Cell cycle profiling reveals protein oscillation, phosphorylation, and localization dynamics.

The cell cycle is a highly conserved process involving the coordinated separation of a single cell into two daughter cells. To relate transcriptional regulation across the cell cycle with oscillatory changes in protein abundance and activity, we carried out a proteome- and phospho-proteome-wide mass spectrometry profiling. We compared protein dynamics with gene transcription, revealing a large number of transcriptionally regulated G2 mRNAs that only produce a protein shift after mitosis. Integration of CRISPR/Cas9 survivability studies further highlighted proteins essential for cell viability. Analyzing the dynamics of phosphorylation events and protein solubility dynamics over the cell cycle, we characterize predicted phospho-peptide motif distributions and predict cell cycle-dependent translocating proteins, as exemplified by the S-adenosylmethionine synthase MAT2A. Our study implicates this enzyme in translocating to the nucleus after the G1/S-checkpoint, which enables epigenetic histone methylation maintenance during DNA replication. Taken together, this dataset provides a unique integrated resource with novel insights on cell cycle dynamics.


Introduction
The mechanism of cell division has been extensively studied for many decades resulting in a very detailed picture of the genes and proteins involved and their temporal function within the dividing cell. Historically, the cell cycle is divided into a DNA synthesis phase (S-phase) and a cell division phase (Mitosis; M-phase), with these two phases separated by two gap phases, G1 and G2.
To date, many large-scale studies addressing the cell cycle focus on the transcriptional control of cell cycle regulated genes. Transcription can be used as a proxy for protein abundance and transcript dynamics translated to protein abundance on a larger scale for systems at the steady state (1), but extrapolation between different mRNA-protein pairs has very little explanatory power (2).

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HeLa-Fucci cells (obtained from Riken Cell Bank) were cultured in DMEM with 10% FBS, penicillin (100 µg/ml), streptomycin (100 µg/ml) at 37°C containing 5% CO2, in a humidified incubator. Cells were regularly checked for Mycoplasma contamination (Lonza, MycoAlert). This cell line was the same as used in Boström et al to generate the transcriptomic data that we compared our data with, and was thawed up from freezing vials from a similar passage number (9).

Western blotting
Western blotting was carried out following standard protocols with Bio-Rad SDS gradient gels and the Trans-Blot Turbo transfer system (Bio-Rad). Cells were lysed in RIPA buffer for 20 minutes on ice in presence of protease inhibitor cocktail (Roche), followed by sonication with a needle sonicator (Hielscher UP100H; 70% amplitude; 0.7 cycle; 10 cycles). Protein concentration was measures according to BCA (Pierce). Images were taken at a LI-COR Odyssey FC.

Cell fractionation
The soluble fraction was isolated based on a protocol published by (12). Lysis buffer to isolate the cytosolic (soluble) fraction contained 42 ug/ml Digitonin, 2 mM DTT, 2 mM MgCl2, 150 mM NaCl, 0.2 mM EDTA, 20 mM Hepes-NaOH at pH 7.4. Protease and phosphatase inhibitors (including 1 mM sodium orthovanadate) were added fresh. The insoluble fraction was separated by centrifugation. The remaining insoluble fraction containing nucleus, chromatin, larger organelles, and membranes was isolated according to a protocol by (13). Lysis buffer contained 8 M Urea, 20 mM HEPES pH 7.5, 1 mM bglycerophosphate, 2.5 mM Sodium pyrophosphate, 1 mM Na3VO4. Protease and phosphatase inhibitor were added fresh. Lysate was sonicated.

FACS
HeLa-Fucci cells were seeded on 10 cm 2 dishes and grown to 70-80% confluency. Cells were washed in 10 ml 37°C PBS. PBS was removed and replaced with 1ml Trypsin for 4 minutes until cells were in single cell suspension. Cells were then collected in 10 ml warm DMEM and counted. Cells were spun down for 3 minutes at 300 g at 4 °C. Media was by guest on April 7, 2020 6 removed and cells were washed in 10 ml of cold PBS. Cells were spun again for 3 minutes at 300 g at 4°C. The cell pellet was diluted in cold PBS with 5 mM EDTA to 3 million/ml and strained through a cell strainer FACS tube. Cells were sorted on a BD Influx. After every 30 minutes the sorted cells were snap frozen and pellets were stored at -80°C.

Immunocytochemistry and image analysis
Cells were grown on 96 well plates, washed in PBS and fixed in 4% PFA in PBS for 20 minutes. Samples were permeabilised for 10 minutes with 0.3% Triton X-100 in PBS. Copper(II)Sulphate, 10mM ascorbic acid in PBS for 10 minutes. Fixation and analysis were done as described above except prior to DAPI incubation, cells were treated with 10 µg/mL RNAse A in PBS for 1h at 4°C in order to enhance DAPI quantification, and images were acquired on a Evos M5000 Imaging System (Thermo Fisher).

Proteomic analyses
For the unfractionated proteome and phosphoproteome analyses of each of the three FACS sorted phases (G1, S, G2/M), cell pellets were harvested and resuspended in lysis buffer (1% Sodium deoxycholate, 100 mM Hepes pH 8, 1 mM sodium orthovanadate, 1 tablet of Complete mini EDTA-free mixture (Roche Applied Science) and one tablet of PhosSTOP by guest on April 7, 2020 7 phosphatase inhibitor mixture per 10 ml of lysis buffer (Roche Applied Science)). Cells were then lysed by 10 rapid passages through a 23-gauge hypodermic syringe needle and by sonication on ice. After centrifugation (20, fraction collection was halted, and the gradient was held at 3% B for 20 min. The concatenated fractions were collected in a plate, dried at room temperature using a Speed Vac (SPD 111V, Thermo), and stored at -20°C until LC-MS/MS analyses.

Phosphoproteomics analysis
For phosphoproteomics analyses, one hundred μg of tryptic digest of each cell sorted population and their respective replicates were labelled by TMT10plex as described above.

Proteomic and phosphoproteomic data analyses
The raw data were analyzed using MaxQuant 1.5.3.30 (15) and Andromeda (16)  complemented with a list of common contaminants, the two fluorescent proteins used in the Fucci system, and concatenated with the reversed version of all sequences. In total the searched database contained 42 168 entries. TMT10plex was chosen as quantification platform. Trypsin/P was chosen as cleavage specificity allowing for two missed cleavages.
Carbamidomethylation (C) was set as a fixed modification, while oxidation (M) (and phosphorylation of STY in the case of the phosphoproteome analysis) were used as variable modifications. The database search was performed with a mass deviation of the precursor ion of up to 4.5 ppm (main search). The mass tolerance for fragment ions was 0.5 Da. Data filtering was carried out using the following parameters: peptide and protein FDRs were set to 1%, minimum peptide length was set to 7 and Andromeda minimum score for modified peptides was set to 40, min reporter precursor ion fraction=0.75. The reverse and common contaminant hits were removed from the output as well as those with localization probability <0.75. Phosphopeptide motif prediction was performed using Perseus (1.5.3.2) with Perseus' integrated motif list, additionally provided in Suppl Table 3. Motif enrichment analysis was performed with R using Fisher's exact test or a simulated Fisher's test.
To account for changes in total cell content during the cell cycle, normalization was deemed very important. Aside from using equal weight amounts of tryptic digest for TMT10plex-labeling, logarithmic reporter intensity values were normalized by subtracting the median logarithmic intensity for each sample prior to further analysis.

Experimental Design and Statistical Rationale
All experiments were performed on three replicates from each of three cell cycle stages.
This was deemed adequate to identify the magnitude of differences intended in this study.
In the fractionation proteomic experiment, two insoluble fraction samples were excluded between peptide mapping and ANOVA analysis. After mapping, all proteomic samples (from full proteomics, insoluble fractions, soluble fractions) were hierarchically clustered in a heatmap, which independently clustered all triplicates of samples together except for by guest on April 7, 2020 10 two individual samples, one replicate for G-phase-Insoluble and one for S-phase-Insoluble. Therefore, the ANOVA for the insoluble data was performed on 8 total samples (3vs2vs2).
All enrichment analyses were performed using either Fisher's exact test for comparing two different distributions (simulated if too large to calculate) using the fisher.test function in base R, or Log-Likelihood-Ratio analysis for comparing a distribution relative to its parental distribution, using the xmulti (xmonte for simulation) function in the XNomial package in R.
All datasets with three groups underwent ANOVA analysis in R

Data Analysis
As the cells studied are unsynchronized and constantly dividing, any significant difference in protein or phosphopeptide amount between the cell cycle phases in the study indicates a cell-cycle-dependent change in abundance. We thus classify these as exhibiting oscillatory behavior. For visualization of oscillatory patterns and to be able to compare patterns of oscillation between experiments, the algorithm TriComp was used according to (9). A generalized version of the TriComp code in R script can be found in Suppl File 1.
Finally, a summarising group variable is calculated by checking which hexagonal section the data point belongs to using equation (5).
The TriComp algorithm generates two polar coordinates from the relative logarithmic relationship between three groups, in our study the three cell cycle phases, without losing any information. The output degree coordinate corresponds to the pattern of relationship, and the radius coordinate corresponds to the quantification/intensity of the relationship. By filtering on statistics and radius, the degree coordinate was used throughout the study as a quantitative variable describing the kind of cell cycle oscillation pattern a protein or phosphopeptide exhibited.
Preparation of figures were performed in R (3.4.2) using the libraries ggplot and extrafont.

Annotation and Enrichment Analysis
by guest on April 7, 2020 12 GO-terms associated with cell cycle were classified as being annotated with GO:7049 or a child GO-term of GO:7049 (17). A grouping of these terms into five major cell cycle processes was done using the same grouping as (9 analysis of comparing Insoluble-specific proteins to Soluble-specific proteins was performed with DAVID after specifying hit lists using multiple T-tests corrected for multiplicity with the qvalue module in R. Uniprot IDs were provided to DAVID. Fractionspecific proteins were specified as more than two-fold higher in soluble than insoluble and vice-versa, with a significance cutoff of FDR<0.001. Only proteins with available data in both unsorted Soluble and unsorted insoluble fractions were used.
For the literature-based kinase enrichment analysis, the Ma'ayan Lab Kinase Enrichment Analysis 2 tool was used, and each phase group of significantly fluctuating phosphopeptides was submitted, with the results presented as a network analysis.

Characterizing protein oscillation patterns over the cell cycle
To analyze the dynamics of the proteome across the cell cycle without introducing potential artifacts from chemical synchronization, we combined fluorescence-based cell sorting with mass spectrometry-based proteomic analysis. The cell cycle reporter cell line HeLa-Fucci To be able to quantify the relative abundance of a given protein in G1, S or G2/M phases of 13 the cell cycle we employed a TMT isobaric labeling approach (20) of three replicates for each cell cycle phase (Fig 1A, Suppl Fig 2G). Nearly 7500 proteins were identified per cell cycle phase (Fig 1A, Suppl Table 1), and our ANOVA statistical analysis (corrected by FDR) revealed that 3317 proteins were significantly altered across the cell cycle (Suppl Fig 2H), of which 219 undergo changes larger than 50%, and 87 more than two-fold. We refer to these dynamic protein fluctuations between cell cycle phases as oscillatory behaviors. Visualization and comparison of the three groups was achieved with the use of the TriComp algorithm (9) (Suppl Fig 1A-D). TriComp translates three quantitative measurements into two polar descriptive variables: one variable describes the relationship type (θ, degree), while the other reports the relative intensity of the relationship (radius).
Thus, each angle in the polar coordinate system corresponds to a specific relationship between G1, S and G2/M phase, whilst the distance from origo is a quantifying descriptor of that relationship and can be used for filtering. The angle (degree) was also used to distinguish six separate groups, corresponding to an upregulation in either one or two of the three cell cycle phases. We further categorized a group of high-oscillating proteins as having at least a 50% increase from one group to another, while also being statistically  Table 1).
Among the 20 highest oscillating proteinsexcluding the cell cycle probes CDT1 and Geminin, which rank number 1 and 2we identified well-known cell cycle regulators, as well as proteins not previously associated with the cell cycle (Fig 1C). The thymine DNA glycosylase TDG is high in G1 while the uracil DNA glycosylase UNG2 peaks during Sphase. TK1 (thymidine kinase), involved in the production of the nucleotide dTTP, is increased during S-phase carrying on through G2/M phase. Proteins involved in Rho signaling such as ARHGAP11A and NET1 as well as the mitotic spindle protein CKAP2L have increased abundance in S and G2/M, which is likely associated with mitosis. Some of the highest oscillating proteins that are not GO-annotated as related to the cell cycle have previously been identified to be cell cycle regulated, including ARHGAP11A (4) which has previously been identified to have a critical function in mitosis (21) , and KIAA0101 by guest on April 7, 2020 14 and TK1 which have both been identified to undergo cell-cycle-regulated degradation (22,23).
Performing gene-set enrichment analysis (GSEA) on the entire hit-list, we observed a major enrichment for cell-cycle-annotated proteins, as expected (Fig 1D). A closer inspection of these oscillating proteins with cell cycle-related GO terms revealed a strong bias towards the GO annotation of S+G2/M-phase enriched proteins, while proteins with higher abundance in G1-or G1+S-phase were underrepresented (Fig 1E). Confirming the quality of our cell cycle sorting we detected bona fide cell cycle regulators in their corresponding phases (Fig 1F). After removing all non-cell-cycle-annotated proteins, GSEA reveals that GO cellular process terms such as Locomotion, Cell Motility, Adhesion, and Extracellular Structure Organization are still enriched, suggesting that many proteins involved in these processes are under regulation of the cell cycle machinery (Fig 1G). We Chromosome Segregation in the G2/M+G1 pattern (Fig 1H). We conclude that many vital cellular processes, even those not directly cell cycle-regulatory, are still affected by the cell cycle.

Substantiating the relationship between mRNA expression and protein abundance
A comparison between our proteome dataset with a recently published transcriptomic dataset on cell cycle dynamics using the Fucci system as a biological model (9) revealed an overlap of 1467 transcripts that were significantly oscillating in both datasets.
Interestingly, 56% of these showed a variation at the protein level but not at the transcript level, implying that a large extent of protein turnover is regulated via translational or posttranslational mechanisms (Fig 2A). Using the θ variable of both datasets on proteins significantly oscillating in both systems, we could compare variations at transcript and protein abundance, and these strongly correlated. The oscillation patterns for most proteins were either synchronized in the same cell cycle phase as the corresponding transcript or by guest on April 7, 2020 15 delayed into the next phase (Fig 2B). By investigating and comparing the relationship between mRNA and protein in the six different cell cycle phase groups, we detected profound differences between mRNA categories. The percentage of proteins that are delayed differ depending on how the mRNA is regulated. Some mRNA-protein relationships are essentially self-supporting, such as G1-upregulated mRNA leading to G1or G1+S-upregulated protein. Genes transcribed in G2/M have the highest percentage of delayed proteins, as a majority of corresponding oscillating proteins are enriched in the following G1 and G1+S groups (Fig 2C, D), with similar magnitudes as the total hit list of oscillating proteins (Suppl Fig 2I). These findings suggest that the majority of processing and ribonucleoprotein assembly (Fig 2E). These three groups were also analyzed on the level of magnitude they were affected, and found to share a magnitude distribution with the total list of significantly changed proteins, except for the group which was upregulated in G2 in both mRNA and Protein, which had a higher overall magnitude (Suppl Fig 2F).
We compared our dataset with a collection of ten different genome-wide CRISPR/Cas9 viability experiments (10) by using the number of cell lines out of ten affected in this collection as a broad "Essentiality Score" to denote importance for cellular proliferation.
We found a clear positive correlation between cell cycle-dependent changes of proteins and their essentiality for cellular viability (Fig 2F), the percentage of essential proteins was more than 70% higher in >2FC oscillators than non-oscillating proteins. Investigating the cell-cycle distribution of the group of essential-and-oscillating proteins we observed a by guest on April 7, 2020 16 significant decrease of protein enrichment in S phase compared to non-essential oscillating proteins (Fig 2G).

Cell cycle dynamics of phosphorylation patterns
Analyzing the abundance of kinases and phosphatases in our dataset using the human KinBase (18) and the Drug-Gene Interaction database v3 (DGIdb3) (19), we detected an enrichment of phosphatases upregulated in S-phase, while kinases are significantly enriched in G2/M (Fig 3A). This is also evident after comparison of protein and mRNA (Fig 3B). To integrate the phosphorylation patterns for individual proteins across the cell cycle on a proteome-wide scale, we sorted cells in G1, S and G2/M phases and coupled a phospho-enrichment step to a TMT-based LC/MS/MS analysis (Fig 3C). We identified 5829 phosphosites with a localization probability >75%. An ANOVA test was performed on 4833 unique phosphopeptides, out of which 3317 were significantly oscillating across the cell cycle after FDR correction (Fig 3C; Suppl Fig 2J-M). We applied TriComp to visualize the overall changes in phosphorylation over the cell cycle (Fig 3D-E). The most prominent patterns of phosphorylation enrichments were detected during S+G2/M phases of the cell cycle, which coincides with the increase in kinase abundance previously identified (Fig 3A-B) and overall increase in protein regulation during that phase (Fig 1B).
Among the top oscillating phosphopeptides we could identify many known cell cycle regulators including MKI67, MCM4, CDC20 and CDT1 (Fig 3F). Highlighted phosphopeptides, which have corresponding genes not annotated with GO:7409 or child terms among the top fluctuating, are multiple phosphorylations on nuclear and nucleolar proteins; Nucleolin and multiple Histone 1 proteins (Fig 3G).
Comparing the patterns of oscillations for the proteome and phosphoproteome, we observed many occurrences that are similar. However, there are also a number of phosphorylation events in other phases (Fig 3H). Marker of Proliferation KI-67 (MKI67) provides an example of how protein regulation and activity can be decoupled from protein abundance. MKI67 is transcriptionally restrained to the G2/M phase, but the protein abundance is surprisingly stable throughout the cell cycle (Fig 3I). Still, a characterization of phosphorylation patterns on MKI67 reveals a high degree of G2/M-phase by guest on April 7, 2020 phosphorylation of MKI67, with 11 unique phosphopeptides being enriched more than two-fold in G2/M. This suggests a high degree of upstream kinase regulation on MKI67, which corresponds to its role in cellular proliferation (Fig 3I).
We then utilized a motif-matching enrichment analysis to gain insights into which phosphopeptide patterns were cell-cycle dependent. We used a motif recognition algorithm in Perseus, with Perseus' integrated motif library which mostly contains known kinase motifs; both general motifs for kinase families, as well as specific kinase motifs (24).

Subcellular fractionation reveals cell cycle-dependent protein relocalization.
To analyze protein translocation events, we performed a simple subcellular fractionation on cell cycle-sorted HeLa-Fucci cells (Fig 4A, Suppl Fig 4A). Using digitonin as a mild detergent enabled us to separate the cytosol (soluble fraction) from cellular organelles, nuclei and membranes (insoluble fraction). Cellular components of the two fractions were validated using GSEA on GO cell cycle (GO_CC) Terms (Suppl Fig 4B, Suppl Table 4).
TMT-based proteomics analyses of both fractions detected ~4500 proteins. ANOVA analysis retrieved 1393 significantly oscillating proteins in the soluble and 2000 significantly oscillating proteins in the insoluble fraction (Fig 4A, Suppl Fig 4C). Tricomp visualization of the two fractions revealed clearly distinct patterns. Soluble proteins were mostly enriched in S phase, while insoluble proteins were more prevalent in G1 and G2/M (Fig 4B, C). We used these oscillation patterns to screen for possible translocation events by looking at proteins that oscillated diametrically opposite in the soluble and insoluble fraction but showed no extensive variation (<20% change) in the total proteome. This identified 169 potential cell cycle-dependent solubility-changing proteins (Fig 4D). The essentiality score on this group of proteins revealed a strong enrichment for essential proteins (Fig 4E), underscoring the functional significance of these events. Characterizing by guest on April 7, 2020 these 169 proteins disclosed multiple overarching trends with different kinds of solubleinsoluble relationships (Fig 4F), some indicating a cell-cycle dependent translocation between subcellular compartments, and some indicating a change in binding configuration.
We performed GSEA on a broad categorization of these distributions, using the phase they  Table 2).  replication. We can also see NUP93, a nuclear-membrane-associated protein becoming more soluble in G2, possibly due to a fragmentation of the nuclear membrane in mitosis.
The shortlist of potential translocating proteins that are also classified as essential can be subdivided into four broad groups, depending on specific phases (Fig 5B). One of these groups, which becomes more insoluble during S-phase, is composed of just five proteins,  Fig 5C). For example, MCM3 is dephosphorylated on many different serine sites during S-phase. This dephosphorylation is in accordance with a previous report describing negative regulation of MCM3 activity by the cell cycle regulator Chk1 (26). The highest regulation of MCM3 was on Ser728, a site which has been described by (27) to be phosphorylated by ATM in response to DNA damage (Fig 5D). Similarly, we can find the highest regulations of MCM2 and MCM4 in the literature (28, 29) ( Fig 5D).

MAT2A nuclear localization is enriched in S-and G2/M-phase
MAT2A is one of the proteins that do not oscillate over the cell cycle in total abundance but undergoes significant changes in oscillation patterns of the soluble and insoluble fractions. This protein is involved in methyl donor production and was previously found to have a dynamic nuclear localization (30). We identified increasingly insoluble MAT2A in S-phase and G2/M-phase cells, with a corresponding decline of soluble MAT2A in these phases (Fig 6A). We confirmed these findings by independent immunofluorescence experiments, using a machine-learning-based classifier in CellProfiler Analyst (v2.2.1) to categorize MAT2A localization. We identified a strong correlation between MAT2A nuclear localization and S+G2 cell cycle phase using Fucci categorization (Fig 6B-D).
Arresting cells prior to replication (but post G1/S-checkpoint) using two inhibitors of DNA replication (Aphidicolin and Camptothecin), increased the proportion of cells with nuclear MAT2A (Fig 6D, Suppl Fig 5B, C). We can thus conclude that passing the G1/S checkpoint is in itself sufficient to facilitate the nuclear translocation of MAT2A, without active replication or entry into G2 phase. by guest on April 7, 2020 20

Discussion
The importance of understanding the cell cycle on a proteomic level has only recently become the focus of intense research, as methods to measure protein stability have been employed on cell cycle separated cell lines (31,32). Additionally, the complexity of the relationship between transcriptomics and proteomics has been highlighted, and in many cases, transcriptomic data cannot confidently be used to predict protein dynamics (33).
With the current study, we provide a system-level understanding of the cell cycle in 21 mRNA identified as strongly upregulated in G2 might not actually be relevant for the G2phase, but might be preparatory for G1-requisite protein translation instead. Overall, it is clear that with a mixed cell cycle population such as with tissues or cell samples, the mRNA abundance is a reliable predictor of steady-state protein content (1). However, for the finetuned temporal dynamics of the cell cycle, the delay between transcription and translation is oscillatory. As such, the cell will start synthesizing an mRNA in one cell cycle phase in preparation for protein translation in the next phase, especially during the transcriptional hiatus of mitosis. This highlights the need to re-assess the cell cycle classifications of proteins using protein-based techniques, such as western blot or mass spectrometry, rather than relying solely on transcriptomics data.
The thymine DNA glycosylase TDG is not cell cycle associated using gene ontology (GO) enrichment analysis, however it was described to be degraded by the UPS in cells entering S-phase. The authors argue that this maintains a separation of function between TDG and the uracil DNA glycosylase UNG2 which peaks during S-phase (37). While UNG2 was not detected in our dataset, we can confirm the G1 prevalence of TDG, and UNG is upregulated in S and G2/M phases. However, TDG was also linked to DNA demethylation raising the possibility that it is also implicated in regulating gene expression (38). The last reason is related to translocation events between subcellular compartments. We identified and confirmed a distinct translocating phenotype of the S-adenosylmethionine synthase MAT2A. This methyl transferase was previously reported to be localized to the nucleus as well as to the cytosol, but the functional significance was unknown (30). We show here that MAT2A is enriched in the nucleus of cells undergoing replication and during the subsequent G2-phase. We suggest that the role for this translocation is due to the high methylation requirement inside the nucleus during S-phase for both DNA and histone methylation processes. While the highest requirement of methyl donor is during Sphase, recent evidence (44) suggests that the copying of histone methylation patterns is performed throughout G2 phase. In addition, methylation is required during G2 phase to facilitate heterochromatin formation (45). MAT2A and methionine metabolism was just recently found to be a critical feature of tumor initiating cells (46). Together, this provides a plausible explanation as to why MAT2A nuclear localization persists until mitosis. We speculate that localized nuclear S-adenosylmethionine is essential to provide materials for the copying of epigenetic methylation patterns in proliferating cells.
Throughout our characterizations of protein abundance, phosphorylation and translocation patterns, we investigated the essentiality of the hit lists using CRISPR/Cas9 survival screening data, and we could identify a clear correlation between the level of regulation over the cell cycle and essentiality for cellular proliferation. This supports the argument that essential genes are more likely to have specific cell cycle-dependent functions. Thus, by guest on April 7, 2020 the essentiality score itself is a useful tool to narrow large hit lists and identify proteins that are also proven to be functionally necessary for cell division, and to filter out candidates that are regulated but not functionally essential.