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. 2024 Jul 24;12:RP91072. doi: 10.7554/eLife.91072

Single-molecule analysis reveals the phosphorylation of FLS2 governs its spatiotemporal dynamics and immunity

Yaning Cui 1,2,†, Hongping Qian 2,†, Jinhuan Yin 2, Changwen Xu 2, Pengyun Luo 2, Xi Zhang 2, Meng Yu 3, Bodan Su 4, Xiaojuan Li 2, Jinxing Lin 1,2,✉
Editors: Jian-Min Zhou5, Jürgen Kleine-Vehn6
PMCID: PMC11268883  PMID: 39046447

Abstract

The Arabidopsis thaliana FLAGELLIN-SENSITIVE2 (FLS2), a typical receptor kinase, recognizes the conserved 22 amino acid sequence in the N-terminal region of flagellin (flg22) to initiate plant defense pathways, which was intensively studied in the past decades. However, the dynamic regulation of FLS2 phosphorylation at the plasma membrane after flg22 recognition needs further elucidation. Through single-particle tracking, we demonstrated that upon flg22 treatment the phosphorylation of Ser-938 in FLS2 impacts its spatiotemporal dynamics and lifetime. Following Förster resonance energy transfer-fluorescence lifetime imaging microscopy and protein proximity indexes assays revealed that flg22 treatment increased the co-localization of GFP-tagged FLS2/FLS2S938D but not FLS2S938A with AtRem1.3-mCherry, a sterol-rich lipid marker, indicating that the phosphorylation of FLS2S938 affects FLS2 sorting efficiency to AtRem1.3-associated nanodomains. Importantly, we found that the phosphorylation of Ser-938 enhanced flg22-induced FLS2 internalization and immune responses, demonstrating that the phosphorylation may activate flg22-triggered immunity through partitioning FLS2 into functional AtRem1.3-associated nanodomains, which fills the gap between the FLS2S938 phosphorylation and FLS2-mediated immunity.

Research organism: A. thaliana

Introduction

Plasma membrane (PM) localized receptor kinase (RK) plays a crucial role in pattern-triggered immunity (PTI), the initial defense layer in plants, features with an extracellular domain, a single transmembrane region, and a cytoplasmic kinase domain (Liang and Zhou, 2018). FLAGELLIN-SENSING 2 (FLS2), a star RK in PTI, senses a conserved N-terminal epitope (flg22) of the bacterial flagellin (Hudson et al., 2024). Upon flg22 perception, FLS2 rapidly interacts with its co-receptor brassinosteroid insensitive1-associated kinase1 (BAK1) to form an active receptor complex (Chinchilla et al., 2006; Chinchilla et al., 2007; Heese et al., 2007), initiating phosphorylation events through activating receptor-like cytoplasmic kinases (RLCKs) such as BOTRYTIS-INDUCED KINASE 1 (BIK1) to elicit downstream immune responses (Lu et al., 2010; Zhang et al., 2010).

Protein phosphorylation, a vital post-translational modification, plays an essential role in signal transduction. Previous studies suggest that protein phosphorylation alterations affect protein dynamics and subcellular trafficking (Kaiserli et al., 2009; Kontaxi et al., 2023). In plants, phosphorylation of proteins can coalesce into membrane nanodomains, forming platforms for active protein function at the PM (Bücherl et al., 2017). For example, Xue et al., 2018 demonstrated that phosphorylation of the blue light receptor phot1 accelerates the protein movement, enhancing its interaction with a sterol-rich lipid marker AtRem1.3-mCherry, which underscores the crucial role of protein phosphorylation in protein dynamics and membrane partitioning. Upon flg22 treatment, multiple FLS2 phosphorylation sites are activated, with the Serine-938 phosphorylation playing a pivotal role in defense activation (Cao et al., 2013); nevertheless, how the phosphorylation of FLS2S938 impacts on plant immunity remains elusive.

Membrane nanodomains, which are crucial in regulating PM protein behavior, are dynamic structures enriched with sterols and sphingolipids (Boutté and Jaillais, 2020) and uniquely labeled by proteins such as Flotillins and Remorins within living cells (Martinière and Zelazny, 2021). Various stimuli are able to trigger PM proteins moving into mobile or immobile nanodomains, suggesting a connection between nanodomains and signal transduction (Wang et al., 2015). For example, Xing et al., 2022 demonstrated that sterol depletion significantly impacts the dynamics of flg22-activated FER-GFP, emphasizing the role of nanodomains in the lateral mobility and dissociation of FER from the PM under flg22 treatment. However, the spatial coordination of FLS2 dynamics and signaling at the PM, and their relationships with nanodomains remain poorly understood.

To investigate whether the FLS2S938 phosphorylation activates immune responses through nanodomains, we analyzed the diffusion and lifetime of FLS2 phospho-dead and phospho-mimic mutants at the PM before and after flg22 treatments. Our results show that flg22-induced dynamic and lifetime changes were abolished in FLS2S938A. Using FLIM-FRET and single-molecule protein proximity index (smPPI) techniques, we found that FLS2 phosphorylation variants exhibited distinct membrane nanodomain distribution and endocytosis. Importantly, we demonstrated that the immune response of phosphor-mimic FLS2S938D was comparable to wild-type FLS2, while the phosphor-dead version of FLS2S938A weakened the immune response. Our findings pinpoint the missing piece of FLS2-mediated immune signaling in planta.

Results and discussion

Ser-938 phosphorylation site changed the spatiotemporal dynamics of flg22-induced FLS2 at the plasma membrane

Previous studies highlight the crucial role of membrane protein phosphorylation in fundamental cellular processes, including PM dynamics (Vitrac et al., 2019; Offringa and Huang, 2013). In vitro mass spectrometry (MS) identified multiple phosphorylation sites in FLS2. Genetic analysis further identified Ser-938 as a functionally important site for FLS2 in vivo (Cao et al., 2013). FLS2 Ser-938 mutations impact flg22-induced signaling, while BAK1 binding remains unaffected, thereby suggesting Ser-938 regulates other aspects of FLS2 activity (Cao et al., 2013). To unravel the immune response regulation mechanisms via FLS2 phosphorylation, we generated transgenic Arabidopsis plants expressing C-terminal GFP-fused FLS2, S938A, or S938D under the FLS2 native promoter in the fls2 mutant background (Figure 1—figure supplement 1). Using VA-TIRFM with single-particle tracking (SPT) (Figure 1A and B, Figure 1—source data 1), we investigated the diffusion dynamics of FLS2 phospho-dead and phospho-mimic mutants, following previous reports (Geng et al., 2022). Upon flg22 treatment, FLS2/FLS2S938D-GFP spots demonstrated extended motion trajectories, contrasting with the shorter motion tracks of FLS2S938A-GFP spots (Figure 1C). The results indicate significant changes in the diffusion coefficients and motion ranges of FLS2/FLS2S938D-GFP after flg22 treatment, whereas FLS2S938A-GFP showed no significant differences (Figure 1D and E and Figure 1—source data 2 and 3). Similar results were obtained using Uniform Manifold Approximation and Projection (UMAP) technology (Dorrity et al., 2020; Figure 1F and Figure 1—figure supplement 2) and fluorescence recovery after photobleaching (FRAP) (Greig and Bulgakova, 2021; Figure 1G and H; Figure 1—figure supplement 3), supporting the essential role of the S938 phosphorylation site in flg22-induced lateral diffusion of FLS2 at the PM.

Figure 1. Effects of Ser-938 phosphorylation on the spatiotemporal dynamics of FLS2 at the plasma membrane.

(A) VA-TIRFM images of a FLS2-expressing hypocotyl cell were analyzed. The 5-day-old transgenic Arabidopsis plant cells were observed under VA-TIRFM. The red balls indicate the positions of the identified points that appeared. Trajectories represent the track length of the identified points. Bar = 10 μm. (B) Time-lapse images of FLS2, FLS2S938A, and FLS2S938D. Bar = 2 μm. The fluorescence intensity changes among different 3D luminance plots. (C) The trajectories of representative individual FLS2, FLS2S938A, and FLS2S938D under 30 min for 10 μM flg22 processing and control. Bar = 0.5 μm. (D) Diffusion coefficients of FLS2 (control, n = 42 spots; flg22, n = 22 spots), FLS2S938A (control, n = 44 spots; flg22, n = 25 spots), and FLS2S938D (control, n = 27 spots; flg22, n = 23 spots) under different environments. Statistical significance was assessed using Student’s t-test (***p<0.001). Error bars represent the SD. (E) Frequency of long- and short-range motions for FLS2 (control, n = 13 spots; flg22, n = 5 spots), FLS2S938A (control, n = 16 spots; flg22, n = 14 spots), and FLS2S938D (control, n = 14 spots; flg22, n = 10 spots) under different environments. Statistical significance was assessed using Student’s t-test (***p<0.001). Error bars represent the SD. (F) Uniform Manifold Approximation and Projection (UMAP) visualization of FLS2 samples under different conditions (control, n = 25 spots; flg22, n = 22 spots). Dots represent the individual images and are colored according to the reaction conditions. (G) The representative FRAP time course of FLS2-GFP, FLS2S938A, and FLS2S938D under flg22 treatments. White squares indicate bleached regions. Bar = 5 μm. (H) Fluorescence recovery curves of the photobleached areas with or without the flg22 treatment. Three biological replicates were performed. Each experiment was repeated thrice independently (n = 3 images). Statistical significance was assessed using Student’s t-test (***p<0.001). Error bars represent the SD. (I) Lifetime were analyzed for FLS2 (control, n = 13 spots; flg22, n = 9 spots), FLS2S938A (control, n = 28 spots; flg22, n = 21 spots), and FLS2S938D (control, n = 13 spots; flg22, n = 15 spots) under the control and flg22 treatments. The data points were collected from a TIRFM time series using an exposure time of 100 ms to capture a total duration of 20 s. Statistical significance was assessed using Student’s t-test (*p<0.05, ***p<0.001). Error bars represent the SD.

Figure 1—source data 1. The original VA-TIRFM image and single-particle tracking of FLS2-expressing hypocotyl cells are shown in Figure 1A.
Figure 1—source data 2. The list of the diffusion coefficients of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments is shown in Figure 1D.
Figure 1—source data 3. The list of the motion range of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments is shown in Figure 1E.
Figure 1—source data 4. The list of the lifetime of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments is shown in Figure 1I.

Figure 1.

Figure 1—figure supplement 1. The subcellular localization of FLS2-GFP, FLS2S938A-GFP, and FLS2S938D-GFP was determined by confocal imaging.

Figure 1—figure supplement 1.

The left images represent GFP signal showing the subcellular localization of FLS2 molecules under different phosphorylation states. The middle images represent red fluorescence signal showing plasma membrane (PM) stained with FM4-64. The right images represent the merge of GFP and FM4-64 staining. Bar = 20 μm.
Figure 1—figure supplement 1—source data 1. The original confocal images of FLS2-GFP, FLS2S938A-GFP, and FLS2S938D-GFP.
Figure 1—figure supplement 2. Uniform Manifold Approximation and Projection (UMAP) visualization of FLS2S938A (control, n = 27 spots; flg22, n = 30 spots) and FLS2S938D (control, n = 11 spots; flg22, n = 14 spots) samples in the different conditions.

Figure 1—figure supplement 2.

Dots represent individual images and are colored according to the reaction conditions.
Figure 1—figure supplement 3. The representative fluorescence recovery after photobleaching (FRAP) time course of FLS2-GFP, FLS2S938A, and FLS2S938D under control treatments.

Figure 1—figure supplement 3.

White squares indicate bleached regions. Bar = 5 μm.
Figure 1—figure supplement 4. Images of FLS2-GFP, FLS2S938A-GFP, and FLS2S938D-GFP with flg22 treatment were collected by VA-TIRFM.

Figure 1—figure supplement 4.

For short presentation, one frame is displayed for every 10 frames captured (at 100 ms intervals). Bar = 300 nm.
Figure 1—figure supplement 5. Representative kymographs showing individual FLS2-GFP, FLS2S938A-GFP, and FLS2S938D-GFP dwell times under the control and flg22 treatment.

Figure 1—figure supplement 5.

Bar = 2 s.
Figure 1—figure supplement 6. The single-molecule trajectories of FLS2-GFP, FLS2S938A-GFP, and FLS2S938D-GFP analyzed by Imaris could be faithfully tracked under control and flg22 treatments.

Figure 1—figure supplement 6.

Bar = 1 μm.
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Using VA-TIRFM, we analyzed FLS2 particle lifetime across various phosphorylation states. The results revealed that flg22 treatment significantly reduced the fluorescence trajectory of FLS2 molecules compared to the control (Figure 1—figure supplement 4), indicating that Ser-938 phosphorylation influences flg22-induced lifetime of FLS2 at the PM. Subsequently, real-time dynamic analysis through the Kymograph technique provided spatiotemporal information via frame-by-frame tracking (Zhou et al., 2020; Su et al., 2023). Compared to FLS2-GFP and FLS2S938D-GFP, FLS2S938A-GFP showed nearly linear fluctuations in fluorescence intensity under flg22 conditions, and the duration of fluorescence retention was essentially unchanged (Figure 1—figure supplement 5). To validate this, we used an exposure time of 100 ms to capture a time series with a total duration of 20 s. Therefore, we divided the times into three segments: 0–1 s, 1–4 s, and >4 s (Bücherl et al., 2017). Numerous FLS2 molecules exhibited a short-lived lifetime, which could be attributed to fluorescence bleaching or potentially reflect the occurrence of abortive endocytic events (Bertot et al., 2018). Additionally, we focused on lifetimes exceeding 4 s. The results showed after flg22 treatment the lifetime of FLS2S938D-GFP greater than 4S significantly decreased, resembling that of FLS2-GFP. While FLS2S938A-GFP plants showed a minor decrease in lifetime upon flg22 treatment, this change was insignificant (Figure 1I, Figure 1—source data 4, Figure 1—figure supplement 6). This aligns with previous findings that indicated that NRT1.1 phosphorylation affects dynamics and lifetime (Zhang et al., 2019). Therefore, these results underscore the impact of Ser-938 phosphorylation on the spatiotemporal dynamics of flg22-induced FLS2.

Ser-938 phosphorylation enhances recruitment of FLS2/BAK1 heterodimerization into AtRem1.3-associated nanodomains

Proteins rarely act independently; they typically form multimers to enhance downstream signaling (Li et al., 2022). Flg22 treatment induces FLS2 and BAK1 to heterodimerize at the PM, signifying flg22 as a ligand promoting this heterodimerization (Orosa et al., 2018). To investigate the impact of Ser-938 phosphorylation on FLS2/BAK1 heterodimerization, we employed Tesseler technology, analyzing FLS2 and BAK1 co-localization in Nicotiana benthamiana epidermal cells. Partial co-localization of FLS2-GFP, FLS2S938A-GFP, and FLS2S938D-GFP with BAK1-mCherry was observed after treatment (Figure 2—figure supplement 1), suggesting that heterodimerization formation is independent of Ser-938 phosphorylation. To further verify if FLS2/BAK1 heterodimerization requires Ser-938 phosphorylation, we analyzed the donor fluorescence lifetime (t) and corrected fluorescence resonance energy transfer (FRET) efficiency (IPS value). A significant decrease in GFP lifetime (t) was observed in leaves expressing FLS2/FLS2S938D/FLS2S938A and BAK1-mCherry upon flg22 application (Figure 2A, Figure 2—source data 1). The FRET efficiency (IPS) between FLS2, FLS2S938D, and FLS2S938A with BAK1-mCherry significantly increased after flg22 treatment, suggesting that the FLS2 Ser-938 phosphorylation site is not essential for flg22-induced heterodimerization. To validate this observation, we quantified FLS2/FLS2S938D/FLS2S938A and BAK1-mCherry co-localization using Pearson correlation coefficients. A marked increase in the coefficient after flg22 treatment for FLS2/FLS2S938D/FLS2S938A and BAK1-mCherry (Figure 2B, Figure 2—source data 2, Figure 2—figure supplement 2) indicated flg22-induced interaction between FLS2-GFP and BAK1-mCherry, irrespective of FLS2 Ser-938 phosphorylation. Additionally, we applied protein proximity indexes (PPIs) to estimate the degree of co-localization between FLS2/FLS2S938D/FLS2S938A and BAK1-mCherry. After flg22 treatment, mean PPIs of FLS2, FLS2S938D, and FLS2S938A with BAK1 increased (Figure 2C, Figure 2—source data 3, Figure 2—figure supplement 3), thereby further supporting our findings. This aligns with the previous finding that flg22 acts as a molecular glue for FLS2 and BAK1 ectodomains (Sun et al., 2013), confirming the independence of FLS2/BAK1 heterodimerization from phosphorylation, with these events occurring sequentially.

Figure 2. Different Ser-938 phosphorylation states of FLS2 affect its partitioning into AtRem1.3-associated nanodomains.

(A) FLIM-FRET was used to detect the co-expression of FLS2/FLS2S938A /FLS2S938D-GFP and BAK1-mCherry in the N. benthamiana epidermal cells stimulated by 1/2 MS or flg22 (10 μM) for 30 min. Average fluorescence lifetime (t) and the FRET efficiency were analyzed for FLS2 (control, n = 21 images; flg22, n = 14 images), FLS2S938A (control, n = 18 images; flg22, n = 12 images), or FLS2S938D (control, n = 24 images; flg22, n = 14 images) and BAK1. The fluorescence mean lifetime (t) of FLS2-GFP (n = 7 images). Statistical significance was assessed using Student’s t-test (***p<0.001). Error bars represent the SD. (B) Pearson correlation coefficient values of co-localization between FLS2 (control, n = 21 images; flg22, n = 11 images), FLS2S938A (control, n = 4 images; flg22, n = 20 images), or FLS2S938D (control, n = 8 images; flg22, n = 10 images) and BAK1 upon stimulation with control or flg22. Statistical significance was assessed using Student’s t-test (*p<0.05). Error bars represent the SD. (C) Mean protein proximity indexes showing FLS2 (control, n = 6 images; flg22, n = 5 images), FLS2S938A (control, n = 4 images; flg22, n = 3 images), or FLS2S938D (control, n = 4 images; flg22, n = 3 images) and BAK1 degree of proximity under different conditions. Statistical significance was assessed using Student’s t-test (*p<0.05, **p<0.01). Error bars represent the SD. (D) TIRF-SIM images of the Arabidopsis leaf epidermal cells co-expressing FLS2/FLS2S938A/FLS2S938D-GFP and AtRem1.3-mCherry under different environments. The distribution of FLS2 and AtRem1.3 signals along the white line in the merged image. Bar = 5 μm. (E) The histogram shows the co-localization ratio of FLS2-GFP (control, n = 46 images; flg22, n = 59 images), FLS2S938A-GFP (control, n = 24 images; flg22, n = 36 images), or FLS2S938D-GFP (control, n = 30 images; flg22, n = 54 images) and AtRem1.3-mCherry. The sizes of the ROIs used for statistical analysis are 13.38 μm and 13.38 μm. Statistical significance was assessed using Student’s t-test (*p<0.05). Error bars represent the SD. (F) FLS2/FLS2S938A/FLS2S938D-GFP and AtRem1.3-mCherry fluorescence signals as shown in (D). (G) The fluorescence mean lifetime (t) and the corrected fluorescence resonance efficiency (Rate) of FLS2 (control, n = 8 images; flg22, n = 13 images), FLS2S938A (control, n = 9 images; flg22, n = 11 images), or FLS2S938D (control, n = 10 images; flg22, n = 11 images) with co-expressed AtRem1.3. The fluorescence mean lifetime (t) of FLS2-GFP (n = 9 images). Statistical significance was assessed using Student’s t-test (***p<0.001). Error bars represent the SD. (H) Intensity and lifetime maps of the Arabidopsis leaf epidermal cells co-expressing FLS2/FLS2S938A/FLS2S938D-GFP and AtRem1.3-mCherry as measured by FLIM-FRET. Bar = 2 µm. (I) Quantification of co-localization between FLS2, FLS2S938A or FLS2S938D and AtRem1.3 with and without stimulation with ligands. Pearson correlation coefficient values (r) were obtained between FLS2 (control, n = 62 images; flg22, n = 53 images)/FLS2S938A (control, n = 26 images; flg22, n = 40 images)/FLS2S938D-GFP (control, n = 28 images; flg22, n = 38 images) and AtRem1.3-mCherry. Statistical significance was assessed using Student’s t-test (*p<0.05). Error bars represent the SD.

Figure 2—source data 1. The list of the fluorescence mean lifetimes (t) of FLS2/FLS2S938A/FLS2S938D-GFP and BAK1-mCherry under different treatments is shown in Figure 2A.
Figure 2—source data 2. The list of the Pearson correlation coefficient of FLS2/FLS2S938A/FLS2S938D-GFP and BAK1-mCherry under different treatments is shown in Figure 2B.
Figure 2—source data 3. The list of the mean protein proximity indexes of FLS2/FLS2S938A/FLS2S938D-GFP and BAK1-mCherry under different treatments is shown in Figure 2C.
Figure 2—source data 4. The original intensity and lifetime maps of FLS2/FLS2S938A/FLS2S938D-GFP and AtRem1.3-mCherry under different treatments are shown in Figure 2H.
Figure 2—source data 5. The list of the fluorescence mean lifetimes (t) of FLS2/FLS2S938A/FLS2S938D-GFP and AtRem1.3-mCherry under different treatments is shown in Figure 2G.
Figure 2—source data 6. The list of the Pearson correlation coefficient of FLS2/FLS2S938A/FLS2S938D-GFP and AtRem1.3-mCherry under different treatments is shown in Figure 2I.

Figure 2.

Figure 2—figure supplement 1. SR-Tesseler analysis shows the distribution and co-localization of all spots on FLS2-GFP, FLS2S938A-GFP and FLS2S938D-GFP and BAK1 under control or flg22 treatment.

Figure 2—figure supplement 1.

Bar = 50 μm.
Figure 2—figure supplement 1—source data 1. The list of the coordinates of FLS2/FLS2S938A/FLS2S938D-GFP and BAK1-mCherry under different treatments.
Figure 2—figure supplement 2. Images show co-localization of FLS2-GFP, FLS2S938A-GFP, or FLS2S938D-GFP with BAK1 in N. benthamiana leaves cells under control or flg22 treatment.

Figure 2—figure supplement 2.

Figure 2—figure supplement 3. 3D plot of FLS2-GFP, FLS2S938A-GFP, or FLS2S938D-GFP and BAK1 cross-correlation versus pixel shift under control or flg22 treatment.

Figure 2—figure supplement 3.

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Membrane nanodomains serve as pivotal platforms for protein regulation, impacting cellular signaling dynamics (Martinière and Zelazny, 2021). Studies reveal that specific PM proteins, responsive to developmental cues and environmental stimuli, exhibit dynamic movements within and outside these nanodomains (Lee et al., 2019). For instance, treatment with the secreted peptides RAPID ALKALINIZATION FACTOR (RALF1 and RALF23) enhances the presence of FERONIA (FER) in membrane nanodomains (Gronnier et al., 2022). Conversely, flg22-activated BSK1 translocates from membrane nanodomains to non-membrane nanodomains (Su et al., 2021). In a previous investigation, we demonstrated that flg22 induces FLS2 translocation from AtFlot1-negative to AtFlot1-positive nanodomains in the plasma membrane, implying a connection between FLS2 phosphorylation and membrane nanodomain distribution (Cui et al., 2018). To validate this, we assessed the association of FLS2/FLS2S938D/FLS2S938A with membrane nanodomains, using AtRem1.3-associated nanodomains as representatives (Lv et al., 2017; Huang et al., 2019). Upon observation using dual-color TIRM-SIM, partial co-localization was detected between FLS2-GFP, FLS2S938D-GFP, FLS2S938A-GFP, and AtRem1.3-mCherry foci on cell surfaces (Figure 2D). SPT was employed to quantify the co-localization ratio, revealing a small overlap in the cross-correlation signal for FLS2S938A-GFP and AtRem1.3-mCherry after flg22 treatment, similar to untreated cells. In contrast, flg22-treated seedlings displayed higher cross-correlation signals for FLS2-GFP/FLS2S938D-GFP and AtRem1.3-mCherry compared to untreated seedlings (Figure 2E and F), suggesting that phosphorylation increased the movement of FLS2 protein to membrane nanodomains. To further verify this result, FRET-FLIM was applied to examine the correlation between FLS2/FLS2S938D/FLS2S938A-GFP and AtRem1.3-mCherry. The results showed that, after flg22 treatment, plants co-expressing FLS2S938D-GFP with AtRem1.3-mCherry had a strongly reduced average GFP fluorescence lifetime compared to control seedlings, which was similar to that of FLS2-GFP seedlings (Figure 2G and H, Figure 2—source data 4 and 5). Meanwhile, the flg22 treatment increased the IPS for FLS2 and FLS2S938D with AtRem1.3-mCherry. No significant difference was observed in the fluorescence lifetime and IPS of FLS2S938A-GFP co-expressed with AtRem1.3-mCherry, with or without flg22 treatment (Figure 2G and H, Figure 2—source data 4 and 5), suggesting that Ser-938 phosphorylation may influence FLS2 partitioning into PM nanodomains. To further investigate the role of phosphorylation in FLS2 distribution into nanodomains, we used Pearson correlation coefficients to quantify the co-localization between FLS2/FLS2S938D/FLS2S938A-GFP and AtRem1.3-mCherry. It was found that the mean co-localization values between FLS2-GFP/FLS2S938D-GFP and AtRem1.3-mCherry under flg22 treatment were significantly higher compared to the control group (Figure 2I, Figure 2—source data 6). Notably, FLS2S938A-GFP and AtRem1.3-mCherry demonstrated similar co-localization values under both control group and flg22 conditions (Figure 2I, Figure 2—source data 6), indicating that the phosphorylated form of FLS2 may modify the distribution of PM nanodomains. These findings suggest that the phosphorylation state of FLS2 at Ser-938 influences its aggregation into AtRem1.3-associated nanodomains, providing an efficient mechanism for triggering the immune response.

Ser-938 phosphorylation maintains FLS2 protein homeostasis via flg22-induced endocytosis

The PM protein endocytosis is crucial for regulating intercellular signal transduction in response to environmental stimuli. Notably, Thr 867 mutation, a potential phosphorylation site on FLS2, results in impaired flg22-induced endocytosis, underscoring the significance of phosphorylation in FLS2 endocytosis (Robatzek et al., 2006). As shown in Figure 1I, both FLS2 and FLS2 phospho-mimetic mutants showed a reduced lifetime under flg22 treatment, implying a probable connection between FLS2 lifetime and endocytosis. We further found that FLS2/FLS2S938D/FLS2S938A-GFP accumulated in brefeldin A (BFA) compartments labeled with FM4-64, indicating that various Ser-938 phosphorylation states of FLS2 undergo BFA-dependent constitutive endocytosis (Figure 3A, Figure 3—source data 1, Figure 3—figure supplement 1).

Figure 3. Ser-938 phosphorylation site affects flg22-induced endocytosis.

(A) Confocal images of FLS2/FLS2S938A/FLS2S938D-GFP in Arabidopsis thaliana leaf epidermal cells. Firstly, experiments were performed after pretreatment with CHX (50 μM) for 30 min. Subsequently, to observe subcellular localization, FLS2/FLS2S938A/FLS2S938D-GFP were treated with 60 min of BFA (50 μM) and then exposed or not to flg22 (10 μM). Finally, the transgenic seedlings were stained with FM4-64 (5 μM, 30 min). White arrows indicate BFA bodies. Bar = 3 μm. (B) Images of FLS2/FLS2S938A/FLS2S938D-GFP in Arabidopsis thaliana leaf epidermal cells treated with 10 μM flg22 for 15, 30, and 60 min. Bar = 3 μm. (C) Analysis of FLS2 (15 min, n = 4 images; 30 min, n = 4 images; 60 min, n = 4 images), FLS2S938A (15 min, n = 3 images; 30 min, n = 3 images; 60 min, n = 3 images), and FLS2S938D (15 min, n = 3 images; 30 min, n = 3 images; 60 min, n = 3 images) endocytic vesicle numbers in cells treated with 10 μM flg22 treatment over time. Statistical significance was assessed using Student’s t-test (***p<0.001). Error bars represent the SD. (D) The signal density of FLS2 (control, n = 45 images; flg22, n = 24 images), FLS2S938A (control, n = 27 images; flg22, n = 31 images), and FLS2S938D (control, n = 18 images; flg22, n = 15 images) in cells after control or 10 μM flg22 treatments for 30 min as measured by fluorescence correlation spectroscopy (FCS). Statistical significance was assessed using Student’s t-test (**p<0.01). Error bars represent the SD. (E) Immunoblot analysis of FLS2 protein in 10-day-old transgenic Arabidopsis plant upon stimulation with or without 10 μM flg22. CBB, a loading control dyed with Coomassie Brilliant Blue.

Figure 3—source data 1. The original confocal images of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments are shown in Figure 3A.
Figure 3—source data 2. The original confocal images of FLS2/FLS2S938A/FLS2S938D-GFP in Arabidopsis thaliana leaf epidermal cells treated with 10 μM flg22 for 15, 30, and 60 min are shown in Figure 3B.
Figure 3—source data 3. The list of the endocytic vesicle numbers of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments is shown in Figure 3C.
Figure 3—source data 4. The list of the signal density of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments is shown in Figure 3D.
Figure 3—source data 5. The original file for the western blot analysis in Figure 3E.
Figure 3—source data 6. The original file for the western blot analysis with highlighted bands and sample labels is shown in Figure 3E.

Figure 3.

Figure 3—figure supplement 1. Distribution and co-localization with FM4-64 of FLS2-GFP, FLS2S938A-GFP, and FLS2S938D-GFP in leaves epidermal cells treated with or without 10 μM flg22 for 30 min after 30 min pretreatment with CHX.

Figure 3—figure supplement 1.

Bar = 5 μm.
Figure 3—figure supplement 1—source data 1. The list of the number, diameter, and fluorescence intensity of FLS2/FLS2S938A/FLS2S938D-GFP BFA bodies under different treatments.
Figure 3—figure supplement 2. Confocal images of FLS-GFP, FLS2S938A-GFP, and FLS2S938D-GFP in leaves epidermal cells treated with or without 10 μM flg22 after 30 min pretreatment with BFA.

Figure 3—figure supplement 2.

Arrows indicate BFA bodies of FLS2, inset images show details of BFA bodies. White arrows indicate BFA bodies. Bar = 5 μm.
Figure 3—figure supplement 3. Effects of Ser-938 phosphorylation on the endocytosis of FLS2.

Figure 3—figure supplement 3.

(A) Number analysis of FLS2-GFP (control, n = 19 images; flg22, n = 9 images), FLS2S938A-GFP (control, n = 28 images; flg22, n = 9 images) and FLS2S938D-GFP (control, n = 21 images; flg22, n = 7 images) signal in leaf epidermal cells of Arabidopsis.Statistical significance was assessed using Student’s t-test. Error bars represent SD (n = 3). (B) Quantification of BFA body diameter in FLS2-GFP (control, n = 12 images; flg22, n = 8 images), FLS2S938A-GFP (control, n = 9 images; flg22, n = 10 images), and FLS2S938D-GFP (control, n = 8 images; flg22, n = 22 images). Statistical significance was assessed using Student’s t-test (**p<0.01). Error bars represent the SD. (C) The fluorescence intensity of FLS2-GFP (control, n = 6 images; flg22, n = 5 images), FLS2S938A-GFP (control, n = 5 images; flg22, n = 5 images), and FLS2S938D-GFP (control, n = 3 images; flg22, n = 7 images) in the cytoplasm relative to the sum of the cytoplasm and PM in leaves epidermal cells under CHX + BFA + FM4-64 treatment; three biological replicates were performed. Statistical significance was assessed using Student’s t-test (**p<0.01). Error bars represent the SD.
Figure 3—figure supplement 4. The fluorescence intensity of FLS2-GFP (0 min, n = 9 images; 15 min, n = 5 images; 30 min, n = 7 images; 60 min, n = 9 images), FLS2S938A-GFP (0 min, n = 5 images; 15 min, n = 5 images; 30 min, n = 5 images; 60 min, n = 5 images), and FLS2S938D-GFP (0 min, n = 10 images; 15 min, n = 6 images; 30 min, n = 7 images; 60 min, n = 8 images) in the cytoplasm relative to the sum of the cytoplasm and PM in leaves epidermal cells treated with flg22 treatment over time; Three biological replicates were performed.

Figure 3—figure supplement 4.

Statistical significance was assessed using Student’s t-test (*p<0.05, ***p<0.001). Error bars represent the SD.
Figure 3—figure supplement 4—source data 1. The list of the fluorescence intensity of FLS2-GFP, FLS2S938A-GFP, and FLS2S938D-GFP in the cytoplasm relative to the sum of the cytoplasm and PM in leaves epidermal cells under different treatments.
Figure 3—figure supplement 5. The confocal images of FLS2-GFP signal density in Arabidopsis leaf epidermal cells upon ligand stimulation were analyzed using fluorescence correlation spectroscopy.

Figure 3—figure supplement 5.

Control (CK) plants received no treatment. All the samples except CK were pretreated with flg22 for different times. Bar = 10 μm.
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Next, we investigated the impact of Ser-938 on flg22-induced FLS2 internalization. Under co-treatment with BFA and flg22, FLS2/FLS2S938D produced strong signals in both the BFA compartments. Interestingly, we found that FLS2S938A-GFP only produced BFA compartments, and most remained at the PM region with few intracellular puncta upon stimulation with BFA and flg22 (Figure 3—figure supplements 2 and 3). We further tracked FLS2 endocytosis and quantified vesicle numbers over time; the results showed that both FLS2 and FLS2S938D vesicles appeared 15 min after-flg22 treatment, significantly increasing thereafter. Notably, only a few vesicles were detected in FLS2S938A-GFP, indicating Ser-938 phosphorylation of FLS2 impact on flg22-induced FLS2 endocytosis (Figure 3B and C, Figure 3—source data 2, Figure 3—source data 3, Figure 3—figure supplement 4). Results revealed increased endocytic vesicles for FLS2 during flg22 treatment, aligning with previous studies (Leslie and Heese, 2017; Loiseau and Robatzek, 2017). Additionally, fluorescence correlation spectroscopy (FCS) (Chen et al., 2009) monitored molecular density of FLS2 changes on the PM before and after flg22 treatment (Figure 3—figure supplement 5). Figure 3D shows that both FLS2-GFP and FLS2S938D-GFP densities significantly decreased after flg22 treatment, while FLS2S938A-GFP exhibited minimal changes, indicating Ser-938 phosphorylation affects FLS2 internalization (Figure 3D, Figure 3—source data 4). Western blotting confirmed that Ser-938 phosphorylation influences FLS2 degradation after flg22 treatment (Figure 3E, Figure 3—source data 5 and 6), consistent with single-molecule analysis findings. Therefore, our results strongly support the notion that Ser-938 phosphorylation expedited FLS2 internalization, potentially regulating its immune response capacity.

Ser-938 phosphorylation affects FLS2-mediated responses

PTI plays a pivotal role in host defense against pathogenic infections (Macho and Zipfel, 2014; Ding et al., 2022; Ngou et al., 2022). Previous studies demonstrated that the perception of flg22 by FLS2 initiates a complex signaling network with multiple parallel branches, including calcium burst, mitogen-activated protein kinases (MAPKs) activation, callose deposition, and seedling growth inhibition (Chi et al., 2021; Zipfel et al., 2004; Li et al., 2016; Sanguankiattichai et al., 2022). Our focus was to investigate the significance of Ser-938 phosphorylation in flg22-induced plant immunity. Our results illustrate diverse immune responses in FLS2 and FLS2S938D plants following flg22 treatment (Figure 4A–F, Figure 4—source data 1–3, Figure 4—figure supplements 1–3). These responses encompass calcium burst activation, MAPKs cascade reaction, callose deposition, hypocotyl growth inhibition, and activation of immune-responsive genes. In contrast, FLS2S938A exhibited limited immune responses, underscoring the importance of Ser-938 phosphorylation for FLS2-mediated PTI responses.

Figure 4. Ser-938 phosphorylation is essential for various flg22-induced pattern-triggered immunity (PTI) responses.

(A) The flg22-induced transient Ca2+ flux in 20-day-old transgenic leaf cells. The Ca2+ flux was continuously recorded for 12 min in the test medium. Each point represents the average value for about 12 individual plants ± SEM. (B) Phenotypes of 5-day-old etiolated seedlings grown in the presence of 1/2 MS (control) or 10 μM flg22 solid medium. Bar = 0.5 cm. (C) Hypocotyl length of FLS2 (control, n = 13 seedlings; flg22, n = 13 seedlings), FLS2S938A (control, n = 13 seedlings; flg22, n = 13 seedlings), and FLS2S938D (control, n = 13 seedlings; flg22, n = 13 seedlings) transgenic plants. Statistical significance was assessed using Student’s t-test (***p<0.001). Error bars represent the SD. (D–F) mRNA levels of the PTI marker genes FRK1/WRKY33/CYP81 were significantly different between the FLS2, FLS2S938A, and FLS2S938D 10-day-old transgenic Arabidopsis plant after treatment with 10 μM flg22 for 30 min. Total RNA was extracted from 10-day-old seedlings and analyzed using qRT-PCR, with transcript levels being normalized to UBQ5. Three biological replicates were performed. Each experiment was repeated thrice independently (n = 3). Statistical significance was assessed using Student’s t-test (*p<0.05, **p<0.01, ***p<0.001). Error bars represent SD. (G) The working model for the spatiotemporal dynamic regulation of FLS2 phosphorylation at the plasma membrane upon flg22 stimulation. (H) Dynamic model of FLS2 with different Ser-938 phosphorylation states upon stimulation with flg22.

Figure 4—source data 1. The list of the transient Ca2+ flux of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments is shown in Figure 4A.
Figure 4—source data 2. The list of the hypocotyl length of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments is shown in Figure 4C.
Figure 4—source data 3. The list of the mRNA levels of the PTI marker genes FRK1/WRKY33/CYP81 of the FLS2, FLS2S938A, and FLS2S938D 10-day-old transgenic Arabidopsis plants under different treatments is shown in Figure 4D–F.

Figure 4.

Figure 4—figure supplement 1. MAPKs phosphorylation in FLS2-GFP, FLS2S938A-GFP, and FLS2S938D-GFP 10-day-old seedlings incubated with 10 μM flg22 for 0 min, 5 min, and 15 min.

Figure 4—figure supplement 1.

CBB, a loading control dyed with Coomassie brilliant blue.
Figure 4—figure supplement 1—source data 1. The original file for the western blot analysis.
Figure 4—figure supplement 1—source data 2. The original file for the western blot analysis with highlighted bands and sample labels.
Figure 4—figure supplement 2. Ser-938 phosphorylation affects flg22-induced callose deposition.

Figure 4—figure supplement 2.

(A) Detection of callose deposition in leaves of FLS2-GFP, FLS2S938A-GFP, and FLS2S938D-GFP Arabidopsis seedlings after treated with CK (1/2 MS liquid medium) or 10 μM flg22 for 12 hr. Callose was observed using a fluorescence microscope. Bar = 5 mm. (B) The amount of callose stained with aniline blue per unit area on FLS2-GFP (control, n = 7 images; flg22, n = 14 images), FLS2S938A-GFP (control, n = 10 images; flg22, n = 12 images), and FLS2S938D-GFP (control, n = 10 images; flg22, n = 13 images) image was quantitatively analyzed. Statistical significance was assessed using Student’s t-test (***p<0.001). Error bars represent the SD.
Figure 4—figure supplement 2—source data 1. The list of the callose deposition of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments.
Figure 4—figure supplement 3. Phenotypes of 5-day-old etiolated seedlings grown in the 1/2 MS (CK) or solid medium with or without 10 μM flg22.

Figure 4—figure supplement 3.

Scale bar = 0.5 cm.
Figure 4—figure supplement 3—source data 1. The original images of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments.
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In summary, our study confirmed that FLS2 phosphorylation regulated PAMP-triggered plant immunity by influencing spatiotemporal dynamics at the PM. Following flg22 treatment, activated FLS2 undergoes hetero-oligomerization and phosphorylation sequentially (Figure 4G and H). Crucially, phosphorylation at the Ser-938 site promotes FLS2 recruitment into AtRem1.3-associated nanodomains and endocytosis (Figure 4G and H). These results provided new insights into the phosphorylation-regulated dynamics of plant immunity, thereby providing a reference for future studies on signal transduction in intracellular complex nanostructures.

Materials and methods

Plant materials and construction

Mutants and transgenic lines used in all experiments were in the Arabidopsis thaliana Colombia-0 (Col-0) background. To generate the transgenic plants, specific constructs were PCR-amplified and cloned into the vector pCAMBIA2300. Plasmids were introduced into the mutant plants by Agrobacterium-mediated transformation. Dual-color lines expressing FLS2S938A-GFP and FLS2S938D-GFP with AtREM1.3-mCherry were generated by hybridization.

Transient infiltration of N. benthamiana leaves

The Agrobacterium tumefaciens strains GV3101 (obtained from Shanghai Weidi Biotechnology), harboring eukaryotic expression vectors, were infiltrated into the leaves of 8-week-old N. benthamiana plants. Plants were incubated at 22°C for 2 days before imaging.

Drug treatments

All chemicals were obtained from Sigma-Aldrich and dissolved in 100% DMSO to yield stock solutions at the following concentrations: BFA (50 mM in DMSO, 50 µM in working solution), CHX (50 mM in DMSO, 50 µM in working solution), and FM4-64 (5 mM in DMSO, 5 µM in working solution). The flg22 of flagellin peptides was synthesized by Shanghai GL Biochem Company and was used at a concentration of 10 µM in double-distilled H2O. Arabidopsis seedlings were treated in 1/2 MS growth liquid medium with added hormone or drug.

Quantitative reverse-transcription PCR

Total RNA was extracted using the Plant Kit (Tiangen) and then reverse transcribed into cDNA with FastQuant RT Kit (Tiangen). Next, qPCR was performed using TiangenSurperRealPreMix Plus (SYBR Green). The primers used were as follows: WRKY33 (At At2g38470) 5′-GAAACAAATGGTGGGAATGG-3′ and 5′-TGTCGTGTGATGCTCTCTCC-3′; CYP81 (At At2g23190) 5′-AAATGGAGAGAGCAACACAATG-3′ and 5′-ATCGCCCATTCCAATGTTAC-3′; FRK1 (AtAt2g19190) 5′-TATATGGACACCGCGTATAGTG-3′ and 5′-ATAAAACTTTGCGTTAGGGTCG-3′.

Aniline blue staining

To detect callose deposition, aniline blue staining was performed as described. Arabidopsis thaliana leaves were completely de-colored by the destaining solution (3 ml ethyl alcohol and 1 ml glacial acetic acid), rinsed in water and 50% ethanol, and then stained in 150 mM KH2PO4 (pH 9.5) plus 0.01% aniline blue for 2 hr. Samples were mounted in 25% glycerol, and then observed under a microscope that was equipped with a Leica DM2500 UV lamp.

Confocal laser scanning microscopy and image analysis

Confocal microscopy was done with a TCS SP5 Confocal Microscope fitted with a ×63 water-immersion objective. GFP and FM4-64 were assayed using 488 nm and 514 nm wavelengths (multitrack mode). The fluorescence emissions were respectively detected with spectral detector set LP 560-640 (FM4-64) and BP 520-555 (GFP). Image analysis was performed with Leica TCS SP5 software and quantified using the ImageJ software bundle (NIH).

VA-TIRFM and single-particle fluorescence image analysis

The dynamics of FLS2 phospho-dead and phospho-mimic mutants were recorded using VA-TIRFM. This was done using an inverted microscope (IX-71, Olympus) equipped with a total internal reflective fluorescence illuminator (model no. IX2-RFAEVA-2; Olympus) and a 1003 oil-immersion objective (numerical aperture = 1.45). To track GFP-labeled proteins at the PM, living leaf epidermal cells of 6-day-old seedlings were observed under VA-TIRFM. To visualize GFP or mCherry fluorescent proteins, appropriate corresponding laser excitation (473 nm or 561 nm) was used and emission fluorescence was obtained with filters (BA510IF for GFP; HQ525/50 for mCherry). A digital EMCCD camera (Andor Technology, ANDOR iXon DV8897D-CS0-VP, Belfast, UK) was used to acquire the fluorescent signals, which were stored directly on computers and then analyzed using ImageJ software. Images of single particles were acquired with 100 ms exposure time.

UMAP analysis

We used an R package to perform dimensionality reduction and clustering analysis of single-particle tracking data. We performed nonlinear dimensionality reduction for visualization with the function ‘UMAP’ in the R package. We proceeded to cluster cells using the Louvain algorithm with the ‘FindNeighbors’ and ‘FindClusters’ functions in the R package carried out as previously described (Dorrity et al., 2020).

TIRF-SIM imaging and the co-localization analysis

The SIM images were taken using a 60×NA 1.49 objective on a structured illumination microscopy (SIM) platform (DeltaVision OMX SR) with a sCMOS camera (camera pixel size, 6.5 μm). The light source for TIRF-SIM included diode laser at 488 nm and 568 nm with pixel sizes (μm) of 0.0794 and 0.0794 (Barbieri et al., 2021).

For the dual-color imaging, FLS2/FLS2S938A/FLS2S938D-GFP (488 nm/30.0%) and AtRem1.3-mCherry (561 nm/30.0%) were excited sequentially. The exposure time of the camera was set at 50 ms throughout single-particle imaging. The time interval for time-lapse imaging was 100 ms, the total time was 2 s, and the total time points were 21 s. The Imaris intensity correlation analysis plugin was used to calculate the co-localization ratio as described previously. The sizes of the ROIs used for statistical analysis are 13.38 μm and 13.38 μm.

Ca2+ flux measurements in Arabidopsis leaves

Net Ca2+ fluxes in Arabidopsis leaf cells were measured using the non-invasive micro-test technique, as described previously (Zhong et al., 2023). First, a small incision was made in the leaves of 14-day-old seedling. Then it was fixed at the bottom of 35 mm Petri dish and incubated in the test buffer (pH = 6.0, 0.1 mmol l−1 KCl/CaCl2/MgCl2, 0.2 mmol l−1 Na2SO4, 0.3 mmol l−1 MES, 0.5 mmol l−1 NaCl) for approximately 30 min. The Ca2+ concentration in the leaf cells was measured at 0.2 Hz near and 30 µm away from the cells. Each plant was measured once, and then using 1/2 MS (CK) or 10 μM flg22 treated the leaf and measured Ca2+flux again. The Ca2+ flux was calculated as described (Jiao et al., 2022).

Analysis of root growth

The transgenic seedlings were treated with different conditions, and imaging was performed by scanning the root systems at 500 dpi (Canon EOS 600D). ImageJ was used to analyze the root growth parameters. Three biological replicates were performed.

Fluorescence recovery after photobleaching analysis

Plants were grown on 1/2 MS solid medium for 4 days prior to conducting FRAP analysis. The FRAP analysis was carried out using an Olympus FV1200 confocal microscope with an inverted microscope setup. The imaging was performed using 488 nm diode laser excitation and a water-immersed ×63 objective. The area of interest was bleached with a 488 nm laser at 100% laser power. The time interval for monitoring fluorescence recovery was 3 s. The fluorescence recovery data was subsequently analyzed using ImageJ and Origin 8.6 software following the methods outlined by Xing et al., 2022.

Fluorescence correlation spectroscopy

FCS was performed in point-scanning mode on a Leica TCS SP5 FCS microscope equipped with a 488 nm argon laser, an Avalanche photodiode, and an in-house coupled correlator. After acquiring images on the PM of a cell in transmitted light mode, the diffusion of protein molecules into and out of the focal volume transformed the local concentration of fluorophores, leading to spontaneous fluctuation in the fluorescence intensity. Finally, the protein density was calculated on the basis of the protocol described previously.

FRET-FLIM

FRET-FLIM analysis was performed using an inverted Olympus FV1200 microscope equipped with a Picoquant picoHarp300 controller. The excitation at 488 nm was implemented by a picosecond pulsed diode laser at a reduplication rate of 40 MHz by way of a water immersion objective (603, NA 1.2). The emitted light passed through a 520/35 nm bandpass filter and was detected by an MPD SPAD detector. Data were collected and performed using the SymphoTime 64 software (PicoQuant).

Western blot

Total proteins were extracted from 10-day-old seedlings of the acidic phosphomimic mutants FLS2S938A-GFP and FLS2S938D-GFP and transgenic FLS2-GFP lines under different conditions. Proteins were extracted using buffer E (includes 1.5125 g Tris-HCl [pH 8.8], 1.2409 g Na2S2O5, 11.1 ml glycerine, 1 g SDS, and 5 mM DTT). The proteins in PM fractions were obtained using the Invent Minute kit. Proteins were separated by 10% SDS-polyacrylamide gel and transferred to a nitrocellulose membrane. The membrane was blotted with anti-GFP antibody (Sigma-Aldrich) at a 1:4000 dilution.

SR-Tesseler

The association of FLS2/FLS2S938D/FLS2S938A with AtRem1.3-associated nanodomains was characterized using the open-source software packages ImageJ and SR-Tesseler. The specific operational steps were as previously described (Levet et al., 2015).

PPI analysis

The degree of correlation between proteins was analyzed by measuring the PPIs (Zinchuk et al., 2011). Recently, an smPPI was developed. Using single-molecule images acquired via VA-TIRFM, the smPPI can be calculated to quantitatively assess the co-localization of FLS2/FLS2S938D/FLS2S938A with AtRem1.3.

Kymograph analysis

The original images were uploaded to ImageJ, which includes the ‘multiple kymograph’ plugin. The image can be adjusted for optimum contrast by selecting ‘Image/Adjustment/Brightness/Contrast’. Subsequently, the individual particles of interest are selected using the ‘straight line’ tool, and the dynamic features are analyzed using the ‘Multiple Kymograph’ tool. Finally, the ‘Image/Type/RGB color’ tool is utilized to obtain typical images, which are then saved in TIFF format.

Pearson analysis

The co-localization analysis for FLS2/FLS2S938D/FLS2S938A with AtRem1.3/BAK1 was carried out using ImageJ as described previously (Zhang et al., 2019). The background subtraction was performed using the 'Rolling Ball' method with a radius of 50 pixels. The plugin ‘PSC co-localization’ in ImageJ was employed to derive the Pearson correlation coefficient (Rr).

Data analysis

The significance of arithmetic mean values for all data sets was assessed using Student’s t-test. Error bars were calculated with the s.d. function in Microsoft Excel. The differences at p<0.05 were considered statistically significant. According to Student’s t-test, characters in the figure represent statistically significant differences compared with control (*p<0.05, **p<0.01, and ***p<0.001).

Acknowledgements

This work was supported by the National Natural Science Foundation of China (32030010, 91954202, 32370740, 32000483), National Key Research and Development Program of China (2022YFF071250, 2022YFF0712500, 2022YFD2200603), and Beijing Nova Program (20230484251).

Funding Statement

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Contributor Information

Jinxing Lin, Email: linjx@ibcas.ac.cn.

Jian-Min Zhou, Chinese Academy of Sciences, China.

Jürgen Kleine-Vehn, University of Freiburg, Germany.

Funding Information

This paper was supported by the following grants:

  • National Natural Science Foundation of China 32030010 to Jinxing Lin.

  • National Natural Science Foundation of China 91954202 to Xiaojuan Li.

  • National Natural Science Foundation of China 32370740 to Yaning Cui.

  • National Natural Science Foundation of China 32000483 to Yaning Cui.

  • National Key Research and Development Program of China 2022YFF071250 to Jinxing Lin.

  • National Key Research and Development Program of China 2022YFF0712500 to Jinxing Lin.

  • National Key Research and Development Program of China 2022YFD2200603 to Jinxing Lin.

  • Beijing Nova Program 20230484251 to Yaning Cui.

Additional information

Competing interests

No competing interests declared.

Author contributions

Conceptualization, Writing - review and editing.

Investigation, Writing - original draft.

Data curation, Visualization, Methodology.

Formal analysis, Visualization.

Methodology.

Writing - review and editing.

Data curation.

Validation.

Software.

Resources, Funding acquisition.

Additional files

MDAR checklist

Data availability

All data are included in the manuscript and supporting files.

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eLife assessment

Jian-Min Zhou 1

This potentially important study employs advanced imaging techniques to directly visualize molecular dynamics and of the immune receptor kinase FLS2 in specific microenvironments. The evidence supporting the ligand-induced association with remorin and the requirement of a previously reported phosphosite as presented is solid, although support by independent methods would be welcome. The work will be of interest to plant biologists working on cell surface receptors.

Reviewer #1 (Public Review):

Anonymous

Summary:

Organization of cell surface receptors in membrane nanodomains is important for signaling, but how this is regulated is poorly understood. In this study the authors employ TIRFM single-molecule tracking combined with multiple analyses to show that ligand exposure increases diffusion of the immune receptor FLS2 in the plasma membrane and its co-localization with remorin REM1.3 in a manner dependent on the phosphosite S938. They additionally show that ligand increases dwell time of FLS2, and this is linked to FLS2 endocytosis, also in a manner dependent on S938 phosphorylation. The study uncovers a regulatory mechanism of FLS2 localization in the nanodomain crucial for signaling.

Strengths:

TIRFM single-molecule tracking, FRAP, FRET and endocytosis experiments were nicely done. A role of S938 phosphorylation is convincing.

Weaknesses:

In the previous submission, reviewers pointed out multiple issues, which the reviewers believed the authors can address in the revision. The revised version does improve to some extent but still contains many issues in terms of data analysis and writing.

Reviewer #2 (Public Review):

Anonymous

Summary:

The research conducted by Yaning Cui and colleagues delves into understanding FLS2-mediated immunity. This is achieved by comparing the spatiotemporal dynamics of a FLS2-S938A mutant and FLS2-WT, especially in relation to their association with the remorin protein. To delineate the differences between the FLS2-S938A mutant and FLS2-WT, they utilized a plethora of advanced fluorescent imaging techniques. By analyzing surface dynamics and interactions involving the receptor signal co-receptor BAK1 and remorin proteins, the authors propose a model of how FLS2 and BAK1 are assembled and positioned within a remorin-specific nano-enviroment during FLS2 ligand-induced immune responses.

Strengths:

These techniques offer direct visualizations of molecular dynamics and interactions, helping us understand their spatial relationships and interactions during innate immune responses.

Advanced cell biology imaging techniques are crucial for obtaining high-resolution insights into the intracellular dynamics of biomolecules. The demonstrated imaging systems are excellent examples to be used in studying plant immunity by integrating other functional assays.

Weaknesses:

It's essential to acknowledge that every fluorescence-based method, just like biochemical assays, comes with its unique limitations. These often pertain to spatial and temporal resolutions, as well as the sensitivity of the cameras employed in each setup. Meticulous interpretation is pivotal to guarantee an accurate depiction and to steer clear of potential misunderstandings when employing specific imaging systems to analyze molecular attributes. Moreover, a discerning interpretation and accurate image analysis can offer invaluable guidance for future studies on plant signaling molecules using these nice cell imaging techniques.

For instance, although single-particle analysis couldn't conclusively link FLS2 and remorin, FLIM-FRET effectively highlighted their ligand-triggered association and the disengagement brought on by mutations. While these methodologies seemed to present differing outcomes, they were described in the manuscript as harmonious. In reality, these differences could highlight distinct protein populations active in immune responses, each accentuated differently by the respective imaging techniques due to their individual spatial and temporal limitations. Addressing these variations is imperative, especially when designing future imaging explorations of immune complexes.

eLife. 2024 Jul 24;12:RP91072. doi: 10.7554/eLife.91072.3.sa3

Author response

Yaning Cui 1, Hongping Qian 2, Jinhuan Yin 3, Changwen Xu 4, Pengyun Luo 5, Xi Zhang 6, Meng Yu 7, Bodan Su 8, Xiaojuan Li 9, Jinxing Lin 10

The following is the authors’ response to the original reviews.

We greatly thank you and the reviewers for your expert comments and valuable suggestions on our manuscript. After reading these comments, we realized that the previous version of the manuscript contained some weak points. Surely, the issues raised by the six reviewers were of great help in the revision of our manuscript.

According to the comments, we have now fully revised the manuscript to address most of the questions and suggestions. In addition, we reworded some parts of the Introduction, Results and Discussion, Figures, Figure legends and Experimental Methods to increase the rigor of our conclusions.

Overall, you will see that we have paid serious attention to all the concerns and criticisms expressed by reviewers. Addressing these various issues has most certainly allowed us to prepare a much-improved manuscript and for this we offer our hearty thanks.

Reviewer #1 (Public Review):

Summary:

The organization of cell surface receptors in membrane nanodomains is important for signaling, but how this is regulated is poorly understood. In this study, the authors employ TIRFM single-molecule tracking combined with multiple analyses to show that ligand exposure increases the diffusion of the immune receptor FLS2 in the plasma membrane and its co-localization with remorin REM1.3 in a manner dependent on the phosphosite S938. They additionally show that ligand increases the dwell time of FLS2, and this is linked to FLS2 endocytosis, also in a manner dependent on S938 phosphorylation. The study uncovers a regulatory mechanism of FLS2 localization in the nanodomain crucial for signaling.

Strengths:

TIRFM single-molecule tracking, FRAP, FRET, and endocytosis experiments were nicely done. The role of S938 phosphorylation is convincing.

Weaknesses:

Question 1: The model suggests that S938 is phosphorylated upon flg22 treatment. This is actually not known.

Reply: Thank you for your expert comments. Although the phosphorylation of Ser-938 upon flg22 treatment is not known, the model presented in the manuscript is based on previous studies that have shown the importance of Ser-938 phosphorylation for the function of FLS2 (Cao et al, 2013). When it is mutated to the phosphorylation-mimicking residues aspartate or glutamate, immune responses remain normal. These findings suggest that the phosphorylation of Ser-938 plays a critical role in activating defense mechanisms upon flagellin detection (Cao et al, 2013). Now we added the results of Cao et al. (2013) to the introduction to strengthen in the revised manuscript.

Question 2: In addition, the S938D mutant does not show constitutively increased diffusion and co-localization with remorin. It is necessary to soften the tone in the conclusion.

Reply: We appreciate the valuable suggestions from the reviewer. Based on our findings, we observed that the phosphorylation of Ser-938 significantly impacts the dynamics of flg22-induced FLS2. However, it does not alter the diffusion coefficient of FLS2 itself. In the revised manuscript, we have carefully adjusted the conclusion by softening the tone to reflect these findings.

Question 3: The introduction (only two paragraphs) and discussion are not properly written in the context of the current understanding of plant receptors in nanodomains. The authors basically just cited a few publications of their own, and this is not acceptable.

Reply: We accepted the criticisms here. Now, we have reworded the introduction and discussion sections to improve clarity. Furthermore, we have incorporated several new reports on plant receptors in nanodomains into the revised manuscript. Besides, we deleted some publications from our own group, while citing the latest references on plant receptors and nanodomains.

Reviewer #2 (Public Review):

Summary:

The research conducted by Yaning Cui and colleagues delves into understanding FLS2-mediated immunity. This is achieved by comparing the spatiotemporal dynamics of an FLS2-S938A mutant and FLS2-WT, especially in relation to their association with the remorin protein. To delineate the differences between the FLS2-S938A mutant and FLS2-WT, they utilized a plethora of advanced fluorescent imaging techniques. By analyzing surface dynamics and interactions involving the receptor signal co-receptor BAK1 and remorin proteins, the authors propose a model of how FLS2 and BAK1 are assembled and positioned within a remorin-specific nano-environment during FLS2 ligand-induced immune responses.

Strengths:

These techniques offer direct visualizations of molecular dynamics and interactions, helping us understand their spatial relationships and interactions during innate immune responses. Advanced cell biology imaging techniques are crucial for obtaining high-resolution insights into the intracellular dynamics of biomolecules. The demonstrated imaging systems are excellent examples to be used in studying plant immunity by integrating other functional assays.Weaknesses:

It's essential to acknowledge that every fluorescence-based method, just like biochemical assays, comes with its unique limitations. These often pertain to spatial and temporal resolutions, as well as the sensitivity of the cameras employed in each setup. Meticulous interpretation is pivotal to guarantee an accurate depiction and to steer clear of potential misunderstandings when employing specific imaging systems to analyze molecular attributes. Moreover, a discerning interpretation and accurate image analysis can offer invaluable guidance for future studies on plant signaling molecules using these nice cell imaging techniques. For instance, although single-particle analysis couldn't conclusively link FLS2 and remorin, FLIM-FRET effectively highlighted their ligand-triggered association and the disengagement brought on by mutations. While these methodologies seemed to present differing outcomes, they were described in the manuscript as harmonious. In reality, these differences could highlight distinct protein populations active in immune responses, each accentuated differently by the respective imaging techniques due to their individual spatial and temporal limitations. Addressing these variations is imperative, especially when designing future imaging explorations of immune complexes.

Reply: Thank you for your insightful comments and suggestions. We appreciate your expertise in fluorescence-based methods and the importance of careful interpretation and accurate image analysis. We agree with you that different imaging techniques may have their limitations and can highlight distinct aspects of protein dynamics and interactions.

In our study, we used single-particle analysis and FLIM-FRET to investigate the spatiotemporal dynamics of FLS2 and its association with remorin. While single-particle analysis did not conclusively link FLS2 and remorin, FLIM-FRET effectively highlighted their ligand-triggered association and the disengagement caused by mutations. We acknowledge that these techniques may have different spatial and temporal resolutions, leading to the discrepancy in their results. However, after the normalized treatment, we can provide very similar conclusions. Accordingly, we have revised the manuscript.

Reviewer #3 (Public Review):

Summary:

Receptor kinases (RKs) perceive extracellular signals to regulate many processes in plants. FLS2 is an RK that acts as a pattern-recognition receptor (PRR) to recognize bacterial flagellin and activate pattern-triggered immunity (PTI). PRRs such as FLS2 have been previously shown to reside within PM nanodomains, which can regulate downstream PTI signaling. In the current manuscript, Cui et al use single particle tracking to characterize the effect of previously-described phosposite mutants (FLS2-S938A/D) on the PM organization, endocytosis, and signaling functions of FLS2. The authors confirm that FLS2-S938D but not -S938A is functional for flg22-induced responses, while also demonstrating that phopshodead mutation at this site (S938A) prevents flg22-induced sorting into nanodomains and endocytosis. These results are consistent with S938 being an important phosphorylation site for FLS2 function, however, they fall short of demonstrating that membrane disorganization of FLS2-938A is responsible for downstream signaling defects.

Strengths:

The authors' experiments (single particle tracking, co-localization, etc) do a good job of demonstrating how a non-functional version of FLS2 (S938A) does not alter its spatio-temporal dynamics, nanodomain organization, and endocytosis in response to flg22, suggesting that these require a functional receptor and are regulated by intracellular signaling components.

Weaknesses:

Question 1: The authors do not provide direct evidence that S938 phosphorylation specifically affects membrane organization, rather than FLS2 signaling more generally. All evidence is consistent with S938A being a non-functional version of FLS2, wherein an activated/functional receptor is required for all downstream events including membrane re-organization, downstream signalling, internalization, etc. Furthermore, the authors never demonstrate that this site is phosphorylated in planta in the basal or flg22-elicited state.

Reply: Sorry that we did not describe clearly in the original manuscript. In fact, we found in our study that the phosphorylation of the Ser-938 site influences the efficient sorting of FLS2 into AtRem1.3-associated microdomains rather than membrane organization, as depicted in Figure 2. Furthermore, we found that the immune responses are disrupted when Ser-938 is mutated to alanine, which is consistent with previously reported results (Cao et al, 2013). However, they remain normal when mutated to the phosphorylation-mimicking residues aspartate or glutamate. These results suggest that the phosphorylation of Ser-938 is crucial for activating defense mechanisms upon flagellin detection. Although the phosphorylation of Ser-938 in plant at the basal or flg22-elicited state is not known, the model presented in the manuscript is based on the results of our current investigation together with those in the previous study that have shown the importance of Ser-938 phosphorylation for FLS2 function (Cao et al, 2013).

Question 2: As written, the manuscript also has numerous scientific issues, including a misleading/incomplete description of plant immune signaling, lack of context from previous work, and extensive use of inappropriate references.

Reply: We accept the criticism here. After reading the comments, we realized the problem. Now we have revised the misleading or incomplete description of plant immune signaling, added the context of previous works and deleted inappropriate references in the revised manuscript.

Reviewer #1 (Recommendations For The Authors):

Question 1: The description of the data has no details. How many biological repeats were done? How were statistical analyses done? What is the concentration of flg22? How was the calcium flux done (Fig. 4A)? The method also lacks details and relevant references.

Reply: We apologize for the lack of detail in presenting the data. Following your suggestion, we added comprehensive figure legends that provide clear explanations for each figure. Additionally, we included supplementary information on the measurement methods and references pertaining to calcium flux in the revised manuscript.

Question 2: Data in Fig. 4 basically repeated the 2013 PLoS Pathog paper. Why were these experiments even performed? Were GFP-tagged FLS2 lines used in these experiments? If this is the case, the data just verified that the GFP-tagged FLS2 functions as expected and should be moved to supporting data.

Reply: Thanks for the expert suggestions. In our study, we utilized GFP-tagged FLS2 lines to generate FLS2-S938 mutants and conducted experiments to investigate the flg22-induced immune response. Although some experiments in Figure 4 are similar to those reported (Cao et al, 2013), we provided a more detailed analysis of the immune response. The comprehensive analysis included early immune responses and late immune responses, e.g., the activation of a calcium burst, mitogen-activated protein kinases (MAPKs), the induction of immune-responsive genes and callose deposition, ultimately resulting in the inhibition of plant growth. As some results are analogous to the previous paper, we transfer some of the experiments as suggested, including the analysis of MAPKs and callose deposition, to the supporting data section of the revised manuscript.

Question 3: Flg22-induced FLS2-BAK1 association does not require S938, this is consistent with prior study that flg22 acts as a molecular glue for the ectodomains of FLS2 and BAK1 (Sun et al., 2013 Science). This needs to be cited.

Reply: Yes, we agree with the comment. Now we added an additional sentence in the revised manuscript: “ This aligns with the previous finding that flg22 acts as a molecular glue for FLS2 and BAK1 ectodomains (Sun et al., 2013).”

Question 4: Line 50, the references cited do not match what they say here.

Reply: We are sorry for the mistake in citing inappropriate references. In the revised manuscript, we deleted this sentence as well as the incorrect reference.

Question 5: Line 105, "flg22 can act as a ligand-like factor". It is a ligand!

Reply: Sorry for the mistake. Now, the sentence was corrected in the revised manuscript by deleting the word “like”.

Question 6: Line 107, FLS2/BAK1 heterodimerization, not heteroologomerization.

Reply: Now we used “heterodimerization” to replace “heteroologomerization” in the revised manuscript.

Question 7: Line 114, are these really the best references to cite here?

Reply: After reading the comment, we found the references were not suitable here. Now we changed references by citing “(Martinière et al., 2021)” in the revised manuscript.

Question 8: Lines 123-124, the sentence is incomplete.

Reply: In the revised manuscript, we reworded the sentence to make it complete now. We changed “In a previous investigation, we demonstrated that flg22 induces FLS2 translocation from AtFlot1-negative to AtFlot1-positive nanodomains in the plasma membrane, implying a connection between FLS2 phosphorylation and membrane nanodomain distribution (Cui et al., 2018). To validate this, we assessed the association of FLS2/FLS2S938D/FLS2S938A with membrane microdomains, using AtRem1.3-associated microdomains as representatives (Huang et al., 2019).” in the revised manuscript.

Question 9: Lines 169-170, Why is this "most important"?

Reply: Sorry for the unsuitable description. As we have dramatically changed the manuscript, this sentence was deleted from the new version.

Reviewer #2 (Recommendations For The Authors):

Here are some specific areas of ambiguity in the study to be improved.

Question 1: Clarity in statistical analysis is necessary. Many figure legends omit details such as the sample size "n", and the nature of the measurements, like ROIs, images, and dots, the size of the seedlings, etc.

Reply: We appreciated this suggestion, which was raised by the reviewer I as well. Now, we provided the details for each figure, including the sample size, the nature of the measurements in the revised manuscript.

Question 2: Additional background about the choice of FLS2-S938 mutant would be beneficial, given that this mutant doesn't affect the BAK1 interaction but nullifies several PTI responses.

Reply: Yes, we agreed that some additional background is required for the FLS2-S938 mutant. Therefore, we added a sentence here: “FLS2 Ser-938 mutations impact flg22-induced signaling, while BAK1 binding remains unaffected, thereby suggesting Ser-938 regulates other aspects of FLS2 activity (Cao et al., 2013).” in the revised manuscript.

Question 3: A specific segment "... Using CLSM, Fluorescence Correlation Spectroscopy (FCS) and Western blotting, we found that the endocytic vesicles of FLS2S938D increased significantly after flg22 treatment (Figure 3B-3E)..." is not easy to follow. The author may want to differentiate these methods and highlight them by indicting them as endocytic vesicle counting, receptor density on PM measurement by FCS, and WB-based protein degradation characterization to understand such mixed descriptions better. By the way, "Number of Endocytosis" should be "number of endocytic vesicles". Endocytosis is a process and uncountable.

Reply: We thank the reviewer for kindly reminding us to differentiate experimental methods. Therefore, we changed the sentences in the revised manuscript: “Employing confocal laser-scanning microscopy (CLSM) during 10μM flg22 treatment, we tracked FLS2 endocytosis and quantified vesicle numbers over time (Figure 3B). It is evident that both FLS2 and FLS2S938D vesicles appeared 15 min after-flg22 treatment, significantly increasing thereafter (Figure 3C). Notably, only a few vesicles were detected in FLS2S938A-GFP, indicating Ser-938 phosphorylation's impact on flg22-induced FLS2 endocytosis. Additionally, fluorescence correlation spectroscopy (FCS) (Chen et al., 2009) monitored molecular density changes at the PM before and after flg22 treatment (Figure S3F). Figure 3D shows that both FLS2-GFP and FLS2S938D-GFP densities significantly decreased after flg22 treatment, while FLS2S938A-GFP exhibited minimal changes, indicating Ser-938 phosphorylation affects FLS2 internalization. Western blotting confirmed that Ser-938 phosphorylation influences FLS2 degradation after flg22 treatment (Figure 3E), consistent with single-molecule analysis findings.” Besides, we also changed “number of endocytosis” to “the number of endocytic vesicles” in Figure 3C as suggested.

Question 4: In Figure 1 E, a discrepancy exists where the total percentages in the red and black columns don't sum up to 100%, while other groups look right. This needs clarification.

Reply: We are sorry for our carelessness in making the data incomplete. Now we thoroughly supplemented, collated, and rechecked the data in Figure 1E. Due to an oversight during the production of the figure, some data was inadvertently omitted, resulting in the red column not reaching 100%. Besides, we checked the data in the black column again, and the total percentage indeed added up to 100%.

Question 5: Although Figure 1F uses UMAP analysis to differentiate between FLS2WT and A mutants, only data pertaining to the "D" mutant is shown.

Reply: Thank you for the expert comments. Because there are several images in Figure 1, we only selected the data related to the “D” mutant as a representative for display. As suggested, we have added all the UMAP images in the revised supplement figure S1F.

Question 6: There are apparent inconsistencies in the FRAP results, particularly regarding the initial recovery points post-bleaching. A detailed statistical analysis, supplemented with FRAP images over time, should be included for clarity. Were they bleached to a similar ground level before monitoring their recovery? The data points from "before" and "after "bleaching were not shown. I found the red and blue curves showed similar recovery slop, which suggests no long-distance movement changes for all three FLS2 versions, with or without flg22. This is opposite from the conclusions made by the author.

Reply: Thank you for the expert comments. After reading the comments, we recognized this terrible problem. Therefore, we carried out a new FRAP experiment. The new results showed that, following complete bleaching of three samples of FLS2 to ground level, the recovery rates of FLS2 and FLS2S938D under flg22 treatment were significantly higher compared to the control group (Fig. 1G). In contrast, the recovery rates of the FLS2S938A-GFP after flg22 treatment remain similar to that before treatment (Fig. 1G), indicating that the Ser-938 phosphorylation site indeed affects the flg22-induced lateral diffusion of FLS2 at the PM. The new results are basically consistent with the motion range of single-molecule results, which is not contradictory to long-distance movement changes. Accordingly, we incorporated the new time-lapse FRAP images into Figure 1G and S1B.

Question 7: There's a potential typo in Figure 1B regarding the bar size. It could neither possibly be 200 um nor 200 nm. Figure 1A also needs a scale bar.

Reply: Apologies for the mistake. We now corrected “200 μm” to “2 μm”. Besides, we also included a scale bar in Figure 1A in the revised manuscript.

Question 8: Due to the unreliable tracking for a long-time by Imaris, the authors analyzed the tracks within 10s and quantified very short live particles under 4s. Such 4S surface retention for a receptor does not seem to match functional endocytic internalization time for cargo. Even after the endocytic adaptor module recruitment, it would take at least more than 10s to finish the internalization. In the field of endocytosis, these events are often described as abortive endocytic events. However, the disappearance of cargoes, FLS2 in this case, indicates internalization into the cytoplasm, which is interesting. May the author discuss more on how these short events analyzed enhance our understanding of the functional behavior of FLS2?

Reply: We greatly appreciated the valuable comments provided by the reviewer. After thorough consideration, we acknowledged that in our original manuscript, we failed to distinguish the short-lived from the long-lived particles and vaguely put them collectively into the internalized particles. We realized that and it is inappropriate to ambiguously categorize all particles as internalized. Therefore, we added the sentence “Additionally, numerous FLS2 exhibited short-lived dwell times, indicating abortive endocytic events associated with the endocytic pathway and signal transduction (Bertot et al., 2018)” in the revised manuscript.

Question 9: Figure 2D should be comprehensive, presenting data for the WT, A, and D versions.

Reply: Yes, we agreed with the suggestions. Now, we added several representative images for the WT, A, and D versions in the revised manuscript.

Question 10: In Figure 2D, TIRM-SIM should be a typo and rectified to TIRF-SIM. Also, a detailed explanation of the TIRF-SIM setup and its specifics would be important. The imaging approach of SIM, especially the time duration for finishing all frames before reconstruction, is essential to rationalize its use in capturing and measuring an appropriate speed range of particle movement. May the author elaborate on the technique details and the use of TIRF-SIM for colocalization analysis? To clarify these, the author may provide additional TIRF-only movies of FLS2 (WT, A, D) and AtRem1.3 for comparison with TIRF-SIM still images.

Reply: Sorry for the mistake. In the revised manuscript, we have corrected “TIRM-SIM” to “TIRF-SIM”. In order to rationalize its use in capturing and measuring an appropriate speed range of particle movement, we included a more detailed description of the imaging approach and the colocalization analysis of TIRF-SIM in the Materials and Methods section as follows: “The SIM images were taken by a 60 × NA 1.49 objective on a structured illumination microscopy (SIM) platform (DeltaVision OMX SR) with a sCMOS camera (Camera pixel size, 6.5 μm). The light source for TIRF-SIM included diode laser at 488 nm and 568 nm with pixel sizes (μm) of 0.0794 and 0.0794 (Barbieri et al., 2021). For the dual-color imaging, FLS2/FLS2S938A/FLS2S938D-GFP (488 nm/30.0%) and AtRem1.3-mCherry (561 nm/30.0%) were excited sequentially. The exposure time of the camera was set at 50 ms throughout single-particle imaging. The time interval for time-lapse imaging was 100 ms, the total time was 2s, and the total time points were 21s. The Imaris intensity correlation analysis plugin was used to calculate the co-localization ratio.” in the revised manuscript. Furthermore, we provided additional TIRF-SIM movies of FLS2 (WT, A, D) and AtRem1.3.

Question 11: The colocalization displayed in Figure 2D is hard to tell. A colocalization ratio of FLS2-AtRem1.3 is shown as ~0.8%, which has only ~0.2% difference from the flg22-treated condition. "n" of Figure 2F should be specified in the legend, such as a line with a specific length, or an ROI with a specific area size.

Reply: Thank you for the expert comments. Although the increased colocalization after flg22 treatment is not high, the change is statistically significant as compared with the wild type. We agreed that every fluorescence-based method, like biochemical analysis, has its own unique limitations, which were raised by the Reviewer #2 (Public Review) as well. In order to provide strong evidence, we also carried out the FLIM-FRET experiment as a supplement, which can effectively detect their ligand-triggered association or disassociation. From figure 2G and H, we clearly found that the co-localization of FLS2/FLS2S938D-GFP with AtRem1.3-mCherry significantly increase in response to flg22 treatment (FLS2-GFP control: 2.45 ± 0.019 s; FLS2-GFP flg22-treated: 2.39 ± 0.016 s; FLS2S938D-GFP control: 2.42 ± 0.010 ns; FLS2S938D-GFP flg22-treated: 2.35 ± 0.028 ns). In contrast, FLS2S938A-GFP shows no significant changes (control: 2.53 ± 0.011 ns; flg22-treated: 2.56 ± 0.013 ns), indicating that Ser-938 phosphorylation influences efficient sorting of FLS2 into AtRem1.3-associated microdomains. Following the suggestion of the reviewer, we now rearranged the order of 2E and 2F, in which N represents the entire image region used for analysis rather than a specific region of interest.

Question 12: I appreciate the nice results of the FLIM-FRET results for FLS2-Rem1.3. Figure 2H should be supplemented with additional representative images of all FLS2 variants including WT and mutants.

Reply: Thanks for your warm encouragement. As suggested, we added all the representative images in the revised manuscript.

Question 13: The unit of the X-axis of Figure 2E can not be pixel. Should it be, um? In the method, the author could specify the camera model and magnification for TIRF-SIM to understand pixel size of the image better.

Reply: Sorry for the mistake here. Indeed, the unit of the X-axis in Figure 2E should be μm. Now we correct this mistake in Figure 2E in the revised manuscript. Besides, we included a detailed description of the imaging approach of TIRF-SIM in the Materials and Methods section as follows: “The SIM images were taken by a 60 × NA 1.49 objective on a structured illumination microscopy (SIM) platform (DeltaVision OMX SR) with a sCMOS camera (Camera pixel size, 6.5 μm)”.

Question 14: "... as shown in A..." in Figure Legend 2E should be "... as shown in D..."

Reply: Thanks for pointing out this mistake. In the revised manuscript, we used “as shown in D” to replace “as shown in A”.

Question 15: I recommend that the authors exercise caution when drawing conclusions based on the Rem1.3 data and when representing the "microdomain" concept in their final model. While Rem1.3 punctate is a nanometer-sized protein cluster specific to its identity, its shape can be categorized as a nanodomain. Conceptually, however, it neither universally represents all nanodomains nor microdomains, as depicted in Figure 4. We should exercise caution to prevent providing misleading information to the field.

Reply: We thank the reviewer for expert comments. To avoid misleading conclusions, we changed “nanodomains” to “AtRem1.3-associated microdomains” in the revised manuscript. Besides, we have also made modifications to Figure 4.

Reviewer #3 (Recommendations For The Authors):

Question 1: The manuscript needs to be extensively re-written and has severe issues as-is. Many references are either not quite appropriate or are completely unrelated to the use in the text. In general, the current state-of-the-art of PTI and RK signaling is not correctly described or incorporated.

Reply: We accepted the criticisms here. As suggested, we thoroughly rewrote the manuscript to address the concerns raised. Furthermore, we have thoroughly checked and revised the manuscript by removing 21 irrelevant references and adding 30 relevant references. We also incorporated the most up-to-date descriptions of the PTI and RK signaling pathways.

Question 2: Receptor-like kinase (RLK) should generally be receptor kinase (RK) as receptor functions are now well established.

Reply: Yes, we agreed with your expert comment here. Now, we changed “Receptor-like kinase (RLK)” into “receptor kinase (RK)” in the revised manuscript.

Question 3: Line 20 - is this really true?

Reply: Sorry for the mistake. In the revised manuscript, we changed “However, the mechanisms underlying the regulation of FLS2 phosphorylation activity at the plasma membrane in response to flg22 remain largely enigmatic.” to “However, the dynamic FLS2 phosphorylation regulation at the plasma membrane in response to flg22 needs further elucidation.”

Question 4: S938D sorts better in response to Flg22; S938A is unaffected - suggests phosphorylation of S938 is not dynamic in response to Fig 22 but is required for pre-elicitation sorting. Overall, there is a chicken-and-egg problem in this paper: which comes first, immune/signalling functionality or nanodomain sorting? And which is explaining the defects of S938A?

Reply: We thank the reviewer for expert suggestions. In fact, the previous studies showed that membrane microdomains serve as signaling platforms that mediate cargo protein sorting and protein-protein interactions in a variety of contexts (Goldfinger et al. 2017). Since our previous research showed that the disruption of membrane microdomains affected flg22-induced immune signaling (Cui et al. 2018), we speculate that the immune signal occurred after entering the membrane microdomains.

As shown in Figure 1 and 2, ligand exposure leads to an increase in diffusion coefficient and enhanced co-localization with REM1.3, both of which are dependent on the phosphorylation of the Ser-938 site. Deducing from these results, we inferred that the defects in S938A resulted largely from its failure to sort into membrane microdomains. The phosphorylation of the Ser-938 site can regulate FLS2 into functional AtRem1.3-associated microdomains, thereby affecting flg22-induced plant immunity.

Question 5: Line 37 conserved, not conservative (though not technically true - the domain organization is conserved but the ECDs are not conserved).

Reply: Thank you for pointing this mistake out. In the revised manuscript, we used “conserved” to replace “conservative”.

Question 6: Lines 40-42 - not all phosphorylation sites are within the kinase domain, for example, sites are well-described on the JM and/or C-tail regions outside of the kinase domain.

Reply: We accepted the criticisms here. We have corrected the sentence to “with phosphorylation sites mainly located in PKC” in the revised manuscript.

Question 7: Line 42 - what is BIK1? Intro to relevant topics is severely lacking.

Reply: Sorry for the incomplete introduction here. We added the relevant introduction of BIK1 by adding that “Upon recognizing flg22, FLS2 interacts with the co-receptor Brassinosteroid-Insensitive 1-associated Kinase 1 (BAK1), initiating phosphorylation events through the activation of receptor-like cytoplasmic kinases (RLCKs) such as BOTRYTIS-INDUCED KINASE 1 (BIK1) to elicit downstream immune responses (Chinchilla et al., 2006; Li et al., 2016b; Majhi et al., 2021). ” in the revised manuscript.

Question 8: Lines 42-44 - not sure this sequence of events is being properly described (e.g. BIK1 release is unlikely to precede activation by BAK1/SERKs).

Reply: We apologize for not expressing this sentence clearly. Now, we reworded the sentence: “Upon recognizing flg22, FLS2 interacts with the co-receptor Brassinosteroid-Insensitive 1-associated Kinase 1 (BAK1), initiating phosphorylation events through the activation of receptor-like cytoplasmic kinases (RLCKs) such as BOTRYTIS-INDUCED KINASE 1 (BIK1) to elicit downstream immune responses (Chinchilla et al., 2006; Li et al., 2016b; Majhi et al., 2021).” in the revised manuscript.

Question 9: Line 61 - S938 was identified by Cao et al (2013) based on in vitro MS, but was functionally validated using genetic assays, not based on MS.

Reply: Thank you for your comments. Now, we changed the sentence: “In vitro mass spectrometry (MS) identified multiple phosphorylation sites in FLS2. Genetic analysis further identified Ser-938 as a functionally important site for FLS2 in vivo (Cao et al., 2013).” in the revised manuscript.

Question 10: Line 68-69 - phospho-dead and phospho-mimic, not phosphorylated/non-phosphorylated.

Reply: We thank the reviewer for expert suggestions. In the revised manuscript, we changed the sentence by replacing “phosphorylated/non-phosphorylated” with “phospho-mimic” and “phospho-dead”.

Question 11: Lines 104-106 - this is wildly misleading. Flg22 is more than a ligand-like factor, as it is a bona fide ligand, and the heterodimerization with BAK1/SERKs is extremely well-established (and relevant foundational papers should be cited here in place of the authors' previous work).

Reply: We apologize for the incorrect expression here. After reading the comments, we realized the problem which was raised by the reviewer I as well. Now, we changed “ligand-like factor” to “ligand”. Besides, we cited the new references “(Orosa et al., 2018)” to replace the references of our group in the revised manuscript.

Question 12: Lines 107-112 - again, this is confusing. There is a decade of (uncited, undiscussed) work previously establishing that heterodimerization of RK-co-receptor complexes is mediated by extracellular ligand binding and independent of intracellular phosphorylation.

Reply: We thank the reviewer for expert suggestions. Now, we added several sentences in the revised manuscript: “Therefore, we further investigated if Ser-938 phosphorylation affects FLS2/BAK1 heterodimerization. Tesseler segmentation, FRET-FLIM, and smPPI analyses revealed no impact of Ser-938 phosphorylation on FLS2/BAK1 heterodimerization (Figure 2A-C and S2). This aligns with the previous finding that flg22 acts as a molecular glue for FLS2 and BAK1 ectodomains (Sun et al., 2013), confirming the independence of FLS2/BAK1 heterodimerization from phosphorylation, with these events occurring sequentially.”

Question 13: Line 119 - this is the wrong citation - Yu et al 2020 is a review and does not cover RALFs; correct citation is Gronnier et al 2022 eLife.

Reply: In the revised manuscript, we updated the reference from “ (Yu et al., 2020)” to “(Gronnier et al., 2022)”.

Question 14: Lines 123-124 - this sentence is incomplete.

Reply: Sorry for the incomplete sentence. Now we reworded the sentence to “In a previous investigation, we demonstrated that flg22 induces FLS2 translocation from AtFlot1-negative to AtFlot1-positive nanodomains in the plasma membrane, implying a connection between FLS2 phosphorylation and membrane nanodomain distribution (Cui et al., 2018). To validate this, we assessed the association of FLS2/FLS2S938D/FLS2S938A with membrane microdomains, using AtRem1.3-associated microdomains as representatives (Huang et al., 2019).” in the revised manuscript.

Question 15: Line 126 - this requires a reference.

Reply: Yes, we added a new reference: “(Huang et al., 2019)” in the revised manuscript.

Question 16: Lines 125-128 - should clarify that the authors are not looking at direct interaction between FLS2 and REM1.3.

Reply: Sorry for the inappropriate expressions here. In the revised manuscript, we reworded the sentence as follows: “To validate this, we assessed the association of FLS2/FLS2S938D/FLS2S938A with membrane microdomains, using AtRem1.3-associated microdomains as representatives (Huang et al., 2019)” .

Question 17: Line 138 - these are odd references to use for such a broad statement.

Reply: Now the inappropriate references cited here have been deleted.

Question 18: Line 161 - incorrect reference, again.

Reply: Sorry for this mistake. In the revised manuscript, we reworded the sentence and changed the reference.

Question 19: Lines 160-165 - this is very confusing and misleading. I would suggest just having a short section introducing PTI earlier on (with appropriate references).

Reply: As suggestion, we reworded and added a section in the revised manuscript as follows: “PTI plays a pivotal role in host defense against pathogenic infections (Lorrai et al., 2021; Ma et al., 2022). Previous studies demonstrated that FLS2 perception of flg22 initiates a complex signaling network with multiple parallel branches, including calcium burst, mitogen-activated protein kinases (MAPKs) activation, callose deposition, and seedling growth inhibition (Baral et al., 2015; Marcec et al., 2021; Huang et al., 2023). Our focus was to investigate the significance of Ser-938 phosphorylation in flg22-induced plant immunity. Figure 4A-F illustrates diverse immune responses in FLS2 and FLS2S938D plants following flg22 treatment. These responses encompass calcium burst activation, MAPKs cascade reaction, callose deposition, hypocotyl growth inhibition, and activation of immune-responsive genes. In contrast, FLS2S938A (Figure S4A-D) exhibited limited immune responses, underscoring the importance of Ser-938 phosphorylation for FLS2-mediated PTI responses”.

Question 20: Line 166 - these are not appropriate references, again.

Reply: Thank you for the suggestion. In the revised manuscript, we removed the inappropriate references. Besides, we added new references by citing: “(Baral et al., 2015; Marcec et al., 2021)”.

Question 21: Lines 169-173 - this is not relevant, the inhibition of growth by elicitors is extremely well-documented (though not by the refs cited here).

Reply: We reworded the sentence and deleted the inappropriate reference in the revised manuscript.

Question 22: Lines 174-175 - I don't see why this is unexpected, as nanodomain organization of PRRs has been previously described.

Reply: Sorry for the inappropriate expressions here. As we have dramatically changed the manuscript, this sentence was deleted from the new version.

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    Figure 1—source data 1. The original VA-TIRFM image and single-particle tracking of FLS2-expressing hypocotyl cells are shown in Figure 1A.
    elife-91072-fig1-data1.xlsx (12.8KB, xlsx)
    Figure 1—source data 2. The list of the diffusion coefficients of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments is shown in Figure 1D.
    elife-91072-fig1-data2.zip (10.8KB, zip)
    Figure 1—source data 3. The list of the motion range of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments is shown in Figure 1E.
    elife-91072-fig1-data3.zip (11.7KB, zip)
    Figure 1—source data 4. The list of the lifetime of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments is shown in Figure 1I.
    elife-91072-fig1-data4.zip (15.5KB, zip)
    Figure 1—figure supplement 1—source data 1. The original confocal images of FLS2-GFP, FLS2S938A-GFP, and FLS2S938D-GFP.
    elife-91072-fig1-figsupp1-data1.zip (10.7MB, zip)
    Figure 2—source data 1. The list of the fluorescence mean lifetimes (t) of FLS2/FLS2S938A/FLS2S938D-GFP and BAK1-mCherry under different treatments is shown in Figure 2A.
    elife-91072-fig2-data1.zip (11.7KB, zip)
    Figure 2—source data 2. The list of the Pearson correlation coefficient of FLS2/FLS2S938A/FLS2S938D-GFP and BAK1-mCherry under different treatments is shown in Figure 2B.
    elife-91072-fig2-data2.zip (11.9KB, zip)
    Figure 2—source data 3. The list of the mean protein proximity indexes of FLS2/FLS2S938A/FLS2S938D-GFP and BAK1-mCherry under different treatments is shown in Figure 2C.
    elife-91072-fig2-data3.zip (11.4KB, zip)
    Figure 2—source data 4. The original intensity and lifetime maps of FLS2/FLS2S938A/FLS2S938D-GFP and AtRem1.3-mCherry under different treatments are shown in Figure 2H.
    elife-91072-fig2-data4.zip (1.8MB, zip)
    Figure 2—source data 5. The list of the fluorescence mean lifetimes (t) of FLS2/FLS2S938A/FLS2S938D-GFP and AtRem1.3-mCherry under different treatments is shown in Figure 2G.
    elife-91072-fig2-data5.zip (11.4KB, zip)
    Figure 2—source data 6. The list of the Pearson correlation coefficient of FLS2/FLS2S938A/FLS2S938D-GFP and AtRem1.3-mCherry under different treatments is shown in Figure 2I.
    elife-91072-fig2-data6.zip (12.9KB, zip)
    Figure 2—figure supplement 1—source data 1. The list of the coordinates of FLS2/FLS2S938A/FLS2S938D-GFP and BAK1-mCherry under different treatments.
    elife-91072-fig2-figsupp1-data1.zip (23.7KB, zip)
    Figure 3—source data 1. The original confocal images of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments are shown in Figure 3A.
    elife-91072-fig3-data1.zip (17.7MB, zip)
    Figure 3—source data 2. The original confocal images of FLS2/FLS2S938A/FLS2S938D-GFP in Arabidopsis thaliana leaf epidermal cells treated with 10 μM flg22 for 15, 30, and 60 min are shown in Figure 3B.
    elife-91072-fig3-data2.zip (12.5MB, zip)
    Figure 3—source data 3. The list of the endocytic vesicle numbers of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments is shown in Figure 3C.
    elife-91072-fig3-data3.xlsx (10.9KB, xlsx)
    Figure 3—source data 4. The list of the signal density of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments is shown in Figure 3D.
    elife-91072-fig3-data4.zip (10.7KB, zip)
    Figure 3—source data 5. The original file for the western blot analysis in Figure 3E.
    elife-91072-fig3-data5.zip (963.1KB, zip)
    Figure 3—source data 6. The original file for the western blot analysis with highlighted bands and sample labels is shown in Figure 3E.
    elife-91072-fig3-data6.zip (13.5MB, zip)
    Figure 3—figure supplement 1—source data 1. The list of the number, diameter, and fluorescence intensity of FLS2/FLS2S938A/FLS2S938D-GFP BFA bodies under different treatments.
    elife-91072-fig3-figsupp1-data1.zip (30.2KB, zip)
    Figure 3—figure supplement 4—source data 1. The list of the fluorescence intensity of FLS2-GFP, FLS2S938A-GFP, and FLS2S938D-GFP in the cytoplasm relative to the sum of the cytoplasm and PM in leaves epidermal cells under different treatments.
    elife-91072-fig3-figsupp4-data1.zip (11KB, zip)
    Figure 4—source data 1. The list of the transient Ca2+ flux of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments is shown in Figure 4A.
    elife-91072-fig4-data1.zip (33.6KB, zip)
    Figure 4—source data 2. The list of the hypocotyl length of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments is shown in Figure 4C.
    elife-91072-fig4-data2.zip (10.3KB, zip)
    Figure 4—source data 3. The list of the mRNA levels of the PTI marker genes FRK1/WRKY33/CYP81 of the FLS2, FLS2S938A, and FLS2S938D 10-day-old transgenic Arabidopsis plants under different treatments is shown in Figure 4D–F.
    elife-91072-fig4-data3.zip (11.3KB, zip)
    Figure 4—figure supplement 1—source data 1. The original file for the western blot analysis.
    elife-91072-fig4-figsupp1-data1.zip (297.1KB, zip)
    Figure 4—figure supplement 1—source data 2. The original file for the western blot analysis with highlighted bands and sample labels.
    elife-91072-fig4-figsupp1-data2.zip (8.2MB, zip)
    Figure 4—figure supplement 2—source data 1. The list of the callose deposition of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments.
    elife-91072-fig4-figsupp2-data1.zip (10.4KB, zip)
    Figure 4—figure supplement 3—source data 1. The original images of FLS2/FLS2S938A/FLS2S938D-GFP under different treatments.
    elife-91072-fig4-figsupp3-data1.zip (131.5KB, zip)
    MDAR checklist
    elife-91072-mdarchecklist1.docx (50KB, docx)

    Data Availability Statement

    All data are included in the manuscript and supporting files.

    Articles from eLife are provided here courtesy of eLife Sciences Publications, Ltd

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