Characterization of Cerebrospinal Fluid via Data-Independent

Article pubs.acs.org/jpr

Cite This: J. Proteome Res. XXXX, XXX, XXX−XXX

Characterization of Cerebrospinal Fluid via Data-Independent Acquisition Mass Spectrometry Katalin Barkovits,† Andreas Linden,† Sara Galozzi,† Lukas Schilde,† Sandra Pacharra,† Brit Mollenhauer,‡ Nadine Stoepel,† Simone Steinbach,† Caroline May,† Julian Uszkoreit,† Martin Eisenacher,† and Katrin Marcus*,† †

Ruhr University Bochum, Medical Faculty, Medizinisches Proteom-Center, Universitaetsstrasse 150, D-44801 Bochum, Germany Paracelsus-Elena-Klinik, Klinikstraße 16, D-34128 Kassel, Germany

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‡

S Supporting Information *

ABSTRACT: Cerebrospinal fluid (CSF) is in direct contact with the brain and serves as a valuable specimen to examine diseases of the central nervous system through analyzing its components. These include the analysis of metabolites, cells as well as proteins. For identifying new suitable diagnostic protein biomarkers bottom-up data-dependent acquisition (DDA) mass spectrometry-based approaches are most popular. Drawbacks of this method are stochastic and irreproducible precursor ion selection. Recently, dataindependent acquisition (DIA) emerged as an alternative method. It overcomes several limitations of DDA, since it combines the benefits of DDA and targeted methods like selected reaction monitoring (SRM). We established a DIA method for in-depth proteome analysis of CSF. For this, four spectral libraries were generated with samples from native CSF (n = 5), CSF fractionation (15 in total) and substantia nigra fractionation (54 in total) and applied to three CSF DIA replicates. The DDA and DIA methods for CSF were conducted with the same nanoLC parameters using a 180 min gradient. Compared to a conventional DDA method, our DIA approach increased the number of identified protein groups from 648 identifications in DDA to 1574 in DIA using a comprehensive spectral library generated with DDA measurements from five native CSF and 54 substantia nigra fractions. We also could show that a sample specific spectral library generated from native CSF only increased the identification reproducibility from three DIA replicates to 90% (77% with a DDA method). Moreover, by utilizing a substantia nigra specific spectral library for CSF DIA, over 60 brain-originated proteins could be identified compared to only 11 with DDA. In conclusion, the here presented optimized DIA method substantially outperforms DDA and could develop into a powerful tool for biomarker discovery in CSF. Data are available via ProteomeXchange with the identifiers PXD010698, PXD010708, PXD010690, PXD010705, and PXD009624. KEYWORDS: proteomics, data-independent acquisition mass spectrometry, DIA, cerebrospinal fluid, CSF, spectral library

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INTRODUCTION Cerebrospinal fluid (CSF) surrounds the brain, as well as the spinal cord, and is therefore often used to diagnose central nervous system (CNS) disorders or to further confirm an established diagnosis.1 Especially for CNS disorders like infectious or neurodegenerative diseases, CSF is routinely examined including the analysis of proteins, blood cells, viruses, bacteria, chemicals, or other substances.2 Aside from these routine clinical diagnostics, CSF is intensely researched and constantly analyzed using various methods in a wide range of neurological disorders. The aim of these studies has been to identify new protein biomarkers helping to ensure a clinical diagnosis, to monitor disease progression, or to evaluate therapeutic interventions.3 Various proteomic studies utilizing liquid chromatography−mass spectrometry (LC−MS) were executed for protein biomarker © XXXX American Chemical Society

discovery in CSF including characterization of the global CSF proteome4−7 and quantitative differential profiling, for example in the field of HIV,8 Multiple Sclerosis9−11 Alzheimer’s disease (AD)12 or Parkinson’s disease (PD) (reviewed in ref 13). Those studies faced several challenges: (1) 80% of the proteins in the CSF originate from blood and only 20% from the CNS,3,14 which can mask specific CNS proteins. (2) High content of albumin (about 60%) and immunoglobulins (about 10%)3 results in a large dynamic range of protein concentration (12 orders of magnitude15). (3) Blood protein contamination of the CSF during lumbar puncture can skew the CSF proteome.16,17 Received: May 3, 2018

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DOI: 10.1021/acs.jproteome.8b00308 J. Proteome Res. XXXX, XXX, XXX−XXX

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Journal of Proteome Research

almost all precursor ions are used for fragmentation and are available for quantification with the advantage of more complete and reproducible data. Several DIA based studies are shown to overcome limitations of DDA.31−33 Muntel et al. compared DDA and DIA for urine proteomics studies and observed with DIA a doubling in the number of identified peptides (2536 for DDA vs 5219 for DIA) and proteins (622 for DDA vs 1200 for DIA) per sample. Furthermore, superior coefficients of variation for DIA with ∼8% compared to DDA with ∼16% were shown. Similar observations were reported for a quantitative serum proteome profiling from 1 μL of blood serum, in which with a DIA approach 3-fold more proteins could be identified and quantified compared to DDA. In summary, with DDA 103 and with DIA 325 proteins were quantified without depleting or fractionating the sample. Among the DDA data set 80 proteins and within the DIA set 193 proteins showed a CV < 10%.34 In this study, we evaluate the use of DIA for native CSF analytics with particular focus on spectral library generation for data analysis, DIA data reproducibility and identification of brain originated proteins. For the first time, we show a DIA method for in-depth proteome analysis of CSF utilizing various complementary spectral libraries. Our results illustrate that the implementation of sample specific spectral libraries increases reproducibility. With our DIA approach the number of identified protein groups increases by 140% (DDA 649 protein groups vs 1574 with DIA) and especially more brain originated proteins are identified (64 by DIA vs 11 by DDA).

The large dynamic range of protein concentrations in CSF makes the identification and quantification of low abundant potential protein biomarkers for neurodegeneration and other disorders, like autoimmune diseases, difficult.3 Therefore, different strategies were implemented in LC-MS based studies such as depletion of high abundant proteins (including albumin, IgG, antitrypsin, IgA, transferrin, haptoglobin, fibrinogen, alpha2-macroglobulin, IgM, apolipoprotein AI, apolipoprotein AII, complement C3, and transthyretin) and sample fractionation, like strong cation exchange.4−7 With a combined approach utilizing depletion and fractionation Guldbrandsen et al. and Zhang et al. reported the identification of over 3.000 proteins from CSF.6,7 Both strategies have not only advantages, but also disadvantages. Depletion holds the risk of co-depleting low abundant proteins, hindering their detection.18,19 Recently, it was shown that depletion of CSF resulted in the loss of over 20 proteins suggested to be biomarker candidates for neurological diseases like junction plakoglobin, a marker for atherosclerosis or plasminogen a marker for Alzheimer’s disease.14,20,21 In the case of fractionation strategies, high volumes of starting material are required (>500 μL), though in most cases only low volumes of CSF are available, when analyzing samples from clinical cohorts. Moreover, any additional step during sample processing may result in a decrease of reproducibility.22 Therefore, strategies with less complex sample preparation are needed for CSF-based quantitative proteomics. Hence, to allow the detection of minor CSF proteome changes when comparing patients to healthy controls, a robust, as well as highly reproducible quantitative method, has to be applied specially to facilitate quantification of proteins affected by disease progression in the brain and released into the CSF. Various label-free approaches in quantitative neuroproteomics have been performed with CSF in the last years aiming to identify suitable disease related biomarkers.23 Most studies utilized the precursor ion abundance quantification method that uses data-dependent acquisition (DDA). Here, the precursor ion is used for quantification and the product ion information is used for identification in contrast to the fragment ion-based quantification in data independent acquisition (DIA) where quantification is based on the product ions. In DDA, an initial survey scan is performed and the most N intensive precursor ions (Top N) are selected for subsequent fragmentation.24 Due to preferred selection of high intensity ions within Top N approaches, low intensive ions might not be fragmented and hence not included in data analysis. This can lead to technical variances among different samples and results in inconsistent data sets. Moreover, in almost all proteomic biomarker studies concerning neurodegenerative diseases low fold changes (below 2) are reported for differentially abundant proteins and mostly multiple testing correction like p-value adjustment were not performed.13,25,26 Hence, contrary observations in the regulation of potential protein candidates are described in different studies: for example within two PD biomarker studies for vitamin D binding protein D, an upregulation was found in the study of Yin et al. while Abdi et al. observed a downregulation of this protein in PD patients.27,28 Due to intrinsic biological variances and marginal differences in the CSF proteome between patients with CNS disorders versus healthy controls (as mentioned above) the technical variance should be kept as low as possible. With DIA an alternative platform for label-free quantification has been established.29,30 With this technique,

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EXPERIMENTAL PROCEDURES

Ethical Statement

This study was approved by the ethics committee of the Physician’s Board Hesse, Germany (approval no. FF89/2008), which is registered at the German Register for Clinical Trials (DRKS00000540) according to the WHO Trial Registration Data Set. Participants provided written, fully informed consent. The relevant documents relating to this process are archived in the Paracelsus-Elena-Klinik in Kassel, Germany. Only the anonymous data and materials from the participants were provided to the scientists carrying out the research. The data concerning this study were stored separately from the hospital charts of the patients. The ethics committee of the Universitätsklinik Würzburg (approval no. 78/99) and of the Ruhr-Universität Bochum (approval no. 4760−13) approved all studies and fully informed consents were signed by each next-of-kin. Subjects and Samples

This CSF proteome study included four human subjects suffering hydrocephalus. Samples were selected as previously described by Mollenhauer et al.35 All subjects (volunteer cerebrospinal fluid donors) were recruited in the ParacelsusElena-Klinik in Kassel, Germany. Blood was collected with BD Vacutainer system tubes (BD, Franklin Lakes, NJ) by venous puncture and processed according to published standard operating procedures (SOPs).35 Aliquots were stored at −80 °C within 30 min after the venous puncture. Human substantia nigra tissue was provided by the Universitätsklinik Würzburg. Sample Preparation

CSF samples − Five native CSF samples were used for insolution digestion. Twenty μL of CSF (a pool of CSF samples from 4 hydrocephalus patients) were mixed with the same B


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performed overnight. For this, three times cell lysate (40 μg) was mixed with acetone at the ratio of 1:4 (v/v) in (J.T. Baker) and stored at −20 °C overnight. Finally, gel electrophoresis was performed. 40 μg precipitated proteins were loaded on a NuPAGE 4−12% BisTris gel as well as on a NuPAGE 3−8% Tris-Acetate gel (both from Fisher Scientific) and further processed as described for CSF proteins above.

volume of 0.2% RapiGest (Waters, Eschborn, Germany). After adding dithiotreitol (DDT) to a final concentration of 5 mM, samples were incubated for 30 min at 60 °C. For cysteine alkylation iodoacetamide (IAA) was added to a final concentration of 15 mM and incubated for 30 min in the dark. Protein digestion was performed with trypsin (Serva, Heidelberg, Germany) at an enzyme to substrate ratio of 1:50 at 37 °C overnight. On the next day, the digestion was stopped and RapiGest precipitated by adding trifluoroacetic acid (TFA) to a final concentration of 0.5% by subsequent incubation for 45 min at 37 °C. After centrifugation, the supernatant was collected and the peptide concentration was determined by amino acid analysis (AAA) as described by Plum et al.36 According to AAA, 500 ng per sample were taken for MS analysis and spiked with 1 μL HRM calibration peptides (Biognosys, Schlieren, Switzerland). All five samples were used for DDA measurements and with three out of the five CSF samples DIA was performed. In addition, CSF was fractionated by gel electrophoresis: 40 μg CSF proteins were loaded on a NuPAGE 4−12% BisTris gel (Fisher Scientific, Schwerte, Germany). Gel electrophoresis was performed at 200 mA and 200 W per gel, the voltage profile was as following: 50 V for 15 min and 180 V for 50 min. After this, gels were stained with Coomassie Blue Staining (SimpleBlue SafeStain, Thermo Fisher Scientific) according to the manufacturer’s protocol. The protein lane was cut into single fractions (in total 15 fractions), destained and pH adjusted by incubating the gel pieces for 10 min with 50 mM ammonium bicarbonate (Sigma-Aldrich, Steinheim, Germany), as well as 50% (v/v) 50 mM ammonium bicarbonate with 50% (v/v) 100% acetonitrile (Merck KGaA, Darmstadt, Germany), three times alternately. After the second incubation with 50 mM ammonium bicarbonate, samples were treated with 50 μL 10 mM DTT (AppliChem GmbH, Darmstadt, Germany) for 1 h at 56 °C and with 50 μL 50 mM IAA (Merck KGaA) for 45 min at room temperature before the destaining protocol was continued. Finally, gel pieces were dried in a vacuum concentrator (RVC2−25CD plus, Martin Christ Gefriertrocknungsanlagen, Osterode am Harz, Germany) and resuspended in 6 μL 50 mM ammonium bicarbonate. Digestion was initiated by adding 6 μL of trypsin solution (0.012 μg/μL, Promega Corp.) and was performed overnight. The digestion was stopped, and peptides eluted by incubating the gel pieces two times for 15 min with 30 μL of a 1:1 solution containing 100% acetonitrile and 0.1% (v/v) TFA (Merck KGaA) in an ice-cooled ultrasonic bath. Samples were transferred in MS vials, dried in a vacuum concentrator and resuspended in 15 μL 0.1% (v/v) TFA. Finally, samples were spiked with 1 μL of iRT peptides (Biognosys), before they were analyzed via HPLC-MS. Substantia nigra samples, Substantia nigra tissue was fractionated by gel electrophoresis and in-gel digested. For this, 100 mg substantia nigra tissue was homogenized in 400 μL RIPA buffer (cell signaling). Following, glass beads were added to the lysate and a sonication of the sample was performed with an ultrasound probe (Potter S. Homogenizer, B. Braun; amplitude 90, power 50%, 4 times 1 min). After this, the sample was washed off the glass beads (Ø 1.25−1.65 nm; Ø 0.25−0.5 nm; Carl Roth) with 50 μL ultrapure water and sonicated in an ice-cooled ultrasonic bath six times for 10 s. After this, the protein concentration of the cell lysate was determined via Bradford assay (Sigma-Aldrich) according to the manufacturer’s protocol. An acetone precipitation was

DDA Analysis

Samples were analyzed by LC-MS/MS, as previously described.37,38 In brief, a Dionex nanoHPLC system Ultimate 3000 (Thermo Fisher Scientific, Bremen, Germany) with a PepMap C18 (75 μm x 50 cm, particle size 2 μm, pore size 100 Å; Thermo Scientific, Rockford, IL, USA) was used as analytical column. Peptide separation was performed by applying a stepwise 3 h gradient of buffer A (0.1% FA) and buffer B (84% ACN, 0.1% FA) with a flow rate of 400 nL· min−1. The gradient was run from 5 to 40% buffer for 180 min followed by a 5 min washing step at 95% buffer B and a 5 min equilibration step at 5% buffer B, the column oven temperature was set to 60 °C. Subsequently, peptides were ionized by electrospray ionization (ESI) and injected into a Q Exactive HF mass spectrometer (Thermo Fisher Scientific, Bremen, Germany). The instrument was operated in data-dependent acquisition (DDA) mode performing HCD fragmentation of the top 10 abundant precursor ions at 27% NCE. The mass range was set to 350−1400 m/z with a resolution of 60 000 at 200 m/z (AGC 3e6, 80 ms maximum injection time, 2.2 m/z wide isolation window). The capillary temperature was set to 250 °C and the spray voltage to 1600 V. The lock mass polydimethylcyclosiloxane (445.120 m/z) was used for internal recalibration. The fragment analysis was performed in an orbitrap mass analyzer with a resolution of 30 000 at 200 m/z (AGC 5e5, 120 ms maximum injection time). The LC-MS/MS analysis of CSF fractionation and substantia nigra fractions were performed with slight differences. The HPLC gradient was a 117 and 141 min segmented gradient from 5−52% (v/v) and 5−60% (v/v) buffer B, respectively. For substantia nigra, the mass range of the mass spectrometric analysis was set to 350−1,100 m/z with a resolution of 120,000 and maximum injection time was 20 ms and the isolation window was set as 1.6 m/z. HCD fragmentation was performed for both at 24%, 27%, and 30% NCE. The fragment analysis was performed as described previously, with the only difference that the maximum injection time was set to 25 ms. The mass spectrometry proteomics data from the DDA analysis have been deposited to the ProteomeXchange Consortium via the PRIDE39 partner repository with the data set identifier PXD010698 for native CSF, PXD009624 for fractionated CSF and PXD010690 for substantia nigra. Generation of Spectral Libraries

The generated .RAW files were directly analyzed by Proteome Discoverer (PD) 1.4 (Thermo Fisher Scientific). The recorded spectra were matched to peptides by the search algorithm Mascot 2.5 (,40 Matrix Science, London, UK) using a database containing the human Swiss-Prot part of UniProtKB41 (version 2016_05) and additionally the HRM calibration peptide sequences (all together 20,205 protein entries). Trypsin was selected as digestion enzyme with two maximum missed cleavage sites. Precursor mass tolerance was set to 5 ppm and fragment mass tolerance to 20 mmu. Oxidation at methionine was set as dynamic modification, carbamidomethylation as static modification at cysteine. Using the target decoy PSM C


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Figure 1. Experimental Workflow. Four different libraries were generated for the identification of CSF DIA maps including library A (from native CSF sample), library B (from native CSF and fractionated CSF), library C (from native CSF and human substantia nigra tissue) and library D (from native CSF, fractionated CSF and human substantia nigra tissue). Properties of spectral libraries are shown in the scheme and for each library the utilized sample, the total number of including protein groups as well as peptides and the number of included samples for library generation are indicated. Furthermore, DDA- and DIA-performances were compared in general, as well as, with respect to their capability of the identification of brain originated proteins using the human proteome map (HPM) database. For both, DIA and DDA, three CSF replicates were analyzed each.

at 200 m/z (AGC 1e6, auto maximum injection time, default charge state 2). This ended up in a cycle time of approximately 2.2 s. The .RAW files were converted into .HTRMS files by using the Raw to HTRMS Converter Professional provided by Biognosys. Those .HTRMS files were analyzed via Spectronaut 9.0 in the “Review” perspective by applying the four different libraries in single experiments. The following settings were applied: dynamic XIC RT Extraction Window, enabled Interference Correction, enabled Cross Run Normalization, quantitation on MS2 level, automatic Protein Inference Workflow, and p-value normal distribution estimator. All results were filtered at a Q value of 0.01 on peptide and protein levels. The mass spectrometry proteomics data and the quantitative data tables have been deposited to the PRIDE repository with the data set identifier PXD009623.

validator, decoy database search was performed with target FDR of 1%. Four different spectral libraries were generated using Spectronaut 9.0 (Biognosys). Therefore, different .MSF files from PD were uploaded to the “Prepare” perspective of Spectronaut and treated with the following settings: for identification, only peptides with high confidence level (equals a false discovery rate (FDR) of 1%) were considered and protein inference was performed by Spectronaut. Spectral Library Filters were set to no missed cleavage filters, no peptide charge filters, number of best fragment ions between 3 and 6 in a mass range of 300−1800 m/z. The first library (library A) consisted of five .MSF files of native CSF samples (5069 precursors). The second library (library B) was generated using library A plus 15 .MSF files of analyzed CSF fractions (11556 precursors). The third library (library C) contained library A and additional 54 .MSF files originating from human substantia nigra analyses (67551 precursors). The fourth library (library D) contained all files from library B and additional 54 .MSF files originating from human substantia nigra analyses (69364 precursors, Figure 1). The spectral libraries have been deposited to the PRIDE repository with the data set identifier PXD009623.

Data Analysis

The Human proteome map (HPM) was used for classification of brain specific proteins.42 In brief, the list of DDA and DIA specific proteins were imported into the HPM and correlated to the relative expression profile within 17 adult tissues. Results were visualized by a heat map. Raw files were uploaded to the Proteome Discoverer, version 1.4 (Thermo Fisher, San Jose, CA), and searched with Mascot algorithms against the human part of the UniProtKB Swiss-Prot protein database (release 2016_5). Searching parameters included: two maximum missed cleavages, static modification with cysteine carbamidomethylation and methionine oxidation as a dynamic modification, precursor mass tolerance of 5 ppm, fragment mass tolerance of 20 mmu. Target decoy PSM validator was performed with target FDR of 1%. A Venn diagram was prepared using Venny 2.1.43

DIA Analysis

Native CSF was measured with each setup on the already mentioned instrument with identical HPLC settings. Here, the Q Exactive HF was run in data-independent (DIA) mode, subdividing a mass range of 400−1000 m/z into 30 isolation windows with an equal width of 20 m/z and one full scan with a loop cycle of 10. The full MS was performed with a resolution of 30 000 at 200 m/z (AGC 3e6, 80 ms maximum injection time). DIA was operated with a resolution of 15 000 D


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RESULTS AND DISCUSSION

Limits of CSF Analytics Using DDA

One aim of our analysis was to investigate, for the first time, if a DIA method leads to increased protein identification when analyzing CSF samples, especially when no sample depletion or fractionation was applied. During our DDA method development, we investigated different tryptically digested CSF sample amounts (150 ng, 500 ng, and 1 μg) for MS analyses. We found a total amount of 500 ng peptides optimal for the analysis of CSF detecting consistently over 700 protein groups (Supporting Information (SI) Figure 1) while with 150 ng a high variability was observed in the number of identified proteins between different samples, 1 μg peptide did not result in increased protein identification (700 identified protein groups, data not shown). As CSF is mainly composed of serum albumin (about 60%), higher peptide quantities on column might increase protein identifications but with the risk of carryover of serum albumin. Therefore, we examined how many protein groups were present in subsequent blank runs (injection without sample, only solvent) after measuring CSF and identified serum albumin, as well as 13 other proteins (SI Figure 2A). In the second and third blank, the number of proteins decreased to 5 and 2 proteins, respectively. In order to consider carry over effects on quantitative analysis, we compared the abundance of albumin after eight CSF measurements (SI Figure 2B). Therefore, the albumin abundance (using the Mascot protein score as a proxy) from the last CSF samples was set to 100% and the abundances within the blank measurements were relatively correlated to this. In the first blank, a relative abundance of 7% was observed and in the second blank it was 0.86%, showing the need of blank runs between CSF sample measurements. Further attempts to increase the protein identification by modifying the LC-MS method (data not shown) failed, facing the limit of DDA method optimization for unfractionated CSF samples. In order to examine DIA for the analysis of CSF in this study, all experiments were performed with a CSF pool from four patients with hydrocephalus (Figure 1). Initially, the pool was analyzed with a standard DDA workflow (including in-solution digestion, peptide separation via LC and subsequent online MS analysis) as described in,44 generating three technical replicates. Within all three technical replicates, comparable numbers of peptides and protein groups were identified with an average of 3180 and 573, respectively. A total of 3732 peptides and 649 protein groups were identified cumulatively in all three technical replicates, whereas 497 protein groups were identified consistently in all replicates showing an overlap of 77% (Figure 2A). On peptide level, 2621 peptides were identified in all three replicates resulting in a lower overlap of 70%, as compared to the protein level of 77% (Figure 2B). Similar observation (lower overlap on peptide level for DDA) was reported by Tsou et al. for HEK-293 cells and human liver tissue, showing identification overlap of 38−54% at the peptide level and 69−81% at the protein level.45 Observed differences in the overlap is based on the combination of dynamic exclusion settings and coelution of a different (more abundant) peptide during a DDA analysis.46 This fact can lead to missing MS/MS values, which is a major concern in label free proteomics.47 Bruderer et al. demonstrated that DDA generated 51% missing values, while with the application of DIA only 1.6% missing values were observed in HEK cells.48

Figure 2. Comparative analysis of protein and peptide identifications from DDA. Numbers of protein groups (A) and peptides (B) identified at 1% FDR for three triplicate CSF analyses. Indicated are the individual proteins and peptides for each analysis and in the Venn diagram the overlapping proteins with the respective percentage are shown.

Furthermore, they showed increased peptide and protein identification rates when using a comprehensive DIA workflow.49 For CSF, Pavelek et al. reported in their study using DDA label-free high-resolution shotgun proteomics an initial data set of 922 quantified protein groups and after subsequent filtering (a protein has to be present in ≥50% of samples in at least one cohort without missing value), the final data set reduced to 627 quantifiable protein groups.50 By applying DIA, we want to overcome the limits of DDA for label-free based CSF proteome quantification resulting in increased reproducibility and increased proteome coverage. Therefore, the above-described results from the standard DDA method will be compared in the following with the DIA method. Impact of Comprehensive Library on Identification and Reproducibility Using DIA

The application of an adequate spectral library is a major aspect in DIA workflows. These libraries are generated from DDA measurements and are used as a template for protein identification. In our study, we included substantia nigra tissue in addition to CSF samples for the generation of spectral libraries, in order to enhance the identification rate through more available peptide ion information.3,51 A similar approach to improve the amount of reliable peptide identifications from DIA for human brain tissue showed an increase of the analysis depth of DIA data sets.49 Here, Bruderer et al. used spinal cord and prefrontal cortex from humans and mice and constructed extended mouse and human ad hoc libraries for the analysis of human brain tissue and compared the results with results generated with a pan human spectral library.42,52,53 The data showed that ad hoc library-based analysis revealed a high number of proteins involved in physiological functions associated with spinal cord and prefrontal cortex. To generate spectral libraries, DDA measurements from native CSF (n = 5), fractionated CSF (15 fractions obtained by gel fractionation) and substantia nigra tissue (three times 18 fractions obtained by gel fractionation) were executed and identification was performed using proteome discoverer (version 1.4, settings described above). In total, four different libraries (A to D) were generated (Figure 1). Library A included five DDA analyses of native CSF resulting in 662 E


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Journal of Proteome Research protein groups and 3575 peptides. Library B consisted of DDA analyses of five native CSF samples and fractionated CSF (15 fractions) leading to 1620 protein groups and 8738 peptides. Library C was generated with five DDA analyses of native CSF and fractionated substantia nigra tissue (54 fractions) comprising 5486 protein groups and 49970 peptides. For library D, DDA analysis of five native CSF, fractionated CSF (15 fractions) and fractionated substantia nigra tissue (54 fractions) were used resulting in 5600 protein groups and 51 159 peptides. In the next step, we evaluated the suitability of the generated libraries for protein identification in human CSF. Therefore, DIA maps from three technical replicates of one CSF sample were produced and compared with the standard DDA analysis (likewise from three technical replicates using the same CSF sample as for DIA) (Figure 1). With the latter approach, overall 649, and in average 573, protein groups were identified with three replicates (Figure 3 and Figure 4). Utilizing DIA for

Figure 4. Comparative analysis of protein identifications from DIA utilizing different spectral libraries. Indicated are the total number of protein groups (PGs) identified from three replicates within one analysis and the corresponding overlapping proteins with the respective percentage.

Figure 3. Comparison of different libraries for CSF analysis. The number of peptide ions and proteins identified in DDA and in DIA from CSF samples. For DIA four different spectral libraries (A to D) were used with following DDA analyses. library A: native CSF; library B native CSF samples and fractionated CSF; library C native CSF and fractionated substantia nigra tissue; library D five native CSF, fractionated CSF and fractionated substantia nigra tissue. Each LCanalysis was performed with three replicates. Standard deviation is indicated.

proteins out of 1389), respectively. The lower overlap with the libraries C and D suggests that with the application of DIA (using complementary libraries) so far unidentified proteins can be mapped. One reason for this could be that for DIA, a Q Exactive mass spectrometer was used and the protein set from CSF-PR was generated by an LTQ Orbirap Velos Pro mass spectrometer, which could impact the identification of proteins. Furthermore, the condition for peptide separation is an important aspect for the identification of proteins and it was reported by several groups that longer columns improve peptide separations.55−57 In our study, we used 50 cm separation column, while Guldbrandsen et al. utilized a 15 cm column for the generation of the CSF-PR data. While an increase of the CSF proteome coverage could be achieved utilizing complementary libraries, we observed that the identification reproducibility across the applied libraries strongly varied. DIA with the CSF sample specific library resulted in an identification overlap of 90% on protein level, while with each additional comprehensive library a decrease of reproducibility was observed down to an overlap of 43% with the most comprehensive library C. For quantitative workflows, a high reproducibility is necessary in order to generate valid data. DIA with sample specific libraries were shown to have protein identification overlap in the range of 8−91%, outperforming DDA with 69−81%.45 Hence, DIA could improve results of quantitative CSF analysis when using a sample specific library. This is especially important for biomarker discovery in the field of neuroproteomics, since the total protein concentration in CSF can vary strongly from

three replicates with a CSF specific library (library A), overall 637 and in average 607 protein groups could be identified. In comparison to this, the combined libraries B, C, and D lead in total to the identification of 901, 1574, and 1389 protein groups and in average 764, 1056, and 996 protein groups were identified, respectively. The extension of the libraries with more DDA analyses (including CSF fractionation and substantia nigra samples) increased the numbers of identified protein groups, as well as the peptide identifications. With library C, an enhancement of protein identifications from over 140% using overall identification rate of the three replicates and 86% for the average identification was achieved extending the CSF proteome information to DDA with over 900 proteins. To verify our results we compared our data with the comprehensive CSF Proteome Resource (CSF-PR) repository.54 The CSF-PR consists of a protein set of 3081 proteins obtained by SDS-PAGE, mixed mode reversed phase-anion exchange and hydrazide-based glycopeptide. With our results utilizing the overall identified protein groups from three DIA replicates, we obtained an overlap for the analysis with the two CSF based libraries A and B of 94% (600 proteins out of 637) and 82% (741 proteins out of 901), respectively. With the two libraries C and D, containing substantia nigra samples, the overlap was 57% (898 proteins out of 1574) and 61% (845 F


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Figure 5. Identification of DIA specific proteins based on the used spectral library and functional classification of these proteins. (A) shows the overlap of protein groups identified with DDA (left circle) and DIA method (right circle), each analyzed with three replicates. With all four libraries, a high number of DIA-specific proteins were obtained. Using these proteins gene ontology functional classification was performed for molecular function (B) and cellular component (C) using the PANTHER classification system (http://www.pantherdb.org/).63

one individual to the next due to differences in CSF flow, the presence of blood-derived protein and/or produced immunoglobulins (own observations). In the case of DIA, all identified peptides/proteins can be quantified since quantification is based on MS2 level.58,59 We believe usage of the DIA approach applying a comprehensive library for an overall identification can have a tremendous benefit to select and/or include potential biomarkers for a targeted screening like PRM based analysis. In case of neurological diseases, a list of additional proteins (like brain originated), could increase the identification and quantification numbers of putative disease biomarkers. For standard label-free quantification, the use of comprehensive libraries requires further development of integrated data analysis strategy. One reason for the low identification reproducibility when using comprehensive libraries is false negatives, which means that a true peptide match is discarded by the software due to existence of too many interfering signals that can be assigned to other peptides. This is especially true in the case of very large search spaces as

in case of our libraries C and D, where we included several different samples for the generation of the spectral library.32 As shown in SI Figure 3 with heat maps based on normalized intensities increased missing intensities were obtained with increased library size. Narrowing the targeted quantification search space could help to create much smaller libraries.60 For this, different strategies have been proposed in the literature such as selecting proteins of interest from other proteomics measurements with subsequent library filtering so that the transition list only contains the information for these targeted proteins.61 Another approach is the application of an alternative spectral-matching tool for identifying peptides in DIA data, named MSPLIT-DIA (mixture-spectrum partitioning using libraries of identified tandem mass spectra).62 Here, peptide identification is directly optimized from the data excluding absent peptides. The application of these strategies for CSF samples must be further evaluated in future studies. G


Article

Journal of Proteome Research Increasing the Number of Brain Specific Proteins by Applying a Substantia Nigra Library

approach was described by Begcevic et al., where they used the Human Protein Atlas (HPA) to generate a list of brain-related proteins from DDA analysis based on mRNA expression values.4,64 With this strategy, they reported 78 brain-specific proteins for CSF and suggested a combination of database (like the HPA) and experimental search (like reported Immunohistochemistry-based expression or mRNA expression) of identified proteins in specific body fluids as an initial step in the discovery of disease biomarkers to reveal association to particular tissue. We decided to use the human proteome map (HPM) to select brain originated proteins.42 In contrast to the mRNA based expression profiles of the HPA, the HPM is based on proteomic profiling of 30 histologically normal human tissues and primary cells using high resolution mass spectrometry including 17 adult tissues, 6 primary hematopoietic cells, and 7 fetal tissues. We matched the 60 DDA and 242 DIA specific proteins from the analysis with library C (as shown in Figure 5A) against the proteomic profile of the 17 adult tissues (comprising 15 peripheral tissues, as well as two tissues from the central nervous system namely spinal cortex and frontal cortex). Proteins showing highest expression in the spinal or frontal cortex were specified to be brain originated. With this, 11 DDA and 64 DIA specific proteins could be assigned as brain originated (Figure 6, SI

With the application of a comprehensive library, we could successfully increase the number of identified proteins for CSF samples. Because we included a brain originated library for the DIA experiment, we wanted to know if we would be able to identify more brain originated proteins with the DIA approach compared to DDA. To achieve this, we used the generated protein lists for DDA and DIA of three technical replicates and compared only proteins consistently identified in all three replicates of the DDA and DIA, respectively. In summary, for the analysis with library A 30 DDA-specific and 108 DIAspecific proteins were identified. With library B, 28 DDA- and 183 DIA-; for library C, 60 DDA- and 242 DIA-; and for library D, 60 DDA- and 248 DIA-specific proteins were obtained (Figure 5A). The DIA-specific proteins were further categorized by gene ontology classification for molecular function, cellular components and pathways (Figure 5C to D) using PANTHER (http://www.pantherdb.org/).63 For all analyses, similar molecular functions were assigned including “binding, catalytic activity”, “receptor activity signal”, “transducer activity”, “structural molecule activity”, “transporter activity”, “antioxidant activity”. Only for the analysis with library C we obtained one additional category of molecular function: “translation regulator activity”. Overall, it was observed that with increasing library size, the number of assigned genes increased. For example, the two categories “binding” and “catalytic activity”, were in library A both assigned with 24, in library B with 41 and 44, in library C with 70 and 78 and in library D with 66 and 68 gene respective ontology classes. These increasing numbers are presumably obtained because of the total increase of identified proteins in the respective analysis. Similar results were obtained for the classification of cellular components. Here, the same number of classes were assigned and the category “cell part” showed the highest increase in assigned genes due to library size. For library A 15, for library B 33, for library C 79 and for library D 60 genes were assigned. The higher assignments for library C and D are likely a result of the additional substantia nigra data included in the spectral library. Functional classification varied according to the library size. For library A 22, for library B 33, for library C 41, and for library D 59 pathways were mapped for the specifically identified protein. In Figure 5D, the nine overlapping pathways from all four analyses with relation to neurodegeneration are shown. For CNS-related pathways like “axon guidance”, similar assignment was obtained for all analyses, while for the category “inflammation”, library size dependent increase of assigned genes occurred. Interestingly, the pathway with relation to neurodegeneration increased with library size. While no pathway with relation to neurodegeneration was identified for the analysis with library A, in library B, C, and D the pathways “Alzheimer diseasepresenilin”, “Huntington disease”, and “Parkinson disease” were obtained. For the two libraries C and D, the pathway “Alzheimer disease-amyloid secretase” was also classified. Interestingly, within these classes the number of assigned genes was higher in the libraries including substantia nigra. For example, the pathway “Huntington disease” was assigned with two genes for library B, with six for library C, and with four for library D, suggesting an increased detection of brain-originated proteins with these libraries. In the next step, we wanted to define brain originated proteins among the DDA and DIA specific proteins. A similar

Figure 6. Brain originated protein groups identified with DDA and DIA, respectively. Proteins exclusively identified by DDA (60) or DIA (242) were mapped against the human protein map and 11 proteins from DDA and 64 from DIA were assigned as brain-originated based on the expression profile.

Figures 4 and 5, Table 1) showing that application of a substantia nigra spectral library increases the number of proteins that are originated from the brain. We compared our results with the data from Begcevic et al. and found three overlapping proteins for the DDA originated brain proteins among the reported 78 brain-specific proteins: (1) SLITRK1, a transmembrane protein controlling the neurite outgrowth, (2) PCDH9, a potential calcium-dependent cell-adhesion protein, and (3) ICAM5, a ligand for the leukocyte adhesion protein LFA-1. Among the brain originated proteins detected using DIA four overlapped with the literature protein candidates: (1) MAG, a cell membrane glycoprotein that functions as ligand of the NoGo-66 receptor, (2) LINGO1, a transmembrane protein which is a functional component of the Nogo (neurite H


Article

Journal of Proteome Research Table 1. List of Selected 64 Brain Originated Brain Proteins Identified with the DIA Approach accession uniprot

gene name

O00451 O43157 O60282 O60313 O75711 P05771 P06307 P07108 P12036 P13637

GFRA2 PLXNB1 KIF5C OPA1 SCRG1 PRKCB CCK DBI NEFH ATP1A3

P14174

MIF

P17677 P19367 P20916 P23634

GAP43 HK1 MAG ATP2B4

P26232 P31150

CTNNA2 GDI1

P32004 P41217 P42858 P55786

L1CAM CD200 HTT NPEPPS

P61981 P62760 Q00610 Q01082 Q01484 Q03001 Q05329 Q09470

YWHAG VSNL1 CLTC SPTBN1 ANK2 DST GAD2 KCNA1

Q12756 Q13813 Q14204

KIF1A SPTAN1 DYNC1H1

Q14289 Q15111

PTK2B PLCL1

Q15149 Q15223 Q16555

PLEC PVRL1 DPYSL2

protein name GDNF family receptor alpha-2 plexin-B1 kinesin heavy chain isoform 5C dynamin-like 120 kDa protein scrapie-responsive protein 1 protein kinase C beta type cholecystokinin acyl-CoA-binding protein neurofilament heavy polypeptide sodium/potassium-transporting ATPase subunit alpha-3 macrophage migration inhibitory factor neuromodulin hexokinase-1 myelin-associated glycoprotein plasma membrane calciumtransporting ATPase 4 catenin alpha-2 rab GDP dissociation inhibitor alpha neural cell adhesion molecule L1 OX-2 membrane glycoprotein huntingtin puromycin-sensitive aminopeptidase 14−3−3 protein gamma visinin-like protein 1 clathrin heavy chain 1 spectrin beta chain ankyrin-2 dystonin glutamate decarboxylase 2 potassium voltage-gated channel subfamily A member 1 kinesin-like protein KIF1A spectrin alpha chain cytoplasmic dynein 1 heavy chain 1 protein-tyrosine kinase 2-beta inactive phospholipase C-like protein 1 plectin nectin-1 dihydropyrimidinase-related protein 2

accession uniprot

highest abundancy

gene name

FC SC FC FC FC FC FC SC SC FC

Q16620

NTRK2

Q5FWE3

PRRT3

Q5HYI7 Q86T65

MTX3 DAAM2

Q86VP3

PACS2

Q8IVF2 Q8NHG7

AHNAK2 SVIP

SC

Q8TAM6 Q92598 Q92747

ERMN HSPH1 ARPC1A

Q93050

ATP6 V0A1

Q96FE5

LINGO1

Q96GD0 Q96MZ0

PDXP GDAP1L1

Q96RR4

CAMKK2

Q9BXM9 Q9BYH1 Q9HCM2 Q9NVA2 Q9P265

FSD1L SEZ6L PLXNA4 SEPT11 DIP2B

Q9UBQ6 Q9UI12 Q9ULU8

EXTL2 ATP6 V1H CADPS

Q9UQM7

CAMK2A

Q9Y2A7 Q9Y2T3 Q9Y6N8

NCKAP1 GDA CDH10

FC FC FC/SC FC FC FC FC FC FC FC/SC FC FC FC FC FC SC FC FC FC FC FC FC FC/SC

a

protein name BDNF/NT-3 growth factors receptor proline-rich transmembrane protein 3 metaxin-3 disheveled-associated activator of morphogenesis 2 phosphofurin acidic cluster sorting protein 2 protein AHNAK2 small VCP/p97-interacting protein Ermin heat shock protein 105 kDa actin-related protein 2/3 complex subunit 1A V-type proton ATPase 116 kDa subunit a isoform 1 leucine-rich repeat and immunoglobulin-like domaincontaining nogo receptorinteracting protein 1 pyridoxal phosphate phosphatase ganglioside-induced differentiation-associated protein 1-like 1 calcium/calmodulin-dependent protein kinase kinase 2 FSD1-like protein seizure 6-like protein plexin-A4 septin-11 disco-interacting protein 2 homologue B exostosin-like 2 V-type proton ATPase subunit H calcium-dependent secretion activator 1 calcium/calmodulin-dependent protein kinase type II subunit alpha Nck-associated protein 1 guanine deaminase cadherin-10

highest abundancy FC FC FC FC/SC SC SC FC/SC FC FC FC FC FC

FC FC FC SC FC FC FC FC FC FC FC FC FC FC FC

a

Indicated is the CNS tissue, spinal cord (SC) or frontal cortex (FC), in which the corresponding protein was highest expressed according to the human proteome map.42

SC FC FC

neurons, Lewy bodies and microglia of substantia nigra region in PD) are included.65−67 Showing the increase of numbers for brain specific proteins, as well as the correlation of these proteins to a neurodegenerative disease like AD, emphasizes the advantage of DIA with a comprehensive spectral library compared to DDA in order to identify putative biomarkers within the CSF proteome.

outgrowth inhibitor) receptor, (3) SEZ6L, a substrate of BACE1, and (4) CAMK2A, a kinase in the central nervous system. All four proteins are functionally linked to the central nervous system supporting our strategy to select brainoriginated proteins with the HPM. However, due to the low overlap with the data set from Begcevic et al., we further examined if any of the reported 78 brain specific proteins are present in the list of our DIA data set and found 37 proteins from the 78 proteins. Although we analyzed native CSF and did not perform a peptide fractionation via strong cation exchange chromatography, we achieved almost 50% overlapping proteins between the study of Begcevic et al. and our study. Within this overlap, proteins previously linked to neurodegenerative diseases like APLP1 (a substrate of BACE1, an enzyme linked to in AD pathology) and SPP1(expressed in

DIA Identified Proteins Suitable As Biomarker

In the next step we further evaluated whether within the DIA data set disease relevant proteins are identified. Here, we focused on AD related proteins. Recently, Olsson et al.25 performed a systematic review and meta-analysis of CSF and blood biomarkers reflecting neurodegeneration, APP metabolism, tangle pathology and glial activation. Besides the I


Article


was reported, showing the benefits of this method compared to DDA.34 Here, the DIA approach doubled the identified peptides and proteins from samples which were not depleted and prefractionated using a 50 min LC gradient. The quantified proteins contain over 50 disease-related biomarkers for renal cell carcinoma. We are convinced that application of DIA with a brain associated library extents the current label free DDA based biomarker discovery for CSF. DIA provides a panoramic view of all possible generated product ions and a sample overlapping library greatly improves the accuracy and sensitivity of DIAbased data analysis.

approved biomarkers Aβ 40/42 and Tau, YKL-40, HFABP, VLP-1, and NSE were also reported as promising AD biomarkers in more than five different studies.25 In our study, YKL-40 could be identified with DDA and DIA, whereas HFABP, VLP-1, and NSE were exclusively identified via DIA. NSE is a neuron-enriched enzyme of the glycolytic pathway; VLP-1 is a calcium-sensor protein found in the neuronal cytoplasm; HFABP is an intracellular fatty acid transport protein expressed in skeletal muscle, heart, and neurons; and YKL-40 is a marker of activated microglia and astrocytes.68−71 Although these proteins do not reflect the main pathology of AD, Olsson et al. suggest that they are useful markers in clinical trials of drugs for the prevention or treatment of AD as Tau independent and Aβ-independent measures of neurodegeneration and glial activation. By crosschecking the Alzheimer forum database (https://www.alzforum.org/alzbiomarker), we could further reveal 39 of the 64 selected brain-originated proteins to be associated with AD such as protein kinase C beta (P05771) and Catenin (P26232) or other neurological disorders72,73 (SI Table 1). To examine if these proteins could be used for quantification, we compared their relative protein abundance to the relative abundance of the entire CSF proteome by plotting the respective MS2 quantities (Figure 7). As a result,

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CONCLUSION Here, we present the application of DIA for the efficient and reproducible mapping of CSF proteome. Without any prefractionation, we identified over 140% more proteins from 20 μL CSF sample, when using a comprehensive spectral library with DIA compared to DDA, leading to an increase of 900 identified proteins. Furthermore, with this strategy we were able to detect 64 brain originated proteins, increasing the confidence in identifying relevant biomarker candidates for neurodegenerative diseases. Particularly for AD, already postulated biomarkers could be found, which can be implemented in a biomarker discover pipeline for high throughput analysis of clinical samples. In summary, DIA for CSF proteomics is a powerful platform for biomarker discovery studies, giving the possibility to combine discovery and verification, since all identified proteins can be precisely quantified in hundreds of samples. Future developments of adequate integrated data analysis strategy will enable application of comprehensive libraries in label free quantification studies to establish robust and high reproducible DIA-based workflows for CSF proteomic profiling.

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ASSOCIATED CONTENT

S Supporting Information *

Figure 7. Relative abundance plot of the CSF proteome based on DIA using library C (combined library of CSF and brain) with selected AD-linked proteins (highlighted in black) according to the Alzheimer forum database (https://www.alzforum.org/alzbiomarker) distributed over the whole abundance range of the CSF proteome. AD biomarkers reported in more than 5 different studies according to Olsson et al.25 are highlighted in in blue and the corresponding gene name is indicated.

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.jproteome.8b00308.

most of the proteins were positioned in the upper and middle, but not in the lower, range of the relative CSF proteome abundance, suggesting a medium to high abundance of these proteins. This information is important to assess whether these proteins could be quantified in clinical CSF samples using for example targeted assays like MRM or PRM. Our results indicate that the DIA approach can be used to identify and select diseases related proteins as demonstrated for AD, that could be used as suitable candidates for targeted proteomic assays, which would be undiscovered in a DDA experiment. With this strategy, combining DIA identification with interactive proteome resource applications, like HPM, to perform proteomic profiling could be used to generate specific proteome signatures for individual diseases. Subsequently, these results can be used to establish high throughput targeted MS-based methods for diagnostic approaches. Recently, a high throughput and accurate serum proteome profiling using DIA

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Supplemental Figure 1: Reproducible protein identification from native CSF using DDA. Supplemental Figure 2: Carryover of proteins after measuring 500 ng total peptide from CSF sample. Supplemental Figure 3: Normalized intensities of the four CSF DIA approaches. Supplemental Figure 4: Protein expression profiles of brain originated proteins from DDA experiment. Supplemental Figure 5: Protein expression profiles of brain originated proteins from DIA experiment. Supplemental Table 1: List of proteins linked to Alzheimer’s disease (AD) or other neurological disorders (OND) (PDF)

AUTHOR INFORMATION

Corresponding Author

*Phone: +49 234 32-28444; e-mail: [email protected]. ORCID

Katalin Barkovits: 0000-0003-2554-8796 Julian Uszkoreit: 0000-0001-7522-4007 J


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Journal of Proteome Research Author Contributions

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K.M. and K.B. directed and conceived the work. A.L., S.G., S.S., N.S., J.U, and M.E. carried out the experiments and data analysis. K.B. wrote the manuscript with input from the other authors. Notes

The authors declare no competing financial interest. The mass spectrometry data have been deposited to the PRIDE Archive (http://www.ebi.ac.uk/pride/archive/) via the PRIDE partner repository with the following submission details. Native CSF DDA MS data. Project Name: Characterization of cerebrospinal fluid via data-independent acquisition mass spectrometry. Project accession: PXD010698. Project DOI: 10.6019/PXD010698. Fractionated CSF DDA MS data. Project Name: Characterization of cerebrospinal fluid via data-independent acquisition mass spectrometry. Project accession: PXD010708. Project DOI: 10.6019/PXD010708 Substantia Nigra DDA MS data. Project Name: Characterization of cerebrospinal fluid via data-independent acquisition mass spectrometry. Project accession: PXD010690. Project DOI: 10.6019/PXD010690. CSF DIA MS data. Project Name: Characterization of cerebrospinal fluid via data-independent acquisition mass spectrometry. Project accession: PXD010705.

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ACKNOWLEDGMENTS This work was supported by the Deutsche Parkinson Gesellschaft, Medical Faculty at RUB (FoRUM), the German Federal Ministry of Education and Research (WTZ with Brasil, FKZ 01DN14023), the HUPO Brain Proteome Project (HBPP), PURE, a project of North Rhine-Westphalia, a federal German state.). In addition, it was supported by de.NBI (FKZ 031 A 534A), a project of the German Federal Ministry of Education and Research (Bundesministerium für Bildung und Forschung BMBF) and the H2020 project NISCI, GA no. 681094.

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