ZFP36L2 orchestrates stress-adaptive plasticity in regeneration and cancer

The intestinal epithelium is characterized by a hierarchy of cell states that balance self-renewal and differentiation to maintain homeostasis². Crypt base ISCs, which are marked by the expression of genes such as LGR5, drive the continuous production of short-lived, specialized differentiated daughter cells that are eventually shed into the intestinal lumen¹⁶. However, when LGR5⁺ ISCs are lost during injury, the epithelium displays substantial plasticity. A broad range of differentiated cells have the capacity to undergo injury-induced reprogramming. Initially, cells enter into a transient regenerative state with fetal-like properties and then they dedifferentiate into a LGR5⁺ ISC state to repopulate the crypt base and restore homeostasis^2,3,17 (Fig. 1a). Although the transcription factor ASCL2 is necessary for this process¹⁸, little is known about injury-induced triggers of dedifferentiation. In CRC, the capacity of the intestinal epithelium to undergo stress-induced reprogramming contributes to tumour progression, metastasis and therapy resistance¹⁹. Primary tumours largely adopt LGR5⁺ ISC states either through the acquisition of oncogenic driver mutations in crypt base ISCs or through the dedifferentiation of mutated differentiated cells into tumour-permissive ISC-like states^20,21,22,23. Cells at the primary tumour-invasion front that detach from the tumour mass enter into LGR5^– metastasis-initiating states marked by increased expression of injury-associated regenerative and fetal markers (for example, L1CAM, PROX1, TROP2 and ANXA1)^7,10,24. These cells also exhibit increased expression of epithelial differentiation markers (for example, EMP1)⁹, and are associated with increased activity of the AP-1 family of stress-response transcription factors^25,26 (henceforth termed an ‘injury–repair’ state). After extravasation in distant organs, differentiated LGR5^– metastasis-initiating cells can dedifferentiate into canonical LGR5⁺ ISC states to seed metastasis and regenerate tumours, a process that mirrors the restoration of homeostasis during intestinal regeneration^{8,9,10,26,27,28} (Fig. 1a). Alternatively, CRC metastases can undergo lineage plasticity into non-canonical squamous-like and neuroendocrine-like states not observed during regeneration in the intestinal niche^10,26. Such non-canonical states are common among patients and are associated with adverse clinical outcomes compared with patients with tumours that primarily express canonical states. The mechanisms that underpin such cell-state reprogramming and cell-fate decision-making are largely unknown. To gain insight, we sought to identify the molecular switch that controls stress-induced dedifferentiation into a LGR5⁺ ISC state, a conserved process that is essential for both epithelial regeneration and canonical metastases.

To identify mediators of cell-state switching, we focused on genes co-expressed in ISC-like cells in a cohort of same-patient trios of normal, primary and metastatic CRC tissue samples. Genes were identified through Hotspot gene module analyses of single-cell RNA sequencing (scRNA-seq) data from fresh tissue¹⁰, and we termed them ‘tumour ISC-like’. ZFP36L2 (also known as TIS11D, ERF2 and BRF2) stood out as the gene most highly expressed in ISC-like cells. ZFP36L2 expression progressively decreased with differentiation in both normal intestinal crypts and in CRC, including canonical intestinal and non-canonical lineages (Fig. 1b–e and Extended Data Fig. 1a–d). Whole-exome and whole-genome sequencing studies^{11,12,13,14,15} have identified recurrent ZFP36L2 mutations in 5–10% of tumours from patients with CRC (Extended Data Fig. 1e,f). Most of these mutations are frameshift deletions with truncation of the coding region upstream of the zinc finger domain, which probably results in loss of ZFP36L2 function (Fig. 1f). ZFP36L2 is an RNA-binding protein of the ZFP36 (also known as tristetraprolin) family of CCCH-tandem zinc finger proteins. These proteins bind AU-rich elements in 3′ untranslated regions (UTRs) and recruit the RNA exosome or the CCR4–NOT complex to mediate mRNA degradation in haematopoiesis and early embryogenesis^29,30,31,32. However, little is known about ZFP36L2 function in epithelial cells and cancer.

To gain insight, we performed gene expression autocorrelation analysis across normal ISCs, transit-amplifying (TA) cells and tumour cells (Methods). Genes most strongly co-expressed with ZFP36L2 in both normal colon and CRC samples included AP-1 transcription factors and other components of the AP-1 stress-response pathway (for example, FOSB, FOS, MYC, EGR1, MYB, ATF3 and DUSP1; Fig. 1g–j, Extended Data Fig. 1g and Supplementary Table 1c–f). Although ZFP36L2 was most broadly expressed in tumour ISC-like cells, a subset of ZFP36L2-high CRC cells displayed an injury–repair state marked by high expression of the RevCSC signature²⁸, and the metastasis-initiating cell markers L1CAM⁷ and EMP1⁹ (Extended Data Fig. 2). The AP-1 transcription factor complex, formed by FOS, JUN and ATF family of heterodimers, has a central role in orchestrating stress-induced transcriptional responses during tissue regeneration³³. AP-1 is rapidly activated in response to cytokines, growth factors or microbial or physical stressors. Subsequently, AP-1 integrates upstream signals—primarily via MAPK, JNK and ERK pathways—to drive expression programs that regulate proliferation, survival, differentiation and epithelial remodelling. ZFP36 family members are well-characterized immediate-early genes and are rapidly induced in response to such stimuli^34,35,36,37. EGF stimulation of cells leads to the sequential induction of AP-1 followed by ZFP36L2³⁵, which indicates that ZFP36L2 has a role downstream of mitogenic signalling. Together, these data suggest that ZFP36L2 might be a molecular link between AP-1-driven stress response in the injury–repair state and re-entry into the ISC state.

We first explored the relationship between ZFP36L2, the stress response and the ISC state. As in humans, mouse Zfp36l2 mRNA (Fig. 2a) and ZFP36L2 protein (Fig. 2b) were most highly expressed in crypt base ISCs, with expression declining with differentiation. Colitis induced by dextran sodium sulfate (DSS) leads to destruction of intestinal epithelial cells (IECs), followed by dedifferentiation of surviving differentiated progenitors into LGR5⁺ ISCs and restoration of homeostasis once DSS is withdrawn³⁸. ZFP36L2 expression increased by day 2 of DSS administration, concurrent with LGR5 loss, whereas both ZFP36L2 and LGR5 expression were downregulated at the peak of injury by day 7 (Fig. 2c). During regeneration, ZFP36L2 was re-expressed first in the mid-crypt (TA compartment) and crypt base (ISC compartment) by day 10, preceding restoration of crypt base Lgr5 expression by day 14.

These observations of a temporospatial sequence of ZFP36L2 and LGR5 expression during crypt regeneration suggested that ZFP36L2 might have a functional role in the injury-induced dedifferentiation of TA progenitors into a LGR5⁺ ISC state. To test this hypothesis, we generated Vil1^creZfp36l2^fl/fl mice to knockout Zfp36l2 in IECs^39,40 (henceforth called Zfp36l2^IEC mice) and Vil1^creZfp36l2^+/+ (Zfp36l2^WT) and Vil1^creZfp36l2^fl/+ (Zfp36l2^HET) littermate controls (Extended Data Fig. 3a,b). Zfp36l2^IEC mice displayed normal colon morphology and body weight (Extended Data Fig. 3b-d). Immunofluorescence (IF) and RNA fluorescence in situ hybridization (FISH) staining of the colon revealed a reduction in LGR5⁺ ISCs from Zfp36l2^IEC mice (Extended Data Fig. 3e,f). This result was quantified by crossing Zfp36l2^IEC and Zfp36l2^WT mice with Lgr5^eGFP-creERT2 reporter mice¹⁶ and performing flow cytometry for LGR5–eGFP⁺ cells on isolated colon crypts (Extended Data Fig. 3g–i). Thus, at homeostatic baseline, Zfp36l2^IEC mice seem to maintain normal intestinal morphology and function despite having a reduced LGR5⁺ ISC population. To determine the function of intestinal epithelial ZFP36L2 during stress, we treated mice with 3.5% DSS for 7 days to induce intestinal injury, followed by 7 days of regeneration once DSS treatment was stopped. Zfp36l2^IEC mice displayed increased weight loss during DSS treatment and incomplete weight recovery 7 days after DSS withdrawal relative to Zfp36l2^WT controls. This result was consistent with more substantial epithelial injury and impaired regeneration after Zfp36l2 knockout (Fig. 2d). Pathology revealed shortening of the colon and caecum with persistent inflammation, crypt loss and decreased Lgr5 expression at day 14 in Zfp36l2^IEC mice compared with Zfp36l2^WT controls (Fig. 2e and Extended Data Fig. 3j–m).

To determine whether epithelial cell-intrinsic mechanisms inhibit dedifferentiation in colonic epithelia of Zfp36l2^IEC mice, we used an orthogonal Lgr5^DTR mouse model in which LGR5⁺ ISCs are directly ablated using diphtheria toxin (DT)⁵. Direct ablation of LGR5⁺ ISCs induced Zfp36l2 expression first in the TA compartment, with expression moving down the crypt and preceding the restoration of crypt base Lgr5 expression (Fig. 2f and Extended Data Fig. 4a). Next, we generated Lgr5^DTR-eGFP+Zfp36l2^IEC and Lgr5^DTR-eGFP+Zfp36l2^WT mice, administered DT to selectively ablate LGR5⁺ ISCs and assayed the restoration of Lgr5 expression on the basis of the dedifferentiation of LGR5^– cells (Fig. 2g,h). Consistent with results from the DSS-induced colitis model, loss of Zfp36l2 impaired the recovery of LGR5⁺ crypt base ISCs following the direct ablation of LGR5⁺ cells. To exclude potential confounders from in vivo DT administration, we established organoids from the colon epithelium of Lgr5^DTR-eGFP+Zfp36l2^IEC and Lgr5^DTR-eGFP+Zfp36l2^WT mice. Organoids were treated with vehicle or DT at day 4 for 24 h, and DT-dependent ISC ablation was verified by flow cytometry for LGR5–eGFP expression (Extended Data Fig. 4b,c). Flow-sorted single cells were seeded at low density to facilitate the regeneration of new organoids, a function that requires dedifferentiation into an LGR5⁺ ISC state⁴¹. Lgr5^DTR-eGFP+Zfp36l2^IEC organoids regenerated significantly fewer organoids than Lgr5^DTR-eGFP+Zfp36l2^WT controls (Extended Data Fig. 4e,f). Flow cytometry confirmed re-expression of LGR5–eGFP, with reduced LGR5–eGFP re-expression in organoids recovered from Lgr5^DTR-eGFP+Zfp36l2^IEC mice (Extended Data Fig. 4d). Thus, Zfp36l2 deletion inhibits the dedifferentiation of LGR5^– cells into LGR5⁺ cells and impairs organoid regeneration after DT-mediated LGR5⁺ ISC ablation.

We also established colon organoids from Zfp36l2^IEC, Zfp36l2^HET and Zfp36l2^WT mice by plating intact crypts (Extended Data Fig. 4g–l). After 3 days of ISC culture in mWRENAFI medium⁴¹ (Methods), WNT and R-spondin1 were withdrawn to induce differentiation for 3 days (Extended Data Fig. 4g–i). To assess dedifferentiation capacity, differentiated organoids were dissociated into single cells, flow-sorted for live cells and reseeded at low density to regenerate new organoids. Zfp36l2^IEC cells re-established fewer and smaller organoids than Zfp36l2^WT and Zfp36l2^HET controls (Extended Data Fig. 4j–l). In summary, both the DSS and Lgr5^DTR ablation models demonstrated that ZFP36L2 is required for injury-induced dedifferentiation of IECs into an ISC state, which in turn is required for epithelial regeneration and wound healing (Fig. 2i).

As in tissue repair, disseminating metastasis-initiating cells enter injury-induced differentiated states. However, they can also dedifferentiate into an ISC state during metastatic seeding^7,8,9,10 (Fig. 1a). To determine whether ZFP36L2 has a role in dedifferentiation into an ISC state during CRC progression, we engineered four patient-derived CRC organoid lines and used doxycycline-inducible short-hairpin RNAs (shRNAs) to knockdown ZFP36L2 (shZFP36L2). Scrambled shRNA was used as a control (shCtrl) (Extended Data Fig. 5a–d). We also sought to clarify how the underlying canonical or non-canonical differentiation potential of the tumour cell population influences ZFP36L2 function. Therefore, we included two primary tumour-derived organoids (MSK125P and OKG146P) with canonical gene expression (largely ISC with some intestinal differentiation)¹⁰ and two metastasis-derived organoids (MSK107Li and OKG146Li) with both canonical and non-canonical differentiation potential^7,10 (Extended Data Fig. 5a,b). To assess the role of ZFP36L2 in tumour growth in the intestinal niche, organoids transduced with shZFP36L2 or shCtrl were orthotopically injected into the caecum of NSG mice, and animals were administered doxycycline to knockdown ZFP36L2 (Fig. 3a). Primary tumour-derived canonical organoids (OKG146P and MSK125P) exhibited decreased primary tumour growth after ZFP36L2 knockdown (Fig. 3b, left). By contrast, non-canonical organoids derived from liver metastases (OKG146Li and MSK107Li) did not exhibit a decrease in primary tumour growth. (Fig. 3b, right). MSK107Li can generate spontaneous metastasis from orthotopic caecal primary tumours. However, despite having larger caecal primary tumours, mice with shZFP36L2 MSK107Li organoids had significantly decreased liver and lung metastatic burden (Fig. 3c).

Spontaneous metastasis requires multiple cell-state switches from ISC to differentiated during dissemination and to dedifferentiation during metastasis seeding (Fig. 1a). To isolate the function of ZFP36L2 specifically in the dedifferentiation switch, we used a metastasis-seeding model using intrasplenic injection (Fig. 3d). As the original OKG146Li organoid is capable of efficient intrahepatic outgrowth¹⁰ but does not efficiently initiate metastatic seeding, we serially passaged OKG146Li cells through mouse liver twice to generate a highly metastatic seeding line (OKG146Li-MS2; Methods). ZFP36L2 knockdown in both MSK107Li and OKG146Li-MS2 organoids significantly impaired liver metastasis seeding after intrasplenic injection, and fewer and smaller tumours were observed (Fig. 3e,f and Extended Data Fig. 5e,h,i). shCtrl-expressing MSK107Li organoids generated secondary lung metastases from the liver, a process nearly completely blocked by shZFP36L2 (Extended Data Fig. 5f). Notably, the first 2 weeks after intrasplenic injection is a period of high attrition. Disseminating cancer cells extravasating from liver sinusoids and infiltrating into the hepatic parenchyma must overcome immune, biochemical and growth factor stressors to survive, with only a small fraction of injected cells overcoming these hurdles to reinitiate tumour growth⁴. During this early seeding period, shCtrl tumours displayed a slow but steady increase in bioluminescence imaging (BLI) signals. By contrast, shZFP36L2 tumours showed a sustained decrease in BLI signals from immediately after injection until week 2, a result consistent with the observed substantial reduction in early metastatic seeding and reinitiation of growth in the liver. Beginning around week 2, the BLI signal in shZFP36L2 tumour-bearing mice increased, which suggests that the rare cells that successfully seeded were able to subsequently regrow. Indeed, growth kinetics were similar to controls and ultimately gave rise to macroscopic tumour nodules (Fig. 3g). Thus, ZFP36L2 knockdown substantially impairs the ability of tumour cells to initially seed metastasis in the liver and lungs.

To investigate the mechanism underlying the inefficient metastatic seeding of shZFP36L2 organoids, we tested the response of canonical primary CRC organoid cells to ex vivo differentiation and dedifferentiation cycles. OKG146P CRC organoids were cultured in human intestinal stem cell (HISC) medium, differentiated through growth factor withdrawal for 7 days (ref. ¹⁰) and then induced to dedifferentiate by passaging in HISC medium for 7 days (Fig. 3h). ZFP36L2 knockdown decreased the proportion of tumour ISC cells in both HISC and dedifferentiated conditions (Extended Data Fig. 6a–e). Control organoids re-expressed ISC markers (LGR5, ASCL2 and AXIN2) and downregulated differentiation markers (TFF3 and KRT20) within 7 days of the addition of ISC growth factors (to induce dedifferentiation) compared with differentiated cells cultured in intestinal growth-factor-free (IGFF) conditions, albeit to a slightly lower level than in stable HISC conditions. By comparison, shZFP36L2-treated organoids showed persistently marked reductions in ISC markers and elevated differentiation marker expression following dedifferentiation (Fig. 3i,j and Extended Data Fig. 6a–e). These data suggest that ZFP36L2 knockdown enforces differentiation and renders cancer cells incompetent to dedifferentiate back into an ISC state, which is critical for metastasis seeding. ZFP36L2 expression was itself markedly upregulated during dedifferentiation (Fig. 3j and Extended Data Fig. 6a), which mirrors the observed induction of ZFP36L2 expression during injury–repair in the mouse colon (Fig. 2c,f).

To exclude potential shRNA off-target effects, we used an independent CRISPR–Cas9 ZFP36L2 knockout strategy (Extended Data Fig. 7). Consistent with our observations with shRNA-mediated ZFP36L2 knockdown, CRISPR–Cas9 ZFP36L2 knockout or truncation decreased dedifferentiation and organoid regeneration capacity (Extended Data Fig. 7a–c). Single-sample gene set enrichment analysis (ssGSEA) of bulk RNA-seq of the CRISPR-mediated knockout organoids similarly revealed decreased ISC-associated gene signatures and increased expression of canonical and multiple non-canonical differentiation programs (osteoblast, squamous and neuroendocrine) in ZFP36L2 knockout organoids compared with ZFP36L2 WT controls (Extended Data Fig. 7d,e). In summary, ZFP36L2 function is important for the maintenance and progression of canonical primary CRC tumours (Fig. 3b, left). Metastatic CRC caecal xenografts are less reliant on ZFP36L2 for the propagation of established tumours in the intestinal microenvironment (Fig. 3b, right); however, ZFP36L2 depletion reduces metastatic seeding (Fig. 3c–g). When metastases grow in ZFP36L2-depleted contexts, the surviving cancer cell population exhibits more non-canonical gene expression (Extended Data Fig. 7d,e).

shZFP36L2-expressing MSK107Li and MSK146Li-MS2 liver metastases displayed significantly decreased LGR5 expression and increased expression of the canonical differentiation marker KRT20 (Fig. 3k and Extended Data Fig. 5g). In patients, CRC metastases progressively adopt a non-canonical differentiation trajectory that is associated with poor clinical outcomes, and the ISC program is simultaneously switched off¹⁰. We therefore proposed that metastases that grow out in the absence of ZFP36L2-dependent ISC dedifferentiation might adopt non-canonical lineage identities. Immunostaining of shZFP36L2-expressing OKG146Li-MS2 liver metastases revealed significantly increased staining for the neuroendocrine differentiation marker chromogranin B (CHGB) compared with liver metastases from shCtrl organoids (Fig. 3l). We reasoned that loss-of-function ZFP36L2 mutations might confer a selective disadvantage to canonical, intestinal-lineage-restricted primary tumours (for example, KG146P and MSK125P) that depend on ZFP36L2-driven dedifferentiation to continually replenish the primary tumour ISC state and seed metastasis (Fig. 3b). Conversely, by preventing ISC dedifferentiation, ZFP36L2 loss-of-function mutations would exert selective pressure for the emergence and outgrowth of tumours expressing ISC-independent non-canonical programs, which are associated with metastasis and poor prognosis¹⁰. To test this hypothesis, we performed immunostaining for LGR5 mRNA (ISC), CK5 (squamous) and CHGB (neuroendocrine) on ZFP36L2 mutated and wild-type CRC metastasis samples from patients (Fig. 3m,n and Supplementary Table 3b). Expression of the ISC marker LGR5 was significantly decreased, whereas neuroendocrine (CHGB) and squamous (CK5) non-canonical differentiation marker expression was increased in ZFP36L2 mutated relative to ZFP36L2 wild-type CRC metastasis samples (Fig. 3m,n). Among individual ZFP36L2 mutated tumours and across patients, clusters of cells expressed either CK5 or CHGB, but seldom both markers (Extended Data Fig. 8). This result is consistent with ZFP36L2 loss promoting a shift towards a more plastic state permissive for divergent differentiation into non-canonical states.

Together, these data indicate that ZFP36L2 acts as a crucial stress-induced mediator of dedifferentiation into an ISC state. In cancers with less plasticity (that is, primary tumours with intestinal-lineage-restricted programs), ZFP36L2 loss is a selective disadvantage. By contrast, in more plastic settings (that is, cancer cells adapted to metastatic environments), ZFP36L2 loss blocks re-entry into the ISC state, but the inherent plasticity of metastatic cells enables survival and metastatic outgrowth through the adoption of non-canonical programs.

Next, we sought to understand the molecular mechanism of ZFP36L2-dependent stress adaptation. Immunostaining of primary and metastatic CRC tissue samples showed that endogenous ZFP36L2 localized to largely cytoplasmic punctate structures (Fig. 4a). ZFP36L2 puncta were conserved in CRC organoids and xenografts, and decreased after ZFP36L2 knockdown (Fig. 4b and data not shown). AlphaFold2 structural analysis predicted extensive intrinsically disordered regions (IDRs) in ZFP36L2 N and C termini, which were characterized by low structural prediction scores, an absence of a secondary structure and high disorder propensity^42,43 (Fig. 4c and Extended Data Fig. 9a,b). This result suggests that ZFP36L2 might form IDR-driven biomolecular condensates⁴⁴. In patients with CRC, we identified a frequently mutated hotspot region downstream of the N-terminal IDR that leads to frameshift deletions and loss of the zinc finger domains (Figs. 1f and 4d). We therefore asked whether ZFP36L2 forms dynamic condensates with phase-separation properties. We engineered CRC organoids (MSK107Li, OKG146P and OKG146Li) with doxycycline-inducible constructs expressing eGFP, wild-type ZFP36L2–eGFP or the clinical frameshift deletion mutation Gly144Alafs*43/Gly5Argfs*329 (fsZFP36L2–eGFP) together with H2B–mCherry (Fig. 4e,f and Supplementary Video 1a–c). Tracking of ZFP36L2 puncta in motion-corrected 3D time-lapse images revealed that they were mobile in cells (Fig. 4e). Neither fsZFP36L2–eGFP nor eGFP formed condensates (Fig. 4f, Extended Data Fig. 9c–f and Supplementary Video 1). To track condensate dynamics during organoid formation, we dissociated 7-day-old organoids, seeded them as single cells and performed live-cell imaging during organoid regeneration (Fig. 4f,g and Extended Data Fig. 9c–f). ZFP36L2 condensates could be identified about 2 h after dissociation, and the number of condensates peaked around 10–12 h after dissociation (day 0) (Supplementary Video 2). This result is consistent with the previously established timing of entry into an L1CAM-high injury–repair or metastasis-initiating state⁷. The number of condensates reduced with further cell divisions and organoid regeneration (Fig. 4f,g, Extended Data Fig. 9c–f and Supplementary Video 2). In addition to tissue-disruptive stress, the withdrawal of growth factors or treatment with low-dose irinotecan (a chemotherapy) significantly increased the number of ZFP36L2 condensates. This result suggests that diverse cellular stresses converge to promote ZFP36L2 condensate formation (Extended Data Fig. 9g,h). ZFP36L2 protein levels increased on days 1–3 of organoid regeneration and declined to steady-state by day 7 (Extended Data Fig. 9i). Conversely, peak condensation occurred on day 1, preceding peak protein levels on day 3 (Fig. 4f,g). At each time point, growth factor withdrawal or irinotecan treatment induced condensate formation (Extended Data Fig. 9g,h) without changing overall ZFP36L2 protein levels (Extended Data Fig. 9i). These observations suggest that although ZFP36L2 protein levels are one determinant of condensate formation, cellular stresses can rapidly nucleate condensation through mechanisms independent of protein abundance.

Fluorescence recovery after photobleaching (FRAP) revealed that ZFP36L2–eGFP condensates exhibit gradual fluorescence recovery compared with the freely diffusible eGFP. This result is characteristic of phase-separated biomolecular condensates, which exhibit reduced fluidity (Fig. 4h, Extended Data Fig. 10a,b and Supplementary Video 3a–c). The clinical hotspot mutation fsZFP36L2, which lacks the zinc finger domain, exhibited free diffusion and did not form condensates, similar to eGFP (Fig. 4h and Extended Data Fig. 10a,b). Consistent with phase-separation characteristics, ZFP36L2 condensates could be dissolved by treating cells with 1% 1,6-hexanediol⁴⁵ (Extended Data Fig. 10c). Treatment with 1 μg μl^–1 RNase A caused dissipation of ZFP36L2–eGFP condensates (Fig. 4i), consistent with a requirement for RNA binding to nucleate condensate formation. Replacing eGFP with monomeric eGFP (meGFP), which has low fluorophore-autonomous condensation levels⁴⁶, did not alter the pattern of intracellular puncta (Fig. 4j). Staining patterns were similar for ZFP36L2–eGFP, ZFP36L2–meGFP (Fig. 4j) and endogenous ZFP36L2 (Fig. 4a), which indicated that the fluorescent protein tags do not induce aberrant condensation. ZFP36L2 condensates showed partial overlap with DDX6, an mRNA decay factor also associated with P bodies, but did not overlap with the stress granule components G3BP1 and PABPC1⁴⁷, which did not form condensates under the same conditions (Extended Data Fig. 10d,e). Together, these results highlight the distinct biology of ZFP36L2: it forms dynamic, RNA-dependent, stress-inducible biomolecular condensates in tumour tissues and organoids.

Next, we used HyperTRIBE^48,49 to investigate ZFP36L2 RNA-binding targets. Full-length ZFP36L2 and fsZFP36L2 (which cannot bind RNA) were tagged with the hyperactive catalytic domain of adenosine deaminase acting on RNA (ADAR) containing the E488Q mutation, which enhances editing efficiency and reduces sequence bias⁴⁸ (Fig. 5a). Two patient-derived CRC liver metastasis organoids (MSK107Li and OKG146Li) and matched primary tumour and normal colon organoids from patient KG146 (OKG146P and OKG146N) expressing doxycycline-inducible ZFP36L2–ADAR or fsZFP36L2–ADAR were subjected to RNA-seq to identify adenosine to inosine (read as guanosine) edits (Supplementary Table 5a). The majority of edits were in the ZFP36L2–ADAR-expressing organoids, a result consistent with the requirement of full-length protein-specific mRNA interactions, which are absent in the fsZFP36L2–ADAR control (Fig. 5b,c and Extended Data Fig. 11a–d). More than 80% of ZFP36L2-specific edits occurred in mRNA 3′ UTRs, predominantly at the conserved UAUUUA motif (Fig. 5b and Extended Data Fig. 11b), which is consistent with the known binding properties of the ZFP36 protein family³⁴. As ZFP36L2 3′ UTR binding can promote mRNA decay^37,50,51, we treated OKG146Li and MSK107Li organoids with the transcriptional inhibitor actinomycin D (ActD) to inhibit de novo mRNA synthesis⁵¹, and measured RNA decay rates (Extended Data Fig. 12a). Among the mRNA transcripts for which abundance decreased after 5 h of ActD treatment relative to 0 h baseline (indicative of mRNA decay), we identified mRNAs for which expression increased in shZFP36L2 organoids relative to shCtrl organoids (that is, ZFP36L2-dependent mRNA decay) (Fig. 5c and Extended Data Fig. 12b). Overlaying ZFP36L2-bound (HyperTRIBE) and ZFP36L2-dependent mRNA decay (ActD pulse-chase) transcripts revealed substantial overlap (Fig. 5c). Gene ontology (GO) enrichment analysis of ZFP36L2-dependent mRNA decay targets shared between the OKG146Li and MSK107Li datasets revealed significant enrichment in biological processes related to stress and stimulus responses (Fig. 5d). Notably, these pathways included responses to diverse stressors, including hypoxia (for example, FOXO3, STC2 and CA9), TNF signalling (for example, BIRC2, IL1B and FOSL2) and apoptosis (for example, CDKN1A, BNIP3L and RHOB) (Supplementary Table 5c). As ActD can itself induce cellular stress, we used SLAM-seq⁵² to measure transcript half-lives after 4-thiouridine (4sU) labelling, which does not disrupt transcription. Consistent with ActD-mediated decay, ZFP36L2 knockdown significantly stabilized transcripts associated with stress-response programs (Fig. 5e,f, Extended Data Fig. 12c and Supplementary Table 5e,f).

Dual mRNA FISH and ZFP36L2 IF for three putative ZFP36L2 mRNA targets (GDF15, GADD45A and HILPDA) in OKG146Li-MS2 organoids identified colocalization of a subset of these mRNAs with ZFP36L2 condensates (Fig. 5g). Acute exposure to growth factor withdrawal or irinotecan, conditions that induce ZFP36L2 condensate formation (Extended Data Fig. 9g,h), decreased the percentage of ZFP36L2 foci positive for target mRNA expression and the intensity of mRNA staining in ZFP36L2 foci (Fig. 5g,h and Extended Data Fig. 12d). Passaging organoids that had undergone long-term IGFF culture back into HISC medium, thus alleviating mitogen-deprivation-associated stress and inducing dedifferentiation (Figs. 2 and 3), promoted recovery of mRNA–ZFP36L2 colocalization (Fig. 5h and Extended Data Fig. 12d). We observed an inverse correlation between the intensity of ZFP36L2 and target mRNA staining per cell (Extended Data Fig. 12e,f). These data support a model in which stress-induced ZFP36L2 foci engage and promote rapid degradation of target mRNAs, which reduces their steady-state abundance and apparent colocalization. These observations spatiotemporally link ZFP36L2 condensate dynamics with its stress-response mRNA targets. That is, expression and colocalization are detectable at baseline and decrease during acute stress, consistent with dynamic mRNA degradation in ZFP36L2 condensates. This process reaches a minimum under prolonged growth factor stress conditions, is reversible after the reintroduction of growth factors and temporally coupled with dedifferentiation into an LGR5⁺ ISC state (Extended Data Fig. 12g).

We uncovered a pivotal role for the RNA-binding protein ZFP36L2 in linking epithelial stress responses with cellular plasticity. In association with AP-1 stress responses, ZFP36L2 binds to AU-rich 3′ UTRs of stress-associated mRNAs, which in turn mediates the formation of biomolecular condensates and the degradation of stress transcripts, thus terminating the stress response. This process subsequently triggers dedifferentiation into an ISC state, which enables epithelial regeneration and restoration of homeostasis. Loss-of-function mutations in ZFP36L2 are enriched in metastatic CRC^14,53 relative to non-metastatic tumours, and are associated with decreased recurrence-free survival¹⁵ and overall survival⁵³ in patients with CRC. Our study provides a mechanistic explanation for the context-specific functions of ZFP36L2. In canonical primary tumours, in which the tumour mass is maintained through continual dedifferentiation into LGR5⁺ ISC states⁶, ZFP36L2 mutations provide an initial selective disadvantage, manifesting as decreased tumour growth of canonical primary tumours (Fig. 3b). In metastasis, the ZFP36L2 mutation-dependent inability to dedifferentiate from an LGR5^– injury–repair metastasis-initiating state into an LGR5⁺ ISC state confers an initial seeding disadvantage (Fig. 3c–g). However, this property selects for the outgrowth of metastases with non-canonical gene expression, which is associated with poor clinical outcomes¹⁰ (Fig. 3k–n). ZFP36L2 mutations therefore act as a two-way molecular switch, blocking the entry of metastasis-initiating cells into canonical ISC state but at the same time triggering lineage plasticity into heterogeneous non-canonical states. Although this study focused on CRC, in which ZFP36L2 mutations occur most frequently, ZFP36L2 is also recurrently mutated in pancreatic cancer^54,55, urothelial cancers⁵⁶ and melanoma, and its paralogues ZFP36L1 and ZFP36 are mutated in other cancer types^15,57. ZFP36L2 was recently identified in a screen for non-genetic drivers of acute myeloid leukaemia⁵⁸. In this context, ZFP36L2 acts by degrading myeloid differentiation-associated mRNAs to maintain an undifferentiated leukaemic state. Moreover, the paralogue ZFP36L1 was identified as a regulator of neuroendocrine plasticity in a screen for LSD1 inhibitor sensitivity in small cell lung cancer⁵⁹. These studies suggest that the function of the ZFP36L2 family in regulating differentiation and plasticity might be conserved across tissue lineages.

The dynamic interplay between differentiation and reprogramming underscores a broader paradigm in which cellular plasticity governs both tissue maintenance and malignant progression. In tissue repair, plasticity is tightly regulated and self-limiting. By contrast, metastatic progression reflects a failure of this stress ‘off switch’, which fuels sustained canonical or non-canonical differentiation programs that drive tumour growth and dissemination. Notably, our work identified a role for ZFP36L2 in maintaining the ISC pool even under homeostatic conditions. Moreover, ZFP36L2 binds a similar repertoire of stress-related mRNA transcripts in both normal and neoplastic cells but is dispensable for epithelial function at homeostasis. Basal ZFP36L2 expression in ISCs may reflect a preparedness to buffer transient stress-associated transcriptional programs and replenish stem cell pools during homeostasis^60,61,62. By identifying ZFP36L2 as a crucial orchestrator of stress-induced cellular plasticity, this study opens many avenues for further investigation. Discerning the mechanisms that control context-dependent ZFP36L2 function—through regulation of its RNA binding and condensate formation—could lead to approaches that prevent or reverse plasticity into aggressive phenotypic states, metastasis and therapy resistance. The development of strategies to exploit these vulnerabilities could pave the way for new cancer treatments.

Patient biospecimens and data

All patient material was obtained through Memorial Sloan Kettering (MSK) Institutional Review Board protocols 06-107, 12-245, 14-244 and 22-404. No statistical method was used to predetermine sample sizes. scRNA-seq datasets from matched normal colon, primary tumour and metastasis samples were obtained from a previous study¹⁰. Patient-derived organoids from two primary tumours (MSK125P and OKG146P) and two metastases (MSK125Li and OKG146Li) were generated and validated as previously described^7,10. Tumour whole-exome sequencing (WES) was performed by recapturing DNA processed originally for targeted exon sequencing using MSK-IMPACT⁶⁷. The median WES target coverage for tumour and normal samples was 129× and 101×, respectively. WES data were analysed using the TEMPO pipeline (available from GitHub: https://github.com/mskcc/tempo). The OncoKB precision oncology knowledgebase, a US Food and Drug Administration (FDA)-recognized human genetic variant database curated by experts at MSK⁶⁸, was used to distinguish between oncogenic alterations (presumed drivers) and variants of unknown significance (presumed passengers). Only somatic alterations labelled as oncogenic, likely oncogenic or predicted oncogenic by OncoKB were included for analyses. Archival formalin-fixed, paraffin-embedded (FFPE) ZFP36L2 WT and ZFP36L2 mutant clinical tissue blocks for immunostaining were identified through WES of corresponding tumour DNA originally collected for MSK-IMPACT. Tissue processing, FFPE section selection and histopathological data interpretation were overseen by an expert gastrointestinal pathologist (J.S.).

scRNA-seq data analysis of CRC samples from patients

Normalized gene expression matrices were obtained from the Human Tumour Atlas Network (HTAN) Data Portal (http://humantumouratlas.org/publications/hta8_crc_moorman_2024) and processed as previously described¹⁰. Downstream analyses were restricted to patient-matched pairs of primary tumour and liver metastasis samples (n = 25 patients). After sample filtering, a total of 38,272 cells remained: primary tumour (13,102 cells), metastatic (12,215 cells) ISC (1,145 cells), absorptive precursor (2,805 cells), enterocyte (1,214 cells), BEST4⁺ enterocyte (1,339 cells), secretory precursor (3,818 cells), goblet (1,720 cells), tuft (751 cells) and enteroendocrine (163 cells). All subsequent differential expression, pathway analyses and correlation-based analyses were restricted to this filtered epithelial compartment.

Data visualization

Two-dimensional embeddings were generated using Scanpy (v.1.9.1). A k-nearest neighbours (KNN) graph was constructed on the principal components using Euclidean distance (k = 20), followed by visualization with a force-directed layout (ForceAtlas2). Subsequent plots were produced using Matplotlib (v.3.6.0)

Gene signature scores

Gene signature scores were calculated using the score_genes function from Scanpy (v.1.9.1), which calculates the average expression of a given gene set relative to matched reference genes. To reduce bias from differences in gene expression levels in each signature, we used z-scored expression data as input.

Generation of ISC-like cell annotations and gene signature

To identify tumour cells exhibiting an ISC-like transcriptional program, we first filtered the dataset to retain only malignant epithelial cells. Raw counts from these primary tumour and metastatic cells were normalized using total-count normalization followed by log-transformation. Highly variable genes (HVGs; n = 3,000) were selected for principal component analysis (PCA), neighbourhood graph construction (n_neighbours=20) and Leiden clustering.

To assess stemness expression across cells, we computed the median expression per cluster of ISC-specific marker genes from Hotspot-derived tumour ISC-like gene module 29 (Supplementary Table 1a), as previously identified¹⁰. The distribution of cluster-level ISC module scores was bimodal, which informed the selection of a threshold in identifying clusters that were ISC-like or not ISC-like. This tumour-derived ISC-like threshold value was then kept constant in labelling ISC-like normal cell types. We calculated a gene signature score on the z-normalized expression of these genes using the score_genes function in Scanpy (v.1.9.1) (Fig. 1b and Extended Data Fig. 1a).

DEG analysis between normal and tumour ISC-like cells

Differential gene expression (DEG) analysis was performed using the Wilcoxon rank-sum test as implemented in scanpy.tl_rank_genes_groups, comparing normal and tumour cells stratified by their ISC-like annotations. The analysis was conducted on the log-normalized gene expression matrix. We filtered for previously defined¹⁰ tumour ISC-like Hotspot gene module 29 genes and selected the most significant ones for downstream visualization (Fig. 1b). The log₂ fold changes were computed relative to each gene across the specified cell-type groups.

To further characterize activity of the tumour ISC-like module, we computed the mean imputed expression of each gene in ISC module 29 across canonical ISC cells. Genes were then ranked by mean imputed expression to highlight ISC-enriched transcripts.

Computation of gene autocorrelation signatures in normal and tumour cells

We used Hotspot⁶⁹ (v.0.9.1) to calculate pairwise local correlations between genes on the basis of the KNN graph, which captures local cell–cell similarity and genes with high local autocorrelation. This local strategy is also inherently more robust to gene dropouts and technical noise, which enables sensitive detection of context-specific gene co-expression patterns.

We first partitioned the data to only include tumour cells (25,317 cells) or normal cells (12,955 cells). HVGs (n = 2,000) were selected on the untransformed expression layer, with the manual inclusion of ZFP36L2. Genes prone to technical or non-informative variation (for example, ribosomal, mitochondrial and long non-coding RNAs) were excluded from the analyses, as were genes with non-zero counts across the dataset. A 50-nearest neighbour graph was constructed using the precomputed PCA embedding, and raw UMI counts were re-added to a desiccated counts layer. The resulting dataset was passed to Hotspot, using a depth-adjusted negative binomial model with the neighbourhood structure defined by a 15-nearest neighbourhood graph. Spatial autocorrelation scores (Moran’s I) were computed for all genes. Genes with a FDR < 0.05 were considered significant and retained for downstream analyses (Fig. 1i and Extended Data Fig. 1g).

GSEA

GSEA using GO biomolecular process, all Hallmark, PID and KEGG gene sets and Hotspot modules have been previously defined¹⁰ (Supplementary Table 1b). GSEA was performed using the prerank function of the Python package GSEApy (v.0.14.0) with 10,000 permutations and the default parameters.

Statistical analyses

Pairwise comparisons of PID–AP-1 pathway⁶⁵ scores (Fig. 1j), Hotspot tumour ISC-like module 29 scores¹⁰ and ZFP36L2 expression (Extended Data Fig. 1) were calculated across all cell-type groups by Mann–Whitney U-tests, and P values were adjusted for multiple testing using Bonferroni correction (q < 0.05).

Mouse colon epithelial cell scRNA-seq data analysis

Data acquisition and preprocessing

scRNA-seq data (GSE168448)⁶⁶ were downloaded from the Gene Expression Omnibus (GEO). Cells were first filtered on the basis of mitochondrial content, excluding those with mitochondrial transcript percentages exceeding 0.2. Outliers were identified using the median absolute deviation (MAD) method, following criteria adapted from previous studies^70,71. A cell was classified as an outlier if it met at least two of the following five conditions: (1) log-transformed library size below 3 MADs under the median or above 5 MADs over the median; (2) log-transformed number of expressed genes below 3 MADs under the median or above 5 MADs over the median; (3) percentage of counts from the top 20 most highly expressed genes exceeding 5 MADs below or above the median; (4) feature-count distance (as previously defined⁷⁰) exceeding 5 MADs below or above the median; and (5) percentage of mitochondrial counts exceeding 2.5 MADs above the median and greater than 8%. Same-sample doublets were identified and removed using scDblFinder (v.1.16.0)⁷². Outliers and doublets were excluded from downstream analyses.

Normalization, HVG selection, PCA and denoising

Count data were median-normalized and log-transformed with a pseudo-count of 1. The top 2,000 HVGs were selected using Scanpy⁷³ (v.1.9.8) with the Seurat (v.3) method⁷⁴. PCA was performed on these HVGs, retaining 280 principal components that accounted for 75% of the variance. Denoising was conducted using MAGIC^74,75,76 via the Scanpy external API, using default parameters except for the number of principal components, for which all 280 retained components were used for neighbourhood calculations.

Cell typing

Cell-type annotation was performed by scoring the denoised expression of cell-type marker gene sets using Scanpy’s score_genes function, which reimplements a previously described method⁷⁷. Each cell was assigned to the cell type for which it exhibited the highest score.

Mouse experimental methods

Mouse strains

All animal procedures were approved by the Institutional Animal Care and Use Committee of the Memorial Sloan Kettering Cancer Center (MSKCC). The following mouse strains were obtained from The Jackson Laboratory: C57BL/6J (strain 000664); Vil1^cre mice³⁹ (B6.Cg-Tg(Vil1-cre)1000Gum/J; stock 021504); Lgr5^{eGFP-IRES-creERT2} mice¹⁶ (B6.129P2-Lgr5^{tm1(cre/ERT2)Cle}/J; stock 008875); and NSG mice (NOD.Cg-Prkdc^scidIl2rg^tm1Wjl/SzJ; stock 005557). Zfp36l2^fl/fl mice⁴⁰ were provided by M. Turner and Lgr5^DTR-eGFP mice⁵ by F. de Sauvage. Primers and relevant information used for genotyping are listed in Supplementary Table 4b.

Xenograft experiments used only female NSG mice. All other experiments used male and female mice in equal proportions, with littermates allocated across groups to minimize variability. Sample sizes were not predetermined by statistical methods; group sizes (n = 5–15 per condition) were based on previous experience with similar xenograft and colitis models, keeping animal numbers to the minimum required under the 3Rs principles. Mice were randomly assigned to treatment groups, with blinding applied wherever feasible.

DSS-induced colitis

Experimental cohorts were generated by inbreeding on a C57BL/6J background, and all experiments included littermate controls. To evaluate the role of ZFP36L2 in colonic regeneration, Zfp36l2^fl/fl mice were crossed with Vil1^cre mice to generate intestinal epithelial knockout mice (Zfp36l2^IEC). Mice (6–8 weeks old) received 3.5% (w/v) DSS (molecular weight 36–50 kDa; MP Biomedicals) in drinking water ad libitum for 7 days, followed by 7 days of regular water before euthanasia. Body weight was recorded daily. Caecal and colon tissues were collected at specified time points, processed as Swiss rolls, fixed in 4% paraformaldehyde and embedded in paraffin. Tissue sections were subjected to haematoxylin and eosin staining, immunostaining or FISH (see below).

Quantification of LGR5–eGFP by crypt flow cytometry

Lgr5^{eGFP-IRES-creERT2} mice (Jackson Laboratory, stock 008875) were crossed with Vil1^cre (stock 004586) and Zfp36l2^fl/fl mice to generate Lgr5^{eGFP-IRES-creERT2+};Vil1^cre+;Zfp36l2^IEC mice. Littermate controls included Lgr5^{eGFP-IRES-creERT2+};Vil1^cre+;Zfp36l2^WT and Lgr5^{eGFP-IRES-creERT2–};Vil1^cre+;Zfp36l2^WT mice. For each experimental batch, three littermate pairs of Lgr5^{eGFP-IRES-creERT2+};Vil1^cre+;Zfp36l2^WT and Lgr5^{eGFP-IRES-creERT2+};Vil1^cre+;Zfp36l2^IEC mice (aged 6–8 weeks) were collected. Colons were dissected, flushed with PBS to remove faecal content, opened longitudinally and cut into around 1-cm segments. Tissue samples were incubated in dissociation buffer (PBS with 8 mM EDTA, 0.5 mM DTT and 10 U ml^–1 DNase I (Roche 04716728001)) at 4 °C with gentle shaking for 60 min. Crypts were detached by vigorous shaking, pelleted and further dissociated with TrypLE (Thermo Fisher Scientific). The resulting cell suspension was treated with DNase at room temperature for 5 min and passed through a 40 μm strainer to generate single-cell suspensions for flow cytometry. Viable epithelial single cells were gated by forward scatter, side scatter and negative staining for 4′,6-diamidino-2-phenylindole (DAPI). Lgr5^{eGFP-IRES-creERT2–};Vil1^cre+;Zfp36l2^WT mice were processed in parallel as gating controls. Two independent experimental batches were performed, and data were pooled for analyses.

In vivo Lgr5 ^DTR ablation

Mice were bred to generate Lgr5^DTR-eGFP+ and littermate Lgr5^DTR-eGFP− controls. Lgr5^DTR-eGFP+ZFP36L2^IEC mice were generated by crossing Lgr5^DTR-eGFP+;Vil1^cre+ and ZFP36L2^fl/fl together with Lgr5^DTR-eGFP+;Vil1^cre− and ZFP36L2^fl/fl to generate littermate controls. For in vivo depletion of Lgr5^DTR-expressing cells, DT (0.005 μg μl^–1 in PBS) was administered intraperitoneally at a dose of 0.05 μg per g of body weight every 48 h, for a total of 4 doses. Mice aged 8–10 weeks were used for all experiments. Colons were collected at the time points indicated in the figures or figure legends, Swiss-rolled and fixed in 4% paraformaldehyde for 24 h at room temperature. Tissues were then transferred to 70% ethanol and stored at 4 °C before paraffin embedding.

In vitro Lgr5 ^DTR ablation and dedifferentiation assay

Colon organoids were established from Lgr5^DTR-eGFP+;Vil1^cre+;ZFP36L2^fl/fl (Lgr5^DTR-eGFP+ZFP36L2^IEC) and Lgr5^DTR-eGFP+;Vil1^cre−;ZFP36L2^fl/fl (Lgr5^DTR-eGFP+ZFP36L2^WT) mice (6–8 weeks old; n = 3 mice per genotype). Approximately 1,000 crypts, isolated as described above, were suspended in 40 μl Matrigel and cultured in mouse WRENAFI (mWRENAFI) medium containing advanced DMEM/F12 (AdDF12; Thermo Fisher Scientific), 2 mM GlutaMAX (Thermo Fisher Scientific), 10 mM HEPES (Thermo Fisher Scientific), 1 mM N-acetyl-l-cysteine (Sigma-Aldrich), 1:50 B27 supplement (Thermo Fisher Scientific), 1:100 N2 supplement (Thermo Fisher Scientific) and 100 μg ml^–1 Pimocin (InvivoGen), supplemented with growth factors and the following inhibitors: 50 ng ml^–1 EGF (Peprotech), 100 ng ml^–1 murine Noggin (Peprotech), 500 nM A8301 (Sigma-Aldrich), 50 ng ml^–1 FGF2 (Peprotech), 100 ng ml^–1 IGF-I (Peprotech), 1 nM NGS-WNT (ImmunePrecise N0001) and 1 μg ml^–1 murine R-spondin1 (Peprotech). After stable organoid lines were established, organoids were dissociated into single cells and seeded at 20,000 cells per 40 μl Matrigel. Cells were cultured in mWRENAFI medium for 4 days, with medium changed every 2 days. On day 4, cultures were switched to fresh mWRENAFI medium with or without 0.6 μg ml^–1 DT for 24 h. Organoids were then dissociated into single cells, and depletion of LGR5–eGFP⁺ cells in the DT-treated group was confirmed by flow cytometry using organoids derived from C57BL/6J mice as a gating control. Live DAPI-negative cells were sorted, embedded in Matrigel at 2,000 cells per 40 μl and cultured in mWRENAFI medium for 5 days to induce dedifferentiation. Regenerated organoid numbers were quantified using BioTek bright-field imaging with whole-dome z-projection, and eGFP⁺ cells from each group were quantified by flow cytometry.

Orthotopic xenograft experiments

NSG mice (Jackson Laboratory, stock 005557) were used for transplantation experiments at 6 weeks of age. Mice were housed in a specific pathogen-free facility under controlled temperature and humidity, with a 12-h light–dark cycle and ad libitum access to either standard chow or an irradiated diet supplemented with 2,500 ppm doxycycline (Modified LabDiet 5053), along with water.

For orthotopic caecal and intrasplenic injections, organoid lines expressing thymidine kinase–eGFP–luciferase and transduced with pTRIPZ lentivirus expressing doxycycline-inducible shRNAs targeting ZFP36L2 or a Renilla control were transduced with lentivirally expressed pLenti-PGK-Akaluc. Transplantation procedures were performed as previously described^7,10. For generation of the OKG146Li-MS2 line, OKG146Li organoids were transduced with pLenti-PGK-Akaluc, and around 500,000 cells were injected orthotopically into the mouse liver in 50% Matrigel as previously described¹⁰ to generate liver tumours. At the end point, tumours were collected, minced and plated in HISC medium containing antibiotics to select for CRC organoids. This in vivo selection procedure was repeated twice to generate the liver-metastasis-selected OKG146Li-MS2 organoid line. For caecal injections, 5 × 10⁵ cells from canonical primary tumour organoids (MSK125P and OKG146P) or liver metastasis (OKG146Li and MSK107Li) organoids, representing canonical and non-canonical cell states, respectively, transduced with lentivirus expressing eGFP–luciferase and either shZFP36L2 or shCtrl were resuspended in 10 μl (50% Matrigel, 50% PBS) and injected into the caecal submucosa of 6-week-old NSG mice. Mice were monitored by BLI 1 week after injection to confirm primary tumour engraftment. Any BLI-signal-negative animals were excluded from subsequent analyses. Mice were then started on a doxycycline diet with weekly BLI monitoring for 15 weeks before euthanasia and organ collection. For splenic vein injections, 6-week-old NSG mice were placed on a doxycycline diet 24 h before surgery. CRC liver-metastasis-derived organoids MSK107Li and OKG146Li-MS2 transduced with lentivirus expressing eGFP-luciferase and either shZFP36L or shCtrl were treated with doxycycline ex vivo for 7 days before surgery and injected into the spleens of mice (5 × 10⁵ cells per mouse). Around 500,000 cells were injected into the splenic vein in 50 μl PBS supplemented with 10% Matrigel, immediately followed by splenectomy. BLI was performed immediately after surgery to establish a post-injection baseline (week 0). Mice were maintained on doxycycline diet for 10–15 weeks until they reached the humane end point.

BLI was performed immediately after surgery and weekly thereafter using an IVIS Spectrum Xenogen system (Caliper Life Sciences). Data were analysed using Living Image (v.2.50) software. Experimental group sizes (n ≥ 5 mice per group) were determined based on practical constraints (five mice per cage) and were age-matched and sex-matched.

Mice with caecal or intrasplenic tumours were monitored daily. Humane end points were defined as the onset of distress, including hunched posture, impaired grooming or weight loss of >20%, together with a route-specific criterion: a maximum experimental duration of 6 months for caecal tumours, or tumour burden exceeding 10% of body mass and/or a body condition score <2 for intrasplenic tumours (with ascites as an additional distress sign specific to intrasplenic tumours). These criteria were not exceeded in any experiment. Animals were euthanized at the end point, and tissues were collected for further analyses. Where indicated, tissues were fixed in 4% paraformaldehyde for 24 h. Histopathological scoring was performed in a blinded manner.

Mouse organoid dedifferentiation assay

Three mice from each genotype (aged 6–8 weeks) were euthanized, and colons were dissected and processed to collect crypts as described above. Approximately 1,000 crypts were suspended in 40 μl Matrigel and cultured in mWRENAFI medium, consisting of advanced DMEM/F12 (AdDF12; Thermo Fisher Scientific), 2 mM GlutaMAX (Thermo Fisher Scientific), 10 mM HEPES (Thermo Fisher Scientific), 1 mM N-acetyl-l-cysteine (Sigma-Aldrich), 1:50 B27 supplement (Thermo Fisher Scientific), 1:100 N2 supplement (Thermo Fisher Scientific) and 100 μg ml^–1 Primocin (InvivoGen), supplemented with growth factors and the following inhibitors: 50 ng ml^–1 EGF (Peprotech), 100 ng ml^–1 murine Noggin (Peprotech), 500 nM A8301 (Sigma-Aldrich), 50 ng ml^–1 FGF2 (Peprotech), 100 ng ml^–1 IGF-I (Peprotech), 1 nM NGS-WNT (ImmunePrecise N0001) and 1 μg ml^–1 murine R-spondin1 (Peprotech). Once stable organoid lines were established, 2,000 single cells from the organoids were plated in 40 μl Matrigel and cultured in mWRENAFI medium supplemented with 10 μM Y-27632 for 3 days. The medium was then switched to mENAFI (removing NGS-WNT and R-spondin1) to induce organoid differentiation for 2 days. Efficient loss of LGR5⁺ ISCs under these differentiation conditions was confirmed by flow cytometry analysis of GFP signal in LGR5–eGFP⁺ organoids cultured under the same conditions. The differentiated mature organoids were dissociated into single cells using TrypLE, and viable cells were sorted and plated at 2,000 cells per 40 μl Matrigel domes to evaluate dedifferentiation via organoid formation. Five to six technical replicates from three biological replicates were pooled for subsequent analysis and plotting.

Multiplexed staining and imaging

Organoid collagen embedding

Day 7 patient-derived organoids (PDOs) from patients with CRC were recovered from Matrigel as described above. Intact organoids were resuspended in neutralized collagen I (250 μl collagen I solution mixed with 28.4 μl of 10× PBS and 5.7 μl of 1 M NaOH) and deposited into a tissue capsule in a 6-well plate to form small domes. Once the collagen I droplets solidified in the incubator for 30 min, the domes were covered with tissue culture medium and cultured overnight.

The medium was aspirated the following day, and the tissue capsule was washed two to three times with PBS until the solution appeared clear, free of residual medium. The tissue capsule was then transferred to freshly prepared 4% paraformaldehyde for 24 h with gentle agitation at room temperature. After fixation, the capsule was washed with PBS and stored in 70% ethanol at 4 °C until paraffin embedding.

FISH

Paraffin-embedding and tissue sectioning were performed by the MSKCC Molecular Cytology Core. Paraffin-embedded tissue sections (5 μm) were stored at 4 °C until use. Sections were processed on a Leica Bond RX stainer, baked for 30 min at 60 °C, dewaxed using Bond Dewax solution (Leica, AR9222) and subjected to EDTA-based epitope retrieval (Leica, AR9640) for 15 min at 95 °C.

For single FISH, RNA probes (Advanced Cell Diagnostics, ready-to-use) were hybridized to the tissue sections for 2 h at 42 °C. Probe detection was performed using a RNAscope 2.5 LS Reagent kit–Brown (ACD, 322100) following the manufacturer’s instructions, with the modification of substituting DAB application with Fluorescent CF594/Tyramide (Biotium, 92174) for 20 min at room temperature. After washing in PBS, slides were incubated with 5 μg ml^–1 DAPI (Sigma-Aldrich) in PBS for 5 min, rinsed and mounted using Mowiol 4–88 (Calbiochem).

For double FISH, a mixture of two probes (hLGR5-C1 and hZFP-C2) was hybridized to 5-μm FFPE tissue sections at 42 °C for 2 h. The hybridized probes were detected using a RNAscope LS Multiplex Reagent kit (ACD, 322800) according to the manufacturer’s protocol. Fluorescence detection was achieved by incubating the slides with Alexa Fluor 488 Tyramide (Life Technologies, B40953) and CF594 Tyramide (Biotium, 92174) for 30 min at room temperature. After PBS washing and DAPI incubation (5 μg ml^–1 for 5 min), slides were mounted in Mowiol 4–88.

The following RNAscope 2.5 LS probes were used in this study: Hs-ZFP36L2 (581268), Mm-Zfp36l2 (518908), Mm-Lgr5 (312178), Hs-LGR5 (311028), Hs-GDF15 (600308), Hs-HILPDA (501958), Hs-GADD45A (477518), positive control probe_Hs-PPIB (313908) and negative control probe_dapB (312038).

Multiplex FISH with IF

After FISH staining, slides were scanned, and coverslips were carefully removed. The slides were then processed following the standard IF protocol (as detailed below).

Single IF staining

For single IF, 5-μm FFPE tissue sections were loaded onto a Leica Bond RX system and dewaxed as the first step. Epitope retrieval was performed using EDTA-based ER2 solution (Leica, AR9640) for 20 min at 100 °C. Primary antibodies against each target were incubated for 1 h at room temperature. Following primary antibody incubation, slides were incubated with Leica Bond Polymer anti-rabbit HRP (included in the Polymer Refine Detection kit, Leica, DS9800) for 8 min. For pan-cytokeratin staining, after primary antibody incubation, slides were incubated with Leica Bond Post-Primary reagent (rabbit anti-mouse linker, included in the Polymer Refine Detection kit, Leica, DS9800) for 8 min, followed by an additional 8 min of incubation with Leica Bond Polymer anti-rabbit HRP (included in the Polymer Refine Detection kit, Leica, DS9800). Signal detection was performed using either Alexa Fluor Tyramide signal amplification reagents (Life Technologies, B40953, B40958) or CF dye Tyramide conjugates (Biotium, 92172, 96053, 92174). After IF staining, slides were washed in PBS and incubated with 5 μg ml^–1 DAPI (Sigma Aldrich) in PBS for 5 min, rinsed in PBS and mounted using Mowiol 4–88 (Calbiochem). Slides were stored overnight at –20 °C before imaging.

Multiplex IF staining

For multiplex IF, antibody staining and detection were performed sequentially according to the single IF staining procedures. Secondary antibody detection was performed using Leica Bond Post-Primary rabbit anti-mouse linker (Leica, DS9800) for human samples stained with mouse-derived primary antibodies, or with rabbit anti-mouse secondary antibody linker (Abcam, ab133469) for mouse samples stained with mouse-derived primary antibodies, followed by Leica Bond Polymer anti-rabbit HRP (Leica, DS9800). After each round of IF staining, epitope retrieval was carried out to denature the primary and secondary antibodies before applying the next primary antibody. After the final staining round, slides were washed in PBS, incubated with DAPI, rinsed, and mounted in Mowiol 4–88 (Calbiochem). Slides were stored at –20 °C overnight before imaging.

The following primary antibodies were used for IF: ZFP36L2 (mouse, 200 µg ml^–1, Santa Cruz, sc-365908); MUC2 (rabbit, 0.0625 μg ml^–1, Abcam, ab97386); pan-cytokeratin (mouse, 0.125 μg ml^–1, Abcam, ab8068); KI67 (rabbit, 0.25 μg ml^–1, Abcam, ab15580); CK20 (rabbit, 1 μg ml^–1, Novus Biologicals, NBP3-03605); CK5 (rabbit, 0.016 μg ml^–1, Cell Signaling Technology, 81817); CHGB (rabbit, 0.1 μg ml^–1, Invitrogen, PA5-52605); DDX6 (rabbit, 0.517 mg ml^–1, Abcam, ab307418); G3BP1 (mouse, 1 mg ml^–1, Abcam, ab56574) and PABPC1 (rabbit, 1 mg ml^–1, Abcam, ab21060). The following secondary antibodies were used for IF: Leica Bond Post-Primary (rabbit anti-mouse linker) (Leica, DS9800) for staining human samples with mouse-derived antibodies, rabbit anti-mouse secondary antibody linker (Abcam, ab133469) for staining mouse samples with mouse-derived antibodies, Leica Bond Polymer anti-rabbit HRP (Leica, DS9800).

FISH and IF imaging and quantification

FISH or IF slides were scanned on a Pannoramic scanner (3DHistech) using a ×40/0.95NA objective.

Mouse crypt FISH and IF multiplex quantification

A total of 31 crypts were drawn and exported as TIFF files from these scans using Slide Viewer (3DHistech). These images were then analysed using ImageJ/FIJI (NIH). Thresholding and Watershedding were used to segment the cells using the DAPI channel. The Lgr5 channel was thresholded and the mask was used to create a distance map. Then, the segmented cells were overlaid onto the distance map and thresholded images to determine their positivity for each marker and their distance from Lgr5.

Tumour marker gene FISH and IF quantification

ROIs around tissues were drawn and exported as TIFF files from scans using Slide Viewer (3DHistech). Images were then analysed using ImageJ/FIJI (NIH). Thresholding and Watershedding were used to segment cells using the DAPI channel. Each marker was thresholded to measure the area and intensity of the fluorescent signal and to classify positivity for each marker per cell.

Patient-derived CRC organoid experimental methods

CRC organoid culture

Organoids were cultured and expanded in HISC medium comprising advanced DMEM/F12 (ADF12; Thermo Fisher Scientific), 2 mM GlutaMAX (Thermo Fisher Scientific), 10 mM HEPES (Thermo Fisher Scientific), 1 mM N-acetyl-l-cysteine (Sigma-Aldrich), 1:50 B27 supplement (Thermo Fisher Scientific), 1:100 N2 supplement (Thermo Fisher Scientific) and 100 μg ml^–1 Primocin (InvivoGen), supplemented with growth factors and inhibitors, including 50 ng ml^–1 EGF (Peprotech), 100 ng ml^–1 Noggin (Peprotech), 500 nM A8301 (Sigma-Aldrich), 50 ng ml^–1 FGF2 (Peprotech) and 100 ng ml^–1 IGF-I (Peprotech). Where indicated, organoids were cultured in IGFF containing all base components of the medium but lacking EGF, Noggin, A8301, FGF2 and IGF. Medium was supplemented with 2 μg ml^–1 doxycycline where indicated. Medium was refreshed every 2 days. Human organoid lines used in this study were verified by short tandem repeat analysis at the time of establishment and before each experiment. All organoid lines used in this study were routinely tested for mycoplasma contamination using a MycoAlert PLUS Detection kit (Lonza), and all tested negative.

For downstream applications, organoids were collected on day 7 by dissolving Matrigel with 2 mM EDTA in DPBS, followed by orbital agitation at 4 °C for 30–60 min. Organoids were then dissociated into single cells using TrypLE (Thermo Fisher Scientific) for 5–10 min at 37 °C. The reaction was quenched by adding 5 volumes of wash buffer (DMEM/F12 supplemented with 2 mM GlutaMAX, 10 mM HEPES, 100 U ml^–1 penicillin–streptomycin (Thermo Fisher Scientific) and 5% FBS (Sigma-Aldrich)), and the cell suspension was filtered through a 40 μm cell strainer. For passaging, cells were embedded at 375 cells per μl Matrigel. For organoid formation and IC₅₀ assays, cells were seeded at 50 cells per μl Matrigel. Matrigel domes were incubated at 37 °C for 30 min to solidify before adding culture medium. Lentiviral transduction was performed as previously described⁴², and antibiotic selection was initiated after the first passage post-transduction using 2 μg ml^–1 puromycin, 15 μg ml^–1 blasticidin and 400 μg ml^–1 geneticin (Thermo Fisher Scientific).

Plasmids

For doxycycline-inducible knockdown of ZFP36L2, miR-30a-based shRNA sequences (RHS4430-200267269 and RHS4430-200276644, pGIPZ) were subcloned into the pTRIPZ lentiviral vector using XhoI and MluI restriction sites. The resulting constructs encoded the following inducible mature antisense sequences: 5′-TGCACAAGAAGTCGACATC-3′ and 5′-TGTTGAGCAGGGCTGTGCC-3′. Lentiviral particles were produced as previously described⁷⁸ in HEK293T cells (American Type Culture Collection) using a non-targeting control vector (Addgene, 127696). For ZFP36L2 knockout, the doxycycline-inducible Cas9 vector TLCV2 (Addgene, 87360) was cloned with either sgRNA1 (5′-ACCCTTAAGGAGCCGTCGGG-3′) or sgRNA2 (5′-TGCTGGCCGAGTGCCGTCGG-3′) targeting ZFP36L2 to generate indel-mediated gene disruption. These constructs were delivered to organoids by lentiviral transduction. Transduced organoids were selected using puromycin and treated with 2 μg ml^–1 doxycycline for 1 week before sorting eGFP⁺ cells. Sorted cells were seeded at 2,000 cells per 40 μl Matrigel dome and cultured in the continued presence of doxycycline. On day 10, individual organoid clones were picked, dissociated and expanded as single clones. ZFP36L2 knockout was confirmed by western blot analysis. For inducible protein expression, the coding sequence of WT human ZFP36L2 was PCR-amplified from organoid cDNA, in-frame fused to an eGFP or meGFP tag and cloned into AgeI-digested and NheI-digested TLCV2 vector (Addgene, 87360) via Gibson assembly using 20-bp homologous overlaps. A frameshift mutant (fsZFP36L2) was generated using a similar approach by dividing the ZFP36L2 coding sequence into two fragments (nucleotides 1–429 and 433–1359) for Gibson assembly. A single-nucleotide deletion (guanine deletion at codon Gly143) was introduced on the primer through site-directed mutagenesis. Lentiviral particles carrying doxycycline-inducible WT ZFP36L2–eGFP, ZFP36L2–meGFP, fsZFP36L2–eGFP or eGFP-only control were produced for downstream experiments. To generate ADAR-fusion constructs for HyperTRIBE, the hyperactive catalytic domain of Drosophila melanogaster ADAR was PCR-amplified from a plasmid template (Addgene, 166969) and inserted in-frame into the doxycycline-inducible WT and fsZFP36L2 expression constructs. The resulting fusion constructs encoded ADAR-tagged ZFP36L2 or fsZFP36L2, followed by a self-cleaving T2A peptide linked to eGFP (Extended Data Fig. 11a). Lentiviral particles were generated and used to transduce organoids. For nuclear labelling during live-cell imaging, organoids were stably transduced with mCherry-tagged histone H2B in a pLenti6/V5-DEST backbone (Addgene, 89766). All the plasmid sequences are listed in Supplementary Table 4a.

Western blotting

Approximately 2 million organoid cells were recovered from Matrigel by disrupting domes in 2 mM EDTA in DPBS with orbital shaking at 4 °C for 30 min. Cells were washed, centrifuged (500g, 5 min, 4 °C) and lysed in 1× RIPA buffer (50 mM Tris-HCl, pH 8.0, 150 mM NaCl, 0.1% SDS, 0.5% sodium deoxycholate and 1% NP-40) supplemented with protease inhibitors (Roche, complete EDTA-free protease inhibitor cocktail) and phosphatase inhibitors (Sigma-Aldrich, 524636-1SET) and benzonase nuclease (2% v/v; Thermo Fisher Scientific, 70-664-3) for 30 min on ice. Lysates were sonicated using a Bioruptor (10 s on–10 s off for 10 cycles at 4 °C). The protein concentration was determined using a Pierce BCA Protein Assay kit (Thermo Fisher Scientific, 23227).

For immunoblotting, 50 μg of total protein per sample was separated by SDS–PAGE on Bis–Tris polyacrylamide gels (Thermo Fisher Scientific), transferred to methanol-activated 0.45 μm PVDF membranes (Millipore, IPFL00010) and blocked with 5% non-fat milk in TBST for 30 min at room temperature. Membranes were incubated overnight at 4 °C with the following primary antibodies: mouse anti-β-actin (1:2,000; Abcam, ab8226), anti-GAPDH (1:2,000; Abcam, ab181602) and anti-ZFP36L2 (1:200; Santa Cruz, clone A-3, sc-365908).

After washing, membranes were incubated with HRP-conjugated anti-mouse or anti-rabbit secondary antibodies (1:10,000; Cell Signaling Technology, 7076 for mouse and 7074 for rabbit). Signals were detected using an iBright CL1000 imaging system (Invitrogen) with SuperSignal West Femto Maximum Sensitivity substrate (Thermo Fisher Scientific).

Reverse transcription and qPCR

Total RNA was extracted from organoids using a RNeasy Mini kit (Qiagen) according to the manufacturer’s instructions. cDNA was synthesized from 2 μg of total RNA using a Transcriptor First-Strand cDNA Synthesis kit (Roche). qPCR was performed using TaqMan gene expression assay primers (ZFP36L2: Hs00272828_m1; GAPDH: Hs02758991_g1, Mm99999915_g1; ACTB: Hs01060665_g1; Thermo Fisher Scientific) on an ABI QuantStudio 7 Pro Real-Time PCR system (Applied Biosystems). Relative expression was computed using Design & Analysis software (v.2.6.0) using the \({2}^{-\Delta \Delta {C}_{t}}\) method and normalized to GAPDH or ACTB expression.

CRC organoid dedifferentiation and scRNA-seq

OKG146P organoids expressing shZFP36L2 or shCtrl were cultured initially in HISC (with 2 μg ml^–1 doxycycline) for 7 days, and live cells were collected for scRNA-seq. A portion of the same batch of organoids was then passaged into IGFF medium (with 2 μg ml^–1 doxycycline) for 7 days to induce differentiation as previously described¹⁰. Organoids were then passaged into complete HISC medium (with 2 μg ml^–1 doxycycline) for an additional 7 days to induce dedifferentiation into an ISC-like state, followed by scRNA-seq. Organoids continuously maintained in complete HISC medium (with 2 μg ml^–1 doxycycline) for 7 days served as controls.

Organoids were dissociated into single cells as described above. For sample multiplexing, single-cell suspensions were incubated with TotalSeq hashtag antibodies (BioLegend, B0251, B0252, B0253) for 30 min on ice. Viable (DAPI-negative) cells were sorted using a 130 μm nozzle on a SH800S (Sony) cell sorter and collected into DPBS containing 0.04% BSA. Equal numbers of hashtag-labelled shZFP36L2 and control cells from each condition were pooled for scRNA-seq using a Chromium Single Cell 3′ (v.3.1) platform (10x Genomics) according to the manufacturer’s protocol. FACS-sorted cells were washed with DPBS + 0.04% BSA and resuspended at 700–1,300 cells per μl. Cell viability was above 90%, as determined by 0.2% Trypan Blue exclusion. Up to 10,000 cells per sample were targeted for droplet-based encapsulation and barcoding. After reverse transcription, emulsions were broken and cDNA was purified using Dynabeads MyOne SILANE, followed by PCR amplification. Libraries were sequenced on an Illumina NovaSeq S4 platform using the following read configuration: read 1, 28 cycles; i7 index, 8 cycles; read 2, 90 cycles.

Data processing, quality control and batch correction

Raw scRNA-seq data were pre-processed to remove ambient RNA contamination using CellBender (v.1.0.0). Doublets were identified and excluded using DoubletDetection (v.3.0). Cells with log-transformed library sizes deviating by more than 3 MADs below or 5 MADs above the median were considered outliers and removed. Additional quality control metrics, including total UMI counts, number of detected genes, mitochondrial gene fraction and ribosomal gene fraction, were computed per cell. Cells with fewer than 500 total counts, fewer than 400 detected genes, greater than 20% mitochondrial content or otherwise poor-quality metrics were excluded. Hashtag-related genes were removed to eliminate multiplexing artefacts.

Normalization and batch correction

After quality filtering, normalization was performed using Scran (v.3.20), and log-transformed expression values were generated. The dataset was aggregated for downstream analyses. Batch correction was performed using single-cell variational inference (scVI)⁷⁹. A raw count matrix was input, and Seurat (v.3) was used to identify 5,000 HVGs. The scVI model was initialized with 100 latent dimensions, incorporating mitochondrial content as a covariate.

Dimensionality reduction and clustering

PCA was performed, retaining 280 principal components that explained 75% of the variance. Cells were clustered using the Leiden algorithm (resolution of 1.8), and UMAP embeddings were generated for visualization. Differential gene expression analysis was performed using the Wilcoxon rank-sum test.

Local variability analysis

To assess local variability in cell states, KNN entropy was computed by calculating the Shannon entropy of ZFP36L2 expression in each cell’s 15-nearest neighbour graph. Entropy distributions across experimental groups (for example, shZFP36L2 versus shCtrl) were visualized using boxplots. Kernel density estimation plots were also used to visualize expression variability in low-dimensional space.

Differential abundance analysis

To evaluate shifts in cell population composition, MELD⁸⁰ was used to partition the UMAP embedding into transcriptionally similar neighbourhoods. The method quantified relative differences in the abundance of these regions between conditions (for example, shZFP36L2 versus shCtrl).

Cell-state classification

Cell-state classification was performed using PhenoGraph⁸¹, which integrates graph-based clustering with phenotypic similarity. An affinity matrix was constructed based on nearest-neighbour relationships, and batch effects were corrected using Harmony⁸². Cell-state labels were assigned to organoid cells by propagating annotations from reference patient-derived cell states.

Bulk RNA-seq

Total RNA was extracted from organoids with conditions specified in the figure legends using a RNeasy Mini kit (Qiagen) according to the manufacturer’s instructions. Three technical replates for each sample were prepared for all the experiments. After RiboGreen quantification and quality control by Agilent BioAnalyzer, 300 ng of total RNA with RIN values of 9.8–10 underwent polyA selection and TruSeq library preparation according to instructions provided by Illumina (TruSeq Stranded mRNA LT kit, RS-122-2102), with 8 cycles of PCR. Samples were barcoded and run on NovaSeq 6000 in a PE100 flowcell, using a NovaSeq 6000 S4 NovaSeq Reagent kit (200 Cycles) (Illumina). An average of 109 million paired reads was generated per sample. Ribosomal reads represented 0.4–0.7% of the total reads generated and the per cent of mRNA bases averaged 92%.

Bulk RNA-seq data analysis

Adapter sequences were trimmed using Cutadapt (v.4.8), and processed reads were aligned to the human reference genome (hg38) using STAR (v.2.7.11b). Gene-level read counts were quantified using HTSeq (v.2.0.5), and differential expression analysis was performed using DESeq2 (v.1.42.0).

HyperTRIBE data analysis

WT ZFP36L2 or fsZFP36L2 was expressed as a fusion with the hyperactive E488Q mutant catalytic domain of ADAR, which enables the detection of ZFP36L2-interacting mRNAs through capture of adenosine (A) to inosine (I, read as guanosine (G)) edits through RNA-seq⁴⁸. The construct design is shown in Extended Data Fig. 11a. Following stable lentiviral integration, organoids were seeded as single cells, cultured in HISC medium for 3 days and then treated with doxycycline for 4 days before flow sorting for eGFP⁺ (ADAR fusion protein expressing) cells and RNA-seq. Day 5 organoids stably transduced with doxycycline-inducible ZFP36L2–ADAR or fsZFP36L2–ADAR constructs were treated with 2 μg ml^–1 doxycycline for 2 days to induce transgene expression. eGFP⁺ live cells were isolated by flow cytometry, and total RNA was extracted using a RNeasy Mini kit (Qiagen). mRNA libraries were prepared and sequenced as described above. ADAR-seq data were processed as previously described⁴⁹. In brief, adapter sequences were trimmed using Cutadapt (v.4.8), and reads were aligned to the hg38 genome and construct sequences using STAR (v.2.7.11b). RNA editing sites were identified using GATK HaplotypeCaller (v.3.8.1.0), with stringent filtering to retain only A-to-G substitutions on the forward strand and U-to-C substitutions on the reverse strand. Known variants annotated in dbSNP (human_9606_b144_GRCh38p2) were excluded. The editing frequency at each site was assessed using a Beta-binomial test, with multiple testing correction performed using the Benjamini–Hochberg method.

Gene decay rate comparison

PDO MSK107Li and OKG146Li organoids expressing shCtrl or shZFP36L2 were cultured as described above, supplemented with 2 μg ml^–1 doxycycline. On day 5, organoids were treated with or without 5 μg ml^–1 ActD for 5 h. Organoids were then collected, and total RNA was extracted using a RNeasy Mini kit (Qiagen). mRNA libraries were prepared and sequenced as described above.

Adapter sequences were trimmed using Cutadapt (v.4.8), and processed reads were aligned to the human reference genome (hg38) using STAR (v.2.7.11b). Gene-level read counts were quantified using HTSeq (v.2.0.5), and differential expression analysis was performed using DESeq2 (v.1.42.0) to compare the 5-h-treated versus untreated samples for shCtrl or shZFP36L2 separately to capture gene decay rates in each condition. To compare decay rates between shCtrl and shZFP36L2 experiments, gene-level read counts were normalized globally for both shCtrl and shZFP36L2 cells, with and without treatment. The decay rate dependency on ZFP36L2 for each gene was calculated using the following formula:

Thiol-linked alkylation for the metabolic sequencing of RNA

To measure RNA decay kinetics, we performed SLAM-seq⁵² in CRC organoids using a SLAM-seq Kinetics kit–Catabolic Kinetics Module (Lexogen) followed by library preparation with a QuantSeq 3′ mRNA-seq V2 Library Prep kit FWD with Unique Dual Indices (Lexogen). The SLAM-seq workflow is based on metabolic labelling of newly synthesized RNA with 4sU, followed by alkylation of 4sU-containing RNA and detection of T > C conversions after sequencing. For all steps from 4sU handling up until library preparation, samples and reagents were protected from white light and handled in the dark or wrapped in foil whenever possible, as recommended by the manufacturer because 4sU is light sensitive and can crosslink.

We first performed an organoid viability titration to determine a non-toxic working concentration of 4sU. shCtrl-expressing MSK107L organoids were dissociated and seeded in 96-well plates as 40 μl Matrigel domes containing 2,000 cells per well in HISC medium. On day 6, organoids were treated with a range of 4sU concentrations, with medium replaced every 2 h, and cell viability was measured after 13 h using CellTiter-Glo assays. The concentration causing 10% growth inhibition over the assay window was selected for the final experiment, as per the manufacturer’s recommendations. On the basis of this titration, 500 μM 4sU was used for all subsequent labelling experiments.

For the catabolic labelling experiment, organoids were labelled with 500 μM 4sU for 6 h, with fresh 4sU-containing medium supplied every 2 h to maintain labelling efficiency. At the end of labelling, one set of samples was collected immediately as the t₀ time point. To measure mRNA decay kinetics, 4sU-containing medium was removed and organoids were washed by adding 3 ml warm PBS and incubating for 10 min to allow residual labelling medium to equilibrate out. The PBS was then replaced with HISC medium containing 50 mM uridine and samples were collected at 2 h, 5 h and 12 h. At each time point, the medium was removed completely and organoids were lysed directly in the culture well by adding 1 ml TRIzol and pipetting 15 times to fully dissolve the Matrigel domes. Lysates were transferred to pre-labelled 1.5 ml tubes, snap-frozen and stored at −80 °C until RNA extraction. Total RNA was subsequently isolated using a standard TRIzol-based extraction protocol.

Purified RNA was then subjected to chemical derivatization according to the SLAM-seq Kinetics kit–Catabolic Kinetics Module manufacturer’s instructions. In brief, 4sU-labelled RNA was alkylated with iodoacetamide under the recommended reaction conditions to modify incorporated 4sU residues before library preparation. Alkylated RNA was purified and used as input for sequencing library construction. SLAM-seq chemistry enables reverse transcriptase to incorporate G opposite alkylated 4sU during cDNA synthesis, which results in diagnostic T > C conversions in sequencing reads derived from newly labelled transcripts. Sequencing libraries were generated from total RNA using a QuantSeq 3′ mRNA-seq V2 Library Prep kit FWD with Unique Dual Indices according to the manufacturer’s instructions. This protocol uses oligo(dT)-primed first-strand synthesis, followed by RNA removal, random-primed second-strand synthesis, bead purification and PCR amplification to generate strand-specific Illumina-compatible libraries enriched for the 3′ ends of polyadenylated transcripts. Unique dual indices were introduced during PCR amplification. Libraries were prepared from total RNA without previous poly(A) enrichment or rRNA depletion, consistent with the QuantSeq workflow.

SLAM-seq data processing and half-life analysis

Raw SLAM-seq reads were processed using the nf-core/slamseq pipeline (v.1.0.0) implemented in Nextflow. Sequencing reads were trimmed for adapters and low-quality bases using Trim Galore, and processed reads were aligned using Slamdunk, which performs conversion-aware mapping, alignment filtering, multimapper recovery, SNP calling to mask genomic T > C variants and quantification of both total and T > C-converted reads. Read counts were first generated at the UTR level and then collapsed to the gene level. The pipeline also generated quality-control metrics summarizing nucleotide-conversion frequencies across reads, read positions and gene–UTR positions, together with trimming and alignment statistics compiled in a MultiQC report. Human organoid datasets were processed against the GRCh38 reference genome. Generation of 3′ UTR annotations and Nextflow pipeline configurations followed the settings described in a previous study⁸³. For RNA half-life estimation, gene-level T > C conversion rates were background-corrected using matched unlabelled samples, with control samples corrected using unlabelled control samples and knockdown samples corrected using unlabelled knockdown samples. In organoid samples, residual 4sU retention in Matrigel resulted in delayed peak labelling, with many transcripts reaching maximal T > C signal at 2 h after chase initiation. Therefore, for the 5,888 genes that peaked at t = 2 h in control samples, t₂ was considered the first effective decay time point, and half-lives were calculated by fitting a single-exponential decay model to the t₂, t₅ and t₁₂ time points. RNA half-lives were calculated as t_1/2 = ln(2)/k, where k is the fitted first-order decay constant. For control versus knockdown comparisons, only these 5,888 genes were considered, and genes were further filtered for good decay-model fit in the control condition (R² exp > 0.6, where R² exp is the coefficient of determination of the single-exponential decay model fit for each gene).

Live-cell confocal microscopy

Confocal imaging was performed on stably transduced, doxycycline-inducible eGFP, fsZFP36L2–eGFP, ZFP36L2–eGFP and meGFP organoids seeded in a 1:1 mixture of culture medium and Matrigel on Ibidi glass-bottom chamber slides (80821). Imaging was carried out using a Nikon CSU-W1 SoRa spinning disk confocal microscope equipped with a ×60 oil-immersion objective, 1.49 NA, 300 ms exposure time, 5% laser power and 1 μm z-stack intervals. Variable settings, treatments or imaging time windows specific to each experiment are detailed in the figure legends. For nuclear–cytoplasmic shuttling experiments, organoids expressing eGFP-tagged constructs were co-transduced with a H2B–mCherry nuclear marker. Live-cell imaging was performed using the same microscope setup at ×60 magnification, with 300 ms exposure time, 5% laser power and continuous acquisition of 0.2 μm z stacks.

Condensate tracking analysis

Time-lapse videos were first motion-corrected for sample drift using the Correct 3D Drift plugin in Fiji, with drift correction restricted to the x–y plane and based on the condensate channel to avoid instability in z axis estimation. Background subtraction was then performed on the condensate channel using a rolling-ball radius of 50 μm to reduce variability arising from elevated background fluorescence in some cells. Condensates were subsequently detected and tracked in 3D using the TrackMate plugin in Fiji (TrackMate 7)⁸⁴. Spot detection was performed with the difference-of-Gaussians detector using a sigma of 1.25 μm. The quality threshold was manually adjusted for each video on the first frame to optimize condensate detection while minimizing false-positive assignments. Tracking was performed using the Simple LAP tracker, with a maximum linking distance of 4 μm between consecutive frames and a maximum gap-closing distance of 2 μm. Tracklets spanning fewer than two frames were excluded from further analyses. The remaining trajectories were overlaid on maximum-intensity projections of the drift-corrected videos. For visualization, tracks were displayed retrospectively for up to five preceding frames across all z planes.

FRAP

FRAP experiments were performed using a Stellaris confocal imaging system (Leica) equipped with a ×63 1.4 NA oil-immersion objective. Stably transduced, doxycycline-inducible eGFP, fsZFP36L2–eGFP and WT ZFP36L2–eGFP organoid lines were seeded on Lab-Tek chamber slides (155409) 3 days before imaging, with doxycycline (2 μg ml⁻¹) added 24 h before the experiment to induce construct expression. A ROI measuring 1.5 × 1.5 μm was photobleached at 35% laser power for 15 frames (0.6 s per frame), followed by acquisition of 30 frames (1 s per frame) to monitor fluorescence recovery. Fluorescence intensity in the ROI was quantified using Fiji (ImageJ), and the mobile fraction was calculated as:

where F(∞) is the fluorescence intensity at steady state after recovery, F(0) is the intensity immediately after bleaching and F(pre) is the pre-bleach intensity.

Statistics and reproducibility

No statistical method was used to predetermine sample sizes. The number of samples (n) is indicated in each figure panel, and statistical analyses are described in the corresponding figure legends. In vitro experiments were repeated a minimum of three times independently with similar results unless otherwise noted. All staining experiments were performed with a minimum of three biological replicates. Mice that died within 24 h of surgery were excluded from analysis as procedure-related complications; no other data were excluded.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

1. Hanahan, D. Hallmarks of cancer: new dimensions. Cancer Discov. 12, 31–46 (2022).

   Article  PubMed  CAS  Google Scholar 

2. de Sousa e Melo, F. & de Sauvage, F. J. Cellular plasticity in intestinal homeostasis and disease. Cell Stem Cell 24, 54–64 (2019).

   Article  PubMed  CAS  Google Scholar 

3. Varga, J. & Greten, F. R. Cell plasticity in epithelial homeostasis and tumorigenesis. Nat. Cell Biol. 19, 1133–1141 (2017).

   Article  PubMed  CAS  Google Scholar 

4. Gerstberger, S., Jiang, Q. & Ganesh, K. Metastasis. Cell 186, 1564–1579 (2023).

   Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

5. Tian, H. et al. A reserve stem cell population in small intestine renders Lgr5-positive cells dispensable. Nature 478, 255–259 (2011).

   Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

6. de Sousa e Melo, F. et al. A distinct role for Lgr5⁺ stem cells in primary and metastatic colon cancer. Nature 543, 676–680 (2017).

   Article  ADS  PubMed  CAS  Google Scholar 

7. Ganesh, K. et al. L1CAM defines the regenerative origin of metastasis-initiating cells in colorectal cancer. Nat. Cancer 1, 28–45 (2020).

   Article  PubMed  PubMed Central  CAS  Google Scholar 

8. Fumagalli, A. et al. Plasticity of Lgr5-negative cancer cells drives metastasis in colorectal cancer. Cell Stem Cell (2020).

9. Cañellas-Socias, A. et al. Metastatic recurrence in colorectal cancer arises from residual EMP1⁺ cells. Nature 611, 603–613 (2022).

   Article  ADS  PubMed  PubMed Central  Google Scholar 

10. Moorman, A. et al. Progressive plasticity during colorectal cancer metastasis. Nature 637, 947–954 (2025).

    Article  ADS  PubMed  CAS  Google Scholar 

11. Martínez-Jiménez, F. et al. Pan-cancer whole-genome comparison of primary and metastatic solid tumours. Nature 618, 333–341 (2023).

    Article  ADS  PubMed  PubMed Central  Google Scholar 

12. Nunes, L. et al. Prognostic genome and transcriptome signatures in colorectal cancers. Nature 633, 137–146 (2024).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

13. Cornish, A. J. et al. The genomic landscape of 2,023 colorectal cancers. Nature 633, 127–136 (2024).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

14. Mendelaar, P. A. J. et al. Whole genome sequencing of metastatic colorectal cancer reveals prior treatment effects and specific metastasis features. Nat. Commun. 12, 574 (2021).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

15. Kishigami, F. et al. Exploration of predictive biomarkers for postoperative recurrence of stage II/III colorectal cancer using genomic sequencing. Cancer Med. 11, 3457–3470 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

16. Barker, N. et al. Identification of stem cells in small intestine and colon by marker gene Lgr5. Nature 449, 1003–1007 (2007).

    Article  ADS  PubMed  CAS  Google Scholar 

17. Viragova, S., Li, D. & Klein, O. D. Activation of fetal-like molecular programs during regeneration in the intestine and beyond. Cell Stem Cell 31, 949–960 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

18. Murata, K. et al. Ascl2-dependent cell dedifferentiation drives regeneration of ablated intestinal stem cells. Cell Stem Cell 26, 377–390 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

19. Tape, C. J. Plastic persisters: revival stem cells in colorectal cancer. Trends Cancer 10, 185–195 (2024).

    Article  PubMed  CAS  Google Scholar 

20. Barker, N. et al. Crypt stem cells as the cells-of-origin of intestinal cancer. Nature 457, 608–611 (2009).

    Article  ADS  PubMed  CAS  Google Scholar 

21. Schwitalla, S. et al. Intestinal tumorigenesis initiated by dedifferentiation and acquisition of stem-cell-like properties. Cell 152, 25–38 (2013).

    Article  PubMed  CAS  Google Scholar 

22. Shimokawa, M. et al. Visualization and targeting of LGR5⁺ human colon cancer stem cells. Nature 545, 187–192 (2017).

    Article  ADS  PubMed  CAS  Google Scholar 

23. Bala, P. et al. Aberrant cell state plasticity mediated by developmental reprogramming precedes colorectal cancer initiation. Sci. Adv. 9, eadf0927 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

24. Vasquez, E. G. et al. Dynamic and adaptive cancer stem cell population admixture in colorectal neoplasia. Cell Stem Cell 29, 1213–1228 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

25. Mzoughi, S. et al. Oncofetal reprogramming drives phenotypic plasticity in WNT-dependent colorectal cancer. Nat. Genet. 57, 402–412 (2025).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

26. Ogden, S. et al. Phenotypic heterogeneity and plasticity in colorectal cancer metastasis. Cell Genom. (2025).

27. Heinz, M. C. et al. Liver colonization by colorectal cancer metastases requires YAP-controlled plasticity at the micrometastatic stage. Cancer Res. 82, 1953–1968 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

28. Qin, X. et al. An oncogenic phenoscape of colonic stem cell polarization. Cell 186, 5554–5568 (2023).

    Article  PubMed  CAS  Google Scholar 

29. Varnum, B. C., Ma, Q. F., Chi, T. H., Fletcher, B. & Herschman, H. R. The TIS11 primary response gene is a member of a gene family that encodes proteins with a highly conserved sequence containing an unusual Cys-His repeat. Mol. Cell. Biol. 11, 1754–1758 (1991).

    PubMed  PubMed Central  CAS  Google Scholar 

30. Ramos, S. B. V. et al. The CCCH tandem zinc-finger protein Zfp36l2 is crucial for female fertility and early embryonic development. Development 131, 4883–4893 (2004).

    Article  PubMed  CAS  Google Scholar 

31. Stumpo, D. J. et al. Targeted disruption of Zfp36l2, encoding a CCCH tandem zinc finger RNA-binding protein, results in defective hematopoiesis. Blood 114, 2401–2410 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

32. Zhang, L. et al. ZFP36L2 is required for self-renewal of early burst-forming unit erythroid progenitors. Nature 499, 92–96 (2013).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

33. França, G. S. & Yanai, I. A mechanism for adaptive genome regulation in cancer. Nature 652, 581–590 (2026).

    Article  ADS  PubMed  Google Scholar 

34. Wells, M. L., Perera, L. & Blackshear, P. J. An ancient family of RNA-binding proteins: still important! Trends Biochem. Sci 42, 285–296 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

35. Amit, I. et al. A module of negative feedback regulators defines growth factor signaling. Nat. Genet. 39, 503–512 (2007).

    Article  PubMed  CAS  Google Scholar 

36. Canzoneri, R. et al. Identification of an AP1-ZFP36 regulatory network associated with breast cancer prognosis. J. Mammary Gland Biol. Neoplasia 25, 163–172 (2020).

    Article  PubMed  CAS  Google Scholar 

37. Sha, Q. et al. CNOT6L couples the selective degradation of maternal transcripts to meiotic cell cycle progression in mouse oocyte. EMBO J. 37, e99333 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

38. Kim, J. J., Shajib, M. S., Manocha, M. M. & Khan, W. I. Investigating intestinal inflammation in DSS-induced model of IBD. JoVE https://doi.org/10.3791/3678 (2013).

39. Madison, B. B. et al. cis Elements of the villin gene control expression in restricted domains of the vertical (crypt) and horizontal (duodenum, cecum) axes of the intestine. J. Biol. Chem. 277, 33275–33283 (2002).

    Article  PubMed  CAS  Google Scholar 

40. Hodson, D. J. et al. Deletion of the RNA-binding proteins ZFP36L1 and ZFP36L2 leads to perturbed thymic development and T lymphoblastic leukemia. Nat. Immunol. 11, 717–724 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

41. Sato, T. et al. Single Lgr5 stem cells build crypt–villus structures in vitro without a mesenchymal niche. Nature 459, 262–265 (2009).

    Article  ADS  PubMed  CAS  Google Scholar 

42. Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

43. Varadi, M. et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 50, D439–D444 (2022).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

44. Shin, Y. & Brangwynne, C. P. Liquid phase condensation in cell physiology and disease. Science 357, eaaf4382 (2017).

    Article  PubMed  Google Scholar 

45. Zheng, T. et al. Molecular insights into the effect of 1,6-hexanediol on FUS phase separation. EMBO J. 44, 2725–2740 (2025).

46. Dörner, K. et al. Fluorescent protein and peptide tags alter condensate formation and dynamics in vivo and in vitro. EMBO Rep. 27, 89–121 (2026).

    Article  PubMed  Google Scholar 

47. Riggs, C. L., Kedersha, N., Ivanov, P. & Anderson, P. Mammalian stress granules and P bodies at a glance. J. Cell Sci. 133, jcs242487 (2020).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

48. Rahman, R., Xu, W., Jin, H. & Rosbash, M. Identification of RNA-binding protein targets with HyperTRIBE. Nat. Protoc. 13, 1829–1849 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

49. Nguyen, D. T. T. et al. HyperTRIBE uncovers increased MUSASHI-2 RNA binding activity and differential regulation in leukemic stem cells. Nat. Commun. 11, 2026 (2020).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

50. Dumdie, J. N. et al. Chromatin modification and global transcriptional silencing in the oocyte mediated by the mRNA decay activator ZFP36L2. Dev. Cell 44, 392–402 (2018).

    Article  CAS  Google Scholar 

51. Cicchetto, A. C. et al. ZFP36-mediated mRNA decay regulates metabolism. Cell Rep. 42, 112411 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

52. Herzog, V. A. et al. Thiol-linked alkylation of RNA to assess expression dynamics. Nat. Methods 14, 1198–1204 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

53. Chen, H.-N. et al. Genomic evolution and diverse models of systemic metastases in colorectal cancer. Gut https://doi.org/10.1136/gutjnl-2020-323703 (2021).

54. Zhong, J. et al. Large-scale multiomic analysis identifies non-coding somatic driver mutations and nominates ZFP36L2 as a driver gene for pancreatic ductal adenocarcinoma. Gut https://doi.org/10.1136/gutjnl-2025-335152 (2025).

55. Yonemori, K. et al. ZFP36L2 promotes cancer cell aggressiveness and is regulated by antitumor microRNA-375 in pancreatic ductal adenocarcinoma. Cancer Sci. 108, 124–135 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

56. Su, X. et al. Comprehensive integrative profiling of upper tract urothelial carcinomas. Genome Biol. 22, 7 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

57. Priestley, P. et al. Pan-cancer whole-genome analyses of metastatic solid tumours. Nature 575, 210–216 (2019).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

58. Wang, E. et al. Surface antigen-guided CRISPR screens identify regulators of myeloid leukemia differentiation. Cell Stem Cell 28, 718–731 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

59. Chen, H.-Y. et al. Regulation of neuroendocrine plasticity by the RNA-binding protein ZFP36L1. Nat. Commun. 13, 4998 (2022).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

60. Malagola, E. et al. Isthmus progenitor cells contribute to homeostatic cellular turnover and support regeneration following intestinal injury. Cell 187, 3056–3071 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

61. Capdevila, C. et al. Time-resolved fate mapping identifies the intestinal upper crypt zone as an origin of Lgr5⁺ crypt base columnar cells. Cell 187, 3039–3055.e14 (2024).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

62. Islam, M. et al. Unbiased recording of clonal potency reveals species-specific regulation of mammalian intestine. Preprint at bioRxiv https://doi.org/10.1101/2025.06.10.658707 (2025).

63. Aaltonen, L. A. et al. Pan-cancer analysis of whole genomes. Nature 578, 82–93 (2020).

    Article  Google Scholar 

64. Giannakis, M. et al. Genomic correlates of immune-cell infiltrates in colorectal carcinoma. Cell Rep. 15, 857–865 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

65. Schaefer, C. F. et al. PID: the Pathway Interaction Database. Nucleic Acids Res. 37, D674–679 (2009).

    Article  PubMed  CAS  Google Scholar 

66. Širvinskas, D. et al. Single-cell atlas of the aging mouse colon. iScience https://doi.org/10.1016/j.isci.2022.104202 (2022).

67. Cheng, D. T. et al. Memorial Sloan Kettering–Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): a hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology. J. Mol. Diagn. 17, 251–264 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

68. Chakravarty, D. et al. OncoKB: a precision oncology knowledge base. JCO Precis. Oncol. 2017, PO.17.00011 (2017).

    PubMed  PubMed Central  Google Scholar 

69. DeTomaso, D. & Yosef, N. Hotspot identifies informative gene modules across modalities of single-cell genomics. Cell Syst. 12, 446–456 (2021).

    Article  PubMed  CAS  Google Scholar 

70. Germain, P.-L., Sonrel, A. & Robinson, M. D. pipeComp, a general framework for the evaluation of computational pipelines, reveals performant single cell RNA-seq preprocessing tools. Genome Biol. 21, 227 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

71. Heumos, L. et al. Best practices for single-cell analysis across modalities. Nat. Rev. Genet. 24, 550–572 (2023).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

72. Germain, P.-L., Lun, A., Meixide, C. G., Macnair, W. & Robinson, M. D. Doublet identification in single-cell sequencing data using scDblFinder. F1000Research https://doi.org/10.12688/f1000research.73600.2 (2022).

73. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

74. Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

75. Dijk et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

76. Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. 37, 1482–1492 (2019).

    Article  ADS  PubMed  PubMed Central  CAS  Google Scholar 

77. Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

78. Maru, Y., Orihashi, K. & Hippo, Y. Lentivirus-based stable gene delivery into intestinal organoids. Methods Mol. Biol. 1422, 13–21 (2016).

    Article  PubMed  CAS  Google Scholar 

79. Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

80. Burkhardt, D. B. et al. Quantifying the effect of experimental perturbations at single-cell resolution. Nat. Biotechnol. 39, 619–629 (2021).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

81. Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

82. Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

83. Muhar, M. et al. SLAM-seq defines direct gene-regulatory functions of the BRD4–MYC axis. Science 360, 800–805 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

84. Ershov, D. et al. TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines. Nat. Methods 19, 829–832 (2022).

    Article  PubMed  CAS  Google Scholar 

85. Cheng, J. et al. Accurate proteome-wide missense variant effect prediction with AlphaMissense. Science 381, eadg7492 (2023).

    Article  PubMed  CAS  Google Scholar 

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Authors and Affiliations

1. Molecular Pharmacology Program, Sloan Kettering Institute, New York, NY, USA

   Qingwen Jiang, Manisha S. Raghavan, Aileen M. Rodriguez, Saskia Hartner, Ahmed Mahmoud, Fabian Zincke, Sasha Balkaran, Kathleen Luckett, Jura Pintar, Bernadette Mödl, Michael G. Kharas & Karuna Ganesh

2. Gerstner Sloan Kettering Graduate School of Biomedical Sciences, New York, NY, USA

   Manisha S. Raghavan & Kathleen Luckett

3. Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA

   Morgan Lallo, Britney Forsyth & Joseph M. Chan

4. Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

   Cyrus L. Tam, Andrew Moorman, Ilyes Baali, Dana Pe’er & Quaid Morris

5. Tri-Institutional PhD Program in Computational Biology and Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

   Cyrus L. Tam & Ilyes Baali

6. Pharmacology Graduate Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA

   Ahmed Mahmoud

7. Antitumor Assessment Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY, USA

   Huiyong Zhao & Elisa de Stanchina

8. Tri-Institutional MD–PhD Program, New York, NY, USA

   Kathleen Luckett

9. Molecular Cytology Core Facility, Memorial Sloan Kettering Cancer Center, New York, NY, USA

   Yevgeniy Romin, Eric Chan & Anthony Santella

10. Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Farheen Shah & Francisco Sanchez-Vega

11. Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Nil Urganci & Jinru Shia

12. Howard Hughes Medical Institute, Chevy Chase, MD, USA

    Dana Pe’er

13. Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Francisco Sanchez-Vega

14. Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Richard Koche

15. Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Joseph M. Chan & Karuna Ganesh

Contributions

Q.J. and K.G. conceived the study, designed the experiments, interpreted the data and wrote the manuscript. M.S.R. performed the live imaging and FRAP experiments. Y.R., E.C. and A.S. assisted with microscopy and image processing. Q.J., A.M.R., S.H., F.Z. and S.B. performed organoid and mouse experiments. M.L., B.F., J.P. and A. Moorman performed scRNA-seq data analyses (supervised by D.P. and J.M.C.). R.K. and A. Mahmoud performed data analyses. C.L.T. and I.B. performed HyperTRIBE, ActD decay and SLAM-seq analyses (supervised by Q.M.). M.G.K. assisted with HyperTRIBE experimental design. F.S. and F.S.-V. analysed and plotted mutation data from patient samples. N.U. and J.S. facilitated the procurement of human samples. H.Z., K.L., B.M. and E.d.S. assisted with mouse experiments. K.G. supervised the study and obtained funding. All authors reviewed, edited and approved the manuscript.

Corresponding author

Correspondence to Karuna Ganesh.

Competing interests

K.G. is an inventor in US patent 11,464,874, and in US provisional patent applications 63/478,809, 63/478,829 and 63/748,114, submitted by the MSKCC. D.P. is on the scientific advisory board of Insitro. J.S. is a consultant for Paige AI. M.G.K is a member of the scientific advisory board of 858 Therapeutics and receives research support from AstraZeneca and Transition Bio. All other authors declare no competing interests.

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