Spectral counting assessment of protein dynamic range in cerebrospinal fluid following depletion with plasma-designed immunoaffinity columns

Protein recovery after immunodepletion

Figure 1 schematically illustrates the sample processing strategies adopted in this study. The amount of protein recovered in the flow-through (~ 3 or 4 mL for IgYHSA or IgY14 columns, respectively) following sample concentration with Amicon filters was around 13% and 30% of applied protein for the IgY14 and IgYHSA columns, respectively ( Table 1). Furthermore, the amount of protein recovered in the fractions bound to the IgY14 and IgYHSA columns was 52% and 37%, respectively.

Reproducibility

Dynamic range

Under the premise that spectral counting is correlated with peptide abundance [16, 17], we evaluated the changes in CSF proteome content after depletion of highly abundant plasma proteins. Recently, the protein abundance calculated by APEX has been demonstrated to be a close approximation of the relative abundance of a particular protein [10]. Figure 2 shows a comparison of the dynamic range profile of CSF proteome achieved after immunodepletion as measured by APEX algorithm. Our data demonstrate an improvement in the overall number of low abundance proteins (LAP; below 2 logs of magnitude from the most abundant protein) in samples subjected to IgYHSA (14 proteins) or IgY14-depletion (18 proteins) compared to non-depleted (5 proteins) samples.

Peptide and protein identification

At the protein level, we found 90 proteins common to the three IgY14 replicates from a total of 156 proteins; 72 proteins were common to all three IgYHSA replicates from a total of 131 proteins; and 55 proteins were common to all three non-depleted replicates from a total of 90 proteins (Figure 3). Overall, approximately 80% of the proteins identified in each method were found in at least 2 replicates.

Figure 4 shows the similarities in terms of peptide and protein identification across the three methods. 231 peptides and 67 proteins were commonly identified in the three methods, while 432 peptides and 107 proteins were commonly identified in both depleted samples. The differences between proteins identified in the IgYHSA-depleted replicates and undetected in the IgY14-depleted replicates are attributed, in part, to more proteins being targeted for depletion in the latter method. A manual inspection of the protein list not identified in samples subjected to IgY14 depletion indicates that 13 proteins (out of 24) were removed by IgY14 column (isoforms of haptoglobulin, fibrinogen, complement C3, and a number of immunoglobulin fragments). The lists of proteins and peptides identified are available as Additional File 1 Table S1 and Additional File 2 Table S2, respectively, along with corresponding protein abundance as calculated by APEX (Additional File 3 Tables S3, Additional File 4 Table S4 and Additional File 5 Table S5). The distribution of most abundant proteins showed that 9-10 proteins accounted each for more than 2% of total identified proteins (Figure 5). The 10 most abundant proteins following immunodepletion accounted for 41% (IgY14) and 46% (IgYHSA) of total CSF protein content, whereas they accounted for 64% of total protein content in non-depleted CSF samples. Except for abundant proteins common with plasma, our data also point out other proteins, such as Prostaglandin H2 D-isomerase (PTGDS) and Cystatin-C (CSTC3) that account for approximately 40% of total CSF content after depletion vs 20% in non-depleted CSF. On the other hand, low and medium abundance proteins account for 59%, 54% and 36% in IgY14, IgYHSA and non depleted samples respectively, thus demonstrating significant enrichment of low- and medium-abundance proteins.

Peptide fractionation

Peptide fractionation techniques are expected to increase the depth of analysis while possibly deteriorating experimental reproducibility. We set out to evaluate: (1) the gain in proteome coverage attained after peptide fractionation using offline reversed-phase; (2) the overall improvement of sample dynamic range; (3) experimental reproducibility in terms of peptide and protein identification.

Albumin-depleted CSF sample was fractionated into 30 fractions using preparative reversed-phase chromatography under basic pH. The numbers of confident peptide and protein identifications obtained from fractionated samples are summarized in Table 4. A total of 3,026 unique peptides were identified among the 3 replicates (1637 were common to the 3 replicates; Figure 6), corresponding to 535 non-redundant proteins (289 were common to the 3 replicates). Moreover RSD (relative standard deviation) was not increased when compared to unfractionated samples.

We compared the protein list generated with Mascot search alone using a target-decoy strategy or Mascot search combined with PeptideProphet and ProteinProphet validation analyses. CSF immunodepletion with IgYHSA column and analysis with 2DLC-MS/MS of one of the replicates led to the identification of 913 proteins with Mascot alone (FDR < 0.001). In contrast, with Mascot-TPP (PeptideProphet and ProteinProphet) strategy, a total of 947 proteins were identified, 402 of which were identified with high confidence and the remaining 545 identifications were grouped into one of the 187 protein groups for which members could not be distinguished on the basis of the peptides observed. The other replicates followed a similar trend.

The increased depth of analysis achieved with fractionation was also evident in terms of number of LAP detected in the sample. The number of proteins below 2 orders of magnitude from the most abundant protein as determined by APEX was used as a parameter to evaluate sample dynamic range following peptide pre-fractionation. Immunodepletion alone improved the number of LAP from 5 to 18 (Figure 2), whereas immunodepletion coupled with reversed-phase pre-fractionation further improved it to 53 proteins (Figure 2D).

CSF samples

Using an atraumatic needle, CSF was obtained by lumbar puncture (2-4 ml per patient) from subjects attending the Department of Neurology at University Hospital Saint Etienne. CSF was collected in 12-mL polypropylene tubes (VWR), transferred on ice to the laboratory and centrifuged (3.000 × g, 10 min, +4°C). Fluid was aliquoted into 0.5 mL polypropylene cryotubes (VWR) and stored at -80°C. The study was approved by the local ethics committee of University of Saint Etienne. CSF samples from 5 ALS patients (Amyotrophic Lateral Sclerosis) aged 50-76 with clinically diagnosed or probable ALS following El Escorial diagnostic criteria were pooled and used for further analysis.

Sample setup

The present study was devised using a single pooled CSF sample that was further divided into 12 aliquots. Each aliquot contained 780 μg total protein (Table 1). Nine of these aliquots were used for immunodepletion evaluation as follows: 3 were depleted of the 14 most abundant proteins (IgY14), 3 were depleted of albumin (IgYHSA), and the remaining 3 were not immunodepleted. The remaining 3 aliquots were depleted of albumin and further offline-fractionated using reversed-phase liquid chromatography under basic pH after protein digestion.

Immunoaffinity depletion of highly abundant proteins

CSF immunodepletion of highly abundant proteins was performed using pre-packed liquid chromatography Seppro^® columns (GenWay Biotech Inc.). The term IgYHSA, refers to the column used for immunodepletion of albumin alone while IgY14 refers to that used for immunodepletion of albumin, IgG, α1-antitrypsin, IgA, IgM, transferrin, haptoglobin, α1-acid glycoprotein, α2-macroglobulin, fibrinogen, complement C3, and apolipoproteins A-I, A-II and B. Prior to injection on the column, each CSF sample was passed through a 0.45 μm pore size filter to remove particulates. As a result of the loading capacity of IgY14 columns, CSF aliquots subjected to these columns were further concentrated using a 3 kDa molecular weight cutoff (MWCO) filter (Millipore). A chromatographic column was set up on an ÄKTA Ettan system (GE Healthcare) and run following manufacturer's instructions. Finally, flow-through was desalted and proteins concentrated using a 3 kDa MWCO filter.

Sample preparation for LC and LC-MS/MS

The final protein concentration of the depleted samples was determined by a bicinchoninic acid colorimetric assay (Pierce Biotechnology) using BSA as standard. Seventy five μg protein of each sample in dissolution buffer (0.1 M triethylammonium bicarbonate, 0.1%SDS) was reduced with 5 mM tris-(2-carboxy-ethyl)-phosphine for 60 min at 60°C. Free sulfhydryl groups of cysteine residues were then blocked with 15 mM iodoacetamide for 20 min at room temperature. Digestion with trypsin (Promega) was performed overnight at 37°C at a 1:50 enzyme-substrate ratio.

Peptide pre-fractionation

To evaluate the impact of peptide fractionation following IgYHSA immunodepletion, trypsin-digested peptides were pre-fractionated offline by reversed phase liquid chromatography under basic pH conditions (RPb) on an ÄKTA system (GE Healthcare) using a 300 Extend C₁₈ column (150 mm length × 2.1 mm ID, 5 μm particles, 300Å pore size; Agilent). CSF peptides were fractionated into 30 fractions. Peptide mixture dissolved in buffer A (25 mM NH₄OH, pH9.5) was loaded onto the column and eluted with a gradient of 0 to 10% buffer B (25 mM NH₄OH in acetonitrile pH9.5) over 3 min, then 10% to 28% buffer B for 8 min, and 28% to 45% buffer B for 4 min at 0.5 mL/min column flow rate. Fractions were collected at intervals of 30 seconds. Finally, acetonitrile was removed by evaporation and fractions were stored at -20°C until further use.

Mass spectrometry

Prior to LC-MS/MS analysis, dried peptide samples were reconstituted with 0.1% aqueous formic acid. Peptide concentration estimates were extrapolated either from protein concentrations (non-fractionated samples) or from peptide absorbance at 215 nm during fractionation (RPb-fractionated samples). Approximately 200 ng of each sample was then loaded onto a 0.180 mm × 20 mm C₁₈ precolumn Symmetry^® (Waters Corp., Milford, MA) coupled to an analytical C₁₈ column (BEH130™ 75 μm × 10 cm, 1.7 μm, Waters Corp.) at 15 μl/min flow rate using nanoACQUITY Ultra Performance LC™ system (Waters Corp., Milford, MA). Peptides were separated in a 70 min gradient of 1-35% buffer B, followed by 15 min of 35-50% B (A = 0.1% formic acid in water, B = 0.1% formic acid in acetonitrile), at 250 nl/min flow rate. The column outlet was directly connected to an Advion Triversa Nanomate (Advion) fitted on an LTQ-FT Ultra mass spectrometer (Thermo). The mass spectrometer was operated in a data-dependent mode. Survey full-scan MS spectra (m/z 400-1800) were acquired in the FT with R = 100.000 at m/z 400 (after accumulation of a target value of 1e⁶). The five most intense ions were sequentially isolated for fragmentation and detection in the linear ion trap using collisionally induced dissociation at a target value of 50.000, 1 microscan averaging and a normalized collision energy of 35%. Target ions already selected for MS/MS were dynamically excluded for 30 s. Spray voltage and delivery pressure in the Nanomate source were set to 1.75 kV and 0.3 psi respectively. Capillary voltage and tube lens on the LTQ-FT were tuned to 35V and 109V. Minimal signal required to trigger MS to MS/MS switch was set to 100 and activation Q was 0.250. The spectrometer was working in positive polarity mode and singly charge state precursors were rejected for fragmentation. We performed at least one blank run before each analysis in order to ensure the absence of cross contamination from previous samples.

Data analysis

MS raw data files were processed with Mascot Distiller (Version 2.3.2, Matrix Science, London). The resulting peak lists were searched with Mascot (Version 2.1) against the human International Protein Index (IPI) database (Version 3.71) concatenated with reversed IPI sequences. Search criteria were as follows: full tryptic specificity was required with up to 2 missed cleavage sites allowed; the precursor ion m/z tolerance was set at 20 ppm; the product ion m/z tolerance at 0.6 Da; carbamidomethylation (Cys) was set as fixed modification and oxidation (Met) as variable modification.

Peptide-spectrum matches (PSMs) were subjected to statistical validation with the PeptideProphet algorithm (TransProteomic Pipeline - TPP v4.3) using the accurate mass model option and the semi-supervised approach [21]. In brief, the expectation-maximization (EM) algorithm used by PeptideProphet to construct a Bayes classifier incorporates decoy peptide hits information from a target-decoy database search. All PSMs with PeptideProphet ≥ 0.95 were kept for further analyses. Finally, Occam's razor logic as implemented in ProteinProphet algorithm was applied to generate the most coherent list of proteins identified. Therefore, redundant protein entries were removed by clustering peptides by matching multiple members of a protein family to a single protein group and considering them as a single identification. Degenerate peptides were discarded before downstream quantitative analysis.

To gain insight into the protein profiling distinctiveness of the three protein depletion strategies, we used the modified spectral counting technique APEX (v1.2)[16]. This approach makes use of a machine-learning classification algorithm to predict peptide detectability. The program generates a correction factor for each protein (O _i value), which is then used to predict the number of tryptic peptides expected to be detected for a given amount of a particular protein. Finally, spectral counts for each protein observed in a given run are corrected with their respective predicted O _i value.The APEX abundance is therefore a modified spectral counting method in which the total observed spectral count for a given protein is normalized by expected (predicted) count (Oi) for one molecule or protein. In this regard, APEX abundance is considered the relative abundance of a particular protein with respect to all other proteins in the same sample.

Pattern similarity and quantitative analyses were performed using SuperHirn algorithm [22]. Briefly, SuperHirn performs peak detection and deisotoping followed by peak integration on each LC-MS run in order to build a peptide feature map. Multiple peptide feature maps are then aligned using 10 ppm precursor tolerance within a window of 60 second retention time.