Microtubules
Microtubules make up one of three major parts of the cytoskeleton (Figure 1). Similar to other cytoskeletal filaments, they play a major role in structural organization and cell shape, but they are also important in a number of other cellular processes, such as cell division, cell motility and intracellular transport. Microtubules form a polar network of filaments that extends from the centrosome towards the plasma membrane. This organization is highly conserved in evolution, reflected in a striking similarity of microtubules across almost all species (Janke C. (2014)).
In the subcellular section, 478 genes (2% of all protein-coding human genes) have been shown to encode proteins that localize to the microtubule cytoskeleton and its substructures, including microtubule ends, cytokinetic bridge, midbody, midbody ring, and mitotic spindle (Figure 2 and 3). Functional enrichment analysis of the genes encoding microtubule-localizing proteins shows highly enriched GO-terms for biological processes related to cytoskeleton organization, cytoskeletal transport, cell division, and post-translational protein folding. More than half of the proteins detected at microtubules also localize to other cellular compartments, most commonly to the nucleoplasm, the cytosol and/or vesicles.
TUBA3D - A-431
TUBA3D - U-251MG
TUBA3D - U2OS
DTNBP1 - U2OS
AURKB - U2OS
CAMSAP2 - U2OS
Figure 1. Examples of proteins localized to the microtubules and its substructures. TUBA3D is a member of the alpha tubulin family, which form one of the major building blocks of microtubules, here shown to localize to the microtubules in three different cell lines (detected in A-431, U-251 and U2OS cells). DTNBP1 is a component of the BLOC-1 protein complex required for biogenesis of lysosome-related organelles. This protein was previously not known to localize to microtubules. By using independent antibodies DTNBP1 is shown to localize to microtubules (detected in U2OS cells). AURKB is a key regulator of mitosis by being part of the chromosomal passenger complex that ensures the correct orientation of the chromosomes during their segregation. AURKB is localized to the cytokinetic bridge (detected in U2OS cells). CAMSAP2 is a microtubule minus end protein that is expected to be involved in the nucleation and polymerization of microtubules. This protein is localized to the microtubule ends (detected in U2OS cells).
- 2% (478 proteins) of all human proteins have been experimentally detected in the microtubules by the Human Protein Atlas.
- 98 proteins in the microtubules are supported by experimental evidence and out of these 15 proteins are enhanced by the Human Protein Atlas.
- 375 proteins in the microtubules have multiple locations.
- 266 proteins in the microtubules show a cell to cell variation. Of these 266 show a variation in intensity and 0 a spatial variation.
- Proteins localizing to microtubules are mainly involved in organization of the cytoskeleton, cytoskeletal transport, protein folding and cell division.
Figure 2. 2% of all human protein-coding genes encode proteins localized to microtubules. Each bar is clickable and gives a search result of proteins that belong to the selected category.
The structure of microtubules
Substructures
- Microtubules: 276
- Microtubule ends: 6
- Cytokinetic bridge: 158
- Midbody: 55
- Midbody ring: 26
- Mitotic spindle: 93
Microtubules are physically robust polymers made up of α/β-tubulin heterodimers (Goodson HV et al. (2018); Wade RH. (2009)). The dimers first assemble into linear protofilaments. Subsequent lateral association of 13 protofilaments gives rise to a hollow tube, with an outer diameter of around 25 nm. Microtubules can grow very long and highly dynamic, with an ability to rapidly polymerize or depolymerize from the ends. The uniform orientation of the subunits results in a polar structure, with one fast-growing plus-end of exposed β-subunits and one slow-growing minus-end of exposed α-subunits. The cellular organization of microtubules varies between cell types, but in most cells, the minus ends of microtubules are anchored to the centrosomes near the nucleus while the plus ends radiate towards the periphery of the cell. The dynamic instability of microtubules is vital for the cell's ability to adapt its structural arrangements in response to different environmental conditions, and for mechanical processes (Desai A et al. (1997); Conde C et al. (2009)).
APC2 - U2OS
KIF18A - U2OS
FAM83D - A-431
Figure 3. Examples of the substructures of the microtubules. Midbody ring: APC2 is localized to the midbody ring (detected in U2OS cells). Cytokinetic bridge: KIF18A is a motor protein of the kinesin family that regulates chromosome aggregation and suppresses centromere movements prior to anaphase, thus contributing to chromosome stability (detected in U2OS cells). Mitotic spindle: FAM83D is localized to the mitotic spindle (detected in A-431 cells).
The dynamics of microtubules are regulated by a group of microtubule-associated proteins (MAPs). In addition, microtubules are subjected to a number of different post-translational modifications that influence the structure in order to meet the requirements for their different functions, for example acetylation of lysine residues, detyrosination, glycosylation and glutamylation (Janke C. (2014); Wloga D et al. (2010)). A selection of proteins suitable to be used as markers for microtubules and its substructures are listed in Table 1. Highly expressed genes encoding proteins that localize to microtubules are listed in Table 2.
Table 1. Selection of proteins suitable as markers for the microtubules structure or its substructures.
Gene |
Description |
Substructure |
TUBB4B
|
Tubulin beta 4B class IVb |
Cytokinetic bridge Microtubules Mitotic spindle |
TUBA1A
|
Tubulin alpha 1a |
Microtubules |
DCTN1
|
Dynactin subunit 1 |
Cytokinetic bridge Microtubules Mitotic spindle |
DTNBP1
|
Dystrobrevin binding protein 1 |
Microtubules Midbody |
CAMSAP2
|
Calmodulin regulated spectrin associated protein family member 2 |
Cytosol Microtubule ends |
Table 2. Highly expressed microtubule proteins across different cell lines.
Gene |
Description |
Average nTPM |
TUBA1B
|
Tubulin alpha 1b |
2713 |
TUBA1A
|
Tubulin alpha 1a |
326 |
BIRC5
|
Baculoviral IAP repeat containing 5 |
92 |
TUBA4A
|
Tubulin alpha 4a |
76 |
VPS4A
|
Vacuolar protein sorting 4 homolog A |
26 |
OFD1
|
OFD1 centriole and centriolar satellite protein |
11 |
See the morphology of microtubules in human iPSCs in the Allen Cell Explorer.
The function of microtubules
Similar to other cytoskeletal networks, a major function of the microtubule cytoskeleton is to supply mechanical strength to the cytoplasm and maintain the organization of organelles and other cellular compartments (Goodson HV et al. (2018)). As components of cilia and flagella, microtubules are also vital for cell migration and motility. These appendages are used in certain human cell types for cellular motility or extracellular transport of fluids, but cilia can also function as sensory organelles. Moreover, microtubules enable intracellular transport of organelles, vesicles and proteins with the help of ATP-driven motor proteins, making them key contributors to the secretory pathway (Schmoranzer J et al. (2003)). Motor proteins and microtubule dynamics are also employed to generate forces and movements. The two largest families of motor proteins are the dyneins and kinesins, which are moving in direction towards the minus and the plus end of microtubules, respectively.
Another highly important and well studied function of microtubules is during cell division through mitosis. Microtubules constitute a major part of the mitotic spindle (see Figure 3), which mediates segregation of sister chromatids to opposite poles. Spindle formation is an intricate process that involves both polymerization and depolymerization of microtubules, as well as movements generated by motor proteins. Sister chromatid separation is followed by cytokinesis, upon which microtubules of the central spindle are rearranged and compacted between the daughter cells, forming a cytokinetic bridge with a dense central structure called the midbody, which is eventually cleaved (see Figure 3) (Skop AR et al. (2004)).
Several diseases are linked to defective cellular transport due to abnormalities in microtubules. Hereditary diseases associated with defects in cilia, known as ciliopathies, and several neurodegenerative disorders, such as Parkinson's syndrome, belong to such diseases (Waters AM et al. (2011); Matamoros AJ et al. (2016)). Moreover, as tumour growth is highly dependent on mitosis, there are many efficient anti-cancer drugs that target microtubules (Jordan MA et al. (2004)).
Gene Ontology (GO)-based analysis of genes encoding proteins localizing to microtubules in the subcellular section shows enrichment of functions and processes well in line with their known functions. The most highly enriched terms for the GO domain Biological Process are related to microtubule-based processes such as cytoskeleton organization, ciliogenesis, mitosis and cell division, organelle organization, and intracellular transport (Figure 4a). Enrichment analysis of the GO domain Molecular Function shows enrichment of terms related to motor activity and tubulin binding (Figure 4b).
Figure 4a. Gene Ontology-based enrichment analysis for the microtubules 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 4b. Gene Ontology-based enrichment analysis for the microtubules 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.
Microtubules proteins with multiple locations
Approximately 78% (n=375) of the microtubule-localizing proteins in the subcellular section also localize to other compartments in the cell (Figure 5). The network plot shows that the most common locations shared with microtubules are the nucleoplasm, the cytosol and vesicles. Proteins with dual localization to the latter two are overrepresented, likely reflecting the important role of microtubules as a transport system in the cell.
Figure 5. Interactive network plot of microtubule proteins with multiple localizations. The numbers in the connecting nodes show the proteins that are localized to the microtubules and to one or more additional locations. Only connecting nodes containing more than one protein and at least 0.9% of proteins in the microtubule 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). Each node is clickable and results in a list of all proteins that are found in the connected organelles.
Expression levels of microtubules proteins in tissue
Transcriptome analysis and classification of genes into tissue distribution categories (Figure 6) shows that genes encoding microtubule-localizing proteins are less likely to be detected in all tissues, but more likely to be detected in many tissues, compared to all genes presented in the subcellular section. This points towards a somewhat more restricted pattern and tissue expression of genes encoding proteins that localize to microtubules.
Figure 6. Bar plot showing the percentage of genes in different tissue distribution categories microtubule-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
Leduc C et al., Intermediate filaments in cell migration and invasion: the unusual suspects. Curr Opin Cell Biol. (2015)
PubMed: 25660489 DOI: 10.1016/j.ceb.2015.01.005
Lowery J et al., Intermediate Filaments Play a Pivotal Role in Regulating Cell Architecture and Function. J Biol Chem. (2015)
PubMed: 25957409 DOI: 10.1074/jbc.R115.640359
Robert A et al., Intermediate filament dynamics: What we can see now and why it matters. Bioessays. (2016)
PubMed: 26763143 DOI: 10.1002/bies.201500142
Fuchs E et al., Intermediate filaments: structure, dynamics, function, and disease. Annu Rev Biochem. (1994)
PubMed: 7979242 DOI: 10.1146/annurev.bi.63.070194.002021
Janmey PA et al., Viscoelastic properties of vimentin compared with other filamentous biopolymer networks. J Cell Biol. (1991)
PubMed: 2007620
Köster S et al., Intermediate filament mechanics in vitro and in the cell: from coiled coils to filaments, fibers and networks. Curr Opin Cell Biol. (2015)
PubMed: 25621895 DOI: 10.1016/j.ceb.2015.01.001
Herrmann H et al., Intermediate filaments: from cell architecture to nanomechanics. Nat Rev Mol Cell Biol. (2007)
PubMed: 17551517 DOI: 10.1038/nrm2197
Gauster M et al., Keratins in the human trophoblast. Histol Histopathol. (2013)
PubMed: 23450430 DOI: 10.14670/HH-28.817
Ouyang W et al., Analysis of the Human Protein Atlas Image Classification competition. Nat Methods. (2019)
PubMed: 31780840 DOI: 10.1038/s41592-019-0658-6
Janke C., The tubulin code: molecular components, readout mechanisms, and functions. J Cell Biol. (2014)
PubMed: 25135932 DOI: 10.1083/jcb.201406055
Goodson HV et al., Microtubules and Microtubule-Associated Proteins. Cold Spring Harb Perspect Biol. (2018)
PubMed: 29858272 DOI: 10.1101/cshperspect.a022608
Wade RH., On and around microtubules: an overview. Mol Biotechnol. (2009)
PubMed: 19565362 DOI: 10.1007/s12033-009-9193-5
Desai A et al., Microtubule polymerization dynamics. Annu Rev Cell Dev Biol. (1997)
PubMed: 9442869 DOI: 10.1146/annurev.cellbio.13.1.83
Conde C et al., Microtubule assembly, organization and dynamics in axons and dendrites. Nat Rev Neurosci. (2009)
PubMed: 19377501 DOI: 10.1038/nrn2631
Wloga D et al., Post-translational modifications of microtubules. J Cell Sci. (2010)
PubMed: 20930140 DOI: 10.1242/jcs.063727
Schmoranzer J et al., Role of microtubules in fusion of post-Golgi vesicles to the plasma membrane. Mol Biol Cell. (2003)
PubMed: 12686609 DOI: 10.1091/mbc.E02-08-0500
Skop AR et al., Dissection of the mammalian midbody proteome reveals conserved cytokinesis mechanisms. Science. (2004)
PubMed: 15166316 DOI: 10.1126/science.1097931
Waters AM et al., Ciliopathies: an expanding disease spectrum. Pediatr Nephrol. (2011)
PubMed: 21210154 DOI: 10.1007/s00467-010-1731-7
Matamoros AJ et al., Microtubules in health and degenerative disease of the nervous system. Brain Res Bull. (2016)
PubMed: 27365230 DOI: 10.1016/j.brainresbull.2016.06.016
Jordan MA et al., Microtubules as a target for anticancer drugs. Nat Rev Cancer. (2004)
PubMed: 15057285 DOI: 10.1038/nrc1317