{"paper_id":"9595a113-6c74-427e-bb6c-970952525426","body_text":"In vitro fertilization (IVF) coupled with embryo transfer into uterus has been applied as treatment for infertility several decades. IVF was initially used to assist the reproduction of sub-fertile women caused by tubal factors [ 1 ]. With the improvement of IVF techniques, IVF is now a routine treatment for many reproductive diseases. However, the success rate of pregnancy is still a problem in clinical IVF practice, which is only about 50% even if the embryos with normal morphology were used for transfer [ 2 ]. In order to select embryos with the best potential good for IVF outcome, morphological assessments of blastocyst and blastocoels have been adopted, but it was still difficult to predict the quality of embryos [ 3 ]. Therefore, it was necessary to develop new strategies for embryo quality evaluation. Epidemiologic investigations showed that many intrinsic and extrinsic factors contributed to the quality of embryo. Because oocyte quality directly influences embryo development, HFF (microenvironment of oocyte maturation) became a main factor contributing to the success of IVF treatment [ 4 ].\nSmall antral follicles respond to ovarian stimulation by increasing in size due to rapid accumulation of follicular fluid, as well as granulosa cell divisions, which necessitate follicular basal lamina expansion. The components of HFF had several origins: secretions from granulosa cells, thecal cells, occytes, and blood plasma composition transferred through the thecal capillaries [ 5 ]. The major components of HFF were proteins [ 6 ], steroid hormones [ 7 ], and metabolites [ 8 ]. HFF provided a special milieu to facilitate the communications between occyte and follicular cells, the development of follicle and the maturation of occytes. The alteration of HFF proteins reflected disorders of main secretary function of granulosa cells and thecae, and the damage of blood follicular barrier, which was associated with abnormal folliculogenesis [ 9 ] and a diminished reproductive potential [ 10 ]. In IVF treatment, HFF was easily accessible during the aspiration of oocytes from follicle, and was an ideal source for noninvasive screening of biomakers for oocyte maturation, fertilization success, IVF outcome, pregnancy, and ovarian diseases.\nIn the postgenomic era, proteomic techniques have been widely used in the field of reproductive medicine. HFF proteome has become a hotspot for research, which not only contributed to discovering proteins related to IVF outcomes, but also improved our comprehensive understanding of physiological process during follicle development and oocyte maturation [ 11 ]. Li and co-workers used surface-enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF-MS) combined with weak cation-exchange protein chip (WCX-2) to search for differentially expressed HFF proteins from mature and antral follicles [ 12 ]. Two-dimensional gel electrophoresis (2D–GE) followed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was also used to identify 8 differentially expressed HFF proteins related to immune and inflammatory responses from controlled ovarian hyperstimulation (COH) and natural ovulatory cycles [ 13 ]. Ambekar and co-workers carried out SDS-PAGE, OFFGEL and SCX-based separation followed by LC-MS/MS analysis to characterize 480 HFF proteins for a better understanding of folliculogenesis physiology [ 14 ]. Chen and co-workers explored the HFF biomarkers between successfully fertilized oocytes and unfertilized mature oocytes through nano-scale liquid chromatography coupled to tandem mass spectrometry (nano LC-MS/MS), and found 53 peptides to be potential candidates [ 15 ]. Although proteomic researches on HFF deepened our understanding of reproductive process and provided candidates related to oocyte quality, follicle development, IVF outcome and ovarian disorders, it was still essential to fully delineate the HFF networks and pathways involved in the physiology of reproduction and pathophysiology of infertility.\nIn the present study, we carried out an in-depth proteomic analysis of HFF from women undergoing successful IVF based on dual RP-HPLC coupled to MALDI TOF/TOF MS. The results profiled candidate biomarkers for the prediction of oocyte maturation, fertilization, and pregnancy and provided a new complement for HFF dataset, which will improve the understanding of biological processes and complicated pathways and interaction networks in HFF.\n\nThe HFF samples were collected from 10 women who underwent IVF treatment and achieved pregnancy. The selected patients met the following criteria: infertility not caused by tubal factor; aged less than 38 years; serum FSH values <12 mIU/mL; undergoing their first fresh egg retrieval cycle; ovulation stimulated with the long protocol. The patients were also without chromosomal abnormalities, polycystic ovary syndrome (PCOS), endometriosis and or endocrine disease. Cause of infertility was simple male factor. The body mass index (BMI) of patients met the normal criteria proposed by WGOC (18.5 ≤ BMI ≤ 23.9 kg/m 2 ) [ 16 – 18 ]. Ovarian stimulation and oocyte retrieval were performed as previously described [ 19 ]. Briefly, when more than two follicles exceeded 18 mm in diameter, 10,000 IU of HCG (Merck Serono, Swiss) was injected intramuscular. After 36 h, HFF was collected during trans-vaginal ultrasound guided aspiration of oocytes. The resultant HFF samples were macroscopically clear and without contamination of the flushing medium.\nThe samples were centrifuged at 10,000×g at 4 °C for 30 min to produce cell debris-free HFF fraction for further analysis. Concentration of HFF was determined by the Bradford method [ 20 ]. This work has been approved by the Ethics Committee of Beijing BaoDao Obstetrics and Gynecology Hospital, and written informed consents were obtained from all participants.\nEqual amounts (50μg) of HFF proteins from each sample were pooled for separation. The samples were sequentially treated with 20 mM dithiothreitol at 37 °C for 120 min, and 50 mM iodoacetamide in dark for 60 min at room temperature. Then the sample was finally digested using trypsin (sequencing grade, Promega, France) ( W /W, 1:50 enzyme/protein) overnight at 37 °C. According to the previous method with appropriate modification [ 21 ], the first dimension RP separation was performed on PF-2D HPLC System (Rigol) by using a Durashell RP column (5 μm, 150 Å, 250 mm × 4.6 mm i.d., Agela). Mobile phases A (2% acetonitrile, adjusted pH to 10.0 using NH 3 .H 2 0) and B (98% acetonitrile, adjusted pH to 10.0 using NH 3 .H 2 0) were used to develop a gradient. The solvent gradient was set as follows: 5% B, 5 min; 5–15% B, 15 min; 15–38% B, 15 min; 38–90% B, 1 min; 90% B, 8.5 min; 90–5% B, 0.5 min; 5% B, 10 min. The tryptic peptides were separated at an eluent flow rate of 0.8 ml/min and monitored at 214 nm. Totally, 28 eluent fractions were collected and dried by a SPD2010 SpeedVac concentrator system (Thermo, USA).\nAccording to the previous method [ 22 ], the samples were dried under vacuum and reconstituted in 30 μl of 0.1% ( v /v) formic acid, 2% ( v /v) acetonitrile in water for subsequent analyses. Each fraction was separated and spotted using the Tempo™ LC-MALDI Spotting System (AB SCIEX, USA). Peptides were separated by a C18 AQ 150 × 0.2 mm column (3 μm, Michrom, USA) using a linear gradient formed by buffer A (2% acetonitrile, 0.1% formic acid) and buffer B (98% acetonitrile, 0.1% formic acid), from 5% to 35% of buffer B over 90 min at a flow rate of 0.5 μL/min. The eluted peptides were mixed with matrix solution (5 mg/mL in 70% acetonitrile, 0.1% trifluoroacetic acid) at a flow rate of 2 μL/min pushed by additional syringe pump. For each fraction, 616 spots were spotted on a 123× 81 mm LC-MALDI plate insert. Then the spots were analyzed using MALDI-TOF/TOF 5800 mass spectrometer (AB SCIEX, USA). A full-scan MS experiment (m/z range from 800 to 4000) was acquired, and then the top 40 ions were detected by MS/MS.\nProtein identification was performed with the ProteinPilot™ software (version 4.0.1; AB SCIEX). Each MS/MS spectrum was searched against a database (2017_03 released UniProtKB/Swiss-Prot human database, 20,183 entries) and a decoy database for FDR analysis (programmed in the software). The search parameters were as follows: trypsin enzyme; maximum allowed missed cleavages 1; Carbamidomethyl cysteine; biological modifications programmed in the algorithm. Proteins with high-confidence (FDR < 0.01) were considered as positively identified proteins.\nThe gene ontology enrichment analysis of HFF proteins were performed by using online bioinformatics tools of PANTHER (Protein ANalysis THrough Evolutionary Relationships) classification system (released 11.1, 2016–10-24) ( http://pantherdb.org/ ) [ 23 ] and DAVID (The Database for Annotation, Visualization and Integrated Discovery) bioinformatics resources 6.8 ( https://david.ncifcrf.gov/ ) [ 24 ]. Each protein was placed in only one category, and those with no annotation and supporting information were categorized as “Unknown”. The pathway map of HFF proteins were achieved through KEGG: Kyoto Encyclopedia of Genes and Genomes (Release 81.0, 2017–01-01) ( http://www.kegg.jp ) [ 25 ]. The protein-protein interaction network for the HFF proteins was annotated using the STRING (search tool for recurring instances of neighbouring genes) database (released 10.0, 2016–04–16) ( http://string-db.org/ ) [ 26 ]. The venn diagram was drawn through a online software “Calculate and draw custom Venn diagrams” ( http://bioinformatics.psb.ugent.be/webtools/ Venn/).\nAccording to the method described previously [ 27 ,  28 ], 50 μg HFF protein were separated by a 12% SDS-PAGE gel and then electronically transferred onto a nitrocellulose membrane. The resultant membrane was blocked with 5% ( w / v ) skimmed milk for 1 h at 37 °C, and then was incubated with the primary antibody (Abcam, Cambridge, USA, diluted 1:2000) at 4 °C overnight. After washing with TBST for three times, the membranes were incubated with horse-radish peroxidase-conjugated secondary antibody (diluted 1:5000, Zhong-Shan Biotechnology, Beijing, China) at room temperature for 1 h. The immunoreactive proteins was visualized by enhanced chemiluminescence detection reagents (Pierce, Rockford, IL, USA) (Additional file  1 : Table S1).\n\nIdentification of high-confidence HFF proteome by dual RP-HPLC coupled with MALDI TOF/TOF mass spectrometry.\nA peptide sequencing strategy was applied by using two-dimensional chromatography-MALDI TOF/TOF mass spectrometry. We employed high pH (pH 10) reverse phase liquid chromatography to decrease the complexity of the tryptic digest of the HFF proteins, and collected 28 fractions. Then each fraction was further separated by low pH (pH 3) reverse phase liquid chromatography, and spotted on the plate using the Tempo™ LC-MALDI Spotting System. After sequencing by a 5800 MALDI TOF/TOF mass spectrometry, the resultant spectra were analyzed by ProteinPilot™ software by searching the reviewed Swiss-Prot human database (20,183 sequences, 2017_03 released). A total of 219 unique high-confidence (FDR < 0.01) proteins were identified by two replicates (Table  1 ). Experiment 1 and 2 identified 188 with 2747 unique peptides and 179 proteins with 2800 unique peptides, respectively. 148 common proteins were shared between the two experiments. Figure  1  showed representative MS/MS spectra of peptides from the identified HFF proteins. The m/z of precursor (Fig.  2c ) was over 2500, and almost all b-ions and y-ions were still obtained based on a 5800 MALDI TOF/TOF mass spectrometry. Table 1 A list of 219 identified high-confidence HFF proteins from women underwent successful IVF by LC MALDI TOF/TOF mass spectrometry (FDR < 0.01) No SwissProt AC Name protein description Gene Name Molecular Weight experiment 1 experiment 2 Coverage(%) Matched Peptides number Coverage(%) Matched Peptides number 1 P43652 Afamin AFM 69,069 31.9 10 35.7 10 2 P02763 Alpha-1-acid glycoprotein 1 ORM1 23,512 40.8 17 40.8 15 3 P19652 Alpha-1-acid glycoprotein 2 ORM2 23,603 45.8 15 53.2 15 4 P01011 Alpha-1-antichymotrypsin SERPINA3 47,651 53 15 44.2 16 5 P01009 Alpha-1-antitrypsin SERPINA1 46,737 62.7 86 64.4 76 6 P04217 Alpha-1B-glycoprotein A1BG 54,254 39.8 17 48.5 19 7 P08697 Alpha-2-antiplasmin SERPINF2 54,566 29.1 9 47.1 11 8 P02765 Alpha-2-HS-glycoprotein AHSG 39,325 42.8 14 55.9 18 9 P01023 Alpha-2-macroglobulin A2M 163,291 46.8 47 47.4 46 10 P48728 Aminomethyltransferase, mitochondrial AMT 43,946 2.2 1 - - 11 P01019 Angiotensinogen AGT 53,154 37.7 14 25.8 11 12 C9JTQ0 Ankyrin repeat domain-containing protein 63 ANKRD63 39,620 15 1 - - 13 P01008 Antithrombin-III SERPINC1 52,602 61.9 21 54.7 24 14 P02647 Apolipoprotein A-I APOA1 30,778 73.8 67 82.4 69 15 P02652 Apolipoprotein A-II APOA2 11,175 70 9 64 9 16 P06727 Apolipoprotein A-IV APOA4 45,399 67.2 24 63.1 25 17 P02654 Apolipoprotein C-I APOC1 9332 26.5 3 37.4 3 18 P02655 Apolipoprotein C-II APOC2 11,284 39.6 2 50.5 3 19 P02656 Apolipoprotein C-III APOC3 10,852 34.3 2 51.5 6 20 P05090 Apolipoprotein D APOD 21,276 24.9 3 28.6 3 21 P02649 Apolipoprotein E APOE 36,154 43.2 6 43.5 4 22 Q13790 Apolipoprotein F APOF 35,399 - - 8 1 23 O95445 Apolipoprotein M APOM 21,253 26.6 2 30.3 2 24 Q9H2U1 ATP-dependent RNA helicase DHX36 DHX36 114,760 - - 17.9 1 25 O75882 Attractin ATRN 158,537 15 1 - - 26 P98160 Basement membrane-specific heparan sulfate proteoglycan core protein HSPG2 468,830 30.8 43 31 46 27 P02749 Beta-2-glycoprotein 1 APOH 38,298 51 15 41.5 16 28 Q96KN2 Beta-Ala-His dipeptidase CNDP1 56,706 18.9 1 - - 29 P43251 Biotinidase BTD 61,133 9.2 2 14.2 1 30 Q7L273 BTB/POZ domain-containing protein KCTD9 KCTD9 42,567 - - 30.1 1 31 P04003 C4b-binding protein alpha chain C4BPA 67,033 11.9 2 27 4 32 Q96IY4 Carboxypeptidase B2 CPB2 48,424 13 2 16.1 2 33 P22792 Carboxypeptidase N subunit 2 CPN2 60,557 - - 10.8 2 34 Q9ULM6 CCR4-NOT transcription complex subunit 6 CNOT6 63,307 - - 2.3 1 35 Q8N8E3 Centrosomal protein of 112 kDa CEP112 112,749 17.4 1 - - 36 Q5SW79 Centrosomal protein of 170 kDa CEP170 175,293 - - 5.9 1 37 P00450 Ceruloplasmin CP 122,205 59.6 47 58.1 58 38 O14647 Chromodomain-helicase-DNA-binding protein 2 CHD2 211,344 - - 12 1 39 P10909 Clusterin CLU 52,495 41.4 14 50.1 12 40 P00740 Coagulation factor IX F9 51,778 15.2 1 - - 41 P00742 Coagulation factor X F10 54,732 24.6 1 14.1 1 42 P00748 Coagulation factor XII F12 67,792 29.9 4 20.8 4 43 Q5TID7 Coiled-coil domain-containing protein 181 CCDC181 60,103 - - 7.9 1 44 P02746 Complement C1q subcomponent subunit B C1QB 26,722 20.2 1 18.6 1 45 Q9NZP8 Complement C1r subcomponent-like protein C1RL 53,498 8.6 1 6.2 1 46 P06681 Complement C2 C2 83,268 21.5 4 22.7 6 47 P01024 Complement C3 C3 187,148 67.1 121 74.1 119 48 P0C0L4 Complement C4-A C4A 192,785 46.6 53 54.8 66 49 P0C0L5 Complement C4-B C4B 192,751 46.3 52 53 66 50 P01031 Complement C5 C5 188,305 20.3 7 27.1 12 51 P13671 Complement component C6 C6 104,786 26 6 25.5 6 52 P10643 Complement component C7 C7 93,518 35.2 8 23.1 5 53 P07357 Complement component C8 alpha chain C8A 65,163 24.8 5 23.5 4 54 P07358 Complement component C8 beta chain C8B 67,047 37.1 4 37.2 6 55 P07360 Complement component C8 gamma chain C8G 22,277 48.5 7 48 5 56 P02748 Complement component C9 C9 63,173 36.5 8 35.8 10 57 P00751 Complement factor B CFB 85,533 41.4 20 51.4 25 58 P08603 Complement factor H CFH 139,096 55.4 43 56.9 45 59 Q03591 Complement factor H-related protein 1 CFHR1 37,651 33.9 2 27.3 5 60 P05156 Complement factor I CFI 65,750 31.1 7 31.7 5 61 P08185 Corticosteroid-binding globulin SERPINA6 45,141 19.5 3 17.3 2 62 Q9UBG0 C-type mannose receptor 2 MRC2 166,674 3.2 1 - - 63 P01034 Cystatin-C CST3 15,799 22.6 1 - - 64 P30876 DNA-directed RNA polymerase II subunit RPB2 POLR2B 133,897 - - 10.7 1 65 Q8NHS0 DnaJ homolog subfamily B member 8 DNAJB8 25,686 16.8 1 - - 66 Q96DT5 Dynein heavy chain 11, axonemal DNAH11 520,369 - - 9.8 1 67 Q9C0C9 E2 ubiquitin-conjugating enzyme UBE2O 141,293 - - 3.9 1 68 O95071 E3 ubiquitin-protein ligase UBR5 UBR5 309,352 7.6 1 - - 69 A4FU69 EF-hand calcium-binding domain-containing protein 5 EFCAB5 173,404 8.1 1 - - 70 Q16610 Extracellular matrix protein 1 ECM1 60,674 20.7 2 11.5 2 71 Q9UGM5 Fetuin-B FETUB 42,055 12.8 1 18.3 1 72 P02671 Fibrinogen alpha chain FGA 94,973 44.8 40 47.6 44 73 P02675 Fibrinogen beta chain FGB 55,928 72.1 53 68.6 42 74 P02679 Fibrinogen gamma chain FGG 51,512 69.1 36 68 34 75 P02751 Fibronectin FN1 262,625 30.3 33 31.2 34 76 Q08380 Galectin-3-binding protein LGALS3BP 65,331 22.9 1 28.7 4 77 P06396 Gelsolin GSN 85,698 43.9 16 43.6 20 78 P07093 Glia-derived nexin SERPINE2 44,002 34.7 4 28.6 3 79 P22352 Glutathione peroxidase 3 GPX3 25,552 16.4 2 27 1 80 Q7Z4J2 Glycosyltransferase 6 domain-containing protein 1 GLT6D1 36,274 2.6 1 - - 81 P0CG08 Golgi pH regulator B GPR89B 52,917 - - 7.7 1 82 P00738 Haptoglobin HP 45,205 61.1 26 58.6 23 83 P00739 Haptoglobin-related protein HPR 39,030 44.3 10 - - 84 Q9Y6N9 Harmonin USH1C 62,211 7.8 1 - - 85 P69905 Hemoglobin subunit alpha HBA1/HBA2 15,258 - - 28.2 1 86 P68871 Hemoglobin subunit beta HBB 15,998 43.5 2 52.4 1 87 P02790 Hemopexin HPX 51,676 55.8 44 76.4 50 88 P05546 Heparin cofactor 2 SERPIND1 57,071 21 6 34.9 6 89 Q04756 Hepatocyte growth factor activator HGFAC 70,682 5.3 1 - - 90 P04196 Histidine-rich glycoprotein HRG 59,578 33 15 37.9 18 91 O43365 Homeobox protein Hox-A3 HOXA3 46,369 6.5 1 - - 92 P78426 Homeobox protein Nkx-6.1 NKX6–1 37,849 16.4 1 - - 93 Q14520 Hyaluronan-binding protein 2 HABP2 62,672 15.4 2 11.8 3 94 P0DOX2 Immunoglobulin alpha-2 heavy chain N/A 48,935 39.1 14 40.9 12 95 P0DOX3 Immunoglobulin delta heavy chain N/A 56,224 19.9 1 23.4 1 96 P0DOX4 Immunoglobulin epsilon heavy chain N/A 60,323 8.4 2 15.7 2 97 P0DOX5 Immunoglobulin gamma-1 heavy chain N/A 49,330 70.6 144 71.9 123 98 P01876 Immunoglobulin heavy constant alpha 1 IGHA1 37,655 42.8 23 48.2 16 99 P01859 Immunoglobulin heavy constant gamma 2 IGHG2 35,901 74.9 104 69.9 92 100 P01860 Immunoglobulin heavy constant gamma 3 IGHG3 41,287 72.4 69 78.3 65 101 P01861 Immunoglobulin heavy constant gamma 4 IGHG4 35,941 79.8 101 68.8 85 102 P01871 Immunoglobulin heavy constant mu IGHM 49,440 33.1 10 34.7 12 103 A0A0C4DH31 Immunoglobulin heavy variable 1–18 IGHV1–18 12,820 53 7 48.7 9 104 P23083 Immunoglobulin heavy variable 1–2 IGHV1–2 13,085 47.9 6 - - 105 A0A0C4DH33 Immunoglobulin heavy variable 1–24 IGHV1–24 12,824 38.5 2 38.5 3 106 A0A0C4DH29 Immunoglobulin heavy variable 1–3 IGHV1–3 13,008 38.5 3 - - 107 A0A0A0MS14 Immunoglobulin heavy variable 1–45 IGHV1–45 13,508 9.4 2 - - 108 P01743 Immunoglobulin heavy variable 1–46 IGHV1–46 12,933 - - 32.5 5 109 P01742 Immunoglobulin heavy variable 1–69 IGHV1–69 12,659 - - 34.2 5 110 P01762 Immunoglobulin heavy variable 3–11 IGHV3–11 12,909 38.5 10 53.9 11 111 P01766 Immunoglobulin heavy variable 3–13 IGHV3–13 12,506 60.3 6 - - 112 A0A0B4J1V0 Immunoglobulin heavy variable 3–15 IGHV3–15 12,926 55.5 8 42.9 7 113 P01764 Immunoglobulin heavy variable 3–23 IGHV3–23 12,582 60.7 15 54.7 10 114 A0A0B4J1X8 Immunoglobulin heavy variable 3–43 IGHV3–43 13,077 - - 34.8 6 115 A0A0A0MS15 Immunoglobulin heavy variable 3–49 IGHV3–49 13,056 47.1 3 50.4 3 116 A0A075B6Q5 Immunoglobulin heavy variable 3–64 IGHV3–64 12,891 59.3 2 18.6 1 117 A0A0C4DH42 Immunoglobulin heavy variable 3–66 IGHV3–66 12,698 61.2 14 55.2 10 118 P01780 Immunoglobulin heavy variable 3–7 IGHV3–7 12,943 76.9 14 77.8 12 119 A0A0B4J1Y9 Immunoglobulin heavy variable 3–72 IGHV3–72 13,203 55.5 9 - - 120 A0A0B4J1V6 Immunoglobulin heavy variable 3–73 IGHV3–73 12,858 58 3 58 4 121 P01782 Immunoglobulin heavy variable 3–9 IGHV3–9 12,945 51.7 8 51.7 9 122 P06331 Immunoglobulin heavy variable 4–34 IGHV4–34 13,815 - - 38.2 4 123 P01824 Immunoglobulin heavy variable 4–39 IGHV4–39 13,917 19.2 4 - - 124 A0A0C4DH38 Immunoglobulin heavy variable 5–51 IGHV5–51 12,675 66.7 9 50.4 8 125 P01834 Immunoglobulin kappa constant IGKC 11,765 88.8 50 92.5 37 126 P0DOX7 Immunoglobulin kappa light chain N/A 23,379 61.2 52 62.6 39 127 P04430 Immunoglobulin kappa variable 1–16 IGKV1–16 12,618 - - 34.2 2 128 A0A075B6S5 Immunoglobulin kappa variable 1–27 IGKV1–27 12,712 47 8 65 8 129 P01594 Immunoglobulin kappa variable 1–33 IGKV1–33 12,848 49.6 5 42.7 4 130 P01602 Immunoglobulin kappa variable 1–5 IGKV1–5 12,782 30.8 3 30.8 6 131 A0A0C4DH72 Immunoglobulin kappa variable 1–6 IGKV1–6 12,697 47 4 47 5 132 A0A0C4DH69 Immunoglobulin kappa variable 1–9 IGKV1–9 12,715 74.4 5 44.4 5 133 P01611 Immunoglobulin kappa variable 1D-12 IGKV1D-12 12,620 44.4 5 49.6 7 134 A0A0B4J2D9 Immunoglobulin kappa variable 1D-13 IGKV1D-13 12,569 13.7 1 - - 135 A0A075B6S4 Immunoglobulin kappa variable 1D-17 IGKV1D-17 12,835 28.2 1 43.6 2 136 P04432 Immunoglobulin kappa variable 1D-39 IGKV1D-39 12,737 47 6 47.9 6 137 P06310 Immunoglobulin kappa variable 2–30 IGKV2–30 13,185 50 5 63.3 7 138 P01615 Immunoglobulin kappa variable 2D-28 IGKV2D-28 12,957 33.3 5 40.8 5 139 A0A075B6S2 Immunoglobulin kappa variable 2D-29 IGKV2D-29 13,143 - - 20.8 5 140 P01614 Immunoglobulin kappa variable 2D-40 IGKV2D-40 13,310 37.2 6 37.2 5 141 P04433 Immunoglobulin kappa variable 3–11 IGKV3–11 12,575 54.8 16 49.6 10 142 P01624 Immunoglobulin kappa variable 3–15 IGKV3–15 12,496 42.6 9 50.4 8 143 P01619 Immunoglobulin kappa variable 3–20 IGKV3–20 12,557 70.7 16 70.7 14 144 A0A087WSY6 Immunoglobulin kappa variable 3D-15 IGKV3D-15 12,534 42.6 10 56.5 8 145 A0A0C4DH25 Immunoglobulin kappa variable 3D-20 IGKV3D-20 12,515 64.7 10 64.7 8 146 P06312 Immunoglobulin kappa variable 4–1 IGKV4–1 13,380 34.7 10 40.5 6 147 A0M8Q6 Immunoglobulin lambda constant 7 IGLC7 11,254 54.7 13 53.8 10 148 A0A0B4J1U3 Immunoglobulin lambda variable 1–36 IGLV1–36 12,478 13.7 1 13.7 1 149 P01703 Immunoglobulin lambda variable 1–40 IGLV1–40 12,302 21.2 2 - - 150 P01700 Immunoglobulin lambda variable 1–47 IGLV1–47 12,284 54.7 4 39.3 3 151 P01706 Immunoglobulin lambda variable 2–11 IGLV2–11 12,644 22.7 3 - - 152 A0A075B6K4 Immunoglobulin lambda variable 3–10 IGLV3–10 12,441 40 4 40 3 153 P01714 Immunoglobulin lambda variable 3–19 IGLV3–19 12,042 50 2 42.9 1 154 P80748 Immunoglobulin lambda variable 3–21 IGLV3–21 12,446 35.9 3 - - 155 P01717 Immunoglobulin lambda variable 3–25 IGLV3–25 12,011 - - 43.8 3 156 P01721 Immunoglobulin lambda variable 6–57 IGLV6–57 12,566 20.5 2 - - 157 P0DOX8 Immunoglobulin lambda-1 light chain N/A 22,830 44.4 23 44.4 20 158 P15814 Immunoglobulin lambda-like polypeptide 1 IGLL1 22,963 23 5 23 5 159 P35858 Insulin-like growth factor-binding protein complex acid labile subunit IGFALS 66,035 23.1 4 27.4 6 160 P16144 Integrin beta-4 ITGB4 202,167 4.9 1 - - 161 P19827 Inter-alpha-trypsin inhibitor heavy chain H1 ITIH1 101,389 33.6 20 33.7 25 162 P19823 Inter-alpha-trypsin inhibitor heavy chain H2 ITIH2 106,463 35.9 18 42.6 20 163 Q06033 Inter-alpha-trypsin inhibitor heavy chain H3 ITIH3 99,849 5.2 1 15.5 1 164 Q14624 Inter-alpha-trypsin inhibitor heavy chain H4 ITIH4 103,357 38.4 23 47 26 165 Q15811 Intersectin-1 ITSN1 195,422 - - 9.9 1 166 P29622 Kallistatin SERPINA4 48,542 26.5 4 23 5 167 Q92764 Keratin, type I cuticular Ha5 KRT35 50,361 - - 16.7 1 168 P13645 Keratin, type I cytoskeletal 10 KRT10 58,827 5.8 1 - - 169 P04264 Keratin, type II cytoskeletal 1 KRT1 66,039 23.6 3 30 2 170 P01042 Kininogen-1 KNG1 71,957 53.7 25 41 23 171 P02750 Leucine-rich alpha-2-glycoprotein LRG1 38,178 21.6 4 27.1 5 172 P18428 Lipopolysaccharide-binding protein LBP 53,384 14.8 1 13.3 1 173 P51884 Lumican LUM 38,429 30.2 3 27.8 3 174 P14174 Macrophage migration inhibitory factor MIF 12,476 18.3 2 - - 175 P01033 Metalloproteinase inhibitor 1 TIMP1 23,171 18.8 2 34.8 2 176 Q7Z5P9 Mucin-19 MUC19 805,253 4.3 1 - - 177 P35579 Myosin-9 MYH9 226,532 - - 15.8 1 178 Q96PD5 N-acetylmuramoyl-L-alanine amidase PGLYRP2 62,217 26 7 29.3 6 179 A6NHN0 Otolin-1 OTOL1 49,422 15.3 1 - - 180 P04180 Phosphatidylcholine-sterol acyltransferase LCAT 49,578 15.5 2 - - 181 P36955 Pigment epithelium-derived factor SERPINF1 46,312 22.3 5 17.9 5 182 P03952 Plasma kallikrein KLKB1 71,370 23 6 26.5 6 183 P05155 Plasma protease C1 inhibitor SERPING1 55,154 34.8 9 33.2 16 184 P05154 Plasma serine protease inhibitor SERPINA5 45,675 13.6 3 - - 185 P00747 Plasminogen PLG 90,569 63 30 58.8 32 186 Q96GD3 Polycomb protein SCMH1 SCMH1 73,354 4.7 1 - - 187 Q8WUM4 Programmed cell death 6-interacting protein PDCD6IP 96,023 - - 14.1 1 188 P46013 Proliferation marker protein Ki-67 MKI67 358,694 11.9 1 21.8 1 189 P15309 Prostatic acid phosphatase ACPP 44,566 25.1 4 17.9 2 190 P02760 Protein AMBP AMBP 38,999 38.9 11 42.1 12 191 Q9UK55 Protein Z-dependent protease inhibitor SERPINA10 50,707 15.5 2 18.9 2 192 Q96PF1 Protein-glutamine gamma-glutamyltransferase Z TGM7 79,941 - - 7.5 1 193 P00734 Prothrombin F2 70,037 59.8 33 62.4 31 194 P02753 Retinol-binding protein 4 RBP4 23,010 40.3 11 55.7 13 195 O94885 SAM and SH3 domain-containing protein 1 SASH1 136,653 - - 10.3 1 196 P04279 Semenogelin-1 SEMG1 52,131 30.5 5 32.3 5 197 Q02383 Semenogelin-2 SEMG2 65,444 21 3 18 5 198 P57059 Serine/threonine-protein kinase SIK1 SIK1 84,902 - - 7.3 1 199 P02787 Serotransferrin TF 77,064 71.4 143 79.4 185 200 P02768 Serum albumin ALB 69,367 89.3 607 91.3 550 201 P35542 Serum amyloid A-4 protein SAA4 14,747 30 2 49.2 6 202 P02743 Serum amyloid P-component APCS 25,387 26.5 5 25.1 5 203 P27169 Serum paraoxonase/arylesterase 1 PON1 39,731 24.5 7 19.2 5 204 P04278 Sex hormone-binding globulin SHBG 43,779 18.7 4 21.9 3 205 P09486 SPARC SPARC 34,632 - - 5.3 1 206 Q6N022 Teneurin-4 TENM4 307,957 5.3 1 - - 207 P05452 Tetranectin CLEC3B 22,537 22.8 2 30.2 2 208 P05543 Thyroxine-binding globulin SERPINA7 46,325 14.5 1 23.6 2 209 Q8WZ42 Titin TTN 3,816,030 10.6 1 - - 210 P21675 Transcription initiation factor TFIID subunit 1 TAF1 212,677 - - 7 1 211 Q66K66 Transmembrane protein 198 TMEM198 39,475 2.5 2 2.5 1 212 P02766 Transthyretin TTR 15,887 69.4 12 69.4 19 213 P13611 Versican core protein VCAN 372,820 - - 5.2 2 214 P02774 Vitamin D-binding protein GC 52,964 63.9 29 60.3 28 215 P04070 Vitamin K-dependent protein C PROC 52,071 - - 2.2 1 216 P07225 Vitamin K-dependent protein S PROS1 75,123 12.6 2 - - 217 P04004 Vitronectin VTN 54,306 32.6 11 32.2 15 218 Q6PF04 Zinc finger protein 613 ZNF613 70,143 6.6 1 - - 219 P25311 Zinc-alpha-2-glycoprotein AZGP1 34,259 52.7 14 52 17 \n Fig. 1 Identification of HFF proteins by LC MALDI TOF/TOF MS Spectra. The MS/MS map ( a ,  b ) marked with b ions and y ions for vitamin D-binding protein identification. The sequences of precursor at m/z 2053.8506 and 2353.9646 were analyzed by MS/MS to be GQELCADYSENTFTEYK and SYLSMVGSCCTSASPTVCFLK and the protein identified as vitamin D-binding protein. The MS/MS map ( c ,  d ) marked with b ions and y ions for retinol-binding protein 4 identification. The sequences of precursor at m/z 2692.0667 and 1197.6047 were analyzed by MS/MS to be GNDDHWIVDTDYDTYAVQYSCR and YWGVASFLQK and the protein identified as retinol-binding protein 4 \n Fig. 2 Pie diagrams of the proportion of HFF proteins categorized by GO classifications based on their ( a ) molecular function, ( b ) subcellular localization, ( c ) biological process\nA list of 219 identified high-confidence HFF proteins from women underwent successful IVF by LC MALDI TOF/TOF mass spectrometry (FDR < 0.01)\nIdentification of HFF proteins by LC MALDI TOF/TOF MS Spectra. The MS/MS map ( a ,  b ) marked with b ions and y ions for vitamin D-binding protein identification. The sequences of precursor at m/z 2053.8506 and 2353.9646 were analyzed by MS/MS to be GQELCADYSENTFTEYK and SYLSMVGSCCTSASPTVCFLK and the protein identified as vitamin D-binding protein. The MS/MS map ( c ,  d ) marked with b ions and y ions for retinol-binding protein 4 identification. The sequences of precursor at m/z 2692.0667 and 1197.6047 were analyzed by MS/MS to be GNDDHWIVDTDYDTYAVQYSCR and YWGVASFLQK and the protein identified as retinol-binding protein 4\nPie diagrams of the proportion of HFF proteins categorized by GO classifications based on their ( a ) molecular function, ( b ) subcellular localization, ( c ) biological process\nThe proteins identified by mass spectrometry were broadly placed into several GO categories on the basis of the PANTHER, DAVID and PubMed databases (Fig.  2 ). Based on molecular function, the majority (31%) of proteins were related to immunity, whereas other involved protein functions were mainly complement and coagulation (17%), protease or inhibitor (14%), and transportation (10%) (Fig.  2a ). Based on subcellular localization, the majority (64%) of the identified proteins located in extracellular region. Other main locations were extracellular matix (7%), nuleus (6%), and cytoskeleton (5%) (Fig.  2b ). Based on biological process, the majority (28%) of proteins was related to developmental process, and the next prevalence was immunological system process (26%). The other groups were involved into protein metabolic process (12%), reproduction (5%), lipid metabolic process (3%), and transportation (2%) (Fig.  2c ).\nKEGG pathway analysis was performed to map HFF protein interactions, Pathways associated with complement and coagulation cascades (P_Value = 5.8E-52),  vitamin digestion and absorption  (P_Value = 0.023), and (P_Value = 0.066) were significantly enriched. Figure  3  showed the complement and coagulation cascades pathway which included 17 (7.8%) and 21 (9.6%) highlighted HFF proteins in coagulation cascade and complement cascade, respectively. Fig. 3 Presentative Network of protein HSPG2 in the identified HFF proteome. A total of 21 genes are connected with 105 paired relationships annotated by STRING database. The relationships among proteins were derived from evidence that includes textmining, co-expression, protein homology, gene neighborhood, from curated databases, experimentally determined, gene fusions, and gene co-occurrence (as shown in the legend with different color)\nPresentative Network of protein HSPG2 in the identified HFF proteome. A total of 21 genes are connected with 105 paired relationships annotated by STRING database. The relationships among proteins were derived from evidence that includes textmining, co-expression, protein homology, gene neighborhood, from curated databases, experimentally determined, gene fusions, and gene co-occurrence (as shown in the legend with different color)\nA protein-protein interaction network was constructed by retrieving the STRING database. 151 proteins were in connection with other proteins, which lead to 738 paired relationships. As an example, 21 of 151 proteins related to basement membrane-specific heparan sulfate proteoglycan core protein (HSPG) was chosen, and 105 paired relationships were connected (Fig.  4 ). Fig. 4 The KEGG pathway of complement and coagulation cascades with the identified HFF proteins highlighted. Generated by the KEGG online (hsa04610), this diagram showed the roles if HFF proteins in complement ( Red ) and coagulation cascades ( Blue )\nThe KEGG pathway of complement and coagulation cascades with the identified HFF proteins highlighted. Generated by the KEGG online (hsa04610), this diagram showed the roles if HFF proteins in complement ( Red ) and coagulation cascades ( Blue )\nTo disclose the overlap of the HFF proteomes between different labs and to explore the orign of the HFF proteins, the previous reported HFF proteins [ 14 ] and the human plasma proteome [ 29 ] were selected, whose protein identification criteria were both at a false discovery rate (FDR) of 1%. The results reflected the overlap of our HFF proteins and the previously reported HFF proteins with human plasma proteins (Fig.  5 ). A total of 49% proteins in our HFF data were common to the previous HFF data. Compared with human plasma proteins, 69% proteins from our HFF data and 64% proteins from previous HFF data were common to human plasma proteins. Fig. 5 Venn diagram of the overlap of HFF and human plasma protein datasets. Distribution of our present findings or the previously reported HFF proteins (Aditi S. Ambekar et al. 2013) and their overlap with those reported in human plasma (Terry Farrah et al. 2011)\nVenn diagram of the overlap of HFF and human plasma protein datasets. Distribution of our present findings or the previously reported HFF proteins (Aditi S. Ambekar et al. 2013) and their overlap with those reported in human plasma (Terry Farrah et al. 2011)\nTo verify the confidence of the proteome data, the expression patterns of 3 HFF proteins (retinol-binding protein 4, vitamin D-binding protein and lactotransferrin) from 10 women undergoing successful IVF were analyzed by western blotting (Fig.  6 ). Those three proteins could be detected in all 10 HFF samples. Compared with retinol-binding protein 4 and lactotransferrin, the expression of vitamin D-binding protein was relatively constant level in the HFF of ten women. Fig. 6 Immunoblot analysis of retinol-binding protein 4, vitamin D-binding protein and lactotransferrin in 10 HFF samples of women underwent successful IVF. Protein lysates prepared from 10 HFF samples were examined by immunoblots using specific antibodies recognizing the retinol-binding protein 4(23 kDa), vitamin D-binding protein (53 kDa) and lactotransferrin (78 kDa)\nImmunoblot analysis of retinol-binding protein 4, vitamin D-binding protein and lactotransferrin in 10 HFF samples of women underwent successful IVF. Protein lysates prepared from 10 HFF samples were examined by immunoblots using specific antibodies recognizing the retinol-binding protein 4(23 kDa), vitamin D-binding protein (53 kDa) and lactotransferrin (78 kDa)\n\nProteomics has been carried out to discover HFF biomarkers for decades, and liquid chromatography coupled with ion trap MS became widely available with the development of high-throughput sequencing. The identification of HFF proteins from women with and without endometriosis was performed using ESI MS/MS [ 30 ]. Nanoflow LC-MS/MS combined with TMT labeling was used to identify HFF biomarkers from women undergoing IVF/ICSI treatment with or without folic acid supplement [ 31 ]. Another advance LTQ Orbitrap system coupled with LC was also applied to comparing HFF proteins between fertilized oocytes and non-fertilized oocytes from the same patient [ 32 ]. Based on sample pre-fractionation using microscale in-solution isoelectric focusing (IEF), capillary electrophoresis (CE) coupled off-line to matrix assisted laser desorption/ionization time of flight tandem mass spectrometry (MALDI TOF MS/MS) identified 73 unique proteins [ 33 ]. Hanrieder and co-workers [ 34 ] utilized a proteomic strategy of IEF and reversed-phase nano-liquid chromatography coupled to MALDI TOF/TOF mass spectrometry to identify 69 proteins related to controlled ovarian hyper stimulation (COH) during IVF. However, limited proteins were identified which delayed the research of HFF protein networks.\nIn the present work, a dual RP-HPLC coupled with MALDI TOF/TOF mass spectrometry was performed to identify HFF protein profiles associated with successful IVF, and 219 unique high-confidence (FDR < 0.01) HFF proteins were identified by searching the reviewed Swiss-Prot human database (20,183 sequences). Meanwhile, the new strategy indicated that the effective dual reverse LC pre-fractionation [ 21 ] could identify more HFF proteins.\nAmbekar and co-workers carried out SDS-PAGE, OFFGEL and SCX-based separation followed by LC–MS/MS (an LTQ-Orbitrap Velos MS) to identify 480 HFF proteins with high confidence (FDR < 0.01) [ 14 ]. A comparison with our results and these data showed that more than 50% proteins in present study were not found in previous dataset (Additional file  2 : Figure S1), which indicated that the data from different MS platforms were complementary. Retinol-binding protein 4 and vitamin D-binding protein were verified by western blotting, and the results showed they were all expressed in the 10 HFF samples. Lactotransferrin was uniquely included in Ambekar’s data, and was also successfully detected by western blotting in our study. This result not only testified the good quality of Ambekar’s data, but also facilitated to integrate the data from different MS platform in the future. Interestingly, more than 60% of combined HFF proteins from our data and Ambekar’s data were found in the reported human plasma data [ 29 ]. HFF was a complex mixture, and the content of HFF mainly originates from the transfer of blood plasma constituents via theca capillaries, and the secretion of granulosa and thecal cells [ 5 ]. From the above contrast, we considered the transfer of plasma proteins was the major source of HFF, and the alternative permeability of theca capillaries would change the HFF compositions which inevitably impaired the oocyte quality, and even caused unsuccessful IVF outcome.\nBioinformatics analysis showed that 5% HFF proteins were involved in lipid metabolism and transport process. It has been reported that ageing could decrease apolipoprotein A1 and apolipoprotein CII, while increase apolipoprotein E, which were associated with the decline in production of mature oocytes and the decline in fertility potential [ 35 ]. Preconception folic acid supplementation upregulated apolipoprotein A-I and apolipoprotein C-I of the HDL pathway in human follicular fluid, which increased embryo quality and IVF/ICSI treatment outcome [ 30 ]. In our HFF data, apolipoprotein A-I, apolipoprotein A-II, apolipoprotein A-IV, apolipoprotein C-I, apolipoprotein C-II, apolipoprotein C-III, apolipoprotein D, apolipoprotein E, apolipoprotein F, and apolipoprotein M were all found, which indicated that those apolipoproteins were related to cholesterol homeostasis and steroidogenesis and played important roles in the maintenance of oocyte maturation microenvironment.\nPathway analysis showed that complement and coagulation cascades were the most prominent pathways (P_Value = 5.8E-52). Complement cascade promoted coagulation through the inhibition of fibrinolysis, and coagulation cascade in return amplified complement activation. Complement cross_talked with coagulation in a reciprocal way [ 36 ]. For example, plasmin, thrombin, elastase and plasma kallikrein could activate C3 [ 37 ]. Coagulation activation factor XII could cleave C1 to activate the classical complement pathway [ 38 ]. And thrombin could also directly cleave C5 to generate active C5a [ 39 ]. Among our HFF proteins, components (F12, KLKB1, PLG, KNG1, F9, F10, SERPINC1, SERPIND1, SERPINA5, F2, PROS1, PROC, SERPINA1, SERPINF2, A2M, CPB2, and FGA) of extrinsic pathway and intrinsic pathway in coagulation cascade and those (FH, FI, FB, C3, C1qrs, SERPING1, C2, C4, C4BP, C5, C6, C7, C8A, C8B, C8G, C9, FGA, FGG, PLG, FGB, F10) of alternative pathway, classical pathway, and lectin pathway in complement cascade were all identified. During follicle development and ovulation, coagulation system in HFF contributed to HFF liquefaction, fibrinolysis and the breakdown of follicle wall [ 40 ,  41 ]. Follicle development had been hypothesized as the controlled inflammatory processes in 1994 [ 42 ], and inappropriate complement activation was linked to abortion [ 43 ]. Inhibition of complement activation improved angiogenesis failure and rescued pregnancies [ 44 ]. The paired comparison of HFF with plasma showed C3, C4, C4a, and C9 as well as complement factor H and clusterin might contribute to the inhibition of complement cascade activity for women undergoing controlled ovarian stimulation for IVF [ 45 ]. However there were still debates on the role of complement cascade in IVF. Physiologic complement activation protected the host against infection in normal pregnancy [ 46 ]. In comparison with those non-fertilized oocytes, C3 was more abundant in HFF from fertilized oocytes [ 47 ]. In the course of IVF treatment, the functions of complement and coagulation cascade were very complicated during ovarian hyperstimulation. More works were still deserved in both mechanism research and clinical practice.\nBased on the analysis of STRING, we discovered a profound HFF protein-protein interaction networks. 151 of 219 HFF proteins participated in the network with 738 paired relationships. Basement membrane-specific HSPG was found as a node, which was also a potential biomarker for oocyte maturation in HFF. HSPG was widely distributed on the surface of animal cells, and especially strongly expressed in granulosa cells. HSPG played a critical role in controlling inflammation control through binding and activating antithrombin III during folliculogenesis [ 48 ]. Women with PCOS showed HSPG defect in follicular development [ 49 ], and on the contrary, HSPG was up-regulated in the fertilized-oocyte HFF [ 32 ]. In the network, HSPG interacted with 20 of 219 HFF proteins, and constructed 105 paired relationships. We deduced that the loss of HSPG might affect the function of the whole network or more complicated interaction maps, which might cause subsequent failures of oocyte maturation, fertilization, and IVF treatment.\n\nHFF had a natural advantage for the noninvasive prediction of oocyte quality and IVF treatment outcome. The present study would provide a new complementary dataset for better understanding of oocyte maturation, and also delineate a new networks and pathways involved into the folliculogenesis. Furthermore, those novel findings would facilitate to testify the potential biomarkers associated with oocyte quality and IVF outcome. In the future, international laboratory collaboration should be established to standardize and optimize experimental design, patient selection, HFF handling, analysis methods, data standard, and clinical verification, which will greatly promote basic research of reproductive medicine, and ultimately accelerate the clinical transformation.\n\nAdditional file 1: The information of antibodies and secondaries for Western blotting. (XLSX 10 kb) \n Additional file 2: The overlap of known data and novel findings. (JPEG 1344 kb)\nThe information of antibodies and secondaries for Western blotting. (XLSX 10 kb)\nThe overlap of known data and novel findings. 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