Fig 2: TfR1 (TFRC) proteins interactome. TfR1 can exert varied actions on important biological pathways within the cancer cells such as metabolism of RNA, translation initiation complex formation, immune system, cell cycle, clathrin-mediated endocytosis, vesicle-mediated transport, membrane trafficking, and clathrin-derived vesicle budding, mainly mediated by its direct interaction with dysregulated actin-related proteins and ubiquitous scaffolding proteins such as YWHAZ.

Fig 3: METTL16-mediated mRNA regulation requires MTR4-nuclear RNA exosome complex.a, b Workflow of the pairwise CRISPR screen for genetic interaction analysis. Top 500 TfR1 regulators were selected from the initial genome-wide CRISPR screen results. Then a dual gRNA expression library (M16GI) was constructed (a). K562-CRISPRi cells were transduced with the M16GI gRNA library and then cells were sorted according to the surface TfR1 expression (b, c). d Results of the pairwise CRISPR screen. Genes were sorted by T-score, which indicates the degrees of genetic interaction with METTL16. e–h Validation of the screen hits. Two gRNAs per gene were chosen for validation experiments. K562-CRISPRi cells were transduced with lentiviral vectors encoding individual dual gRNAs. Then cells were treated with DFO overnight, stained with anti-TfR1 antibody and analyzed using flow cytometry (e, g). Change in TfR1 expression levels [(MFI of METTL16-sufficient cells - MFI of METTL16-deficient cells) / MFI of METTL16-sufficient cells] in each condition were summarized (f, h, n = 3 biological replicates from independent experiments for MTR4, n = 4 for DIS3). Horizontal lines indicate the mean. i mRNA expression levels of indicated genes in the cultured erythroblasts from control and Mettl16fl/flCreERT2+ mice which were nucleofected with indicated crRNAs and Cas9 complexes (n = 3 biological replicates from independent experiments). j Model overview of METTL16-mediated mRNA regulation in developing erythroid cells, where METTL16 expression is upregulated. METTL16 regulates m6A deposition on transcripts of DNA-repair-related genes. The m6A deposition regulates the expression of these genes in a manner dependent on the MTR4-nuclear exosome, thereby safeguarding genome integrity and erythropoiesis. Data are expressed as mean ± SD (i). The p-values were calculated using two-tailed Student’s t-test (e–i). ns not significant. Source data are provided as a Source Data file.

Fig 4: RNA methyltransferase METTL16 regulates erythroid differentiation in vivo.a Mettl16 mRNA expression levels in each hematopoietic compartment (n = 3 technical replicates). HSC hematopoietic stem cells, MPP multipotent progenitors, CMP common myeloid progenitors, GMP granulocyte-monocyte progenitors, MEP megakaryocyte-erythrocyte progenitors. b Gene expression patterns of Mettl16 mRNA during erythroid differentiation generated from the single-cell RNA-seq analysis48. c Survival rates of mice with each genotype. d Fetuses on E12.5 of control and Mettl16fl/flEpor-Cre+ mice. e–g Flow cytometric analysis of the E10.5 peripheral blood from Mettl16fl/flEpor-Cre+ mice (e). The histogram of CD71 intensity (f) and the relative CD71 mean fluorescence intensity (MFI) in the gated population (g, n = 4–5 mice). Data were pooled from two independent experiments. h Flow cytometric analysis of the E11.5 FL from Mettl16fl/flEpor-Cre+ mice (n = 4–5 mice). i May-Grünwald-Giemsa stain of cytospin slides of E11.5 FL cells from Mettl16fl/flEpor-Cre+ mice. j The histogram of FSC intensity in each population in h. k Flow cytometric analysis of the E12.5 FL from Mettl16fl/flEpor-Cre+ mice. Data are expressed as mean ± SD (a, g, h). The p-values were calculated using two-tailed Student’s t-test (g, h). Images are representative of samples obtained from at least three mice (d and i–k). Source data are provided as a Source Data file.

Fig 5: 3F3-FMA, Anti-TfR1 3B8 2A1, Anti-TfR1 H68.4, Anti-MDA 1F83, and Anti-4-HNE Antibodies Can Be Used as Ferroptosis Markers by Immunofluorescence(A) HT-1080 cells were incubated with 1 µM RSL3 for 4 h and then were fixed, permeabilized, and stained with DAPI (nuclei, blue), 3F3-FMA, anti-TfR1 3B8 2A1, or anti-TfR1 H68.4 antibodies (red). The white arrows indicate differences. The quantification of membrane intensities of anti-TfR1 antibodies is shown at bottom (3F3-FMA n = 107 and 96; TfR1 3B8 2A1 n = 32 and 9; TfR1 H68.4 n = 22 and 13). ****p = 0.0001 (2-tailed t test). Data plotted are mean ± SEM. Each dot represents 1 cell.(B) HT-1080 cells were incubated with 1 µM RSL3 for 4 h and then were fixed, permeabilized, and stained with DAPI (nuclei, blue), anti-MDA 1F83 antibody (red), and anti-4-HNE ab46545 antibodies (green). The white arrows indicate differences. The quantification of membrane intensities of the antibodies is shown at bottom (MDA 1F83 n = 35 and 73; 4-HNE ab46545 n = 33 and 26). ****p = 0.0001 (2-tailed t test). Data plotted are mean ± SEM. Each dot represents 1 cell.(C) HT-1080 cells were incubated with 1 µM STS for 6 h and then were fixed, permeabilized, and stained with DAPI (nuclei, blue), 3F3-FMA (red), anti-TfR1 3B8 2A1 (red), anti-TfR1 H68.4 (red), anti-MDA 1F83 (red), and anti-4-HNE ab46545 antibodies (green). The white arrows indicate the stain outside intact cells. The quantification of membrane intensities of the antibodies is shown at bottom (3F3-FMA n = 71 and 53; TfR1 3B8 2A1 n = 103 and 63; TfR1 H68.4 n = 93 and 80; MDA 1F83 n = 81 and 71; 4-HNE ab46545 n = 56 and 69). ns, p > 0.05 (2-tailed t test). Data plotted are mean ± SEM. Each dot represents 1 cell.See also Figure S4.