The Golgi apparatus is named after the Italian physician and scientist Camillo Golgi, who discovered the fine membranous structure of the organelle in 1898. In mammalian cells, the Golgi apparatus has a morphologically distinct architecture. It consists of stacks of interconnected membrane cisternae, and resides close to the nucleus in proximity to the microtubule organizing center. It plays a central role in the intracellular transport of newly synthesized proteins and membrane lipids to other organelles, as well as in the transport of substances that are secreted to the extracellular space. Proteins present in the Golgi apparatus take part in various steps in this trafficking process, being involved in the post-translational modification, packaging and sorting of proteins.
In the subcellular section, 1163 genes (6% of all protein-coding human genes) have been shown to encode proteins that localize to the Golgi apparatus (Figure 2). A Gene Ontology (GO)-based functional enrichment analysis of genes encoding proteins that localize to the Golgi apparatus mostly shows enrichment of terms related to transmembrane- and vesicle transport, as well as protein metabolism and processing. Around 76% (n=881) of the Golgi apparatus proteins localize to one or more additional cellular compartments, the most common ones being the nucleus and vesicles. Examples of Golgi-associated proteins can be found in Figure 1.
Figure 1. Examples of proteins localized to the Golgi apparatus. GORASP1 is a key protein for maintaining the structure of the Golgi apparatus, especially for the reassembly of the fragmented Golgi apparatus after its breakdown during mitosis (detected in HeLa cells). GORASP2 has a similar function to GORASP1 and is also involved in the assembly and stacking of Golgi-cisternae (detected in A-431 cells). SLC30A6 is a Golgi membrane protein that regulates the zinc ion transport between the Golgi lumen and the cytosol (detected in A-431 cells).
6% (1163 proteins) of all human proteins have been experimentally detected in the golgi apparatus by the Human Protein Atlas.
274 proteins in the golgi apparatus are supported by experimental evidence and out of these 70 proteins are enhanced by the Human Protein Atlas.
881 proteins in the golgi apparatus have multiple locations.
161 proteins in the golgi apparatus show single cell variation.
Proteins localizing to the Golgi apparatus are mainly involved in transport and modification of proteins.
Figure 2. 6% of all human protein-coding genes encode proteins localized to the Golgi apparatus. Each bar is clickable and gives a search result of proteins that belong to the selected category.
The structure of the Golgi apparatus
In human cells, the Golgi apparatus is made up of a series of flattened membrane-bound disks, known as cisternae, originating from fusion of vesicular clusters that bud off the endoplasmatic reticulum (ER) (Kulkarni-Gosavi P et al. (2019); Short B et al. (2000)). The membrane disks are arranged in consecutive compartments that are named after the direction in which proteins move through them. Proteins coming from the ER or from the ER-Golgi intermediate compartment (ERGIC) enter in the cis Golgi network (CGN), followed by the medial-Golgi compartment, and ultimately exit via the adjacent trans Golgi Network (TGN) on route to their final destinations. The Golgi-membranes are characterized by constant emergence and fusion of small transport vesicles trafficking between the compartments. In most human cells, the individual stacks of the Golgi apparatus are interconnected with each other and form a twisted ribbon-like network (Figure 3). However, in some cell lines, like MCF7, the Golgi apparatus is more fragmented and scattered throughout the cytosol, making it easier to distribute between daughter cells in mitosis. The shape of the Golgi ribbon is not necessary for its function in post-translational modifications nor in secretion. However, it has been suggested the the ribbon structure and its positioning close to the nucleus has a role in cell polarization, including polarized secretion and migration (Wei JH et al. (2010)).
Figure 3. Examples of the morphology of the Golgi apparatus in different cell lines, represented by immunofluorescent staining of the protein encoded by YIPF3 in U2OS, SH-SY5Y, and MCF7 cells.
Figure 4. 3D-view of the Golgi apparatus in U2OS, visualized by immunofluorescent staining of GORASP2. The morphology of the Golgi apparatus in human induced stem cells can be seen in the Allen Cell Explorer.
The function of the Golgi apparatus
The Golgi apparatus is the key organelle in the secretory pathway and essential for the sorting and intracellular trafficking of proteins and membranes (Short B et al. (2000); Kulkarni-Gosavi P et al. (2019); Wilson C et al. (2011); Farquhar MG et al. (1998). Most newly synthesized proteins that enter the secretory pathway move from the ER through the Golgi apparatus to their final destination (Brandizzi F et al. (2013)). During transit through the Golgi apparatus they are heavily modified by post-translational modifications mediated by Golgi-resident proteins. These modifications include for exaMPLE glycosylation, sulfation, phosphorylation, and proteolytic cleavage. Such modifications are often important for the functional characteristics of the modified protein as well as for the proper sorting and transportation of the protein. Therefore, it is not surprising that malfunctions of Golgi-associated proteins that affect the morphology, the trafficking or post-translational modifications (especially glycosylation) that occur in the compartment, can lead to human diseases such as Congenital Disorder of Glycosylation (CDG) (Potelle S et al. (2015)).
Gene Ontology (GO)-based enrichment analysis of genes encoding proteins that localize to the Golgi apparatus reveals several functions associated with this organelle. The most highly enriched terms for the GO domain Biological Process are related to Golgi localization and organization, vesicle- and transmembrane transportation, and posttranslational modifications of proteins (Figure 5a). Enrichment analysis of the GO domain Molecular Function reveals enrichment of genes with various enzymatic activities, and proteins related to SNAP receptors (SNARES) (Figure 5b). The latter play important roles in fusion of vesicles (Yoon TY et al. (2018)).
Figure 5a. Gene Ontology-based enrichment analysis for the Golgi apparatus proteome showing the significantly enriched terms for the GO domain Biological Process. Each bar is clickable and gives a search result of proteins that belong to the selected category.
Figure 5b. Gene Ontology-based enrichment analysis for the Golgi apparatus proteome showing the significantly enriched terms for the GO domain Molecular Function. Each bar is clickable and gives a search result of proteins that belong to the selected category.
Proteins that are involved in the maintenance of the Golgi apparatus are suitable markers of the Golgi apparatus, e.g. members of the Golgin protein family (Table 1). However, they do not belong to the group of proteins with the highest expression, which contains several proteins related to vesicle transport, such as CAV1 and COPE (Table 2).
Table 1. Selection of proteins suitable as markers for the Golgi apparatus.
Golgi apparatus-associated proteins with multiple locations
Approximately 76% (n=881) of the Golgi apparatus-associated proteins detected in the subcellular section also localize to other compartments in the cell. The network plot (Figure 6) shows that dual locations between the Golgi apparatus and vesicles, as well as the ER, are overrepresented. This is in agreement with the interplay between the ER, Golgi and vesicles in the secterory pathway. Figure 7 shows examples of the most common and/or overrepresented combinations for multilocalizing proteins in the proteome of the Golgi apparatus.
Figure 6. Interactive network plot of Golgi-associated proteins with multiple localizations. The numbers in the connecting nodes show the proteins that are localized to the Golgi apparatus and to one or more additional locations. Only connecting nodes containing more than one protein and at least 0.7% of proteins in the Golgi apparatus proteome are shown. The circle sizes are related to the number of proteins. The cyan colored nodes show combinations that are significantly overrepresented, while magenta colored nodes show combinations that are significantly underrepresented as compared to the probability of observing that combination based on the frequency of each annotation and a hypergeometric test (p≤0.05). Note that this calculation is only done for proteins with dual localizations. Each node is clickable and results in a list of all proteins that are found in the connected organelles.
Figure 7. Examples of multilocalizing proteins in the proteome of the Golgi apparatus. SLC39A14 is a zinc transporter that was identified in the Golgi apparatus, ER, and plasma membrane. It might be involved in the regulation of the zinc ion homeostasis (detected in A-431 cells). RAB20 is a protein that was identified in the Golgi apparatus as well as in cytoplasmic vesicles, and is involved in endocytosis (detected in U2OS cells). TMEM87A is a transmembrane protein whose subcellular location and function have not been described previously, but was detected in the Golgi apparatus and nucleoplasm (detected in A-431 cells).
Expression levels of Golgi apparatus-associated proteins in tissue
Transcriptome analysis and classification of genes into tissue distribution categories (Figure 8) shows that genes encoding Golgi apparatus-associated proteins have a similar distribution over these categories as for all genes presented in the subcellular section, with the exception that a higher fraction of these genes are detected in many tissues, while a slightly lower fraction of these genes belong to those not detected in any of the tissues that have been analyzed.
Figure 8. Bar plot showing the percentage of genes in different tissue distribution categories for Golgi apparatus-associated protein-coding genes, compared to all genes in the subcellular section. Asterisk marks a statistically significant deviation (p≤0.05) in the number of genes in a category based on a binomial statistical test. Each bar is clickable and gives a search result of proteins that belong to the selected category.
Stadler C et al., Systematic validation of antibody binding and protein subcellular localization using siRNA and confocal microscopy.J Proteomics. (2012)
PubMed: 22361696 DOI: 10.1016/j.jprot.2012.01.030
Poser I et al., BAC TransgeneOmics: a high-throughput method for exploration of protein function in mammals.Nat Methods. (2008)
PubMed: 18391959 DOI: 10.1038/nmeth.1199
Hildreth AD et al., Single-cell sequencing of human white adipose tissue identifies new cell states in health and obesity.Nat Immunol. (2021)
PubMed: 33907320 DOI: 10.1038/s41590-021-00922-4
He S et al., Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs.Genome Biol. (2020)
PubMed: 33287869 DOI: 10.1186/s13059-020-02210-0
Bhat-Nakshatri P et al., A single-cell atlas of the healthy breast tissues reveals clinically relevant clusters of breast epithelial cells.Cell Rep Med. (2021)
PubMed: 33763657 DOI: 10.1016/j.xcrm.2021.100219
Lukassen S et al., SARS-CoV-2 receptor ACE2 and TMPRSS2 are primarily expressed in bronchial transient secretory cells.EMBO J. (2020)
PubMed: 32246845 DOI: 10.15252/embj.20105114
Wang W et al., Single-cell transcriptomic atlas of the human endometrium during the menstrual cycle.Nat Med. (2020)
PubMed: 32929266 DOI: 10.1038/s41591-020-1040-z
Menon M et al., Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration.Nat Commun. (2019)
PubMed: 31653841 DOI: 10.1038/s41467-019-12780-8
Ulrich ND et al., Cellular heterogeneity of human fallopian tubes in normal and hydrosalpinx disease states identified using scRNA-seq.Dev Cell. (2022)
PubMed: 35320732 DOI: 10.1016/j.devcel.2022.02.017
Wang L et al., Single-cell reconstruction of the adult human heart during heart failure and recovery reveals the cellular landscape underlying cardiac function.Nat Cell Biol. (2020)
PubMed: 31915373 DOI: 10.1038/s41556-019-0446-7
MacParland SA et al., Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations.Nat Commun. (2018)
PubMed: 30348985 DOI: 10.1038/s41467-018-06318-7
Tabula Sapiens Consortium* et al., The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans.Science. (2022)
PubMed: 35549404 DOI: 10.1126/science.abl4896
Wagner M et al., Single-cell analysis of human ovarian cortex identifies distinct cell populations but no oogonial stem cells.Nat Commun. (2020)
PubMed: 32123174 DOI: 10.1038/s41467-020-14936-3
Qadir MMF et al., Single-cell resolution analysis of the human pancreatic ductal progenitor cell niche.Proc Natl Acad Sci U S A. (2020)
PubMed: 32354994 DOI: 10.1073/pnas.1918314117
Vento-Tormo R et al., Single-cell reconstruction of the early maternal-fetal interface in humans.Nature. (2018)
PubMed: 30429548 DOI: 10.1038/s41586-018-0698-6
Wang Y et al., Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine.J Exp Med. (2020)
PubMed: 31753849 DOI: 10.1084/jem.20191130
De Micheli AJ et al., A reference single-cell transcriptomic atlas of human skeletal muscle tissue reveals bifurcated muscle stem cell populations.Skelet Muscle. (2020)
PubMed: 32624006 DOI: 10.1186/s13395-020-00236-3
Solé-Boldo L et al., Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming.Commun Biol. (2020)
PubMed: 32327715 DOI: 10.1038/s42003-020-0922-4
Gilvesy A et al., Spatiotemporal characterization of cellular tau pathology in the human locus coeruleus-pericoerulear complex by three-dimensional imaging.Acta Neuropathol. (2022)
PubMed: 36040521 DOI: 10.1007/s00401-022-02477-6
Jin H et al., Systematic transcriptional analysis of human cell lines for gene expression landscape and tumor representation.Nat Commun. (2023)
PubMed: 37669926 DOI: 10.1038/s41467-023-41132-w
Schubert M et al., Perturbation-response genes reveal signaling footprints in cancer gene expression.Nat Commun. (2018)
PubMed: 29295995 DOI: 10.1038/s41467-017-02391-6
Jiang P et al., Systematic investigation of cytokine signaling activity at the tissue and single-cell levels.Nat Methods. (2021)
PubMed: 34594031 DOI: 10.1038/s41592-021-01274-5
Jin L et al., Targeting of CD44 eradicates human acute myeloid leukemic stem cells.Nat Med. (2006)
PubMed: 16998484 DOI: 10.1038/nm1483
Magis AT et al., Untargeted longitudinal analysis of a wellness cohort identifies markers of metastatic cancer years prior to diagnosis.Sci Rep. (2020)
PubMed: 33004987 DOI: 10.1038/s41598-020-73451-z
Santarius T et al., GLO1-A novel amplified gene in human cancer.Genes Chromosomes Cancer. (2010)
PubMed: 20544845 DOI: 10.1002/gcc.20784
Berggrund M et al., Identification of Candidate Plasma Protein Biomarkers for Cervical Cancer Using the Multiplex Proximity Extension Assay.Mol Cell Proteomics. (2019)
PubMed: 30692274 DOI: 10.1074/mcp.RA118.001208
Virgilio L et al., Deregulated expression of TCL1 causes T cell leukemia in mice.Proc Natl Acad Sci U S A. (1998)
PubMed: 9520462 DOI: 10.1073/pnas.95.7.3885
Saberi Hosnijeh F et al., Proteomic markers with prognostic impact on outcome of chronic lymphocytic leukemia patients under chemo-immunotherapy: results from the HOVON 109 study.Exp Hematol. (2020)
PubMed: 32781097 DOI: 10.1016/j.exphem.2020.08.002
Satelli A et al., Galectin-4 functions as a tumor suppressor of human colorectal cancer.Int J Cancer. (2011)
PubMed: 21064109 DOI: 10.1002/ijc.25750
Harlid S et al., A two-tiered targeted proteomics approach to identify pre-diagnostic biomarkers of colorectal cancer risk.Sci Rep. (2021)
PubMed: 33664295 DOI: 10.1038/s41598-021-83968-6
Sun X et al., Prospective Proteomic Study Identifies Potential Circulating Protein Biomarkers for Colorectal Cancer Risk.Cancers (Basel). (2022)
PubMed: 35805033 DOI: 10.3390/cancers14133261
Bhardwaj M et al., Comparison of Proteomic Technologies for Blood-Based Detection of Colorectal Cancer.Int J Mol Sci. (2021)
PubMed: 33530402 DOI: 10.3390/ijms22031189
Chen H et al., Head-to-Head Comparison and Evaluation of 92 Plasma Protein Biomarkers for Early Detection of Colorectal Cancer in a True Screening Setting.Clin Cancer Res. (2015)
PubMed: 26015516 DOI: 10.1158/1078-0432.CCR-14-3051
Thorsen SB et al., Detection of serological biomarkers by proximity extension assay for detection of colorectal neoplasias in symptomatic individuals.J Transl Med. (2013)
PubMed: 24107468 DOI: 10.1186/1479-5876-11-253
Mahboob S et al., A novel multiplexed immunoassay identifies CEA, IL-8 and prolactin as prospective markers for Dukes' stages A-D colorectal cancers.Clin Proteomics. (2015)
PubMed: 25987887 DOI: 10.1186/s12014-015-9081-x
He W et al., Attenuation of TNFSF10/TRAIL-induced apoptosis by an autophagic survival pathway involving TRAF2- and RIPK1/RIP1-mediated MAPK8/JNK activation.Autophagy. (2012)
PubMed: 23051914 DOI: 10.4161/auto.22145
Enroth S et al., A two-step strategy for identification of plasma protein biomarkers for endometrial and ovarian cancer.Clin Proteomics. (2018)
PubMed: 30519148 DOI: 10.1186/s12014-018-9216-y
Jung CS et al., Serum GFAP is a diagnostic marker for glioblastoma multiforme.Brain. (2007)
PubMed: 17998256 DOI: 10.1093/brain/awm263
Jaworski DM et al., BEHAB (brain enriched hyaluronan binding) is expressed in surgical samples of glioma and in intracranial grafts of invasive glioma cell lines.Cancer Res. (1996)
Zhang X et al., CEACAM5 stimulates the progression of non-small-cell lung cancer by promoting cell proliferation and migration.J Int Med Res. (2020)
PubMed: 32993395 DOI: 10.1177/0300060520959478
Xu F et al., A Linear Discriminant Analysis Model Based on the Changes of 7 Proteins in Plasma Predicts Response to Anlotinib Therapy in Advanced Non-Small Cell Lung Cancer Patients.Front Oncol. (2021)
PubMed: 35070967 DOI: 10.3389/fonc.2021.756902
Dagnino S et al., Prospective Identification of Elevated Circulating CDCP1 in Patients Years before Onset of Lung Cancer.Cancer Res. (2021)
PubMed: 33574093 DOI: 10.1158/0008-5472.CAN-20-3454
Álvez MB et al., Next generation pan-cancer blood proteome profiling using proximity extension assay.Nat Commun. (2023)
PubMed: 37463882 DOI: 10.1038/s41467-023-39765-y
Wik L et al., Proximity Extension Assay in Combination with Next-Generation Sequencing for High-throughput Proteome-wide Analysis.Mol Cell Proteomics. (2021)
PubMed: 34715355 DOI: 10.1016/j.mcpro.2021.100168
Zeiler M et al., A Protein Epitope Signature Tag (PrEST) library allows SILAC-based absolute quantification and multiplexed determination of protein copy numbers in cell lines.Mol Cell Proteomics. (2012)
PubMed: 21964433 DOI: 10.1074/mcp.O111.009613
Peng Y et al., Identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis.Cancer Cell Int. (2020)
PubMed: 32581652 DOI: 10.1186/s12935-020-01355-z
Gyllensten U et al., Next Generation Plasma Proteomics Identifies High-Precision Biomarker Candidates for Ovarian Cancer.Cancers (Basel). (2022)
PubMed: 35406529 DOI: 10.3390/cancers14071757
Enroth S et al., High throughput proteomics identifies a high-accuracy 11 plasma protein biomarker signature for ovarian cancer.Commun Biol. (2019)
PubMed: 31240259 DOI: 10.1038/s42003-019-0464-9
Wang Z et al., DNER promotes epithelial-mesenchymal transition and prevents chemosensitivity through the Wnt/β-catenin pathway in breast cancer.Cell Death Dis. (2020)
PubMed: 32811806 DOI: 10.1038/s41419-020-02903-1
Liu S et al., Discovery of CASP8 as a potential biomarker for high-risk prostate cancer through a high-multiplex immunoassay.Sci Rep. (2021)
PubMed: 33828176 DOI: 10.1038/s41598-021-87155-5
Orchard S et al., The MIntAct project--IntAct as a common curation platform for 11 molecular interaction databases.Nucleic Acids Res. (2014)
PubMed: 24234451 DOI: 10.1093/nar/gkt1115
Varadi M et al., AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models.Nucleic Acids Res. (2022)
PubMed: 34791371 DOI: 10.1093/nar/gkab1061
Bird RP., Observation and quantification of aberrant crypts in the murine colon treated with a colon carcinogen: preliminary findings.Cancer Lett. (1987)
PubMed: 3677050 DOI: 10.1016/0304-3835(87)90157-1
HUXLEY AF et al., Structural changes in muscle during contraction; interference microscopy of living muscle fibres.Nature. (1954)
HUXLEY H et al., Changes in the cross-striations of muscle during contraction and stretch and their structural interpretation.Nature. (1954)
Cho RJ et al., Transcriptional regulation and function during the human cell cycle.Nat Genet. (2001)
PubMed: 11137997 DOI: 10.1038/83751
Whitfield ML et al., Identification of genes periodically expressed in the human cell cycle and their expression in tumors.Mol Biol Cell. (2002)
PubMed: 12058064 DOI: 10.1091/mbc.02-02-0030.
Boström J et al., Comparative cell cycle transcriptomics reveals synchronization of developmental transcription factor networks in cancer cells.PLoS One. (2017)
PubMed: 29228002 DOI: 10.1371/journal.pone.0188772
Lane KR et al., Cell cycle-regulated protein abundance changes in synchronously proliferating HeLa cells include regulation of pre-mRNA splicing proteins.PLoS One. (2013)
PubMed: 23520512 DOI: 10.1371/journal.pone.0058456
Ohta S et al., The protein composition of mitotic chromosomes determined using multiclassifier combinatorial proteomics.Cell. (2010)
PubMed: 20813266 DOI: 10.1016/j.cell.2010.07.047
Ly T et al., A proteomic chronology of gene expression through the cell cycle in human myeloid leukemia cells.Elife. (2014)
PubMed: 24596151 DOI: 10.7554/eLife.01630
Pagliuca FW et al., Quantitative proteomics reveals the basis for the biochemical specificity of the cell-cycle machinery.Mol Cell. (2011)
PubMed: 21816347 DOI: 10.1016/j.molcel.2011.05.031
Ly T et al., Proteomic analysis of the response to cell cycle arrests in human myeloid leukemia cells.Elife. (2015)
PubMed: 25555159 DOI: 10.7554/eLife.04534
Dueck H et al., Variation is function: Are single cell differences functionally important?: Testing the hypothesis that single cell variation is required for aggregate function.Bioessays. (2016)
PubMed: 26625861 DOI: 10.1002/bies.201500124
Snijder B et al., Origins of regulated cell-to-cell variability.Nat Rev Mol Cell Biol. (2011)
PubMed: 21224886 DOI: 10.1038/nrm3044
Cooper S et al., Membrane-elution analysis of content of cyclins A, B1, and E during the unperturbed mammalian cell cycle.Cell Div. (2007)
PubMed: 17892542 DOI: 10.1186/1747-1028-2-28
Davis PK et al., Biological methods for cell-cycle synchronization of mammalian cells.Biotechniques. (2001)
PubMed: 11414226 DOI: 10.2144/01306rv01
Scialdone A et al., Computational assignment of cell-cycle stage from single-cell transcriptome data.Methods. (2015)
PubMed: 26142758 DOI: 10.1016/j.ymeth.2015.06.021
Sakaue-Sawano A et al., Visualizing spatiotemporal dynamics of multicellular cell-cycle progression.Cell. (2008)
PubMed: 18267078 DOI: 10.1016/j.cell.2007.12.033
Grant GD et al., Identification of cell cycle-regulated genes periodically expressed in U2OS cells and their regulation by FOXM1 and E2F transcription factors.Mol Biol Cell. (2013)
PubMed: 24109597 DOI: 10.1091/mbc.E13-05-0264
Semple JW et al., An essential role for Orc6 in DNA replication through maintenance of pre-replicative complexes.EMBO J. (2006)
PubMed: 17053779 DOI: 10.1038/sj.emboj.7601391