The multilocalizing proteome

The immunofluorescence (IF)-based approach used in the subcellular section allows the analysis of protein distribution in all organelles and cellular substructures simultaneously. This allows for the study of spatial distribution of proteins in their cellular context and identification of proteins that localize to more than one compartment, referred to as "multilocalizing proteins" (MLPs).

Figure 1 shows example images of MLPs representing common combinations of locations and gives an idea of the cellular roles of MLPs. The most common case is that MLPs are located at multiple sites at the same time, within the same cell, but there are also MLPs that are associated with cell line-specific variations. For example, ZNF554 is a solely nuclear protein in RT4 and SH-SY5Y cells, but becomes a MLP in U-2 OS due to its additional prominent location in the nucleoli.


IPO7 - A-431

RPL19 - A-431

CCDC51 - U2OS


KIAA1522 - HaCaT

ITM2B - RT-4

ENO1 - U2OS

Figure 1. Examples of MLPs identified in the subcellular section. IPO7 mediates the import of proteins from the cytosol to the nucleus and can cross the nuclear membrane rapidly in both directions (detected in A-431 cells). RPL19 is a component of the ribosomal 60S subunit and was identified in nucleoli, where ribosomes are assembled, and in the cytosol and endoplasmic reticulum, where protein synthesis takes place (detected in A-431 cells). CCDC51 encodes an uncharacterized protein located in the mitochondria and nucleoplasm (detected in U-2 OS). KIAA1522 encodes an uncharacterized protein identified in the plasma membrane and nucleoplasm (detected in HaCaT cells). ITM2B is a transmembrane protein processed in the Golgi apparatus and vesicles. The resulting small peptide is secreted (detected in RT4 cells). ENO1 is a well described moonlighting protein. It has several functions in different compartments including a role in glycolysis in the cytosol, and as a surface protein in the plasma membrane (detected in U-2 OS cells).

MLPs in the subcellular section

More than half of the proteins localized in the subcellular section (57%, n=7413) are MLPs (Figure 2). Of these, around 32% (n=2393) can be found at three or more locations. The distribution of single and multilocalizing proteins for each organelle is shown in Figure 3 and Table 1. The percentage of MLPs in the individual organelle proteomes varies, but is often more than half because of the double counting of MLPs. Organelles such as the plasma membrane, cytosol, nucleus, and nucleoli share the majority of their proteins with other subcellular structures. This may reflect a need for proteins that operate across the borders of these organelles in order to regulate metabolic reactions, control gene expression, and/or to transmit information from the surrounding environment. In contrast, the proteomes of mitochondria contain mainly single localizing proteins, suggesting that this compartment is more self-contained with regards to its biological function.

Figure 2. Bar plot showing the number of protein-coding genes for single or multilocalizing proteins.

Figure 3. Bar plot showing the distribution of proteins localized to one or multiple organelles. Note that proteins localized to different substructures of organelles (e.g. nuclear bodies and nucleoplasm) are considered multilocalizing.

Table 1. Detailed information about single and multilocalizing proteins in the proteome of organelles and substructures.

Location Number of additional protein locations
0%1% 2% 3 or more%
Actin filaments 3013913887363113
Aggresome 001474316211
Centriolar satellite 2915794260322011
Centrosome 84211403514436318
Cleavage furrow 0000001100
Cytokinetic bridge 21473067424227
Cytoplasmic bodies 13183953182534
Cytosol 965202345481266262876
Focal adhesion sites 2820483546331712
Intermediate filaments 51316941402474
Microtubule ends 00233233233
Microtubules 60221053877283412
Midbody 611193518331222
Midbody ring 284151765312
Mitochondria 580523683314213293
Mitotic spindle 00111248523437
Rods & Rings 42194752615
Cell Junctions 50151294011636309
Endoplasmic reticulum 24245189358816204
Endosomes 42495331816
Golgi apparatus 284254654033429706
Lipid droplets 1128225651313
Lysosomes 2111053526211
Peroxisomes 15657301400
Plasma membrane 3601887543641311688
Vesicles 7053291441496221135
Kinetochore 00117233350
Mitotic chromosome 00182432432432
Nuclear bodies 721226245187326611
Nuclear membrane 3814129468731269
Nuclear speckles 18738193398918214
Nucleoli 10710475443763511811
Nucleoli fibrillar center 4314153499129237
Nucleoli rim 75302068444831
Nucleoplasm 1711282753451384232985

To get a better overview of the multilocalizing proteome, organelles can be grouped into three meta-compartments, and genes encoding MLPs can be aligned on a circular plot (Figure 4). The meta-compartments are the nucleus (nuclear and nucleolar structures shown in red), the cytoplasm (cytosol, mitochondria, and the different types of cytoskeleton shown in blue), and the secretory pathway (endoplasmic reticulum, Golgi apparatus, vesicles, plasma membrane shown in yellow). This reveals subordinate organization patterns of the MLPs. For instance, for the meta-compartments cytoplasm and nucleus, a common pattern is multilocalization between the predominant organelles cytosol and nucleoplasm, respectively. There are also many proteins that localize to more than one of the fine substructures within each of these meta- compartments. The MLPs in the secretory pathway exhibit a more sequential pattern likely reflecting the directional protein trafficking. In addition, the secretory pathway shares a strikingly high number of MLPs with the nucleus, despite that they are not in direct physical contact with each other. In agreement, cytoscape plots of each organelle (Figure 5, at end of the page) show that dual locations to the nucleoplasm together with the Golgi apparatus or vesicles are indeed overrepresented. This suggests that the proteomes of organelles in the secretory pathway are more versatile and should not be simplified to their role in protein secretion.


Figure 4. Circular plot with the identified proteins of each compartment presented and sorted by meta-compartments (red: nucleus, blue: cytoplasm, yellow: secretory pathway). Multilocalizing proteins appearing more than once in the plot are connected by a line

Why does the cell have MLPs?

MLPs present several advantages for the cell, some of which are crucial for cell survival. Shuttling proteins constantly switch their location in order to transport other proteins between organelles, making their multilocalization inseparably tied to their function. For example, members of the importin family transport proteins from the cytosol to the nucleus and hence are found in both organelles (Lange A et al. (2007), see also Figure 1). Another advantage of multilocalization is the possibility to make use of the same proteins in similar cellular processes and reactions, even if they occur in different subcellular compartments. For example, it has been shown that mitochondria and peroxisomes share some enzymes in their lipid metabolism (Ashmarina LI et al. (1999)). A switch of the subcellular location can also be an important way of generating a quick cellular response upon environmental changes, and external or internal cues. For example, receptors such as ERBB2 located in the plasma membrane are known to move to the nucleus after stimulation, where they change the expression pattern of target genes. This translocation has a profound impact on cancer initiation, progression, and prognosis of human cancers (Wang SC et al. (2009)).

Some of the MLPs are not just multilocalizing, but also multifunctional proteins. Multifunctional proteins do not fit in the paradigm of "one gene - one protein - one function", and certainly adds another dimension to cellular complexity. Multifunctionality may be the result of eg. gene fusions, expression of several splice variants, different post-translational modifications, different interaction partners and/or multilocalization of the protein. An extreme group of multifunctional proteins are the moonlighting proteins. The term "moonlighting" has been used for people who work in different jobs during daylight and moonlight, and like their human counterpart, moonlighting proteins have two or more completely different biochemical functions (Jeffery CJ. (1999)). Moonlighting proteins may provide connections and switches between different cellular reactions, pathways and processes, making it possible for cells to coordinate responses to a changing environment (Jeffery CJ. (2015)). For example, some biosynthetic enzymes moonlight as transcription factors in order to provide a tightly cpoupled feedback loop for transcription of genes involved in the pathway. An example of a moonlighting and multilocalizing protein is ENO1 (Figure 1) that acts as a glycolytic enzyme in the cytosol, but also as a plasminogen-receptor in the plasma membrane, and as a transcriptional repressor in the nucleus (Pancholi V. (2001)).

The Human Protein Atlas does not provide functional studies of proteins and therefore cannot determine if a MLP is multifunctional. However, the description of proteins at multiple locations is an important step in the discovery of multifunctional and moonlighting proteins and the spatial information provided in the subcellular section could be integrated into existing prediction models (Chapple CE et al. (2015)).

Actin Filaments
Centrosome
Cytosol
Endoplasmic Reticulum
Golgi Apparatus
Intermediate Filaments
Microtubules
Mitochondria
Nuclear membrane
Nucleoli
Nucleoplasm
Plasma Membrane
Vesicles

Figure 5. Cytoscape plots showing the distribution of MLPs that are shared between the major organelle proteomes. The black middle node links to all proteins localizing to the selected major organelle proteome, while the gray nodes links to all MLPs shared with each of the other major organelle proteomes. Only gray nodes with more than one protein and at least 0.5% of all human proteins are shown. The colored connecting nodes show the number of proteins that are exclusively shared between the compartments. The circle sizes of the connecting nodes are related to the number of proteins exclusively shared between the compartments. 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 link to a list of the corresponding genes.

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

Kilfoil ML et al., Stochastic variation: from single cells to superorganisms. HFSP J. (2009)
PubMed: 20514130 DOI: 10.2976/1.3223356

Ansel J et al., Cell-to-cell stochastic variation in gene expression is a complex genetic trait. PLoS Genet. (2008)
PubMed: 18404214 DOI: 10.1371/journal.pgen.1000049

Colman-Lerner A et al., Regulated cell-to-cell variation in a cell-fate decision system. Nature. (2005)
PubMed: 16170311 DOI: 10.1038/nature03998

Liberali P et al., Single-cell and multivariate approaches in genetic perturbation screens. Nat Rev Genet. (2015)
PubMed: 25446316 DOI: 10.1038/nrg3768

Elowitz MB et al., Stochastic gene expression in a single cell. Science. (2002)
PubMed: 12183631 DOI: 10.1126/science.1070919

Kaern M et al., Stochasticity in gene expression: from theories to phenotypes. Nat Rev Genet. (2005)
PubMed: 15883588 DOI: 10.1038/nrg1615

Bianconi E et al., An estimation of the number of cells in the human body. Ann Hum Biol. (2013)
PubMed: 23829164 DOI: 10.3109/03014460.2013.807878

Malumbres M., Cyclin-dependent kinases. Genome Biol. (2014)
PubMed: 25180339 

Collins K et al., The cell cycle and cancer. Proc Natl Acad Sci U S A. (1997)
PubMed: 9096291 

Zhivotovsky B et al., Cell cycle and cell death in disease: past, present and future. J Intern Med. (2010)
PubMed: 20964732 DOI: 10.1111/j.1365-2796.2010.02282.x

Cho RJ et al., A genome-wide transcriptional analysis of the mitotic cell cycle. Mol Cell. (1998)
PubMed: 9702192 

Spellman PT et al., Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell. (1998)
PubMed: 9843569 

Orlando DA et al., Global control of cell-cycle transcription by coupled CDK and network oscillators. Nature. (2008)
PubMed: 18463633 DOI: 10.1038/nature06955

Rustici G et al., Periodic gene expression program of the fission yeast cell cycle. Nat Genet. (2004)
PubMed: 15195092 DOI: 10.1038/ng1377

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

Nunnari J et al., Mitochondria: in sickness and in health. Cell. (2012)
PubMed: 22424226 DOI: 10.1016/j.cell.2012.02.035

Friedman JR et al., Mitochondrial form and function. Nature. (2014)
PubMed: 24429632 DOI: 10.1038/nature12985

Calvo SE et al., The mitochondrial proteome and human disease. Annu Rev Genomics Hum Genet. (2010)
PubMed: 20690818 DOI: 10.1146/annurev-genom-082509-141720

McBride HM et al., Mitochondria: more than just a powerhouse. Curr Biol. (2006)
PubMed: 16860735 DOI: 10.1016/j.cub.2006.06.054

Schaefer AM et al., The epidemiology of mitochondrial disorders--past, present and future. Biochim Biophys Acta. (2004)
PubMed: 15576042 DOI: 10.1016/j.bbabio.2004.09.005

Lange A et al., Classical nuclear localization signals: definition, function, and interaction with importin alpha. J Biol Chem. (2007)
PubMed: 17170104 DOI: 10.1074/jbc.R600026200

Ashmarina LI et al., 3-Hydroxy-3-methylglutaryl coenzyme A lyase: targeting and processing in peroxisomes and mitochondria. J Lipid Res. (1999)
PubMed: 9869651 

Wang SC et al., Nuclear translocation of the epidermal growth factor receptor family membrane tyrosine kinase receptors. Clin Cancer Res. (2009)
PubMed: 19861462 DOI: 10.1158/1078-0432.CCR-08-2813

Jeffery CJ., Moonlighting proteins. Trends Biochem Sci. (1999)
PubMed: 10087914 

Jeffery CJ., Why study moonlighting proteins? Front Genet. (2015)
PubMed: 26150826 DOI: 10.3389/fgene.2015.00211

Pancholi V., Multifunctional alpha-enolase: its role in diseases. Cell Mol Life Sci. (2001)
PubMed: 11497239 DOI: 10.1007/pl00000910

Chapple CE et al., Extreme multifunctional proteins identified from a human protein interaction network. Nat Commun. (2015)
PubMed: 26054620 DOI: 10.1038/ncomms8412