Tuft cells are a rare chemosensory lineage that coordinates immune and neural responses to foreign pathogens in mucosal tissues1. Recent studies have also revealed tuft-cell-like human tumours2,3, particularly as a variant of small-cell lung cancer. Both normal and neoplastic tuft cells share a genetic requirement for the transcription factor POU2F3 (refs. 2,4), although the transcriptional mechanisms that generate this cell type are poorly understood. Here we show that binding of POU2F3 to the uncharacterized proteins C11orf53 and COLCA2 (renamed here OCA-T1/POU2AF2 and OCA-T2/POU2AF3, respectively) is critical in the tuft cell lineage. OCA-T1 and OCA-T2 are paralogues of the B-cell-specific coactivator OCA-B; all three proteins are encoded in a gene cluster and contain a conserved peptide that binds to class II POU transcription factors and a DNA octamer motif in a bivalent manner. We demonstrate that binding between POU2F3 and OCA-T1 or OCA-T2 is essential in tuft-cell-like small-cell lung cancer. Moreover, we generated OCA-T1-deficient mice, which are viable but lack tuft cells in several mucosal tissues. These findings reveal that the POU2F3–OCA-T complex is the master regulator of tuft cell identity and a molecular vulnerability of tuft-cell-like small-cell lung cancer.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
Transcriptional co-activators: emerging roles in signaling pathways and potential therapeutic targets for diseases
Signal Transduction and Targeted Therapy Open Access 13 November 2023
Pulmonary cancers across different histotypes share hybrid tuft cell/ionocyte-like molecular features and potentially druggable vulnerabilities
Cell Death & Disease Open Access 19 November 2022
Selective regulation of tuft cell-like small cell lung cancer by novel transcriptional co-activators C11orf53 and COLCA2
Cell Discovery Open Access 18 October 2022
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 51 print issues and online access
$199.00 per year
only $3.90 per issue
Rent or buy this article
Prices vary by article type
Prices may be subject to local taxes which are calculated during checkout
All genomic datasets are available at the GEO database under accession code GSE186614. The thymic scRNA-seq dataset was obtained from ref. 15; the human colon mucosa dataset was obtained from ref. 14; and the human bronchi dataset was obtained from ref. 24. The Tabula Muris Consortium dataset was obtained from Figshare (https://figshare.com/projects/Tabula_Muris_Transcriptomic_characterization_of_20_organs_and_tissues_from_Mus_musculus_at_single_cell_resolution/27733). The cancer dependency dataset as well as expression was obtained online (https://depmap.org/portal/download/, DepMap Public 21Q2). The transcriptome dataset for patients with SCLC was obtained from ref. 22. The original dataset for analysing isoform level of C11orf53/OCA-T1 and COLCA2/OCA-T2 in mouse tuft cells is derived from ref. 9.
Customized code used to analyse the data is available on GitHub (https://github.com/xlw1207/paper).
Schneider, C., O’Leary, C. E. & Locksley, R. M. Regulation of immune responses by tuft cells. Nat. Rev. Immunol. 19, 584–593 (2019).
Huang, Y.-H. et al. POU2F3 is a master regulator of a tuft cell-like variant of small cell lung cancer. Genes Dev. 32, 915–928 (2018).
Yamada, Y. et al. A tuft cell-like signature is highly prevalent in thymic squamous cell carcinoma and delineates new molecular subsets among the major lung cancer histotypes. J. Thorac. Oncol. 16, 1003–1016 (2021).
Gerbe, F., Legraverend, C. & Jay, P. The intestinal epithelium tuft cells: specification and function. Cell. Mol. Life Sci. 69, 2907–2917 (2012).
Gerbe, F. et al. Intestinal epithelial tuft cells initiate type 2 mucosal immunity to helminth parasites. Nature 529, 226–230 (2016).
von Moltke, J., Ji, M., Liang, H.-E. & Locksley, R. M. Tuft-cell-derived IL-25 regulates an intestinal ILC2–epithelial response circuit. Nature 529, 221–225 (2016).
Howitt, M. R. et al. The taste receptor TAS1R3 regulates small intestinal tuft cell homeostasis. Immunohorizons 4, 23–32 (2020).
McGinty, J. W. et al. Tuft-cell-derived leukotrienes drive rapid anti-helminth immunity in the small intestine but are dispensable for anti-protist immunity. Immunity 52, 528–541 (2020).
Nadjsombati, M. S. et al. Detection of succinate by intestinal tuft cells triggers a type 2 innate immune circuit. Immunity 49, 33–41 (2018).
Lei, W. et al. Activation of intestinal tuft cell-expressed Sucnr1 triggers type 2 immunity in the mouse small intestine. Proc. Natl Acad. Sci. USA 115, 201720758 (2018).
Rudin, C. M. et al. Molecular subtypes of small cell lung cancer: a synthesis of human and mouse model data. Nat. Rev. Cancer 19, 289–297 (2019).
Yamaguchi, T. et al. Skn-1a/Pou2f3 is required for the generation of Trpm5-expressing microvillous cells in the mouse main olfactory epithelium. BMC Neurosci. 15, 13 (2014).
Matsumoto, I., Ohmoto, M., Narukawa, M., Yoshihara, Y. & Abe, K. Skn-1a (Pou2f3) specifies taste receptor cell lineage. Nat. Neurosci. 14, 685–687 (2011).
Elmentaite, R. et al. Single-cell sequencing of developing human gut reveals transcriptional links to childhood Crohn’s disease. Dev. Cell 55, 771–783 (2020).
Park, J.-E. et al. A cell atlas of human thymic development defines T cell repertoire formation. Science 367, eaay3224 (2020).
Haber, A. L. et al. A single-cell survey of the small intestinal epithelium. Nature 551, 333–339 (2017).
Plasschaert, L. W. et al. A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte. Nature 560, 377–381 (2018).
Bornstein, C. et al. Single-cell mapping of the thymic stroma identifies IL-25-producing tuft epithelial cells. Nature 559, 622–626 (2018).
Schaum, N. et al. Single-cell transcriptomics of 20 mouse organs creates a tabula muris. Nature 562, 367–372 (2018).
Smillie, C. S. et al. Intra- and inter-cellular rewiring of the human colon during ulcerative colitis. Cell 178, 714–730 (2019).
Behan, F. M. et al. Prioritization of cancer therapeutic targets using CRISPR–Cas9 screens. Nature 568, 511–516 (2019).
George, J. et al. Comprehensive genomic profiles of small cell lung cancer. Nature 524, 47–53 (2015).
Luo, Y., Fujii, H., Gerster, T. & Roeder, R. G. A novel B cell-derived coactivator potentiates the activation of immunoglobulin promoters by octamer-binding transcription factors. Cell 71, 231–241 (1992).
Duclos, G. E. et al. Characterizing smoking-induced transcriptional heterogeneity in the human bronchial epithelium at single-cell resolution. Sci. Adv. 5, eaaw3413 (2019).
Chasman, D., Cepek, K., Sharp, P. A. & Pabo, C. O. Crystal structure of an OCA-B peptide bound to an Oct-1 POU domain/octamer DNA complex: specific recognition of a protein-DNA interface. Genes Dev. 13, 2650–2657 (1999).
Gstaiger, M., Georgiev, O., Leeuwen, H., van, Vliet, Pvander & Schaffner, W. The B cell coactivator Bob1 shows DNA sequence-dependent complex formation with Oct-1/Oct-2 factors, leading to differential promoter activation. EMBO J. 15, 2781–2790 (1996).
Schubart, D. B., Rolink, A., Kosco-Vilbois, M. H., Botteri, F. & Matthias, P. B-cell-specific coactivator OBF-1/OCA-B/Bob1 required for immune response and germinal centre formation. Nature 383, 538–542 (1996).
Cepek, K. L., Chasman, D. I. & Sharp, P. A. Sequence-specific DNA binding of the B-cell-specific coactivator OCA-B. Genes Dev. 10, 2079–2088 (1996).
Luo, Y. & Roeder, R. G. Cloning, functional characterization, and mechanism of action of the B-cell-specific transcriptional coactivator OCA-B. Mol. Cell. Biol. 15, 4115–4124 (1995).
Lim, J. S. et al. Intratumoural heterogeneity generated by Notch signalling promotes small-cell lung cancer. Nature 545, 360–364 (2017).
Andersen, B. et al. Functions of the POU domain genes Skn-1a/i and Tst-1/Oct-6/SCIP in epidermal differentiation. Genes Dev. 11, 1873–1884 (1997).
Szczepanski, A. P., Tsuboyama, N., Zhao, Z. & Wang, L. POU2AF2/C11orf53 functions as a co-activator of POU2F3 by maintaining chromatin accessibility and enhancer activity. Preprint at bioRxiv https://doi.org/10.1101/2022.03.17.484753 (2022).
Dailey, L. & Basilico, C. Coevolution of HMG domains and homeodomains and the generation of transcriptional regulation by Sox/POU complexes. J. Cell. Physiol. 186, 315–328 (2001).
Malik, V., Zimmer, D. & Jauch, R. Diversity among POU transcription factors in chromatin recognition and cell fate reprogramming. Cell. Mol. Life Sci. 75, 1587–1612 (2018).
Reményi, A. et al. Crystal structure of a POU/HMG/DNA ternary complex suggests differential assembly of Oct4 and Sox2 on two enhancers. Genes Dev. 17, 2048–2059 (2003).
Chu, C.-S. et al. Unique immune cell coactivators specify locus control region function and cell stage. Mol. Cell 80, 845–861 (2020).
Ren, X., Siegel, R., Kim, U. & Roeder, R. G. Direct interactions of OCA-B and TFII-I regulate immunoglobulin heavy-chain gene transcription by facilitating enhancer-promoter communication. Mol. Cell 42, 342–355 (2011).
Montoro, D. T. et al. A revised airway epithelial hierarchy includes CFTR-expressing ionocytes. Nature 560, 319–324 (2018).
Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).
Stephens, M. False discovery rates: a new deal. Biostatistics https://doi.org/10.1093/biostatistics/kxw041 (2016).
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).
Ashkenazy, H. et al. ConSurf 2016: an improved methodology to estimate and visualize evolutionary conservation in macromolecules. Nucleic Acids Res. 44, W344–W350 (2016).
Celniker, G. et al. ConSurf: using evolutionary data to raise testable hypotheses about protein function. Israel J. Chem. 53, 199–206 (2013).
Mészáros, B., Erdős, G. & Dosztányi, Z. IUPred2A: context-dependent prediction of protein disorder as a function of redox state and protein binding. Nucleic Acids Res. 46, W329–W337 (2018).
Romero, P., Obradovic, Z., Kissinger, C., Villafranca, J. E. & Dunker, A. K. Identifying disordered regions in proteins from amino acid sequence. In Proc. International Conference on Neural Networks ICNN′97 Vol. 1, 90–95 (IEEE, 1997).
Romero, P. et al. Sequence complexity of disordered protein. Proteins Struct. Funct. Bioinform. 42, 38–48 (2001).
Waterhouse, A. et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 46, W296–W303 (2018).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and b cell identities. Mol. Cell 38, 576–589 (2010).
Kent, W. J. et al. The Human Genome Browser at UCSC. Genome Res. 12, 996–1006 (2002).
Bailey, T. L. & Elkan, C. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. In Proc. International Conference on Intelligent Systems for Molecular Biology Vol. 2, 28–36 (CAAAAI Press, 1994).
Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).
Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).
Waskom, M. seaborn: statistical data visualization. J. Open Source Softw. 6, 3021 (2021).
Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).
Roe, J.-S. et al. Enhancer reprogramming promotes pancreatic cancer metastasis. Cell 170, 875–888 (2017).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Adrover, J. M. et al. Programmed ‘disarming’ of the neutrophil proteome reduces the magnitude of inflammation. Nat. Immunol. 21, 135–144 (2020).
We acknowledge C. Hammell, E. Luk, J. Sheltzer and K. Adelman for discussions and suggestions throughout the course of this study. This work was supported by Cold Spring Harbor Laboratory NCI Cancer Center Support grant CA045508. Additional funding was provided to C.R.V. by the Pershing Square Sohn Cancer Research Alliance, National Institutes of Health grants CA013106 and CA242919, Department of Defense grant W81XWH1910317, and the Cold Spring Harbor Laboratory and Northwell Health Affiliation. L.J.-T. is an investigator of the Howard Hughes Medical Institute. M.E. was supported by the Dr Marcia Kramer Mayer, William C. and Joyce C. O’Neil Charitable Trust, and the Pershing Square Foundation. X.-Y.H. was supported by the 2021 AACR-AstraZeneca Breast Cancer Research Fellowship (grant no. 21-40-12-HE). J.S. was supported by NIH grant CA231997. Y.T.S. was supported by Singapore National Science Scholarship (PhD) A*STAR.
C.R.V. has received consulting fees from Flare Therapeutics, Roivant Sciences and C4 Therapeutics; has served on the advisory boards of KSQ Therapeutics, Syros Pharmaceuticals and Treeline Biosciences; has received research funding from Boehringer-Ingelheim and Treeline Biosciences; and owns a stock option from Treeline Biosciences. M.E. is a member of the research advisory board for brensocatib for Insmed; a member of the scientific advisory board for Vividion Therapeutics; and a consultant for Protalix. J.S. has received research funding from Abbvie and Pfizer and licensed a patent (11046763 to Stanford University) to Forty Seven/Gilead on the use of CD47-blocking strategies in SCLC.
Peer review information
Nature thanks Christoph Schneider, Dean Tantin and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data figures and tables
(a)-(f) Summary of scRNA-seq data. The plots depict the average expression of indicated genes in the indicated cell types. COLCA2: mouse gene name Gm684. (a) mTEC: medullary thymic epithelial cells; TEC: thymic epithelial cells; VSMC: vascular smooth muscle cell; cTEC: cortical thymic epithelial cells. (b) ILCs: innate lymphoid cells; TA: transit amplifying. (d) NK: natural killer; ETP: early thymic progenitor. NMP: neutrophil-myeloid progenitor. (e) PNEC: pulmonary neuroendocrine cells. (f) APCS: antigen presenting cells; BG: Bergmann glial cell; OLGs: oligodendrocytes; OPCs: oligodendrocyte precursor cell; MSC: mesenchymal stem cell; MuSC: muscle satellite stem cell; AdMSC: adipose mesenchymal stem cell; PTECs: proximal tubular epithelial cells; LSESc: Liver sinusoidal endothelial cells; MPCs: multipotent progenitor cell; CLP: common lymphoid progenitor cells; GMP: granulocyte monocyte progenitor cell; HSC: hematopoietic precursor cell; MEP: megakaryocyte-erythroid progenitor cell; Treg: regulatory T cells; DN1 pro-T: double negative 1 progenitor T cells. (g) Summary of C11orf53 and Colca2 isoform expression in sorted murine tuft cells. (h) mRNA levels of POU2F3 and COLCA2 across 1,379 human cancer cell lines from CCLE database. Data are obtained from the Cancer Dependency Map (DepMap) portal (21Q2). Source of each single-cell or bulk RNA-seq dataset as well as plotting is indicated in method section.
Extended Data Fig. 2 Evaluation of Pou2f3, C11orf53, and Colca2 mRNA level through RNA-FISH in different tissues.
(a)-(c) Representative RNA-FISH analysis of Pou2f3, C11orf53, and Colca2 expression in mouse small intestine (a), trachea (b), and stomach tissue of wild-type mice (c, top). (c, bottom) RNA-FISH analysis Pou2f3, C11orf53, and Colca2 expression in the stomach of C11orf53−/− (KO) mice (one independent experiment from three different mice). All scale bar represents 10 μM.
(a) Isoform information of C11orf53 and COLCA2 extracted from the Ensemble and NCBI databases. (b) RT-qPCR analysis of RNA from SCLC-P cell lines using the primers indicated in (a), that quantify either the short isoform of C11orf53 or all isoforms. (c) RT-qPCR analysis of COLCA2 isoforms in NCI-H1048 cells using primers indicated in (a). for (b) and (c), mRNA level (Ct value) of isoform indicated is normalized to the level of B2M. Bar graph represents the mean of normalized mRNA level from three biological replicates with each replicate depicted as individual dot. Primers are provided in Supplementary Table 6.
Extended Data Fig. 4 C11orf53, COLCA2, and OCA-B share a conserved peptide that corresponds the known binding site of OCA-B with POU2F1/OCT1.
(a) and (c) Predicted intrinsic disorder for C11orf53 and COLCA2. PONDR (Predictor of Natural Disordered Regions) VLXT scores and IUPred2A (Intrinsically Unstructured Proteins Prediction) scores are shown on the y axis, and the amino acid positions are shown on the x axis. (b) and (d) The conservation analysis of C11orf53 and COLCA2 protein sequences across species with Bayesian method on ConSurf Server. The purple region is the conserved peptide (residues 10–32 for C11orf53 and 5–27 for COLCA2). (e) C11orf53, COLCA2, and Pou2af1/OCA-B gene cluster in the mouse genome (mm10). (f) Architecture and conservation of POU homeodomains of POU2F1/OCT1, POU2F2/OCT2, and POU2F3/OCT11. (g) Summary of residues involved in protein-DNA and protein-protein close contacts (<4.0 Å) for OCA-B with POU2F1, C11orf53 with POU2F3, and COLCA2 with POU2F3. The crystal structure of OCA-B1–44 and POU2F1DBD (PDB: 1CQT25)25 was used as a model for predicting the structure of C11orf53 or COLCA2 with POU2F3DBD on an octamer motif (ATGCAAAT) in PyMOL.
Extended Data Fig. 5 Additional biochemical evidence that C11orf53/OCA-T1 and COLCA2/OCA-T2 are paralogues of OCA-B.
(a) GST-pulldown with western blot of endogenous POU2F3 from NCI-H211 nuclear extracts with indicated constructs (n = 1). (b) Co-IP testing the interaction of HA-C11orf53 (HEK293T whole cell lysates, n = 2) or HA-COLCA2 (NCI-H1048 nuclear extract, n = 1) with FLAG-POU2F1, FLAG-POU2F2, and FLAG-POU2F3. (c) Co-IP testing the interaction of HA-C11orf53 or HA-COLCA2 with FLAG-POU3F4/OCT9, FLAG-POU4F3/BRN3C, FLAG-POU5F1/OCT4, and POU2F3/OCT11 in HEK293T whole cell lysates. (d) Analytical gel filtration of His6-GFP-POU2F3 with Strep2SUMO-C11orf53 or untagged C11orf53, performed in the absence of any DNA. (e) Replicate of analytical gel filtration of His6-GFP-POU2F3/Strep2SUMO-C11orf53/octamer motif A alone or in combination, accompanied by Coomassie blue staining of the peak fraction of the ternary complex (red curve). Data are representative of two biological replicates (c-e). (f) Summary of microscale thermophoresis measurements of protein binding affinity for the octamer motif A DNA. Highest protein concentration tested is 1 μM for all experiments. (g) Purity assessment of recombinant OCA-B (expressed and purified from Sf9 cells) and POU2F1/OCT1 (expressed and purified from E. coli) proteins by SDS-PAGE and Coomassie blue staining. (h) Analytical gel filtration of POU2F3, OCA-B, octamer DNA motif A assemblies. (i) Analytical gel filtration of POU2F1, C11orf53, and octamer DNA motif A assemblies. For (h) and (i), the maximum absorbance at 260 nm for each injection was normalized to 1.0 for the ease of comparison. As POU2F1 is larger in molecular weight than POU2F3, the shift in elution volume is less prominent for the ternary complex. Complex formation was validated by SDS-PAGE assessment of the peak fraction to confirm the presence of both proteins (red curve). Data are representative of two biological replicates (i-j). Source data for microscale thermophoresis assay is provided in Supplementary Table 1, uncropped gels are provided in Supplementary Fig. 1b, d–g.
Extended Data Fig. 6 Epigenomic evaluation of POU2F3, C11orf53/OCA-T1, and COLCA2/OCA-T2 in SCLC-P cell lines.
(a) Comparison of POU2F3, C11orf53, and HA-COLCA2 ChIP-seq peak overlap in the indicated cell lines. For each protein, peaks represent the ones that are consistently identified from 2-3 replicate of experiments. Detailed information is in method. (b) Annotation of POU2F3 and C11orf53 overlapping peaks NCI-H211 cells. (c) Position weight matrix of discovered motif enriched on POU2F3/C11orf53 co-binding sites by MEME from NCI-H211 cell line. (d) Western blot analysis of lentivirally overexpressed constructs used for ChIP-qPCR and gene complementation assay in NCI-H211 cells (n = 2). (e) Sequential ChIP-qPCR analysis of overexpressed HA-C11orf53 (NCI-H211) or HA-COLCA2 (NCI-H1048) co-expressed with FLAG-POU2F3 binding sites in NCI-H211 and NCI-H1048 cells respectively with anti-FLAG (1st IP) or protein G beads (1st IP) and anti-HA (2nd) antibodies. The enrichment is adjusted to the input amount. Two biological replicates are performed and represented as individual dots; the bar value represents the average enrichment over input of two biological replicates. (f) Western blot analysis of POU2F3 or C11orf53 in NCI-H211 (Cas9) cells transduced with indicated sgRNAs. Samples were collected 4 days post infection (n = 1). (g) RNA-seq analysis comparing mRNA changes following C11orf53, COLCA2, or POU2F3 knockout compared to control in three SCLC-P cells. RNA was collected five- or six-days post sgRNA infection. Each dot represents the log2fold-change of a single protein-coding genes (read outs > = 10). (h) Western blot analysis of FLAG-POU2F3, HA-C11orf53, and FLAG-GFP expression in murine YT330 SCLC cells. Data is representative of two biological replicates. Uncropped gel is provided in Supplementary Fig. 1i. Source data for gene expression changes upon knockout in different cells are provided in Supplementary Table 2. sgRNA and primer sequences are provided in Supplementary Table 6.
(a) Two-class comparison of gene dependencies of four SCLC-P versus 986 other cancer cell lines. The average essentiality of each gene from SCLC-P is subtracted to its mean in other cell lines. This difference is moderated with an empirical Bayes method using the adaptive shrinkage method described in CRNA package - ashr. (b) Scatter plot of C11orf53, COLCA2, and POU2F3 dependency scores across a panel of human cancer cell lines. Data obtained from DepMap (21Q2). (c) Control sgRNAs (2 negative controls or one sgCDK1) for the competition-based proliferation assays (related to Fig. 4a), (n = 3). Mean ± s.d. is plotted. (d) BrdU incorporation assays following CRISPR-based targeting of POU2F3, C11orf53, or COLCA2 or negative control in the indicated Cas9+ cell lines. Two technical replicates with two independent sgRNAs for each gene as biological replicates. Adjusted P value was calculated with two-way ANOVA with Tukey’s multiple comparison tests. (e-f) Tumour weights and imaging at the terminal timepoint of the xenograft experiments shown in Fig. 4b. Mean ± s.e.m. is plotted for (d-e). P-values are derived from two-tailed unpaired student’s t-test with Welch’s correction. (g) Controls for NCI-H211 gene complementation assay shown in Fig. 4c. (n = 3). Mean ± s.d. is plotted. (h) Competition-based proliferation assays in NCI-H1048 cells cotransduced with indicated cDNAs and sgRNAs lentivirally to assess functionality of indicated mutants. cDNAs were engineered to be resistant to Cas9/sgRNA-mediated cutting. Mean ± s.d. of normalized GFP percentage is plotted (n = 3). (i) anti-HA western blot of the indicated cDNAs from (h). Data are representative of two biological replicates. Source data for all GFP depletion assays, BrdU assays and tumour weights are provided in Supplementary Table 3-4, gating strategy for BrdU assay is provided in Supplementary Figure 2, sgRNA sequences are provided in Supplementary Table 4, uncropped gels are provided in Supplementary Fig. 1j.
Extended Data Fig. 8 Similar weights, morphology, and organ histology of C11orf53+/+ and C11orf53−/− mice. WT: C11orf53+/+ mice, KO: C11orf53−/− mice.
(a) Body weights of C11orf53 F2 mice of each genotype (2 female WT mice, 4 male WT mice; 3 female KO mice, 4 male KO mice; 5 HET male and female mice were included in the experiment with 6–10 weeks old littermate mice). (b) Representative images of C11orf53+/+ and C11orf53−/− mice F2 mice (representative image of four mice). (c) H&E analysis of representative organs from age- and sex-matched C11orf53+/+ and C11orf53−/− littermate mice (one female and one male mice for both genotypes were included in the experiment). All scale bars represent 200 μm. Source data for mice body weight is provided in Supplementary Table 5.
Extended Data Fig. 9 Representative immunofluorescence staining of POU2F3 and DCLK1 in tissues of C11orf53+/+ and C11orf53−/− mice.
WT: C11orf53+/+ mice, KO: C11orf53−/− mice. For trachea staining, 5 WT, 6 KO and 10 HET mice were included, except for POU2F3 staining which 9 HET mice were included in. For small intestine, 10 WT, 10 KO, 8 HET mice were included for POU2F3 staining and 9 WT, 10 KO, 5 HET mice for DCLK1 staining were included in. For urethra, 5 mice were included. For gallbladder, 6 WT and 5 KO mice were included. For caecum, colon, tongue and nasal respiratory, 3 WT and KO mice were included. For thymus, 6 WT and KO mice were included. For stomach, 10 mice were included. Data shown are representative images of acquired for each tissue. All scale bars represent 100 μm.
Extended Data Fig. 10 RNA-FISH, sSingle-cell RNA-seq, and IL-25 treatment experiments confirming in C11orf53−/− mice.
(a) t-distributed stochastic neighbour embedding (t-SNE) of small intestine epithelial cells (points), coloured by cell type assignments. Two independent replicates of single-cell RNA-seq data from C11orf53 wildtype (WT) and knockout (KO) littermate female mice. For the second replicate, cells that expressed a high level of CD45 were depleted by using CD45 microbeads before library preparation (Miltenyi Biotec, 30-052-301). TA: transient amplifying. (b) Quantification of different cell types in the intestinal epithelium from single-cell RNA-seq experiments in small intestine from two independent replicates. Bar graphs represent the mean. (c) H&E and Alcian Blue (Goblet cell) staining of intestines of C11orf53 WT and KO mice treated with PBS control or 500 ng of IL-25 for 8 days (for PBS control, four mice were included. For IL-25, five mice were included). Scale bars represent 100 μm. (d) Immunofluorescence staining of DCLK1 and POU2F3 markers in intestines of C11orf53 WT and KO mice treated with PBS control or IL-25, scale bars represent 250 μm. (e) Same as (d), but higher magnification (scale bars represent 100 μm). For DCLK1 staining, three WT mice were included in PBS control and five WT mice were included in IL-25 treatment. For POU2F3 staining, four and five WT mice were included in PBS and IL-25 treatment respectively, five KO mice were included in both PBS control and IL-25 treatment. (f) Quantification of (d). Mean ± s.e.m. is plotted. Two-tailed unpaired student’s t-test with Welch’s correction was used to evaluate significance. Source data for IF quantification is provided in Supplementary Table 3.
Supplementary Figs. 1 and 2. Supplementary Fig. 1 contains uncropped images of western blots and SDS–PAGE gels. Supplementary Fig. 2 shows representative gating strategies for FACS-based apoptosis assay analysis.
MST and luciferase data. Combined tables for the raw MST data and the normalized luciferase reporter activity for each construct used in the study. Related to Fig. 2.
RNA-seq results. Combined tables for DESeq2 output of sgPOU2F3, sgPOU2AF2 or sgCOLCA2 in all four SCLC-P cancer cell lines and log2[TPM + 1] value of tuft cell markers in YT-330 cell lines that were transduced with different constructs lentivirally. Related to Fig.3 and Extended Data Fig. 6.
GFP competition and gene complementation assay data. Combined tables for GFP competition assays in SCLC cells lines and gene complementation assays in NCI-H211 and NCI-H1048 cells. Related to Fig. 4 and Extended Data Fig. 7.
Combined tables for the xenograft experiments and BrdU apoptosis assay. For the xenograft experiment, the end time point tumour weight as well as the tumour volume at different days are provided. For the BrdU apoptosis assay, the percentile of cells at different cell cycle for each perturbation is provided. Related to Fig. 4 and Extended Data Fig. 7.
Combined tables for Pou2af2−/− mouse experiments. The frequency of tuft cells in different tissues as measured by DCLK1 or POU2F3 staining are provided. The bodyweight for different genotypes are included. Related to Fig. 4 and Extended Data Figs. 8–10.
Combined tables for all of the oligos used in the study. The oligos include RT–qPCR primers, sgRNA sequences, ChIP–qPCR primers and genotyping primers.
DCLK1 immunostaining with DAPI as a control in a whole-mount analysis of the nasal epithelium of Pou2af2 WT (left) and knockout (right) mice.
POU2F3 immunostaining with DAPI as a control in a whole-mount analysis of the nasal epithelium of Pou2af2 WT (left) and knockout (right) mice.
About this article
Cite this article
Wu, X.S., He, XY., Ipsaro, J.J. et al. OCA-T1 and OCA-T2 are coactivators of POU2F3 in the tuft cell lineage. Nature 607, 169–175 (2022). https://doi.org/10.1038/s41586-022-04842-7
This article is cited by
British Journal of Cancer (2023)
Transcriptional co-activators: emerging roles in signaling pathways and potential therapeutic targets for diseases
Signal Transduction and Targeted Therapy (2023)
Immunologic Research (2023)
Transcriptional dynamics of colorectal cancer risk associated variation at 11q23.1 correlate with tuft cell abundance and marker expression in silico
Scientific Reports (2022)
Tuft cell-like carcinomas: novel cancer subsets present in multiple organs sharing a unique gene expression signature
British Journal of Cancer (2022)