558.

38.

Depciuch

, Sowa-Kućma

, Misztak

, Szewczyk

, Nowak

, Pankiewicz

, Parlińska-Wojtan

(2006) Olfactory bulbectomy-induced changes in phospholipids and protein profiles in the hippocampus and prefrontal cortex of rats. A preliminary study using a FTIR spectroscopy. Pharmacol Rep 68, 521–528.

39.

Fukuyama

, Yoshida

, Yanagisawa

, Shimizu

(1999) A study on the differences between oral squamous cell carcinomas and normal oral mucosas measured by Fourier transform infrared spectroscopy. Biospectroscopy 5, 117–126.

40.

Garidel

(2002) Mid-FTIR-Microspectroscopy of stratum corneum single cells and stratum corneum tissue. Phys Chem Chem Phys 4, 5671–5677.

41.

Hendler

, Barnett

, Dracheva

, Bose

, Levin

(2003) Purple membrane lipid control of bacteriorhodopsin conformational flexibility and photocycle activity - An infrared spectroscopic study. Eur J Biochem 270, 1920–1925.

42.

Jung

(2000) Insight into protein structure and protein-ligand recognition by Fourier transform infrared spectroscopy. J Mol Recognit 13, 325–351.

43.

Yonar

, Ocek

, Tiftikcioglu

, Zorlu

, Severcan

(2018) Relapsing-remitting multiple sclerosis diagnosis from cerebrospinal fluids via Fourier transform infrared spectroscopy coupled with multivariate analysis. Sci Rep 8, 1025.

44.

Dovbeshko

, Gridina

, Kruglova

, Pashchuk

(2000) FTIR spectroscopy studies of nucleic acid damage. Talanta 53, 233–246.

45.

Cakmak

, Togan

, Severcan

(2006) 17Beta-estradiol induced compositional, structural and functional changes in rainbow trout liver, revealed by FT-IR spectroscopy: A comparative study with nonylphenol. Aquat Toxicol 77, 53–63.

46.

Liao

, Rak

, Lund

, Unger

, Platt

, Albensi

, Hirschmugl

, Gough

(2013) Synchrotron FTIR reveals lipid around and within amyloid plaques in transgenic mice and Alzheimer’s disease brain. Analyst 138, 3991–3997.

47.

Choo

, Wetzel

, Halliday

, Jackson

, LeVine

, Mantsch

(1996) In situ characterization of beta-amyloid in Alzheimer’s diseased tissue by synchrotron Fourier transform infrared microspectroscopy. Biophys J 71, 1672–1679.

48.

Bayari

, Sevinc

, Tiftikcioglu

, Zorlu

, Severcan

(2015) Molecular differentiation of mild cognitive impairment and dementia cases in Alzheimer’s disease using vibrational spectroscopy. In 9th World Congress International Brain Research Organization.

49.

Schaeberle

, Levin

, Lewis

(2000) Biological vibrational spectroscopic imaging. In Infrared and Raman Spectroscopy of Biological Materials, Gremlich

, ed. CRC Press, Boca Raton, pp. 231–258.

50.

Han

, Fagan

, Cheng

, Morris

, Xiong

, Holtzman

(2003) Cerebrospinal fluid sulfatide is decreased in subjects with incipient dementia. Ann Neurol 54, 115–119.

51.

Kosicek

, Zetterberg

, Andreasen

, Peter-Katalinic

, Hecimovic

(2012) Elevated cerebrospinal fluid sphingomyelin levels in prodromal Alzheimer’s disease. Neurosci Lett 516, 302–305.

52.

Walter

, Korth

, Hilgert

, Hartmann

, Weichel

, Fassbender

, Schmitt

, Klein

(2004) Glycerophosphocholine is elevated in cerebrospinal fluid of Alzheimer patients. Neurobiol Aging 25, 1299–1303.

53.

Mulder

, Wahlund

, Teerlink

, Blomberg

, Veerhuis

, van Kamp

, Scheltens

, Scheffer

(2003) Decreased lysophosphatidylcholine/phosphatidylcholine ratio in cerebrospinal fluid in Alzheimer’s disease. J Neural Transm 110, 949–955.

54.

Mielke

, Haughey

, Bandaru

VVR

, Zetterberg

, Blennow

, Andreasson

, Johnson

, Gleason

, Blazel

, Puglielli

, Sager

, Asthana

, Carlsson

(2014) Cerebrospinal fluid sphingolipids, beta-amyloid, and tau in adults at risk for Alzheimer’s disease. Neurobiol Aging 35, 2486–2494.

55.

Miller

, Bourassa

, Smith

(2013) FTIR spectroscopic imaging of protein aggregation in living cells. Biochim Biophys Acta 1828, 2339–2346.

56.

Gouras

, Willen

, Tampellini

(2012) Critical role of intraneuronal Abeta in Alzheimer’s disease: Technical challenges in studying intracellular Abeta. Life Sci 91, 1153–1158.

57.

Umeda

, Tomiyama

, Sakama

, Tanaka

, Lambert

, Klein

, Mori

(2011) Intraneuronal amyloid beta oligomers cause cell death via endoplasmic reticulum stress, endosomal/lysosomal leakage, and mitochondrial dysfunction in vivo. J Neurosci Res 89, 1031–1042.

58.

Wirths

, Bayer

(2012) Intraneuronal A beta accumulation and neurodegeneration: Lessons from transgenic models. Life Sci 91, 1148–1152.

59.

Maes

, Smith

, Scharpe

(1995) The monocyte T- lympocyte hypothesis of major depression. Psychoneuroendocrinology 20, 111–116.

60.

Litman

, Niu

, Polozova

, Mitchell

(2001) The role of docosahexaenoic acid containing phospholipids in modulating G protein-coupled signaling pathways - Visual transduction. J Mol Neurosci 16, 237–242.

61.

Griebe

, Daffertshofer

, Stroick

, Syren

, Ahmad-Nejad

, Neumaier

, Backhaus

, Hennerici

, Fatar

(2007) Infrared spectroscopy: A new diagnostic tool in Alzheimer disease. Neurosci Lett 420, 29–33.

FTIR Spectroscopy of Cerebrospinal Fluid Reveals Variations in the Lipid: Protein Ratio at Different Stages of Alzheimer’s Disease

Abstract

Keywords

INTRODUCTION

MATERIALS AND METHODS

Materials

FTIR spectroscopy

Deconvolution of the amide I region (1600–1700 cm-1)

Lipid-protein balance

Computational analysis

Statistical analysis

RESULTS AND DISCUSSION

Footnotes

ACKNOWLEDGMENTS

References

Deconvolution of the amide I region (1600–1700 cm^-1)