Golgi apparatus

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, 1153 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 protein 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 75% (n=869 proteins) 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.

GORASP1 - HeLa
GORASP2 - A-431
SLC30A6 - A-431

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).

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)).

YIPF3 - U2OS
YIPF3 - SH-SY5Y
YIPF3 - MCF-7

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 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, but are not limited to, 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 of the Golgi apparatus, or 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 points towards enrichment of genes with various enzymatic activities, and proteins that form or bind 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.

Table 2. Highly expressed single localizing Golgi apparatus-associated proteins across different cell lines.

Golgi apparatus-associated proteins with multiple locations

Approximately 75% (n=869) 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. However, there is also an over-representation of genes that localize to the Golgi apparatus and the nucleus. 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.

SLC39A14 - A-431
RAB20 - U2OS
TMEM87A - A-431

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.

Relevant links and publications

Uhlen M et al., A proposal for validation of antibodies. Nat Methods. (2016)
PubMed: 27595404 DOI: 10.1038/nmeth.3995

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

Skogs M et al., Antibody Validation in Bioimaging Applications Based on Endogenous Expression of Tagged Proteins. J Proteome Res. (2017)
PubMed: 27723985 DOI: 10.1021/acs.jproteome.6b00821

Parikh K et al., Colonic epithelial cell diversity in health and inflammatory bowel disease. Nature. (2019)
PubMed: 30814735 DOI: 10.1038/s41586-019-0992-y

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

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

Liao J et al., Single-cell RNA sequencing of human kidney. Sci Data. (2020)
PubMed: 31896769 DOI: 10.1038/s41597-019-0351-8

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

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

Chen J et al., PBMC fixation and processing for Chromium single-cell RNA sequencing. J Transl Med. (2018)
PubMed: 30016977 DOI: 10.1186/s12967-018-1578-4

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

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

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

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

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

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

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

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

Man L et al., Comparison of Human Antral Follicles of Xenograft versus Ovarian Origin Reveals Disparate Molecular Signatures. Cell Rep. (2020)
PubMed: 32783948 DOI: 10.1016/j.celrep.2020.108027

Guo J et al., The adult human testis transcriptional cell atlas. Cell Res. (2018)
PubMed: 30315278 DOI: 10.1038/s41422-018-0099-2

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

Takahashi H et al., 5' end-centered expression profiling using cap-analysis gene expression and next-generation sequencing. Nat Protoc. (2012)
PubMed: 22362160 DOI: 10.1038/nprot.2012.005

Lein ES et al., Genome-wide atlas of gene expression in the adult mouse brain. Nature. (2007)
PubMed: 17151600 DOI: 10.1038/nature05453

Kircher M et al., Double indexing overcomes inaccuracies in multiplex sequencing on the Illumina platform. Nucleic Acids Res. (2012)
PubMed: 22021376 DOI: 10.1093/nar/gkr771

Uhlén M et al., The human secretome. Sci Signal. (2019)
PubMed: 31772123 DOI: 10.1126/scisignal.aaz0274

Uhlen M et al., A genome-wide transcriptomic analysis of protein-coding genes in human blood cells. Science. (2019)
PubMed: 31857451 DOI: 10.1126/science.aax9198

Zhong W et al., The neuropeptide landscape of human prefrontal cortex. Proc Natl Acad Sci U S A. (2022)
PubMed: 35947618 DOI: 10.1073/pnas.2123146119

Sjöstedt E et al., An atlas of the protein-coding genes in the human, pig, and mouse brain. Science. (2020)
PubMed: 32139519 DOI: 10.1126/science.aay5947

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

Gao L et al., Integrative analysis the characterization of peroxiredoxins in pan-cancer. Cancer Cell Int. (2021)
PubMed: 34246267 DOI: 10.1186/s12935-021-02064-x

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)
PubMed: 8625302 

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

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

Robinson JL et al., An atlas of human metabolism. Sci Signal. (2020)
PubMed: 32209698 DOI: 10.1126/scisignal.aaz1482

Uhlen M et al., A pathology atlas of the human cancer transcriptome. Science. (2017)
PubMed: 28818916 DOI: 10.1126/science.aan2507

Hikmet F et al., The protein expression profile of ACE2 in human tissues. Mol Syst Biol. (2020)
PubMed: 32715618 DOI: 10.15252/msb.20209610

Gordon DE et al., A SARS-CoV-2 protein interaction map reveals targets for drug repurposing. Nature. (2020)
PubMed: 32353859 DOI: 10.1038/s41586-020-2286-9

Karlsson M et al., A single-cell type transcriptomics map of human tissues. Sci Adv. (2021)
PubMed: 34321199 DOI: 10.1126/sciadv.abh2169

Jumper J et al., Highly accurate protein structure prediction with AlphaFold. Nature. (2021)
PubMed: 34265844 DOI: 10.1038/s41586-021-03819-2

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

Berman HM et al., The Protein Data Bank. Nucleic Acids Res. (2000)
PubMed: 10592235 DOI: 10.1093/nar/28.1.235

Pollard TD et al., Actin, a central player in cell shape and movement. Science. (2009)
PubMed: 19965462 DOI: 10.1126/science.1175862

Mitchison TJ et al., Actin-based cell motility and cell locomotion. Cell. (1996)
PubMed: 8608590 

Pollard TD et al., Molecular Mechanism of Cytokinesis. Annu Rev Biochem. (2019)
PubMed: 30649923 DOI: 10.1146/annurev-biochem-062917-012530

dos Remedios CG et al., Actin binding proteins: regulation of cytoskeletal microfilaments. Physiol Rev. (2003)
PubMed: 12663865 DOI: 10.1152/physrev.00026.2002

Campellone KG et al., A nucleator arms race: cellular control of actin assembly. Nat Rev Mol Cell Biol. (2010)
PubMed: 20237478 DOI: 10.1038/nrm2867

Rottner K et al., Actin assembly mechanisms at a glance. J Cell Sci. (2017)
PubMed: 29032357 DOI: 10.1242/jcs.206433

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)
PubMed: 13165697 

HUXLEY H et al., Changes in the cross-striations of muscle during contraction and stretch and their structural interpretation. Nature. (1954)
PubMed: 13165698 

Svitkina T., The Actin Cytoskeleton and Actin-Based Motility. Cold Spring Harb Perspect Biol. (2018)
PubMed: 29295889 DOI: 10.1101/cshperspect.a018267

Malumbres M et al., Cell cycle, CDKs and cancer: a changing paradigm. Nat Rev Cancer. (2009)
PubMed: 19238148 DOI: 10.1038/nrc2602

Massagué J., G1 cell-cycle control and cancer. Nature. (2004)
PubMed: 15549091 DOI: 10.1038/nature03094

Hartwell LH et al., Cell cycle control and cancer. Science. (1994)
PubMed: 7997877 DOI: 10.1126/science.7997877

Barnum KJ et al., Cell cycle regulation by checkpoints. Methods Mol Biol. (2014)
PubMed: 24906307 DOI: 10.1007/978-1-4939-0888-2_2

Weinberg RA., The retinoblastoma protein and cell cycle control. Cell. (1995)
PubMed: 7736585 DOI: 10.1016/0092-8674(95)90385-2

Morgan DO., Principles of CDK regulation. Nature. (1995)
PubMed: 7877684 DOI: 10.1038/374131a0

Teixeira LK et al., Ubiquitin ligases and cell cycle control. Annu Rev Biochem. (2013)
PubMed: 23495935 DOI: 10.1146/annurev-biochem-060410-105307

King RW et al., How proteolysis drives the cell cycle. Science. (1996)
PubMed: 8939846 DOI: 10.1126/science.274.5293.1652

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

Mahdessian D et al., Spatiotemporal dissection of the cell cycle with single-cell proteogenomics. Nature. (2021)
PubMed: 33627808 DOI: 10.1038/s41586-021-03232-9

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

Thul PJ et al., A subcellular map of the human proteome. Science. (2017)
PubMed: 28495876 DOI: 10.1126/science.aal3321

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

Domenighetti G et al., Effect of information campaign by the mass media on hysterectomy rates. Lancet. (1988)
PubMed: 2904581 DOI: 10.1016/s0140-6736(88)90943-9

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

Uhlén M et al., Tissue-based map of the human proteome. Science (2015)
PubMed: 25613900 DOI: 10.1126/science.1260419

Nigg EA et al., The centrosome cycle: Centriole biogenesis, duplication and inherent asymmetries. Nat Cell Biol. (2011)
PubMed: 21968988 DOI: 10.1038/ncb2345

Doxsey S., Re-evaluating centrosome function. Nat Rev Mol Cell Biol. (2001)
PubMed: 11533726 DOI: 10.1038/35089575

Bornens M., Centrosome composition and microtubule anchoring mechanisms. Curr Opin Cell Biol. (2002)
PubMed: 11792541 

Conduit PT et al., Centrosome function and assembly in animal cells. Nat Rev Mol Cell Biol. (2015)
PubMed: 26373263 DOI: 10.1038/nrm4062

Tollenaere MA et al., Centriolar satellites: key mediators of centrosome functions. Cell Mol Life Sci. (2015)
PubMed: 25173771 DOI: 10.1007/s00018-014-1711-3

Prosser SL et al., Centriolar satellite biogenesis and function in vertebrate cells. J Cell Sci. (2020)
PubMed: 31896603 DOI: 10.1242/jcs.239566

Rieder CL et al., The centrosome in vertebrates: more than a microtubule-organizing center. Trends Cell Biol. (2001)
PubMed: 11567874 

Badano JL et al., The centrosome in human genetic disease. Nat Rev Genet. (2005)
PubMed: 15738963 DOI: 10.1038/nrg1557

Clegg JS., Properties and metabolism of the aqueous cytoplasm and its boundaries. Am J Physiol. (1984)
PubMed: 6364846 

Luby-Phelps K., The physical chemistry of cytoplasm and its influence on cell function: an update. Mol Biol Cell. (2013)
PubMed: 23989722 DOI: 10.1091/mbc.E12-08-0617

Luby-Phelps K., Cytoarchitecture and physical properties of cytoplasm: volume, viscosity, diffusion, intracellular surface area. Int Rev Cytol. (2000)
PubMed: 10553280 

Ellis RJ., Macromolecular crowding: obvious but underappreciated. Trends Biochem Sci. (2001)
PubMed: 11590012 

Bright GR et al., Fluorescence ratio imaging microscopy: temporal and spatial measurements of cytoplasmic pH. J Cell Biol. (1987)
PubMed: 3558476 

Kopito RR., Aggresomes, inclusion bodies and protein aggregation. Trends Cell Biol. (2000)
PubMed: 11121744 

Aizer A et al., Intracellular trafficking and dynamics of P bodies. Prion. (2008)
PubMed: 19242093 

Carcamo WC et al., Molecular cell biology and immunobiology of mammalian rod/ring structures. Int Rev Cell Mol Biol. (2014)
PubMed: 24411169 DOI: 10.1016/B978-0-12-800097-7.00002-6

Lang F., Mechanisms and significance of cell volume regulation. J Am Coll Nutr. (2007)
PubMed: 17921474 

Becht E et al., Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol. (2018)
PubMed: 30531897 DOI: 10.1038/nbt.4314

Schwarz DS et al., The endoplasmic reticulum: structure, function and response to cellular signaling. Cell Mol Life Sci. (2016)
PubMed: 26433683 DOI: 10.1007/s00018-015-2052-6

Friedman JR et al., The ER in 3D: a multifunctional dynamic membrane network. Trends Cell Biol. (2011)
PubMed: 21900009 DOI: 10.1016/j.tcb.2011.07.004

Travers KJ et al., Functional and genomic analyses reveal an essential coordination between the unfolded protein response and ER-associated degradation. Cell. (2000)
PubMed: 10847680 

Roussel BD et al., Endoplasmic reticulum dysfunction in neurological disease. Lancet Neurol. (2013)
PubMed: 23237905 DOI: 10.1016/S1474-4422(12)70238-7

Neve EP et al., Cytochrome P450 proteins: retention and distribution from the endoplasmic reticulum. Curr Opin Drug Discov Devel. (2010)
PubMed: 20047148 

Kulkarni-Gosavi P et al., Form and function of the Golgi apparatus: scaffolds, cytoskeleton and signalling. FEBS Lett. (2019)
PubMed: 31378930 DOI: 10.1002/1873-3468.13567

Short B et al., The Golgi apparatus. Curr Biol. (2000)
PubMed: 10985372 DOI: 10.1016/s0960-9822(00)00644-8

Wei JH et al., Unraveling the Golgi ribbon. Traffic. (2010)
PubMed: 21040294 DOI: 10.1111/j.1600-0854.2010.01114.x

Wilson C et al., The Golgi apparatus: an organelle with multiple complex functions. Biochem J. (2011)
PubMed: 21158737 DOI: 10.1042/BJ20101058

Farquhar MG et al., The Golgi apparatus: 100 years of progress and controversy. Trends Cell Biol. (1998)
PubMed: 9695800 

Brandizzi F et al., Organization of the ER-Golgi interface for membrane traffic control. Nat Rev Mol Cell Biol. (2013)
PubMed: 23698585 DOI: 10.1038/nrm3588

Potelle S et al., Golgi post-translational modifications and associated diseases. J Inherit Metab Dis. (2015)
PubMed: 25967285 DOI: 10.1007/s10545-015-9851-7

Yoon TY et al., SNARE complex assembly and disassembly. Curr Biol. (2018)
PubMed: 29689222 DOI: 10.1016/j.cub.2018.01.005