Here, we present a method for in-depth human plasma proteome analysis based on high-resolution isoelectric focusing HiRIEF LC-MS/MS, demonstrating high proteome coverage, reproducibility and the potential for liquid biopsy protein profiling. By integrating genomic sequence information to the MS-based plasma proteome analysis, we enable detection of single amino acid variants and for the first time demonstrate transfer of multiple protein variants between mother and fetus across the placenta. We further show that our method has the ability to detect both low abundance tissue-annotated proteins and phosphorylated proteins in plasma, as well as quantitate differences in plasma proteomes between the mother and the newborn as well as changes related to pregnancy. Blood cells travel through the blood vessels in a soupy mixture of proteins called plasma. Most of these proteins are plasma-specific, yet small amounts of proteins can leak into the plasma from other body parts and may provide hints about what is going on elsewhere in the body. This could allow doctors to use plasma samples to assess health or detect disease. But so far developing methods to detect these leaked proteins has proved difficult.Plasma passing through the placenta can transfer proteins between a pregnant woman and her baby. Learning more about these protein exchanges may help scientists understand how the mother and baby adapt to each other and what triggers child birth. But, so far, they have been hard to study. Using DNA to help trace the origins of proteins found in mother or baby could make it easier.Now, Pernemalm et al. have used DNA sequencing in combination with protein analysis to identify proteins passed between two pregnant mothers and their babies. Comparing the genetic sequences of each mother and child made it possible to trace the origin of the proteins. For example, if a mother had a version of the protein that matched genes the child inherited from its father, they knew it passed from the baby to the mother. This approach found 24 proteins in plasma from two pregnant mothers that had likely passed through the placenta during pregnancy. Pernemalm et al. also analyzed the plasma of 30 healthy individuals and confirmed that it contained several proteins that had likely leaked from other organs, including the lungs and pancreas.Monitoring protein transfer between pregnant mother and baby may help scientists identify what triggers normal or premature deliveries. One advantage of the technique developed Pernemalm et al. is that it can analyze plasma proteins from large numbers of people, which could enable larger studies. More refinement of the technique may also allow scientists to identify leaked proteins in the plasma that provide an early warning of cancer or other diseases. Several studies have presented draft maps of the human tissue proteome using mass spectrometry (MS)-based methods . Due to One major challenge in plasma proteomics is the ability to analyze larger sample cohorts while retaining proteome coverage. Plasma datasets are often characterized by a high proportion of one-peptide identifications resulting in poor overlap between analyses. For example, in the Plasma Proteome Database 10 546 proteins have been reported from 509 publications; however, only 3784 of the identified proteins are present in two or more studies .The main analytical challenge when applying proteomics methods to plasma is the presence of a few extremely high abundant proteins that dominate the protein content. Roughly 55% of the total protein mass in plasma is made up by albumin alone and as few as seven proteins together make up 85% of the total protein mass. This can be compared with estimates from tissue and cellular data where 2300 housekeeping proteins are thought to make up 75% of the protein mass .In general, high plasma proteome coverage is dependent on extensive fractionation. Consequently, aiming for high number of identified proteins comes at the cost of low sample throughput and vice versa. Hence, when larger cohorts have been analyzed the number of proteins monitored drastically decreases. Examples of high-throughput studies include 342 proteins monitored in 230 samples in a longitudinal study of twins using SWATH-MS  or relatEfforts to increase the throughput by shortening the analysis time of shotgun MS have also been made, for example by quantifying 285 proteins from 10 plasma samples in as little as 3 hr analysis time/sample . The samIn 2015, a study by However, despite these advances in the field, in-depth analysis of clinically-relevant-sized plasma cohorts remains a challenge.Here, we present a method to achieve unbiased, reproducible, in-depth plasma proteome analysis. High-resolution isoelectric focusing based pre-fractionation prior MS analysis (HiRIEF LC-MS/MS) has demonstrated capability of in-depth proteome analysis of cell and tissue samples . We demoIn the adaptation of the HiRIEF-LC-MS/MS method to plasma analysis, we analyzed female plasma, depleted of high-abundance plasma proteins, using a label-free MS approach. The optimization of the method included evaluation of the pH range of the peptide isoelectric focusing and amount of peptide sample load onto the strips; leading to in total 16 different HiRIEF conditions being assessed . AdditioWhen optimizing the HiRIEF methodology we identified on average 1505 proteins per condition , with strict 1% FDR cut-off at PSM, peptide and protein level  In compaFrom the HiRIEF pH range evaluation of broad (pH 3–10), narrow (pH 3.7–4.9) and ultra-narrow ranges , we concluded that the pH range did not impact largely on protein identification numbers, which is in line with previously reported studies using HiRIEF on cells and tissue  Zhu et . HoweverIn the second step of the optimization, sample loads on the HiRIEF strip were evaluated in terms of analytical depth  and protTo explore the potential of plasma phosphoprotein identification in the acidic HiRIEF range , we re-sWhen summarizing the protein identifications from the different loads and pI intervals in total 3053 proteins were identified in the optimization experiments . All rawA comparison of our data to the 3509 proteins from the most recent version of the human plasma PeptideAtlas data set  shows thTo evaluate the sensitivity of the HiRIEF method, prostate-specific antigen protein (PSA) was spiked-in at a clinically relevant cut-off level of 4 ng/mL into the female plasma and analyzed on the ultranarrow, narrow and broad pH intervals described in the optimization. Interestingly, PSA was only detected in the 4.0–4.25 strip and not in the broader 3.7–4.9 or 3–10 strips, despite covering the same pI interval. This implies that specific, narrower pH intervals could be used to increase analytical depth for selected proteins. In line with this, analyzing the proportion of extracellular, membrane, cytoplasmic and nuclear proteins detected in the different pH intervals, a slightly higher proportion of intracellular proteins were detected in the ultranarrow ranges .−18 moles of the PSA peptide, demonstrating that findings from the global analysis can be validated using targeted MS-methods by rational selection of HiRIEF fractions based on theoretical peptide pI  and quantitative reproducibility of the method we focused on the broad range strip (pH 3–10), as it would generate the highest number of identified peptides which, in turn, would give the best protein quantification accuracy. Due to the larger pI interval per fraction, the broad range strips give higher peptide focusing accuracy on strips , which iOn each strip, we loaded in total 1 mg of depleted TMT-labeled plasma (100 μg/TMT-label), which was in coherence with the yield from a single 40 μl crude plasma injection per sample on the depletion system .The first cohort contained samples from 30 healthy individuals, 15 men and 15 women. To connect the quantitative information between the sets and enable reproducibility calculations we created a pooled internal standard of depleted and digested plasma from all 30 individuals, which we included in the study. This allowed us to study the peptide level variability by including replicates of the pooled internal standards in each set - four TMT sets, 30 individual samples and 10 pooled internal standards . We idenIn the second cohort, we wanted to explore if we could reduce the MS analysis time, and still retain the proteome coverage. For this purpose, we tailored a condensed 3–10 HiRIEF LC MS/MS analysis approach where fractions in pI areas containing fewer peptides where pooled and analyzed together in the LC-MS/MS analysis. This reduced the number of fractions analyzed from 72 to 40, and the MS analysis time by approximately 30 hr to 55 hr per HiRIEF plate. To test this condensed approach, we analyzed a second longitudinal cohort with plasma samples from 12 women making a total of five TMT-sets. In this analysis, we identified in total 2123 proteins, with 1135 being detected in in all five sets, showing that the reduced analysis time only had a minor impact on the number of identified proteins .Lastly, we compared the identified proteins from the nine TMT-sets from the two different cohorts to define a core set of proteins that could be robustly detected in plasma using the 3–10 strip. In this analysis, we used a gene-centric approach to avoid variation in protein grouping caused by protein inference. Using this approach, we identified in total 828 genes in all nine TMT sets containing samples from 42 individuals (both men and women) .et al.  and the iTRAQ 4-plex, TMT-6 plex, TMT-10 plex analysis (one pooled plasma sample), a set of 1394 genes was robustly identified in all seven sets. In comparison to our data, we identified 1043 genes in seven out of the nine sets, with 42 individuals, starting with 40 μl plasma and 14-protein MARS depletion . While iNext, we used plasma HiRIEF to explore the overall plasma protein inter-individual variability by analyzing the plasma from the 30 healthy donors . To define which plasma proteins were tightly controlled or highly variable between individuals, we ranked all overlapping quantified proteins based on inter-individual CV (%). When examining the classes of proteins associated with low or high variability by comparative GO enrichment analyses, we found that coagulation- and complement cascade proteins such as complement factor I (CF1) and complement component C6 were tightly regulated, which was in line with previous findings . InteresLarge inter-individual variation was observed for lipoproteins and keratins (the latter classified as probable contamination) . One exaTwo other highly variable proteins (APOC2 and CETP) showed strong gender correlation, with lower concentrations in women . Increaswww.proteinatlas.org)  .To explore the potential of detecting biomarkers using plasma HiRIEF, we mined our data for the proteins included in the recently published CancerSEEK test . We iden‘tissue enriched'. We ranked the merged data from the optimization based on abundance and colored the proteins according to protein classes  . The lat classes . As expe classes . Notably classes .As a proof of concept to demonstrate the detection of potential tissue leakage proteins using the HiRIEF method, we searched for the presence of placenta-enriched proteins in the cohort with 30 blood donors  as we would not expect to detect any placenta proteins in these individuals. In this cohort, we detected low levels of eight proteins classified as placental enriched , which were not detected, or detected at low levels prior to pregnancy  .We continued the analysis to search for increased amounts of placental proteins in newborns, as the placenta is mainly of fetal origin. Two mother and newborn pairs were analyzed using plasma HiRIEF on HiRIEF 3–10 strips. In this cohort we found over 30 placenta-enriched proteins at high levels in the maternal plasma , but onlExpanding the comparison between newborn and maternal plasma to the full plasma proteome, we compared protein abundances of mother- versus baby-proteomes and also the pregnant- versus not-pregnant proteomes of the female donor by plotting the protein abundances against each other . Among tFetal hemoglobin was found at very high levels in the newborns , as expected, but a fraction of fetal hemoglobin was also detected in the mother’s blood . The fetal-specific isoform of haemoglobin has previously been detected maternal plasma during pregnancy and transfer across the placenta has been proposed, however, it has also been shown that the level of fetal hemoglobin expressed in adults has a genetic component , making Fetal DNA can easily be detected in maternal plasma and is transferred across the placenta during pregnancy . SeveralTo pinpoint proteins that could have been transferred across the placenta, we adopted a proteogenomics approach . First, The MS data was subsequently curated using SpectrumAI within the iPAW pipeline to reduce false discoveries among the detected single amino acid substitutions . Indeed,We then explored if the curated SAAV peptides detected by MS that could not be explained by the individual’s own genotype could possibly be originating from a transfer across the placenta. Indeed, from the two mother-child pairs we found 32 cases with in total 26 unique peptides sequences from 24 proteins where the amino acid sequence could be explained by the genotype of other the individual, indicating a possible transfer of these proteins across the placenta. Mirror plots of spectra from the peptide in the donor (with DNA support) and the recipient (without DNA support) can be found in For the majority of the proteins (n = 11), the transfer was detected in the direction of baby to the mother, but we also detected nine proteins potentially transferred from the mother to the baby. For three of these transferred proteins, we could see the same transfer directions in both of the mother-child pairs, strengthening our findings . For one additional transferred proteins, we saw opposing transfer directions in the two pairs, suggesting that the protein could transfer in both directions (SERPINB11) .The transferred proteins were spread across the entire abundance range and did not stand out in terms of hydrophilicity/hydrophobicity , size, pI or abundance . A detaiTo our knowledge, this is the first-time plasma proteogenomics has been used to study protein transfer across the placenta in-vivo providing a new possibility to study the molecular communication between mother and baby.In the last 5 years, the MS-based proteomics field has taken major steps forward in terms of proteome coverage and analytical depth. Identification and quantification of over 10 000 proteins can now routinely be performed in cell line and tissue samples, which is in line with the number of transcripts reported using RNA sequencing from same samples . Global et al argues for a rectangular biomarker strategy, where both the discovery and validation cohort are analyzed using global MS-based proteomics  and kept on ice until preparation, to prevent coagulation and minimize protein degradation. EDTA tubes were first centrifuged at 1500 × g at 4°C for 10 min. The supernatant was transferred to a new tube and centrifuged at 3000 × g at 4°C for 10 min. The plasma was then aliquoted and kept at −80°C until analysis. All samples used in this study were prepared within 1 hr of sample collection and showed no signs of hemolysis.Plasma from an anonymous female donor was collected and pooled for the optimization experiment. Aliquots from the same pooled sample was used for all the experiments in the optimization study to reduce experimental variability.Two healthy pregnant women with planned Caesarian section (due to Tokophobia) were asked to join the study. Plasma was sampled from the mothers prior to C-section, and the cord blood was taken from the child directly after birth. Plasma was prepared as described in previous section and in addition buffy coat, containing white blood cells, was collected after the plasma separated. All donors signed informed consent and the study was approved by the local ethics committee, ethical approval number 2014/1622-31/2.Healthy men and women were asked to join the study. Ethical permits were obtained from the ethical committee at Karolinska University Hospital (Dnr 91:164 for men and Dnr 95:298 for women.) All participants signed informed consent prior to inclusion in the study.Individuals were recruited to a longitudinal study where plasma was collected prior to influenza vaccination (day 0) and at day 1, 28, and 90 days post-vaccination. All donors signed informed consent prior to study inclusion and the study was approved by the local ethics committee, ethical approval number 2008/915-31/4 m.Agilent Plasma 14 Multiple Removal System 4.6 × 100 (Agilent Technologies) was set up on an Agilent HPLC system (Agilent technologies) and run according to the manufacturer’s instructions. The depleted plasma flow-through was concentrated on 5 kD molecular weight cut off filter (Agilent Technologies) followed by buffer exchange >100 times to 20 mM Ammonium bicarbonate for the optimization study and 50 mM HEPES for the TMT-analyses.Depleted plasma was denatured at 95°C for 10 min followed by reduction with dithiothreitol and alkylation with iodoacetamide at end concentrations 5 mM and 10 mM, respectively. Trypsin  was added at a 1:50 (w/w) ratio and digestion was performed at 37°C overnight. When applicable TMT-labeling was performed according to manufacturer’s instructions (Thermo scientific).Following digestion (and labeling if applicable), 1 ml Strata X-C 33 µm columns (Phenomenex) were used for sample clean-up. The peptides were subsequently dried in a speedvac.Briefly, HiRIEF was performed as previously described . The samPlasma from healthy female donor with and without PSA spiked-in was depleted and digested as described above. In total, 16 different conditions were evaluated by label-free analysis. A detailed summary of the different pI intervals and sample loads in the HiRIEF optimization can be found in Online LC-MS was performed using a hybrid Q-Exactive mass spectrometer (Thermo Scientific). For each LC-MS/MS run, the auto sampler (Dionex UltiMate 3000 RSLCnano System) dispensed 15 μl of mobile phase A  to the well of the microtiter plate , mixed 10 times (by repeatedly aspirating and dispensing 10 μl), and proceeded to inject 7 μl into a C18 guard desalting column . After 5 min of flow at 5 µl/min with the loading pump, the 10-port valve switched to analysis mode in which the NC pump provided a flow of 250 nL/min through the guard column. The curved gradient (curve six in the Chromeleon software) then proceeded from 3% mobile phase B  to 45% B in 50 min followed by wash at 99% B and re-equilibration. Total LC-MS run time was 74 min. We used a nano EASY-Spray column  on the nano electrospray ionization (NSI) EASY-Spray source (Thermo) at 60°C.FTMS master scans with 70,000 resolution (and mass range 300–1700 m/z) were followed by data-dependent MS/MS (35 000 resolution) on the top five ions using higher energy collision dissociation (HCD) at 30–40% normalized collision energy. Precursors were isolated with a 2 m/z window. Automatic gain control (AGC) targets were 1e6 for MS1 and 1e5 for MS2. Maximum injection times were 100 ms for MS1 and 450 ms for MS2. The entire duty cycle lasted ~3.5 s. Dynamic exclusion was used with 60 s duration. Precursors with unassigned charge state or charge state one were excluded. An underfill ratio of 0.1% was used.Raw MS/MS files were converted to mzML format using msconvert from the ProteoWizard tool suite . SpectraMS data for the optimization study was also analyzed using the MaxQuant software package (version 1.5.3.30). The Andromeda search engine  was usedPurified prostate specific antigen (PSA) protein  was spiked in at 4 ng/mL in crude EDTA plasma from healthy female donor.To ensure that PSA was not removed during the depletion process, intact PSA protein was measured using ELISA in female plasma spiked-in with PSA at 4 ng/mL, depleted spiked-in plasma and two samples from patients diagnosed with metastatic prostate cancer (serving as positive controls) using ELISA kit R and D Systems #DKK300.Fraction 30–35 from the HiRIEF 4.0–4.25 strip from the optimization experiment was analyzed using LC-MRM. Total peptide load on the strip was 0.6 mg of depleted female plasma with PSA spiked-in (described above).−5 Torr, Q1 and Q3 resolution was equal at unit value of 0.7 FWHM.A Capillary pump (from an Agilent 1200 LC system) was used to load sample into a C18 guard desalting column , at a flow rate of 4 µl/min with 100% of loading mobile phase A . After loading for 3 min, the 6-port valve switched and the C18 guard desalting column was put in line with the nano pump, and the sample was eluted and separated using a 15 cm long C18 picofrit column . Peptide separation was obtained using a 0.4 µl/min flow rate and a gradient from 3% to 100% of analytical mobile phase B over 45 min. Analytical mobile phase A consisted of 5% DMSO, 0.1% FA, whereas mobile phase B consisted of 90% ACN, 5% DMSO, 0.1% FA. The gradient method was linear and consisted in (time: %B): 0 min: 3% B; 3 min: 3% B; 20 min: 40% B; 25 min: 100% B; 33 min: 100% B; 34 min: 3% B; 45 min: 3% B. Eluted peptides were ionized into a nano electrospray ionization source and analyzed using a triple quadrupole QQQ Agilent 6490 equipped with iFunnel technology. Default 380 V fragmentor voltage and 5 V cell accelerator potential were used for all peptide transitions. All acquisition methods used the following parameters: Capillary voltage of 2100 V, drying gas flow 11 l/min (UHP Nitrogen) at temperature of 250°C, MS operating pressure of 5 × 10All MRM data were analyzed with Skyline platform and each peak was manually inspected and integrated. Five transitions were used to identify the target peptide and only the area of the most abundant transition was used to quantify the PSA. The concentration of endogenous peptides was calculated by the ratio of heavy area to endogenous area and the known concentration of heavy-spiked-in peptide. Peptide concentration reported in fmol/µl was converted to ng/ml using the molecular weight of the whole protein.http://web.expasy.org/compute_pi/).To predict pI of identified peptides during the optimization, we used the Pred pI Algorithm available as Supplementary software in Thirty plasma samples were depleted, digested and TMT-labeled as described above. An internal standard was created by pooling an aliquote from each of the 30 samples, in order to connect the four TMT sets together and to enable reproducibility. An overview of the TMT-labelling scheme can be found in Online LC-MS was performed using a hybrid Q-Exactive mass spectrometer (Thermo Scientific). For each LC-MS/MS run, the auto sampler (Dionex UltiMate 3000 RSLCnano System) dispensed 15 μl of mobile phase A  to the well of the microtiter plate , mixed 10 times (by repeatedly aspirating and dispensing 10 μl), and proceeded to inject 7 μl into a C18 guard desalting column . After 5 min of flow at 5 µl/min with the loading pump, the 10-port valve switched to analysis mode in which the NC pump provided a flow of 250 nL/min through the guard column. The curved gradient (curve six in the Chromeleon software) then proceeded from 3% mobile phase B  to 45% B in 50 min followed by wash at 99% B and re-equilibration. Total LC-MS run time was 74 min. We used a nano EASY-Spray column  on the nano electrospray ionization (NSI) EASY-Spray source (Thermo) at 60°C.FTMS master scans with 70,000 resolution (and mass range 300–1700 m/z) were followed by data-dependent MS/MS (35 000 resolution) on the top five ions using higher energy collision dissociation (HCD) at 30–40% normalized collision energy. Precursors were isolated with a 2 m/z window. Automatic gain control (AGC) targets were 1e6 for MS1 and 1e5 for MS2. Maximum injection times were 100 ms for MS1 and MS2 150. The entire duty cycle lasted ~3.5 s. Dynamic exclusion was used with 60 s duration. Precursors with unassigned charge state or charge state one were excluded. An underfill ratio of 0.1% was used.Raw MS/MS files were converted to mzML format using msconvert from the ProteoWizard tool suite . SpectraPlasma samples were depleted, digested and TMT-labeled as described above. An internal standard was created to connect the five TMT sets together. An overview of the TMT-labeling scheme can be found in −1, the curved gradient  went from 2% B up to 40% B in each fraction as shown in Online LC-MS was performed using a Dionex UltiMate 3000 RSLCnano System coupled to a Q-Exactive mass spectrometer (Thermo Scientific). Each plate well was dissolved in 20 μl solvent A and 10 μl were injected. Samples were trapped on a C18 guard-desalting column , and separated on a 50cm-long-C18 column . The nano capillary solvent A was 95% water, 5%DMSO, 0.1% formic acid; and solvent B was 5% water, 5% DMSO, 90% acetonitrile, 0.1% formic acid. At a constant flow of 0.25 μl minFTMS master scans with 70,000 resolution (and mass range 300–1600 m/z) were followed by data-dependent MS/MS (35 000 resolution) on the top five ions using higher energy collision dissociation (HCD) at 30% normalized collision energy. Precursors were isolated with a 2 m/z window and an offset of 0.5 m/z. Automatic gain control (AGC) targets were 1e6 for MS1 and 1e5 for MS2. Maximum injection times were 100 ms for MS1 and 450 ms for MS2. Dynamic exclusion was used with 30 s duration. Precursors with unassigned charge state or charge state 1, 7, 8, >8 were excluded.Raw MS/MS files were converted to mzML format and corrected for mass shifts using the msconvert tool from the ProteoWizard suite . SpectraThe reference databases used were the human protein database of Swissprot at 2018-07-18. MSGF +settings included precursor mass tolerance of 10ppm, fully-tryptic peptides, maximum peptide length of 50 amino acids and a maximum charge of 6. Fixed modifications were TMT-10-plex on lysines and N-termini, and carbamidomethylation on cysteine residues, a variable modification was used for oxidation on methionine residues. Quantification of TMT-10plex reporter ions was done using OpenMS project’s IsobaricAnalyzer  (v2.0). https://github.com/lehtiolab/galaxy-workflows/  is available at Plasma samples from two mothers and two babies were subjected to depletion as described above. In this analysis, both the depleted fraction and the proteins bound to the column were analyzed using HiRIEF 3–10 strips as described in the HiRIEF section above. The bound fraction was included because we did not want to exclude any of the high-abundance proteins in the transfer analysis. In total 1 mg of digested peptides were loaded on each strip and analyzed with label-free mass spectrometry.Online LC-MS was performed using a hybrid Q-Exactive mass spectrometer (Thermo Scientific). For each LC-MS/MS run, the auto sampler (Dionex UltiMate 3000 RSLCnano System) dispensed 15 μl of mobile phase A  to the well of the microtiter plate , mixed 10 times (by repeatedly aspirating and dispensing 10 μl), and proceeded to inject 7 μl into a C18 guard desalting column . After 5 min of flow at 5 µl/min with the loading pump, the 10-port valve switched to analysis mode in which the NC pump provided a flow of 250 nL/min through the guard column. The curved gradient (curve six in the Chromeleon software) then proceeded from 3% mobile phase B  to 45% B in 50 min followed by wash at 99% B and re-equilibration. Total LC-MS run time was 74 min. We used a nano EASY-Spray column  on the nano electrospray ionization (NSI) EASY-Spray source (Thermo) at 60°C.FTMS master scans with 70,000 resolution (and mass range 300–1700 m/z) were followed by data-dependent MS/MS  on the top five ions using higher energy collision dissociation (HCD) at 30–40% normalized collision energy. Precursors were isolated with a 2 m/z window. Automatic gain control (AGC) targets were 1e6 for MS1 and 1e5 for MS2. Maximum injection times were 100 ms for MS1 and 450 ms for MS2. The entire duty cycle lasted ~3.5 s. Dynamic exclusion was used with 60 s duration. Precursors with unassigned charge state or charge state one were excluded. An underfill ratio of 0.1% was used.Raw MS/MS files were converted to mzML format using msconvert from the ProteoWizard tool suite . SpectraThe MS raw data was searched in customized peptide database including non-synonymous SNPs annotated in CanProVar 2.0 database, the detected variants were curated using the SpectrumAI pipeline . In totasnp142CodingDbSnp.txt downloaded from UCSC table browser. Variant peptide transfer between mom and baby is inferred when the SNP peptide were detected both in mom and baby, but SNP array genotype data indicates one of them is wild_type and the alternative allele is carried in the other.The genotypes of these detected SNP peptides were determined by SNP array data, and classified into three categories: homo_snp, hetero_snp and wild_type based on the following rules. Wild_type is defined that both alleles are the same to the nucleotide in human reference genome hg19; hetero_snp with one of the allele changed; and homo_snp with both alleles changed. The reference allele sequence for each SNP was extracted from the file DNA were prepared from white blood cells using Qiagen DNAeasy blood and tissue kit (product number 69504) according to manufacturer’s instructions.Purified DNA was sent to the SNP and SEQ Technology Platform  for genotyping. The analysis was performed using the Illumina Infinium OmniExpressExome-8 v1.4, with 960 919 SNP markers and the Illumina Infinium assay. The results were analyzed using the software GenomeStudio 2011.1 from Illumina Inc. BeadChip type: InfiniumOmniExpressExome-8v1-4_A1 Manifest file: InfiniumOmniExpressExome-8v1-4_A1.bpm, Genome build version: 37 Cluster file ICF InfiniumOmniExpressExome-8v1-4_A1_ClusterFile.egt’.https://www.r-project.org). Significance assessed by Student's t-test was FDR-adjusted (Benjamini-Hochberg method) to correct for multiple testing. Heatmap visualizations and hierarchical clustering (Pearson correlation) were performed using the online tool Morpheus (software.broadinstitute.org/Morpheus). Annotations were extracted from BioMart (www.ensembl.org/biomart) and Ingenuity Pathway Analysis (IPA) software . Gene Ontology annotation of ranked lists was performed using the online tool GOrilla  , Microsoft Excel 2011, and R statistical computing software (n.ac.il) . Comparaions.jsp .www.proteinatlas.org) tissue enriched  proteome database. https://commons.wikimedia.org/wiki/File:Female_shadow_anatomy_without_labels.png. In addition, we also mined our data for proteins used in a recently described multi-analyte blood test .http://www.proteomexchange.org/) with identifier PXD010899.Data are available via ProteomeXchange  Novelty compared to the state of the art: There are several prior reports on use of the method for plasma analysis dating back to Heller et al., 2005, and the authors own work published in 2014 in Nature Methods provides much of the detail off-line HiRIEF separations. The first step of using abundant protein depletion to gain depth in the plasma proteome is commonly used and common knowledge. One new aspect reported relative to the authors prior published work is that broad range IEF strips outperform narrow-range IEF strips with respect to the numbers of peptides and proteins identified. This result contradicts the author's own prior results obtained with cell and tissue samples. This important difference between the behavior of cells/tissues and plasma should be described and further explored as it represents an interesting and potentially novel finding.- The section on exploring the proteome demonstrates that the HiRIEF method can replicate already known finding about the differences in the plasma proteome between males and females.- The section on protein enriched during pregnancy and enriched in cord blood is interesting, but largely describes what is already known from other blood analysis papers. Perhaps most interesting, although again simply descriptive, is the finding of fetal hemoglobin in maternal blood of some of the subjects. No mechanistic or biological implications for this are presented.- Variant observation – it is well known that MS-based proteogenomics can do this . The fact that one can do this in blood and show correlation to the genome is therefore fully expected.2) Method analyses 72 fractions from the HiRIEF separation and uses ca. 89 hours of MS instrument time to acquire the data. The number of proteins reported is ca. 1/3 of what has been achieved with slightly greater instrument use time . The HiRIEF method does yield some proteins not detected by other approaches, but overall the depth is limited in comparison to the Keshishian method which makes it somewhat less relevant for biomarker discovery or to inform biology.3) Importantly, the method appears to not be particularly reproducible as many of the supposedly confidently identified proteins must only be being observed in one or a small number of analyses: "When applying the HiRIEF methodology we identified on average 1505 proteins (per sample)…."; "In total 3053 proteins  were identified in the different experiments.' The fact that this aggregate number approaches the 3509 proteins from the most recent version of the human plasma atlas is, therefore, not of much use from the perspective of what the method can do on an individual sample basis for biology or biomarker discovery. What matters is what can be confidently and repeatedly detected across multiple patient/subject samples.4) Importantly, the number of proteins confidently identified by the authors appears to be overstated. From 1/4 to 1/2 of the proteins claimed to be identified are identified on the basis of a single peptide according to Supplementary file 1. It is standard practice in proteomics to only claim confident identification of a protein when 2 or more unique peptides from that protein are identified. Therefore, the large number of proteins claimed (ca. 3000 across multiple experiments) is likely highly inflated as it appears to include "one-hit wonders". It also appears that these numbers derive from analysis of the same plasma pool rather than plasma from different subjects – this needs to be clearly stated in the text.5) The PSA spike experiment is interesting, but simply replicates what is already known: that if you know the fraction that peptide(s) from a protein of interest elute in, you can get significantly higher sensitivity by focusing experiments on those selected fractions. In this case the authors changed to selected fractions a single narrow pI range. This is similar in concept to the PRISM method developed at PNNL by the Smith group.6) Authors state that the "method" is reproducible. As noted above, based on the ability to confidently and repeatedly detect proteins appears to be rather low. Exactly which parts are reproducible  is not entirely clear. The data used to support this statement are TMT intensity ratios obtained in a set of 4 x TMT 10-plex experiments, and focuses on a subset of ca. 1000 proteins found to be in common across the experiments. What is not demonstrated well  is the ability to confidently determine differences in protein abundance between samples. It would be much more convincing to show the reproducibility of the relative abundance differences of proteins from different patient samples and which cover the detection range from high to low abundance. Data plotted appear to be just for the pooled plasma sample in each of the 4 TMT plexes where no differences in abundance are expected across samples for the same protein. Perhaps more importantly, the actual method used for analysis of subject plasma samples does not use TMT labeling, so the reproducibility of the label-free proteomics approach has not being directly evaluated.7) Keshishian et al. report a TMT1- method that provides ca. 600 proteins/sample in ca. 5h of analysis time . This paper should be cited and your results compared.8) Please provide tables: of proteins which are purported to be newly discovered in plasma (n=611); protein affinity assay to provide the identity of the 751 protein assays that are not in the HiRIEF or PeptideAtlas lists, and of whom they are available from; CancerSEEK fingerprint used so readers do not have to go to the original paper to find out this information.9) The figures are a little on the small size of acceptable quality and care should be taken to provide legible axis and text within the figure. If they can be made larger, it would benefit the paper immensely.10) For the identification of new proteins, full identification data provided as tables should provide the MS spectral details of each of the new plasma proteins identified, the MS spectra of peptides with SAAV's, and provide the genomic data used to construct the SAAV databases for proteomics search algorithms.11) Choosing a single amino acid variation (SAAV) as the strategy to distinguish between mother and fetus proteins might hold a weakness. Since the mass difference between the WT peptide and the variant peptide is relatively low (based on one amino acid substitution) it may be explained by additional post-translational modifications (PTMs) from ones that were already mentioned and may not necessarily be explained by SAAV. For example, in Supplementary file 9: peptide 2 (T>S) – the mass difference equals Methylation, peptides 7,25,26,30,32 (V>I/L) – mass difference equal Methylation, peptide 13,15 (E>D) – mass difference equal Methylation, peptide 1,18,19 (P>L) – mass difference almost equals oxidation. We would suggest to closely review the peptide spectra to validate that the source of the different mother-fetus peptides is due to genomic variance and not PTM. This should be addressed or discussed in the manuscript and it may limit the interpretation of transfer.12) When analyzing the inter-individual variability of the plasma proteome  the authors identified HLA proteins in their top 20 list. This may be due to the highly polymorphic nature of HLA causing individual's peptides to not be properly assigned to the reference sequence. This is worth considering or mentioning.13) The authors should clarify what they mean by 'load'. Is this the amount of protein loaded onto the MARS14 column or onto the isoelectric focusing strips. If the latter, did it require multiple depletions of the same sample to reach the amount of total protein?14) In the Methods section they list two types of data processing tools (MS-GF+ and MaxQuant). It is not clear why did they use two different tools and for which samples did they use each tool?eLife. Your revised article has been favorably evaluated by Harry Dietz as the Senior Editor, a Reviewing Editor, and two reviewers.Thank you for resubmitting your work entitled "In-depth human plasma proteome analysis captures tissue proteins and transfer of protein variants across the placenta" for further consideration at The manuscript has been substantially improved but there are some remaining issues that need to be addressed before final acceptance, as outlined below.1) Figure 3B – it is unclear if the colored dots obscure one another – is there a way to statistically strengthen this statement? Perhaps it would be useful to calculate the average abundance of each of the four groups and compare them? It seems that the statement is not supported statistically.2) Overall, the authors have improved the manuscript substantially and the paper is almost ready for acceptance with the new information provided. However, the revised manuscript could have been better with tables that have the required information contained in them as requested, as well as the individual peptide spectral files from all of the novel identifications purported and provided to be more extensive and user-friendly than the current revised tables included. If done, many readers can take the information provided in this paper and perform their own analysis and corroborate the findings described within. For example, the review asked for tables of identification and spectral information of each novel peptide to describe the new protein lists in both the plasma identifications as well as the variant peptides that were found with this work, yet a simplistic table of ensemble identifiers (Supplementary file 4) was only provided for the novel plasma proteins even though the full spectral information was provided for the variant peptides. Given the scrutiny that the work presented in this manuscript will come under from the research community interested in plasma protein analysis, it is beholden to the authors to defend the work to the best of their capabilities as it stands if the work is to be published in a quality journal. The tables of new identifications would encompass all the technical details of the proteins discovered including web-linked accession number to not only ensemble but also to neXtProt and PeptideAtlas given this was one of the databases referred to, how many peptides for each novel protein were found, their individual score and probabilities, as well as any other quality factors used for their positive identification. The PSM MS spectrum plots were not provided as requested for the 611 newly discovered plasma proteins, however, these were supplied for the mother/baby variant peptides identified in the demonstration project of this paper. To complete the resubmission, please provide this updated table (Supplementary file 4) also identifying which files in the raw data repository of this work the novel identifications were made from so readers can quickly get at the data if needed. Essential revisions:1) Novelty compared to the state of the art: There are several prior reports on use of the method for plasma analysis dating back to Heller et al., 2005, and the authors own work published in 2014 in Nature Methods provides much of the detail off-line HiRIEF separations. The first step of using abundant protein depletion to gain depth in the plasma proteome is commonly used and common knowledge. One new aspect reported relative to the authors prior published work is that broad range IEF strips outperform narrow-range IEF strips with respect to the numbers of peptides and proteins identified. This result contradicts the author's own prior results obtained with cell and tissue samples. This important difference between the behavior of cells/tissues and plasma should be described and further explored as it represents an interesting and potentially novel finding.The aim of the current study was to develop and apply a robust in-depth plasma proteomics workflow, capable of analyzing clinically relevant sized cohorts, while still retaining the analytical depth to reach low abundant tissue leakage proteins and potential biomarkers.We agree with the reviewers that the use of isoelectric focusing (IEF) for separation prior to mass spectrometry (MS) -based proteomics is not a novelty itself. However, adapting the high-resolution peptide IEF for plasma has not been demonstrated previously. The previously published work from Heller et al. has applied OFF-gel fractionation on a protein and peptide level as well as in combination with high abundant protein depletion to identify 81 proteins. Here, we elucidate which pI ranges that are most beneficial for the special characteristics of the plasma proteome in terms of proteome coverage and analytical depth.We describe a workflow with capability of reproducible analysis of multiple plasma samples by use of HiRIEF pI-based plasma fractionation on ultra-narrow, narrow and wide ranges. We show that the benefit of wide range lies in the increased number of PSMs and peptides, which improves quantification accuracy– a key factor importance to biomarker discovery with inherent high variation between samples. The benefit of ultra-narrow lies in the increased number of protein identifications in the low protein abundance range . The pH range around 4 is very dense in peptides, and hence protein identification of low abundant proteins can benefit from the additional fractionation by the ultra-narrow strips in that region as shown in the PSA spike-in experiment.We report over 600 novel proteins previously not incorporated in Peptide Atlas plasma proteomics database adding significant number of proteins, and peptides for targeted proteomics, to the public resource. We also show that the identifications are robust enough to cover single amino acid variants in the plasma proteome, which has previously not been shown.Further, we show that the plasma HiRIEF method can detect tissue leakage proteins in untargeted proteomics . We also show, for the first time, that we can use plasma proteogenomics to follow peptides containing single amino acid variants across the placenta during pregnancy.In the new version of the manuscript a new section in the Introduction has been added highlighting the difference between the plasma and tissue/cellular proteome. Data from cell lines separated by HiRIEF has been added to Table 1 for comparison of the cellular analysis in Zhu et al., 2018.- The section on exploring the proteome demonstrates that the HiRIEF method can replicate already known finding about the differences in the plasma proteome between males and females.To benchmark the plasma HiRIEF method performance against current knowledge on the plasma proteome we have chosen to highlight findings that is supported by other studies on plasma. The aim with the benchmarking is to show the validity of the results from the method. The previously reported differences in the plasma proteome between men and women is used as an important reference analysis to demonstrate that the method can replicate previous observations shown by other techniques.- The section on protein enriched during pregnancy and enriched in cord blood is interesting, but largely describes what is already known from other blood analysis papers. Perhaps most interesting, although again simply descriptive, is the finding of fetal hemoglobin in maternal blood of some of the subjects. No mechanistic or biological implications for this are presented.This is a good point that is missing in the current version of the manuscript. The expression of fetal hemoglobin among adults has been reported previously and can partly be explained by a hereditary component . A short section describing this and the references has been added to this section . However, we see the novelty here in detecting proteins transferring cross placenta unequivocally identified.- Variant observation – it is well known that MS-based proteogenomics can do this . The fact that one can do this in blood and show correlation to the genome is therefore fully expected.While it is true that both we and others have shown variant detection by MS-based proteomics in cells and tissues, we also concluded in Branca et al., 2014, that a prerequisite in variant detection is in-depth coverage of proteome and peptidome. Given the analytical difficulties associated with plasma proteome analysis it cannot necessarily been taken for granted that variants expressed by different organs of the human body can be detected, as it for other sample types such as cell cultures, particularly considering the skewed distribution of proteins concentrations.Here we have applied the detection of variants to study proteins passing across the placenta. This opens up a new possibility to study fundamental questions linked to fetal-maternal communication in vivo.2) Method analyses 72 fractions from the HiRIEF separation and uses ca. 89 hours of MS instrument time to acquire the data. The number of proteins reported is ca. 1/3 of what has been achieved with slightly greater instrument use time . The HiRIEF method does yield some proteins not detected by other approaches, but overall the depth is limited in comparison to the Keshishian method which makes it somewhat less relevant for biomarker discovery or to inform biology.In the first version of the manuscript, a comparison with the Keshishian dataset from 2015  was only made indirectly, as it is one of the datasets included in the latest version of the Plasma Proteome database compiled within the PeptideAtlas .We fully agree that work by Keshishian et al. is an important benchmark in the field of plasma proteomics and have therefore added a head to head comparison with their method and the data from the MCP paper from 2015. The raw data from the Nature protocols paper by Keshishian et al. is not available deposited in the repository, so no direct comparative analysis with the novel data sets from that publication has been performed. We acknowledge that there is a growing diversity of MS-based approaches and that these require to highlight their added value in relation to the benchmarks of the field.In the initial response to the editor we included several preliminary analyses where we had re-searched the available raw data from Keshishian et al. using same search parameters as for our HiRIEF analysis. After feedback from the editor, these preliminary analyses were not added in updated version of the manuscript. Instead, we have introduced a short section using the re-searched data to show a core set of proteins that can be confidently identified across the different experiments described in Keshishian et al., 2015 and compared it to the core set generated by the HiRIEF method, as we believe this is a key feature in the analysis of larger clinical cohorts and a core set analysis was not reported in the original MCP paper (subsection “Performance assessment of the plasma HiRIEF method”).In addition, we have also added a head to head comparison of the main results describing the performance of the method from Keshishian et al. as reported by the authors from the MCP study in terms of sample load, CV, MS time and fractionation (see the aforementioned subsection).In the revised version of the manuscript we have added a second TMT-cohort which is analyzed in a condensed HiRIEF fraction pooling approach to reduce the MS time to approx. 55h. We show that the reduction in MS-time does not affect the number of identified proteins, showing improved throughput of the HiRIEF method. In addition, the amount of plasma per sample required in the HiRIEF TMT-10 experiment is 40uL vs approx. 400uL used for the in-depth analysis in the Keshishian et al.papers.A key feature in the comparison between the method described by Keshishian et al. and the HiRIEF method is the applicability to clinical cohorts including samples from a number of different individuals. We acknowledge that Keshishian et al. report higher number of identified proteins, but we argue that the HiRIEF method has an advantage for in-depth analysis of larger patient cohorts – using less material, less MS analysis time and showing robust identifications across two different cohorts with 42 different individuals, sampled at different times and different hospitals.3) Importantly, the method appears to not be particularly reproducible as many of the supposedly confidently identified proteins must only be being observed in one or a small number of analyses: "When applying the HiRIEF methodology we identified on average 1505 proteins (per sample)…."; "In total 3053 proteins  were identified in the different experiments.' The fact that this aggregate number approaches the 3509 proteins from the most recent version of the human plasma atlas is, therefore, not of much use from the perspective of what the method can do on an individual sample basis for biology or biomarker discovery. What matters is what can be confidently and repeatedly detected across multiple patient/subject samples.While setting up and optimizing the workflow, an average 1505 proteins were identified per experimental condition using different loads and different pI ranges. The pI ranges cover different pI areas and are in that sense complementary and hence are not expected to identify exactly the same peptides and proteins . This flexibility is indeed an advantage of the method . In the new version of the manuscript we have added a table (Table 1) summarizing the findings from the optimization and updated the text to clarify the benefit of being able to use different pH intervals for different analytical purposes.In addition, we have added a second cohort to make a core-set analysis of proteins that can be confidently identified across samples, TMT-sets and cohorts. In this gene-centric core set analysis we show that 828 genes can be confidently identified across nine TMT-sets (subsection “Performance assessment of the plasma HiRIEF method”).4) Importantly, the number of proteins confidently identified by the authors appears to be overstated. From 1/4 to 1/2 of the proteins claimed to be identified are identified on the basis of a single peptide according to Supplementary file 1. It is standard practice in proteomics to only claim confident identification of a protein when 2 or more unique peptides from that protein are identified. Therefore, the large number of proteins claimed (ca. 3000 across multiple experiments) is likely highly inflated as it appears to include "one-hit wonders". It also appears that these numbers derive from analysis of the same plasma pool rather than plasma from different subjects – this needs to be clearly stated in the text.For protein identifications we have used MSGF+ and Percolator within our standard pipeline. In the current manuscript we have been using a strict 1% FDR cut off at PSM, peptide and protein level. In addition, the open source commonly used MaxQuant search engine Andromeda was used for quality control comparison and for calculating modification probabilities and location.We do not agree with the reviewer that one-peptide identifications are inherently poor-quality identifications. Rather, for peptide identification, currently the most accepted result validation method of the quality of the peptide identification is through false discovery rate (FDR) of each individual peptide. This has been well described in the paper by Pevzner et al. .Also see reply to point 3 above regarding number of identified proteins in different experiments and the core set analysis.5) The PSA spike experiment is interesting, but simply replicates what is already known: that if you know the fraction that peptide(s) from a protein of interest elute in, you can get significantly higher sensitivity by focusing experiments on those selected fractions. In this case the authors changed to selected fractions a single narrow pI range. This is similar in concept to the PRISM method developed at PNNL by the Smith group.While PRISM method is indeed splendid, however, there are key differences between the methods. The advantage with the HiRIEF methodology for targeted MS is that it uses inherent peptide property that is peptide sequence dependent, the pI of a peptide, which can be predicted. Hence, for any tryptic peptide with known sequence, we can use the pI predictor to find the fraction in which the peptide will be found if present above detection limits. The fractionation method can therefore be optimized a priori based on the calculated peptide pI and do not rely on indexing of large previously analyzed experimental datasets. Moreover, the prediction accuracy of where a peptide ends up in LC fractionation is lower (property used in PRISM), especially on variant level detection, due to that it suffers on variation due to inter- chromatographic interactions depending on sample compositions .6) Authors state that the "method" is reproducible. As noted above, based on the ability to confidently and repeatedly detect proteins appears to be rather low. Exactly which parts are reproducible  is not entirely clear. The data used to support this statement are TMT intensity ratios obtained in a set of 4 x TMT 10-plex experiments, and focuses on a subset of ca. 1000 proteins found to be in common across the experiments. What is not demonstrated well  is the ability to confidently determine differences in protein abundance between samples. It would be much more convincing to show the reproducibility of the relative abundance differences of proteins from different patient samples and which cover the detection range from high to low abundance. Data plotted appear to be just for the pooled plasma sample in each of the 4 TMT plexes where no differences in abundance are expected across samples for the same protein. Perhaps more importantly, the actual method used for analysis of subject plasma samples does not use TMT labeling, so the reproducibility of the label-free proteomics approach has not being directly evaluated.This is a valid point and addressing it will help us to improve the manuscript. As we see this, the reproducibility can be divided into two categories: quantitative reproducibility and reliability of repeated detection of overlapping proteome between analyses. The pooled internal standards that have been used for the quantitative reproducibility calculations have been pooled after the digestion of the proteins, meaning that only technical variability post digestion is taken in to account.In the current version we have clarified which steps are included in the reproducibility calculations (subsection “Performance assessment of the plasma HiRIEF method). We have also added a core set analysis of proteins that can be repetitively detected across different TMT sets and cohorts using the 3-10 strip.For quantitative analysis using TMT-labelling we have used that in the analysis looking a proteins with high and low variability in the plasma proteome and differences between abundance between men and women.7) Keshishian et al. report a TMT1- method that provides ca. 600 proteins/sample in ca. 5h of analysis time (Nature Protocols 2016). This paper should be cited and your results compared.We agree, see reply under point 2.8) Please provide tables: of proteins which are purported to be newly discovered in plasma (n=611); protein affinity assay to provide the identity of the 751 protein assays that are not in the HiRIEF or PeptideAtlas lists, and of whom they are available from; CancerSEEK fingerprint used so readers do not have to go to the original paper to find out this information.We have added a supplementary table (Supplementary file 4) describing the proteins from the overlap analysis as well as a supplementary file describing the identified proteins from on the cancer seek panel (Supplementary file 7). The protein assays are described in the original publication by Schwenk et al.9) The figures are a little on the small size of acceptable quality and care should be taken to provide legible axis and text within the figure. If they can be made larger, it would benefit the paper immensely.We agree with the reviewer and have enlarged and simplified Figure 1 and split Figure 3 into two separate figures, as well as enlarged several of the supplementary figures.0) For the identification of new proteins, full identification data provided as tables should provide the MS spectral details of each of the new plasma proteins identified, the MS spectra of peptides with SAAV's, and provide the genomic data used to construct the SAAV databases for proteomics search algorithms.The construction of the VarDB database is described in detail in Zhu et al., 2018. The reference and information has been added in the updated version.Mirror plots, showing spectra with DNA support (from ‘donor’) and without DNA support (from ‘recipient’), for the transferred peptides are included in a supplementary file (Supplementary file 13). We have manually inspected the mirror plots and highlighted those where we believe there is a poor match (n=5) and updated the main text accordingly (subsection “Plasma proteogenomics”).11) Choosing a single amino acid variation (SAAV) as the strategy to distinguish between mother and fetus proteins might hold a weakness. Since the mass difference between the WT peptide and the variant peptide is relatively low (based on one amino acid substitution) it may be explained by additional post-translational modifications (PTMs) from ones that were already mentioned and may not necessarily be explained by SAAV. For example, in Supplementary file 9: peptide 2 (T>S) – the mass difference equals Methylation, peptides 7,25,26,30,32 (V>I/L) – mass difference equal Methylation, peptide 13,15 (E>D) – mass difference equal Methylation, peptide 1,18,19 (P>L) – mass difference almost equals oxidation. We would suggest to closely review the peptide spectra to validate that the source of the different mother-fetus peptides is due to genomic variance and not PTM. This should be addressed or discussed in the manuscript and it may limit the interpretation of transfer.To curate the variant data, we have used the iPAW pipeline described in Zhu et al., 2018. Of particular importance in IPAW is SpectrumAI (Automated Inspection), a tool that curates single amino acid substituted peptides by requiring ions to directly support the residue substitutions in MS/MS spectra. SpectrumAI will make sure that the mass change is exactly on that amino acid position and not elsewhere.We agree that if the residue mass in a position of D>E substitution is a genuine glutamate or it is an aspartate that got a post-translational modification of a methylation in the cell, we can never be 100% sure. However, T>S (peptide 2) would be loss of methyl group, not gain, and that modification is not known at all (and is absent from the unimod database).Moreover, methylations are unlikely in S, V, D, or P. There are papers describing methylation on R and K, for example, but not on S, V, D or P. To our knowledge only methyl-D (PMID: 18220335) has been described, and even in that paper out of a total of 7000 peptides identified in MS, they found 3 methyl-D peptides.Although these alternative explanations for the spectra could be possible, we believe that they less likely than a SAAV with genomic support.To supplement the sequence confirmation of the transferred variants we have included mirror plots showing the peptide spectrum from the donor (with sequence support) mirrored against the postulated same transferred peptide in the recipient (without sequence support), and we have also manually inspected the spectra and highlighted those of poor quality. See reply to point 10.12) When analyzing the inter-individual variability of the plasma proteome  the authors identified HLA proteins in their top 20 list. This may be due to the highly polymorphic nature of HLA causing individual's peptides to not be properly assigned to the reference sequence. This is worth considering or mentioning.This is a good point and we have added it to the revised version of the manuscript (subsection “Exploring the plasma proteome”).13) The authors should clarify what they mean by 'load'. Is this the amount of protein loaded onto the MARS14 column or onto the isoelectric focusing strips. If the latter, did it require multiple depletions of the same sample to reach the amount of total protein?The load is referring to the peptide amount loaded onto the IEF strips. This and other lab jargon terminologies has been clarified in the text .14) In the Materials and methods section they list two types of data processing tools (MS-GF+ and MaxQuant). It is not clear why did they use two different tools and for which samples did they use each tool?We used MSGF+ and Percolator within our standard pipeline. The open source commonly used MaxQuant search engine Andromeda was used for quality control comparison and for calculating modification probabilities and location.The manuscript has been substantially improved but there are some remaining issues that need to be addressed before final acceptance, as outlined below.1) Figure 3B – it is unclear if the colored dots obscure one another – is there a way to statistically strengthen this statement? Perhaps it would be useful to calculate the average abundance of each of the four groups and compare them? It seems that the statement is not supported statistically.We would like to thank the reviewers and editors with thorough work on reviewing the manuscript and we are happy to hear that the revisions that has been made to the manuscript is appreciated by the reviewers. We also agree that all dots in Figure 3B are not separated and hence could mask each other. We have hence added a novel figure supplement  showing the distributions in more detail and have added statistical analyses comparing the different distributions to each other to clarify the differences between classical plasma proteins and other protein groups referred to in the text.2) Overall, the authors have improved the manuscript substantially and the paper is almost ready for acceptance with the new information provided. However, the revised manuscript could have been better with tables that have the required information contained in them as requested, as well as the individual peptide spectral files from all of the novel identifications purported and provided to be more extensive and user-friendly than the current revised tables included. If done, many readers can take the information provided in this paper and perform their own analysis and corroborate the findings described within. For example, the review asked for tables of identification and spectral information of each novel peptide to describe the new protein lists in both the plasma identifications as well as the variant peptides that were found with this work, yet a simplistic table of ensemble identifiers (Supplementary file 4) was only provided for the novel plasma proteins even though the full spectral information was provided for the variant peptides. Given the scrutiny that the work presented in this manuscript will come under from the research community interested in plasma protein analysis, it is beholden to the authors to defend the work to the best of their capabilities as it stands if the work is to be published in a quality journal. The tables of new identifications would encompass all the technical details of the proteins discovered including web-linked accession number to not only ensemble but also to neXtProt and PeptideAtlas given this was one of the databases referred to, how many peptides for each novel protein were found, their individual score and probabilities, as well as any other quality factors used for their positive identification. The PSM MS spectrum plots were not provided as requested for the 611 newly discovered plasma proteins, however, these were supplied for the mother/baby variant peptides identified in the demonstration project of this paper. To complete the resubmission, please provide this updated table (Supplementary file 4) also identifying which files in the raw data repository of this work the novel identifications were made from so readers can quickly get at the data if needed.We agree with the reviewers that the availability of the MS data is of outmost importance and all raw data as well as all result files  from the MS analysis is available in the public repository proteomeXchange as described in the Materials and methods section.With acceptance of the publication the data will be made available for the scientific community.In response to review comment 2 we have added three novel tables with more detailed information about the proteins identified in the optimization as supplementary information (updated Supplementary file 4).Supplementary file 4 is now updated and contains all 3053 proteins that were identified in the optimization, including data on number of peptides, #psms, q-value etc. A column indicating all the 611 proteins that has not been reported in PeptideAtlas has also been added.In addition, the updated Supplementary file 4 also contains all the peptides that has been identified in the optimization, as well as details on #psms, q-value etc. A column indicating which peptides that are mapping to the 611 proteins that has not been reported in PeptideAtlas is also included.Last the last novel table within Supplementary file 4 contains all the PSMs mapping to the 611 proteins that has not been reported in PeptideAtlas and information on in which raw file(s) they can be found and the corresponding scan numbers (spectrum).For the transferred peptides we have also provided annotated spectrum, however we respectfully argue against a figure on the over 40 000 spectrum matching to the 611 novel proteins as a supplementary figure, and even if only selected spectra showing the specific peptides for each protein would be included, the file would be of limited use for the community. In our opinion, having access to the raw data, and the added information on the tables to guide researcher to find the spectra, provides the readers with a much more flexible format and the opportunity for the scientific community to re-analyze and evaluate the data much more thoroughly.reference to each spectrum is required .In our reporting of the mass spectrometry data we have followed the Miape guidelines  http://www.psidev.info/miape postulated by the Human Proteome Organisation where