FLEXTAG: a small and self-renewable protein labeling system for anti-fading multi-color super-resolution imaging

Domain: chemistry
Published: 2026-03-09
Editorial outcome: major_revision

Abstract
Super-resolution fluorescence imaging enables visualization of subcellular structures and molecular interactions at the nanoscale, but its broader application has been hindered by long-standing limitations in current protein tagging systems, including rapid photobleaching, tag-induced artifacts, poor post-fixation labeling efficiency, and restricted multiplexing capability. Here, we present FLEXTAG (Fluorescent Labeling for Exchangeable, X-resilient Tagging in Advanced Generic Nanoscopy), a comprehensive protein labeling system comprising three orthogonal, ultra small (12–18 kDa), and self-renewable protein tags that collectively overcome these major limitations of existing tagging systems, enabling optimized multi-color super-resolution imaging. Through continuous exchange of organic fluorophores, FLEXTAG supports extended durations of high-resolution imaging in both live and fixed cells with minimal photobleaching. It is compatible with major super-resolution modalities, such as SIM, STED, STORM, and PAINT, and is applicable to a wide range of subcellular targets. To further address fixation-induced labeling inefficiency and background fluorescence, we developed an innovative protection-based fixation method and chemical blocking strategies that significantly preserve tag accessibility and enhance signal-to-noise ratio, improvements that are broadly applicable to other protein tagging systems. Altogether, FLEXTAG enables long-term tracking of dynamic behaviors and interactions of subcellular targets, as well as mapping of nanoscale protein organizations and cellular architecture, advancing both basic research and translational applications in cell biology.

Introduction
Fluorescence microscopy has revolutionized biological research by enabling the study of cellular structures and dynamics across scales from single molecules to live animals, due to its unique advantages compared to other microscopies, such as high image contrast, high molecular specificity, non-invasive multi-color labeling capability, and long-term live imaging capability. Over the past decade, super-resolution fluorescence imaging, also known as fluorescence nanoscopy, has been invented to overcome the light diffraction limit and improve the spatial resolution from ~250 nm (this is ~50-fold larger than the average size of a protein) to the molecular scale (1–20 nm)1,2, allowing molecular-resolution fluorescence imaging. The most widely used nanoscopy techniques fall into two categories: (1) patterned light illumination microscopy (PLIM), such as structured illumination microscopy (SIM) and stimulated emission depletion (STED) microscopy; (2) single-molecule localization microscopy (SMLM), such as stochastic optical reconstruction microscopy (STORM)3, photo-activated localization microscopy (PALM)4, and point accumulation for imaging in nanoscale topography (PAINT)5. These advancements in spatial resolution enable the visualization of previously unresolvable subcellular details, such as the organization of proteins within molecular complexes, nanoscale changes in cellular architecture, and dynamic molecular interactions, providing unprecedented mechanistic insights into biological processes.

The effectiveness of fluorescence nanoscopy relies on precise protein labeling strategies, which can be classified into three main approaches: (1) immunofluorescence uses antibodies conjugated with organic dye to recognize target proteins. While this method enables labeling of endogenous proteins, it is restricted to fixed-cell imaging. Additionally, only a small fraction of commercially available antibodies exhibit the high affinity and specificity required for nanoscopy, and their large size (~150 kDa) limits access to densely packed cellular structures; (2) fluorescent protein tags, such as EGFP, mEos3.26, and mMaple37, allow live-cell imaging but are often outperformed in brightness and photostability by organic dyes; and (3) self-labeling protein tags, such as Halo-8, CLIP-9, and SNAP-Tag10, provide site-specific labeling by reacting with small-molecule ligands conjugated to organic dyes. This approach offers improved photostability over fluorescent protein tags and is compatible with both live and fixed cells. However, even with organic dyes, photobleaching remains a major limitation, particularly in fluorescence nanoscopy, which requires prolonged high-intensity laser illumination. Additionally, in post-fixation labeling for fixed-cell imaging, commonly used fixatives such as paraformaldehyde (PFA) and glutaraldehyde (GA) can extensively crosslink proteins, reducing accessibility to dye-conjugated ligands and thereby lowering labeling efficiency of these self-labeling protein tags.

To mitigate photobleaching, self-renewable (i.e., exchangeable) protein tags have been recently developed, allowing continuous fluorescent dye replacement during imaging. Early efforts focused on fluorogenic GFP-like chromophores that reversibly bind to specific protein tags, such as pFAST11 and blc tags12. However, these chromophore-based renewable tags are incompatible with STED imaging due to their low brightness and photostability, and are also unsuitable for SMLM such as STORM or PAINT: these chromophores lack the photoswitchability required for STORM, and under prolonged illumination in PAINT, they degrade into aldehyde derivatives that react with amines in the binding pocket13, preventing chromophore rebinding (Supplementary Fig. 1a–c). Additionally, these tags do not address the low post-fixation labeling efficiency caused by protein crosslinking during cell fixation. Another major challenge lies in expanding the spectral range of these tags for multi-color imaging, because chromophore binding is highly dependent on its precise chemical structure; even minor modifications to extend the color palette can disrupt tag recognition and reduce labeling efficiency. This limitation underscores the need to separate the fluorescent properties of the tag from its binding mechanism. More recently, HaloTag-based systems have been modified for reversible binding by engineering the HaloTag and its ligand to interact non-covalently14. This approach enables prolonged super-resolution imaging using both STED and PAINT. By linking nonfluorescent HaloTag ligands to various nanoscopy-compatible dyes, these systems (i.e., dye-conjugated HaloTag ligands) offer flexible color selection. However, the relatively large size of HaloTag (~33 kDa) can interfere with the native localization and function of tagged proteins, as evidenced by mislocalization when tagging ATP5ME, COX8, YTHDC1, and the coronaviral envelope (E) protein15 (Supplementary Fig. 1d) and there are no similar self-renewable but orthogonal tags available that function across both PLIM- and SMLM-based techniques to enable three or more color super-resolution imaging. Furthermore, these existing tags do not overcome the challenge of low post-fixation labeling efficiency caused by protein crosslinking.

In this work, we present Fluorescent Labeling for Exchangeable, X-resilient Tagging in Advanced Generic Nanoscopy (FLEXTAG), a comprehensive framework that integrates three compact, self-renewable protein tags (FLEXTAG1, FLEXTAG2, and FLEXTAG3) with optimized labeling protocols to enable anti-fading multi-color super-resolution imaging. This FLEXTAG framework (Fig. 1) distinguishes itself from previous protein tagging and labeling methods in the following six key aspects: (1) the tag sizes are small (<20 kDa) with minimal self-aggregation and tag-induced protein mislocalization issues, (2) the tags are self-renewable with minimal photobleaching, (3) it is compatible with both live and fixed cell imaging, (4) a protection-based fixation protocol has been developed to overcome low post-fixation labeling efficiency, (5) a chemical treatment and blocking protocol has been developed to minimize non-specific labeling, and (6) the tags are orthogonal and compatible with both PLIM-based and SMLM-based super-resolution imaging, providing a “one-for-all” solution, making it convenient to switch between different super-resolution imaging methods and enables three or more color super resolution nanoscopy. We envision that this versatile super-resolution protein labeling system would allow for more flexible and detailed investigations on cellular processes, facilitating super-resolution imaging efforts to study molecular interactions, protein dynamics, and cellular functions.

Results
Development and characterization of FLEXTAG1
To develop new protein tags that address the limitations of current protein tagging systems, we sought to create a series of orthogonal, small, self-renewable protein tags (FLEXTAGs). These FLEXTAGs utilize synthetic fluorescent ligands, some of which are cell-permeable for live-cell imaging, while others are designed specifically for fixed-cell applications. Upon entering the live cell or accessing fixed cellular structures, the fluorescent ligands reversibly bind to and dissociate from non-fluorescent genetically encoded protein tags fused to target proteins, thereby minimizing photobleaching. To enable easy expansion for multi-color imaging, we decouple the fluorescent properties of the ligand from its binding to the tag by designing these fluorescent ligands with two components: a non-fluorescent part that specifically recognizes the protein tag and a nanoscopy-compatible fluorescent dye, connected through a polyethyleneglycol (PEG) linker. The criteria used for the tag development are: (1) minimal aggregation and small size (<20 kDa) to minimize tag-induced mislocalization and potential malfunction of the tagged protein, (2) sufficiently high labeling efficiency for compatibility with all major fluorescence nanoscopic methods, and (3) dynamic exchange between the fluorescent ligand and protein tag to support PAINT imaging and effectively mitigate photobleaching across all major fluorescence nanoscopy techniques.

Based on these criteria, we developed FLEXTAG1 from a mutant form of the second bromodomain of human BRD4, BRD4BD2L55A, a small protein degron tag (~15 kDa, about half the size of the commonly used EGFP tag). Originally designed for tag-targeted proteolysis, this tag can be specifically recognized by the small molecule ligand ET-JQ116. JQ1 is a small molecule inhibitor of bromodomains that binds to wild-type BRD4BD2 (BRD4BD2WT)17. To enhance binding specificity using the “bump-and-hole” strategy, an ethyl group was added to JQ1 to generate ET-JQ1, and the L55A mutation was introduced into BRD4BD2 to minimize off-target ligand binding to endogenous bromodomain proteins. Although this degron tag-ligand system has been successfully applied in tag-targeted proteolysis of human cells, it has not yet been further optimized for fluorescence imaging applications due to two challenges: (1) while ET-JQ1 shows minimal cross-reactivity with endogenous bromodomain proteins, as inferred from western blot data where ET-JQ1 induces significantly less degradation of endogenous bromodomains than of BRD4BD2L55A, this does not directly translate to imaging applications16. ET-JQ1 may still weakly and transiently bind to endogenous bromodomain proteins, generating unwanted fluorescence background; and (2) BRD4BD2 has a strong tendency to dimerize18, meaning BRD4BD2L55A could promote aggregation of the tagged protein, potentially altering its subcellular localization and function and leading to experimental artifacts.

To evaluate these two potential issues, we synthesized tetramethylrhodamine (TMR)-conjugated ET-JQ1 and JQ1 (as a control). After labeling U2OS cells, with and without overexpressing a bromodomain protein BRD4, using ET-JQ1-TMR and JQ1-TMR (Fig. 2a), we observed that ET-JQ1-TMR showed no detectable binding to either overexpressed BRD4-GFP or endogenous bromodomain proteins (Fig. 2c), whereas JQ1-TMR exhibited strong binding to overexpressed BRD4-GFP and detectable binding to the nucleus-localized endogenous bromodomain proteins, indicating that the ET-JQ1, in contrast to JQ1, has negligible binding affinity to endogenous bromodomain proteins under imaging conditions (Fig. 2b–d). Next, we assessed the potential aggregation issue of BRD4BD2L55A. Indeed, after labeling cells overexpressing BRD4BD2L55A-tagged TOM20, a mitochondria-localized protein, with ET-JQ1-TMR, we observed severe aggregation of BRD4BD2L55A, drastically altering the expected mitochondrial morphology and distribution (Fig. 2e).

To minimize the aggregation of BRD4BD2L55A, we used ColabFold19, an artificial intelligence system that predicts 3D structures of proteins and protein interactions from their amino acid sequences, to predict its dimer interface and identify the key interfacial interactions (≤3.5 Å bond distances), primarily between the Q115 residues of each monomer and between Y98 of one monomer and M120 of the other (Fig. 2f and Supplementary Fig. 2a). This predicted dimer interface closely resembles the intermolecular contacts observed in the crystal lattice formed by co-crystallizing BRD4BD1 and BRD4BD218, In addition, a previous study identified several mutations that reduce homodimer formation of BRD2BD120, a bromodomain that shares a nearly identical structure with BRD4BD2 (Supplementary Fig. 2b). Based on the key interfacial residues identified from the predicted dimer interface and the previous mutations known to disrupt BRD2BD1 dimerization, we selected five mutations including Q21A, Y98K, Q115A, M120G, and F122E, to test their ability to disrupt BRD4BD2 dimerization while retaining ET-JQ1 binding. We introduced each of these mutations into the BRD4BD2L55A background and quantified the percentage of cells exhibiting abnormal (i.e., either bundled or aggregated) mitochondrial morphology when overexpressing TOM20 tagged with these BRD4BD2 variants (Hereafter referred to as the mitochondria morphology assay, which is a quantitative method for assessing the extent of protein tag aggregation; Supplementary Fig. 3). Among the tested mutations, Q21A, Y98K, Q115A, and F122E failed to reduce BRD4BD2L55A dimerization. In contrast, M120G was the only mutation that effectively minimized dimerization-induced mitochondrial bundling or aggregation, reducing the percentage of cells with abnormal mitochondrial morphology from 80.0±1.4% to 16.2±3.4%, while still maintaining ET-JQ1 binding. Furthermore, M120G not only significantly reduced BRD4BD2L55A dimerization but also disrupted BRD4BD2WT dimerization, confirming the essential role of M120 in BRD4BD2 dimerization (Fig. 2g and Supplementary Fig. 4).

As in our FLEXTAG system, we developed protection-based fixation method (described in a later section) to preserve tag accessibility after cell fixation and address low post-fixation labeling efficiency, where it requires adding a relatively high concentration (~100 μM) of the ligand (ET-JQ1 for BRD4BD2L55A, M120G or JQ1 for BRD4BD2M120G) during the cell fixation process to protect residues surrounding the binding pocket from fixation and hence prevent loss of ligand binding after fixation. Therefore, we performed additional tests to see if the tag remains prone to aggregation in the presence of its ligand ET-JQ1. Unfortunately, when incubating cells overexpressing TOM20 tagged with BRD4BD2L55A or BRD4BD2L55A, M120G with 100 μM ET-JQ1, both constructs exhibited strong mitochondrial bundling and aggregation (Fig. 2h and Supplementary Fig. 4). The percentage of cells overexpressing BRD4BD2L55A, M120G with abnormal mitochondrial morphology increased from 16.2±3.4% back to 100.0±0.0%. Similarly, 100 μM JQ1-TMR increased the percentage of cells overexpressing BRD4BD2M120G with abnormal mitochondrial morphology from 13.9±1.9% to 32.1±2.4%, although this ligand-induced aggregation was less pronounced than in BRD4BD2L55A, M120G, indicating that ligand binding mediates a new dimer interface near the ligand binding pocket for both protein tags (Fig. 2i and Supplementary Fig. 4). To investigate this newly induced dimerization, we used ColabFold to predict the dimer of BRD4BD2L55A,M120G. Although ColabFold does not support structure prediction of ligand-bound dimers, a new dimer interface, predicted in the absence of ligand, emerged near the ligand-binding pocket, with the key interfacial interactions between H105 residues of each monomer and between W42 of one monomer and A52 of the other (Supplementary Fig. 2c). Based on the predicted new dimer interface, and that L55A exhibited more severe ligand-induced aggregation, we designed three variants to disrupt this new dimer interface: BRD4BD2L55G,M120G, BRD4BD2W42A, L55A, M120G, and BRD4BD2L55A, H105A, M120G. Using the mitochondrial morphology assay (Supplementary Fig. 3), we found that only BRD4BD2L55A, H105A, and M120G disrupted the ligand-induced mitochondrial aggregation (Figs. 2g, i and Supplementary Fig. 4), while the other two mutants abolished ET-JQ1-TMR binding (Supplementary Fig. 2d).

Next, following our criteria for FLEXTAG, as aforementioned, we evaluated the labeling efficiencies and the dynamic exchange rates between the fluorescent ligand and protein tag across several BRD4BD2 variants. To this end, we fused each BRD4BD2 variant, along with a fluorescent protein tag (mEGFP, used as an expression level reference for normalization), to the mitochondria-localized protein TOM20 (TOM20-mEGFP-BRD4BD2 variants). After labeling U2OS cells overexpressing a variant of TOM20-mEGFP-BRD4BD2 with JQ1-TMR (for variants without L55A mutation) or ET-JQ1-TMR (for variants with L55A mutation), we determined the fluorescence intensity ratios of TMR to mEGFP, which quantitatively represent the labeling efficiencies of each BRD4BD2 variant. Among the variants tested, BRD4BD2L55A, H105A, M120G exhibited the highest TMR/EGFP fluorescence ratio, indicating superior labeling efficiency (Fig. 2j and Supplementary Fig. 2d). Using PAINT imaging, we further quantified two dynamic exchange parameters in the presence of 1 nM ligand: (1) event rate, defined as the average number of fluorescent ligand binding events per unit imaging area per unit time; (2) apparent off-rate, defined as the average rate of disappearance of the single molecule fluorescent spot. While these rates do not precisely reflect the true ligand binding kinetics, since the apparent off-rate may be overestimated due to TMR photobleaching, and the event rate differs from the true on-rate as it is measured per imaging area rather than per tag, they still provide a practical assessment of PAINT compatibility. When these kinetic properties align with PAINT requirements, they often also help mitigate photobleaching in other nanoscopic techniques such as STORM, SIM, and STED. We found that while the apparent off-rate of BRD4BD2L55A, H105A, M120G was slightly lower than that of other BRD4BD2 variants (Fig. 2k), its event rate was significantly higher (Fig. 2l), making it the most optimized for PAINT imaging.

Finally, we validated the performance of BRD4BD2L55A, H105A, M120G for tagging diverse proteins, localized to actin filaments, focal adhesions, the nucleus, vimentin, the ER, and microtubules, respectively. The observed fluorescence patterns closely matched the expected localization and morphology of the targeted cellular structures (Fig. 2m). We therefore designated BRD4BD2L55A, H105A, M120G as our developed FLEXTAG1.

Development and characterization of FLEXTAG2
We developed FLEXTAG2 from E. coli dihydrofolate reductase (eDHFR), a previously reported small (~18 kDa) protein imaging tag that can be specifically recognized by its specific small molecule inhibitor trimethoprim (TMP)21 (Fig. 3a). Although eDHFR and dye-conjugated TMP have been successfully used for protein imaging in live cells, this system remains largely incompatible with cell fixation, and its fluorescence signal is relatively weak due to low labeling efficiency22,23 (defined as the percentage of protein tags in the fluorescent state at any given time). This inefficiency stems from the relatively low affinity of the non-covalent interaction between eDHFR and dye-conjugated TMP, leaving a large fraction of eDHFR tags in a dark, TMP-unbound state at any given time, limiting its applications in fluorescence nanoscopy, where a high fluorescence signal is often required. To address this, eDHFR mutations (e.g., L28C) and TMP variants have been developed to enable covalent bonding between eDHFR and TMP22,23, However, this covalent interaction eliminates eDHFR’s reversible binding and dissociation properties, which are crucial for mitigating photobleaching. Therefore, to achieve higher labeling efficiency while retaining the self-renewable nature of the tag, it is crucial to engineer eDHFR to enhance the binding rate constant (kon) of TMP-dye binding to eDHFR.

Previous studies have reported that eDHFR has a relatively low thermal denaturation temperature Tm of 43°C24, and exhibits low mechanical stability, with an average unfolding force of ~27 pN, much lower than that of most globular proteins25. These characteristics led us to hypothesize that its inherent instability partially contributes to insufficient labeling. This instability may result in a fraction of eDHFR molecules being in a conformational state in which TMP ligands cannot bind, thereby reducing fluorescence intensity. To test this hypothesis, we identified and designed ten eDHFR variants with potentially improved stability, categorized into four groups: (1) eDHFRT68N, Q108D, T113V, E120P, S138Y (eDHFR with quintuple mutations, termed eDHFRQM), which has been reported to exhibit a higher Tm than wild-type eDHFR (eDHFRWT)26; (2) eDHFRP39C and eDHFRP39GCG. eDHFRP39C, a mutant previously reported to have a higher midpoint denaturant concentration27, features a Proline-39 to Cysteine (P39C) substitution, enabling disulfide bond formation between P39C and the cysteine-85 to stabilize the folded eDHFR structure. In eDHFRP39GCG, P39 is substituted by GCG instead of C to allow additional flexibility for disulfide formation. (3) eDHFRW22C, H149C, and eDHFRW22GCG, H149GCG, where two residues in proximity, at positions 22 and 149, are substituted by either a C or GCG to enable disulfide bond formation between them; (4) C(G)n-eDHFRP89C and C(G)n-eDHFRP89GCG, where n=3, 5, 7. P89 is substituted by either C or GCG, and an additional amino acid sequence C(G)n is introduced at the N-terminus of eDHFR to promote disulfide bond formation between P89C and C(G)n. Using ColabFold, we confirmed that all designed eDHFR variants, except for eDHFRP39GCG, can form the intended disulfide bonds (Fig. 3b and Supplementary Fig. 5a–d).

To compare the labeling efficiencies among the ten eDHFR variants, we utilized similar double-tagged TOM20 constructs (TOM20-mEGFP-eDHFR variants) as used for BRD4BD2 variants. After labeling U2OS cells overexpressing TOM20-mEGFP-eDHFR with TMR-conjugated TMP (TMP-TMR) (Fig. 3a), we determined the fluorescence intensity ratios of TMR to mEGFP, which quantitatively represent the labeling efficiencies of each eDHFR variant. The TMR/mEGFP fluorescence ratios for eDHFRW22C, H149C, and eDHFRW22GCG, H149GCG dropped to nearly zero, indicating that mutations at positions 22 and 149 resulted in a near complete loss of TMP-TMR binding affinity. In contrast, all other eDHFR variants retained their TMP-TMR binding. Among these, eDHFRP39C and eDHFRQM showed a 30-40% increase in labeling efficiency, as indicated by the TMR/mEGFP ratio, compared to eDHFRWT, while eDHFRP39GCG exhibited labeling efficiency comparable to eDHFRWT, consistent with ColabFold predictions indicating its inability to form a disulfide bond. Notably, C(G)n-eDHFRP89C variants exhibited greater labeling efficiency, with the highest enhancement (~3.3-fold) observed at the poly-glycine linker length of n=5. Introducing additional glycine residues on both sides of cysteine (GCG) to increase flexibility decreased the labeling efficiency compared to a single cysteine mutation. Therefore, we selected C(G)5-eDHFRP89C for further optimization (Fig. 3c and Supplementary Fig. 5e).

Having identified that the length of the poly-glycine linker between the N-terminal cysteine and the N-terminus of eDHFRP89C affects labeling efficiency, we further aimed to optimize the linker length and sequence on the other side of the N-terminal cysteine (i.e., the linker between C(G)5-eDHFRP89C and the target protein). We designed the linkers of varying lengths (5, 10, and 20 amino acids) with different flexibilities, predicted using ColabFold (Fig. 3d). By quantifying TMR/mEGFP ratios, we found that the 10-amino-acid linkers yielded the highest labeling efficiency of C(G)5-eDHFRP89C compared to 5- or 20-amino-acid linkers. Among the tested sequences, the moderately flexible linkers resulted in the highest labeling efficiency compared to the flexible (i.e., GGGGS-based) or the rigid linkers (i.e., EAAAR-based). In contrast, the labeling efficiency of eDHFRWT was unaffected by linker length and flexibility, suggesting that the observed linker-dependent effect in C(G)5-eDHFRP89C is likely due to its influence on disulfide bond formation (Fig. 3e and Supplementary Fig. 6).

Next, we assessed the potential aggregation tendency of C(G)5-eDHFRP89C. Although eDHFRWT is a monomer28, we needed to confirm that the C(G)5-eDHFRP89C mutation does not increase aggregation. Using the mitochondrial morphology assay, we found the percentage of cells overexpressing TOM20-mEGFP-eDHFRP89C exhibiting aggregation was comparable to that of those overexpressing TOM20-mEGFP-eDHFRWT. Additionally, the presence of 100 μM TMP, the concentration used in our protection-based fixation method, did not noticeably increase aggregation (Fig. 3f, g and Supplementary Fig. 7). To test whether the ligand binding kinetics of C(G)5-eDHFRP89C supports PAINT imaging, which also allows effectively mitigate photobleaching across all major fluorescence nanoscopic methods, we quantified event rate and apparent off-rate, as done for BRD4BD2 variants, at 1 nM TMP-TMR. C(G)5-eDHFRP89C exhibited the highest event rate (Fig. 3h) and apparent off-rate (Fig. 3i) in PAINT imaging, indicating that TMP binds to and dissociates from C(G)5-eDHFRP89C more rapidly than other eDHFR variants. This faster ligand exchange makes C(G)5-eDHFRP89C particularly well-suited for higher speed PAINT imaging. Indeed, PAINT images C(G)5-eDHFRP89C outperformed those obtained using eDHFRWT (Fig. 3j).

Finally, we evaluated the performance of C(G)5-eDHFRP89C for tagging proteins localized to diverse cell organelles, as done for FLEXTAG1. The obtained fluorescence images showed expected subcellular localization and morphology of the targeted cellular structures (Fig. 3k). We therefore designated C(G)5-eDHFRP89C as FLEXTAG2.

Development and characterization of FLEXTAG3
We developed FLEXTAG3 from a mutant form of human FKBP, FKBPF36V, a previously reported small (~12 kDa) protein tag used for tag-targeted proteolysis, chemical induction of protein interaction, and fluorescence imaging29–31. Based on the “bump-and-hole” strategy, the F36V mutation and its selective ligand SLF’ (also known as AP1867) were developed to prevent undesired ligand binding to endogenous FKBPs. The high binding affinity of SLF’ to FKBPF36V (KD = 0.094 nM30) results in slow exchange dynamics, making it incompatible with PAINT imaging and ineffective for mitigating photobleaching across major fluorescence nanoscopic methods. To enhance ligand exchange kinetics, a recent study identified the FKBPF36L mutant32. However, we found that overexpression of FKBPF36L-tagged TOM20 in cells led to severe mitochondrial aggregation, with 74.3±2.4% of cells exhibiting abnormal mitochondrial morphology (Fig. 4e, f and Supplementary Fig. 10), Structure prediction using ColabFold revealed that the FKBPF36L dimer interface is indeed mediated primarily through L36, with L36 of one monomer interacting with K34 and I90 of the other, along with additional interfacial contacts (Supplementary Fig. 8a).

We next sought to identify mutations that could further disrupt the dimerization tendency of FKBPF36V. ColabFold predictions showed a distinct dimer interface for FKBPF36V compared to FKBPF36L, with interfacial interactions (bond distances ≤3.5 Å) observed between: (1) the side chain of T85 and the backbone of Q53, E54, and V55, (2) the side chains of F46 and P88, and (3) the side chains of Y82 and H87 (Supplementary Fig. 8b). Unfortunately, this dimer interface lies near the ligand binding pocket. Despite multiple prediction rounds, we found that attempts to disrupt dimerization often resulted in structural disruptions to the binding pocket. Specifically, introducing alanine substitutions or rationally designed mutations at all interfacial residues led to poor folding around the binding pocket, while failing to abolish dimer formation—new interfacial interactions formed, resulting in a similar overall dimer structure (Supplementary Fig. 8c, e). To mitigate this, we narrowed our focus to only the strongest interfacial interactions (bond distances ≤3.0 Å), identifying E54–T85 and Y82–H87 as key interfacial contacts (Supplementary Fig. 8d). We tested four mutations on the FKBPF36V background: E54A, H87A, H87D, and H87E. However, mitochondrial morphology assays showed that none significantly further reduce aggregation, with 17.2±4.2%, 18.2±3.5%, 35.3±2.9%, and 18.2±4.6% of cells displaying abnormal (i.e., aggregated or bundled) mitochondria, respectively (Fig. 4f). The double mutant E54A/H87E also failed to further disrupt dimer formation, based on ColabFold predictions (Supplementary Fig. 8e). Additionally, deletion of the Q53–V55 segment, intended to eliminate backbone-mediated interactions, resulted in severe protein misfolding (Supplementary Fig. 8e). Therefore, despite extensive experimental and computational efforts, further reducing FKBP dimerization tendency remains a challenge. We therefore chose to proceed with FKBPF36V, as it exhibits a tolerable aggregation profile, with ~20% of cells displaying mitochondrial bundling or aggregation.

To increase ligand binding kinetics to FKBPF36V, we sought to evaluate alternative ligands and identify new mutations with the FKBPF36V scaffold. SLF”, a derivative of SLF’ with the three methoxy groups removed, was reported to exhibit weaker affinity toward FKBPF36V than SLF’. To compare the labeling efficiencies of SLF’ and SLF”, we labeled U2OS cells overexpressing double tagged TOM20 constructs (TOM20-mEGFP-FKBPF36V) with TMR-conjugated SLF’ (SLF’-TMR) and SLF” (SLF”-TMR) (Fig. 4b). Although SLF”-TMR exhibited reduced labeling efficiency to FKBPF36V, considerable fluorescent signal in the cytosol of untransfected cells suggested off-target binding of SLF”-TMR to endogenous FKBPs, making SLF’ the preferred ligand for imaging applications (Fig. 4c, d).

To further enhance SLF’s binding kinetics while avoiding aggregation issues associated with FKBPF36L, we engineered eight new single-point mutations near the SLF’s binding pocket in the FKBPF36V background: D37A, Q53A, E54A, V55A, I56A, H87A, I90A, and I91A. We assessed these variants for labeling efficiency using the TMR/mEGFP fluorescence ratios, and synthesized SLF’-MaP555 to evaluate the event rates and apparent off-rates using PAINT imaging (Fig. 4a and Supplementary Fig. 9a–h).

Quantification of the TMR/mEGFP fluorescence ratio revealed that FKBPF36V, D37A, FKBPF36V, Q53A, and FKBPF36V, H87A exhibited higher brightness than FKBPF36V, indicating increased affinity (i.e., lower KD) (Fig. 4h and Supplementary Fig. 9i). However, PAINT imaging revealed that their higher affinity was largely due to prolonged binding (i.e., lower koff), leading to the disappearance of localizations primarily due to photobleaching rather than dissociation. As a result, the apparent on- and off-rate calculations for these variants were inaccurate (as indicated by the asterisks in Figs. 4i, j), implying that their exchange dynamics are insufficient to support PAINT imaging. For instance, in PAINT imaging, both FKBPF36V and FKBPF36V, H87A initially exhibited high single-molecule localization density benefit from their low KD. However, the MaP555 fluorophores bound to the protein tag photobleached rapidly, and were not efficiently replenished by fresh SLF’-MaP555. Consequently, localization density dropped sharply and remained at a low level throughout the 45-min (30,000-frame) imaging duration (Fig. 4k, l). Meanwhile, FKBPF36A, FKBPF36V, V55A, FKBPF36V, I56A, FKBPF36V, E54A, FKBPF36V, I90A, and FKBPF36V, I91A exhibited lower brightness than FKBPF36V, suggesting reduced affinity (i.e., higher KD) (Fig. 4h and Supplementary Fig. 9i). We found that FKBPF36V, I91A exhibited reasonable on- and off-rates for PAINT (slightly outperformed FKBPF36V, E54A) (Fig. 4i, j). As a result, the binding and dissociation of SLF’-MaP555 with FKBPF36V, I91A could continuously and stably generate localizations, making it well-suited for PAINT imaging (Fig. 4k, l). Although optimizing exchange dynamics required some trade-off in affinity and fluorescence intensity during conventional imaging, the brightness of FKBPF36V, I91A remained sufficient to meet the demands of conventional fluorescence microscopy (Supplementary Fig. 9i).

Finally, using the mitochondrial morphology assay, we found 19.0±1.3% of TOM20-mEGFP-FKBPF36V, I91A expressing cells exhibited abnormal mitochondrial morphology (Fig. 4e, f), similar to that observed for FKBPF36V, and incubating these cells with 100 μM SLF did not further increase mitochondrial bundling or aggregation (Fig. 4f, g and Supplementary Fig. 10).

Finally, we validated the capability of FKBPF36V, I91A for tagging multiple organelles, as done for FLEXTAG1 and FLEXTAG2. The obtained fluorescence images showed expected subcellular localization and morphology of the targeted cellular structures (Fig. 4m). Based on these results, we designated FKBPF36V, I91A as FLEXTAG3.

To quantitatively assess ligand binding, we purified FLEXTAG proteins from E. coli and measured affinities by isothermal titration calorimetry (ITC), yielding in-vitro KD values of 383±116 nM, 91±19 nM, and 107±14 nM for FLEXTAG1-3, respectively (Supplementary Fig. 11). We further performed a cell-based fluorescence enhancement assay using H2B-mEGFP-FLEXTAG fusions in U2OS cells labeled with a ligand concentration series, which generated apparent cellular KD values of 159 nM, 68 nM, and 140 nM for FLEXTAG1–3, respectively (Supplementary Fig. 12).

Development of post-fixation treatment to reduce non-specific binding for FLEXTAG
Although self-renewable protein tags offer many advantages over other fluorescence labeling methods and perform well for live-cell imaging as demonstrated by our three FLEXTAG tag/ligand systems, they face two major technical challenges in fixed-cell imaging: (1) fixation with PFA or GA induces nonspecific binding of fluorescent ligands to biomolecule surfaces via hydrophobic and electrostatic interactions; and (2) fixation partially hinders ligand access to the tags, thereby reducing labeling efficiency by preventing a portion of the protein tags from binding to their ligands.

Inspired by immunofluorescence strategies, we systematically optimized fixation and post-fixation treatments. Using TMP–TMR as a model ligand, we compared four fixatives (4% PFA, 4% PFA+0.1% GA, 3% glyoxal33, 4% methacrolein34) with or without reduction by NH4Cl or NaBH4. Without reduction, nonspecific binding followed the order glyoxal < PFA < PFA+GA ≈ methacrolein, and both reducing agents lowered background, with NaBH4 being most effective (Fig. 5a–c). Morphology preservation was highest with PFA+GA, followed by PFA and glyoxal, and lowest with methacrolein (Supplementary Fig. 13).

We next evaluated blocking agents, including BSA, Tween-20, salmon sperm DNA, and KSCN (a chaotropic salt previously reported to disrupt hydrophobic interactions in PAINT imaging35). BSA, Tween-20, and KSCN effectively reduced nonspecific binding, and their combination gave the strongest effect in both conventional and PAINT imaging (Fig. 5d–f and Supplementary Fig. 14a). KSCN did not impair FLEXTAG ligand binding, as fluorescence on TOM20 constructs remained unchanged (Supplementary Fig. 14b, c).

Finally, dye properties influenced nonspecific interactions. For six green-channel dyes (Cy2, BODIPY1, BODIPY2, rhodamine 110, fluorescein, and AF488), nonspecific binding correlated with hydrophobicity (LogD) (Supplementary Fig. 15a–c), consistent with prior observations36, whereas rhodamine fluorogenic dyes37,38 showed reduced nonspecific and free-ligand background, with more fluorogenic dyes producing lower background (Supplementary Fig. 15a, d–f).

Together, these results highlight fixation, reduction, blocking, and fluorophore choice as key factors for robust fixed-cell FLEXTAG imaging.

Development of protective fixation to preserve post-fixation labeling efficiency of FLEXTAGs
Chemical fixation reduces labeling efficiency of self-renewable protein tags by: (1) aldehyde-based protein crosslinking and Schiff base formation can occur near or within the ligand-binding pocket, potentially limiting or obstructing ligand access; and (2) crosslinking can restrict the protein tag’s ability to undergo ligand-binding-induced conformational adaptation (i.e., ligand-induced fit), which stabilizes ligand binding. To address this, we developed a “protective fixation” strategy in which live cells are preincubated with dye-free (unconjugated) FLEXTAG ligands, followed by fixation in the presence of these ligands. This approach physically excludes aldehydes from the binding pocket and stabilizes the ligand-bound conformation; dye-conjugated ligands are then applied post-fixation for imaging (Fig. 5g).

Using U2OS cells expressing TOM20-mEGFP-FLEXTAG2, fixation with 4.0% PFA+0.1% GA at room temperature (RT) in increasing concentrations of TMP (0–100 μM) showed TMP-dependent preservation of labeling efficiency that plateaued at 1 μM (Fig. 5h). Substitution with methotrexate (MTX), which binds eDHFR with ~60-fold higher affinity than TMP39,40 (Ki = 21 pM versus 1.3 nM), did not further improve preservation, likely due to limited cellular uptake (Supplementary Fig. 16). Performing protective fixation at 37°C resulted in poorer labeling efficiency preservation compared to RT, likely due to increased ligand exchange dynamics at higher temperatures that prolonged the duration FLEXTAG2 remained unliganded during fixation (Fig. 5j and Supplementary Fig. 16). Fixation using PFA alone yielded similar trends but resulted in slightly lower labeling preservation compared to PFA+GA (Fig. 5i, j and Supplementary Fig. 16).

Protective fixation improved labeling efficiency across all three FLEXTAGs and the GFP-like chromophore-based renewable tag pFAST11 when their corresponding protective agents were used (ET-JQ1, TMP, SLF’, or HMBR) (Fig. 5h). Relative to live-cell labeling, post-fixation preservation reached 65.2±7.5% for FLEXTAG1, 66.8±2.7% for FLEXTAG2, 60.5±1.9% for FLEXTAG3, and 49.7±3.3% for pFAST, representing 4.5-, 8.8-, 3.9-, and 3.1-fold improvements over fixation without a protective agent, respectively (Fig. 5k, l). These results demonstrate the broad applicability of protective fixation for self-renewable tags.

Finally, cytotoxicity testing showed that most protective agents were well tolerated: after 24 h at 37°C and 100 μM, only JQ1 caused acute toxicity (within 1–5 h), whereas ET-JQ1, TMP, and SLF’ exhibited only mild effects (Supplementary Fig. 17). Consistent with this, MTT assays showed IC50 >100 μM for ET-JQ1, TMP, and SLF’ (72 h incubation), while HMBR, JQ1, and MTX displayed substantially lower IC50 values (34.1±5.5 μM, 33.6±12.8 μM, and 60.3±3.3 μM, respectively) (Supplementary Fig. 18), supporting the suitability of FLEXTAG ligands for protective fixation.

FLEXTAG enables PLIM-based nanoscopic imaging with anti-fade performance
After establishing the FLEXTAG framework, including three small self-renewable protein tags, methods for reducing non-specific binding, and a labeling efficiency preservation strategy (i.e, protective fixation), we evaluated its performance in enabling anti-fade multi-color fluorescence nanoscopic imaging. Fluorescence nanoscopy methods fall into two main categories: (1) PLIM uses structured or patterned illumination to improve spatial resolution; and (2) SMLM relies on the precise localization of single fluorophores that stochastically blink on and off, enabling the reconstruction of super-resolved images with nanometer precision.

We first assessed FLEXTAG in SIM, a fast PLIM-based nanoscopy offering ~100 nm spatial resolution. Unlike the pFAST ligand, which is a fluorescent chromophore, FLEXTAGs’ ligand-binding components are non-fluorescent, allowing greater flexibility in spectral selection for multi-color imaging. To demonstrate this versatility, we synthesized FLEXTAG ligands conjugated to five dyes spanning distinct spectral ranges: four cell-permeable dyes (JF525, TMR, MaP555, and JF635) suitable for both live and fixed-cell imaging37,38,41 and one cell-impermeable dye (AF594) for fixed-cell imaging only (Fig. 6a). These enabled three-color live-cell co-staining of cells co-expressing TOM20-FLEXTAG1, MAP7-FLEXTAG2, and FLEXTAG3-Sec61β, targeting mitochondria, microtubules, and the ER, respectively. FLEXTAG-SIM imaging enabled us to directly visualize the spatial interactions among these organelles (Fig. 6b and Supplementary Movie 1). We further evaluated FLEXTAGs’ performance in fixed cells using our protective fixation and nonspecific binding reduction protocols. SIM images from fixed cells closely resembled those from live cells, validating the applicability of FLEXTAG to fixed-cell imaging (Fig. 6d).

To assess the orthogonality of our three FLEXTAGs, we quantified the three-color SIM images and observed minimal crosstalk between color channels (Fig. 6c), demonstrating the high orthogonality of our three protein tags for multiplexed imaging.

Next, we assessed FLEXTAG in STED, a PLIM-based nanoscopy offering higher spatial resolution than SIM by incorporating a depletion laser beam. FLEXTAG enabled two-color STED imaging in fixed cells co-expressing TOM20-FLEXTAG1 and Lifeact-FLEXTAG2 (or TOM20-FLEXTAG2 and Lifeact-FLEXTAG3), showing the expected mitochondrial and actin cytoskeleton localizations, respectively, along with potential interactions between mitochondria and actin stress fibers (Fig. 6e). Fluorescence intensity profiles across the mitochondrial outer membrane revealed a marked resolution improvement over conventional confocal microscopy (Supplementary Fig. 19). Notably, our two-color FLEXTAG-STED imaging was performed on a STEDYCON system equipped with a single depletion laser, which limit emission depletion to two color channels. We anticipate that three-color STED imaging would be feasible with an STED microscope featuring multiple depletion lasers covering a broader wavelength range.

Photobleaching remains a major limitation for PLIM-based nanoscopy: SIM requires acquisition of tens of images to reconstruct a single SIM image; STED’s high-power depletion lasers exacerbate fluorophore bleaching. FLEXTAG addresses this by dynamically replacing photobleached ligands with fresh fluorescent ones, maintaining relatively stable signals over time (Fig. 6f). To quantify this anti-fade performance, we acquired time-lapse SIM images of TOM20 tagged with FLEXTAG1, FLEXTAG2, FLEXTAG3, HaloTag (a non-renewable protein tag), or FKBPF36V (the protein tag described above with slow renewable rate), each labeled with MaP555-conjugated ligands. Over 30 consecutive frames, fluorescence intensity dropped to ~20% for HaloTag and ~50% for FKBPF36V. In contrast, the fluorescence intensity of all three FLEXTAGs remained nearly constant (Fig. 6g, h). Repeating the experiment with JF635-conjugated ligands yielded similar results (Supplementary Fig. 20). These findings highlight FLEXTAG’s rapid self-renewalkinetics and its ability to overcome photobleaching during PLIM-based nanoscopic imaging.

FLEXTAG enables SMLM-based nanoscopic imaging with anti-fade performance
In SMLM, a super-resolved image is reconstructed from a time series of frames, each capturing a sparse subset of fluorescently labeled protein targets that can be localized at the single-molecule level. While SMLM typically achieves higher spatial resolution than PLIM, it requires slower image acquisition. STORM/PALM achieves this sparsity by using photoswitchable fluorophores that stochastically blink on and off; A common PAINT strategy uses dye-conjugated DNA oligonucleotides that transiently hybridize with complementary strands linked to antibody-labeled targets.

We first assessed FLEXTAG in PAINT. FLEXTAG-PAINT offers intrinsic advantages over DNA-PAINT: it does not rely on antibody availability, and its smaller labeling size provides a more accurate representation of protein localization (Fig. 7e). Using FLEXTAG ligands conjugated to green (AF488), red (TMR/MaP555), or far red (JF635/HMSiR/AF647) dyes, we obtained three-dimensional (3D) PAINT images for all three FLEXTAG tag/ligand systems, demonstrating that each tag/ligand pair supports self-renewable dynamics compatible with PAINT (Fig. 7a–d). FLEXTAG tag/ligand pairs can also be combined or integrated with established PAINT probes, such as phalloidin-PAINT35 for actin cytoskeleton imaging, enabling multi-color PAINT imaging. Three-color PAINT imaging enabled direct visualization of the spatial interactions among actin stress fibers, mitochondria, and the ER (Fig. 7f).

We next assessed FLEXTAG in STORM. Unlike PAINT, STORM relies on covalent labeling with photoswitchable dyes or fluorescent proteins, which are prone to photobleaching, limiting long-term imaging of dynamic processes42. FLEXTAG-STORM, which uses dynamically exchanging photoswitchable ligands, can overcome this limitation. We used FLEXTAG3 and SLF’ as a model system and synthesized a chimeric ligand by conjugating SLF’ to HMSiR (a cell-permeable, spontaneously photoswitchable Si-rhodamine dye developed for live-cell STORM43,44) (Fig. 7g). We first confirmed that SLF’-HMSiR successfully labeled fixed cells expressing TOM20-FLEXTAG3 (Supplementary Fig. 21a). Live-cell FLEXTAG-STORM imaging, acquired at a temporal resolution of 10 s, captured dynamic mitochondrial morphology (Fig. 7k) and enabled 3D reconstruction (Supplementary Fig. 21b and Supplementary Movie 2). To evaluate anti-fade performance, we compared FLEXTAG3 with slower-renewing precursor tags, FKBPF36V and FKBPF36V, H87A, and the non-renewable HaloTag. We monitored the number of single-molecule localizations per image frame at 110 Hz, generating apparent photobleaching curves. FLEXTAG3 retained 96.6±2.3% of single-molecule localizations per frame after ~ 4.5 min (30,000 frames), compared to 56.5±3.1%, 48.0±1.1%, and 29.9±5.2% for FKBPF36V, FKBPF36V, H87A, and HaloTag, respectively (Fig. 7h). Furthermore, FLEXTAG3 showed minimal photobleaching in multi-cycle time-lapse live-cell STORM imaging, whereas the other tags exhibited progressive signal loss over time (Fig. 7i, j and Supplementary Fig. 21c). These results demonstrate FLEXTAG-STORM’s superior anti-fade performance.

Methods
U2OS cell culture
Human bone osteosarcoma epithelial cells (U2OS cells; ATCC

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "In this manuscript, Zhang and colleagues report the design and development of three small fluorophore-conjugated protein tags that engage in transient binding and dissociation with target proteins in both live and fixed cells. This reversible interaction enables continuous exchange of labeling molecules, thereby mitigating the issues of fluorophore photobleaching and the limited photon budget that often restrict the performance of fluorescence microscopy. The authors further demonstrate the broad utility of these tags by applying them to a range of advanced fluorescence microscopy techniques, including SIM, STED, PAINT, and STORM, with successful implementation in multicolor imaging. In addition, careful optimization of fixation and blocking protocols, together with the integration of protective fixation strategies, is presented to enhance labeling efficiency while minimizing nonspecific signals and background fluorescence. Overall, the results are convincing and provide strong support for the authors’ conclusions. The work is technically sound, addresses an important methodological limitation in the field, and should be of considerable interest to a broad readership. I recommend publication in Nature Communications following minor revisions in accordance with the comments provided below.\n1. The manuscript should clearly indicate the number of samples used for each statistical analysis.\n2. On Page 17, the first sentence of the third paragraph: the description of the quantification of the TMR/mEGFP fluorescence ratio does not correspond with the findings presented in Fig. 4h.\n3. In evaluating the cytotoxicity of the protective agents (HMBR, JQ1, ET-JQ1, TMP, MTX, and SLF’), the authors may consider employing cell viability assays such as MTT or WST-1, which would be more representative.", "reviewer_2": "Zhou and coworkers present three types of minimal, self-renewable protein labeling tags (FLEXTAG) for super-resolution fluorescence imaging (e.g., STED and PAIN) with anti-fading performance. The authors developed these FLEXTAG tags by engineering small human or bacterial proteins including BRD4, eDHFR, and FKBP, to enable reversible binding with their corresponding fluorescent ligands. Using artificial intelligence system, they first screened and predicted potential tag structures. They then overexpessed mutated proteins (e.g., BRD4BD2L55A) in subcellular structures, and adjusted the linkages between targeting proteins and tags (e.g., C(G)n-eDHFRP89C). They proceeded with tag and ligand dye pairs to conduct extensive characterization of designed FLEXTAG proteins by fluorescence imaging and subsequently subcellular morphologic analysis. The three optimized FLEXTAGs not only significantly reduced dimerization and mislocalization of tagged proteins of interests (e.g., TOM20) but also minimized dimerization-induced bundling or aggregation in labeled subcellular structures. Their labeling performance was extensively evaluated in super-resolution and multi-color fluorescence nanoscopy. Collectively, this work overcomes key limitations of renewable tags. In addition, as a part of FLEXTAG system, authors also developed protective fixation and methods for enhancing imaging specificity and contrast of fluorescent ligands.\nThis study was well done, but some major concerns need to be addressed before this manuscript is ready for publication.\n1. In the introduction, the authors state that the relatively large size of HaloTag (~33 kDa) can interfere with the native localization and function of tagged proteins. While Halo-tag is widely applied in effective protein labeling for imaging and biological research.\nExcept for size, are there any other key reasons for its failure in maintaining fused proteins function? In the results section, the authors investigated many factors to improve the labeling efficiency of eDHFR variants (FLEXTAG2), including the glycine linker length in C(G)n-eDHFRP89C. Could the Halo-tag labeling system similarly benefit from these systematic optimization strategies?\n2. Binding kinetics of tag and fluorescent ligands are critical for self-renewal tag system in consecutive super-resolution imaging. Therefore, the authors should measure and report the affinity constants (KD) of FLEXTAG-ligand pairs using FLI or fluorescence polarization (FP) assays.\n3. While the protective fixation and methods developed by the authors significantly improved the accessibility of fluorescence ligand and addressed low post-fixation labeling efficiency, appropriately condensing the texts and research contents would more effectively highlight FLEXTAG development and its value for super-resolution applications.\n4. While native subcellar morphology was preserved by FLEXTAG-based imaging and optimized cell fixation methods, more quantitative validations in super-resolution imaging are needed. The results such as resolution (e.g., intensity line plot, FWHM) and target-to-background contrast ratios should be reported for highlighting the advantages of FLEXTAGs.\n5. Some descriptions lack clarity. For instance, is the fluorescence signal originating from the tag itself or the ligand in some sentences?\n6. Detailed staining protocols for multi-color imaging should be provided, so that researchers can reuse these techniques in other similar studies.\n7. The authors claim Live-cell FLEXTAG-STORM imaging captured dynamic mitochondrial morphology (Fig.7j). However, I don't see mitochondrial morphology shown in that figure.\n8. This study introduces three prospective FLEXTAGs. For non-experts looking to use these tags for protein/organelle labeling and imaging, which version is the most user-friendly pick? Additionally, comparative benchmarking of their performance would significantly strengthen utility evaluation.\n9. It would be great to see video supplements for the dynamic super-resolution imaging data, focusing on the multi-color experiments."}, "author_response": "Response: We thank the reviewer for their positive assessment of our work and their constructive suggestions. In response, we have conducted additional experiments and analyses to further strengthen the manuscript and revised the text accordingly. Detailed point-by-point responses to each of Reviewer 1’s comments are provided below.\n1. The manuscript should clearly indicate the number of samples used for each statistical analysis.\nResponse: We thank the reviewer for this important suggestion. We have now clearly indicated the sample sizes for all statistical analyses in the corresponding figure legends throughout the revised manuscript.\n2. On Page 17, the first sentence of the third paragraph: the description of the quantification of the TMR/mEGFP fluorescence ratio does not correspond with the findings presented in Fig. 4h.\nResponse: We thank the reviewer for bringing this inconsistency to our attention. Upon re-examining Fig. 4h, we identified a labeling error in which three constructs (FKBPF36A, FKBPF36V, D37A, and FKBPF36V, Q53A) were inadvertently swapped. We have corrected these labels in the revised figure. Importantly, this labeling error did not affect the quantitative analysis or the corresponding description in the main text, which was accurate in the original submission. Furthermore, we thoroughly reviewed the labels for all remaining quantification figures and confirm that this mislabeling issue does not occur elsewhere.\n3. In evaluating the cytotoxicity of the protective agents (HMBR, JQ1, ET-JQ1, TMP, MTX, and SLF’), the authors may consider employing cell viability assays such as MTT or WST-1, which would be more representative.\nResponse: We thank the reviewer for this constructive suggestion. In response, we performed MTT assays to quantitatively determine the IC values of HMBR, JQ1, ET-50 JQ1, TMP, MTX, and SLF′, defined as the concentration at which cell viability is reduced by 50%. The protective agents used in our FLEXTAG system (ET-JQ1, TMP, and SLF’) displayed minimal cytotoxicity, maintaining >70% cell viability even at 100 µM after 72 h (notably, our protective fixation protocol uses ≤1 h exposure), with IC values exceeding 50 100 µM. In contrast, JQ1, MTX, and HMBR showed substantially higher cytotoxicity under the same conditions. These MTT results are consistent with our original cytotoxicity assessments based on nuclear and mitochondrial morphology and cell density observed by fluorescence microscopy (Supplementary Fig. 17). The new MTT data are provided in Supplementary Fig.18 of the revised manuscript.\nResponse: We thank the reviewer for their positive evaluation of our work and their constructive suggestions. In response to the comments, we have performed additional experiments and analyses to further strengthen the manuscript and have revised the text accordingly, as detailed in our point-by-point responses to each of Reviewer 2’s comments below.\n1. In the introduction, the authors state that the relatively large size of HaloTag (~33 kDa) can interfere with the native localization and function of tagged proteins. While Halo-tag is widely applied in effective protein labeling for imaging and biological research. Except for size, are there any other key reasons for its failure in maintaining fused proteins function? In the results section, the authors investigated many factors to improve the labeling efficiency of eDHFR variants (FLEXTAG2), including the glycine linker length in C(G)n-eDHFRP89C. Could the Halo-tag labeling system similarly benefit from these systematic optimization strategies?\nResponse: We thank the reviewer for this insightful comment. Our optimization of eDHFR-based FLEXTAG2 was motivated by the intrinsic folding instability of eDHFR. Previous studies have shown that eDHFR possesses a relatively low melting temperature (~43 °C)1 and low mechanical unfolding force (~27 pN)2, which suggests that a fraction of the protein likely exists in partially or fully unfolded states in mammalian cells. These unfolded eDHFR molecules cannot efficiently bind fluorescent eDHFR ligands, resulting in reduced labeling efficiency. By introducing an intramolecular disulfide bond in C(G)n–eDHFRP89C, we effectively stabilize the folded state of eDHFR, thereby reducing the unfolded population and improving labeling efficiency. In contrast, HaloTag is substantially more stable, with a higher melting temperature (~62 °C)3 and much larger unfolding forces (131–491 pN)4, making spontaneous unfolding at physiological temperatures unlikely. Thus, stabilizing HaloTag via a similar intramolecular disulfide strategy would be expected to yield minimal improvement in labeling efficiency.\nIn addition, our data showed that unlike C(G) -eDHFRP89C, the labeling efficiency of 5 eDHFRWT was unaffected by linker length and flexibility (Supplementary Fig.6), suggesting that the observed linker-dependent effect in C(G) -eDHFRP89C is likely due to 5 its influence on disulfide bond formation. In contrast, the primary performance limitations of HaloTag in maintaining the tagged protein function arise from the large size of HaloTag (~33 kDa; Supplementary Fig.1). The bulky folded domain can sterically hinder protein trafficking, membrane insertion, or assembly into native complexes. These functional perturbations are fundamentally distinct from the folding/stability limitations that motivated our optimization of eDHFR. For this reason, we do not expect the systematic stabilization strategies used for FLEXTAG2 to confer comparable benefit to HaloTag-based systems.\n2. Binding kinetics of tag and fluorescent ligands are critical for self-renewal tag system in consecutive super-resolution imaging. Therefore, the authors should measure and report the affinity constants (K ) of FLEXTAG-ligand pairs using FLI or fluorescence D polarization (FP) assays.\nResponse: We thank the reviewer for raising this important point. Although we were unable to perform FLI or FP assays due to instrument limitations, we quantified FLEXTAG–ligand binding affinities using two other complementary approaches, one in vitro and one in live cells.\nFirst, we expressed and purified FLEXTAG proteins from E. coli and performed isothermal titration calorimetry (ITC) experiments. The dissociation constants (K ) for TMR- D conjugated ligands binding to FLEXTAG1–3 were 383 nM, 91 nM, and 104 nM, respectively.\nSecond, we employed a cell-imaging-based fluorescence enhancement assay. H2B-mEGFP-FLEXTAG fusion proteins were expressed in U2OS cells and labeled with a concentration series (1, 5, 25, 125, 625, and 3125 nM) of ET-JQ1-JF635, TMP-JF635, or SLF’-JF635. Plotting the averaged JF635/mEGFP fluorescence ratio versus ligand concentration yielded apparent cellular K values of 159 nM, 68 nM, and 140 nM for D FLEXTAG1–3, respectively. These values are in good agreement with the ITC measurements, supporting the robustness of the binding characterization across assay formats.\nBoth the ITC and live-cell binding data are now included in Supplementary Figs. 11 and 12 of the revised manuscript.\n3. While the protective fixation and methods developed by the authors significantly improved the accessibility of fluorescence ligand and addressed low post-fixation labeling efficiency, appropriately condensing the texts and research contents would more effectively highlight FLEXTAG development and its value for super-resolution applications.\nResponse: We thank the reviewer for this suggestion. To improve clarity and focus, we have carefully condensed the sections describing protective fixation and related optimization procedures, while preserving all key experimental details that are essential for understanding the improved labeling efficiency of FLEXTAG. Specifically, we streamlined the description of fixation conditions, blocking strategies, and dye choice tests, emphasizing how these methodological refinements enable high-efficiency, low-background labeling. These revisions highlight the core development and advantages of FLEXTAG for multicolor super-resolution imaging without unnecessary methodological detail, ensuring that the impact and utility of the system remain clear.\n4. While native subcellar morphology was preserved by FLEXTAG-based imaging and optimized cell fixation methods, more quantitative validations in super-resolution imaging are needed. The results such as resolution (e.g., intensity line plot, FWHM) and target-to-background contrast ratios should be reported for highlighting the advantages of FLEXTAGs.\nResponse: We thank the reviewer for this valuable suggestion. We have now added quantitative super-resolution metrics to highlight the performance of FLEXTAGs. For PAINT and STORM imaging, we quantified resolution by measuring single-molecule fitting precision and localization precision using Nearest-Neighbor Analysis (NeNA) 5. Across all three FLEXTAGs in fixed cells, the fitting precision was ~2.6–5 nm and the NeNA localization precision was ~20–35 nm. In live cells, the fitting precision remained comparable, while NeNA precision increased to ~41 nm due to motion blurring, which is similar to the performance of HaloTag. These results are now summarized in Supplementary Tables 1 and 2.\nFor STED imaging, we evaluated resolution enhancement by extracting fluorescence intensity line profiles across the mitochondrial outer membrane (TOM20). Full width at half maximum (FWHM) values were obtained from Gaussian fits, and target-to-background ratios were quantified, demonstrating substantial resolution improvement without compromising much signal contrast relative to confocal imaging. These analyses are now included in Supplementary Fig. 19.\n5. Some descriptions lack clarity. For instance, is the fluorescence signal originating from the tag itself or the ligand in some sentences?\nResponse: We thank the reviewer for this clarification. The fluorescence signal is generated by the organic dye component of the FLEXTAG ligand, while the protein tags themselves are non-fluorescent. Each ligand consists of a organic dye conjugated to a small-molecule binder via a short PEG linker. When the dye is non-fluorogenic (e.g., AF488, TMR, AF647), ligand binding to the protein tag does not alter fluorescence intensity. In contrast, fluorogenic dyes (e.g., JF525, MaP555, JF585, JF635, HMSiR) become fluorescent upon binding to the tag, transitioning from a dark to a bright state. In this case, although the signal is physically associated with the tag–ligand complex, the fluorescence still originates from the dye. We have clarified this distinction in the revised manuscript to avoid ambiguity.\n6. Detailed staining protocols for multi-color imaging should be provided, so that researchers can reuse these techniques in other similar studies.\nResponse: We thank the reviewer for this suggestion. To facilitate reproducibility, we have now included detailed staining protocols for multi-color SIM, STED, and PAINT imaging in the Methods section of the revised manuscript. We believe these additions will enable researchers to readily apply FLEXTAG-based multi-color imaging in their own studies.\n7. The authors claim Live-cell FLEXTAG-STORM imaging captured dynamic mitochondrial morphology (Fig.7j). However, I don't see mitochondrial morphology shown in that figure.\nResponse: We thank the reviewer for this helpful comment. In the original Fig. 7j, the boxed region indicating mitochondrial morphology changes was too small, and its magnification was insufficient to clearly show the structural dynamics. We have now enlarged this region with higher magnification, which reveals the mitochondrial transition from a curved morphology to an elongated shape over a 50-second imaging window. This updated visualization is provided in the new Fig. 7k of the revised manuscript.\n8. This study introduces three prospective FLEXTAGs. For non-experts looking to use these tags for protein/organelle labeling and imaging, which version is the most user-friendly pick? Additionally, comparative benchmarking of their performance would significantly strengthen utility evaluation.\nResponse: We thank the reviewer for this important question. As noted in our response to Comment #4, we have now included quantitative super-resolution metrics that enable direct comparison across the three FLEXTAG versions. In PAINT and STORM imaging of fixed cells, all three FLEXTAG variants yielded comparable imaging resolution, with single-molecule fitting precision of ~2.6–5 nm and NeNA localization precision of ~20–35 nm. In live-cell imaging, fitting precision remained similar, whereas NeNA precision increased to ~41 nm due to motion blurring, comparable to that of HaloTag. It is worth noting that in PAINT imaging, FLEXTAG2 exhibited the most favorable kinetics among the three tags, showing higher binding affinity and faster apparent on/off rates, leading to superior anti-photobleaching performance and shorter acquisition times in PAINT imaging, despite comparable imaging resolution across FLEXTAG1–3.\nTo evaluate physiological compatibility, we assessed mitochondrial morphology preservation for each tag. FLEXTAG3 resulted in a moderate fraction of cells with mild mitochondrial abnormalities (~24%), whereas FLEXTAG1 and FLEXTAG2 showed substantially reduced effects (~11% and ~9%, respectively), comparable to monomeric mEGFP (~10%). These results were previously dispersed across multiple sections and are now consolidated in Supplementary Table 3.\nTaken together, FLEXTAG2 offers the most user-friendly combination of labeling performance, stability, and minimal perturbation to protein/organelle morphology. We believe the inclusion of Supplementary Tables 1–3 will help researchers identify the most suitable FLEXTAG for their applications.\n9. It would be great to see video supplements for the dynamic super-resolution imaging data, focusing on the multi-color experiments.\nResponse: We thank the reviewer for this excellent suggestion. To better visualize the dynamic performance of FLEXTAG-based imaging, we have now included video supplements. Specifically, we added a three-color live-cell SIM movie showing mitochondrial, ER, and microtubule dynamics within the same cell over a 10-minute period, without noticeable photobleaching. This movie highlights both the multiplexing capability and the strong anti-photobleaching performance of FLEXTAG. In addition, we included a live-cell STORM raw movie to demonstrate the characteristic blinking kinetics of FLEXTAG-based STORM imaging. These videos are now provided as Supplementary Movies 1 and 2."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed all my concerns and revised the manuscript accordingly. Therefore, I recommend this manuscript for publication in Nature Communications.", "reviewer_2": "In the revised manuscript, the authors have diligently addressed most of the major concerns raised in this revision. Specifically,\n1. They have provided convincing experimental validation of dynamic super-resolution imaging, presented effectively by in a video supplement.\n2. A thorough study of binding kinetics of tag and fluorescent ligands has been conducted.\n3. Key imaging resolution metrics (e.g., intensity line plot, FWHM) and target-to-background contrast ratios has been provided.\n4. The other comments from my previous review have been addressed, including the addition of video supplements and experimental protocols, text condensation, and clarification of descriptions.\nI recommend acceptance of the manuscript for publication in its current form. No further changes are needed."}, "author_response": "Response: We sincerely thank the reviewer for their careful evaluation of our work and for their positive assessment. We appreciate the reviewer’s support and recommendation for publication.\nResponse: We sincerely thank the reviewer for their thorough evaluation of the revised manuscript and for their positive assessment. We appreciate the reviewer’s recognition of the added experimental validation, quantitative analyses, and clarifications, as well as their recommendation for publication."}]

Decision letter