Clinical Samples

The Ethical Committee of the Medical Faculty of the University of Cologne reviewed and approved the protocol of this study and the procedures for sample collection and analysis. All study participants gave their written informed consent. All clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki. CSF and serum samples were collected from drug-naïve patients diagnosed with first-episode paranoid schizophrenia or brief psychotic disorder due to duration of illness (DSM-IV 295.30 [n = 54] or 298.8 [n = 4], total n = 58), from 16 patients diagnosed with major depressive disorder (DSM-IV 296.22–4; 296.33–4), from five patients with OCD (DSM-IV 300.3), and from demographically matched healthy volunteers (n = 80) (Tables 1 and 2). Fresh-frozen prefrontal cortex tissue (Brodmann area 9) from gray matter of eight schizophrenia and eight well-matched control individuals was obtained from the Neuropathology Consortium of the Stanley Brain Collection (Stanley Medical Research Institute, http://www.stanleyresearch.org). A detailed history of antipsychotic drug use of included patients is listed in Tables S1 and S2.

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Table 1.

Demographic Details of Subjects in the First CSF SELDI Experiment

https://doi.org/10.1371/journal.pmed.0030428.t001

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Table 2.

Demographic Details of Subjects in the CSF Validation Sample Set

https://doi.org/10.1371/journal.pmed.0030428.t002

Preparation of CSF Samples for SELDI Analysis

An amount (5 μl) of each CSF sample was applied to protein chips with different chemical properties at various pH conditions. The best condition was chosen at pH 9.0 on strong anion exchanger Q10 chips, based on number and separation of peaks resolved. Briefly, the array spots were pre-activated twice with binding buffer (100 mM Tris-HCl [pH 9.0]) at room temperature for 10 min on a shaker (frequency = 600 rpm). An amount (5 μl) of binding buffer was added to each spot prior to the addition of 5 μl of CSF sample. The protein chips were incubated on a shaker for 60 min at room temperature, then washed twice with binding buffer and once with H₂O, and air-dried. The chips were then sequentially treated twice with 0.6 μl of a 100% saturated sinapinic acid (3,5-dimethoxy-4-hydroxycinnamic acid) in 50% acetonitrile and 0.5% trifluoroacetic acid. The chips were analyzed with the Ciphergen ProteinChip Reader (Ciphergen ProteinChip System Series 4000, Ciphergen Biosystems, http://www.ciphergen.com). Each sample was analyzed twice to confirm reproducibility in identifying the differentially expressed proteins and to ensure stability of instrument performance.

SELDI–Time-of-Flight Mass Spectrometer Analysis

The arrays were analyzed with the Ciphergen ProteinChip System Series 4000 (Ciphergen Biosystems). Mass spectra of proteins were generated by using an average of 254 laser shots at a laser intensity of 1,800 arbitrary units. For data acquisition, the detection size ranged between 2.5 and 200 kDa. The laser was focused at 10 kDa. The mass-to-charge ratio (m/z) of each of the proteins captured on the array surface was determined relative to external calibration standards (Ciphergen Biosystems): bovine insulin (5,733.6 Da), human ubiquitin (8,564.8 Da), bovine cytochrome c (12,230.9 Da), bovine superoxide dismutase (15,591.4 Da), horseradish peroxidase (43,240 Da), and BSA (66,410 Da). The data were analyzed with ProteinChip data analysis software version 3.0 and Ciphergen Express Software 3.0 (Ciphergen Biosystems). Matrix attenuation was set at 2,500 Da, and shot sequence was set with one warming shot at 2,000 nJ followed by four shots at 1,800 nJ. The Ciphergen Express Software 3.0 was used to compile all spectra and to autodetect quantified mass peaks within a mass range of between 2,500 and 200,000 Da, and the signal-to-noise threshold was set at 5. Peak labeling was completed using second-pass peak selection with 0.2% of the mass window, and estimated peaks were added. The peak information of all spectra was exported for further statistical analysis.

Peptide and Protein Identification

Typically, 10-μl CSF samples from both the control and schizophrenia groups were applied to Q10 protein chips at pH 9.0 (50 mM Tris-HCl). Proteins/peptides bound to the chip were eluted with 5 μl of elution buffer (30% acetonitrile, 50 mM sodium acetate [pH 3.0]) and desalted using a C18 Ziptip (Millipore, Billerica, Massachusetts, United States) according to the manufacturer's instructions. The peptides were eluted with 0.1% formic acid/50% aqueous acetonitrile (2 μl) and were further examined by MALDI mass spectrometry for the confirmation of the enriched 3,959-Da peak in the CSF sample of the patients with schizophrenia. A CSF sample with a high 3.959 peptide level was also loaded onto a C18 nano-column for online LC-ESI-MS/MS on a QSTAR quadrupole-TOF mass spectrometer (Applied Biosystems, Foster City, California, United States) for de novo sequencing.

For identification of protein biomarkers, CSF proteins were purified from pooled CSF by a combination of anion exchange chromatography (HyperD, Ciphergen Biosystems) followed by SDS-PAGE. CSF protein (~1 mg) was diluted in two volumes of buffer (50 mM Tris-HCl [pH 9]) and applied to the HyperD column equilibrated with the same buffer. The bound protein was then eluted by step gradients using pH 7.0, 6.0, 5.0, 4.0, and 3.0 buffers (Expression Difference Mapping Kit, Ciphergen Biosystems). The fractions were concentrated using Centricon YM10 (Millipore) to ~20 μl, followed by SDS-PAGE analysis (NuPAGE Bis-Tris gel, 4%–12%, Invitrogen, Carlsbad, California, United States). The band expected to correspond to the SELDI peak was cut from the gel. Approximately one third of the excised gel band was used for passive elution of the protein followed by SELDI analysis to confirm that the correct protein had been isolated. The remaining two thirds of the gel band was in-gel digested with trypsin (1:50; Promega, Madison, Wisconsin, United States) overnight at room temperature. The resulting peptide mixtures were then analyzed by LC-ESI-MS/MS (QSTAR, Applied Biosystems), and the protein was identified by database searching using Mascot software (Matrix Science, London, United Kingdom).

S-cysteinylated or S-glutathionylated isoforms of proteins were confirmed by comparing the spectra before and after on-chip reduction using β-mecaptoethanol. In brief, CSF protein and peptide binding was performed as described above and, in the final step, each spot was washed with 100 μl of 1 mM HEPES [pH 7.5]. The proteins and peptides on the chips were then reduced with 1/40 β-mecaptoethanol (1 μl) for 30 min at room temperature. Water (1 ml) was added onto each spot and evaporated. This procedure was repeated twice. Matrix was then added, and data were acquired using ProteinChip Reader (Ciphergen ProteinChip System Series 4000).

Quantitative analysis of transthyretin in human serum samples was carried out by enzyme-linked immunosorbent assay (ELISA). Samples were defrosted from −80 °C and mixed on a benchtop vortexer for 10 min before experimental work commenced. All samples were assayed blind to the clinical conditions. The identities of all subjects were kept blind by code numbers until all biochemical analyses had been completed.

Controls and patient-derived human serum samples were diluted 1,000 times with PBS (pH 7.4) (Sigma, St. Louis, Missouri, United States), and 100 μl of each diluted sample was loaded onto ELISA Maxisorb plates (Nunc, http://www.nuncbrand.com) together with transthyretin standards (Sigma) before being incubated for 1 h. All samples were tested in duplicate. After washing with washing buffer (0.03% Tween 20 in PBS), the plates were blocked with 100 μl of 5% skimmed-milk powder for 60 min. Transthyretin antibody (100 μl) (DakoCytomation, Glostrup, Denmark, 1:500 diluted in 2.5% skimmed-milk powder) was incubated in 96-well plates for 60 min. The plates were washed four times with washing buffer followed by the addition of 100 μl of secondary antibody (anti-rabbit HPP-linked IgG [Cell Signaling Technology, Beverly, Massachusetts, United States] 1:2000) to each well and incubated for 60 min. After washing with washing buffer three times, 100 μl of substrate (TMB One solution, Promega) was added into each well, and the mixture was incubated at room temperature for 10 min. The plate was read at 450 nm (Model 680, Bio-Rad, Hercules, California, United States).

Western Blot Analysis

The preparation of human brain samples for Western blot analysis and the details of performing Western blotting were as described previously [8]. In brief, equivalent amounts of protein (30 μg per sample) were resolved by electrophoresis on 10% polyacrylamide gels and transferred onto nitrocellulose, which was then incubated with primary antibody (anti-transthyretin [DakoCytomation] 1:1,000) or anti-VGF (Santa Cruz Biotechnology [Santa Cruz, California, United States] 1:500) in 3% milk–PBS overnight at 4 °C, followed by incubation of a secondary antibody (HRP-conjugated anti-rabbit secondary antibody [Cell Signaling Technology, 1:2500]) at room temperature for 1 h. Enhanced chemiluminescence (LumiGlu, Cell Signaling Technology) was used to detect signals from the blot. Consistency of protein loading and transfer was determined by Ponseau S staining.

Statistical Analysis

Multivariate statistical analysis techniques including principal component analysis (PCA), partial least-squares discriminate analysis (PLS-DA), and partial least-squares (PLS) were employed to summarize the data output from Ciphergen Express. Holdout cross-validation was performed three times so that the sensitivity and specificity of the PLS model could be estimated. In each of the three rounds of holdout cross-validation, one third of the samples were randomly selected to form the validation data and the remaining samples were used as the training data. All multivariate analyses were performed using SIMCA-P+ 10 (Umetrics, http://www.umetrics.com). Sensitivity is defined as the proportion of true positives that it detects of all the positives, and specificity is defined as the proportion of true negatives that it detects of all the negatives. Where appropriate, a t-test was performed using the Statistical Package for Social Scientists (SPSS/PC+; SPSS, Chicago, Illinois, United States). Correlation analysis was performed using Matlab 7.0.1 (Mathwork, Natick, Massachusetts, United States). Two-way analysis of variance (ANOVA) was performed using the “ANOVA” function from the CAR package in R (R project, http://cran.r-project.org). Type III sums of squares was used as this study had an unbalanced experimental design.

Alterations of CSF Protein/Peptide Profiles in First-Onset, Drug-Naïve, Paranoid Schizophrenia Patients

In a first set of experiments, we examined protein/peptide profiles of CSF samples from 41 first-onset, drug-naïve, paranoid schizophrenia patients and 40 demographically matched healthy volunteers using SELDI mass spectrometry. CSF proteins and peptides were profiled using Q10 (strong anion exchanger) chips at pH 9.0. An example of the CSF protein/peptide profile of a healthy volunteer is shown in Figure 1A. Approximately 75 peaks can be readily detected with a signal-to-noise ratio of >5 under this binding condition. Plots of PLS-DA scores based on SELDI spectra of CSF samples showed a clear differentiation between healthy volunteers and drug-naïve patients with first-onset, paranoid schizophrenia (Figure 1B and 1C). Similar results were found using PCA (unpublished data). The loading coefficients indicated that the peak with an m/z of 3,959 contributed the most to the separation of the two groups (Figure 1D).

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Figure 1. Protein/Peptide Profiling of CSF Samples from First-Onset, Drug-Naïve Schizophrenia Patients Using SELDI Mass Spectrometry

(A) Typical CSF protein/peptide spectrum using an anion exchanger chip (Q10; 50 mM Tris-HCl [pH 9.0]) showing the m/z range of 2,500–15,000 from a healthy volunteer is shown.

(B) The peak intensity of protein/peptide peaks from SELDI spectra were analyzed using PCA and PLS-DA models. A 3D PLS-DA scores plot indicates clusters of healthy volunteers (in blue) and untreated, drug-naïve schizophrenia patients (in red).

(C) and (D) PLS-DA scores and loadings plots. The scores plot is similar to (B) but only the first two components were used to discriminate between healthy controls and patients. The loading plot as shown in (D) indicates the key protein/peptide peaks contributing the most towards the separation as shown in (C). The 3,959-Da VGF23–62 peptide was found to predominantly contribute to the separation between the two groups.

https://doi.org/10.1371/journal.pmed.0030428.g001

Further analyses showed a 2.8-fold elevation of an m/z = 3,959 peptide in first-onset, drug-naïve, paranoid schizophrenia patients as compared to the demographically matched control group (p = 10⁻⁸, t-test; Figure 2). In addition, the loading plot also showed significant reductions in peaks of m/z = 23,490 and clusters of peaks between m/z = 13,600–14,000 and m/z = 6,800–7,300 (~20% decreases, p < 0.01, t-test), all of which also contributed to the separation between classes. We have identified the proteins and peptides at m/z = 3,959 and in the cluster of m/z = 13,600–14,000. The m/z = 6,800–7,300 biomarkers are likely to be influenced by the doubly charged ion signals of the m/z = 13,600–14,000 protein cluster and may only reflect the altered levels in m/z = 13,600–14,000 (Figure S1). The sensitivity and specificity of this model based on holdout cross validation was 80% and 95%, respectively (Table 3; see Materials and Methods). These alterations were not affected by most demographic variables including age, gender, alcohol, and illicit drug use, although we identified a correlation with smoking (Table S3). However, a disease-associated increase in VGF levels remains significant after controlling for smoking effects using a two-way ANOVA (Table S3.5).

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Figure 2. Up-Regulation of m/z = 3,959 VGF23–62 Peptide Peak in CSF from First-Onset, Drug-Naïve Schizophrenia Patients

Forty healthy volunteers and 41 untreated paranoid schizophrenia CSF samples were included in this study. A clear up-regulation of the peak at 3,959 Da was observed (as shown in [A], with five representative spectra from each group). Figure 2B reveals the relative intensity of a 3,959-Da peak in healthy volunteers and first-onset, drug-naïve schizophrenia patients. Figure 2C shows a Western blot analysis of VGF protein in the prefrontal cortex of age-matched controls and patients with schizophrenia. Eight schizophrenia and eight control brains (prefrontal cortex, age matched) were randomly selected, and Western analysis was performed using a polyclonal antibody against the C-terminal region of the VGF protein. VGF was found up-regulated in four patients with schizophrenia (out of eight patients), while no signal was detected in the control patients. This experiment was repeated twice on the same samples under identical conditions, except that a different batch antibody from the same company was used. Similar results were obtained. For demographic details, see Table 4.

https://doi.org/10.1371/journal.pmed.0030428.g002

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Table 3.

Sensitivity and Specificity of PLS Models Calculated from the Two Independent Experiments

https://doi.org/10.1371/journal.pmed.0030428.t003

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Table 4.

Demographic Details of Subjects Investigated for VGF Expression by Western Blot Analysis of Post-Mortem Brain

https://doi.org/10.1371/journal.pmed.0030428.t004

Identification of the 3,959-Da Peptide as a VGF Fragment

The 3,959-Da peptide was purified using identical conditions as employed in the profiling experiment followed by C18-Ziptip purification (see Figure S2). The eluted proteins and peptides from Ziptips were subjected to MALDI to confirm the mass, and the peak was identified as a 40-amino acid peptide using de novo sequencing (Figure S2). This peptide sequence was mapped to amino acids 23–62 of the native VGF protein (in bold underlined, Figure 3A), a neurosecretory protein known to regulate metabolism [9] and synaptic plasticity [10], and is immediately next to a predicted secretory signal peptide. Interestingly, another VGF-derived peptide with an m/z of 3,690 in CSF that did not appear to differ between control and schizophrenia patients was identified (p = 0.85, t-test). This peptide showed the same peptide sequence as the 40-amino acid VGF peptide except that three amino acids were absent at the first N-terminal (Figure 3B; de novo sequencing data not shown). This indicates that the 40-amino acid VGF peptide with the “APP” sequence in the amino terminus may have specific functions and/or may be linked to the pathophysiology of schizophrenia.

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Figure 3. Mapping and Identification of the Biomarker Peptide Derived from the Native VGF Protein

The 3,959-Da “schizophrenia” peptide was mapped to amino acids 23–62 of the native VGF protein (in bold; underlined), immediately next to a predicted secretory signal peptide (using InterProScan: http://www.ebi.ac.uk/cgi-bin/iprscan).

Another native VGF-derived peptide did not show differential expression between healthy volunteers and patients with schizophrenia (as shown in Figure 2A). This peptide has an identical sequence except that it is three amino acids shorter at the N-terminus compared to the “disease-specific” VGF peptide.

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Increased VGF Protein Expression in Post-Mortem Brain Tissue from Patients with Schizophrenia

To examine whether VGF protein expression is also altered in schizophrenic post-mortem brain tissue, we performed Western blot analysis on gray matter in prefrontal cortex tissue from eight patients with schizophrenia and eight demographically matched control subjects. Western blot analysis using an antibody recognizing the C-terminal sequences of VGF showed a strong expression in four out of eight patients while, in control brains, the level of VGF was below the detection level (Figure 2C).

Identification of the 13.6–14-kDa Protein Cluster as Transthyretin

The protein cluster between 13.6 and 14.1 kDa contains four peaks (Figure 4A and 4B), three of which were consistently down-regulated in CSF from first-onset, drug-naïve schizophrenia patients (p < 0.01; Figure 4B, lower panel). Studies have suggested that these peaks may be from S-cysteinylated derivatives of transthyretin protein [11,12], a thyroid hormone–binding protein that transports thyroxine from the bloodstream to the brain. On-chip reduction of CSF peptide/protein performed using β-mercaptoethanol at room temperature showed that the three peaks m/z = 13,741, 13,875, and 13,923 were reduced to a single peak (Figure 4C), confirming that they are derived from the same protein. To determine the protein's identity, a pair of CSF samples from a healthy volunteer and a patient with schizophrenia were applied to an anion exchanger column (HyperD, Ciphergen Biosystems) and eluted with pH 9–pH 3 buffers. A major band (~13–15 kDa) was eluted in the pH 3 fraction (Figure 4D, left panel). The band was confirmed to be the peak cluster at around 13.6–14 kDa in the SELDI spectrum by eluting the protein from the band and running on an NP20 chip to match the mass (Figure 4E). This protein was then digested with trypsin and sequenced using LC-MS/MS. The protein was identified as transthyretin (Figure 4D, right panel).

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Figure 4. Down-Regulation of Three Different Forms of Transthyretin in CSF from First-Onset, Drug-Naïve Schizophrenia Patients

Examples of CSF spectra from healthy volunteers and patients with schizophrenia within 6–17 kDa are shown in (A). The peak cluster indicated (red cursor) is enlarged in (B). Statistical details of each sub-peak are listed in the table presented just below (B). On-chip reduction of CSF peptide/protein with β-mecaptoethanol at room temperature showed that the three peaks 13,741, 13,875, and 13,923 Da were reduced into a single peak (C), suggesting that they are different S-cysteinylated derivatives of the same protein. To identify the protein, CSF samples from a healthy volunteer and a patient with schizophrenia were applied to an anion exchanger column (HyperD, Ciphergen Biosystems) and eluted with pH 9–pH 3 buffers. A major ~14–15-kDa band was eluted in the pH 3 fraction (D, left panel). The protein was identified as transthyretin using LC-MS/MS (D, right panel) and the sequence coverage is shown in red. Note, that all the peptides shown have an E-score of < 0.05, indicating a high probability for the assigned protein identity. In addition, the band was confirmed to be the peak cluster at around 13.5–14 kDa in the spectra by eluting the protein(s) from the gel and SELDI analysis (E).

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Down-Regulation of Transthyretin in Serum Samples from the Same Subjects as well as Prefrontal Cortex Tissue from Patients with Schizophrenia

It has been estimated that 3% of transthyretin in the ventricular CSF and 10% of the transthyretin in lumbar CSF are derived from blood [13]. To evaluate the contribution of blood transthyretin to the changes found in CSF in patients with schizophrenia, we also investigated serum transthyretin levels taken from the same individuals (at the same time as the CSF was collected) on whom we had reported in this study (for demographics, see Tables 1 and 2) using ELISA. We found a moderate, but significant, decrease in transthyretin levels in sera from patients with schizophrenia compared to controls (15% decrease, p = 0.0007, t-test) (Figure 5A). However, we found no correlation between CSF and serum transthyretin from the same individuals, suggesting that the transthyretin level is regulated independently in CSF and serum (Figure 5B). Interestingly, we found an ~40% down-regulation of transthyretin levels in post-mortem prefrontal cortex from patients with schizophrenia compared with controls using Western blot (Figure 5C).

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Figure 5. Transthyretin Levels in Sera of First-Onset, Drug-Naïve Schizophrenia Patients and Prefrontal Cortex Post-Mortem Tissue from Schizophrenia Patients

The serum samples from the same patients whose CSF protein profiles were measured in Figures 1 and 6 were included in this study. Figure 5A shows serum tranythyretin levels in patients with schizophrenia significantly decreased by ~15% compared to control subjects. Data are shown in mean ± standard deviation, and the asterisk denotes p = 0.0007 (t-test). Figure 5B indicates no correlation between serum transthyretin and CSF SELDI signals from one of the transthyretin isoforms (m/z = 13,741) in the second sample set (for demographics, see Table 2). Similar results were found when comparing with signals from other isoforms in CSF (unpublished data). Figure 5C shows an ~40% decrease of transthyretin expression in prefrontal cortex of five patients with schizophrenia and in five control subjects. For demographic details, see Table 5.

https://doi.org/10.1371/journal.pmed.0030428.g005

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Figure 6. PLS-DA Analysis of CSF Protein/Peptide Profiles from an Independent Validation Sample Set Containing 17 First-Onset, Drug-Naïve Schizophrenia Patients and 40 Healthy Volunteers

The demographic details of this sample set are listed in Table 2. The PLS-DA scores plot shows a separation between patients (in red) and healthy volunteers (in blue). The loadings plot indicates similar key variables (e.g., the 3,959-Da VGF23–62 peptide [in red] and transthyretin protein signals between 13,600 and 14,000 [in blue]) identified in the first experiment (see Figure 1).

https://doi.org/10.1371/journal.pmed.0030428.g006

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Table 5.

Demographic Details of Subjects Investigated for Transthyretin Expression by Western Blot Analysis of Post-Mortem Brain

https://doi.org/10.1371/journal.pmed.0030428.t005

Validation of Protein/Peptide Biomarkers in an Independent Sample Set

We validated our biomarker model in Figure 1 using an independent sample set comprising a further 17 first-onset, drug-naïve schizophrenia patients and 40 demographically matched healthy volunteers. These samples were run using identical conditions as in the previously described experiment. PLS-DA scores and loadings plots showed a very similar result as found in Figure 1 with predominant changes in m/z = 3,959, the cluster of 6,800–7,300-Da peptides (likely influenced by the doubly charged ions from transthyretin proteins), and the cluster of 13,600–14,000 proteins (Figure 6). This suggests that these identified alterations in CSF proteins and peptides are a consistent finding and thus may reflect genuinely the early pathophysiology of schizophrenia. The sensitivity and specificity of this model were 88% and 95%, respectively (Table 3). As with the results from the first study, we did not find an influence of most demographic variables on these alterations, with the exception of a potential effect of cannabis use (urine-positive drug screen) on CSF transthyretin levels (p = 0.08; Table S3). However, as for the smoking effect, disease-associated changes in transthyretin levels remain significant after controlling for cannabis use using a two-way ANOVA (Table S3.6–S3.8).

Disease Specificity of the Schizophrenia Biomarker Panel

As many psychiatric disorders and neurological diseases share similar symptoms with schizophrenia, it is crucial to test the disease specificity of these protein/peptide biomarkers. We therefore examined CSF samples of three additional psychiatric/neurological disorders, namely depression, OCD, and Alzheimer disease. CSF samples from 16 patients with depression and five patients with OCD, together with CSF samples from 40 healthy volunteers, were analyzed with SELDI-MS using the condition described previously. We found a partial separation between controls and patients with depression (Figure 7A); the key variables contributing to the separation were the above-mentioned VGF23–62 peptide and a secretogranin II peptide 529–566 (Figure 7B; sequencing data not shown). No difference was found between controls and the five patients with OCD (unpublished data). These results indicate that the VGF peptide alone may not be a specific marker for a given psychiatric disease (possibly due to overlapping disease processes, which are not least implied by the fact that patients with a family history of affective disorder have an increased risk for developing schizophrenia, and vice versa). Our data thus suggest that disease specificity may be achieved with a panel of biomarkers (Table S4).

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Figure 7. CSF Proteomic Profiles of Depression Patients

CSF samples from 16 patients with depression (green triangles), together with 40 healthy volunteers (blue dots), were analyzed with SELDI-MS using Q10 anion exchanger protein chips at pH 9 (50 mM Tris-HCl).

(A) The scores plot shows a partial separation between controls and patients with schizophrenia.

(B) The loading plot indicates VGF23–62 and a depression-specific secretogranin II 529–566 peptide to be the key protein/peptide peaks contributing the most towards the group separation.

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As three out of 16 depression patients presented with psychotic symptoms, we analyzed the psychotic and non-psychotic subgroups separately to examine whether the identified biomarkers are specific for schizophrenia or merely for acute psychosis. No differences could be observed between these two subgroups, and the psychotic subgroup did not co-cluster with schizophrenia patients, suggesting that VGF23–62 and transthyretin peptide/protein changes are biomarkers for schizophrenia rather than acute psychosis (Figure S3). However, as the sample size of patients with psychotic depression was very small, the specificity of the biomarker signature will need to be confirmed on a larger set of samples from patients with psychotic depression, bipolar affective psychosis, and other neuropsychiatric illnesses that present with psychotic states.

In addition, we also analyzed CSF samples from ten patients with Alzheimer disease and from ten age-matched and sex-matched controls using identical experimental conditions. No significant differences between the two groups were identified, although some clear alterations were found using other experimental conditions (unpublished data).

Limitations and Clinical Potential of the Findings

One of the key limitations of the present study is that no (or very few) samples from patients with non-schizophrenia psychosis were investigated. The inclusion of samples from patients with psychosis in the context of bipolar disorder would be particularly desirable. However, it represents a great challenge to collect CSF from patients with acute mania (as the patient must give informed consent and must lie still for safe CSF collection).

A larger number of patient samples, ideally collected in different hospital settings, and the inclusion of samples from patients with “prodromal” schizophrenia (most patients with schizophrenia present with vague mental disturbances for months or years prior to developing overt psychosis) as well as patients with chronic schizophrenia, would increase the confidence in the findings and would have prognostic utility. The responsiveness of the biomarker signature to drug treatment would also be of great interest, as this could be used as a surrogate end point in drug-discovery studies and could help to differentiate drug-responders from non-responders and generally may allow for the development of personalized treatment approaches.

The main limitation of the findings with regards to translation into clinical practice is that, in most countries (with the exception of Germany and some Scandinavian countries), CSF is not routinely collected in patients with first-onset psychosis. A CSF-based diagnostic assay would therefore require a sea-change in clinical practice.

The identification of serum biomarkers would be much more desirable, but is technically challenging. However, our laboratory has identified a number of putative serum biomarkers, which are currently being followed up and validated (unpublished data).

Once sensitive and specific markers are established, diagnostic assays can be developed to validate the identified biomarkers on a larger sample set and, ultimately, in the clinical setting. A likely scenario could be a multi-analyte array configuration based on up to ten indicative biomarkers. Such systems will be configured initially into bench-top instruments and, subsequently, into disposable, easy-to-use diagnostic platforms for the physician's office or the patient's bedside based on hand-held readers. The sensors will be designed with an in-built ability to allow the collected data to be electronically transmitted directly to the physician for interpretation.