A Protocol to Map the Spatial Proteome Using HyperLOPIT in Saccharomyces cerevisiae

引用 收藏 提问与回复 分享您的反馈 Cited by



Apr 2014



The correct subcellular localization of proteins is vital for cellular function and the study of this process at the systems level will therefore enrich our understanding of the roles of proteins within the cell. Multiple methods are available for the study of protein subcellular localization, including fluorescence microscopy, organelle cataloging, proximity labeling methods, and whole-cell protein correlation profiling methods. We provide here a protocol for the systems-level study of the subcellular localization of the yeast proteome, using a version of hyperplexed Localization of Organelle Proteins by Isotope Tagging (hyperLOPIT) that has been optimized for use with Saccharomyces cerevisiae. The entire protocol encompasses cell culture, cell lysis by nitrogen cavitation, subcellular fractionation, monitoring of the fractionation using Western blotting, labeling of samples with TMT isobaric tags and mass spectrometric analysis. Also included is a brief explanation of downstream processing of the mass spectrometry data to produce a map of the spatial proteome. If required, the nitrogen cavitation lysis and Western blotting portions of the protocol may be performed independently of the mass spectrometry analysis. The protocol in its entirety, however, enables the unbiased, systems-level and high-resolution analysis of the localizations of thousands of proteins in parallel within a single experiment.

Keywords: Saccharomyces cerevisiae (酿酒酵母), Subcellular fractionation (亚细胞分离), Nitrogen cavitation (氮空化), Quantitative proteomics (定量蛋白质组学), Yeast (酵母), Spatial proteomics (空间蛋白质组学)


Knowledge of the subcellular localization of a protein is crucial in defining its functions within the cell and regulation of the spatial organization of the proteome is essential for cellular homeostasis. Subcellular protein localization allows proteins to act as members of higher-order complexes with binding partners such as proteins, metabolites and co-factors, and contribute to the functions of organelles. In some cases, the sequestration of enzymes within specific subcellular compartments is critical to ensure that intermediates are not exchanged between different metabolic pathways or that potentially detrimental reactions (e.g., proteolysis) are confined to a ‘safe’ space. Protein localization can be influenced by post-translational modification, such as the phosphorylation of proteins within signaling cascades that ultimately allow proteins such as transcription factors (TFs) to localize within the nucleus to exercise their gene regulatory functions, or allowing other proteins to traverse the nuclear membrane and regulate nuclear-localized transcription factors. One example is the case of the osmostress response pathway in yeast, where a signaling cascade is activated that results in the phosphorylation of the protein Hog1p, whereupon it can translocate to the nucleus and phosphorylate the TF Hot1p which, in turn, can recruit RNA polymerase II to the promoters of responsive genes (Alepuz et al., 2003).

A growing body of evidence suggests that differential protein localization, even within the same cell type, may allow the protein to fulfil different roles depending on its respective localization within a cell, sometimes referred to as “moonlighting” (Gancedo et al., 2016). In Saccharomyces cerevisiae, examples of such proteins include Gcn4p, which is the general transcription factor that controls amino acid biosynthesis (Hope and Struhl, 1986) and also acts as a ribonuclease (Nikolaev et al., 2010); as well as Aco1p, which has roles in mitochondrial DNA stability in addition to its more widely known function as tricarboxylic acid cycle enzyme (Chen et al., 2005). Isoforms of the same protein have further been observed with different localizations in the same cell type, suggesting that this differential localization may offer another level of regulation with regard to protein function. An example of this is the case of a DNA methyltransferase (Dnmt1) in murine stem cells, in which one isoform localized to chromatin and the other isoform is found in an alternative nuclear location (Christoforou et al., 2016).

It is clear that there are many levels of complexity regarding protein subcellular localization. In order to fully understand the in vivo functions of proteins, it is vital to ascertain where, within the cell, they are located. Several methods exist that can be applied to approach this challenge, including fluorescence microscopy imaging, organelle proteomics, proximity biotinylation methods, and protein correlation profiling methods; all of which have been applied in Saccharomyces cerevisiae in different contexts to determine the subcellular localization of yeast proteins. Whole-cell, high-throughput studies based on fluorescence microscopy have been performed using GFP- or other fluorescence protein-tagged protein libraries (Huh et al., 2003; Tkach et al., 2012; Breker et al., 2013; Denervaud et al., 2013; Chong et al., 2015; Weill et al., 2018; Yofe et al., 2016). Although powerful, these methods have documented problems in that the fluorescence signal of a particular protein of interest may not exceed the background level of cellular autofluorescence, particularly in yeast, and therefore it may not be possible to determine the location of such proteins. Additionally, some proteins may require their unmodified C- or N-terminus for correct localization (Weill et al., 2018; Yofe et al., 2016). As such, performing protein localization studies where proteins are tagged with a fluorescent protein either N- or C-terminally to the ORF in question may result in a given protein being reported with different localizations within the same biological system, depending on which strategy was used (Stadler et al., 2013).

Organelle enrichment strategies have also been employed to define organelle proteomes, thereby contributing to our knowledge on protein localization. These strategies often exploit the characteristic buoyant densities of organelles in their enrichment. The relative ease with which mitochondria can be highly enriched, for example, has been exploited in many studies that have attempted to define the mitochondrial proteome, with varying levels of specificity, in S. cerevisiae. This includes some studies that used more crude approaches to attempt to define the mitochondrial proteome (Sickmann et al., 2003; Reinders et al., 2006) or specific sub-mitochondrial proteomes (Zahedi et al., 2006; Vogtle et al., 2012) in some cases without control for other organelle proteins that were co-enriched along with the mitochondrial proteins. Improvements have been made in recent studies that mapped the entirety of the mitochondrial sub-organellar proteome using more complex fractionation and proteome quantitation methods (Morgenstern et al., 2017; Vogtle et al., 2017). For organelles of more heterogeneous density, however, the difficulty of such studies can be compounded by the presence of contaminating membranes from other organelles that co-enrich with the organelle of interest (Wiederhold et al., 2010). In focusing on a specific organelle, such methods fail to report on proteins that may be present at multiple locations in vivo. Further, there is no control for the proportion of an organelle that is lost upon enrichment.

Recently, new proximity labeling methods based on promiscuous biotinylation have shown promise in determining protein subcellular localization in organisms other than yeast. This includes the more detailed study of sub-organellar proteomes (Rhee et al., 2013; Hung et al., 2014), including cytoplasmic faces of organelles (Hung et al., 2017). The BirA-based BioID method (Roux et al., 2012) has been applied in S. cerevisiae to define the subcellular proteins that interact with a ribosomal scaffold protein (Opitz et al., 2017) and the APEX2 method (Lam et al., 2015) has been demonstrated to traverse the cell wall in proof-of-principle experiments in both S. cerevisiae and the fission yeast Schizosaccharomyces pombe (Hwang and Espenshade, 2016). Methods such as these focus only on the proximal interactors of a protein of interest, that are currently in the range of tens of nanometres (Rhee et al., 2013; Kim et al., 2014) and do not provide a whole-cell picture of the spatial organization of the proteome.

To overcome some of the inherent limitations of the methods described above, it is imperative to look at protein subcellular localization in the context of the whole cell. This has been commonly studied using whole-cell protein correlation profiling methods. Such methods are predicated on the observation that, when cell lysates are fractionated by some means, such as differential or density gradient centrifugation, proteins that localize to the same organelle will exhibit similar distributions when the protein distributions across the entire fractionation scheme are monitored (de Duve, 1971). This was originally monitored using enzyme assays that are representative of the organelles in question, but now employs quantitative mass spectrometry methods. Several such methods have been published, which offer differing levels of proteome coverage and subcellular resolution, employing different fractionation and mass spectrometry quantitation methods (Christoforou et al., 2016; Itzhak et al., 2016; Jean Beltran et al., 2016; Mulvey et al., 2017; Jadot et al., 2017; Geladaki et al., 2019). The hyperplexed Localization of Organelle Proteins by Isotope Tagging (hyperLOPIT) method uses density gradient centrifugation for subcellular fractionation, followed by multiplexed proteome quantitation using 10-plex TMT isobaric tags (Thompson et al., 2003) and SPS-MS3 quantitation on an Orbitrap Fusion Lumos mass spectrometer. The exquisite subcellular resolution offered by the density gradient coupled with multiplexed quantitation, followed by sophisticated machine learning analysis methods means that hyperLOPIT allows unbiased protein localization determination of thousands of proteins in parallel in a single experiment. Indeed the method has afforded the highest resolution spatial map of any whole-cell correlation profile-based spatial proteome study to date (Thul et al., 2017; Gatto et al., 2019).

We recently published a yeast spatial proteome mapping study in which we mapped the proteome in high throughput using hyperLOPIT, under nitrogen-sufficient conditions (Nightingale et al., 2019). In our study, we produced a dataset that provided localization data for 2,846 proteins. Only 936 of these proteins were found to map to a unique subcellular location, a number which corresponded to 32% of our observable spatial proteome. This suggests, in common with previous studies (Christoforou et al., 2016; Thul et al., 2017), that a large proportion of the proteome is dynamic or resides at multiple locations.

We provide here a protocol that describes how to perform nitrogen cavitation lysis (Hunter and Commerford, 1961; Simpson, 2010; Wang et al., 2014) subcellular fractionation, and a version of the hyperLOPIT protocol that has been modified for use with the yeast S. cerevisiae (see Figure 1 for an overview of the protocol). We further draw the reader’s attention to the fact that the subcellular fractionation and Western blotting portion of this protocol may be performed as a standalone experiment without TMT labeling. Further to this, in cases where a mass spectrometer capable of resolution of TMT 10- or 11-plex tags (such as the Thermo Fusion series) is not available, isobaric tags with lower multiplexing capability may instead be used. This may be achieved using iTRAQ (Ross et al., 2004) 4- or 8-plex, or TMT 6-plex, but, importantly, will result in reduced overall subcellular resolution relative to TMT 10- or 11-plex, as less of the subcellular fractionation gradient will be sampled in the experiment. HyperLOPIT and its predecessor, LOPIT, have been employed to great effect to map the subcellular proteomes of multiple different species and under numerous conditions (Sadowski et al., 2006; Nikolovski et al., 2012; Groen et al., 2014; Christoforou et al., 2016; Thul et al., 2017). We envisage that this protocol will therefore be applicable and modifiable to study the spatial proteomes of other yeasts, including the industrial yeast Komagataella phaffii (syn. Pichia pastoris) and pathogenic yeasts such as Candida albicans.

We recently published a protocol in Methods in Molecular Biology (Nightingale et al., 2018) that describes how to perform hyperLOPIT in S. cerevisiae. The lysis and subcellular fractionation portions of this protocol are substantially updated from a previous protocol by Wang and colleagues (2014). Here we provide a protocol with updated mass spectrometry data processing parameters using Proteome Discoverer 2.1 (Thermo Fisher Scientific, www.thermofisher.com), and with more focus on post-mass spectrometry informatics analysis. For a more in-depth and detailed informatics protocol than is presented here, we direct the reader to the publication of Breckels and colleagues (Breckels et al., 2016b). The subcellular fractionation portion of the hyperLOPIT protocol is technically challenging, requiring an unbroken stretch of experimental work lasting from cell harvest to gradient fraction collection. We strongly recommend that experimental work not be paused until the gradient fractions have been collected.

Figure 1. Overview of the hyperLOPIT method for Saccharomyces cerevisiae. Also included in parentheses are approximate timescales for each step, and potential stopping points.

Materials and Reagents

  1. 96-well plates (Thermo Fisher Scientific, catalog number: 167008) 
  2. Falcon tubes, 15 and 50 ml capacity (Corning, catalog numbers: 430791 and 430291)
  3. Luer-lock syringes, 2 ml, 5 ml and 10 ml capacity (BD Plastipak, catalog numbers: 300185, 302187 and 300912, respectively)
  4. Microcentrifuge tubes, 1.5 ml capacity (Eppendorf, catalog number: 0030120086)
  5. Pipette tips (Rainin LTS 1,000 μl, LTS 250 μl and LTS 20 μl; catalog numbers: 17001864, 17001863 and 17001865; or equivalent tips compatible with pipettes used for this protocol)
  6. Polycarbonate thickwall ultracentrifuge tubes, 32 ml capacity (Beckman Coulter, catalog number: 355631)
  7. Polypropylene OptiSealTM ultracentrifuge tubes, 11.2 ml capacity (Beckman Coulter, catalog number: 362181) and associated tube adaptors (Beckman Coulter, catalog number: 362202)
  8. Semi-micro cuvettes (Sarstedt, catalog number: 67.742)
  9. Silicone tubing (1 mm inner diameter, 1 mm wall diameter) (Fisher Scientific, catalog number: 10430313)
  10. Stainless steel blunt-ended needle, 14 gauge (Sigma-Aldrich, catalog number: Z261408)
  11. Total recovery glass vials and caps with pre-slit septa (Waters, catalog number: 186000385C)
  12. Waste container
  13. X-ray film (Fujifilm, catalog number: 4741019289)
  14. Trans-Blot® TurboTM polyvinylidene fluoride (PVDF) membranes (Bio-Rad, catalog number: 1704157)
  15. Saccharomyces cerevisiae strain appropriate to experimental aims (e.g., BY4741 (Baker Brachmann et al., 1998), which we used for our recent hyperLOPIT study; Nightingale et al., 2019)
  16. Acetic acid (Sigma-Aldrich, catalog number: 320099)
  17. Acetone, analytical grade (Sigma-Aldrich, catalog number: 32201)
  18. Acetonitrile, HPLC gradient-grade (Fisher Scientific, catalog number: A/0627/17)
  19. Adenine hemisulfate (Sigma-Aldrich, catalog number: A3159)
  20. AmershamTM ECLTM Prime Enhanced Chemiluminescent Western Blotting Detection Reagent (GE Healthcare, catalog number RPN2232)
  21. Ammonium formate (Sigma-Aldrich, catalog number: 70221)
  22. Ammonium hydroxide (Sigma-Aldrich, catalog number: 30501)
  23. Arginine (Sigma-Aldrich, catalog number: A8094)
  24. DifcoTM BactoTM agar (Fisher Scientific, catalog number: 10455513)
  25. Difco Bacto peptone (Fisher Scientific, catalog number: DF0118-17-0)
  26. Difco Bacto yeast extract (Fisher Scientific, catalog number: DF0127-17-9)
  27. cOmpleteTM Mini protease inhibitor tablets (Roche, catalog number: 11836170001)
  28. cOmpleteTM protease inhibitor tablets (Roche, catalog number: 11873580001)
  29. D-(+)-Glucose (Sigma-Aldrich, catalog number: G7021)
  30. Dibasic potassium phosphate (Sigma-Aldrich, catalog number: P3786)
  31. DL-Dithiothreitol (DTT) (Melford Laboratories, catalog number: D11000)
  32. Ethylenediaminetetraacetic acid (EDTA) (Sigma-Aldrich, catalog number: E5134)
  33. Ficoll® PM-400 (Sigma-Aldrich, catalog number: F4375)
  34. Formic acid (VWR, catalog number: 20318.297)
  35. HEPES free acid (Melford Laboratories, H75030)
  36. Histidine (Sigma-Aldrich, catalog number: H6034)
  37. Hydrochloric acid, 37% (v/v) (Fisher Scientific, catalog number: H/1200/PB15)
  38. Hydroxylamine 50% (w/v) solution (Thermo Fisher Scientific, catalog number: 90115)
  39. Iodoacetamide (Sigma-Aldrich, catalog number: I6125)
  40. Isoleucine (Sigma-Aldrich, catalog number: I5281)
  41. Leucine (Sigma-Aldrich, catalog number: L8912)
  42. Lysine (Sigma-Aldrich, catalog number: L5501)
  43. Magnesium acetate (Sigma-Aldrich, catalog number: M5661)
  44. Magnesium chloride (Sigma-Aldrich, catalog number: M8266)
  45. Methionine (Sigma-Aldrich, catalog number: M5308)
  46. Non-fat dry milk powder, such as MarvelTM
  47. OptiPrepTM (Sigma-Aldrich, catalog number: D1556)
  48. Phenylalanine (Sigma-Aldrich, catalog number: P5482)
  49. Potassium acetate (Sigma-Aldrich, catalog number: P1190)
  50. Potassium chloride (Sigma-Aldrich, catalog number: 31248)
  51. Protein concentration estimation assay, such as PierceTM BCA protein assay (Thermo Fisher Scientific, catalog number: 23225), DCTM protein assay (Bio-Rad, catalog number: 5000112) or Quick StartTM Bradford assay (Bio-Rad, catalog number: 5000202)
  52. Sequencing Grade Modified Trypsin (Promega, catalog number: V5111)
  53. Sodium dodecyl sulfate (SDS) (Sigma-Aldrich, catalog number: 436143)
  54. Sodium hydroxide (Sigma-Aldrich, catalog number: 06203)
  55. Sorbitol (Sigma-Aldrich, catalog number: W302902)
  56. Sucrose (Sigma-Aldrich, catalog number: S0389)
  57. Threonine (Sigma-Aldrich, catalog number: T8441)
  58. TMT 10-plex or 11-plex isobaric tagging reagents (Thermo Fisher Scientific, catalog number: 90111 or A37725, respectively)
  59. Trichloroacetic acid (TCA) (Fisher Scientific, catalog number: 421455000)
  60. Trifluoroacetic acid (TFA) (Thermo Fisher Scientific, catalog number: 85183)
  61. Tris base (Melford Laboratories, catalog number: T60040)
  62. Tris-(2-carboxyethyl) phosphine (TCEP) (Generon, catalog number: GEN-TCEP)
  63. Tryptophan (Sigma-Aldrich, catalog number: T8941)
  64. Tween® 20 (Sigma-Aldrich, catalog number: P1379)
  65. Tyrosine (Sigma-Aldrich, catalog number: T8566)
  66. Uracil (Sigma-Aldrich, catalog number: U1128)
  67. Valine (Sigma-Aldrich, catalog number: V4638)
  68. Water, HPLC gradient-grade (Fisher Scientific, catalog number: W/0106/17)
  69. Yeast nitrogen base without amino acids (Sigma-Aldrich, catalog number: Y0626)
  70. Zymolyase 100-T (Nacalai-Tesque, catalog number: 07665-55)
  71. Anti-Pgk1p antibody, for use as cytosol Western blotting marker (Abcam, catalog number: ab113687)
  72. Anti-Dpm1p antibody, for use as endoplasmic reticulum Western blotting marker (ThermoFisher Scientific, catalog number: A-6429)
  73. Anti-Pep1p antibody, for use as Golgi apparatus Western blotting marker (Abcam, catalog number: ab113690)
  74. Anti-Cox4p antibody, for use as mitochondrial Western blotting marker (ThermoFisher Scientific, catalog number: 459150)
  75. Anti-Hh3p antibody, for use as nuclear Western blotting marker (Abcam, catalog number: ab1791)
  76. Anti-Pma1p antibody, for use as plasma membrane Western blotting marker (Abcam, catalog number: ab4645)
  77. 5x SDS-PAGE sample buffer (Jena Biosciences, catalog number: BU-117) 
  78. 4-20% Mini-PROTEAN® TGXTM Precast Protein Gels (Bio-Rad, catalog number: 4561096)
  79. TGS buffer 10x (Bio-Rad, catalog number: 1610732)
  80. 10x yeast nitrogen base (YNB) (see Recipes)
  81. 10x complete supplement mixture (see Recipes)
  82. 40% (w/v) glucose (see Recipes)
  83. Synthetic medium with 2% glucose (see Recipes)
  84. YPD agar medium (see Recipes)
  85. TCEP reduction buffer (see Recipes)
  86. Spheroplasting medium (see Recipes)
  87. Zymolyase 100-T solution (see Recipes)
  88. Spheroplast wash medium (see Recipes)
  89. (Optional) Ficoll lysis buffer (Kizer et al., 2006) (see Recipes)
  90. (Optional) Buffer NP (Kizer et al., 2006) (see Recipes)
  91. 1x LB (see Recipes)
  92. 6x LB (see Recipes)
  93. Iodixanol working solution (IWS) (see Recipes)
  94. 18% (w/v) OptiPrep solution (see Recipes)
  95. 16% (w/v) OptiPrep solution (see Recipes)
  96. Protein resolubilization buffer (see Recipes)
  97. Digestion buffer (see Recipes)
  98. 5% (v/v) hydroxylamine (see Recipes)
  99. TTBS (see Recipes)
  100. Blocking solution (see Recipes)
  101. Dithiothreitol (DTT) solution (see Recipes)
  102. Iodoacetamide (IAA) solution (see Recipes)
  103. Equilibration buffer (see Recipes)
  104. Desalting buffer 1 (see Recipes)
  105. Desalting buffer 2 (see Recipes)
  106. Elution buffer (see Recipes)
  107. RP mobile phase stock solution (see Recipes)
  108. RP mobile phase A (see Recipes)
  109. RP mobile phase B (see Recipes)
  110. RP resolubilization buffer (see Recipes)


  1. 4 °C refrigerator (Polestar cooling, model: MR 100 E)
  2. -20 °C freezer (Hotpoint, model: FZS175)
  3. -80 °C freezer (New Brunswick Scientific, model: U725-G) 
  4. Acquity Ultra Performance Liquid Chromatography (UPLC) system with photodiode array (PDA) detector (Waters)
  5. Acquity UPLC BEH C18 column (Waters, catalog number: 186002353)
  6. Analytical balance ( Explorer, model: EX124)
  7. Auto-Densiflow peristaltic pump with automatic meniscus detection (Labconco)
  8. Avanti® JXN-26 floor-top preparative centrifuge (Beckman Coulter)
  9. Bioruptor® Plus sonicator (Diagenode, catalog number: B01020001)
  10. Centrifuge bottle assemblies, 500 ml capacity (Beckman Coulter, catalog number: 355649)
  11. Cuvette spectrophotometer (Cecil Instruments, model: 2041)
  12. Dounce homogenizer, 7 ml capacity (Wheaton, catalog number: 357542)
  13. Erlenmeyer flasks, 250 ml, 500 ml and 2 L capacity (Fisher Scientific, catalog numbers: 15429103, 15439103 and 11383454)
  14. Handheld analog refractometer (Bellingham + Stanley, E-line, catalog number: 44-803)
  15. JLA-10.500 rotor (Beckman Coulter, catalog number: 369681)
  16. Nitrogen cavitation vessel (Parr Instrument Company, model: 4639)
  17. OptimaTM L80-XP floor-top ultracentrifuge (Beckman Coulter)
  18. Orbitrap FusionTM LumosTM TribridTM mass spectrometer (Thermo Fisher Scientific)
  19. Oxygen-free nitrogen cylinder (BOC, Nitrogen [Oxygen-Free] 230bar Cylinder, size: W) 
  20. Pipettes (Rainin LTS L-1000 XLS, L-200 XLS, L-20 XLS and L-10 XLS, catalog numbers: 17014382, 17014391, 1704392 and 17014388; or equivalent pipettes capable of dispensing the same volumes)
  21. Plate-reader spectrophotometer, capable of measuring wavelengths between 250 nm and 850 nm (Molecular Devices, Spectramax M2e)
  22. Pre-column for mass spectrometer-coupled nanoLC (Thermo Fisher Scientific, catalog number: 160454)
  23. Proxeon EASY-Spray column (Thermo Fisher Scientific, catalog number: ES803)
  24. Refrigerated benchtop centrifuge, capable of centrifuging 15 ml and 50 ml Falcon tubes and running at 4,500 x g (Eppendorf, model: 5804R)
  25. Refrigerated benchtop microcentrifuge, capable of centrifuging microfuge tubes and running at 16,100 x g (Eppendorf, model: 5415R)
  26. Sep-Pak® tC18 cartridges, 100 mg sorbent per cartridge (Waters, catalog number: WAT036820)
  27. Shaking incubator for cell culture (Sartorius, Certomat, model: BS-1)
  28. SW32Ti rotor (Beckman Coulter, catalog number: 369650)
  29. Thermo ScientificTM SterilinTM Standard 90mm Petri Dishes (Fisher Scientific, catalog number: 15370366)
  30. Tube holder for microfuge and Falcon tubes (Starlab, catalog number: E2345-1000)
  31. Trans-Blot® TurboTM system (Bio-Rad, catalog number: 1704150)
  32. Type 70 Ti rotor (Beckman Coulter, catalog number: 337922)
  33. Ultimate RSLCnano chromatography system (Thermo Fisher Scientific)
  34. Vacuum centrifuge (Labconco, CentriVap concentrator)
  35. VTi65.1 rotor (Beckman Coulter, catalog number: 362759)
  36. Electrophoresis system and associated power pack (Bio-Rad, model: Mini-PROTEAN® II)


  1. Proteome Discoverer version 2.1 (Thermo Fisher Scientific, www.thermofisher.com)
  2. R programming language (R Core Team, 2018) (www.r-project.org)
  3. Bioconductor (Gentleman et al., 2004) MSnbase package (Gatto and Lilley, 2012) (http://bioconductor.org/packages/release/bioc/html/MSnbase.html)
  4. Bioconductor (Gentleman et al., 2004) pRoloc package (Gatto et al., 2014) (http://bioconductor.org/packages/release/bioc/html/pRoloc.html)
  5. Bioconductor (Gentleman et al., 2004) pRolocGUI package (Breckels et al., 2018) (http://bioconductor.org/packages/release/bioc/html/pRolocGUI.html)
  6. RStudio R programming language editor (RStudio Team, 2016) (www.rstudio.com)
  7. Mascot Server (Matrix Science, www.matrixscience.com)
  8. Microsoft Excel


  1. Cell culture
    1. At least 3 days before you plan to carry out the experiment, streak-plate an appropriate yeast strain from a cryostock onto a YPD agar plate. Incubate at 30 °C in a stationary incubator for approximately 2 days until colonies form and subsequently store at 4 °C for several weeks. 
    2. The following day, transfer the starter culture to a flask filled with fresh medium. For a typical hyperLOPIT experiment we recommend culturing 720 OD units of yeast, which equates to 1.2 L of culture at OD600 of 0.6. For this volume of culture, we use 2 L Erlenmeyer flasks.
    3. Dilute such that the cells undergo a minimum of 2 doublings prior to harvest so that the desired cell density is reached at a convenient time.
      Note: The amount of cells required for a new growth condition, which yields enough protein per collected density gradient fraction for downstream steps of the hyperLOPIT protocol (> 50 μg per fraction), should be empirically determined for each experiment in question. For experiments where the goal is not to perform hyperLOPIT, smaller cultures should be sufficient, but this should be determined by the investigator. 
    4. When the required OD600 has been reached, harvest cells by centrifugation at 3,000 x g for 5 min at room temperature and discard the supernatant. For this step, use 500 ml bottle assemblies and the JLA 10.500 rotor in the Avanti centrifuge.

  2. Cell pre-treatment for lysis
    1. Resuspend the cell pellet, still in the 500 ml bottle assemblies, in TCEP reduction buffer at 5 OD units per ml and incubate for 5 min at room temperature without shaking. Centrifuge at 3,000 x g for 5 min in the Avanti centrifuge to harvest the cells and discard the supernatant.
    2. Resuspend the cells in spheroplasting medium at 20 OD units per ml and transfer to 50 ml Falcon tubes. Add 1 μl of zymolyase 100-T solution per OD unit of yeast used in the experiment.
      Note: Spheroplasting medium is composed of the same constituents as the medium that was used for cell culture, to maintain the same conditions as were used for culture. We do not, however, recommend the use of conditioned media for this step. The media should be made fresh and include 1.0-1.2 M sorbitol and 20 mM Tris-HCl, pH 7.5, to maintain osmotic support and the appropriate pH during spheroplasting.
    3. Withdraw a 10 μl aliquot of the solution and dilute in 990 μl of water. Measure the OD600 after blanking the spectrophotometer with an equivalent dilution of spheroplasting medium in water.
      Note: Spheroplast conversion efficiency is conveniently monitored by spectrophotometry. Absorbance of the cell suspension at 600 nm is proportional to the number of intact cells that are present in the suspension. Spheroplasts are very fragile and sensitive to changes in the tonicity of a solution. As such, transferring them to an osmotically unsupported solution will result in lysis of the spheroplasts. Only the remaining intact cells contribute to the absorbance at 600 nm, indicating the amount of cells that have undergone conversion to spheroplasts.
    4. Incubate at 30 °C for 10 min with shaking at 200 rpm and measure OD600 of a 1:100 dilution. The OD600 should be around 10% of the pre-digestion OD600, indicating around 90% zymolyase digestion efficiency.
      Note: This step should not be allowed to continue for more than 10 min, as incubation in the presence of zymolyase for too long can result in uncontrolled cell lysis.
    5. Harvest cells, without transferring to new tubes, by centrifugation for 5 min at 1,500 x g, 4 °C and resuspend in spheroplast wash medium.
      Note: The pellet can be sticky and difficult to resuspend, but do not vortex or pipette vigorously as this risks lysis of the fragile spheroplasts. Resuspend with a trimmed 1 ml pipette tip, use a glass rod or gently invert to resuspend. Due to the risk of lysis, do not aim for a homogeneous suspension of cells at this point.
    6. Harvest the cells for 5 min at 1,500 x g, 4 °C and discard supernatant.

  3. Optional: Nuclear preparation
    Note: If greater nuclear resolution is not of particular interest, skip to Section D.
    1. Optionally perform a nuclear preparation according to Kizer et al. (2006) with modifications described subsequently. For this protocol, we use approximately 120 OD units of spheroplasts for a nuclear preparation and 600 OD units for the main subcellular fractionation.
      Note: This step is optional and if other organelles are of special interest, enrichments for these organelles may be performed instead of a nuclear preparation, as was performed in two previous hyperLOPIT studies (Christoforou et al., 2016; Thul et al., 2017).
    2. Resuspend the spheroplasts in Ficoll lysis buffer and transfer to a Dounce homogenizer. Lyse with 20 up-down strokes on ice.
      Note: As in the original paper (Kizer et al., 2006), we use the “tight” Dounce pestle for the entirety of lysis, and do not advocate “pre-resuspension” with the “loose” pestle.
    3. Pre-clear the lysate for 10 min at 3,220 x g, 4 °C and transfer the supernatant to a 32 ml round-bottomed ultracentrifuge tube. Balance the opposite position of a Type 70 Ti rotor (Beckman Coulter) with a tube of the same mass and density and centrifuge at 50,000 x g, 4 °C for 35 min. Use “MAX” acceleration and deceleration.
    4. Discard the supernatant and resuspend pellet in buffer NP with vigorous pipetting. Store resuspended nuclear-enriched pellet at -80 °C.

  4. Lysis for density gradient centrifugation
    1. Resuspend the remaining 600 OD units of spheroplasts in 1x LB at 20 OD units per ml and transfer to the chamber of the nitrogen cavitation vessel.
    2. Lyse using a method modified from Wang et al. (2014). Charge the vessel with oxygen-free nitrogen to 500 psi and incubate for 3 min.
      Note: The nitrogen within the cylinder is under pressure and if the inlet tap is opened too quickly, it can lead to the target pressure of 500 psi being overshot. Therefore, open the inlet tap slowly and closely monitor the pressure gauge to obviate the chance of this occurring.
    3. Release the pressure by opening the gas inlet valve, so that the pressure drops to 300 psi.
    4. Allow a further 3 min of incubation and discharge the vessel by opening the outlet port at the base of the vessel. Collect the lysate at approximately 3 drops per second.
      Note: As the nitrogen is released from solution during lysis, the pressure within the nitrogen cavitation vessel will drop and consequently the lysate will be released more slowly. Therefore monitor the rate at which the lysate is released from the outlet port and, if necessary, open the port more to maintain a steady rate of approximately 3 drops per second.
    5. Clear the lysate of debris, aggregates, and unlysed cells and spheroplasts by centrifugation for 5 min at 1,000 x g, 4 °C. Subject the supernatant to another round of centrifugation for 10 min at 3,000 x g, 4 °C.

  5. Crude membrane preparation
    1. Prepare 18% (w/v) OptiPrep solution with the aid of a handheld refractometer and calibration curve of known OptiPrep concentrations and their refractive index measurements, measured in degrees Brix (°Bx) (see Note 1).
    2. Transfer the cleared lysate into four round-bottomed ultracentrifuge tubes (~7.5 ml lysate per tube). Underlay the contents of each tube with 5 ml 18% (w/v) OptiPrep cushion solution, prepared in 1x LB, using a syringe attached to a blunt-ended, wide-bore needle.
      1. Even though it is possible to fit the entirety of the lysate in a single round-bottomed centrifuge tube, we do not recommend this. If this is done, upon ultracentrifugation, the sheer amount of protein in the entirety of lysate will cause it to aggregate on top of the cushion solution. This may cause damage to organelle membranes and affect the results of the experiment as a whole. We find that if the lysate is split as described, this does not occur.
      2. When underlaying with 18% (w/v) OptiPrep cushion solution, take the utmost care to not to introduce any air bubbles into the cushion solution. If these are introduced it will result in mixing of the lysate with the OptiPrep solution, disrupting the crisp interface that should be present between the two solutions. Ultracentrifugation of a solution where this has occurred will prevent formation of a crude membrane interphase that would normally form between the two solutions, defeating the object of performing this step. Instead this will potentially cause the membranes to pellet and aggregate at the bottom of the tube. To prevent this happening, use the graduations on the syringe as a guide to the amount of OptiPrep solution withdrawn before underlaying. Before commencing the underlaying of the lysate, press down gently on the syringe plunger to allow a single drop of OptiPrep to be released from the wide-bore needle (to expel any air that may be present). Submerge the needle at the bottom of the ultracentrifuge tube and begin injection of the cushion solution, keeping a close eye on the level of the liquid within the syringe. As soon as the meniscus of the liquid drops to the level of the connection between the syringe and needle, stop injecting. Remove the needle and discard the remainder of the solution.
    3. Balance the four tubes to within 10 mg of each other and place the tubes in opposing sides of an SW32Ti swinging-bucket rotor.
    4. Centrifuge the tubes for 2 h at 28,000 rpm (96,300 x g), 4 °C. Select an acceleration profile of “MAX” and a deceleration profile of “9” (the slowest deceleration that uses a brake). This minimizes disruption of the membrane interphase that forms upon ultracentrifugation.
    5. Withdraw the cytosol-enriched supernatant from the tube, leaving between 1 and 2 cm of the supernatant above the membrane interphase, so as not to disturb the crude membrane interphase. Store the supernatant at -20 °C for further processing.
    6. Withdraw the membrane-containing interphase using a trimmed 1 ml pipette tip into a separate tube, taking care not to withdraw too much solution from either above or below the interphase.

  6. Isopycnic density gradient ultracentrifugation and fraction collection
    1. Adjust the concentration of OptiPrep in the collected membrane interphase solution to 16% (w/v), monitoring the concentration using a handheld refractometer (see Note 1).
      1. Using 600 OD units of culture typically yields around 2-3 ml of crude membranes at this step. The aim should be to adjust the refractive index of the crude membranes to the required value in as small a volume as possible and to make up the remainder of the 11.2 ml volume of the resolving gradient tube with pre-prepared 16% (w/v) OptiPrep. If this is not adhered to, it may result in the undesirable situation that the membranes, in the correct OptiPrep concentration, are split across multiple density gradients.
      2. We find that preparing a self-generating gradient using 16% (w/v) OptiPrep provides good separation of the major subcellular organelles in yeast (Figure 2). This step may be modified if a different gradient shape is desired or specific subcellular organelles are the main focus of the experiment.

        Figure 2. Representative data obtained using the protocol described here. After performing hyperLOPIT in S. cerevisiae with the optional nuclear preparation, two-dimensional Principal Components Analysis reveals the resolution of 12 separate organelles and protein complexes within the S. cerevisiae spatial proteome map, across three principal components. These encompass the main subcellular organelles of the organism (data reproduced from a single replicate performed in our previous study [Nightingale et al., 2019]). Within each plot each point represents a single protein group, points that are colored represent proteins determined by hyperLOPIT to localize to a specific organelle and light grey points represent protein that do not have a localization predicted by hyperLOPIT.

    2. Prepare a balance tube containing 16% (w/v) OptiPrep and use to fill a blank vertical rotor tube, which does not contain any membranes and acts as a balance tube.
    3. Balance the tubes to within 10 mg of each other, using 16% (w/v) OptiPrep solution. Place the two tubes in opposing sides of a VTi65.1 rotor and float aluminum spacers on top of the tubes.
    4. Screw the rotor lids on and centrifuge at 65,000 rpm (362,900 x g), for 4 h at 4 °C. Select an acceleration profile of “MAX” and a deceleration profile of “9” (the slowest deceleration that uses a brake). This acts to minimize disruption to the density gradient that has formed on ultracentrifugation.
    5. Remove the tubes from the VTi65.1 rotor. Remove and discard the plastic plugs.
    6. Prepare a Labconco Auto-Densiflow peristaltic pump with meniscus tracking probe for fraction collection by connecting a piece of collection tubing approximately 40 cm long to the probe. Clamp on to the peristaltic pump.
      Note: If an Auto-Densiflow fraction collector is unavailable, fractions may be collected by puncturing the bottom of the tube and allowing the gradient fractions to drip out from the bottom and most dense, fraction to the top and least dense fraction. Alternative fractionators are available, from Brandel and Teledyne Isco, but these have not been tested for use with hyperLOPIT. We do NOT recommend collecting fractions by pipetting from the top of the gradient as this requires a high level of technical skill and is very susceptible to error, which will result in mixing of different parts of the gradient and ruin the entire experiment.
    7. Flush the tubing with distilled water into a waste container and follow this by flushing through with air. Flush through with 1x LB and then air.
    8. Insert the gradient tube into the tube holder and set the probe direction to “down” until the probe finds the gradient meniscus, at which point the probe will stop moving.
    9. Set the probe to “deposit” and collect 23 fractions from the gradient into 1.5 ml microcentrifuge tubes, of which 22 should be 0.5 ml and one should be ~0.2 ml.
      Note: This step is not automated, so volumes collected per fraction should be monitored closely by eye until the required volume has been collected for the fraction in question. Store fractions at -80 °C until further processing.

  7. Monitoring gradient shape
    1. Thaw gradient fractions and vortex well to ensure that the iodixanol content in the OptiPrep-containing fractions is uniformly suspended within the fraction.
    2. Determine refractive index of each fraction using a handheld, analog refractometer. Use approximately 20 μl of each fraction for this step.
      Note: If the refractive index of any fraction exceeds the maximum refractive index of the refractometer, dilute that fraction with water and re-measure. Do not use 1x LB. Multiply the resultant refractive index by the dilution factor to obtain the undiluted refractive index measurement.
    3. Monitor density gradient shape by plotting fraction number against the refractive index of that fraction. The result should be a slightly sigmoidal curve that is relatively flat in the middle of the gradient and becomes steeper in the last 2-3 fractions (Figure 3).
    4. If monitoring organelle distribution by enzyme assay, take an aliquot here with which to do this, and perform enzyme assays.
      Note: We have not attempted such methods in S. cerevisiae but methods are available in the literature if this is desired.

      Figure 3. Typical gradient shape achieved using the concentration of OptiPrep and the density gradient ultracentrifugation parameters detailed within this protocol. Fractions were collected using an Auto-Densiflow fraction collector and are numbered in ascending order from the least to the most dense fraction of the gradient.

  8. Fraction processing
    1. Process all fractions (including separate organelle preparations and cytosol-enriched fraction) further by TCA precipitation, followed with acetone washing. For this add ¼ of the volume of 100% (w/v) TCA, prepared in water and vortex well.
    2. Incubate for 2 h at 4 °C to allow the protein content of each of the fractions to precipitate from solution.
    3. Harvest precipitates by centrifugation at maximum speed in a benchtop microcentrifuge at 4 °C for 10 min. Discard supernatants.
    4. Wash pellets in acetone that has been pre-chilled to -20 °C. Vortex extensively and subject to sonication cycles (30 s on and 30 s off) in a Diagenode BioRuptor Plus sonicator, until the pellets break down completely and become powdery.
      1. If the pellets do not break down completely, they may be difficult to resolubilize in downstream steps of this protocol.
      2. If a BioRuptor is not available, other sonicators may be used for this step.
    5. Repeat centrifugation, acetone washing, and sonication twice more; each time discarding the supernatant.
    6. Discard supernatants and allow pellets to dry briefly at room temperature until acetone evaporates (see Note 2).

  9. Sample resolubilization and protein concentration estimation
    1. Resolubilize each pellet in protein resolubilization buffer with extensive sonication.
      Note: Resolubilize in the minimum volume in which the entire protein content is soluble.
    2. For this step, begin with 50 μl of protein resolubilization buffer per pellet and monitor for the presence of any pellet in the fraction by centrifugation, as in the TCA precipitation step (Section H, step 3). Add further aliquots of protein resolubilization buffer, sonicate, and spin down to monitor the presence of any protein pellet. As soon as no pellet is visible after centrifugation, this indicates that the protein content has been resolubilized to completion.
    3. Estimate protein concentration in each fraction using a protein concentration estimation assay, preferably in 96-well plate format.
      1. If the recommended protein assay kits are not available, other protein estimation assays may be used, as long as they are compatible with 100 mM HEPES and 0.1% (w/v) SDS.
      2. We recommend the use of 96-well plate format assay as sample amounts in hyperLOPIT experiments can be extremely limited and this conserves the maximum amount of each sample for Western blotting and isobaric tag labeling.
    4. Determine overall protein yield. The ideal yield per fraction for isobaric tag labeling should be > 50 μg, allowing for an extra 1 μg per fraction used for downstream Western blotting. If fractionation is required without isobaric tag labeling, lower yields will be suitable.

  10. Monitoring organelle resolution by Western blot
    1. Monitor organelle resolution using Western blotting against a panel of antibodies raised against marker proteins whose resolution is characteristic of the organelle which they represent (see Note 3) (Table 1).

      Table 1. Suggested antibodies for use with hyperLOPIT in S. cerevisiae. The suggested primary and secondary antibody dilutions are based on Western blots that are carried out using 1 μg of total protein per lane of an SDS-PAGE gel.

    2. Normalize the amounts of each fraction (including the nuclear preparation, cytosol-enriched fraction, and density gradient fractions) to 1 μg of total protein and heat to 95 °C in the presence of SDS-PAGE sample buffer for 5 min. Load each fraction on a single well of a pre-cast, gradient SDS-PAGE gel.
    3. Subject the samples to electrophoresis in SDS-PAGE minigels in TGS buffer and using the full length of the gels. Use a Mini-PROTEAN II electrophoresis system and power pack for this step and run the gel at 200 V. 
    4. Transfer to Trans-Blot Turbo polyvinylidene fluoride (PVDF) membranes, using the mixed molecular weight program on a Bio-Rad Trans-Blot Turbo apparatus.
    5. Cut membranes into appropriate molecular weight ranges (see Note 3) and incubate each strip in blocking solution for 1 h at room temperature on a rotary mixer.
    6. Probe each strip with the appropriate primary antibody, diluted in blocking solution (see Note 4). Incubate overnight at 4 °C, with shaking on a rotary mixer (see Table 1 for suggested dilutions).
    7. The following day, place the membranes back at room temperature and wash three times for 5 min each in TTBS.
    8. Probe with the appropriate horseradish peroxidase-conjugated secondary antibody diluted in blocking solution (see Table 1 for suggested dilutions), for 1 h at room temperature.
    9. Discard secondary antibody and wash 3 times for ten minutes each in TTBS.
    10. Proceed to detection of signal using enhanced chemiluminescent (ECL) substrate.
    11. Expose each membrane with X-ray film until the desired level of signal has been reached (see Figure 4 for an example Western blot).
    12. Assuming a suitable yield (see Note 5) has been obtained, proceed to the next section if it is desired to proceed with the hyperLOPIT protocol.

      Figure 4. Representative Western blot typical of an experiment on S. cerevisiae, performed using the hyperLOPIT protocol described here, in the absence of a nuclear preparation and probing using the antibodies described in Table 1. The fractions are numbered in the order in which they were collected using the Labconco fraction collector, from the first and least dense fraction, to the twenty-third and most dense fraction. The separate cytosol-enriched fraction is denoted by “C”. Modified from (Nightingale et al., 2019).

  11. Protein reduction, alkylation and digestion
    1. For experiments including nuclear preparations, select the nuclear preparation, cytosol-enriched fraction, and use the remainder of the isobaric tags to label fractions from the density gradient. In experiments that do not include nuclear preparations, select the cytosolic fraction and use the remainder of the tags to label gradient fractions (see Note 4). This recommendation is the same whether TMT 10- or 11-plex tags are being used, or isobaric tags of lower multiplexing capability.
      Note: Fractions should be chosen for labeling on the basis of relative enrichment of organelles of interest, and relative depletion of other contaminating organelles, as determined by Western blotting. It should be noted that, even though proteins from only 7 organelles are probed in this Western blotting scheme, we are able to resolve 12 subcellular locations within our final dataset (Figure 2). Indeed, a well-designed fractionation scheme will result in the resolution of subcellular locations within the final spatial proteome map that were not included within the Western blotting experiment, as they will resolve discretely within the density gradient.
    2. Normalize each fraction to the same amount of protein (50-100 μg) and make up to the same volume (100 μl) with protein resolubilization buffer.
      Note: The amount of total protein may be reduced if yields are not high enough to use 100 μg of each fraction. We do not, however, recommend labeling < 50 μg of each of the selected fractions.
    3. Add 11.1 μl of dithiothreitol (DTT) solution prepared in protein resolubilization buffer to each sample.
    4. Incubate for 1 h at 56 °C to allow reduction of disulfide bonds.
    5. Return to room temperature and add 12.3 μl of iodoacetamide (IAA) solution to each sample. Incubate for 1 h at room temperature in the dark.
    6. Add 1 ml of acetone to each fraction, vortex the fractions briefly and allow protein content to precipitate in acetone overnight at -20 °C.
    7. The following day, centrifuge all precipitated fractions at maximum speed in a benchtop microcentrifuge for ten minutes at 4 °C.
    8. Discard the supernatants and briefly air-dry the pellets (see Note 2).
    9. Resuspend the pellets in 87.5 μl of digestion buffer by vortexing.
    10. Digest with trypsin at 1:20 (w/w) protease:protein ratio, at 37 °C for 16 h. Add trypsin in two aliquots (1:40 (w/w) protease:protein ratio) spaced 1 h apart, such that the final volume of the digest is 100 μl.

  12. TMT labeling (modified from the TMT 10-plex kit user guide)
    1. This section describes labeling of digests with TMT tags. If using a different isobaric labeling chemistry, consult the relevant labeling protocol that comes with the kit.
    2. The following day, clear the digests of any insoluble material by centrifugation for 15 min in a microcentrifuge at maximum speed.
    3. Allow a TMT kit to equilibrate to room temperature. Before opening the vials, briefly pulse-centrifuge them.
    4. Add 41 μl of acetonitrile to each tag vial. Vortex well and allow to solubilize for 5 min.
    5. Transfer each resolubilized tag to a different digest and incubate for 2 h at room temperature on a shaking platform.
    6. Add 8 μl of 5% (v/v) hydroxylamine, prepared in digestion buffer to each sample and incubate for 30 min on a shaking platform at room temperature.
    7. Add 100 μl of HPLC-grade water to each sample and incubate for 1 h at 4 °C.
    8. Pool all labeled digests into a single tube and dry to completion in a vacuum centrifuge at 10 °C. Store at -80 °C until sample clean-up by solid-phase extraction (SPE).

  13. Sample clean-up by solid-phase extraction (SPE) (modified from a previous publication [Villen and Gygi, 2008])
    1. Equilibrate a Sep-Pak tC18 cartridge with 1.8 ml of 100% (v/v) HPLC-grade acetonitrile (ACN).
    2. Flush through with 600 μl Equilibration Buffer.
    3. Flush through with 1.8 ml of Desalting Buffer 1.
    4. Resolubilize sample in 1 ml 0.4% TFA and ensure that pH < 2. To achieve this, check pH using pH paper on a small aliquot (~10 μl) of sample. If pH > 2, keep adding aliquots of 10% TFA, checking pH after each addition until pH < 2.
      Note: The buffering capacity of residual HEPES from the digestion and isobaric tag labeling steps can keep pH high, even in the presence of 0.4% TFA. Ensure that pH < 2 before loading on the SPE cartridge, or the labeled peptides will not bind to the tC18 material and will, instead, be lost.
    5. Load resolubilized peptides onto the cartridge and allow to bind to the packing material.
    6. Desalt in 1.8 ml Desalting Buffer 1.
    7. Flush with 180 μl Desalting Buffer 2.
    8. Elute the desalted peptides from the cartridge using 1 ml Elution Buffer and remove a small aliquot corresponding to approximately 5-10 μg of labeled peptides.
    9. Dry the samples to completion in a vacuum centrifuge at 10 °C.

  14. Pre-fractionation by high pH reversed phase (RP) chromatography
    1. Fractionate the sample using high pH reversed phase chromatography prior to mass spectrometric analysis, on a Waters Acquity UPLC system.
    2. Resolubilize solid phase extracted sample in 100 μl RP resolubilization buffer by vortex mixing and spin down in a benchtop microcentrifuge for 10 min at maximum speed.
    3. Transfer supernatant to an Acquity autosampler vial and place in the autosampler of a Waters Acquity UPLC system, maintained at 10 °C.
      Note: If desired another chromatography system may be used, that is capable of running in normal flow.
    4. Inject the sample onto the system and run using the gradient parameters shown in Table 2. Monitor sample elution using a photodiode array (PDA) detector, scanning wavelengths from 210-400 nm. Collect fractions at 1-min intervals with the aid of a timed fraction collector and freeze to completion on dry ice.

      Table 2. Parameters for use during high pH reversed phase pre-fractionation of hyperLOPIT samples on a Waters Acquity UPLC system. The entire LC gradient is 75 min long. Within the gradient, 0-10 min, 62-67.5 and 67.6-75 min should be isocratic. The gradient between each of the switching steps that is documented within this Table should be linear (represented by “6” with the Waters UPLC Inlet Method software).

    5. Dry fractions to completion in a vacuum centrifuge at 10 °C. Store dried fractions at -80 °C.
    6. Use the collected chromatographic data to determine the collected fractions that correspond to the peptide-containing chromatographic space.

  15. LC-SPS-MS3 analysis of samples
    1. Resuspend the dried samples in 0.1% (v/v) formic acid and pool equal amounts in the following scheme: Pool the first with the middle fraction, the second with the second-from-middle, and so on until the end of the peptide-containing elution space.
      Note: Pooling the samples in this fashion not only makes better use of the second dimension of peptide fractionation (low pH RP coupled in-line to the mass spectrometer) (Zhang et al., 2011), but also reduces mass spectrometry analysis time by half.
    2. Load approximately 1 μg of each pooled fraction into the autosampler of the Dionex Ultimate 3000 RSLCnano chromatography system.
      Note: Base this measurement either on the protein estimation that was performed prior to Western blotting and protein digestion or, perform a peptide estimation assay to determine the amount to load.
    3. Analyze each pooled fraction by liquid chromatography-mass spectrometry using 120-min gradient on an Orbitrap Fusion Lumos Tribrid mass spectrometer coupled in line to the Dionex Ultimate 3000 RSLC nanoUPLC system. Acquire data using an LC-SPS-MS3 method (McAlister et al., 2014). For details about the methods used for LC and MS, see previous publications that have employed TMT 10-plex quantitation coupled with SPS-MS3 to perform hyperLOPIT studies (Christoforou et al., 2016; Thul et al., 2017; Mulvey et al., 2017). Alternatively, in the absence of a Fusion series mass spectrometer, use an appropriate LC-MS/MS method on the mass spectrometer that is being used for analysis.
      1. We recommend using a Thermo Fisher Scientific Fusion series mass spectrometer, as the SPS-MS3 capability (McAlister et al., 2014) offers greater quantitative accuracy and precision in the measurement of the abundance of each TMT 10- or 11-plex reporter ion in a specific scan, relative to the sole use of MS/MS. SPS-MS3 involves synchronous selection of multiple MS2 fragment ions, arising from the target precursor ion, for further fragmentation in an MS3 scan. This liberates TMT reporter ions that arise solely from the target precursor ion, in the absence of reporter ion signal arising from the fragmentation of contaminating ions that were co-selected in the MS1 scan. Other tandem mass spectrometers may be used for these experiments, but they will be more susceptible to issues (Ting et al., 2011; Wenger et al., 2011) with co-selection of contaminating precursor ions in addition to the ion targeted for fragmentation by MS/MS. This will result in a reduction in the overall quantitative accuracy and precision of quantitation in the experiment, a direct readout of which will be diminished organelle resolution in the final hyperLOPIT spatial map.
      2. Analysis of TMT labeled samples using mass spectrometers that are not of high enough resolution may preclude the use of 10- or 11-plex TMT tags due to an inability to resolve the isotopolog N and C tags, which will result in reporter ion coalescence (McAlister et al., 2012) and, consequently, in inaccurate quantitation. In such cases, isobaric tags with lower multiplexing capability should be used.
    4. Analyze the extra, non-fractionated, sample collected at the SPE step using the same mass spectrometry method as was used in Step O3 above. This will enable characterization of the isobaric tag labeling efficiency.
      Note: It is recommended that the isobaric labeling efficiency be characterized for every hyperLOPIT experiment, independently of the analysis of the pre-fractionated samples by LC-SPS-MS3 or other LC-MS/MS method. The test ensures that the efficiency of labeling of both peptide N-termini and ε-amino groups of lysine residues with isobaric tag reagents is high enough (i.e., essentially complete). Indeed, we observe that labeling efficiencies are typically > 99% using this method. It should be noted that if either the pH or acetonitrile concentration is modified during isobaric tag labeling, it will result in diminished stability of the isobaric tag labeling reagents, hydrolysis of the reagents, or undesirable side-reactions with residues such as tyrosine, serine, and threonine. This will result in incomplete labeling of peptides, which will lead to inaccurate PSM-level and overall protein-level quantitation. This will result in non-representative and reduced overall organelle resolution in the final hyperLOPIT spatial map.

Data analysis

  1. Analysis of mass spectrometry data using Proteome Discoverer version 2.1
    1. Analyze the raw mass spectrometry data files using Proteome Discoverer 2.1 interfaced with an in-house Mascot server. Search the data against a canonical S. cerevisiae database, such as a UniProt or SwissProt database, either in the presence or absence of protein isoformal information. Alternatively download a protein sequence database from the Saccharomyces Genome Database (SGD–www.yeastgenome.org) (Cherry et al., 2012). Search the non-fractionated test sample file separately from the remainder of the fractionated samples. Perform the search for the fractionated sample files together, such that there is a single results report for the entire group of hyperLOPIT experimental mass spectrometry runs.
    2. Perform the labeling test search before carrying out the search of the fractionated hyperLOPIT raw data files. For the labeling test search, specify TMT (or other isobaric tags) labeling of both N-termini and lysine residues as dynamic modifications, but keep all other search parameters the same as described in remainder of this section. Export the Results files at peptide-spectral match (PSM) level and calculate the percentage of peptides that exhibit each isobaric tag modification, relative to all peptides that could theoretically exhibit that modification (i.e., all peptides with a lysine ε-amino group that can be labeled, and all peptide N-termini that are not otherwise modified). This gives an estimate of the labeling efficiency for that particular form of labeling, which should be > 99% using this method.
    3. For the fractionated hyperLOPIT data, load each set of raw data files into Proteome Discoverer as a set of Fractions, and not Files. Perform the Mascot search allowing a peptide tolerance of ±15 ppm and fragment match tolerance of ±0.6 Da if using LC-SPS-MS3. If using another LC-MS/MS method on a different mass spectrometry platform, use peptide and fragment match tolerances that are appropriate to the mass analyzers being used in the MS1 and MS2 scans. Include the Percolator algorithm (Brosch et al., 2009) node within the software, to allow filtering of peptide-spectral matches based on their respective false discovery rate.
    4. Set carbamidomethylation of cysteines, isobaric tag labeling of lysines and N-termini as static modifications. Set oxidation of methionine, and deamidation of asparagine and glutamine as dynamic modifications.
    5. For analyses that use LC-SPS-MS3, in the reporter ion quantitation node, use the sum of centroided peaks within a window of ±2 mmu around m/z of each monoisotopic reporter ion for quantitation and set MS order for activation to MS3. For experiments using other tandem mass spectrometric analysis methods and/or isobaric tags, set MS order for activation to MS2 and adjust the window accordingly for summing around the m/z of the reporter ion. Set protein group-level quantitation to be equal to the median of all quantified PSMs for any given protein group and report quantitation using the S/N measurement option that is available in Proteome Discoverer version 2.1.
    6. Export the protein group-level results information to an Excel workbook or tab-delimited text file. If exported to an Excel workbook, convert the workbook to CSV format before proceeding to the next section.

  2. Data analysis using the MSnbase and pRoloc suite of packages
    1. Analyze the results files from Proteome Discoverer using an R programming language editor such as RStudio (RStudio Team, 2016) that is running the Bioconductor (Gentleman et al., 2004) pRoloc (Gatto et al., 2014), pRolocGUI (Breckels et al., 2018) and MSnbase packages (Gatto and Lilley, 2012). For an exhaustive analysis protocol, we direct the reader to recent work published by Breckels and colleagues (Breckels et al., 2016b).
    2. Read the raw data into the R environment language, using the “readMSnSet2()” function, where “ecol” are specified as the columns that contain the quantitation data.
    3. Filter protein groups from the experiment that contain missing values for quantitation, using the “filterNA()” function. Alternatively, allow a certain number of missing values for quantitation and impute them in some way.
      Note: The missing values may be set to zero, as has been performed previously (Christoforou et al., 2016) or they may be imputed using one of the “impute()” methods that are described in the MSnbase package (Gatto, and Lilley, 2012).
    4. Normalize the quantitation data for each row (i.e., protein group) such that the summed ratios are equal to 1. Use the “normalize ()” function and the “sum” method.
      Note: Other normalization methods are available within the MSnbase package, which may be used to normalize the Proteome Discoverer quantitation data but we find the “sum” method useful for this particular Data analysis.
    5. Observe the data using dimensionality reduction such as Principal Components Analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-SNE) (Van Der Maaten and Hinton, 2008) plots in the absence of any annotation. Note any cluster resolution that is evident from the data.
    6. Curate a list of marker proteins that are known to localize to subcellular organelles in S. cerevisiae. Alternatively, use the markers that are provided within the pRoloc package (using pRolocmarkers(“scer”)).
      Note: Ensure that marker annotation is included for all organelles that are expected to resolve discretely within the experiment. When performing classification of proteins to organelle classes using supervised machine learning approaches, proteins can only be classified to one of the organelle classes that was defined prior to starting the analysis. Therefore, if annotation is omitted for a specific organelle or set of organelles, it will result in proteins from that organelle or set of organelles being subsumed into another organelle that resolved similarly to the organelle that has been omitted within the experiment. The net result will be not only lack of the organelle in question in the final dataset, but also failure to classify any proteins to that organelle.
    7. Append the marker annotation to the MSnSet and observe the resolution within the data at the level of subcellular organelles.
    8. Optionally, perform automated protein marker annotation using the “addGOAnnotation()” function within pRoloc, in which the user can add Gene Ontology (GO) (Ashburner et al., 2000; The Gene Ontology Consortium, 2017) annotation from any GO name space to search for clustering of proteins based on common molecular function, biological process, or cellular component. The resolution of GO annotated clusters that resolve discretely after this analysis can be observed using the pRolocGUI package. This can further be used to augment the marker protein data used for classification of proteins of unknown localization to subcellular organelles.
    9. Perform protein classification using a binary classifier to classify proteins of unknown localization to one of the pre-defined organelle classes. This may encompass use of a supervised machine learning method, such as a Support Vector Machine (SVM) (Vapnik, 1995) as has been widely used in multiple previous hyperLOPIT studies (Christoforou et al., 2016; Thul et al., 2017). Newer methods may instead be employed which include a semi-supervised machine learning method (“phenoDisco”) (Breckels et al., 2013), which can predict presence of novel protein clusters without a priori knowledge of their existence. A variant method is available that uses additional, lower quality, auxiliary data in addition to the highly curated marker protein list to improve classification performance (Breckels et al., 2016a). Alternatively, a new probabilistic classification method may be used, which is based on Bayesian statistics (Crook et al., 2018) and is now available for use within the pRoloc package.
    10. Observe resolution of all proteins after classification to organelles.
    11. Export all classification data on an organelle-by-organelle basis and sort SVM scores in descending order for all proteins classified as belonging to a given organelle.
    12. Consult the original literature to look for corroboration between protein localizations predicted by hyperLOPIT and low throughput studies. Allow a certain permitted percentage of false discoveries for protein localization to a given organelle, based on the literature (e.g., 5% or 1%).
      Note: Due to the nature of such machine-learning methods, all proteins in the dataset will be classified to a single location that was pre-defined in the organelle marker list training data. This does not faithfully recapitulate what happens in vivo, where proteins can localize to multiple subcellular organelles or traffic between multiple organelles.
    13. Set the SVM score cut-off for each organelle at this threshold and use the resultant data as the final spatial proteome map. See Figure 2 for representative data from a single biological replicate performed in reference (Nightingale et al., 2019), without any protein classification. Further, see the Bioconductor (Gentleman et al., 2004) pRolocdata (Gatto et al., 2014) package (version 1.19.4 onwards) and https://proteome.shinyapps.io/yeast2018/ (Nightingale et al., 2019) for a fully processed dataset comprising data from four biological replicate experiments, performed with and without optional nuclear preparations, that can be interactively explored.
    14. Finally, optionally validate the localizations of a subset of proteins that have been predicted by the hyperLOPIT analysis in a low throughput manner and using an orthogonal method. This may be performed using fluorescence microscopy, affinity purification-mass spectrometry, subtractive proteomics, proximity labeling, or density gradient protein co-fractionation experiments.


  1. Prepare this solution by mixing iodixanol working solution (IWS) with 1x LB. To increase the OptiPrep concentration and refractive index, add IWS. To decrease, add 1x LB.
  2. Do not over-dry pellets as they will become very difficult to resolubilize in subsequent steps. Drying for 5 min at room temperature should enable most of the acetone to evaporate. If the protein pellet begins to change from white to colorless, it is a sign that it is starting to over-dry.
  3. To minimize the amount of protein used for Western blotting, run as few gels as possible. Cut membranes, post-blotting, into strips of appropriate molecular weight range based on their reported molecular weight and probe each strip with a single antibody. We typically find that we can probe for all of the markers in Table 1 running only two SDS-PAGE gels.
  4. Primary antibodies diluted in blocking solution may be stored at -20 °C after use and reused at least 5 times, with no appreciable loss of signal. Do not re-use horseradish peroxidase-conjugated secondary antibodies.
  5. If it is necessary to obtain high enough protein yield, the protein contents of adjacent fractions can be pooled prior to labeling. If pooling is performed multiple times within the same gradient, however, it will diminish the resolving ability of the density gradient, meaning that overall organelle resolution within the experiment will be impacted.


  1. Cell culture
    1. 10x yeast nitrogen base (YNB)
      Dissolve 6.7 g yeast nitrogen base without amino acids in 1 L water and filter-sterilize
    2. 10x complete supplement mixture
      0.03% (w/v) isoleucine
      0.15% (w/v) valine
      0.04% (w/v) adenine hemisulfate
      0.02% (w/v) arginine
      0.02% (w/v) histidine
      0.1% (w/v) leucine
      0.03% (w/v) lysine
      0.02% (w/v) methionine
      0.05% (w/v) phenylalanine
      0.2% (w/v) threonine
      0.04% (w/v) tryptophan
      0.03% (w/v) tyrosine
      0.02% (w/v) uracil
      Dissolve all components in water and filter sterilize
    3. 40% (w/v) glucose
      Dissolve 40 g glucose in 100 ml water and sterilize by autoclaving
    4. Synthetic medium with 2% glucose (1.2 L per experiment)
      Mix 120 ml 10x YNB, 60 ml 40% (w/v) glucose and 120 ml 10x complete supplement mixture
      Make up to 1.2 L with sterile water
    5. YPD agar medium
      1. Suspend 4 g Bacto agar, 4 g Bacto peptone and 2 g Bacto yeast extract in 190 ml water
      2. Sterilize by autoclaving
      3. Whilst still molten, add 10 ml 40% (w/v) glucose and pour agar plates (approximately 20 ml per plate)

  2. Cell pre-treatment and spheroplast generation
    1. TCEP reduction buffer (150 ml per experiment)
      20 mM Tris-HCl, pH 7.5
      10 mM Tris-(2-carboxyethyl) phosphine (TCEP)
    2. Spheroplasting medium (40 ml per experiment)
      Composed of the same constituents as synthetic medium, but with the addition of 20 mM Tris-HCl, pH 7.5 and 1.2 M sorbitol
      Freshly supplement with 1 Complete EDTA-free protease inhibitor tablet per 50 ml buffer
    3. Zymolyase 100-T solution (700 μl per experiment)
      5 μg/ml zymolyase 100-T prepared in spheroplasting medium
      Prepare 1 μl for every OD unit of yeast used in the experiment
    4. Spheroplast wash medium (150 ml per experiment)
      Composed of the same constituents as spheroplasting medium, with the omission of the Tris-HCl buffer
      Freshly supplement with 1 Complete EDTA-free protease inhibitor tablet per 50 ml buffer

  3. (Optional) Nuclear preparation 
    1. Ficoll lysis buffer (Kizer et al., 2006) (15 ml per experiment)
      18% (w/v) Ficoll PM-400
      20 mM dibasic potassium phosphate, pH 6.8
      1 mM magnesium chloride
      0.5 mM EDTA
      Freshly supplement with 1 Complete Mini EDTA-free protease inhibitor tablet per 10 ml buffer
    2. Buffer NP (Kizer et al., 2006) (300 μl per experiment)
      340 mM sucrose
      20 mM Tris-HCl, pH 7.4
      50 mM potassium chloride
      5 mM magnesium chloride
      Freshly supplement with 1 Complete Mini EDTA-free protease inhibitor tablet per 10 ml buffer

  4. Cell lysis
    1. 1x LB (75 ml per experiment)
      250 mM sucrose
      50 mM potassium acetate
      2 mM magnesium acetate
      1 mM EDTA, pH 8.0
      Freshly supplement with 1 Complete EDTA-free protease inhibitor tablet per 50 ml buffer
    2. 6x LB (4 ml per experiment)
      300 mM potassium acetate
      12 mM magnesium acetate
      6 mM EDTA, pH 8.0

  5. Density gradient centrifugation
    1. Iodixanol working solution (IWS) (18 ml per experiment)
      Mix 3 ml 6x LB with 15 ml OptiPrep
    2. 18% (w/v) OptiPrep solution (25 ml per experiment)
      1. Mix 9 ml IWS with 16 ml 1x LB
      2. Double-check refractive index against a standard curve of OptiPrep concentrations of known refractive index
      3. Adjust if needed, using IWS to increase refractive index and 1 x LB to reduce refractive index
    3. 16% OptiPrep solution (20 ml per experiment)
      1. Mix 6.4 ml IWS with 13.6 ml 1x LB
      2. Double check refractive index against a standard curve of OptiPrep concentrations of known refractive index
      3. Adjust if needed, using IWS to increase refractive index and 1x LB to reduce refractive index

  6. Fraction processing, digestion and TMT labeling
    1. Protein resolubilization buffer (20 ml per experiment)
      100 mM HEPES-NaOH, pH 8.5
      0.1% (w/v) SDS
    2. Digestion buffer (2 ml per experiment)
      100 mM HEPES-NaOH, pH 8.5
    3. 5% (v/v) hydroxylamine (120 μl per experiment)
      Mix 108 μl of digestion buffer with 12 μl 50% (w/v) hydroxylamine solution
      Prepare just before use
    4. TTBS (1 L per experiment)
      25 mM Tris-HCl, pH 7.6
      150 mM NaCl
      0.1% (v/v) Tween-20
    5. Blocking solution (100 ml per antibody used)
      5% (w/v) non-fat milk powder, prepared in TTBS
    6. Dithiothreitol (DTT) solution
      100 mM DL-dithiothreitol, prepared in protein resolubilization buffer
    7. Iodoacetamide (IAA) solution
      250 mM iodoacetamide, prepared in protein resolubilization buffer

  7. Sample clean-up by solid phase extraction (SPE)
    1. Equilibration buffer (600 μl per experiment)
      50% (v/v) acetonitrile
      0.5% (v/v) acetic acid
    2. Desalting buffer 1 (1.8 ml per experiment)
      0.1% (v/v) trifluoroacetic acid
    3. Desalting buffer 2 (180 μl per experiment)
      0.5% (v/v) acetic acid
    4. Elution buffer (1 ml per experiment)
      80% (v/v) acetonitrile
      0.5% (v/v) acetic acid

  8. High pH reversed-phase chromatography
    1. RP mobile phase stock solution (100 ml per experiment)
      200 mM ammonium formate, pH 10.0. Adjust pH with ammonium hydroxide.
    2. RP mobile phase A (500 ml per experiment)
      20 mM ammonium formate, pH 10.0
      Prepare by mixing 50 ml RP mobile phase stock solution with 450 ml HPLC-grade water
      Re-adjust to pH 10.0 with ammonium hydroxide, if necessary
    3. RP mobile phase B (500 ml per experiment)
      20 mM ammonium formate, pH 10.0
      80% (v/v) acetonitrile
      Prepare by mixing 50 ml RP mobile phase stock solution with 400 ml HPLC-grade ACN and 50 ml HPLC-grade water
      Do not adjust pH
    4. RP resolubilization buffer (100 μl per experiment)
      95% (v/v) RP mobile phase A
      5% (v/v) RP mobile phase B


We acknowledge funding from the BBSRC (Strategic Longer and Larger grant awarded to KSL (award BB/L002817/1) and CASE studentship awarded to KSL and SGO (award BB/I016147/1)). We thank Owen Vennard for critically reading this protocol.

Competing interests

There are no conflicts of interest or competing interest.


  1. Alepuz, P. M., de Nadal, E., Zapater, M., Ammerer, G. and Posas, F. (2003). Osmostress-induced transcription by Hot1 depends on a Hog1-mediated recruitment of the RNA Pol II. EMBO J 22(10): 2433-2442.
  2. Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., Harris, M. A., Hill, D. P., Issel-Tarver, L., Kasarskis, A., Lewis, S., Matese, J. C., Richardson, J. E., Ringwald, M., Rubin, G. M. and Sherlock, G. (2000). Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25(1): 25-29.
  3. Brachmann, C. B., Davies, A., Cost, G. J., Caputo, E., Li, J., Hieter, P. and Boeke, J. D. (1998). Designer deletion strains derived from Saccharomyces cerevisiae S288C: a useful set of strains and plasmids for PCR-mediated gene disruption and other applications. Yeast 14(2): 115-132.
  4. Breckels, L. M., Holden, S. B., Wojnar, D., Mulvey, C. M., Christoforou, A., Groen, A., Trotter, M. W., Kohlbacher, O., Lilley, K. S. and Gatto, L. (2016a). Learning from heterogeneous data sources: an application in spatial proteomics. PLoS Comput Biol 12(5): e1004920.
  5. Breckels, L. M., Mulvey, C. M., Lilley, K. S. and Gatto, L. (2016b). A Bioconductor workflow for processing and analysing spatial proteomics data. F1000Res 5: 2926.
  6. Breckels, L. M., Naake, T. and Gatto L. (2018). Interactive visualisation of spatial proteomics data. R package version 1.14.0.
  7. Breckels, L. M., Gatto, L., Christoforou, A., Groen, A. J., Lilley, K. S. and Trotter, M. W. (2013). The effect of organelle discovery upon sub-cellular protein localisation. J Proteomics 88: 129-140.
  8. Breker, M., Gymrek, M. and Schuldiner, M. (2013). A novel single-cell screening platform reveals proteome plasticity during yeast stress responses. J Cell Biol 200(6): 839-850.
  9. Brosch, M., Yu, L., Hubbard, T. and Choudhary, J. (2009). Accurate and sensitive peptide identification with Mascot Percolator. J Proteome Res 8(6): 3176-3181.
  10. Cherry, J. M., Hong, E. L., Amundsen, C., Balakrishnan, R., Binkley, G., Chan, E. T., Christie, K. R., Costanzo, M. C., Dwight, S. S., Engel, S. R., Fisk, D. G., Hirschman, J. E., Hitz, B. C., Karra, K., Krieger, C. J., Miyasato, S. R., Nash, R. S., Park, J., Skrzypek, M. S., Simison, M., Weng, S. and Wong, E. D. (2012). Saccharomyces Genome Database: the genomics resource of budding yeast. Nucleic Acids Res 40(Database issue): D700-705.
  11. Chen, X. J., Wang, X., Kaufman, B. A. and Butow, R. A. (2005). Aconitase couples metabolic regulation to mitochondrial DNA maintenance. Science 307(5710): 714-717.
  12. Chong, Y. T., Koh, J. L., Friesen, H., Duffy, S. K., Cox, M. J., Moses, A., Moffat, J., Boone, C. and Andrews, B. J. (2015). Yeast proteome dynamics from single cell imaging and automated analysis. Cell 161(6): 1413-1424.
  13. Christoforou, A., Mulvey, C. M., Breckels, L. M., Geladaki, A., Hurrell, T., Hayward, P. C., Naake, T., Gatto, L., Viner, R., Martinez Arias, A. and Lilley, K. S. (2016). A draft map of the mouse pluripotent stem cell spatial proteome. Nat Commun 7: 8992.
  14. Crook, O. M., Mulvey, C. M., Kirk, P. D. W., Lilley, K. S. and Gatto, L. (2018). A Bayesian mixture modelling approach for spatial proteomics. PLoS Comput Biol 14(11): e1006516.
  15. Denervaud, N., Becker, J., Delgado-Gonzalo, R., Damay, P., Rajkumar, A. S., Unser, M., Shore, D., Naef, F. and Maerkl, S. J. (2013). A chemostat array enables the spatio-temporal analysis of the yeast proteome. Proc Natl Acad Sci U S A 110(39): 15842-15847.
  16. de Duve, C. (1971). Tissue fraction-past and present. J Cell Biol 50(1): 20.
  17. Gancedo, C., Flores, C. L. and Gancedo, J. M. (2016). The expanding landscape of moonlighting proteins in yeasts. Microbiol Mol Biol Rev 80(3): 765-777.
  18. Gatto, L. and Lilley, K. S. (2012). MSnbase-an R/Bioconductor package for isobaric tagged mass spectrometry data visualization, processing and quantitation. Bioinformatics 28(2): 288-289.
  19. Gatto, L., Breckels, L. M., Wieczorek, S., Burger, T. and Lilley, K. S. (2014). Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata. Bioinformatics 30(9): 1322-1324.
  20. Gatto, L., Breckels, L. M. and Lilley, K. S. (2019). Assessing sub-cellular resolution in spatial proteomics experiments. Curr Opin Chem Biol 48: 123-149.
  21. Groen, A. J., Sancho-Andres, G., Breckels, L. M., Gatto, L., Aniento, F. and Lilley, K. S. (2014). Identification of trans-golgi network proteins in Arabidopsis thaliana root tissue. J Proteome Res 13(2): 763-776.
  22. Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., Hornik, K., Hothorn, T., Huber, W., Iacus, S., Irizarry, R., Leisch, F., Li, C., Maechler, M., Rossini, A. J., Sawitzki, G., Smith, C., Smyth, G., Tierney, L., Yang, J. Y. and Zhang, J. (2004). Bioconductor: open software development for computational biology and bioinformatics. Genome Biol 5(10): R80.
  23. Geladaki, A., Kocevar Britovsek, N., Breckels, L. M., Smith, T. S., Vennard, O. L., Mulvey, C. M., Crook, O. M., Gatto, L. and Lilley, K. S. (2019). Combining LOPIT with differential ultracentrifugation for high-resolution spatial proteomics. Nat Commun 10(1): 331.
  24. Hope, I. A. and Struhl, K. (1986). Functional dissection of a eukaryotic transcriptional activator protein, GCN4 of yeast. Cell 46(6): 885-894.
  25. Huh, W. K., Falvo, J. V., Gerke, L. C., Carroll, A. S., Howson, R. W., Weissman, J. S. and O'Shea, E. K. (2003). Global analysis of protein localization in budding yeast. Nature 425(6959): 686-691.
  26. Hung, V., Lam, S. S., Udeshi, N. D., Svinkina, T., Guzman, G., Mootha, V. K., Carr, S. A. and Ting, A. Y. (2017). Proteomic mapping of cytosol-facing outer mitochondrial and ER membranes in living human cells by proximity biotinylation. Elife 6: e24463.
  27. Hung, V., Zou, P., Rhee, H. W., Udeshi, N. D., Cracan, V., Svinkina, T., Carr, S. A., Mootha, V. K. and Ting, A. Y. (2014). Proteomic mapping of the human mitochondrial intermembrane space in live cells via ratiometric APEX tagging. Mol Cell 55(2): 332-341.
  28. Hunter, M. J. and Commerford, S. L. (1961). Pressure homogenization of mammalian tissues. Biochim Biophys Acta 47: 580-586.
  29. Hwang, J. and Espenshade, P. J. (2016). Proximity-dependent biotin labelling in yeast using the engineered ascorbate peroxidase APEX2. Biochem J 473(16): 2463-2469.
  30. Itzhak, D. N., Tyanova, S., Cox, J. and Borner, G. H. (2016). Global, quantitative and dynamic mapping of protein subcellular localization. Elife 5: e16950.
  31. Jadot, M., Boonen, M., Thirion, J., Wang, N., Xing, J., Zhao, C., Tannous, A., Qian, M., Zheng, H., Everett, J. K., Moore, D. F., Sleat, D. E. and Lobel, P. (2017). Accounting for protein subcellular localization: A compartmental map of the rat liver proteome. Mol Cell Proteomics 16(2): 194-212.
  32. Jean Beltran, P. M., Mathias, R. A. and Cristea, I. M. (2016). A portrait of the human organelle proteome in space and time during cytomegalovirus infection. Cell Syst 3(4): 361-373 e366.
  33. Kim, D. I., Birendra, K. C., Zhu, W., Motamedchaboki, K., Doye, V. and Roux, K. J. (2014). Probing nuclear pore complex architecture with proximity-dependent biotinylation. Proc Natl Acad Sci U S A 111(24): E2453-2461.
  34. Kizer, K. O., Xiao, T. and Strahl, B. D. (2006). Accelerated nuclei preparation and methods for analysis of histone modifications in yeast. Methods 40(4): 296-302.
  35. Lam, S. S., Martell, J. D., Kamer, K. J., Deerinck, T. J., Ellisman, M. H., Mootha, V. K. and Ting, A. Y. (2015). Directed evolution of APEX2 for electron microscopy and proximity labeling. Nat Methods 12(1): 51-54.
  36. McAlister, G. C., Huttlin, E. L., Haas, W., Ting, L., Jedrychowski, M. P., Rogers, J. C., Kuhn, K., Pike, I., Grothe, R. A., Blethrow, J. D. and Gygi, S. P. (2012). Increasing the multiplexing capacity of TMTs using reporter ion isotopologues with isobaric masses. Anal Chem 84(17): 7469-7478.
  37. McAlister, G. C., Nusinow, D. P., Jedrychowski, M. P., Wuhr, M., Huttlin, E. L., Erickson, B. K., Rad, R., Haas, W. and Gygi, S. P. (2014). MultiNotch MS3 enables accurate, sensitive, and multiplexed detection of differential expression across cancer cell line proteomes. Anal Chem 86(14): 7150-7158.
  38. Morgenstern, M., Stiller, S. B., Lubbert, P., Peikert, C. D., Dannenmaier, S., Drepper, F., Weill, U., Hoss, P., Feuerstein, R., Gebert, M., Bohnert, M., van der Laan, M., Schuldiner, M., Schutze, C., Oeljeklaus, S., Pfanner, N., Wiedemann, N. and Warscheid, B. (2017). Definition of a high-confidence mitochondrial proteome at quantitative scale. Cell Rep 19(13): 2836-2852.
  39. Mulvey, C. M., Breckels, L. M., Geladaki, A., Britovsek, N. K., Nightingale, D. J. H., Christoforou, A., Elzek, M., Deery, M. J., Gatto, L. and Lilley, K. S. (2017). Using hyperLOPIT to perform high-resolution mapping of the spatial proteome. Nat Protoc 12(6): 1110-1135.
  40. Nikolaev, Y., Deillon, C., Hoffmann, S. R., Bigler, L., Friess, S., Zenobi, R., Pervushin, K., Hunziker, P. and Gutte, B. (2010). The leucine zipper domains of the transcription factors GCN4 and c-Jun have ribonuclease activity. PLoS One 5(5): e10765.
  41. Nightingale, D. J., Geladaki, A., Breckels, L. M., Oliver, S. G. and Lilley, K. S. (2019). The subcellular organisation of Saccharomyces cerevisiae. Curr Opin Chem Biol 48: 86-95.
  42. Nikolovski, N., Rubtsov, D., Segura, M. P., Miles, G. P., Stevens, T. J., Dunkley, T. P., Munro, S., Lilley, K. S. and Dupree, P. (2012). Putative glycosyltransferases and other plant Golgi apparatus proteins are revealed by LOPIT proteomics. Plant Physiol 160(2): 1037-1051.
  43. Nightingale, D. J., Oliver, S. G. and Lilley, K. S. (2018). Mapping the Saccharomyces cerevisiae spatial proteome with high resolution using hyperLOPIT. In: Methods in Molecular Biology. In: Oliver. S. G. (Ed.). Humana Press.
  44. Opitz, N., Schmitt, K., Hofer-Pretz, V., Neumann, B., Krebber, H., Braus, G. H. and Valerius, O. (2017). Capturing the Asc1p/Receptor for Activated C Kinase 1 (RACK1) Microenvironment at the head region of the 40S ribosome with quantitative BioID in yeast. Mol Cell Proteomics 16(12): 2199-2218.
  45. Stadler, C., Rexhepaj, E., Singan, V. R., Murphy, R. F., Pepperkok, R., Uhlen, M., Simpson, J. C. and Lundberg, E. (2013). Immunofluorescence and fluorescent-protein tagging show high correlation for protein localization in mammalian cells. Nat Methods 10(4): 315-323.
  46. Sickmann, A., Reinders, J., Wagner, Y., Joppich, C., Zahedi, R., Meyer, H. E., Schonfisch, B., Perschil, I., Chacinska, A., Guiard, B., Rehling, P., Pfanner, N. and Meisinger, C. (2003). The proteome of Saccharomyces cerevisiae mitochondria. Proc Natl Acad Sci U S A 100(23): 13207-13212.
  47. Reinders, J., Zahedi, R. P., Pfanner, N., Meisinger, C. and Sickmann, A. (2006). Toward the complete yeast mitochondrial proteome: multidimensional separation techniques for mitochondrial proteomics. J Proteome Res 5(7): 1543-1554.
  48. Rhee, H. W., Zou, P., Udeshi, N. D., Martell, J. D., Mootha, V. K., Carr, S. A. and Ting, A. Y. (2013). Proteomic mapping of mitochondria in living cells via spatially restricted enzymatic tagging. Science 339(6125): 1328-1331.
  49. Roux, K. J., Kim, D. I., Raida, M. and Burke, B. (2012). A promiscuous biotin ligase fusion protein identifies proximal and interacting proteins in mammalian cells. J Cell Biol 196(6): 801-810.
  50. Ross, P. L., Huang, Y. N., Marchese, J. N., Williamson, B., Parker, K., Hattan, S., Khainovski, N., Pillai, S., Dey, S., Daniels, S., Purkayastha, S., Juhasz, P., Martin, S., Bartlet-Jones, M., He, F., Jacobson, A. and Pappin, D. J. (2004). Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents. Mol Cell Proteomics 3(12): 1154-1169.
  51. R Core Team. (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
  52. RStudio Team. (2016). RStudio: Integrated Development Environment for R.
  53. Sadowski, P. G., Dunkley, T. P., Shadforth, I. P., Dupree, P., Bessant, C., Griffin, J. L. and Lilley, K. S. (2006). Quantitative proteomic approach to study subcellular localization of membrane proteins. Nat Protoc 1(4): 1778-1789.
  54. Simpson, R. J. (2010). Disruption of cultured cells by nitrogen cavitation. Cold Spring Harb Protoc 2010(11): pdb prot5513.
  55. Thompson, A., Schafer, J., Kuhn, K., Kienle, S., Schwarz, J., Schmidt, G., Neumann, T., Johnstone, R., Mohammed, A. K. and Hamon, C. (2003). Tandem mass tags: a novel quantification strategy for comparative analysis of complex protein mixtures by MS/MS. Anal Chem 75(8): 1895-1904.
  56. Thul, P. J., Akesson, L., Wiking, M., Mahdessian, D., Geladaki, A., Ait Blal, H., Alm, T., Asplund, A., Bjork, L., Breckels, L. M., Backstrom, A., Danielsson, F., Fagerberg, L., Fall, J., Gatto, L., Gnann, C., Hober, S., Hjelmare, M., Johansson, F., Lee, S., Lindskog, C., Mulder, J., Mulvey, C. M., Nilsson, P., Oksvold, P., Rockberg, J., Schutten, R., Schwenk, J. M., Sivertsson, A., Sjostedt, E., Skogs, M., Stadler, C., Sullivan, D. P., Tegel, H., Winsnes, C., Zhang, C., Zwahlen, M., Mardinoglu, A., Ponten, F., von Feilitzen, K., Lilley, K. S., Uhlen, M. and Lundberg, E. (2017). A subcellular map of the human proteome. Science 356(6340).
  57. The Gene Ontology Consortium. (2017). Expansion of the Gene Ontology knowledgebase and resources. Nucleic Acids Res 45(D1): D331-D338.
  58. Ting, L., Rad, R., Gygi, S. P. and Haas, W. (2011). MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics. Nat Methods 8(11): 937-940.
  59. Tkach, J. M., Yimit, A., Lee, A. Y., Riffle, M., Costanzo, M., Jaschob, D., Hendry, J. A., Ou, J., Moffat, J., Boone, C., Davis, T. N., Nislow, C. and Brown, G. W. (2012). Dissecting DNA damage response pathways by analysing protein localization and abundance changes during DNA replication stress. Nat Cell Biol 14(9): 966-976.
  60. Van Der Maaten, L. and Hinton, G. (2008). Visualizing Data using t-SNE. J Mach Learn Res 9: 2579-2605.
  61. Vapnik, V. (1995). The nature of statistical learning theory. Springer.
  62. Villen, J. and Gygi, S. P. (2008). The SCX/IMAC enrichment approach for global phosphorylation analysis by mass spectrometry. Nat Protoc 3(10): 1630-1638.
  63. Vogtle, F. N., Burkhart, J. M., Rao, S., Gerbeth, C., Hinrichs, J., Martinou, J. C., Chacinska, A., Sickmann, A., Zahedi, R. P. and Meisinger, C. (2012). Intermembrane space proteome of yeast mitochondria. Mol Cell Proteomics 11(12): 1840-1852.
  64. Vogtle, F. N., Burkhart, J. M., Gonczarowska-Jorge, H., Kucukkose, C., Taskin, A. A., Kopczynski, D., Ahrends, R., Mossmann, D., Sickmann, A., Zahedi, R. P. and Meisinger, C. (2017). Landscape of submitochondrial protein distribution. Nat Commun 8(1): 290.
  65. Wang, Y., Lilley, K. S. and Oliver, S. G. (2014). A protocol for the subcellular fractionation of Saccharomyces cerevisiae using nitrogen cavitation and density gradient centrifugation. Yeast 31(4): 127-135.
  66. Weill, U., Yofe, I., Sass, E., Stynen, B., Davidi, D., Natarajan, J., Ben-Menachem, R., Avihou, Z., Goldman, O., Harpaz, N., Chuartzman, S., Kniazev, K., Knoblach, B., Laborenz, J., Boos, F., Kowarzyk, J., Ben-Dor, S., Zalckvar, E., Herrmann, J. M., Rachubinski, R. A., Pines, O., Rapaport, D., Michnick, S. W., Levy, E. D. and Schuldiner, M. (2018). Genome-wide SWAp-Tag yeast libraries for proteome exploration. Nat Methods 15(8): 617-622.
  67. Wiederhold, E., Veenhoff, L. M., Poolman, B. and Slotboom, D. J. (2010). Proteomics of Saccharomyces cerevisiae organelles. Mol Cell Proteomics 9(3): 431-445.
  68. Wenger, C. D., Lee, M. V., Hebert, A. S., McAlister, G. C., Phanstiel, D. H., Westphall, M. S. and Coon, J. J. (2011). Gas-phase purification enables accurate, multiplexed proteome quantification with isobaric tagging. Nat Methods 8(11): 933-935.
  69. Yofe, I., Weill, U., Meurer, M., Chuartzman, S., Zalckvar, E., Goldman, O., Ben-Dor, S., Schutze, C., Wiedemann, N., Knop, M., Khmelinskii, A. and Schuldiner, M. (2016). One library to make them all: streamlining the creation of yeast libraries via a SWAp-Tag strategy. Nat Methods 13(4): 371-378.
  70. Zahedi, R. P., Sickmann, A., Boehm, A. M., Winkler, C., Zufall, N., Schonfisch, B., Guiard, B., Pfanner, N. and Meisinger, C. (2006). Proteomic analysis of the yeast mitochondrial outer membrane reveals accumulation of a subclass of preproteins. Mol Biol Cell 17(3): 1436-1450.
  71. Zhang, R., Sioma, C. S., Wang, S. and Regnier, F. E. (2001). Fractionation of isotopically labeled peptides in quantitative proteomics. Anal Chem 73(21): 5142-5149.


【背景】亚细胞蛋白质允许定位于蛋白质,作为细胞内蛋白质形成的一部分,蛋白质形成的调节对细胞稳态至关重要。亚细胞蛋白质允许蛋白质作为更高水平的成员作为蛋白质,代谢物和辅因子,并有助于细胞器的功能。在某些情况下,特定亚细胞区室内的酶的隔离不会在不同的代谢途径之间交换蛋白质定位可能受到翻译后修饰的影响,例如信号级联中蛋白质的磷酸化,最终允许蛋白质作为转录因子(例如,,蛋白质水解)定位于“安全”空间。在细胞核内定位以锻炼其基因调节功能,或允许其进行一个例子是酵母中响应反应途径的情况,其中信号级联被激活,导致蛋白质的磷酸化,这是蛋白质磷酸化中磷酸化反应的结果。核酸酶和磷酸化TF Hot 1p,反过来,它可以将RNA聚合酶II募集到响应基因的启动子(Alepuz et al。,2003)。

越来越多的证据表明差异蛋白质定位,即使在相同的细胞类型中,可能允许蛋白质根据其在细胞内的视图定位而发挥不同的作用,有时被称为“兼职”。 ,2016)。酿酒酵母,此类蛋白质的例子包括Gcn4p,它是控制氨基酸生物合成的一般转录因子(Hope和Struhl,1986),也可作为核糖酶(Nikolaev et al。,2010);以及Aco1 p,除了其广泛已知的三羧酸循环酶功能外,它还具有线粒体DNA稳定性的作用(Chen et al。在相同的细胞类型中,在不同的定位中进一步观察到相同蛋白质的同种型,表明这种差异定位可以提供关于蛋白质功能的另一水平的调节。 DNA甲基转移酶(D小鼠干细胞中的nmt1),其定位于染色质,另一种同种型位于另一个核位置(Christoforou 等,,2016)。为了充分理解蛋白质的体内功能,确定它们在细胞内的位置至关重要。包括已经应用于接近该挑战的方法,包括包含荧光显微镜成像,细胞器蛋白质组学,邻近生物素化方法和蛋白质相关性分析方法;使用GFP或其他荧光蛋白标记的蛋白质文库进行了基于荧光显微镜的全细胞,高通量研究(Huh et al。,2003; Tkach et al。,2012; Breker et al。,2013; Denervaud et al。,2013; Chong et al。 >,2015; Weill et al。,2018; Yofe et al。,2016)。功能强大,这些方法有d感兴趣的特定蛋白质的荧光信号可能不会超过细胞自发荧光的背景水平,特别是在酵母中,并且不太可能决定确定这种蛋白质完整性的位置, C-或N-末端用于正确定位(Weill et al。,2018; Yofe et al。,2016)。因此,进行蛋白质定位研究,其中蛋白质被标记对于所讨论的ORF的N或C末端的荧光蛋白可能导致给定的蛋白质在同一生物系统内以不同的定位报告,这取决于使用的策略(Stadler等人。 >,2013)。

细胞器富集策略也包含了对细胞器蛋白质组的定义,通过对我们蛋白质定位的知识做出贡献。这些策略在丰富中已经过时。这包括一些研究,这些研究已经对酿酒酵母中具有不同特异性水平的线粒体蛋白质组的态度进行了各种研究。 et al。,2003; Reinders et al。,2006)或特定的亚线粒体蛋白质(Zahedi et al。,2006; Vogtle et al。,2012)在某些情况下无法控制与线粒体蛋白质共同富集的其他细胞器蛋白质。最近的研究已经对线粒体亚细胞器蛋白质的整体性进行了改进。 USI更复杂的分馏和蛋白质组定量方法(Morgenstern et al。,2017; Vogtle et al。,2017)。这些研究可能因来自其他细胞器的污染膜的存在而加剧,这些细胞膜与感兴趣的细胞器共富集(Wiederhold et al。,2010)。报告可能存在于体内多个位置的蛋白质。此外,无法控制富集时丢失的细胞器的比例。

最近,基于益生菌生物化学的新型邻近标记方法已经显示出在确定除酵母以外的生物体中蛋白质亚细胞定位的前景。这包括对子器官蛋白质组的更详细研究(Rhee等人; Hung et al。,2014),包括细胞器的细胞毒性面(Hung et al。,2017)。基于BirA的BioID方法(Roux et al。 ,2012)已应用于 S. cerevisiae ,以确定与核糖体支架蛋白相互作用的亚细胞蛋白(Opitz et al。,2017)和APEX2方法(Lam et al。,2015)已经证明在 S. Cerevisiae 和裂殖酵母 Schizosaccharomyces pombe (Hwang和Espenshade,2016)。这样的方法只关注目前感兴趣的蛋白质的近端相互作用因子。 / em>,2013 Kim et al。,2014)并没有提供蛋白质组空间组织的全细胞图片。这通常使用全细胞蛋白质相关性相关性分析方法进行研究.S必须在上述方法的基础上进行,重要的是在整个细胞的背景下观察蛋白质亚细胞定位。观察到,当通过某种方式分离细胞裂解物时,例如差异或密度梯度离心,定位于同一细胞器的蛋白质将表现出相似性正如最初报道的那样,它已作为框架的一部分出版,该框架已经出版,已经出版,已经出版,已被用作代表所讨论的细胞器代表但现在采用大量定量方法的例子。 Christoforou et al。,2016; Itzha k et al。,2016; Jean Beltran et al。,2016; Mulvey et al。,2017; Jadot et al。 ,2017; Geladaki et al。,2019)。通过同位素标记(hyperLOPIT)方法对细胞器蛋白质的多重定位使用密度梯度进行亚细胞分级,然后使用10进行多重蛋白质组定量。 -with TMT同量异位标签(Thompson et al。,2003)和Orbitrap Fusion Lumos质谱仪上的SPS-MS3定量。精确的亚细胞分辨率由密度和多重定量提供,其次是复杂的机器学习分析方法意味着超级LOPIT允许在单个实验中平行地对数千种蛋白质进行无偏差的蛋白质定位测定。提供任何体表的最高分辨率分辨率图谱em> et al。,2017; Gatto et al。,2019)。

在我们的研究中,我们发表了一篇酵母蛋白质组作图研究,其中我们使用hyperLOPIT在氮充足条件下以高通量绘制蛋白质组(Nightingale et al。,2019)。这为2,846种蛋白质提供了定位数据,发现这些蛋白质中只有936种位于一个独特的亚细胞位置,这个数字相当于我们可观察到的蛋白质组的32%。这表明与以前的研究相同(Christor) al。,2016; Thul et al。,2017),大部分蛋白质是动态的或存在于多个位置。

我们在这里提供了一个描述如何进行氮气穴裂解的方案(Hunter和Commerford,1961; Simpson,2010; Wang et al。,2014)亚细胞分离,以及一个版本的hyperLOPIT方案我们进一步引起读者注意的事实是亚细胞分级和蛋白质印迹方案的蛋白质印迹部分(参见图1的方案概述)。此外,在能够分辨TMT 10或11-plex标签的质谱仪(不是可用的,具有较低能力的同量异位标签)的情况下这可以使用iTRAQ(Ross et al。,2004)4-或8-plex或TMT 6-plex来实现,但重要的是,这将导致整体亚细胞分辨率降低相对于TMT 10或11重,因为亚细胞分馏较少HyperLOPIT及其前身LOPIT已被用于在不同条件下绘制多种不同物种的亚细胞蛋白质(Sadowski et al。,2006; Nikolovski)。 et al。,2012; Groen et al。,2014; Christoforou et al。,2016; Thul et al。,2017)。我们设想该协议将适用于可修改的其他酵母的空间蛋白质组,包括工业酵母 Komagataella phaffii (syn。 Pichia pastoris >)和致病酵母,如白色念珠菌。我们最近在 Methods in Molecular Biology (Nightingale et al。,2018)中发表了一个协议,描述了如何在 S. Cerevisiae 中进行hyperLOPIT。在这里,我们使用Proteome Discoverer 2.1(Thermo Fisher Scientific, www.thermofisher.com ),更多关注后质谱信息学分析。更深入细致的信息学协议hyperLOPIT方案的亚细胞分数部分在技术上具有挑战性,需要不间断的实验。从细胞收获到梯度馏分收集的工作。我们强烈建议在收集梯度分数之前不要暂停实验工作。

图1. 酿酒酵母的hyperLOPIT方法概述。括号中还包括每个步骤的大致时间尺度和潜在的停止点。

关键字:酿酒酵母, 亚细胞分离, 氮空化, 定量蛋白质组学, 酵母, 空间蛋白质组学


  1. 96孔板(Thermo Fisher Scientific,目录号:167008)&nbsp;
  2. 猎鹰管,15和50毫升容量(康宁,目录号:430791和430291)
  3. Luer-lock注射器,2 ml,5 ml和10 ml容量(BD Plastipak,目录号:300185,302187和300912,分别)
  4. 微量离心管,1.5 ml容量(Eppendorf,目录号:0030120086)
  5. 移液器吸头(Rainin LTS1,000μl,LTS250μl和LTS20μl;目录号:17001864,17001863和17001865;或与本协议使用的移液器兼容的等效吸头)
  6. 聚碳酸酯厚壁超速离心管,容量32毫升(Beckman Coulter,目录号:355631)
  7. 聚丙烯OptiSeal(TM)超速离心管,11.2 ml容量(Beckman Coulter,目录号:362181)和相关的管适配器(Beckman Coulter,目录号:362202)
  8. 半微量比色皿(Sarstedt,目录号:67.742)
  9. 硅胶管(内径1 mm,壁直径1 mm)(Fisher Scientific,目录号:10430313)
  10. 不锈钢钝头针,14号(Sigma-Aldrich,目录号:Z261408)
  11. 具有预切隔垫的总回收玻璃瓶和瓶盖(Waters,目录号:186000385C)
  12. 垃圾箱
  13. X光胶片(Fujifilm,目录号:4741019289)
  14. Trans-Blot®TurboTM聚偏二氟乙烯(PVDF)膜(Bio-Rad,目录号:1704157)
  15. Saccharomyces cerevisiae 菌株适合于实验目的(例如,BY4741(Baker Brachmann et al。,1998),我们用于最近的hyperLOPIT研究夜莺 et al。,2019)
  16. 乙酸(Sigma-Aldrich,目录号:320099)
  17. 丙酮,分析纯(Sigma-Aldrich,目录号:32201)
  18. 乙腈,HPLC梯度级(Fisher Scientific,目录号:A / 0627/17)
  19. 腺嘌呤半硫酸盐(Sigma-Aldrich,目录号:A3159)
  20. AmershamTM ECL Prime增强化学发光蛋白质印迹检测试剂(GE Healthcare,目录号RPN2232)
  21. 甲酸铵(Sigma-Aldrich,目录号:70221)
  22. 氢氧化铵(Sigma-Aldrich,目录号:30501)
  23. 精氨酸(Sigma-Aldrich,目录号:A8094)
  24. Difco Bacto 琼脂(Fisher Scientific,目录号:10455513)
  25. Difco Bacto蛋白胨(Fisher Scientific,目录号:DF0118-17-0)
  26. Difco Bacto酵母提取物(Fisher Scientific,目录号:DF0127-17-9)
  27. cOmpleteTM Mini蛋白酶抑制剂片(罗氏,目录号:11836170001)
  28. cOmpleteTM蛋白酶抑制剂片(罗氏,目录号:11873580001)
  29. D - (+) - 葡萄糖(Sigma-Aldrich,目录号:G7021)
  30. 磷酸氢二钾(Sigma-Aldrich,目录号:P3786)
  31. DL-二硫苏糖醇(DTT)(Melford Laboratories,目录号:D11000)
  32. 乙二胺四乙酸(EDTA)(Sigma-Aldrich,目录号:E5134)
  33. Ficoll <®> PM-400(Sigma-Aldrich,目录号:F4375)
  34. 甲酸(VWR,目录号:20318.297)
  35. HEPES游离酸(Melford Laboratories,H75030)
  36. 组氨酸(Sigma-Aldrich,目录号:H6034)
  37. 盐酸,37%(v / v)(Fisher Scientific,目录号:H / 1200 / PB15)
  38. 羟胺50%(w / v)溶液(Thermo Fisher Scientific,目录号:90115)
  39. 碘乙酰胺(Sigma-Aldrich,目录号:I6125)
  40. 异亮氨酸(Sigma-Aldrich,目录号:I5281)
  41. 亮氨酸(西格玛奥德里奇,目录号:L8912)
  42. 赖氨酸(Sigma-Aldrich,目录号:L5501)
  43. 醋酸镁(Sigma-Aldrich,目录号:M5661)
  44. 氯化镁(Sigma-Aldrich,目录号:M8266)
  45. 蛋氨酸(Sigma-Aldrich,目录号:M5308)
  46. 脱脂奶粉,如Marvel TM
  47. OptiPrep (Sigma-Aldrich,目录号:D1556)
  48. 苯丙氨酸(Sigma-Aldrich,目录号:P5482)
  49. 醋酸钾(Sigma-Aldrich,目录号:P1190)
  50. 氯化钾(Sigma-Aldrich,目录号:31248)
  51. 蛋白质浓度估计测定,例如PierceTM BCA蛋白质测定(Thermo Fisher Scientific,目录号:23225),DC TM蛋白质测定(Bio-Rad,目录号:5000112) )或Quick StartTM Bradford检测(Bio-Rad,目录号:5000202)
  52. 测序级改良胰蛋白酶(Promega,目录号:V5111)
  53. 十二烷基硫酸钠(SDS)(Sigma-Aldrich,目录号:436143)
  54. 氢氧化钠(Sigma-Aldrich,目录号:06203)
  55. 山梨糖醇(Sigma-Aldrich,目录号:W302902)
  56. 蔗糖(西格玛奥德里奇,目录号:S0389)
  57. 苏氨酸(Sigma-Aldrich,目录号:T8441)
  58. TMT 10-plex或11-plex同量异位标记试剂(Thermo Fisher Scientific,目录号:90111或A37725,反映)
  59. 三氯乙酸(TCA)(Fisher Scientific,目录号:421455000)
  60. 三氟乙酸(TFA)(赛默飞世尔科技,目录号:85183)
  61. Tris base(Melford Laboratories,目录号:T60040)
  62. 三 - (2-羧乙基)膦(TCEP)(Generon,目录号:GEN-TCEP)
  63. 色氨酸(Sigma-Aldrich,目录号:T8941)
  64. Tween ® 20(Sigma-Aldrich,目录号:P1379)
  65. 酪氨酸(Sigma-Aldrich,目录号:T8566)
  66. Uracil(Sigma-Aldrich,目录号:U1128)
  67. 缬氨酸(Sigma-Aldrich,目录号:V4638)
  68. 水,HPLC梯度级(Fisher Scientific,目录号:W / 0106/17)
  69. 不含氨基酸的酵母氮碱(Sigma-Aldrich,目录号:Y0626)
  70. Zymolyase 100-T(Nacalai-Tesque,目录号:07665-55)
  71. 抗Pgk1p抗体,用作cytosol蛋白质印迹标记(艾博抗(上海)贸易有限公司,目录号:ab113687)
  72. 抗Dpm1p抗体,用作内质网Western印迹标记物(ThermoFisher Scientific,目录号:A-6429)
  73. 抗Pep1p抗体,用作高尔基体蛋白质印迹标记物(艾博抗(Abcam),目录号:ab113690)
  74. 抗Cox4p抗体,用作线粒体蛋白质印迹标记物(ThermoFisher Scientific,目录号:459150)
  75. 抗Hh3p抗体,用作核免疫印迹标记(艾博抗(上海)贸易有限公司,目录编号:ab1791)
  76. 抗Pma1p抗体,用作质膜western blotting标签(艾博抗(上海)贸易有限公司,目录号:ab4645)
  77. 5x SDS-PAGE样品缓冲液(Jena Biosciences,目录号:BU-117)&nbsp;
  78. 4-20%Mini-PROTEANTGX<<> <<>预制蛋白凝胶(Bio-Rad,目录号:4561096)
  79. TGS缓冲液10x(Bio-Rad,目录号:1610732)
  80. 10x酵母氮碱(YNB)(见食谱)
  81. 10倍完全补充混合物(见食谱)
  82. 40%(w / v)葡萄糖(见食谱)
  83. 含2%葡萄糖的合成培养基(见食谱)
  84. YPD琼脂培养基(见食谱)
  85. TCEP减少缓冲液(见食谱)
  86. 原生质球介质(见食谱)
  87. Zymolyase 100-T解决方案(见食谱)
  88. 原生质球洗涤介质(见食谱)
  89. (可选)Ficoll裂解缓冲液(Kizer et al。,2006)(见食谱)
  90. (可选)Buffer NP(Kizer et al。,2006)(参见食谱)
  91. 1x LB(见食谱)
  92. 6x LB(见食谱)
  93. 碘克沙醇工作溶液(IWS)(见食谱)
  94. 18%(w / v)OptiPrep解决方案(参见食谱)
  95. 16%(w / v)OptiPrep解决方案(参见食谱)
  96. 蛋白质再溶解缓冲液(见食谱)
  97. 消化缓冲液(见食谱)
  98. 5%(v / v)羟胺(见食谱)
  99. TTBS(见食谱)
  100. 阻止解决方案(见食谱)
  101. 二硫苏糖醇(DTT)溶液(见食谱)
  102. 碘乙酰胺(IAA)溶液(见食谱)
  103. 平衡缓冲液(见食谱)
  104. 脱盐缓冲液1(见食谱)
  105. 脱盐缓冲液2(见食谱)
  106. 洗脱缓冲液(见食谱)
  107. RP流动相储备液(见食谱)
  108. RP流动相A(见食谱)
  109. RP流动相B(见食谱)
  110. RP重新溶解缓冲液(见食谱)


  1. 4°C冰箱(Polestar冷却,型号:MR 100 E)
  2. -20°C冰柜(Hotpoint,型号:FZS175)
  3. -80°C冰箱(New Brunswick Scientific,型号:U725-G)&nbsp;
  4. Acquity超高效液相色谱(UPLC)系统,带光电二极管阵列(PDA)检测器(沃特世)
  5. Acquity UPLC BEH C 18 专栏(Waters,目录号:186002353)
  6. 分析天平(Explorer,型号:EX124)
  7. 具有自动弯月面检测功能的Auto-Densiflow蠕动泵(Labconco)
  8. Avanti ® JXN-26落地式制备离心机(Beckman Coulter)
  9. Bioruptor ® Plus超声波发生器(Diagenode,目录号:B01020001)
  10. 离心瓶组件,500 ml容量(Beckman Coulter,目录号:355649)
  11. 比色皿分光光度计(Cecil Instruments,型号:2041)
  12. Dounce均质器,7 ml容量(Wheaton,目录号:357542)
  13. Erlenmeyer烧瓶,250 ml,500 ml和2 L容量(Fisher Scientific,目录号:15429103,15439103和11383454)
  14. 手持式模拟折射仪(Bellingham + Stanley,E-line,目录号:44-803)
  15. JLA-10.500转子(Beckman Coulter,目录号:369681)
  16. 氮气蚀容器(Parr Instrument Company,型号:4639)
  17. Optima TM L80-XP落地式超速离心机(Beckman Coulter)
  18. Orbitrap Fusion TM Lumos TM Tribrid TM 质谱仪(赛默飞世尔科技)
  19. 无氧氮气瓶(BOC,氮气[无氧] 230 bar气瓶,尺寸:W)&nbsp;
  20. 移液器(Rainin LTS L-1000 XLS,L-200 XLS,L-20 XLS和L-10 XLS,目录号:17014382,17014391,1704392和17014388;或能够分配相同容量的等效移液器)
  21. 读板分光光度计,能够测量250 nm和850 nm之间的波长(Molecular Devices,Spectramax M2e)
  22. 质谱仪耦合nanoLC的预柱(Thermo Fisher Scientific,目录号:160454)
  23. Proxeon EASY-Spray色谱柱(Thermo Fisher Scientific,目录号:ES803)
  24. 冷冻台式离心机,能够离心15 ml和50 ml Falcon管并以4,500 x g 运行(Eppendorf,型号:5804R)
  25. 冷冻台式微量离心机,能够离心微量离心管并以16,100 x g 运行(Eppendorf,型号:5415R)
  26. Sep-Pak®tC<18>滤芯,每个滤芯100 mg吸附剂(Waters,目录号:WAT036820)
  27. 摇动培养箱进行细胞培养(Sartorius,Certomat,型号:BS-1)
  28. SW32钛转子(Beckman Coulter,目录号:369650)
  29. Thermo Scientific TM Sterilin 标准90mm培养皿(Fisher Scientific,目录号:15370366)
  30. 用于微量离心管和Falcon管的管架(Starlab,目录号:E2345-1000)
  31. Trans-Blot
    ®Turbo TM 系统(Bio-Rad,目录号:1704150)
  32. 70型Ti转子(Beckman Coulter,目录号:337922)
  33. 终极RSLC纳米色谱系统(赛默飞世尔科技)
  34. 真空离心机(Labconco,CentriVap浓缩器)
  35. VTi 65.1转子(Beckman Coulter,目录号:362759)
  36. 电泳系统和相关的电源组(Bio-Rad,型号:Mini-PROTEAN ® II)


  1. Proteome Discoverer 2.1版(Thermo Fisher Scientific, www.thermofisher.com )
  2. R编程语言(R核心团队,2018年)( www.r-project.org )
  3. Bioconductor(Gentleman et al。,2004)MSnbase package(Gatto and Lilley,2012)( http://bioconductor.org/packages/release/bioc/html/MSnbase.html )
  4. Bioconductor(Gentleman et al,2004)pRoloc package(Gatto et al。,2014)( http://bioconductor.org/packages/release/bioc/html/pRoloc.html )
  5. Bioconductor(Gentleman et al。,2004)pRolocGUI包(Breckels et al。,2018)( http://bioconductor.org/packages/release/bioc/html/pRolocGUI.html )
  6. RStudio R编程语言编辑器(RStudio Team,2016)( www.rstudio.com )
  7. Mascot Server(Matrix Science, www.matrixscience.com )
  8. Microsoft Excel


  1. 细胞培养
    1. 在30°C的固定培养箱中孵育约2天,直到您计划进行实验前4天进行定殖,进行实验,将菌株板从低温恒温器中取出适当的酵母菌株到YPD琼脂平板上。 °C持续数周。&nbsp;
    2. 对于典型的超LOPIT实验,我们推荐培养物720 OD单位的酵母,其等于1.2 600 0.6的培养物。对于这种文化体积,我们使用2 L Erlenmeyer flaksks。
    3. 稀释,因为细胞在收获前经历至少2次倍增,以便在方便的时间达到所需的细胞密度。
    4. 当达到所需的OD <600>时,通过在室温下以3,000 xg 离心5分钟收集细胞并弃去上清液。对于该步骤,使用500ml瓶组件和Avanti离心机中的JLA 10.500转子。

  2. 用于裂解的细胞预处理
    1. 在还原细胞沉淀中,在500ml瓶组件中,以5个OD单位/ ml的TCEP还原缓冲液离心,在3,000 xg 中离心5分钟,并在室温下无振荡孵育5分钟。 Avanti离心收集细胞并弃去上清液。
    2. 在实验中使用的每单位酵母OD单位中加入1μl酶解酶100-T溶液。
      将细胞以20 OD单位/ ml重悬浮于原生质球培养基中,并转移至50 ml Falcon试管中。 注意:原生质球介质由与用于细胞培养的培养基相同的成分组成,以保持与培养相同的条件。但是,我们不建议在该步骤中使用条件培养基。培养基应该是新鲜的,包括1.0-1.2 M山梨醇和20 mM Tris-HCl,pH 7.5,以维持渗透压支持和原生质球时的适当pH值。
    3. 用等效稀释的原生质体介质在水中冲洗分光光度计后测量OD <600>。取出10μl等份的溶液并在990μl水中稀释 注意:通过分光光度法可以方便地监测原生质球转换效率。细胞悬浮液在600 nm处的吸光度与悬浮液中存在的完整细胞数量成正比。球形细胞非常脆弱和敏感将溶液转移到渗透性无支撑溶液中的溶液将仅产生孢子孢子。剩余的完整细胞有助于600nm处的吸光度,表明经历转化的细胞量。
    4. 在30°C下孵育10分钟,同时以200 rpm的速度摇动,测量OD 600为1:100稀释度.OD 600应为预处理的10%左右。消化OD <600>,表明约90%的酶解酶消化效率。
    5. 通过在1,500 x g ,4°C下离心5分钟并在原生质球洗涤介质中重新悬浮,收集细胞而不转移到新管中。
    6. 在1,500 x g ,4°C下收获细胞5分钟并弃去上清液。

  3. 可选:核准备
    1. 对于该方案,我们使用大约120个OD单位的原生质球用于核制备,600个OD单位用于主要亚细胞性(2006),随后进行修改。分馏。
      注意:此步骤是可选的,如果其他细胞器特别感兴趣,可以进行这些细胞器的富集而不是核制备,如在之前的两次超LOPIT研究中所进行的(Christoforou 等人等。 ,2016; Thul 等。 ,,2017)。
    2. 在冰上用20个上下冲程裂解。
      将原生质球重悬于Ficoll裂解缓冲液中并转移至Dounce匀浆器。 注意:正如在原始论文(Kizer 等 ,2006)中,我们使用“紧”Dounce杵来完成裂解的完整性,而不是提倡“松散”杵“重新悬浮”。
    3. 70型Ti转子(Beckman Coulter)的平衡位置在3,220℃,4℃下预裂解裂解物10分钟,并将上清液转移到32ml圆底超速离心管中。使用“最大”加速和减速。“斧”4分钟。使用“MAX”加速和减速。
    4. 将响应的富含核的沉淀储存在-80℃。

  4. 用于密度梯度离心的裂解
    1. 将剩余的600个OD单位的原生质体以每毫升20个OD单位重悬于1×LB中,并转移到氮气蚀容器的腔室中。
    2. 使用Wang et al。(2014)修改的方法进行裂解。使用无氧氮气将容器装入500 psi并孵育3分钟。
      注意:气缸内的氮气处于压力状态,如果进气口打开太快,可能会导致500 psi的目标压力超负荷。然后,缓慢打开进气口并密切监测压力表的压力避免发生这种情况的可能性。
    3. 通过打开进气阀释放压力,使压力降至300 psi。
    4. 以每秒约3滴的速度收集裂解液。
      再孵育3分钟,然后通过打开容器底部的出口将容器排出。 注意:当氮从溶液中释放出来时,氮气蚀容器内的压力会下降,相反裂解液的释放速度会更慢。监测裂解液从出口释放的速度,如有必要,再打开端口以保持每秒约3滴的稳定速率。
    5. 通过在1,000℃,4℃下离心5分钟来清除聚集体,聚集体和未裂解细胞的裂解物。使上清液在3,000g下进行另一轮离心10分钟。 ,4°C

  5. 粗膜制剂
    1. 借助手持折射仪和已知OptiPrep浓度的校准曲线及其折射率测量值,以白利糖度(°Bx)(参见注释1)测量,制备18%(w / v)OptiPrep溶液。
    2. 使用连接到四个圆底超速离心管(每管约7.5ml裂解物)的注射器,用1ml LB制备的5ml 18%(w / v)OptiPrep缓冲溶液在每个管的内容物下面。一个钝端的宽口径针。
      1. 即使可以将整个裂解液配合在一个圆底离心管中,我们也不建议这样做。如果这样做,超速离心时,全身的蛋白质含量很高这可能会导致对细胞器记忆的损害并影响整个实验的结果。我们发现如果裂解物被描述为发生这种情况,因为它不会发生。
      2. 当使用18%(w / v)OptiPrep缓冲溶液进行垫层时,最小心的是不要将气泡引入缓冲溶液中。如果引入气泡,将导致裂解液与OptiPrep溶液混合,发生这种情况的溶液的超速离心将在两种溶液之间进行两百五十次,从而破坏了溶液的性质在预防之前,发生在管的底部。为了防止这种情况发生,使用注射器上的刻度作为在垫下之前绘制的OptiPrep溶液量的指导。在开始裂解物的底层之前,将针头浸没在注射器中,以便从宽口径针头上释放一滴OptiPrep(以排出任何可能存在的空气)用注射器溶液密切注意液体,用注射器用液体注射器液体注射器密切注意液体,取下针头并丢弃剩余的溶液。
    3. 将四个管彼此平衡至10毫克以内,并将管放置在SW32 Ti摆动转子的相对侧。
    4. 离心机在28,000rpm下将管2小时(96300 XG ),4℃下选择“MAX”的加速曲线和(即使用一个制动最慢的减速)“9”的减速分布这最小化了在超速离心时形成的膜间期的破坏。
    5. 将上清液保存在-20℃进行进一步处理。从管中取出富含胞质溶胶的上清液,在膜间期上方留下1至2cm的上清液,以免干扰粗膜间期。
    6. 使用修剪过的1 ml移液器吸头将含有膜的间期取出到单独的管中,注意不要从间期上方或下方取出过多的溶液。

  6. Isopycnic密度梯度超速离心和馏分收集
    1. 将收集的膜间期溶液中OptiPrep的浓度调节至16%(w / v),使用手持式折光仪监测浓度(见注1)。
      1. 使用600 OD单位的培养物通常在该步骤中产生约2-3ml的粗膜。目的应该是以尽可能小的体积将所需指数的折射率调节到所需的值并制造如果不是这样,可能会导致膜在正确的Optipre浓度下,11.2 ml体积的分离梯度管,预先准备好16%(w / v)被分成多个密度梯度。
      2. 我们发现,制备使用16%(W / V)OptiPrep提供在酵母中的主要亚细胞器的良好分离自发电梯度(图2)。该步骤可以,如果不同的梯度的形状是理想的或者特定的改性亚细胞细胞器是实验的主要焦点。

        图2.使用此处描述的方案获得的代表性数据。使用可选的核制备物在 S.cerevisiae 中进行超LOPIT后,二维主成分分析分析12的分辨率在酿酒酵母空间蛋白质组图中分离出三种主要成分的细胞器和蛋白质复合物,包括细胞器的主要亚细胞器。 em> et al。, 2019])。在每个图中代表每个单个蛋白质组,由hyperLOPIT确定为有色代表性蛋白质的点到地点灰色点负责蛋白质由hyperLOPIT预测的本地化。

    2. 准备一个含有16%(w / v)OptiPrep的平衡管,用于填充空白的垂直转子管,该转子管不含任何膜,可用作平衡管。
    3. 将两个管放在VTi 65.1转子的相对侧,并在管顶部浮动铝垫片。
    4. 拧上转子盖和离心机中以65000转(362900 XG ),在4℃下4小时,选择“MAX”的加速度曲线和“9”(最慢减速的减速分布这有助于最小化在超速离心时形成的对密度梯度的破坏。
    5. 从VTi 65.1转子上拆下管子,取下并丢弃塑料塞子。
    6. 夹紧蠕动泵。
      准备一台带有弯月面跟踪探头的Labconco Auto-Densiflow蠕动泵,通过将一块约40厘米长的收集管连接到探头上来收集馏分。 注意:如果Auto-Densiflow馏分收集器不可用,可以通过刺穿管底部并使梯度馏分从底部和最密集的部分滴出来收集馏分。我们不建议通过从Brandel和Teledyne Isco的高级技术技能和可用技术中吸取来收集馏分,但这些尚未经过测试用于hyperLOPIT。错误,这将导致梯度实验的不同部分的混合。
    7. 用蒸馏水冲洗到废液容器中,然后用空气冲洗,用1x LB冲洗,然后用空气冲洗。
    8. 将梯度管插入管架并将探头方向设置为“向下”,直到探头找到梯度弯月面,此时探头将停止移动。
    9. 将探针设置为“沉积”并从梯度中收集23个级分到1.5ml微量离心管中,其中22个应为0.5ml,一个应为0.2ml。
      将馏分储存在-80°C直至进一步处理。 注意:此步骤不是自动化的,因此应按密切监控每个馏分收集的体积。

  7. 监控渐变形状
    1. 解冻梯度级分并充分涡旋以确保含OptiPrep的级分中的碘克沙醇含量均匀地悬浮在级分中。
    2. 使用手持式模拟折光仪确定每个馏分的近似指数。在此步骤中使用大约20μl的每个馏分。
      不要使用1x LB.将得到的折射率乘以稀释系数,得到无界折射率测量。
    3. 结果应该是一个轻微的S形曲线,在梯度中间相对平坦,在最后2-3个分数中变得更陡(图3)。
    4. 如果通过酶测定监测细胞器分布,请在此处取一份等分试样,然后进行酶测定。
      注意:我们尚未在 S.cerevisiae 中尝试此类方法,但如果需要,可在文献中找到方法。


  8. 分数处理
    1. 为此添加1/4体积的100%(w / v)TCA,在水中制备并充分涡旋。通过TCA沉淀进一步处理所有级分(包括单独的细胞器制剂和富含胞质溶胶的级分),然后用丙酮洗涤。
    2. 在4°C孵育2小时,使每个级分的蛋白质含量从溶液中沉淀出来。
    3. 通过在台式微量离心机中在4℃下以最大速度离心10分钟来收获沉淀物。丢弃上清液。
    4. 在Diagenode BioRuptor,超声波仪中进行广泛涡旋并进行超声处理(30秒和30秒),直到颗粒完全分解并变成。 br /> 注意:
      1. 如果颗粒没有完全分解,则可能难以在本协议的下游步骤中解决。
      2. 如果没有BioRuptor,可以使用其他超声波仪进行此步骤。
    5. 重复离心,丙酮洗涤,超声处理两次;每次丢弃上清液。
    6. 弃去上清液,让颗粒在室温下短暂干燥,直至丙酮蒸发(见注2)。

  9. 样品重新溶解和蛋白质浓度估计
    1. 用广泛的超声处理重新溶解蛋白质再溶解缓冲液中的每个沉淀物。
    2. 再添加等份的蛋白质重新溶解缓冲液,对于该步骤,每个沉淀用50μl蛋白质重新溶解缓冲液开始,并通过离心监测馏分中任何沉淀物的存在,如在TCA预处理中那样一旦离心后没有可见颗粒,这表明蛋白质含量已经解决完成。
    3. 使用蛋白质浓度估计测定估计每个级分中的蛋白质浓度,优选以96孔板形式。
      1. 如果没有推荐的蛋白质检测试剂盒,可以使用其他蛋白质估算试剂,只要它们与100 mM HEPES和0.1%(w / v)SDS相容即可。
      2. 我们建议使用96孔板格式分析,因为高渗实验中的样品量可能非常有限,这样可以保留每个样品的最大量,用于Western印迹和同量异位标记标记。
    4. 如果同量异位标签标记,应该是50毫克,允许每个部分额外1微克用于下游蛋白质印迹。如果需要分馏,没有同量异位标记标记,

  10. 通过Western印迹监测细胞器分辨率
    1. 使用蛋白质印迹监测细胞器分辨率,针对一组针对标记蛋白的抗体,这些蛋白的分辨率是它们所代表的细胞器的特征(见注1)(表1)。

      表1.建议的抗体与hyperLOPIT一起使用 S.cerevisiae 。建议的一抗和二抗抗体基于Western印迹,每个泳道使用1μg总蛋白质的SDS-PAGE凝胶进行。

    2. 将每种级分(包括核制剂,富含胞质溶胶的级分和密度梯度级分)的量标准化为1μg总蛋白质,并在SDS-PAGE样品缓冲液存在下加热至95℃5分钟。在预制的梯度SDS-PAGE凝胶的单个孔上。
    3. 使用mini-PROTEAN II电泳系统和电源组进行此步骤,并在200 V下运行凝胶。使样品在TGS缓冲液中的SDS-PAGE微型凝胶电泳,并使用全长凝胶。
    4. 使用Bio-Rad Trans-Blot Turbo装置上的混合分子量程序转移至Trans-Blot Turbo聚偏二氟乙烯(PVDF)膜。
    5. 将膜切割成适当的分子量范围(参见注释3),并将每个条带在封闭溶液中在室温下在旋转混合器上搅拌1小时。
    6. 在4°C孵育过夜,在旋转混合器上振荡(建议稀释度见表1)。用适当的一抗探测每个条带,用封闭溶液稀释(见注4)。
    7. 第二天,将膜置于室温下,并在TTBS中洗涤三次,每次5分钟。
    8. 用在封闭溶液中稀释的适当的辣根过氧化物酶缀合的二抗(参见表1的建议稀释度)在室温下探测1小时。
    9. 弃去二抗并在TTBS中洗涤3次,每次10分钟。
    10. 继续使用增强的化学发光(ECL)底物检测信号。
    11. 用X射线胶片暴露每个膜,直到达到所需的信号水平(参见图4的Western印迹)。
    12. 假设已获得合适的产量(参见注释5),如果需要继续使用hyperLOPIT协议,则进入下一部分。

      图4.使用本文所述的超LOPIT方案进行的酿酒酵母实验的典型代表性蛋白质印迹,在没有核制备和使用表中描述的抗体进行探测的情况下进行使用Labconco馏分收集器,从第一个和最不稠密的部分,到第二十三个和最密集的部分,按照它们的收集顺序编号。 C“。修改自(Nightingale et al。,2019)。

  11. 蛋白质还原,烷基化和消化
    1. 对于包括核制备在内的实验,选择核制剂,富含胞质溶胶的部分,并使用等压标记的其余部分来标记密度梯度中的部分。在涉及核制备的实验中,选择手性当使用TMT 10或11-plex标签或具有较低多路复用能力的同量异位标签时,此建议是相同的。
    2. 将每个级分标准化为相同量的蛋白质(50-100μg),并用蛋白质回收缓冲液补足相同的体积(100μl)。
    3. 向每个样品中加入11.1μl在蛋白质分离缓冲液中制备的二硫苏糖醇(DTT)溶液。
    4. 在56°C孵育1小时,以减少二硫键。
    5. 回到室温,每个样品中加入12.3μl碘乙酰胺(IAA)溶液,在室温下避光孵育1小时。
    6. 向每个级分中加入1ml丙酮,短暂涡旋各级分,使蛋白质含量在-20℃下在丙酮中沉淀过夜。
    7. 第二天,在台式微量离心机中以4℃以最大速度离心所有沉淀的级分10分钟。
    8. 弃去上清液,短暂风干颗粒(见注2)。
    9. 通过涡旋将沉淀重悬于87.5μl消化缓冲液中。
    10. 用1:20(w / w)蛋白酶:蛋白质比例的胰蛋白酶消化,在37℃下消化16小时。将胰蛋白酶分成间隔1小时的两个等分试样(1:40(w / w)蛋白酶:蛋白质比例),例如消化的最终体积是100μl。

  12. TMT标签(从TMT 10-plex套件用户指南修改)
    1. 如果使用不同的同量异位标记化学品,请参阅试剂盒随附的相关标签协议。
    2. 第二天,通过在微量离心机中以最大速度离心15分钟,清除任何不溶物质的消化物。
    3. 在打开样品瓶之前,将其短暂脉冲离心。让TMT试剂盒平衡至室温。
    4. 在每个标签样品瓶中加入41μl乙腈,充分涡旋并溶解5分钟。
    5. 将每个分离的标签转移至不同的消化物并在摇动平台上在室温下孵育2小时。
    6. 向每个样品中加入8μl5%(v / v)羟胺,在消化缓冲液中制备,并在室温下在振荡平台上孵育30分钟。
    7. 向每个样品中加入100μlHPLC级水,并在4°C下孵育1小时。
    8. 将样品标记的消化物合并到单个管中,并在10°C的真空离心机中干燥至完成。在-80°C下储存,直到通过固相萃取(SPE)进行样品净化。

  13. 通过固相萃取(SPE)进行样品净化(根据以前的出版物修改[Villen and Gygi,2008])
    1. 用1.8ml 100%(v / v)HPLC级乙腈(ACN)平衡Sep-Pak tC18柱。
    2. 用600μl平衡缓冲液冲洗。
    3. 用1.8毫升脱盐缓冲液1冲洗。
    4. 将样品在1ml 0.4%TFA中重新溶解并确保pH <2。为此,使用pH试纸在小等分试样(~10μl)的样品上检查pH。如果pH> 2,则继续加入10%TFA的等分试样。 ,每次添加后检查pH,直至pH <2 注意:即使在0.4%TFA存在下,来自消化和同量异位标签标记步骤的残留HEPES的缓冲能力也可保持高pH值。确保在加载到SPE柱上之前pH <2,或标记的肽不会绑定到tC 18 材料,而是会丢失。
    5. 将解析的肽加载到盒上并允许与包装材料结合。
    6. 在1.8毫升脱盐缓冲液中脱盐1。
    7. 用180μl脱盐缓冲液2冲洗。
    8. 使用1ml洗脱缓冲液从柱中洗脱缓冲液,并取出对应于约5-10μg标记肽的小等分试样。
    9. 将样品在10°C的真空离心机中干燥至完成。

  14. 通过高pH反相(RP)色谱预分馏
    1. 在Waters Acquity UPLC系统上,在质谱分析之前使用高pH反相色谱法对样品进行分级。
    2. 通过涡旋混合在100μlRP分辨率缓冲液中重新溶解固相提取的样品,并在台式微量离心机中以最大速度旋转10分钟。
    3. 将上清液转移至Acquity自动进样器样品瓶中,置于Waters Acquity UPLC系统的自动进样器中,保持在10°C。
    4. 借助于系统样品以1分钟的间隔收集级分,并使用表2中所示的梯度参数运行。使用光电二极管阵列(PDA)检测器监测样品洗脱,扫描210-400nm的波长。馏分收集器在干冰上冻结完成。

      表2.在Waters Acquity UPLC系统上pH值反相超级LOPIT样品预分级时使用的参数整个LC梯度长75分钟。在梯度内,0-10分钟,62每个切换步骤之间的梯度是本表中描述的应该是线性的(用UPLC入口方法软件用“6”表示)。

    5. 将干燥级分在真空离心机中在10℃下完成。将干燥的级分在-80℃下储存。
    6. 使用收集的色谱数据确定与含肽色谱空间对应的收集馏分。

  15. LC-SPS-MS3样品分析
    1. 重悬干燥的样品在0.1%(V / V)在下列方案中的甲酸和池等量:普尔所述第一与所述中间馏分,第二与第二从 - 中间,依此类推,直到所述肽的末端 - 含有洗脱空间。
      注:池的样品以这种方式不仅使更好地利用肽分馏的第二维的(低pH RP耦合在线质谱仪)(张 等人 ,2011),但也将质谱分析时间缩短了一半。
    2. 将约1μg的每个合并的级分加载到Dionex Ultimate 3000 RSLC纳米色谱系统的自动进样器中。
    3. 分析通过使用耦合线到戴安终极3000 RSLC nanoUPLC系统中的轨道阱融合荧光闪烁Tribrid质谱仪120分钟梯度液相色谱 - 质谱法各汇集级分。用LC-SPS-MS3方法获取数据(麦卡利斯特等人,2014)。有关用于LC和MS方法的详细信息,请参阅已采用TMT加上SPS-MS3进行hyperLOPIT研究10-PLEX定量以前的出版物(Christoforou 等。 ,2016 ;.书尔等人,2017年; ..马尔维等人,2017年)。另外,在不存在的Fusion系列质谱仪,使用适用于质谱仪的LC-MS / MS方法用于分析。
      1. 我们建议使用Thermo Fisher Scientific Fusion系列质谱仪,因为SPS-MS3功能(McAlister 等 ,2014)提供更高的定量准确度和精确度。 SPS-MS3涉及同时选择多个MS2碎片离子,这些离子源自特定扫描中的靶前体,TMT 10或11重报告离子,相对于MS T的唯一用途。这解放了由靶前体离子产生的TMT报道分子,在没有来自污染物碎片的报告信号起源的情况下,在MS1扫描中共同选择了污染物。可用于这些实验,但它们更容易受到问题的影响(Ting 等。 ,2011; Wenger 等。 ,2011)除了通过MS / MS进行碎裂的离子外,共同选择污染前体。将导致实验中定量的总体准确度和精确度降低,其直接读数将在最终的hyperLOPIT空间图中区分细胞器分辨率。
      2. 使用质谱仪分析TMT标记样品并非高分辨率分辨率可能会因为无法分辨同位素N和C标记而排除使用10或11重TMT标签,这将导致报告离子合并(McAlister et al。, ,2012),因此,定量不准确。在这种情况下,应使用具有较低多路复用能力的等压标签。
    4. 使用与上述步骤O3中使用的相同的质谱分析方法分析在SPE步骤中收集的额外的非分馏样品。这将使得能够表征同量异位素标签标记效率。
      注意:建议对每个超LOPIT实验表征同量异位标记效率,与通过LC-SPS-MS3或其他LC-MS / MS方法分析预分级样品无关。用同量异位标签试剂标记溶素残基的肽N-末端和ε-氨基的效率足够高(即,基本上完全)。实际上,我们观察到使用该方法标记特异性地> 99%。检测同量异位标签浓度,在同量异位标签标记过程中进行修饰,这将导致同量异位标签标记试剂,不可逆侧试剂或不良副反应的分化稳定性这将导致肽的标记不完整,这将导致不准确的PSM水平和整体蛋白质水平定量。这将导致非代表性和减少整体细胞器重新生成最终hyperLOPIT空间映射中的解决方案。


  1. 使用Proteome Discoverer 2.1版分析质谱数据
    1. 使用与内部Mascot服务器连接的Proteome Discoverer 2.1分析原始质谱数据文件。使用规范的 S. Cerevisiae 数据库搜索数据,例如UniProt或SwissProt数据库,在现场备选缺少蛋白质序列信息。从Saccharomyces基因组数据库中下载蛋白质序列数据库(SGD - www.yeastgenome.org 执行分馏样品的搜索。执行分馏样品文件文件的搜索,这是))(Cherry et al。,2012)。整个hyperLOPIT实验质谱运行组的单一结果报告。
    2. 对于标记测试搜索,指定TMT(或其他同量异位标记)标记N末端和赖氨酸残基作为动态修饰,但保留所有其他搜索以肽 - 光谱匹配(PSM)水平导出结果文件,并计算相对于理论上可能暴露的所有肽,显示每个同量异位标签修饰的肽的百分比> \ ,所有具有可被标记的赖氨酸ε-氨基的肽,以及未经其他修饰的所有肽N-末端。这给出了对该特定形式的效率的估计使用此方法应该> 99%。
    3. 如果使用LC,则执行Mascot搜索,允许肽耐受性为±15 ppm,片段匹配容差为±0.6 Da。对于分级的超LOPIT数据,将每组原始数据文件作为一组分数加载到Proteome Discoverer中,而不是文件。 SPS-MS3。如果在不同的质谱平台上使用另一种LC-MS / MS方法,请使用适合MS1和MS2扫描中使用的质谱分析仪的肽和片段匹配公差。 > et al。,2009)软件中的节点,允许基于其错误发现率过滤肽 - 光谱匹配。
    4. 设定蛋氨酸的氧化,并将赖氨酸和N-末端的同量异位标记标记为静态修饰。
    5. 对于使用LC-SPS-MS3的分析,在报告离子定量节点中,使用每个单同位素报告基因的m / z周围±2mmu窗口内的质心峰的总和进行定量,并将MS活化顺序设置为MS3。将蛋白质组水平定量设置为对于其他报告分析方法和/或同量异位标签是相等的,将MS激活顺序设置为MS2并相应地调整窗口以总结报道分子的m / z。任何给定蛋白质组的所有定量PSM的中位数,并使用Proteome Discoverer 2.1版中提供的S / N测量选项报告定量。
    6. 如果导出到Excel工作簿,请在继续下一部分之前将工作簿转换为CSV格式。

  2. 使用MSnbase和pRoloc软件包进行数据分析
    1. 使用R编程语言编辑器分析来自Proteome Discoverer的结果文件,例如运行Bioconductor的RStudio(RStudio Team,2016)(Gentleman et al。,2004)pRoloc(Gatto et al ,2014),pRolocGUI(Breckels et al。,2018)和MS nbase软件包(Gatto和Lilley,2012)。对于详尽的分析协议,我们引导读者阅读最近发表的工作Breckels及其同事(Breckels et al。,2016b)。
    2. 使用“readMSnSet2()”函数将原始数据读入R环境语言,其中“ecol”被指定为包含定量数据的列。
    3. 使用“filterNA()”函数过滤实验中包含缺失定量值的蛋白质组。或者,允许一定数量的缺失值进行定量,并以某种方式对主题进行估算。
      注意:缺失值可以设置为零,如先前所执行的(Christoforou 等 ,2016)或者可以使用其中一个MSnbase包中描述的“Impute()”方法(Gatto和Lilley,2012)。
    4. 标准化每行的量化数据( i。E. ,蛋白质组),使得总和的比率等于1.使用“normalize()”函数和“sum”方法。
      注意:MSnbase软件包中提供了其他标准化方法,可用于标准化Proteome Discoverer定量数据,但我们发现“sum”方法对此特定数据分析很有用。
    5. 请注意任何群集位置,即维度减少这一事实,例如主成分分析(PCA)或t分布式随机邻域嵌入(t-SNE)(Van Der Maaten和Hinton,2008)从数据。
    6. 策划一系列标记蛋白,这些标记蛋白已知定位于酿酒酵母中的亚细胞细胞器。替代方案,使用pRoloc包中提供的标记(使用pRolocmarkers(“scer”))。 /> 当使用有监督的机器学习监督学习对蛋白质类进行细胞分类时,蛋白质只能被归类为类别exe之一。注意:确保包含预期在实验中单独解析的所有细胞器的标记注释。在那里,如果针对特定的细胞器或一组细胞器发出注释,它将导致来自该细胞器或一组细胞器的蛋白质被包含到另一个细胞器中,该细胞器与其他受试者类似地解析在实验中。最终结果不仅是最终数据集中缺乏有问题的细胞器,而且还没有将任何蛋白质分类到该细胞器。
    7. 将标记注释附加到MS n Set并观察亚细胞器水平数据内的分辨率。
    8. (可选)使用pRoloc中的“addGOAnnotation()”函数执行自动蛋白质标记注释,用户可以在其中添加基因本体论(GO)(Ashburner et al。,2000; The Gene Ontology Consortium,2017 )使用pRolocgui软件包可以观察到在该分析后单独解析的GO注释簇的分辨率。这是来自GO名称空间的GO,用于基于共同的分子功能,生物过程或细胞组分搜索蛋白质的聚类。用于增加用于将未知定位的蛋白质分类为亚细胞器的标记蛋白质数据。
    9. 使用二元分类器进行分类,将未知定位的蛋白质分类为预定义的细胞器类别之一。这可能涉及使用监督机器学习方法,例如支持向量机(SVM)(Vapnik,1995)。新的方法可以用于多个先前的hyperLOPIT研究(Christoforou et al。,2016; Thul et al。,2017)。可以使用更新的方法,包括半监督机器学习方法(“ phenoDisco ”)(Breckels et al。,2013),它可以预测新蛋白质簇的存在,而不需要先验知识除了高度标记的标记蛋白列表之外,替代方案,变体方法可用,质量较低,辅助数据可提高分类分类性能(Breckels et al。,2016a)。可以使用新的概率分类方法,该方法基于贝叶斯统计(Crook et al。, 2018)并且现在可以在pRoloc包中使用。
    10. 在分类为细胞器后观察所有蛋白质的分辨率。
    11. 在逐个细胞器的基础上输出所有分类数据,并按降序对所有归类为给定细胞器的蛋白质的SVM分数进行分类。
    12. 允许在hyperLOPIT和低通量研究预测的蛋白质定位之间进行一定的允许百分比的假性解剖蛋白质降解。对于给定的细胞器,基于识字 %或1%)。
    13. 参见图2,来自在参考文献中进行的单个生物学重复的代表性数据(Nightingale等人,等人 在该阈值下对每个细胞器设定SVM得分截止值,并使用所得数据作为最终空间蛋白质组图谱。 >,2019),没有任何蛋白质分类。此外,参见Bioconductor(Gentleman et al。,2004)pRolocdata(Gatto et al。,2014)包(版本1.19) .4以后)和 https://proteome.shinyapps.io/yeast2018/ (Nightingale et al。,2019),包含来自四个生物重复实验的数据的完全处理数据集,在有和没有可选的核制备的情况下进行,可以进行交互式探索。
    14. 通过hyperloPIT分析以低通量方式预测并使用正交方法分析已通过蛋白质定位验证的蛋白质子集。或密度梯度蛋白共分馏实验。


  1. 要准备OptiPrep浓度和折射率,请添加IWS。要减少,请添加1x LB.
  2. 在室温下干燥5分钟应使大部分丙酮蒸发。如果蛋白质颗粒开始从白色变为无色,则很难分辨。它开始过度干燥。
  3. 基于其报道的分子量将膜(后印迹)切成适当分子量范围的条带,并用单一抗体探测每个条带。我们可以探测表1中仅运行两个SDS-PAGE凝胶的所有标记物。
  4. 不要再使用辣根过氧化物酶偶联的二抗。在封闭液中稀释的一抗可在使用后储存在-20°C,并可重复使用至少5次,没有明显的信号丢失。
  5. 如果需要获得足够高的蛋白质产量,可以在标记之前合并相邻级分的蛋白质含量。如果在同一时间内多次进行合并,则可以区分分辨能力实验中的整体细胞器分辨率将受到影响。


  1. 细胞培养
    1. 10x酵母氮碱(YNB)
    2. 10倍完全补充剂混合物
      0.03%(w / v)异亮氨酸
      0.15%(w / v)缬氨酸
      0.04%(w / v)腺嘌呤半硫酸盐
      0.02%(w / v)精氨酸
      0.02%(w / v)组氨酸
      0.1%(w / v)亮氨酸
      0.03%(w / v)赖氨酸
      0.02%(w / v)蛋氨酸
      0.05%(w / v)苯丙氨酸
      0.2%(w / v)苏氨酸
      0.04%(w / v)色氨酸
      0.03%(w / v)酪氨酸
      0.02%(w / v)尿嘧啶
    3. 40%(w / v)葡萄糖
    4. 含2%葡萄糖的合成培养基(每个实验1.2L)
      混合120ml 10x YNB,60ml 40%(w / v)葡萄糖和120ml 10x完全补充混合物
    5. YPD琼脂培养基
      1. 在190毫升水中悬浮4克Bacto琼脂,4克Bacto蛋白胨和2克Bacto酵母提取物
      2. 通过高压灭菌消毒
      3. 在仍然熔化的同时,加入10毫升40%(w / v)葡萄糖并倒入琼脂平板(每块约20毫升)

  2. 细胞预处理和成球细胞生成
    1. TCEP还原缓冲液(每个实验150毫升)
      20mM Tris-HCl,pH 7.5
      10 mM三 - (2-羧乙基)膦(TCEP)
    2. 原生质球培养基(每个实验40毫升)
      由与合成培养基相同的成分组成,但添加20 mM Tris-HCl,pH 7.5和1.2 M山梨糖醇
      每50 ml缓冲液中新鲜补充1片完全不含EDTA的蛋白酶抑制剂片剂
    3. Zymolyase 100-T溶液(每个实验700μl)
      在原生质球培养基中制备的5μg/ ml酶解酶100-T 为实验中使用的每个酵母OD单位准备1μl
    4. 原生质球洗涤培养基(每个实验150毫升)
      由与原生质球介质相同的成分组成,具有Tris-HCl缓冲液的发射 每50 ml缓冲液中新鲜补充1片完全不含EDTA的蛋白酶抑制剂片剂

  3. (可选)核准备&nbsp;
    1. Ficoll裂解缓冲液(Kizer et al。,2006)(每个实验15 ml)
      18%(w / v)Ficoll PM-400
      20 mM磷酸氢二钾,pH 6.8
      1 mM氯化镁
      0.5 mM EDTA
      每10 ml缓冲液新鲜补充1片完全不含EDTA的无蛋白酶抑制剂片剂
    2. 缓冲液NP(Kizer 等人,2006)(每个实验300μl)
      340 mM蔗糖
      20mM Tris-HCl,pH 7.4
      50 mM氯化钾
      5 mM氯化镁
      每10 ml缓冲液中新鲜补充1片完全不含EDTA的无蛋白酶抑制剂片剂

  4. 细胞裂解
    1. 1x LB(每次实验75 ml)
      50 mM醋酸钾
      2 mM醋酸镁
      1 mM EDTA,pH 8.0
      每50 ml缓冲液中新鲜补充1片完全不含EDTA的蛋白酶抑制剂片剂
    2. 6x LB(每次实验4 ml)
      300 mM醋酸钾
      12 mM醋酸镁
      6 mM EDTA,pH 8.0

  5. 密度梯度离心
    1. 碘克沙醇工作溶液(IWS)(每个实验18毫升)
      将3 ml 6x LB与15 ml OptiPrep混合
    2. 18%(w / v)OptiPrep溶液(每个实验25毫升)
      1. 将9 ml IWS与16 ml 1x LB混合
      2. 双折检查折射率与已知折射率的OptiPrep浓度的标准曲线
      3. 根据需要进行调整,使用IWS增加折射率和1 x LB以降低折射率
    3. 16%OptiPrep溶液(每个实验20毫升)
      1. 将6.4ml IWS与13.6ml 1x LB混合
      2. 双重检查折射率与已知折射率的OptiPrep浓度的标准曲线
      3. 根据需要进行调整,使用IWS增加折射率和1x LB以降低折射率

  6. 馏分处理,消化和TMT标记
    1. 蛋白质分辨率缓冲液(每个实验20毫升)
      100mM HEPES-NaOH,pH 8.5
      0.1%(w / v)SDS
    2. 消化缓冲液(每次实验2毫升)
      100mM HEPES-NaOH,pH 8.5
    3. 5%(v / v)羟胺(每个实验120μl)
      将108μl消化缓冲液与12μl50%(w / v)羟胺溶液混合
    4. TTBS(每个实验1升)
      25mM Tris-HCl,pH 7.6
      150 mM NaCl
      0.1%(v / v)吐温-20
    5. 封闭溶液(每种抗体使用100毫升)
      用TTBS制备的5%(w / v)脱脂奶粉
    6. 二硫苏糖醇(DTT)溶液
      在蛋白质再溶解缓冲液中制备100mM DL-二硫苏糖醇
    7. 碘乙酰胺(IAA)溶液

  7. 通过固相萃取(SPE)进行样品净化
    1. 平衡缓冲液(每个实验600μl)
      50%(v / v)乙腈
      0.5%(v / v)乙酸
    2. 脱盐缓冲液1(每个实验1.8毫升)
      0.1%(v / v)三氟乙酸
    3. 脱盐缓冲液2(每个实验180μl)
      0.5%(v / v)乙酸
    4. 洗脱缓冲液(每个实验1毫升)
      80%(v / v)乙腈
      0.5%(v / v)乙酸

  8. 高pH反相色谱
    1. RP流动相原液(每个实验100毫升)
      200 mM甲酸铵,pH 10.0。用氢氧化铵调节pH。
    2. RP流动相A(每个实验500毫升)
      20 mM甲酸铵,pH 10.0
      通过将50ml RP流动相储备溶液与450ml HPLC级水混合来制备
      如有必要,用氢氧化铵重新调节至pH 10.0
    3. RP流动相B(每个实验500毫升)
      20 mM甲酸铵,pH 10.0
      80%(v / v)乙腈
      通过将50ml RP流动相储备溶液与400ml HPLC级ACN和50ml HPLC级水混合来制备
    4. RP重新溶解缓冲液(每个实验100μl)
      95%(v / v)RP流动相A
      5%(v / v)RP流动相B.


我们承认Owen Vennard的批判性阅读,读取这个交易所,并且大于a到交流,而不是比从那里开始,而不是比从那里开始。 。




  1. Alepuz,PM,de Nadal,E.,Zapater,M.,Ammerer,G。和Posas,F。(2003)。 Hot 1的Osmostress诱导的转录取决于Hog1介导的RNA Pol II的募集。 EMBO J 22(10):2433-2442。
  2. Ashburner,M.,Ball,CA,Blake,JA,Botstein,D.,Butler,H.,Cherry,JM,Davis,AP,Dolinski,K.,Dwight,SS,Eppig,JT,Harris,MA,Hill, DP,Issel-Tarver,L.,Kasarskis,A.,Lewis,S.,Matese,JC,Richardson,JE,Ringwald,M.,Rubin,GM and Sherlock,G。(2000)。基因本体:生物学统一的工具。基因本体联盟。 Nat Genet 25(1):25-29。
  3. Brachmann,CB,Davies,A.,Cost,GJ,Caputo,E.,Li,J.,Hieter,P。和Boeke,JD(1998)。来自酿酒酵母 S288C的设计者缺失菌株:用于PCR介导的基因破坏和其他应用的一组有用的菌株和质粒。 > Yeast 14(2):115-13。
  4. Breckels,LM,Holden,SB,Wojnar,D.,Mulvey,CM,Christoforou,A.,Groen,A.,Trotter,MW,Kohlbacher,O.,Lilley,KS和Gatto,L。(2016a)。 href =“http://www.ncbi.nlm.nih.gov/pubmed/27175778"target =”_ blank“>从异构数据源学习:空间蛋白质组学中的应用。 PLoS Comput Biol 12(5):e1004920。
  5. Breckels,LM,Mulvey,CM,Lilley,KS和Gatto,L。(2016b)。 A用于处理和分析空间蛋白质组学数据的Bioconductor工作流程。 F1000 Res 5:2926。
  6. 空间蛋白质组学数据的交互式可视化。 R包版本1.14.0。
  7. Breckels,LM,Gatto,L.,Christoforou,A.,Groen,AJ,Lilley,KS and Trotter,MW(2013)。细胞器发现对亚细胞蛋白定位的影响。 J Proteomics 88:129-140。
  8. 小说单曲> Breker,M.,Gymrek,M。和Schuldiner,M。(2013)。 -cell筛选平台在酵母应激反应期间强度蛋白质组可塑性。 J Cell Biol 200(6):839-850。
  9. Brosch,M.,Yu,L.,Hubbard,T。和Choudhary,J。(2009)。使用Mascot Percolator进行准确灵敏的肽鉴定。 J Proteome Res 8(6):3176-3181。
  10. Cherry,JM,Hong,EL,Amundsen,C.,Balakrishnan,R.,Binkley,G.,Chan,ET,Christie,KR,Costanzo,MC,Dwight,SS,Engel,SR,Fisk,DG,Hirschman,JE ,Hitz,BC,Karra,K.,Krieger,CJ,Miyasato,SR,Nash,RS,Park,J.,Skrzypek,MS,Simison,M.,Weng,S。和Wong,ED(2012)。 href =“http://www.ncbi.nlm.nih.gov/pubmed/22110037"target =”_ blank“> Saccharomyces基因组数据库:芽殖酵母的基因组学资源。 Nucleic Acids Res 40(数据库问题):D700-705。
  11. Chen,XJ,Wang,X.,Kaufman,BA and Butow,RA(2005)。 Aconitase将代谢调节与线粒体DNA维持结合起来。 Science 307(5710):71-717。
  12. Chong,YT,Koh,JL,Friesen,H.,Duffy,SK,Cox,MJ,Moses,A.,Moffat,J.,Boone,C。and Andrews,BJ(2015)。来自单细胞成像和自动分析的酵母蛋白质组动力学。 Cell 161(6): 1413年至1424年。
  13. Christoforou,A.,Mulvey,CM,Breckels,LM,Geladaki,A.,Hurrell,T.,Hayward,PC,Naake,T.,Gatto,L.,Viner,R.,Martinez Arias,A。和Lilley, KS(2016)。小鼠多能干细胞空间蛋白质组的草图。 Nat Commun 7:8992。
  14. Crook,OM,Mulvey,CM,Kirk,PDW,Lilley,KS和Gatto,L。(2018)。用于空间蛋白质组学的贝叶斯混合物模型。 PLoS Comput Biol 14(11):e1006516。
  15. Denervaud,N.,Becker,J.,Delgado-Gonzalo,R.,Damay,P.,Rajkumar,AS,Unser,M.,Shore,D.,Naef,F。和Maerkl,SJ(2013)。 href =“http://www.ncbi.nlm.nih.gov/pubmed/24019481”target =“_ blank”>一个恒化器阵列可以对酵母蛋白质组进行时空分析。 Proc Natl Acad Sci USA 110(39):15842-15847。
  16. de Duve,C。(1971)。组织分数过去和现在。 J Cell Biol 50(1):20。
  17. 日光照射Gancedo,C.,Flores,CL和Gancedo,JM(2016)。酵母中的蛋白质。 Microbiol Mol Biol Rev 80(3):765-777。
  18. Gatto,L。和Lilley,KS(2012)。 MSnbase-an R / Bioconductor package for isobaric标记的质谱数据可视化,处理和定量。 生物信息学 28(2):288-289。
  19. Gatto,L.,Breckels,LM,Wieczorek,S.,Burger,T。和Lilley,KS(2014)。使用pRoloc和pRolocdata进行基于质谱的空间蛋白质组学数据分析。 生物信息学 30(9):1322-1324。
  20. Gatto,L.,Breckels,LM和Lilley,KS(2019)。评估亚细胞分辨率在空间蛋白质组学实验中。 Curr Opin Chem Biol 48:123-149。
  21. Groen,AJ,Sancho-Andres,G.,Breckels,LM,Gatto,L.,Aniento,F。和Lilley,KS(2014)。 Arabidopsis thaliana 根组织中反式高尔基体网络蛋白的鉴定。 J Proteome Res 13(2): 763-776。
  22. Gentleman,RC,Carey,VJ,Bates,DM,Bolstad,B.,Dettling,M.,Dudoit,S.,Ellis,B.,Gautier,L.,Ge,Y.,Gentry,J.,Hornik,K Hothorn,T.,Huber,W.,Iacus,S.,Irizarry,R.,Leisch,F.,Li,C.,Maechler,M.,Rossini,AJ,Sawitzki,G.,Smith,C。 ,Smyth,G.,Tierney,L.,Yang,JY和Zhang,J。(2004)。 Bioconductor:开放计算生物学和生物信息学的软件开发。 Genome Biol 5(10):R80。
  23. Geladaki,A.,Kocevar Britovsek,N.,Breckels,LM,Smith,TS,Vennard,OL,Mulvey,CM,Crook,OM,Gatto,L。和Lilley,KS(2019)。将LOPIT与差异超速离心相结合,实现高分辨率空间蛋白质组学。 Nat Commun 10(1 ):331。
  24. Hope,IA和Struhl,K。(1986)。真核转录活性蛋白的功能解剖,酵母的GCN4。 细胞 46(6):885-894。
  25. Huh,WK,Falvo,JV,Gerke,LC,Carroll,AS,Howson,RW,Weissman,JS和O'Shea,EK(2003)。芽殖酵母中蛋白质定位的全球分析。 Nature 425(6959):686-691。
  26. Hung,V.,Lam,SS,Udeshi,ND,Svinkina,T.,Guzman,G.,Mootha,VK,Carr,SA和Ting,AY(2017)。通过邻近生物素化对活体人体细胞中面向细胞质的外部线粒体和ER膜进行蛋白质组学定位。 Elife 6: e 24463。
  27. Hung,V.,Zou,P.,Rhee,HW,Udeshi,ND,Cracan,V.,Svinkina,T.,Carr,SA,Mootha,VK and Ting,AY(2014)。通过比率APEX标记对活细胞中人线粒体膜间隙进行蛋白质组学定位。 Mol Cell > 55(2):332-341。
  28. Hunter,MJ和Commerford,SL(1961)。哺乳动物组织的压力均质化。 Biochim Biophys Acta 47:580-586。
  29. Hwang,J。和Espenshade,PJ(2016)。酵母中接近依赖的生物素标记使用工程化抗坏血酸过氧化物酶APEX2。 Biochem J 473(16):2463-2469。
  30. Itzhak,DN,Tyanova,S.,Cox,J。and Borner,GH(2016)。蛋白质亚细胞定位的全局,定量和动态映射。 Elife 5:e16950。
  31. Jadot,M.,Boonen,M.,Thirion,J.,Wang,N.,Xing,J.,Zhao,C.,Tannous,A.,Qian,M.,Zheng,H.,Everett,JK,Moore ,DF,Sleat,DE和Lobel,P。(2017)。蛋白质亚细胞定位的核算:大鼠肝脏蛋白质组的区室图。 Mol Cell Proteomics 16(2):194-212。
  32. Jean Beltran,PM,Mathias,RA和Cristea,IM(2016)。人像细胞巨膜病毒感染期间细胞器蛋白质组在空间和时间上的应用。 Cell Syst 3(4):361-373 e366。
  33. Kim,DI,Birendra,KC,Zhu,W.,Motamedchaboki,K.,Doye,V。和Roux,KJ(2014)。探测具有接近依赖性生物学的核孔复合体结构。 Proc Natl Acad Sci USA 111(24):E2453-2461。
  34. Kizer,KO,Xiao,T。和Strahl,BD(2006)。加速成核准备和方法用于分析酵母中的组蛋白修饰。 方法 40(4):296-302。
  35. Lam,SS,Martell,JD,Kamer,KJ,Deerinck,TJ,Ellisman,MH,Mootha,VK and Ting,AY(2015)。用于电子显微镜和邻近标记的APEX2的定向进化。 Nat Methods 12(1):51-54。
  36. McAlister,GC,Huttlin,EL,Haas,W.,Ting,L.,Jedrychowski,MP,Rogers,JC,Kuhn,K.,Pike,I.,Grothe,RA,Blethrow,JD and Gygi,SP(2012) 使用具有同量异位质量的报告子离子同位素,提高TMT的多路复用能力。 Anal Chem 84(17):7469-7478。
  37. McAlister,GC,Nusinow,DP,Jedrychowski,MP,Wuhr,M.,Huttlin,EL,Erickson,BK,Rad,R.,Haas,W。和Gygi,SP(2014)。 MultiNotch MS3可以对癌细胞系蛋白质的差异表达进行准确,灵敏和多重检测。 Chem Chem 86(14):7150-7158。
  38. Morgenstern,M.,Stiller,SB,Lubbert,P.,Peikert,CD,Dannenmaier,S.,Drepper,F.,Weill,U.,Hoss,P.,Feuerstein,R.,Gebert,M.,Bohnert, M.,van der Laan,M.,Schuldiner,M.,Schutze,C.,Oeljeklaus,S.,Pfanner,N.,Wiedemann,N。和Warscheid,B。(2017)。 //www.ncbi.nlm.nih.gov/pubmed/28658629“target =”_ blank“>定量范围内高置信度线粒体原型的定义。 Cell Rep 19(13 ):2836-2852。
  39. Mulvey,CM,Breckels,LM,Geladaki,A.,Britovsek,NK,Nightingale,DJH,Christoforou,A.,Elzek,M.,Deery,MJ,Gatto,L。和Lilley,KS(2017)。 “=”http://www.ncbi.nlm.nih.gov/pubmed/28471460“target =”_ blank“>使用hyperLOPIT执行空间蛋白质组的高分辨率映射。 Nat Protoc 12(6):111 10-1135。
  40. Nikolaev,Y.,Deillon,C.,Hoffmann,SR,Bigler,L.,Friess,S.,Zenobi,R.,Pervushin,K.,Hunziker,P。和Gutte,B。(2010)。 “=”http://www.ncbi.nlm.nih.gov/pubmed/20505831"target =“_ blank”>转录因子GCN4和c-Jun的亮氨酸拉链结构域具有核糖核酸酶活性。 PLoS One 5(5):e10765。
  41. 南丁格尔,DJ,Geladaki,A.,Breckels,LM,Oliver,SG和Lilley,KS(2019)。 Saccharomyces cerevisiae 的亚细胞组织。 Curr Opin Chem Biol 48:86-95。
  42. Nikolovski,N.,Rubtsov,D.,Segura,MP,Miles,GP,Stevens,TJ,Dunkley,TP,Munro,S.,Lilley,KS和Dupree,P。(2012)。推定的糖基转移酶和其他植物高尔基体蛋白质由LOPIT蛋白质组学揭示。 植物生理学 160 (2):1037-1051。
  43. Nightingale,DJ,Oliver,SG和Lilley,KS(2018)。使用hyperLOPIT以高分辨率绘制 Saccharomyces cerevisiae 空间蛋白质。在:分子生物学方法。 Oliver.SG(编辑).Humana Press。
  44. Opitz,N.,Schmitt,K.,Hofer-Pretz,V.,Neumann,B.,Krebber,H.,Braus,GH and Valerius,O。(2017)。在酵母中用定量BioID捕获40S核糖体头部区域的活化C激酶1(RACK1)微环境的Asc1p /受体。 Mol Cell Proteomics 16(12):2199-2218。
  45. Stadler,C.,Rexhepaj,E.,Singan,VR,Murphy,RF,Pepperkok,R.,Uhlen,M.,Simpson,JC和Lundberg,E。(2013)。免疫荧光和荧光蛋白标记显示哺乳动物细胞中蛋白质定位的高度相关性。 Nat Methods 10( 4):315-323。
  46. Sickmann,A.,Reinders,J.,Wagner,Y.,Joppich,C.,Zahedi,R.,Meyer,HE,Schonfisch,B.,Perschil,I.,Chacinska,A.,Guiard,B.,Rehling Saccharomyces cerevisiae 线粒体。 Proc Natl Acad Sci USA 100(23):13207-13212。
  47. Reinders,J.,Zahedi,RP,Pfanner,N.,Meisinger,C。和Sickmann,A。(2006)。走向完整的酵母线粒体蛋白:线粒体蛋白质组学的多维分离技术。 J Proteome Res 5(7):1543-1554。
  48. Rhee,HW,Zou,P.,Udeshi,ND,Martell,JD,Mootha,VK,Carr,SA and Ting,AY(2013)。通过时间限制酶标记对活细胞中线粒体进行蛋白质组学定位。 Science 339(6125):1328-1331。
  49. Roux,KJ,Kim,DI,Raida,M。和Burke,B。(2012)。混杂的生物素连接酶融合蛋白可识别哺乳动物细胞中的近端和相互作用蛋白。 J Cell Biol 196(6):801-810。
  50. Ross,PL,Huang,YN,Marchese,JN,Williamson,B.,Parker,K.,Hattan,S.,Khainovski,N.,Pillai,S.,Dey,S.,Daniels,S.,Purkayastha,S ,Juhasz,P.,Martin,S.,Bartlet-Jones,M.,He,F.,Jacobson,A。和Pappin,DJ(2004)。使用胺反应性同量异位标记试剂在酿酒酵母中进行多重蛋白定量。 Mol Cell Proteomics 3(12):1154-1169。
  51. R核心团队。(2018)。 R:A语言和统计计算环境。 R统计计算基金会,奥地利维也纳。
  52. RStudio Team。(2016).RStudio:R的集成开发环境
  53. Sadowski,PG,Dunkley,TP,Shadforth,IP,Dupree,P.,Bessant,C.,Griffin,JL和Lilley,KS(2006)。用于研究膜蛋白亚细胞定位的定量蛋白质组学方法。 Nat Protoc 1(4):1777-1789。
  54. Simpson,RJ(2010)。氮气蚀对培养细胞的破坏。 Cold Spring Harb Protoc 2010(11):pdb prot 5513。
  55. Thompson,A.,Schafer,J.,Kuhn,K.,Kienle,S.,Schwarz,J.,Schmidt,G.,Neumann,T.,Johnstone,R.,Mohammed,AK and Hamon,C。(2003。 )。串联质量标签:通过MS / MS比较分析复杂蛋白质混合物的新型定量策略 Anal Chem 75(8):1895-1904。
  56. Thul,PJ,Akesson,L.,Wiking,M.,Mahdessian,D.,Geladaki,A.,Ait Blal,H.,Alm,T.,Asplund,A.,Bjork,L.,Breckels,LM,Backstrom ,A.,Danielsson,F.,Fagerberg,L.,Fall,J.,Gatto,L.,Gnann,C.,Hober,S.,Hjelmare,M.,Johansson,F.,Lee,S.,Lindskog ,C.,Mulder,J.,Mulvey,CM,Nilsson,P.,Oksvold,P.,Rockberg,J.,Schutten,R.,Schwenk,JM,Sivertsson,A.,Sjostedt,E.,Skogs,M ,Stadler,C.,Sullivan,DP,Tegel,H.,Winsnes,C.,Zhang,C.,Zwahlen,M.,Mardinoglu,A.,Ponten,F.,von Feilitzen,K.,Lilley,KS ,Uhlen,M。和Lundberg,E。(2017)。人类蛋白质的亚细胞图谱 科学 356(6340)。
  57. (2017)。扩展基因本体知识库和资源。
  58. Ting,L.,Rad,R.,Gygi,SP和Haas,W。(2011)。同量异位多重定量蛋白酶中的MS 3消除率失真。 Nat Methods 8(11):937-940。
  59. Tkach,JM,Yimit,A.,Lee,AY,Riffle,M.,Costanzo,M.,Jaschob,D.,Hendry,JA,Ou,J.,Moffat,J.,Boone,C.,Davis,TN ,Nislow,C。和Brown,GW(2012)。通过分析蛋白质来解剖DNA损伤反应途径DNA复制应激过程中的定位和丰度变化。 Nat Cell Biol 14(9):966-976。
  60. Van Der Maaten,L。和Hinton,G。(2008)。使用t-SNE可视化数据。 J Mach Learn Res 9:2579-2605。
  61. Vapnik,V。(1995)。统计学习理论的本质。 Springer 。
  62. Villen,J。和Gygi,SP(2008)。用于全球磷酸化的SCX / IMAC富集方法质谱分析。 Nat Protoc 3(10):1630-1638。
  63. Vogtle,FN,Burkhart,JM,Rao,S.,Gerbeth,C.,Hinrichs,J.,Martinou,JC,Chacinska,A.,Sickmann,A.,Zahedi,RP和Meisinger,C。(2012)。 a href =“http://www.ncbi.nlm.nih.gov/pubmed/22984289”target =“_ blank”>酵母线粒体的膜间空间蛋白质组。 Mol Cell Proteomics 11 (12):1840-1852。
  64. Vogtle,FN,Burkhart,JM,Gonczarowska-Jorge,H.,Kucukkose,C.,Taskin,AA,Kopczynski,D.,Ahrends,R.,Mossmann,D.,Sickmann,A.,Zahedi,RP和Meisinger, C.(2017)。提交线粒体蛋白分布的景观。 Nat Commun 8(1):290。
  65. Wang,Y.,Lilley,KS和Oliver,SG(2014)。亚细胞的协议使用氮气穴和密度梯度离心分离 Saccharomyces cerevisiae 。 Yeast 31(4):127-135。
  66. Weill,U.,Yofe,I.,Sass,E.,Stynen,B.,Davidi,D.,Natarajan,J.,Ben-Menachem,R.,Avihou,Z。,Goldman,O.,Harpaz,N。 Chuartzman,S.,Kniazev,K.,Knoblac,B.,Laborenz,J.,Boos,F.,Kowarzyk,J.,Ben-Dor,S.,Zalckvar,E.,Herrmann,JM,Rachubinski, RA,Pines,O.,Rapaport,D.,Michnick,SW,Levy,ED和Schuldiner,M。(2018)。用于蛋白质组探索的全基因组SWAp-Tag酵母文库。 Nat Methods 15(8):617-622。
  67. Wiederhold,E.,Veenhoff,LM,Poolman,B。和Slotboom,DJ(2010)。 酿酒酵母细胞器的蛋白质组学。 Mol Cell Proteomics 9(3):431-445。
  68. Wenger,CD,Lee,MV,Hebert,AS,McAlister,GC,Phanstiel,DH,Westphall,MS and Coon,JJ(2011)。气相纯化可以使用同量异位标记进行准确的多重蛋白质组定量分析。 Nat Methods 8(11):933-935。
  69. Yofe,I.,Weill,U.,Meurer,M.,Chuartzman,S.,Zalckvar,E.,Goldman,O.,Ben-Dor,S.,Schutze,C.,Wiedemann,N.,Knop,M。 。,Khmelinskii,A。和Schuldiner,M。(2016)。一个图书馆让他们全部:通过SWAp-Tag策略简化酵母文库的创建。 Nat Methods 13(4):371-378。
  70. Zahedi,RP,Sickmann,A.,Boehm,AM,Winkler,C.,Zufall,N.,Schonfisch,B.,Guiard,B.,Pfanner,N。和Meisinger,C。(2006)。蛋白质组学分析前蛋白亚类的酵母线粒体外膜积累。 Mol Biol Cell 17(3):1436-1450。
  71. Zhang,R.,Sioma,CS,Wang,S。和Regnier,FE(2001)。在定量蛋白质组学中分离同位素标记的肽。 Anal Chem 73(21):5142-5149。
  • English
  • 中文翻译
免责声明 × 为了向广大用户提供经翻译的内容,www.bio-protocol.org 采用人工翻译与计算机翻译结合的技术翻译了本文章。基于计算机的翻译质量再高,也不及 100% 的人工翻译的质量。为此,我们始终建议用户参考原始英文版本。 Bio-protocol., LLC对翻译版本的准确性不承担任何责任。
Copyright: © 2019 The Authors; exclusive licensee Bio-protocol LLC.
引用:Nightingale, D. J., Lilley, K. S. and Oliver, S. G. (2019). A Protocol to Map the Spatial Proteome Using HyperLOPIT in Saccharomyces cerevisiae. Bio-protocol 9(14): e3303. DOI: 10.21769/BioProtoc.3303.