IntroductionCleavage and polyadenylation is a fundamental process in mRNA processing, consisting of two coupled reactions: The endonucleolytic cleavage of pre-mRNA at the polyadenylation site (PAS) followed by the synthesis of a poly(A) tail at the 3′ end1. Notably, a single gene can generate multiple transcript isoforms through the usage of different PASs, a phenomenon known as alternative polyadenylation (APA)2. APA is widespread, with at least 70% of human protein-coding genes expressing APA isoforms3. APA is regulated by cis-acting elements within nascent mRNA and trans-acting factors, including four core protein complexes: Cleavage and polyadenylation specificity factor (CPSF), cleavage stimulation factor (CSTF), cleavage factor I (CFI), and cleavage factor II (CFII). These complexes, along with over 80 additional accessory proteins, interact with pre-mRNA through protein–protein and protein–RNA interactions4. In response to dynamic physiological and pathological conditions, 3′ end processing factors modulate PAS selection, thereby directing APA regulation5. For example, CFIm plays a critical role in maintaining distal PAS usage through the recognition of UGUA motifs6. Downregulation of CFIm typically shifts PAS usage toward proximal sites, leading to widespread 3′ UTR shortening7. In contrast, CSTF has been shown to enhance cleavage at proximal PASs in certain cellular contexts, further emphasizing the importance of identifying novel APA regulators8. Although several studies have successfully identified core 3′ end processing factors using conventional methods such as cellular fractionation, proteomic analysis, and UV cross-linking4,9,10,11, these approaches have limitations in detecting the full spectrum of regulatory proteins. How to systematically screen and identify previously unrecognized APA regulators within the vast human proteome remains a key challenge. Addressing this challenge requires a robust and versatile genome-wide platform.Genome-wide CRISPR knockout screening enables the simultaneous targeting of numerous genes, followed by functional selection to identify cells exhibiting phenotypic changes of interest. After applying selective pressure based on a research question, cells with the desired phenotype are enriched and analyzed using next-generation sequencing (NGS) to identify single guide RNAs (sgRNAs) that are enriched or depleted, thereby pinpointing candidate genes involved in the process under investigation12. However, a major limitation of applying this approach to APA regulation is that APA-related changes are not easily detectable through conventional phenotypic readouts, making it challenging to identify APA-regulating genes using CRISPR screens.A recent study demonstrated that APA regulates the expression and localization of CD47 by generating mRNA isoforms with different 3′ untranslated region (UTR) lengths13. When the distal PAS of CD47 is utilized, the resulting mRNA, termed CD47-LU (LU for long UTR), contains an extended 3′ UTR that serves as a scaffold for recruiting a protein complex, including the RNA-binding protein HuR and SET, to the site of translation. This interaction facilitates the association of SET with newly synthesized CD47 protein, promoting its translocation to the plasma membrane via activated RAC1. In contrast, when the proximal PAS is used, the resulting mRNA, termed CD47-SU (SU for short UTR), has a shorter 3′ UTR that lacks the sequence necessary for HuR and SET recruitment. Consequently, the CD47 protein produced from this transcript predominantly localizes to the endoplasmic reticulum (ER). Notably, CD47-SU has also been shown to play a specific role in stress-induced apoptosis, suggesting that APA-induced changes in CD47 localization contribute to its functional diversification13. These findings suggest that CD47 protein localization serves as a readout for APA and could be leveraged as a reporter system to study APA regulation.In this study, we developed an immunofluorescence-based method that simultaneously detects both cell surface and intracellular CD47 protein, providing a direct visualization of APA-dependent changes. By integrating this method with pooled genome-wide CRISPR screening, we identified several candidate APA regulators. Notably, our findings suggest that POLDIP2 may play a pivotal role in global APA regulation.Materials and methodsCell cultureHeLa cells (ATCC) and HEK293FT cells (ATCC) were cultured in Dulbecco’s modified Eagle’s medium (DMEM) (Gibco) supplemented with 10% fetal bovine serum (FBS) (Biowest), 100 U/ml penicillin, and 100 μg/ml streptomycin (Sigma-Aldrich) at 37 °C in a humidified incubator with 5% CO2. To generate a HeLa cell line stably expressing humanized Streptococcus pyogenes Cas9 (hSpCas9), HeLa cells were transduced with a pLenti-Cas9-BSD-based expression vector using lentivirus produced with the ViraPower Lentiviral Expression Systems (Thermo Fisher Scientific), following the manufacturer’s protocol. After 24 h, transduced cells were selected with 4 μg/ml blasticidin (Invivogen) for 2 weeks. Monoclonal cell lines were isolated using cloning cylinders, expanded in 6-well plates, and hSpCas9 expression was assessed by immunoblot analysis.Gene knockdown using short hairpin RNA (shRNA)shRNA sequences were cloned into pRSI9-U6-sh-UbiC-TagRFP-2A-Puro. For transient knockdown of core 3′ end processing factors, HeLa cells were transfected with either a control plasmid (U6 promoter alone) or a specific shRNA-containing plasmid using Lipofectamine 2000 (Life Technologies). Transfected cells were harvested 48 h post-transfection. For stable knockdown, recombinant lentiviruses were produced using the ViraPower Lentiviral Expression Systems and used to infect HeLa cells, following the manufacturer’s recommendations. Infected cells were selected in growth medium containing 0.5 μg/ml puromycin and harvested 4 days post-infection for confocal microscopy and fluorescence-activated cell sorting (FACS) analysis. The shRNA sequences are provided in Supplementary Table 1.Quantitative PCRTotal RNA was extracted using Sepasol‐RNA I Super G (Nacalai Tesque) following the manufacturer’s instructions. qRT-PCR was performed using the One Step TB Green PrimeScript PLUS RT-PCR Kit (Perfect Real Time) (Takara Bio) on a StepOnePlus Real-Time PCR System (Applied Biosystems). Each experiment was conducted at least three times to obtain a minimum of three biological replicates. The distal PAS usage index was calculated as ΔCT = CTlong—CTtotal, where CT represents the threshold cycle. Data were presented as log2 fold changes by normalizing test samples to controls and calculating log base 2 values. A negative value indicates that the mRNA has a shortened 3′ UTR compared to the control. To precisely quantify the relative expression of the two CD47 3′ UTR isoforms, a standard CD47 cDNA was used to calibrate differences in amplification efficiency between primer sets. The fraction of the short 3′ UTR isoform was determined by subtracting the CT value of the long isoform from the CT value of total CD47 expression. The primers used for qRT-PCR are listed in Supplementary Table 2.Immunofluorescence stainingFor the simultaneous detection of cell surface and intracellular CD47, cells were fixed with 2% paraformaldehyde in phosphate buffered saline (PBS) for 15 min at room temperature (RT), followed by PBS washes. Cells were then blocked with 10% FBS for 15 min at RT and incubated with Alexa Fluor 488-conjugated mouse anti-human CD47 antibody (Thermo Fisher Scientific) for 1 h at RT in 10% FBS. After PBS washes, cells were permeabilized with 0.7% Tween-20 in PBS for 15 min at RT, washed again, and re-blocked with 10% FBS for another 15 min. Cells were then incubated with eFluor 450-conjugated mouse anti-human CD47 antibody (Thermo Fisher Scientific) for 1 h at RT in 10% FBS. Following additional PBS washes, cells were stained with 50 μg/ml 7-aminoactinomycin D (Invitrogen) for 30 min at 4 °C, followed by three PBS washes. Imaging was performed using a Zeiss LSM 780 inverted confocal microscope equipped with a 63×, 1.4 numerical aperture oil objective.FACS analysisFor FACS analysis of cell surface and intracellular CD47, cells were fixed with 2% paraformaldehyde in PBS for 15 min at RT, washed with 1% FBS in PBS, and blocked with 10% FBS for 15 min at RT. Cells were then incubated with Alexa Fluor 488-conjugated mouse anti-human CD47 antibody for 1 h at RT in 10% FBS. After washing with 1% FBS in PBS, cells were permeabilized with 0.7% Tween-20 in PBS for 15 min at RT, washed again, and blocked with 10% FBS for another 15 min at RT. Subsequently, cells were stained with PE-Cyanine5-conjugated mouse anti-human CD47 antibody (Thermo Fisher Scientific) for 1 h at RT in 10% FBS. After final washes with 1% FBS in PBS, cells were resuspended in 1% FBS. Flow cytometry was performed on a BD FACSMelody cell sorter (BD Biosciences) with 3-laser, 8-color (2-2-4) configuration, with at least 10,000 cells analyzed per sample. Data were processed using the FlowJo v10.7 software (BD Biosciences).Immunoblot analysisHeLa cells were resuspended in high salt buffer, sonicated for 10 min, and cooled on ice. Lysates were resolved on 8% sodium dodecyl sulfate polyacrylamide gels and transferred onto PVDF membranes (Merck Millipore). Membranes were blocked with 5% skim milk or Blocking One (Nacalai Tesque) for 1 h at RT. Primary antibodies, including anti-FLAG M2 (Sigma) and anti-actin (Abcam), were diluted in 5% skim milk or Can Get Signal (Toyobo) and incubated with the membranes for 1 h at RT. After washing with Tris-buffered saline containing 0.1% Tween-20, the blots were incubated with HRP-linked horse anti-mouse IgG antibody (Cell Signaling Technology) for 1 h at RT. The blots were washed three times with the same buffer, and signal detection was performed using ECL Western Blotting Detection Reagents (GE Healthcare).T7 endonuclease I (T7EI) assayGenomic DNA was extracted by using the DNeasy Blood & Tissue Kits (Qiagen), following the manufacturer’s protocol. Target regions were amplified by PCR using KOD FX Neo (Toyobo). PCR products were denatured at 95 °C for 5 min, then re-annealed using a temperature ramp of− 2 °C per second to 85 °C, followed by a− 0.1 °C per second ramp to 25 °C. The re-annealed PCR products were incubated with T7EI (New England Biolabs) at 37 °C for 15 min. Digested products were analyzed by electrophoresis using a MutiNA system (Shimadzu). The primers used for the T7EI assay are listed in Supplementary Table 2.FACS-based genome-wide CRISPR screensFor pooled genome-wide CRISPR screening, the Guide-it CRISPR Genome-Wide sgRNA Library (Takara), comprising 76,612 sgRNAs targeting 19,114 human genes, was packaged into lentivirus following the manufacturer’s protocol. Viral supernatants were used to transduce HeLa cells stably expressing hSpCas9 at a multiplicity of infection of 1, where fold change was calculated as the expression in POLDIP2 knockdown samples relative to control samples. Gene expression changes were visualized as a volcano plot using the EnhancedVolcano package in R Studio. For heatmap analysis, DEGs were visualized using the ComplexHeatmap package in R Studio. GO terms were annotated using GO.db and org. Hs.eg.db (both version 3.11.4) Bioconductor packages in R.APA was analyzed using the Quantification of APA (QAPA) bioinformatics algorithm from the RNA-seq data. QAPA quantifies PAS usage within genes by calculating poly(A) usage (PAU), which represents the percentage of PAS usage per transcript. Higher PAU values indicate greater utilization of a specific PAS, whereas lower PAU values indicate reduced usage. To integrate PAU values across all PASs within a gene, the weighted 3′ UTR Length Index (WULI) was calculated using the following formula:$$WULI = \frac{L1 \cdot PAU1 + L2 \cdot PAU2 + L3 \cdot PAU3 + \ldots + LN \cdot PAUN}{{LN}}$$where \(L\) represents the 3′ UTR length up to each PAS, and \(PAU\) denotes the poly(A) usage of the corresponding PAS. To identify significant shifts in 3′ UTR usage, the differences in mean WULI values (∆WULI) were calculated between three POLDIP2 knockdown and three control samples. Genes with |∆WULI/100|≥ 0.1 were considered significant. Using this approach, 1056 genes with significant changes in 3′ UTR usage in response to POLDIP2 knockdown were identified.Statistical analysisStatistical analyses were performed using GraphPad Prism 10. The specific statistical tests used are detailed in the corresponding figure legends. All data are presented as means ± s.d. Statistical significance was determined as follows: Not significant (ns), P ≥ 0.05; *, P