We first sought to quantitatively profile the spatial distribution of centromeres in human cells (Figure 1). We used HTI to visualize endogenous centromeres in eight human cell lines from different tissues and with distinct proliferation properties, including immortalized retinal pigment epithelium RPE1 cells, immortalized human HFF fibroblasts, the induced pluripotent stem cell (iPSC) line WTC-11, and several cancer cell lines of different origin (Supplementary file 1). Some of these cell lines contain numerical aberrations of chromosomes (Giard et al., 1973; Kotecki et al., 1999), which were taken into account when setting the baseline for the quantitative analysis of centromere distribution in individual cell lines. Centromeres were visualized by indirect immunofluorescence (IF) for the integral kinetochore component CENP-C, which localizes to centromeres at all stages of the cell cycle and completely colocalized throughout the cell cycle with the centromere protein CENP-A (Hori et al., 2008; Klare et al., 2015; Figure 1—figure supplement 1a). CENP-C was used as a marker for centromeres in all subsequent experiments.

We quantified the number of centromere spots per nucleus and analyzed centromere spatial distribution in the nucleus by HTI in several thousand cells per cell line by using HiTIPS, an open-source HTI analysis platform that accurately segments nuclei and centromeres in large HTI image datasets (Figure 1—figure supplement 1b; Keikhosravi et al., 2024). For each cell, we measured the number of centromeres per nucleus as spot count and also derived a centromere clustering score, which measures the overall distribution of centromeres in the nucleus (Figure 1—figure supplement 1c and d, also see Methods). The clustering score is a metric derived from the Ripley’s K function, which we established in pilot experiments as a robust and sensitive measure of centromere clustering (Keikhosravi et al., 2025a). The clustering score quantifies deviations of the centromere distribution from uniformly distributed spots and it is normalized to nuclear size. Importantly, the clustering score is robust to changes in the centromere spot number and thus accounts for any differences in centromere numbers in the various cell lines or due to aneuploidy (Keikhosravi et al., 2025b).

We observed significant qualitative differences in centromere distribution among human cell lines (Figure 1a). For example, in WTC-11 cells, centromeres were strongly clustered, in line with centromere association with the nucleolus observed in other human stem cells (Wiblin et al., 2005; Rodrigues et al., 2023). In contrast, A549 basal epithelial cells derived from lung cancer and MDA-MB-231 epithelial-like breast cancer cells showed noticeably less clustering than HFFs, which exhibited the most dispersed distribution among the cell lines tested (Figure 1a). These visual trends were confirmed by quantitative HTI analysis using the clustering score, which was highest for WTC-11 cells and lowest for HFFs (Figure 1b). These differences in clustering were unrelated to spot number or to nuclear area (Figure 1c and d). In addition, WTC-11 cells had the lowest and HFF cells the highest median population values for the mean normalized radial CENP-C distance, which represents the per-cell average distance of centromeres from the center of the nucleus (Figure 1e), consistent with the differential clustering behavior in these two cell lines. Statistical analysis of variance (ANOVA) indicated that most cell lines were significantly different from each other based on centromere clustering score or spot count (Supplementary file 2), indicating cell-type specificity of centromere distribution patterns. We also noted cell-to-cell variation for all centromere distribution parameters within the population (Figure 1b, c, and e), demonstrating single-cell heterogeneity of centromere distributions as previously observed for various other features of genome organization (Finn and Misteli, 2019). In conclusion, quantitative HTI analysis of centromere localizations in thousands of single cells shows that spatial patterns of centromeres in the human cell nucleus are cell-type specific.

Having established the heterogeneous and non-random nature of centromere clustering in the nucleus, we sought to identify the molecular basis for this phenomenon. To do so, we developed an arrayed HTI-based CRISPR-KO screening assay to identify regulators of the spatial distribution of centromeres (Figure 2a). For the screens, we designed an sgRNA library targeting 1064 genes encoding nuclear proteins, enriched in structural components of the nucleus, epigenetic modifiers, and components of the genome maintenance and expression machinery (for library composition, see Supplementary file 3). A non-targeting, scrambled sgRNA and sgRNAs targeting the non-expressing OR10A5 gene were used as negative controls for sgRNA transfection and for CRISPR-induced DNA damage response (Liu et al., 2019), respectively. In addition, as a positive control for sgRNAs transfection, we used sgRNAs against the essential PLK1 gene whose ablation results in rapid and extensive cell death (Schibler et al., 2023). As a positive control, we used sgRNAs targeting the condensin II complex component NCAPH2, whose silencing has previously been shown to induce clustering of centromeres (Hoencamp et al., 2021). In light of our observation that spatial patterns of centromere distribution can be different between cell lines, we performed screens in two cell lines, HCT116 and RPE1, which represent clustered and unclustered centromere patterns, respectively (see Figure 1a). For quantitative HTI analysis, we performed imaging-based phenotypic scoring of centromere distribution patterns using centromere spot count and the Ripley’s K-based clustering score as read-out parameters (Keikhosravi et al., 2025a). All CRISPR-KO screens were performed in biological duplicates and generated data from a few hundred to over a thousand cells per replicate for each target gene (Supplementary files 4 and 5).

The results of the screens indicated consistent phenotypic separation of the positive and negative controls (Figure 2—figure supplement 1a and b) and high reproducibility of hits in the two biological replicates for both cell lines (Figure 2—figure supplement 1c and d). We defined hits as sgRNA perturbations that altered either spot count or clustering by a Z-score of at least 2.5 units from the median phenotype of all sgRNAs included in the library (Supplementary file 3). Any sgRNA KOs that resulted in a large number of dismorphic and/or abnormally sized nuclei, as measured by nucleus compactness and/or nuclear area, were filtered out from subsequent steps of the analysis. We excluded from analysis sgRNAs which resulted in high cytotoxicity (cell number Z-score <–2.5), or which produced inconsistent results across the two biological replicates (see Methods).

Following these criteria, we identified 111 genes whose CRISPR-KO altered centromere distribution in HCT116 cells (Figure 2b, Supplementary file 4). Among these, 80% (89/111) altered the CENP-C clustering score, 41% (45/111) altered CENP-C spot count, and 20% (23/111) altered both parameters. The majority of hits (81%; 72/89) unclustered centromeres, whereas 19% (17/89) increased clustering (Supplementary file 4). Among the 23 genes that altered both parameters, six increased the clustering score and decreased spot count, indicating higher clustering, whereas the opposite trend was observed for four genes, indicating dispersion (Supplementary file 4). The remaining genes (13/23) concomitantly decreased the clustering score and spot count, suggesting global dispersion but local clustering of centromeres into fewer but larger local clusters (Supplementary file 4). A concomitant increase in both spot count and clustering score was not observed. The effects on centromere distribution did not correlate with changes in nuclear area (Figure 2—figure supplement 1e and f).

We similarly identified 113 hits when we performed the CRISPR-KO screen in RPE1 cells, which are characterized by a lower degree of centromere clustering than HCT116 cells (Figure 2—figure supplement 2, Supplementary file 5). Similar to HCT116 cells, we observed a nonlinear relationship of spot count and clustering score in RPE1 cells (Supplementary file 5). The majority (77%, 87/113) of identified sgRNAs altered centromere spot count, 40% altered the clustering score, while 17% altered both parameters (Figure 2c, Supplementary file 5). When analyzed using the clustering score, the majority (58%, 26/45) of hits dispersed centromeres, whereas the rest (42%, 19/45) increased clustering (Supplementary file 5).

Select hits were orthogonally validated using siRNA knockdown with a validation rate of 90% (27/30) (Figure 2—figure supplement 3a). Reassuringly, in line with known centromere-nucleoli association (Bury et al., 2020), several nucleolar proteins, including NPM1, NCL, and FBL, were identified as hits, confirming the validity of our screening approach. In addition, our positive control NCAPH2, represented in the library, and another condensin II component NCAPD3 were strong hits in both cell lines and in all replicates of the screen. We identified both essential and non-essential genes, and only very few hits altered cell-cycle distribution, indicating that the hits were not due to secondary effects on the cell cycle or cell viability (Figure 2—figure supplement 3b and c). In addition, centromere clustering levels were generally similar in G1, S, and G2/M phases for most hits compared to scrambled control, except for a handful of cases where the pattern changed upon knockdown of target genes (Figure 2—figure supplement 3d).

A comparative analysis of the CRISPR-KO screen results in HCT116 and in RPE1 cells indicated that KO of most genes similarly altered centromere distributions in both cell lines, but that the extent of change (Z-score) could vary depending on the initial state of centromere distribution (R=0.47, p<10^–10, Figure 2c). We identified 52 genes that alter centromere distribution in both cell lines (Figure 2—figure supplement 4a). The majority of these genes altered phenotypes in the same direction for clustering score (79%, 41/52) and spot count (73%, 38/52) (Figure 2e). Only rare examples of cell-type-specific opposite effects were observed (Figure 2e and Figure 2—figure supplement 4b). Similarly, we identified genes that altered centromere distribution in only one cell line (Figure 2—figure supplement 4, Supplementary files 4 and 5). Taken together, these data identify both conserved and cell-type-specific regulators of centromere distribution.

To gain insights into the functions of the common hits, we used STRING analysis which identifies pathways based on known physical and genetic interactions (Figure 2d; Szklarczyk et al., 2025). Based on this analysis, centromere distribution modifiers were grouped into six categories: regulators of chromatin structure, kinetochore proteins, nucleolar proteins, nuclear pore complex components, replication factors, and transcription-associated factors (Figure 2d). Interestingly, while KO of most replication- and nuclear pore-associated genes increased clustering, KO of kinetochore components and transcription-associated factors led predominantly to centromere dispersion in both cell types (Figure 2e and Figure 2—figure supplement 4b). Loss of proteins implicated in chromatin structure or the nucleolus either clustered or dispersed centromeres in a gene-specific manner (Figure 2e and Figure 2—figure supplement 4b). It is also important to note that factors grouped into these broad classes may perform functions in multiple categories. For example, the NUP107-160 complex, which is a prominent structural component of the nuclear pore, is known to also interact with kinetochores (Orjalo et al., 2006). Taken together, these results identify major regulators of spatial centromere organization.

The identified regulators of centromere distribution are involved in diverse cellular functions and pathways, suggesting multilayered control of centromere distribution. To gain mechanistic insight, we first asked whether the identified regulators act at particular points in the cell cycle. To establish a baseline for analysis, we quantitated centromere distribution in G1, S, and G2/M of cell-cycle staged HCT116 and RPE1 cells based on DAPI and EdU pulse labeling as described before (Salic and Mitchison, 2008; Bruhn et al., 2014; Roukos et al., 2015; Figure 3a and b). As expected, due to the duplication of the genome during replication, the number of detectable centromere spots increased in S-phase cells (p=0.009) and was highest in G2/M HCT116 cells (p<10^–10; Figure 3c). A marginal increase in clustering score was observed in G2/M cells compared to G1 cells (Figure 3d; p=0.002), while radial positioning of centromeres remained mostly unchanged except for a small increase in G1 cells (Figure 3e; p=0.004). A similar trend was observed in RPE1 cells for all three parameters (Figure 3c, d, and e). As expected, the nuclear area was significantly increased in S and G2/M in both HCT116 and RPE1 cells (Figure 3f, p<10^–10). The lack of strong correlation between nuclear area and clustering score within G1, S, or G2/M subpopulations (R<0.3) indicates that increased clustering scores in G2/M cells are unrelated to nuclear size increase (Figure 3g). We conclude that, in line with observations on radial position of genomic loci (Shachar et al., 2015) and of chromosome territories (Jowhar et al., 2018), the overall distribution of centromeres does not vary strongly within the interphase of the cell cycle.

To specifically ask whether the identified hits required progression through the cell cycle, we performed siRNA knockdown of 30 select hits in asynchronous cells or in cells that were either arrested at the G1/S boundary by standard double thymidine block (Chen and Deng, 2018) or at the G2/M boundary by treatment with the CDK1 inhibitor RO-3306 as previously described (Vassilev et al., 2006; Figure 3g; see Methods). Loss of cell viability upon transfection of siDeath, which simultaneously targets several essential genes, as compared to siScrambled (Figure 3—figure supplement 1a), and reduction in NCAPH2 protein upon transfection of siNCAPH2 (Figure 3—figure supplement 1b) indicates efficient siRNA knockdown in cycling, G1/S, and G2/M cells. While centromere distribution was altered upon knockdown of these genes in cycling cells as expected, no changes in centromere distribution were observed when knockdowns were done in G1/S or G2/M arrested cells (Figure 3h). We conclude that while the distribution of centromeres does not vary during the cell cycle, progression through the cell cycle is required to bring about changes in centromere distribution in the absence of key regulators of centromere clustering. These results demonstrate that the identified modifiers of centromere distribution do not act in the maintenance of centromere distribution during interphase.

Having established that cell-cycle progression is required for the effects of the identified centromere distribution factors, we asked at what stage of the cell cycle the centromere distribution factors act. We measured changes in clustering score before and after progressing through either S-phase or mitosis in cells depleted of a given factor (Figure 4a). We selected four proteins for this analysis that all interact with centromeres and contribute to efficient and error-free chromosome segregation during mitosis but all have distinct functions: NCAPH2 is a component of the Condensin II complex and responsible for axial compaction of chromosomes (Shintomi and Hirano, 2011; Green et al., 2012; Gibcus et al., 2018); KI67 is an established marker of cell proliferation that decorates nucleoli in interphase cells and coats chromosomes during mitosis (Booth et al., 2014; Cuylen et al., 2016); SPC24 and NUF2 are kinetochore components and part of the NDC80 complex which connects the kinetochore to microtubules (McCleland et al., 2004; Ciferri et al., 2008). Auxin-inducible degron cell lines to deplete NCAPH2 or KI67 have previously been characterized (Takagi et al., 2018). In addition, we generated dTAG-SPC24 and NUF2-dTAG cell lines by CRISPR knock-in into HCT116-Cas9 parental cells (Figure 4—figure supplement 1a and b; Methods). Homozygous knock-in in select clones was verified by PCR genotyping (Figure 4—figure supplement 1c and d), and correct localization and expression of dTAG-SPC24 and NUF2-dTAG as compared to the parental cell line was confirmed by indirect IF staining of the tagged proteins and by western blotting, respectively (Figure 4—figure supplement 2a, b, e, and f; McCleland et al., 2004). Effective depletion of each factor by more than 90% as assessed by western blotting was achieved within 3 hrs (Figure 4—figure supplement 2c and d).

Using these four HCT116-based degron lines, we acutely depleted individual factors specifically in cells arrested at the G1/S or G2/M boundaries and then released the cell-cycle block (see Methods). We validated effective depletion of SPC24 and NUF2 in G1/S and G2/M arrested cells (Figure 4—figure supplement 3a, b, c, and d). First, we compared clustering scores in cells progressing through S-phase in the presence or absence of NCAPH2, KI67, SPC24, or NUF2 (Figure 4a). Upon release from a standard double thymidine block, the majority (58–78%) of cells reached G2/M after 6 hrs (Figure 4—figure supplement 4a and c). Progression through S-phase was equally efficient in the presence or absence of KI67, SPC24, or NUF2 (Figure 4—figure supplement 4a and c). Acute NCAPH2 depletion mildly delayed S-phase progression (Figure 4—figure supplement 4a and c), as reported earlier (Rodemoyer et al., 2025). Although loss of KI67 has been reported to delay replication of centromeres and pericentromeric loci (van Schaik et al., 2022; Stamatiou et al., 2024), no effect on bulk S-phase progression after KI67 loss was observed in our hands (Figure 4—figure supplement 4a). No effect on clustering scores was evident as cells progressed through S-phase into G2, regardless of the presence or absence of any of these proteins (p>0.05; Figure 4b and c). We conclude that these centromere distribution modifiers do not act in S-phase.

Next, we tested if the loss of function of these centromere distribution modifiers during mitosis altered centromere localization in the subsequent interphase cells (Figure 5a). HCT116 cells were arrested at the G2/M boundary by treatment for 20 hrs with the CDK1 inhibitor RO-3306 as previously described (Vassilev et al., 2006), and then released for 6 hrs in the absence of each mitotic factor. The newly formed G1 cells were analyzed for centromere distribution. As expected, mitotic progression in the absence of SPC24 or NUF2 was slowed upon release from the G2/M block (McCleland et al., 2004) with 28–35% of cells reaching G1 after 6 hrs compared to 58–60% in the presence of SPC24 or NUF2 (Figure 4—figure supplement 4b and d) and some aberrant nuclear phenotypes were evident in the absence of SPC24 or NUF2 (Figure 5—figure supplement 1a, b, c, and d). Mitotic progression in the absence of NCAPH2 and KI67 was similar to that of control cells (Figure 4—figure supplement 4b and d). While the centromere distribution phenotypes remained unaltered compared to cycling cells in the presence of these proteins, progression through a single mitosis in the absence of any of these proteins altered centromere distribution phenotypes in the subsequent G1 phase as assessed by quantitation using the Ripley’s K clustering score (Figure 5b and c) and visual inspection (Figure 5d). Loss of NCAPH2 had the largest effect and resulted in increased clustering of centromeres in G1 cells (Figure 5c; p<10^–10). Similarly, progression through mitosis in the absence of KI67 reduced clustering in the newly forming G1 cells (p=1.27e-08), as did loss of SPC24 (p<10^–10) or NUF2 (p<10^–10) (Figure 5c). We conclude that the function of NCAPH2, KI67, SPC24, and NUF2 during mitosis determines centromere distribution patterns in the newly formed daughter nuclei. The fact that loss of proteins with distinct mitotic functions perturbs centromere organization in the subsequent G1 phase suggests that, rather than their specific mitotic functions, it is the orderly progression of cells through mitosis that is required to ensure the faithful maintenance of spatial centromere distribution.

Since all four factors act during mitosis but had different effects on centromere distribution, we explored co-depletion phenotypes to understand functional overlap between these factors, if any. We combined siRNA knockdown and degron-based depletion of NCAPH2, KI67, or SPC24 in pairwise combinations along with scrambled siRNAs and non-depleted cells as controls (Figure 5e). We observed additive effects as simultaneous loss of SPC24 and KI67 further reduced the clustering score than the individual loss of either KI67 (p=5.0640e-47) or SPC24 (p=5.8800e-03). Similarly, the centromere unclustering upon KI67 knockdown was rescued by simultaneous NCAPH2 depletion (Figure 5f; p=3.2280e-10). In contrast, centromeres did not cluster more when NCAPH2 was either knocked down (p=5.9760e-56) or depleted (p=3.2280e-10) in the absence of SPC24 (Figure 5f), indicating that SPC24 functions upstream of NCAPH2 in regulating spatial centromere position. These findings point to an intricate interplay of these factors and pathways in determining centromere positioning.

We finally asked whether the aberrant altered centromere distribution in daughter cells upon depletion of mitotic factors can be reversed upon re-expression of NCAPH2, KI67, or SPC24. To test this idea, each of these factors was depleted for 6 hrs in asynchronous cells following washout of degron ligands to allow re-expression of NCAPH2, KI67, or SPC24 as they progress through the cell cycle for 24 hr. Cells with and without depletion are used as controls. We observed partial rescue upon re-expression of all three factors as clustering scores partially returned toward that of the unperturbed cells (Figure 5g).

Taken together, these findings demonstrate a requirement for orderly progression through mitosis for the faithful establishment of the spatial distribution of centromeres and global genome organization in the subsequent interphase nuclei.

All cell lines used in this study are grown in a humidified 37°C incubator in the presence of 5% CO_2. Sources, composition of growth media, relevant references of the respective cell culture protocols are provided in Supplementary file 1. Cells were fixed with 2% paraformaldehyde (PFA, Electron Microscopy Sciences, Cat. No. 15710) solution in media by adding one equal volume of 4% PFA solution in PBS to the cell growth medium in each well for 15 min at room temperature. To activate protein degradation, 500 nM auxin (Millipore Sigma, Cat. No. I3750-25G-A) or 1 µM dTAG13 (Tocris Bioscience, Cat. No. 6605) or 1 μM dTAG^V-1 (Tocris Bioscience, Cat. No. 6914) ligand was used. The identity of all cell lines has been authenticated by sequencing and tested periodically for absence of mycoplasma. Details on cell lines are provided in the Methods section. Cell lines: FLAG-dTAG-SPC24 (this study); NUF2-dTAG-FLAG (this study); NCAPH2-mACh Takagi et al., 2018; KI-67-mACl Takagi et al., 2018; HCT116 Cas9 Hart et al., 2015; A375 Cas9 Hart et al., 2015; hTERT-RPE1 Cas9 Hart et al., 2015; WTC-11 (Coriell Institute, GM25256); MDA-MB-231 Cas9 (Horizon Discovery, HD Cas9-014); HAP1 Cas9 (Horizon Discovery, HD Cas9-011); A549 Cas9 (Horizon Discovery, HD Cas9-001); and HFF-hTERT (Clone 6 4DNucleome project cell line).

Cells were synchronized at the G1/S boundary by double thymidine block as described (Chen and Deng, 2018). Briefly, cells were seeded at 20–30% confluency and grown for 24 hrs following 18 hrs of growth in media containing 2 mM thymidine (Sigma, Cat. No. T9250-5G). Cells were then washed with PBS and grown in fresh media for 9 hrs. The growth media was changed with fresh media containing 2 mM thymidine. Cells were synchronized at the G2/M stage by treating cells with 9 µM RO-3306 (Millipore Sigma, Cat No. 217721-2MG) for 20 hrs as described (Vassilev et al., 2006). Cells were once washed in 300 µL fresh prewarmed growth media and replenished with fresh growth media to release from the G1/S or G2/M block.

Indirect IF staining was performed as previously described (Keikhosravi et al., 2024; Keikhosravi et al., 2025b). Briefly, fixed cells grown on a 96-well plate (Revvity, Cat. No. 6055300) or 384-well plate (CellVis, Cat. No. P384-1.5H-N) were washed with PBS and then permeabilized with 0.1% Triton X-100 (Sigma, Cat. No. T9284-500ML) solution in PBS for 15 min and again washed with PBS. These cells were blocked by incubating with 5% BSA (Millipore Sigma, Cat. No. A3294-100G) solution in PBST (0.05% Tween-20 in PBS) for 15 min at room temperature. Next, cells were incubated with appropriate primary antibody dilution prepared in blocking solution (5% BSA in PBST) for 1 hr at room temperature and then washed with PBS three times. Next, the cells were incubated for 1 hr with fluorescently labeled secondary antibody solution prepared in blocking solution at room temperature and then washed with PBS three times. DAPI (4′,6-diamidino-2-phenylindole) (Thermo Fisher Scientific, Cat. No. 62248) staining was performed by adding 5 µg/mL DAPI solution prepared in 1× PBS to the wells. Anti-CENP-C (MBL Biosciences, Cat. No. PD030) and anti-CENP-A (Abcam, Cat. No. AB13939) antibodies were diluted 1:1000 in blocking buffer (5% BSA solution prepared in PBST) and used for IF staining. In experiments where cells were EdU labeled, CENP-C primary antibody was directly conjugated with fluorophores using Mix-n-Stain CF Dye Antibody Labeling Kit (Biotium, Cat. No. 922235). Anti-FLAG monoclonal antibody (Sigma, Cat. No. F3165250) was diluted 1:250 in blocking buffer and used for IF staining.

A custom arrayed synthetic sgRNA library targeting 1064 genes associated with chromatin biology and nuclear architecture was sourced from Synthego (Cat # SO17105 and 8311960), delivered lyophilized in 96-well plates, resuspended in RNAse-free ddH₂O, and reformatted in 384-well plate format using the PerkinElmer Janus and the Beckman Coulter ECHO525 liquid handlers at a final concentration of 0.25 pmoles/µL. Each gene was targeted in the same well by three pooled sgRNA oligos that included the Synthego-modified EZ Scaffold. The list of genes and sgRNA targeting sequences in the library is included in Supplementary file 3.

For reverse transfection of sgRNA oligos in 384-well format, 325 nL of library sgRNA 0.25 pmoles/µL were spotted in each empty well (0.08 pmoles/well) of an imaging plate (CellVis, Cat. No. P384-1.5H-N) using an ECHO525 acoustic liquid handler. As controls, and in each plate, we also spotted 7 wells each of non-targeting scrambled control sgRNA (Synthego, Cat. No. 063-1010-000-000), sgRNAs targeting each of PLK1, OR10A5, and NCAPH2. Three sgRNAs pooled together in gene KO kits to target PLK1, OR10A5, and NCAPH2 were obtained from Synthego (Cat. No. GKO-HS1-000-0-1.5n-0–0). The control sgRNAs had the same chemistry and were spotted in the same quantities as the sgRNAs in the library. Spotted plates were dried at room temperature under a laminar flow cell culture hood, sealed, and stored at –30°C until the day of the reverse transfection.

The day of the transfection, the spotted sgRNA imaging plates were thawed and equilibrated at room temperature and then spun at 1400 rpm. The seal was removed, and 20 µL of prewarmed serum-free Opti-MEM media (Thermo Fisher Scientific, Cat. No. 31985070) was dispensed into each well for the imaging plate using a Thermo Fisher Multidrop dispenser. The ECHO525 was then used to dispense the required amount of Lipofectamine RNAi MAX (Thermo Fisher Scientific, Cat. No. 13778075). The plates were then incubated at room temperature for 30 min to allow RNA-Lipofectamine complexes to form. Next, a cell suspension prepared in prewarmed Opti-MEM media (Thermo Fisher Scientific, Cat. No. 31985070) containing 20% FBS was dispensed into each well using the Multidrop for a total volume of 40 µL and an effective final sgRNA concentration of 2 nM. Plates were incubated at room temperature inside a laminar airflow hood for 30 min before they were transferred into cell culture incubator and allowed to grow for 72 hr. CRISPR-KO screens were performed each in 2 biological replicates on different days.

384-Well plates containing fixed cells were then stained for CENP-C and DAPI and imaged using a Yokogawa CV8000 spinning disk confocal microscope. Imaging parameters were as described before (Keikhosravi et al., 2024). Briefly, IF images were collected on a multi-laser platform equipped with 405 nm (DAPI), 488 nm (for green fluorophores), 561 nm (for red fluorophores), and 640 nm (for far-red fluorophores) excitation lines that were combined through a 405/488/561/640 nm quad-band dichroic. Fluorescence was captured through a 60× water-immersion objective (NA = 1.2) and routed to either a 445/45 nm band-pass filter for DAPI or a 525/50 nm band-pass filter for green fluorophores, or 600/37 nm band-pass filter for red fluorophores or 676/29 nm band-pass filter for far-red fluorophores. A 16-bit sCMOS detector (2048×2048 pixels, 1×1 binning; effective pixel size = 0.108 µm) recorded Z-stacks with 1 µm steps, while real-time maximal projection was applied. A variable number of fields ranging from 9 to 22 were imaged per well in different imaging experiments to acquire sufficient number of cell images.

Image analysis was performed as described (Keikhosravi et al., 2025a). Raw image stacks were processed using HiTIPS, our previously described high-content analysis pipeline for fixed- and live-cell assay (Keikhosravi et al., 2024). Max-projected DAPI channels provided nuclear masks, whereas CENP-A or CENP-C projections served for centromere spot localization (see Figure 1—figure supplement 1B). Analysis settings in HiTIPS were adjusted to the typical nuclear diameter and the intensity/size characteristics of centromere foci. Nuclear segmentation employed the GPU-accelerated CellPose algorithm (Pachitariu and Stringer, 2022), and centromere detection used a Laplacian-of-Gaussian approach. Final spot coordinates were defined as the centroid of each segmented focus. Average nuclear fluorescence intensity for the DAPI (405 nm) and EdU (640 nm) channels was measured at the single-cell level. Clustering scores were calculated using a metric derived from Ripley’s K function as described (Keikhosravi et al., 2025b).

Single-cell data obtained from HiTIPS (nucleus area, number of CENP-C spots per cell, Ripley’s K clustering score, mean normalized CENP-C spot radial distance) were averaged on a per-well level. Per-well average values and nuclei counts were then used as an input for the screen statistical analysis using R 4.3.3 (R Development Core Team, 2024) and the cellHTS2 package (Boutros et al., 2006). Briefly, per-well raw measurements were normalized on a per-plate basis using the median value of the sgRNA library treatments and the Z-score method. All the per-plate normalized values in different library plates originated from a single biological replicate were further standardized using a robust version of the Z-score. The Z-score values for the same well and plate combination in different biological replicates were then averaged to obtain a mean Z-score.

Screen hits were identified as genes whose KO resulted in a mean Z-score either higher than 2.5 or lower than –2.5 for either number of CENP-C spots per cell or Ripley’s K clustering score. Nuclei that were either abnormally shaped (solidity<0.85) or were abnormally small (area<30 µm²) indicative of micronuclei were not used for analysis. We also excluded sgRNAs that resulted in high cytotoxicity (cell number Z-score<–2.5) or whose mean Z-score was smaller than the Z-score standard deviation of two replicates.

EdU labeling was performed using a kit (Thermo Fisher Scientific, Cat. No. C10340) as per the manufacturer’s instructions. Briefly, cells were incubated with 1 µg/mL EdU for 45 min before fixation. Fixed cells were permeabilized and blocked as described for IF staining protocol. Next, genome-incorporated EdU molecules during replication were fluorescently labeled by performing a click chemistry reaction for 30 min at room temperature. Subsequently, cells were washed twice with 1% BSA solution in PBST and stained with 5 µg/mL of DAPI solution in PBS at room temperature for 1 hr.

Total nuclear fluorescence of the DAPI and EdU channel was quantified using HiTIPS (Keikhosravi et al., 2024). The quantitation data was processed downstream using R packages and log2-transformed DAPI integrated intensity was used to distinguish the G1 and G2/M subpopulations. Similarly, EdU-integrated fluorescence intensity was log2-transformed, and cells with detectable EdU intensity were classified as S-phase cells. Cells with DAPI intensity either higher than G2 cells (>4N population) or lower than G1 cells (subG1 population) were not analyzed.

Candidate guide RNAs targeting the N-terminus of SPC24 and C-terminus of NUF2 were designed using sgRNA Scorer 2.0 (Chari et al., 2017) and CRISPRor (Concordet and Haeussler, 2018; Supplementary file 6). Briefly, candidate guide RNAs were in vitro transcribed and tested for cutting activity in cells, using an approach previously described (Gooden et al., 2021). Based on both the highest indel frequency, as well as proximity to the point of insertion, candidate 207 was selected for SPC24, and candidates 286 and 289 were selected for NUF2. Oligonucleotides corresponding to these three guide RNAs were phosphorylated, annealed, and ligated into either the pX458 backbone (Addgene #48138) (Ran et al., 2013) for SPC24 (pJT142) or pDG458 (Addgene #100900) (Adikusuma et al., 2017) for NUF2 (pMG1040). pSpCas9(BB)-2A-GFP (PX458) was a gift from Feng Zhang (Addgene #48138). Plasmid pDG458 was a gift from Paul Thomas (Addgene #100900).

To generate the homology-directed repair donors for construction of dTAG-SPC24 and NUF2-dTAG cell lines, first, DNA fragments with 5’ and 3’ homology arms were synthesized using Twist Biosciences and subsequently cloned into the pGMC00018 using isothermal assembly (Gibson et al., 2009) to generate intermediate constructs pJT147 (SPC24) and pMG1063 (NUF2). Subsequently, the Puromycin-2A-dTAG-3X-FLAG and 3X-FLAG-dTAG-2A-Puromycin cassettes were then PCR amplified from an existing plasmid (contains 3X-FLAG tagged version of dTAG, generated from Addgene #91796 or #91793) and then cloned into pJT147 to generate pJT152 and pMG1063 to generate pMG1064, respectively, using isothermal assembly approach. The oligonucleotides used to generate these constructs are listed (Supplementary file 6). All plasmids were sequenced completely using nanopore sequencing.

To generate the dTAG-SPC24 homozygous knock-in line, HCT116 Cas9 cells were co-transfected with two constructs: plasmid pJT142 encoding sgRNAs targeting the SPC24 locus and Cas9, and pJT152 encoding a donor template to introduce dTAG-FLAG and puromycin resistance using Lipofectamine LTX with Plus reagent (Thermo Fisher Scientific, Cat. No. 15338100) as instructed by the manufacturer. Similarly, to generate NUF2-dTAG homozygous knock-in line, HCT116 cells were co-transfected with pMG1040 encoding sgRNAs targeting NUF2 locus and Cas9, and pMG1064 encoding a donor template to introduce FLAG-dTAG and puromycin resistance. 24 hrs after transfection, cells were washed with fresh media and grown in fresh media for 24 hr. Next, cells were selected for puromycin resistance by growing them in the presence of 1.5 µg/mL puromycin (Thermo Fisher Scientific, Cat. No. A1113803) for 72 hr. Every 24 hr, old media was replaced with fresh media containing puromycin. Cells were further expanded in the presence of puromycin. Depending on the efficiency of transfection, cells took 5–7 days to achieve confluence. The cells were then harvested by using trypsin (Gibco, Cat. No. 15050065). A portion of the cells was frozen to generate a polyclonal stock, and the remaining cells were taken forward for single-cell cloning.

To generate single-cell clones, 500 cells were plated on a 15 cm dish and allowed to form colonies in the presence of puromycin. Single colonies appeared in 7–10 days. To isolate single colonies, 20–30 single colonies were selected, and a cloning cylinder was placed around them. The bottom of the cloning cylinder was sealed with grease. For clone isolation, 20 µL trypsin was added to each cylinder and cells from individual colonies were resuspended in separate wells in a 96-well plate to recover single-cell clones.

Genomic DNA from single-cell clones was isolated using genomic DNA purification kit (Thermo Fisher Scientific, Cat. No. K0512) as recommended. To identify homozygously tagged dTAG-SPC24 clones, KG49 (5'AGCTCAGACTTACAGGCGTG3') and KG76 (5'TGATGGTGCTGATGGTTGCA3') primers were used to amplify the genomic region flanking the site of integration at the SPC24 locus. This primer pair is expected to amplify a 2925 bp fragment from the tagged allele and an 1821 bp fragment from the untagged allele (Figure 4—figure supplement 1a and c). Similarly, homozygously tagged NUF2-dTAG clones were identified by PCR analysis using primer pair KG37 (5’CTGCTTTTCTTCCCCCACTG3’) and KG64 (5’AGAGGCAGCCTTTTCTCTGA3’). NUF2-dTAG alleles produced a 3046 bp amplicon while the untagged allele produced an amplicon of 1943 bp length (Figure 4—figure supplement 1b and d).

Two PCR-confirmed single-cell clones were used for western blot analysis to validate depletion of FLAG-dTAG-SPC24 or NUF2-dTAG-FLAG. Cells were grown in the presence of 1 µM dTAG13 ligand for 3, 6, and 9 hrs. Cells grown in the absence of the ligand were grown as a control. Protein samples were prepared using Bio-Rad lysis buffer (Bio-Rad, Cat. No. 1610747) as per the manufacturer’s instructions. Briefly, 10⁶ cells were collected, washed with PBS, and lysed in 100 µL lysis buffer (Bio-Rad, Cat. No. 1610747) to isolate proteins.

Anti-FLAG monoclonal antibody (Sigma, Cat. No. F3165) was diluted 1:10,000 in blocking buffer (5% BSA solution prepared in TBST) was used for detection of FLAG-dTAG-SPC24 and NUF2-dTAG-FLAG in western blots. For loading control, anti-beta-actin antibody (Millipore Sigma, Cat. No. A2228) was diluted 1:25,000 in blocking buffer and used in western blots. For comparison between untagged SPC24 and FLAG-dTAG-SPC24 protein levels, antibody against SPC24 (gift from Todd Stukenberg lab) (McCleland et al., 2004) was used at 1:8000 dilution in blocking buffer. For comparison between untagged NUF2 and NUF2-dTAG-FLAG protein levels, antibody against NUF2 (Abcam, Cat. No. ab176556) was used at 1:2000 dilution in blocking buffer.

siRNA knockdown of a select panel of genes identified in the screen was carried out by reverse transfecting siRNAs (Supplementary file 7, list of siRNAs used). Briefly, 150 nL of 5 µM siRNA stock solution was spotted on the 384-well glass-bottom imaging plates using an Echo liquid handler following the addition of 20 µL Opti-MEM media (Thermo Fisher Scientific, Cat. No. 31-985-070) using a Multidrop dispenser. The required amount of Lipofectamine RNAiMAX (Thermo Fisher Scientific, Cat. No. 13778075) was added in each well using the Echo liquid handler and incubated for 30 min at room temperature to allow siRNA-Lipofectamine complex to form. Next, the required number of cells diluted in 20 µL Opti-MEM media containing 20% FBS (Gibco, Cat. No. 10082147) was dispensed into each well. The imaging plate was incubated for 72 hrs at 37°C in a cell culture incubator. Scrambled (Thermo Fisher Scientific, Cat. No. 4390846) and all-star cell death (siDEATH) (QIAGEN, Cat. No. 1027298) siRNAs were used as controls to optimize the amount of Lipofectamine RNAiMax reagent to achieve maximum transfection efficiency with minimum cytotoxicity.

Statistical analysis, including ANOVA, Tukey’s HSD test, and t-test with Bonferroni and FDR correction, was performed using ggpubr (https://github.com/kassambara/ggpubr, kassambara, 2026) and rstatix (https://github.com/kassambara/rstatix/releases, kassambara, 2025) packages in R version 4.3.2. Statistical significance of difference was denoted by stars where * indicates p≤0.05, ** indicates p≤0.01, *** indicates p≤0.001, and **** indicates p<0.0001.