The cell line transcriptome

The word transcriptome refers to the full set of RNA molecules that are transcribed from the genome in a population of cells, or in a specific cell, at a given time point. In contrast to the genome, which is characterized by its stability across different cell types within an organism, the transcriptome varies greatly between cell types, developmental stages, and in response to internal or external cues. The plastic nature of the transcriptome, and its potential to serve as a proxy for cellular identity and diversity, makes it appealing to study and the advances in high-throughput technologies has made it possible to analyze RNA expression in great detail.

In the Subcellular Section, the expression of 20162 protein-coding genes are analyzed by RNA sequencing of mRNA extracted from unsynchronized log phase growing cells. The expression level of gene-specific transcripts are given as normalized expression (NX) values, and transcripts with NX values ≥1 are considered as detected. Genes are then classified according to the specificity and distribution of mRNA expression across a panel of 71 different human cell lines (Figure 1, Thul PJ et al. (2017)).

The Subcellular Section presents RNA expression for 87% (n=17558) of all protein-coding human genes, which can be used for various analyses of transcriptomics, as well as a resource for selection of cell lines expressing particular genes of interest.

A diversity of cell lines

The 71 different cell lines used in the Subcellular Section have been selected to represent various cell populations in different tissue types and organs of the human body. The selection also aims at mimicking the origin and phenotype of solid cancer types represented in the Pathology Atlas (Uhlen et al., 2017), with an additional emphasis on cancer cell types in the hematopoietic and immune systems. In addition to cancer-derived cell lines, there is a number of cell lines that have been generated through in vitro protocols for immortalization of normal cells, some primary cell lines and one type of induced pluripotent stem cells. Details regarding the different cell lines can be found here.

Cell lines are adapted to cultivation in vitro and many of the cell lines used in the Subcellular Section are human cancer cell lines. While this in some aspects limit their ressemblance to normal human cells in the context of tissues and organs, unbiased hierarchical clustering of global RNA expression (Figure 1) shows that the cell lines cluster well in agreement with similarities in origin and phenotype of the cancer cells from which thy are derived. Groups of related cell lines, such as the immortalized and transformed fibroblastic cell lines (BJ derivatives), the glioma cell lines (U-138 MG and U-251 MG), the melanoma cell lines (WM-115 and SK-MEL-30), the breast cancer cell lines (SK-BR-3, MCF7 and T47d) and the endothelial cell lines (TIME and HUVEC), cluster closely together. At the highest level of separation, cell lines that grow in solution and also represent hematopoietic and lymphoid cell systems cluster together and separate into two major clusters dependent on their myeloid or lymphoid origin/phenotype.

Figure 1. Hierarchical clustering based on RNA sequencing data for the 69 cell lines. The color of the cell line name represents its origin: Grey - Lymphoid, Light red - Muscle, Dark red - Myeloid, Bright green - Mesenchymal, Green - Pancreas, Dark green - Lung, Yellow bold - Brain, Yellow thin - Eye, Light pink - Proximal digestive tract, Pink - Female reproductive system, Dark pink - Endothelial, Beige - Skin, Orange - Kidney and urinary bladder, Blue - Gastrointestinal tract, Light blue - Male reproductive system, Light purple - Liver and gallbladder.

Specificity of RNA expression

Approximately one third of all protein-coding genes (n=20162) are expressed in all cell lines, which is indicative of roles in fundamental cellular functions, or 'house-keeping' functions, for the corresponding proteins (Figure 2). In contrast, 13% (n=2604) of all protein-coding genes were not detected in any of the analyzed cell lines, suggesting that the corresponding proteins are only expressed in unrepresented cell types, during specific developmental stages or under specific conditions, such as cellular stress. 0 of the protein-coding genes display high RNA expression in a single cell line, while 874 display high RNA expression in a smaller group of cell lines, relative to any of the other cell lines. 0 of the protein-coding genes show elevated RNA expression in a group of cell lines compared to the average expression in all other cell lines. Table 1 shows the distribution of genes within these expression categories for each of the analyzed cell lines.

Table 1. Table showing the number of detected genes per cell line based on RNA sequencing (NX ≥1), and the number of genes in the enriched and enhanced categories.

The cell line transcriptomes have been compared to the bulk transcriptomes of 40 different normal tissues and organs analyzed in the Tissue Atlas (Uhlén M et al. (2015)).There are 146 protein-coding genes that are only expressed in the panel of cell lines and not detected in any of the analyzed normal tissue types, while there are 1712 protein-coding genes that are only expressed in normal human tissues and not detected in any of the analyzed cell lines. Several of the proteins in the latter category encode proteins that have functions associated with differentiated cells in specialized tissues or subcompartments of tissues, which are not represented in the cell line panel. One example is ADAM30, which is expressed in spermatids of human testis.

• 146 genes found only in cell lines and not tissues
• 1712 genes found only in tissues and not cell lines

Cell line enriched genes

Overall, there is largely an agreement of the RNA expression categories between cell lines and tissues. A majority of the cell line enriched genes, defined as having at least four times higher RNA expression in a single cell line compared to any other cell line, also belong to the tissue elevated gene expression categories (tissue enriched, group enriched and tissue enhanced). For example, the secreted proteins AHSG and ALB that are only expressed in normal liver tissue, are also highly enriched in the liver derived cell line Hep-G2, where immunofluorescent analysis shows localizations to the secretory pathway. The transcription factor HOXB13 that shows expression in the prostate, colon and rectum, is also enriched in the prostate-derived cell line PC-3, where it is localized to the nucleoplasm. The adhesion glycoprotein CDH15 that is enriched in skeletal muscle tissue is also enriched in the sarcoma cell line RH-30, with some expression in the other sarcoma cell line LHCN-M2. The enzyme TYR that is exclusively expressed in skin is highly enriched in the melanoma-derived skin cell line SK-MEL-30, while the epidermal growth factor receptor EGFR that is enriched in female tissues and skin, is enriched in the other skin-derived cell line A-431. The expression pattern in normal tissues and protein function relate to the specific traits and functions of the corresponding normal tissue type and organ.

AHSG
ALB
HOXB13
AHSG - Hep-G2
ALB - Hep-G2
HOXB13 - PC-3
CDH15
TYR
EGFR
CDH15 - Rh30
TYR - SK-MEL-30
EGFR - A-431

Figure 3. Examples of proteins with enriched expression in a cell line and the corresponding tissue of origin. The proteins are AHSG, ALB, HOXB13, CDH15, TYR, and EGFR. The immunohistochemical (IHC) staining shows the protein expression pattern in tissue in brown. The immunofluorescent (IF) staining shows the protein subcellular expression pattern in cell lines in green. The nucleus and microtubules are shown in blue and red respectively in the IF images.

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

Hildreth AD et al., Single-cell sequencing of human white adipose tissue identifies new cell states in health and obesity. Nat Immunol. (2021)
PubMed: 33907320 DOI: 10.1038/s41590-021-00922-4

He S et al., Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs. Genome Biol. (2020)
PubMed: 33287869 DOI: 10.1186/s13059-020-02210-0

Bhat-Nakshatri P et al., A single-cell atlas of the healthy breast tissues reveals clinically relevant clusters of breast epithelial cells. Cell Rep Med. (2021)
PubMed: 33763657 DOI: 10.1016/j.xcrm.2021.100219

Lukassen S et al., SARS-CoV-2 receptor ACE2 and TMPRSS2 are primarily expressed in bronchial transient secretory cells. EMBO J. (2020)
PubMed: 32246845 DOI: 10.15252/embj.20105114

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 W et al., Single-cell transcriptomic atlas of the human endometrium during the menstrual cycle. Nat Med. (2020)
PubMed: 32929266 DOI: 10.1038/s41591-020-1040-z

Menon M et al., Single-cell transcriptomic atlas of the human retina identifies cell types associated with age-related macular degeneration. Nat Commun. (2019)
PubMed: 31653841 DOI: 10.1038/s41467-019-12780-8

Ulrich ND et al., Cellular heterogeneity of human fallopian tubes in normal and hydrosalpinx disease states identified using scRNA-seq. Dev Cell. (2022)
PubMed: 35320732 DOI: 10.1016/j.devcel.2022.02.017

Wang L et al., Single-cell reconstruction of the adult human heart during heart failure and recovery reveals the cellular landscape underlying cardiac function. Nat Cell Biol. (2020)
PubMed: 31915373 DOI: 10.1038/s41556-019-0446-7

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

Tabula Sapiens Consortium* et al., The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans. Science. (2022)
PubMed: 35549404 DOI: 10.1126/science.abl4896

Wagner M et al., Single-cell analysis of human ovarian cortex identifies distinct cell populations but no oogonial stem cells. Nat Commun. (2020)
PubMed: 32123174 DOI: 10.1038/s41467-020-14936-3

Qadir MMF et al., Single-cell resolution analysis of the human pancreatic ductal progenitor cell niche. Proc Natl Acad Sci U S A. (2020)
PubMed: 32354994 DOI: 10.1073/pnas.1918314117

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

Vento-Tormo R et al., Single-cell reconstruction of the early maternal-fetal interface in humans. Nature. (2018)
PubMed: 30429548 DOI: 10.1038/s41586-018-0698-6

Wang Y et al., Single-cell transcriptome analysis reveals differential nutrient absorption functions in human intestine. J Exp Med. (2020)
PubMed: 31753849 DOI: 10.1084/jem.20191130

De Micheli AJ et al., A reference single-cell transcriptomic atlas of human skeletal muscle tissue reveals bifurcated muscle stem cell populations. Skelet Muscle. (2020)
PubMed: 32624006 DOI: 10.1186/s13395-020-00236-3

Solé-Boldo L et al., Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Commun Biol. (2020)
PubMed: 32327715 DOI: 10.1038/s42003-020-0922-4

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

Agaton C et al., Affinity proteomics for systematic protein profiling of chromosome 21 gene products in human tissues. Mol Cell Proteomics. (2003)
PubMed: 12796447 DOI: 10.1074/mcp.M300022-MCP200

Lindskog M et al., Selection of protein epitopes for antibody production Biotechniques (2005)
PubMed: 15945371 

Larsson M et al., High-throughput protein expression of cDNA products as a tool in functional genomics. J Biotechnol. (2000)
PubMed: 10908795 DOI: 10.1016/s0168-1656(00)00258-3

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

Fagerberg L et al., Prediction of the human membrane proteome. Proteomics. (2010)
PubMed: 20175080 DOI: 10.1002/pmic.200900258

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

Gilvesy A et al., Spatiotemporal characterization of cellular tau pathology in the human locus coeruleus-pericoerulear complex by three-dimensional imaging. Acta Neuropathol. (2022)
PubMed: 36040521 DOI: 10.1007/s00401-022-02477-6

Jin H et al., Systematic transcriptional analysis of human cell lines for gene expression landscape and tumor representation. Nat Commun. (2023)
PubMed: 37669926 DOI: 10.1038/s41467-023-41132-w

Schubert M et al., Perturbation-response genes reveal signaling footprints in cancer gene expression. Nat Commun. (2018)
PubMed: 29295995 DOI: 10.1038/s41467-017-02391-6

Jiang P et al., Systematic investigation of cytokine signaling activity at the tissue and single-cell levels. Nat Methods. (2021)
PubMed: 34594031 DOI: 10.1038/s41592-021-01274-5

Jin L et al., Targeting of CD44 eradicates human acute myeloid leukemic stem cells. Nat Med. (2006)
PubMed: 16998484 DOI: 10.1038/nm1483

Magis AT et al., Untargeted longitudinal analysis of a wellness cohort identifies markers of metastatic cancer years prior to diagnosis. Sci Rep. (2020)
PubMed: 33004987 DOI: 10.1038/s41598-020-73451-z

Santarius T et al., GLO1-A novel amplified gene in human cancer. Genes Chromosomes Cancer. (2010)
PubMed: 20544845 DOI: 10.1002/gcc.20784

Berggrund M et al., Identification of Candidate Plasma Protein Biomarkers for Cervical Cancer Using the Multiplex Proximity Extension Assay. Mol Cell Proteomics. (2019)
PubMed: 30692274 DOI: 10.1074/mcp.RA118.001208

Virgilio L et al., Deregulated expression of TCL1 causes T cell leukemia in mice. Proc Natl Acad Sci U S A. (1998)
PubMed: 9520462 DOI: 10.1073/pnas.95.7.3885

Saberi Hosnijeh F et al., Proteomic markers with prognostic impact on outcome of chronic lymphocytic leukemia patients under chemo-immunotherapy: results from the HOVON 109 study. Exp Hematol. (2020)
PubMed: 32781097 DOI: 10.1016/j.exphem.2020.08.002

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

Álvez MB et al., Next generation pan-cancer blood proteome profiling using proximity extension assay. Nat Commun. (2023)
PubMed: 37463882 DOI: 10.1038/s41467-023-39765-y

Wik L et al., Proximity Extension Assay in Combination with Next-Generation Sequencing for High-throughput Proteome-wide Analysis. Mol Cell Proteomics. (2021)
PubMed: 34715355 DOI: 10.1016/j.mcpro.2021.100168

Zeiler M et al., A Protein Epitope Signature Tag (PrEST) library allows SILAC-based absolute quantification and multiplexed determination of protein copy numbers in cell lines. Mol Cell Proteomics. (2012)
PubMed: 21964433 DOI: 10.1074/mcp.O111.009613

Peng Y et al., Identification of key biomarkers associated with cell adhesion in multiple myeloma by integrated bioinformatics analysis. Cancer Cell Int. (2020)
PubMed: 32581652 DOI: 10.1186/s12935-020-01355-z

Gyllensten U et al., Next Generation Plasma Proteomics Identifies High-Precision Biomarker Candidates for Ovarian Cancer. Cancers (Basel). (2022)
PubMed: 35406529 DOI: 10.3390/cancers14071757

Enroth S et al., High throughput proteomics identifies a high-accuracy 11 plasma protein biomarker signature for ovarian cancer. Commun Biol. (2019)
PubMed: 31240259 DOI: 10.1038/s42003-019-0464-9

Wang Z et al., DNER promotes epithelial-mesenchymal transition and prevents chemosensitivity through the Wnt/β-catenin pathway in breast cancer. Cell Death Dis. (2020)
PubMed: 32811806 DOI: 10.1038/s41419-020-02903-1

Liu S et al., Discovery of CASP8 as a potential biomarker for high-risk prostate cancer through a high-multiplex immunoassay. Sci Rep. (2021)
PubMed: 33828176 DOI: 10.1038/s41598-021-87155-5

Orchard S et al., The MIntAct project--IntAct as a common curation platform for 11 molecular interaction databases. Nucleic Acids Res. (2014)
PubMed: 24234451 DOI: 10.1093/nar/gkt1115

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

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

Cellosaurus