A high-throughput, flow cytometry approach to measure phase behavior and exchange in biomolecular condensates

Domain: chemistry
Published: 2026-01-15
Editorial outcome: minor_revision

Abstract
Biomolecular condensates are essential for cellular organization, yet their formation dynamics and molecular content exchange properties remain poorly understood. Here we show that flow cytometry provides a high-throughput, solution-based platform for analyzing condensate behavior at the single-droplet level. Using self-interacting NPM1 condensates as a model, we demonstrate that this approach quantifies phase behavior across protein and salt conditions, measures the partitioning of diverse macromolecules—including antibodies, lipids, small-molecule drugs, and RNA—and detects molecular colocalization with high statistical precision. Importantly, we establish a high-throughput assay to track real-time molecular exchange between preformed condensates and newly added, orthogonally tagged protein. These measurements reveal that condensate aging significantly reduces molecular dynamism, likely due to altered biophysical properties with time. Compared to conventional imaging techniques that require surface immobilization or complex instrumentation, our method enables rapid, quantitative characterization of condensate dynamics and molecular content, providing a scalable framework for probing condensate function.

Introduction
Biomolecular condensates represent a fundamental organizing principle in cell biology, enabling the compartmentalization and regulation of biochemical reactions without the need for membrane-bound organelles1,2. These dynamic structures form via liquid-liquid phase separation (LLPS), a physicochemical process in which biomolecules demix from the surrounding milieu to generate dense, droplet-like assemblies3,4. LLPS is driven by multivalent interactions among proteins and nucleic acids, often facilitated by intrinsically disordered regions (IDRs), low-complexity domains, and specific modular interaction motifs5–8. By concentrating proteins, nucleic acids, and other macromolecules, condensates orchestrate important biochemical and biophysical processes such as gene regulation, signal transduction, RNA metabolism, and stress response (Fig. 1a)9–11. Their ability to assemble and disassemble in response to environmental cues is essential for cellular homeostasis and maintenance. Disruptions in condensate formation or changes in their material properties have been implicated in various pathologies including amyotrophic lateral sclerosis (ALS), frontotemporal dementia, and several cancers12–16. Understanding the molecular principles governing condensate assembly, composition, and dynamics is therefore critical for elucidating cellular functional organization and developing targeted therapies.

As our understanding of condensate biology expands, increasing attention has turned to how specific partner molecules interact with or partition into these structures17–19. While initial studies largely examined the roles of proteins and nucleic acids in driving or enriching within condensates, recent work has broadened this view to include small molecules. For example, one early discovery revealed that cyclic GMP-AMP synthase can undergo phase separation upon binding to oligonucleotides, with these nucleic acids enriching within the resulting condensates20. Building on this, researchers have begun to systematically investigate which classes of molecules can diffuse into, concentrate within, or be excluded from existing condensates. These efforts have primarily focused on small molecules, particularly in the context of how condensates might influence drug distribution. Notably, the Young lab demonstrated that fluorophore-tagged FDA-approved therapeutics showed differential localization across diverse protein condensates21. This work has since expanded to BODIPY-based libraries to further explore physicochemical determinants of condensate partitioning22. More recently, a wide scan of over 1000 small-molecule drugs was evaluated for their colocalization with condensates23.

Despite growing recognition of their significance, in vitro analyses of biomolecular condensates remain challenging tasks, largely constrained by methodological limitations. Confocal microscopy has long been an often-used method to analyze condensate formation, dynamics, and composition, offering high spatial resolution20–22. However, its low throughput in most settings limits experimental scalability, and the temporal resolution is often insufficient to capture rapid dynamics. Moreover, manual image processing can also introduce user bias. Other commonly used methods, such as fluorescence recovery after photobleaching (FRAP), turbidity measurement, and sedimentation assays24–26, provide insights into molecular mobility or condensate composition but are either low throughput or lack spatial and temporal resolution. These techniques often fail to capture condensate heterogeneity or rapid dynamic changes in a quantitative manner. While alternative tools such as microfluidic platforms are being explored27, there remains a need for robust, quantitative, and scalable methods to study condensate behavior with widely accessible instrumentation. In parallel, emerging cell-based approaches such as optogenetic systems enable controlled condensate assembly and live-cell imaging platforms that track condensate dynamics28,29. However, these approaches remain technically demanding and are not inherently high-throughput, limiting their scalability for systematic screening.

To address these limitations, we provide evidence that flow cytometry can be a complementary method for condensate analysis, enabling high-throughput and quantitative information without the constraints of surface immobilization or limited imaging volumes (Fig. 1b). Our results show that we can specifically measure condensate size, morphology, and fluorescence intensity as key readouts of condensate formation, composition, and dissolution. Conventional flow cytometry allows assessment of thousands to millions of individual condensates within minutes, providing high-level statistical power to provide broad descriptions across droplet populations. Compared to confocal microscopy, flow cytometry mostly eliminates issues associated with photobleaching, minimizes user bias, and delivers reproducible fluorescence measurements under consistent excitation conditions. Confocal imaging retains complementary advantages in spatial resolution and direct visualization of condensate morphology, and other commonly used assays (e.g., FRAP) can provide high spatial resolution on molecular mobility.

To benchmark the potential of flow cytometry for characterizing biomolecular condensates, we first conducted a series of experiments comparing results obtained by confocal microscopy with those from flow cytometry. Our data show that flow cytometry reliably captures condensate formation, composition, and dissolution, closely aligning with imaging-based observations. Importantly, this technique also enables real-time monitoring of protein exchange between condensates and their surroundings (Fig. 1c), offering new kinetic insights into phase-separated systems. To further dissect the molecular basis of condensate formation, we complemented our experimental work with coarse-grained molecular dynamics simulations. In parallel, to mechanistically understand the exchange dynamics during aging, we developed a discrete-state stochastic model that quantitatively supports our experimental findings using first-passage calculations. By expanding the analytical toolkit for condensate research, this study establishes flow cytometry as a robust and scalable approach for probing phase-separated systems. Its capacity to resolve dynamic behaviors with high temporal and statistical precision opens new avenues for mechanistic insight and may inform therapeutic strategies targeting condensate-associated pathologies.

Results
Characterization of condensate formation and dissolution
Unlike cellular condensates that assemble under physiological conditions, in vitro condensate formation typically requires crowding agents to drive phase separation. Reagents such as polyethyleneglycol (PEG) and Ficoll mimic the intracellular macromolecular environment by increasing effective protein concentration and promoting demixing through excluded volume effects30,31. As an initial test, we selected four well-characterized scaffolding proteins—NPM1, HP1α, DDX4, and HMGB1—each previously reported to undergo phase separation and function in chromatin organization, RNA metabolism, and nucleolar assembly32–36. Each protein was fused to a fluorescent tag to facilitate visualization of condensate behavior (Fig. 2a). The recombinant proteins were purified and subjected to in vitro droplet assays to assess the formation and dissolution of condensates (Fig. 2e). Similar trends were observed for DDX4, HP1α, and HMGB1, with microscopy and cytometry providing complementary evidence (Fig. 2f, g). Histogram figures and scatter/fluorescence distribution plots for the other proteins are shown in Supplementary Fig. 1. Collectively, these data demonstrate that flow cytometry robustly captures condensate formation and disruption with statistical power across tens of thousands of events, while microscopy provides high-resolution spatial context. We envision that this modality of analysis could be paired with large-scale small molecule libraries to discover novel drug-like agents that can block formation or disrupt droplets.

Characterization of NPM1 condensates using experimental and computational approaches
To gain deeper insight into the condensate behavior of NPM1, we conducted a series of downstream characterization experiments. Turbidity measurements of mCherry-NPM1 in the presence of PEG revealed a concentration-dependent increase in light scattering, consistent with enhanced condensate formation (Fig. 3a). Real-time monitoring of NPM1 turbidity following PEG addition showed a progressive, time-dependent increase, consistent with prior observations of condensate (droplet) formation (Fig. 3b). Confocal microscopy revealed that increasing protein concentrations led to the formation of a higher density and visibly larger droplets (Fig. 3c), reflecting enhanced phase separation under higher protein concentrations. Critically, flow cytometry revealed a clear concentration dependence, showing a progressive increase in mCherry fluorescence intensity with increasing protein concentration (Fig. 3d). Quantitatively, flow cytometry–derived median fluorescence measured on the same samples correlated strongly with confocal image medians (Pearson’s r=0.97, p<0.01; Supplementary Fig. 2), indicating that these orthogonal assays report consistent concentration-dependent trends while providing complementary readouts: confocal imaging resolves droplet morphology and spatial distribution, turbidity reports bulk light scattering, and flow cytometry enables rapid, population-level quantification across tens of thousands of events. In addition, forward and side scatter plots (FSC vs. SSC) indicated a size-dependent shift in condensate populations, supporting the expected increase in the size of the formed droplets at higher protein concentrations (Fig. 3e). While larger condensates generally exhibited higher scattering signals, the distributions did not follow a strictly linear trend, reflecting the previously observed heterogeneity in condensate morphology, internal structure, and refractive index. This non-linear relationship is consistent with predictions from Mie scattering theory, where scattering intensity depends on both particle size and refractive index contrast in a complex manner rather than scaling linearly with size40,41. Condensate diameter analysis from confocal images (Supplementary Fig. 2d) further confirmed the heterogeneity of condensate sizes, supporting the notion that both small and large droplets contribute to the observed bulk and flow cytometry signals.

Recognizing that many condensate-driving interactions are electrostatic in nature42,43, we next evaluated whether flow cytometry could accurately capture the expected salt-dependent (NaCl) changes in droplet behavior described in prior studies44. Upon titration of NaCl from 50 to 500 mM, a clear bimodal response in droplet size was observed, characterized by an initial increase at lower salt concentrations followed by disruption of droplet formation at higher salt levels (Fig. 3f). This observation is consistent with electrostatic screening effects at higher concentrations, where increased ionic strength weakens multivalent protein-protein and protein-RNA interactions essential for condensate stability45. Importantly, flow cytometry results mirrored this trend with corresponding decreases in fluorescence intensity at higher salt concentrations (Fig. 3g).

An important consideration for flow cytometry is whether the focused fluidics generates shear stress or whether the buffer conditions compromise condensate integrity. Previous work has shown that active transport and dynamic cellular processes can reinforce condensate stability, suggesting that physical stress does not necessarily disrupt their integrity46. To confirm this, we analyzed mCherry-NPM1 condensates using imaging flow cytometry (IFC), which combines the high-throughput, quantitative capabilities of flow cytometry with high-resolution imaging across multiple channels47,48. While typically used for cellular analysis, IFC has been applied to study protein aggregates and extracellular vesicles49,50, demonstrating its utility for analyzing non-cell-based biomaterials. IFC confirmed that increasing protein concentrations resulted in larger condensates with higher levels of fluorescence in both brightfield and fluorescence channels (Supplementary Fig. 3a). Plotting surface area (brightfield) against fluorescence intensity (mCherry) demonstrated a concentration-dependent shift toward larger-sized events (Supplementary Fig. 3b). Quantification revealed a concentration-dependent increase in condensate size and fluorescence intensity (Fig. 4a), consistent with confocal microscopy results (Fig. 3c). The mean fluorescence intensity measured by IFC also closely matched the data obtained from standard flow cytometry (Fig. 3d, additional representative images are shown in Supplementary Fig. 4). Critically, these results provided key evidence that the events registered in a standard laboratory flow cytometer mirror the general dimensional features of the condensates observed using a high-end technique that combines both imaging and the throughput of flow cytometry.

Molecular dynamics studies of NPM1
While experimental approaches provide crucial insight into the morphology of NPM1 condensates, they do not directly reveal how intramolecular architecture contributes to phase separation. Specifically, it remains unclear how domain-specific interactions within NPM1 drive condensate formation and what role individual NPM1 domains play in self-association. Previous studies have largely focused on how NPM1 interacts with RNA or other macromolecular partners, leaving gaps in our understanding of how NPM1 alone can organize into phase-separated assemblies51–53. To address this, we performed coarse-grained (CG) molecular dynamics simulations to investigate how structural domains within NPM1 contribute to self-association and condensate formation. Importantly, the aim of these simulations was not to establish direct quantitative comparisons with experimental data, but rather to complement our flow cytometry-based assays by providing molecular-level insights into how the modular domain architecture of NPM1 enables condensate assembly.

NPM1 is a modular protein composed of three domains: an N-terminal oligomerization domain (OD, residues 1–118, red), a central intrinsically disordered region (IDR, residues 119–242, green), and a C-terminal DNA-binding domain (CTD, residues 243–294, blue) (Supplementary Fig. 5a, b). AlphaFold-based structure predictions revealed high-confidence folding (pLDDT>90) for the OD and CTD, and low pLDDT scores for the IDR, indicating a flexible and disordered central region (Supplementary Fig. 5c). Notably, charge analysis revealed striking asymmetries: the OD and IDR are highly negatively charged, with net charges of –7 and –19 respectively, while the CTD is mildly positively charged (+2), consistent with its known affinity for nucleic acids (Supplementary Fig. 6a). Despite their shared negative charge, these domains differ in charge patterning. Using the Sequence Charge Decoration (SCD) parameter, which quantifies local charge clustering in disordered regions54, we found that the IDR region has the most charge segregation (SCD = –1.473), with negative and positive charges clustered within the IDR segment (Supplementary Fig. 6a, b). Such clustering can enhance phase separation55,56, thus motivating further exploration of how these features can potentially govern the self-assembly of NPM1.

To understand the role of these domain-specific properties in NPM1 phase separation, we simulated 100 full-length NPM1 chains using the HPS-Urry model57 under slab geometry58,59 to achieve coexisting dense and dilute phases. We applied rigid body motion to folded domains (OD and CTD), while disordered regions remained fully flexible (see Supporting Information for simulation details). We simulated this system at a fixed temperature of 300 K with 100 mM salt and analyzed protein densities as a function of the z-coordinate (Fig. 4b). The density profile clearly shows that the system phase separates into distinct dense and dilute phases, with NPM1 condensates forming a well-defined dense phase (Fig. 4b). To probe the molecular interactions that mediate condensate formation, we computed 2D and 1D time-averaged intermolecular contact maps across all domains (Fig. 4c, d).

Despite both the OD and IDR being negatively charged, the OD region exhibited the strongest intermolecular contacts, particularly OD–OD interactions (Fig. 4c, d and Supplementary Fig. 6c). Many residues in this region formed the highest number of intermolecular contacts (Fig. 4d). This aligns with its structural role in forming stable pentamers, as seen in the crystal structure of human NPM1 (PDB: 4N8M), and suggests that it acts as a scaffold for multivalent network formation. Interestingly, the IDR also displayed much stronger IDR–IDR interactions, despite its strong negative charge (see Supplementary Fig. 6c). This observation highlights the importance of charge patterning over net charge, as the presence of segregated blocks of charges within the IDR may facilitate transient attractions and enable phase separation. In contrast, CTD–CTD interactions were weak (Supplementary Fig. 6c), consistent with its function as a nucleic acid-binding region rather than a driver of protein–protein condensation. However, we observed favorable CTD–IDR and CTD–OD interactions, suggesting that the CTD may help stabilize the condensate by bridging domains or enhancing multivalency. To further probe how electrostatics shape these interactions, we computed electrostatic surface representations of the OD and CTD (Supplementary Fig. 6d). These revealed complementary charge patches: while the OD exhibits distinct positive regions, the CTD contains negatively charged surfaces (Supplementary Fig. 6d), and the IDR features extended negative patches (Supplementary Fig. 6a). Such spatial charge complementarity provides a molecular rationale for the domain-specific contacts observed in the simulations and explains how the CTD can bridge otherwise negatively charged domains. Importantly, this electrostatic complementarity also connects to our experimental observations that increasing salt concentration disrupts condensate stability (Fig. 3f, g), as electrostatic screening weakens these multivalent charge-driven interactions.

Colocalization study of NPM1 condensates with macromolecules
Having established the formation and characterization of NPM1 condensates, we next aimed to assess whether flow cytometry could capture the interaction and recruitment of diverse macromolecules into these biomolecular condensates. Such condensates often function as organizational hubs, selectively incorporating proteins, nucleic acids, and small molecules according to physicochemical properties, including charge, hydrophobicity, and multivalency23,60–63. To assess the partitioning behavior of different macromolecules within NPM1 condensates, we systematically examined the interaction of NPM1 condensates with antibodies, lipids, small-molecule drugs, and RNA (Fig. 5).

We first benchmarked the association of biomacromolecules with NPM1 condensates using confocal microscopy. NPM1 bearing a C-terminal His tag was probed with Alexa Fluor 488–labeled anti-HisTag (positive control) and anti-biotin (negative control) antibodies. Increasing the concentration of NPM1 led to the formation of larger condensates visible in both the mCherry and Alexa Fluor 488 channels (Fig. 5a), consistent with efficient recruitment of the anti-HisTag antibody. Flow cytometry corroborated these colocalization patterns, showing a corresponding increase in both mCherry and Alexa Fluor 488 fluorescence signals (Fig. 5b). In contrast, the anti-biotin antibody did not show co-association with NPM1 condensates, as indicated by the absence of Alexa Fluor 488 signal in both confocal imaging and flow cytometry (Fig. 5a, b). Together, these results demonstrate selective antibody partitioning with its cognate binding partner and establish flow cytometry as a complementary and quantitative method for assessing condensate–macromolecule colocalization.

Next, we examined whether flow cytometry could be used to assess the association of phospholipids with NPM1-based condensates. Recent studies have shown that phospholipids can partition into biomolecular condensates, potentially modulating their size, morphology, and function64. To explore this possibility, we conducted colocalization assays using two dyes: the dye Oregon Green 488 and Oregon Green–conjugated phosphatidylethanolamine, a lipid-linked dye bearing a hydrophobic tail. Consistent with previous reports, confocal imaging showed that Oregon Green 488 did not colocalize with NPM1 condensates, as only mCherry fluorescence was detected, indicating that the condensates exclude freely soluble small molecules (Fig. 5c). In contrast, Oregon Green phosphatidylethanolamine demonstrated high levels of partitioning into NPM1 condensates, indicating that lipid-modified molecules can associate with these structures. These results mirror the previously described behavior of these two molecules with biomolecular condensates using confocal microscopy64. Complementary flow cytometry analysis reproduced the overall trends while enabling analysis of a much larger sample population (Fig. 5d). Alexa Fluor 488 fluorescence was low when NPM1 was incubated with Oregon Green but markedly increased with Oregon Green-tagged phosphatidylethanolamine, indicating strong partitioning of the lipid-conjugated dye into NPM1 condensates. However, when NPM1 was pre-incubated with neomycin before adding Oregon Green phosphatidylethanolamine, the Alexa 488 signal remained low, suggesting that neomycin disrupts lipid association, likely by interfering with electrostatic or hydrophobic interactions65. Meanwhile, mCherry fluorescence intensity remained high across all conditions (Fig. 5d), indicating that condensate formation itself was not affected. Collectively, these findings demonstrate that flow cytometry enables quantitative assessment of lipid–condensate interactions, providing a means to investigate how biomolecular condensates coordinate lipids in their roles as hubs of biological activity.

Likewise, prior research also highlighted that small-molecule drugs (including anti-cancer drugs) can selectively partition into biomolecular condensates, potentially influencing their local concentration and pharmacodynamics21. To benchmark this feature in a high-volume analysis platform, we performed colocalization assays using mitoxantrone and FLTX1, two small-molecule drugs with distinct physicochemical profiles66,67. Confocal imaging confirmed that both mitoxantrone and FLTX1 strongly partitioned into NPM1 condensates, as evidenced by their respective intrinsic fluorescence signals in the red and green channels, respectively (Fig. 5e). These findings were further supported by flow cytometry analysis, which showed a clear increase in drug-associated fluorescence within condensates (Fig. 5f). These results highlight the potential of flow cytometry to broadly characterize the partitioning or accumulation of small molecules within condensates in a higher-throughput manner than LC-MS/MS. This approach also avoids centrifugation steps that can disrupt condensate interfaces, enabling the analysis of intact droplets that remain as single entities in solution.

Finally, we investigated the association of RNA with NPM1 condensates, given the well-documented role of RNA in nucleating and modulating condensate formation in various cellular contexts9. We labeled total RNA extracted from E. coli with AZDye 488 (Fig. 5g) and incubated the dye-tagged RNA with mCherry-NPM1 in the presence of PEG. Confocal imaging showed strong colocalization of mCherry and AZDye 488 signals, indicating that RNA colocalizes with NPM1 condensates (Fig. 5h). Flow cytometry analysis further confirmed the localization of the two biomacromolecules (Fig. 5i), demonstrating robust RNA association with NPM1-driven condensates. We also incubated RNA with mCherry-NPM1 in the absence of PEG. Under these conditions, RNA alone could promote condensate formation (Supplementary Fig. 7a), and titration of rRNA revealed a concentration-dependent increase in mCherry fluorescence intensity (Supplementary Fig. 7b), providing a quantitative readout of RNA-induced condensate assembly. These results suggest that electrostatic interactions between positively charged regions of NPM1 and negatively charged RNA are sufficient to drive condensate formation even in the absence of crowding agents. Collectively, these colocalization studies demonstrate that NPM1 condensates selectively recruit diverse macromolecules, underscoring their role as compartmentalized microenvironments for molecular interactions. The complementary application of flow cytometry not only corroborated imaging results but also revealed subtle shifts in condensate composition, establishing it as a robust and scalable approach for dissecting the molecular content of biomolecular condensates.

Assessment of biomolecular condensate dynamics
Having benchmarked flow cytometry across a range of established contexts, we next aimed to uncover features of biomolecular condensates that require both finer temporal resolution and high-throughput data acquisition. To this end, we focused on the dynamic behavior of individual proteins and their potential partitioning between the dilute and dense phases. Biomolecular condensates are highly dynamic structures68 that continuously exchange molecules with their surroundings, yet quantitatively capturing these dynamic events remains a major challenge in the field. Traditional approaches, such as confocal microscopy and FRAP, have provided valuable insights into molecular mobility within condensates52,69,70, but they are often limited in throughput and time resolution, making it difficult to assess real-time exchange events between condensates.

To investigate exchange dynamics between condensates, we replaced mCherry with HaloTag to generate an NPM1-HaloTag fusion protein. During affinity purification, we installed three distinct chloroalkane-linked fluorescent dyes (Coumarin, R110, or TAMRA) enabling site-specific labeling of the fusion protein. While the protein was bound to the resin, fluorescent dyes were conjugated, and excess unreacted dye was subsequently washed away. This strategy ensured full removal of the unbound dye (Supplementary Fig. 8). To further confirm that fluorescence accumulation within condensates reflects specific molecular interactions rather than nonspecific partitioning of small molecules, we performed a control using small fluorescent HaloTag ligands. Pre-formed NPM1-Halo condensates were incubated with either TAMRA-chloroalkane, which covalently binds HaloTag, or a structurally similar TAMRA-alkane lacking HaloTag-binding capability. Flow cytometry analysis revealed strong TAMRA fluorescence within condensates only in the presence of TAMRA-chloroalkane, whereas TAMRA-alkane produced no detectable signal (Supplementary Fig. 9). These results demonstrate that small molecules without specific binding affinity do not spontaneously accumulate within NPM1-Halo condensates, supporting the conclusion that observed fluorescence redistribution in our system is driven by defined molecular interactions. We next performed in vitro droplet formation assays using either individual dye-labeled NPM1 variants or mixtures of differentially labeled variants to monitor condensate behavior over time. Confocal microscopy at an early point revealed that condensates exhibited distinct, non-overlapping fluorescence signals (Fig. 6a). After 30 minutes, however, most droplets exhibited complete fluorescence colocalization, indicative of molecular exchange and droplet fusion (Fig. 6a). While these results clearly demonstrate rapid molecular exchange between condensates composed of the same protein, monitoring this dynamic process in real time across large droplet populations is challenging with confocal microscopy due to throughput limitations.

We next leveraged flow cytometry to quantitatively analyze exchange dynamics between condensates at the single-droplet level with high sensitivity and throughput. The ability of flow cytometry to rapidly measure fluorescence intensity across thousands of droplets in solution enables real-time tracking of molecular transfer between condensates. To accomplish this, we designed an assay (Fig. 6b) in which NPM1-Halo-TAMRA (red) condensates were pre-formed by equilibration with PEG. Subsequently, we introduced an NPM1-Halo-Coumarin (blue) protein solution lacking PEG and immediately monitored fluorescence over time by flow cytometry. To confirm that the observed increase in blue fluorescence within red condensates was due to active molecular exchange rather than passive mixing or dye diffusion in the bulk solution, we performed a control using free TAMRA dye. A TAMRA-only solution (without protein) was added to PEG at the same volume ratio used in the exchange assay. After a brief 2-s mixing period, the dye rapidly dispersed and became uniformly distributed throughout the PEG solution (Supplementary Fig. 10), confirming that unbound fluorophores equilibrate freely in the solution phase. This result supports the conclusion that the observed colocalization in our primary assay arises specifically from incorporation of NPM1-Halo-Coumarin into pre-existing condensates, rather than from nonspecific mixing of fluorescent components.

To test the dynamic nature of droplet behavior, we introduced NPM1-Halo-Coumarin into pre-formed NPM1-Halo-TAMRA condensates. This resulted in rapid molecular exchange, with over 40% of events shifting to the double-positive quadrant (TAMRA+/Coumarin+) at the earliest time point measured. We note that the earliest time point reflects an operational dead time of approximately 10–15 s between the addition of NPM1-Halo-Coumarin, rapid mixing, tube loading, and the initiation of flow-cytometry acquisition. All kinetic assays in this study use identical rapid-handling procedures, ensuring internal consistency across all experiments. Because this mixing dead time already captures a portion of the rapid NPM1 exchange process, the earliest measurement represents droplets that have begun to equilibrate. This rapid exchange raised concern that critical transient events might be missed due to the high rate of molecular transfer. To investigate whether condensate dynamics could be artificially slowed, we explored the effect of condensate aging. Prior studies have shown that NPM1 condensates readily undergo fusion within 60 min of incubation but exhibit markedly reduced fusion rates after 180 min, likely due to aging-associated structural changes71. Motivated by these observations, we pre-incubated NPM1-Halo-TAMRA condensates with PEG for 1, 3, or 12 h at room temperature before the addition of NPM1-Halo-Coumarin. We then monitored 10,000 individual events over time using quadrant-based flow cytometry analysis to assess changes in exchange dynamics.

As expected prior to the addition of NPM1-Halo-Coumarin, pre-formed NPM1-Halo-TAMRA condensates exhibited a homogeneous scatter distribution within the TAMRA+/Coumarin- quadrant (Fig. 6c). Upon addition of NPM1-Halo-Coumarin, we observed a progressive shift of events toward the double-positive TAMRA+/Coumarin+ quadrant, indicating active molecular exchange between the two protein pools (Fig. 6c, d). Raw scatter plots corresponding to these distributions are presented in Supplementary Fig. 11. Quantification of the percentage of double-positive events over time revealed that the exchange rate declined with increasing aging time of the pre-formed condensates (Fig. 6e), suggesting that condensate dynamics diminish as they mature. To assess how the introduction of NPM1-Halo-Coumarin impacts the structural integrity of aged NPM1-Halo-TAMRA condensates, we monitored the TAMRA fluorescence intensity in 3-h aged samples. Within 60 min of blue protein addition, the TAMRA signal exhibited a slight but consistent decrease (Fig. 6f). This reduction likely reflects the redistribution of red-labeled NPM1 due to molecular exchange. Initially, the red condensates were in equilibrium with their environment; however, the sudden influx of fresh blue protein may have perturbed this balance, resulting in transient dilution or internal rearrangements that lowered fluorescence intensity.

To further support this interpretation, we performed a control experiment in which pre-formed NPM1-Halo-TAMRA condensates were mixed with an equal concentration of soluble NPM1-Halo-TAMRA protein lacking PEG (Supplementary Fig. 12). Notably, even in this NPM1-Halo-TAMRA followed by NPM1-Halo-TAMRA condition, we observed a similar slight decrease in overall TAMRA fluorescence within 60 minutes. This suggests that the observed drop in fluorescence is not specific to dye identity or heterotypic interactions but may arise from protein influx-induced redistribution or structural remodeling of the condensates. For the 1-h and 3-h aged samples, molecular exchange was highly efficient, with approximately 97% of condensates becoming double-positive within 2 h of mixing (Fig. 6e). In contrast, the 12-h aged sample reached only ~80% double-positive, indicating that condensate aging markedly reduces exchange efficiency. This diminished capacity may result from increased structural rigidity, reduced molecular mobility, or fibril formation that impedes protein incorporation. Supporting this, confocal imaging of 3-h aged condensates after 2 h of mixing with the blue protein showed predominantly spherical morphologies. In contrast, 12-h aged condensates exhibited irregular, broken shapes (Supplementary Fig. 13), consistent with aging-induced structural changes that impair condensate dynamics.

To further gain mechanistic insights into the dynamics of protein exchange during aging, we developed a discrete-state stochastic model that describes transitions of individual blue proteins across three spatially distinct states: the dilute phase (surrounding solution), the interfacial layer, and the dense phase (droplet interior) of a pre-formed red condensate, as shown schematically in Fig. 6g. This interfacial region plays a crucial role in controlling exchange kinetics, because it acts as a kinetic barrier for molecular entry72. In our theoretical model, blue proteins initially located in the dilute phase can enter the interfacial region with rate kon, return or bounce back to the dilute phase with rate kbounce, or proceed inward from interphase into the dense phase of the red condensate with rate kentry (see Fig. 6g). To describe the exchange dynamics of blue proteins into a preformed red condensate, we used a method of first-passage probabilities. More specifically, one can define a function Fdil(t) as the probability density of a blue protein entering the dense phase of the red condensate for the first time at time t if initially at t=0, the system started in the dilute phase. A similar first-passage probability density function for the interphase can be defined as Fint(t). The temporal evolution of these probabilities is governed by the backward master equations73:

dFdil(t)/dt = kon Fint(t) - kon Fdil(t) (1)

dFint(t)/dt = kon Fdil(t) + kbounce Fdil(t) - (kbounce + kentry) Fint(t) (2)

where Fden(t) is the probability density of a blue protein being found in the dense phase immediately after leaving the interphase. So, it is natural to assume that Fden(t) = \delta(t). This physically means that if the system is in this state at t=0, the process is immediately finished. As described in detail in the supporting information, these equations can be solved analytically for all ranges of parameters using Laplace transformations (Fe(s) = \int_0^\infty e^{-st} F(t) dt), producing the first-passage time (FPT) distribution F(t) for entering the dense phase, F(t) = Fdil(t) = (e^{- 2 t} [kon + kbounce + kentry + \sqrt{(kon + kbounce + kentry)^2 - 4 kon kentry}] - e^{t \sqrt{(kon + kbounce + kentry)^2 - 4 kon kentry} - 1} [kon + kbounce + kentry + \sqrt{(kon + kbounce + kentry)^2 - 4 kon kentry}]) / \sqrt{(kon + kbounce + kentry)^2 - 4 kon kentry} * kon kentry (3)

We also obtained the corresponding mean first-passage time (MFPT) for protein exchange, as given by Texchange = (kon + kbounce + kentry) / (kon kentry) (4)

Because the experimental setup introduces blue proteins into solution while red droplets are preformed, the system is inherently nonequilibrium as droplets can grow by recruiting newly arrived proteins. We therefore adopted kinetic rate constants from a nonequilibrium theoretical framework described by Wurtz and Lee74, where chemical reaction-controlled exchange governs droplet formation and dynamics. We then examined how aging affects molecular exchange by modulating kbounce, which controls how readily proteins leave the interfacial region before entering the dense phase. This parameter is particularly sensitive to interfacial structural properties. In a recent experiment by Emmanouilidis et al.75 that combined NMR and Raman spectroscopies with microscopy, droplet maturation or aging were investigated for FUS protein, where the structural properties upon maturation between the inside and the surface of droplets were obtained. It was found that a solid crust-like shell is observed at the surface that comprises of \beta-sheet contents and ultimately matured droplets were converted into fibril-like structures. This suggests that due to aging or maturation of condensates, the interface of the droplet can be much more coarsened due to this structural appearance and therefore exchange upon aging can be limited by the dynamics at the droplet interface, implying the existence of an interface resistance72,76. Based on these observations of interfacial solidification, we considered three conditions: “early” (low interfacial resistance, kbounce = 0.01 s^{-1}), “intermediate” (kbounce = 0.1 s^{-1}), and “late or matured” condensates (high resistance, kbounce = 1.0 s^{-1}). As shown in the inset of Fig. 6h, exchange becomes progressively slower with aging, with MFPT increasing from ~32 s (early) to ~230 s (late), in agreement with experimental delays in double-positive accumulation (Fig. 6e). Moreover, additional structural insights from Chatterjee et al.77 show that interfacial regions become kinetically trapped in \beta-sheet–like conformations, further slowing dynamics compared to the untrapped interior. We mimicked this by decreasing kentry in our model for aged droplets, which also led to slower exchange times (see Supplementary Fig. 14). To further support this connection, we computed the cumulative distribution function (CDF) from the first-passage distribution: CDF(t) = \int_0^t F(\tau) d\tau (5)

which represents the probability that a protein has entered the dense phase by time t, and thus serves as a proxy for the fraction of blue proteins colocalized within red condensates. Under different aging conditions, the CDF curves (Fig. 6h and Supplementary Fig. 14) show a clear trend: rapid rise for early, young condensates and gradual rise for late, aged ones—qualitatively recapitulating the experimental curves in Fig. 6e. Despite the differing timescales (theoretical FPT is in seconds, and experimental mixing time is in minutes), the alignment between the theoretical and experimental dynamics strongly supports that younger condensates support faster exchange, while aged condensates accumulate proteins more slowly due to interfacial resistance and decreased internal mobility.

To test whether condensate dynamics are temperature-sensitive, we performed the protein exchange assay using pre-formed, red-labeled condensates (3 h aged) and kept them on ice. Upon addition of the blue-labeled protein, minimal incorporation was observed over 90 min, with a low percentage of double-positive droplets detected by flow cytometry (Supplementary Fig. 15). However, when the same sample was returned to room temperature, a rapid increase in double-positive events was observed, eventually reaching levels comparable to our standard room temperature assay. These results suggest that lower temperatures suppress dynamic exchange, likely by reducing molecular mobility within aged condensates.

To further demonstrate the versatility of our condensate dynamics assay, we extended its application beyond protein exchange to monitor the incorporation of macromolecular cargo. Specifically, we tested whether lipid molecules could be dynamically recruited into pre-formed condensates by mixing NPM1-Halo-TAMRA condensates with Oregon Green-conjugated phosphatidylethanolamine (OG-PE). Flow cytometry analysis revealed a progressive increase in the double-positive population over 30 min (Supplementary Fig. 16), indicating successful incorporation of the fluorescent lipid into the red condensates. This observation was supported by the migration of events toward the TAMRA+/OG+ quadrant in the scatter plots (Supplementary Fig. 16). These results highlight that our real-time flow cytometry platform can sensitively track condensate dynamics involving diverse molecular components, offering a broadly applicable tool to uncover new principles of phase separation and inform future biological investigations.

In this study, we used NPM1 as a model scaffold protein to show that flow cytometry provides a quantitative, high-throughput method for analyzing condensate formation and macromolecular partitioning, complementing traditional imaging-based approaches. Imaging flow cytometry (IFC) further confirmed that condensates retain their structural integrity under flow, validating the reliability of flow-based analysis.

To investigate the molecular determinants of NPM1 condensate formation, we performed coarse-grained molecular dynamics simulations that revealed key domain-specific interactions driving self-association. The N-terminal oligomerization domain (OD) engaged in strong homotypic contacts, while the intrinsically disordered region (IDR)—despite its net negative charge and charge-segregated sequence—also contributed substantially to condensate stability. In contrast, the C-terminal DNA-binding domain (CTD) displayed weaker self-interactions but formed favorable contacts with both OD and IDR. These results illustrate how architectural features and sequence-level electrostatics work together to promote NPM1 self-assembly in the absence of nucleic acids or other binding partners.

We additionally developed a flow-based molecular exchange assay that revealed reduced exchange efficiency in aged condensates, likely stemming from changes in internal organization and decreased molecular mobility. To interpret these findings, we constructed a discrete-state stochastic model in which exchange proceeds through transitions between dilute, interfacial, and dense regions of a pre-formed red condensate. This framework captures single-molecule exchange kinetics using FPT distributions and mean exchange times. By tuning two key parameters—the escape rate from the interface and the entry rate into the dense phase—we produced the experimentally observed slowing of exchange with condensate aging. The model further predicts cumulative exchange behavior through FPT analysis, closely matching experimental time courses of double-positive droplet accumulation across aging conditions. Together, these theoretical results provide a quantitative explanation for the experimental observations and highlight the importance of interfacial resistance and molecular mobility in aging-dependent exchange dynamics.

Compared to traditional microscopy-based methods, our integrated workflow yields more quantitative and mechanistic insight into condensate behavior. This platform can be readily extended to other condensate systems, including studies of how crowding agents, chaperones, or small molecules modulate condensate dynamics and stability. While flow cytometry offers robustness and scalability, it also presents challenges for future applications. Condensates are dynamic and sensitive to environmental perturbations, and shear stress or buffer composition in flow systems may subtly influence their behavior or stability. Distinguishing bona fide condensates from nonspecific aggregates also requires orthogonal validation, such as imaging or enzymatic assays. Integrating experimental measurements with molecular simulations and theoretical modeling helps mitigate these limitations, enabling a more complete and reliable understanding of condensate structure and dynamics.

In summary, this work introduces a multimodal framework for interrogating the structure, composition, and dynamics of biomolecular condensates. Flow cytometry provides a powerful quantitative complement to imaging-based approaches, molecular simulations reveal how domain-level interactions drive assembly, and a flow-based exchange assay demonstrates how aging slows molecular mobility. Together, these integrated strategies offer a unified and generalizable platform for probing condensate biophysics and their functional roles.

Methods
Instrumentation and reagents
DNA concentrations were quantified using a NanoDrop spectrophotometer (Thermo Fisher, CHEM-PR1-KIT). Images from agarose gels and SDS-PAGE were captured with a ChemiDoc XRS+ gel imager (Bio-Rad, 1708265). A SynergyTM H1 hybrid multi-mode reader (Agilent, BioTek Synergy H1) was employed for condensate turbidity measurement. Confocal images were obtained on a Zeiss LSM 980 Microscopy System and a Leica STELLARIS 8 confocal/FLIM/tauSTED microscope system equipped with a tunable white light laser. For most flow cytometry analyses related to condensates, an AttuneTM NxT Acoustic Focusing Cytometer was used. For imaging flow cytometry (IFC) analysis, the Amnis ImageStreamX Mark II system (Luminex Corporation) was employed.

Protein expression was carried out in Escherichia coli BL21-Gold (DE3) competent cells (Agilent Technologies, 230132). RNA extraction was performed using E. coli BL21 (DE3) pLysS cells. A Monarch Total RNA Purification Kit (New England Biolabs, T2010S) was used to facilitate RNA extraction from bacterial cultures. Protein expression and purification involved the use of Isopropyl-β-D-thiogalactopyranoside (IPTG) (Chem Impex, 00194), cOmplete protease inhibitor cocktail (Millipore Sigma, 11836153001), Econo-Pac Columns (Bio-Rad, 7321010), HisPurTM Ni-NTA resin (Thermo Fisher, 88221), Amicon Ultra-15 Centrifugal Filter Unit (EMD Millipore, UFC900324), Pierce™ Slide-A-Lyzer® Dialysis Cassettes (Thermo Fisher, 66380). Chemicals used for protein and RNA labeling, as well as macromolecule compounds for condensate colocalization studies, were obtained from commercial sources. AZDyeTM 488 hydrazide (Vector Laboratories, FP-1017) was used to label bacterial RNA. Polyethylene Glycol 8000 (Sigma-Aldrich, PHR2894-1G) was used to induce condensate formation, and 1,6-Hexanediol (Sigma-Aldrich, 240117-50G) was used to disrupt condensates. Flow cytometry assays were conducted in 96-well untreated round-bottom plates (VWR, 82050-622).

Protein expression and purification
The plasmids encoding scaffolding proteins were acquired from Addgene: pET-45b-mCherry-NPM1 (Addgene, 194546), pET28C-mCherry-DDX4LCD (Addgene, 204408), pET-45b-mEGFP-HMGB1-WT (Addgene, 194543), pET-45b-HP1a-mCherry (Addgene, 185012). The above plasmids were a gift from Denes Hnisz from Max Planck Institute, and Samie Jaffrey from Cornell University. Codon-optimized DNA sequences of HaloTag fusion scaffolding protein (NPM1-Halo) were synthesized by GenScript and cloned into a pET-28a vector with an N-terminus His-tag. All plasmids were transformed into E. coli BL21-Gold (DE3) cells. The transformed cells were then inoculated into Luria-Bertani (LB) media and shaken at 37 °C until OD600 reached 0.3–0.6. Protein expression was induced by adding 1 mM isopropyl β-D-1-thiogalactopyranoside (IPTG), followed by incubation at 37 °C for 3 h with shaking at 250 rpm. This step was followed by an extended incubation at 18 °C for 18 h. Cells were harvested by centrifugation at 4000 rpm for 30 min. The resulting cell pellets were lysed via sonication in lysis buffer (50 mM Tris-Cl, pH 7.5 at 25 °C; 1 M NaCl; 1 mM DTT) supplemented with protease inhibitors (Sigma-Aldrich, 11836153001). The lysates were centrifuged at 18,000 g for 20 min at 4 °C, and the clarified supernatant was applied to Ni-NTA resins (Thermo Fisher, 88221) in a gravity flow column (Bio-Rad, 7321010). The resin was allowed to bind the proteins at 4 °C overnight with rotation. The column was washed with 100 mL of wash buffer (50 mM Tris-Cl, pH 7.5 at 25 °C; 1 M NaCl; 1 mM DTT; 20 mM imidazole). Recombinant proteins were eluted with 10 mL elution buffer (50 mM Tris-Cl, pH 7.5 at 25 °C; 500 mM NaCl; 500 mM imidazole). The eluted proteins were concentrated using an ultra-15 centrifugal filter unit (EMD Millipore, UFC901024), and dialyzed into 1X storage buffer (50 mM Tris-Cl, pH 7.5 at 25 °C; 125 mM NaCl; 1 mM DTT; 10% glycerol). Protein concentrations were determined using a NanoDrop spectrophotometer (Thermo Fisher, 13-400-518), based on the calculated extinction coefficient and molecular weight of each protein. Corresponding protein sequences are provided in Supplementary Table 1.

Protein labeling
Our lab has previously established protocols to synthesize chloroalkane ligand dyes for HaloTag binding78. Nonetheless, for pursuing more consistent results, we chose to purchase dyes from commercially available vendors: TAMRA-Chloroalkane (TAMRA-Cl, Promega, G8252), Coumarin-Chloroalkane (Coumarin-Cl, Promega, G8582), and R110-Chloroalkane (R110-Cl, Promega, G3221). These dyes were directly conjugated to the NPM1-Halo protein while it was bound to Ni-NTA resin, enabling efficient on-resin labeling. Briefly, clarified lysate containing NPM1-Halo was loaded onto 1 mL of Ni-NTA resin in a gravity flow column. After washing with 20 mL of wash buffer to remove non-specific proteins, the resin was incubated for 15 min at room temperature with 5 mL of 1X storage buffer containing 30 μL of 10 mM stock dye (TAMRA-Cl, Coumarin-Cl, or R110-Cl) under gentle shaking. Following labeling, the column was washed with 100 mL of wash buffer to remove unbound dye, and labeled proteins were eluted using 10 mL of elution buffer. Eluted proteins were then concentrated and dialyzed following the same protocol described in the purification section above.

RNA isolation and labeling
E. coli BL21 (DE3) pLysS cells were grown at 37 °C in LB media until OD600 reached 1.0, then centrifuged at 4000 g for 30 min. All subsequent steps were performed at room temperature to prevent RNA precipitation. RNA isolation was carried out using the Total RNA Extraction kit (NEB, T2010) following the manufacturer’s protocol. In brief, cells were resuspended in 1X DNA/RNA Protection Reagent with 1 mg/mL lysozyme (MP Biomedicals, 100831), followed by sonication for 30 min. The lysates were transferred to an RNase-free microfuge tube and centrifuged at 16,000 g for 2 min. The supernatant containing RNA was mixed with RNA Lysis Buffer and processed according to the manufacturer’s instructions. The eluted RNA was further concentrated using isopropanol and 70% ethanol, and the final RNA concentration was determined using NanoDrop. For RNA labeling, we adapted a periodate oxidation and hydrazide coupling protocol by directly oxidizing total RNA with sodium periodate followed by conjugation to AZDye 488 hydrazide79. Briefly, 87 μL of RNA (3.0 mg/mL) was mixed with 3.3 μL of 3 M sodium acetate (pH 5.2) and 10 μL of freshly prepared 25 mM sodium periodate. The mixture was incubated on ice for 50 min, followed by the addition of 20 μL of 3 M sodium acetate (pH 5.2) and 80 μL of nuclease-free water. RNA was precipitated by adding 400 μL of isopropanol and incubating on ice for 1 h. The RNA was pelleted by centrifugation at 16,000 g for 15 min at 4 °C, then rinsed with 150 μL of ice-cold ethanol. After another centrifugation at 16,000 g for 15 min, the RNA pellets were resuspended in 500 μL of reaction buffer containing 100 mM sodium acetate (pH 5.2) and 25 nmol AZDye 488 Hydrazide (Vector Laboratories, FP-1017-1). The reaction was incubated for 48 h at room temperature with gentle shaking. The labeled RNA was purified via additional isopropanol and ethanol precipitation and dissolved in 100 μL of nuclease-free water. The final RNA-AZDye 488 stock concentration was determined using NanoDrop.

In vitro droplet assays by confocal microscopy
Recombinant proteins were diluted to various stock concentrations using 1X storage buffer without PEG (Polyethylene Glycol 8000). For NaCl titration assays, the original protein stock (with 125 mM NaCl) was either diluted using 1X storage buffer with no NaCl or mixed with 1× storage buffer containing 2 M NaCl to achieve final NaCl concentration ranging from 50 mM to 500 mM before imaging. For imaging, 10 μL of fluorescent protein-fused condensate proteins were mixed with 10 μL of 20% (w/v) PEG solution in a PCR tube and allowed to incubate at room temperature for 30 min. Next, 10 μL of the protein mixture was added into a homemade flow chamber consisting of a glass slide (Corning, 2947–75×25) sandwiched by a coverslip (VWR, 48366-045) with a single layer of double-sided tape serving as a spacer. Images were acquired using a Zeiss LSM 980 Microscopy System, which includes an inverted Axio Observer microscope and a multiplex Airyscan module. Lasers at 405 nm, 488 nm and 567 nm were used for excitation, and appropriate PMT detectors were used for emission. As a negative control, pre-formed condensate solutions were mixed with an equal volume of 20% (w/v) 1,6-hexanediol for 10 min at room temperature before imaging. For the droplet mixing assay, a 20 μM NPM1-Halo protein solution was incubated separately with equimolar amounts of each dye (TAMRA-Cl, Coumarin-Cl, or R110-Cl) for 10 min at room temperature. The different dye-labeled NPM1-Halo proteins were then incubated with an equal volume of 20% (w/v) PEG separately for another 30 min. Subsequently, these colored NPM1-Halo condensates were combined into a single solution and placed in the flow chamber for imaging. For RNA-induced condensate formation, 20 μM mCherry-NPM1 was mixed with 50 ng/μL RNA-AZDye 488 (without PEG) at room temperature for 30 min before imaging.

Turbidity assay
To further characterize condensate formation beyond confocal microscopy, we performed turbidity measurement using mCherry-NPM1 protein. Briefly, condensate formation was assessed by mixing mCherry-NPM1 solutions with 1X storage buffer, followed by incubation with 20% (w/v) PEG for 30 min at room temperature. The resulting condensate solutions were transferred to an opaque 384-well-plate (Millipore Sigma, MZHVN0W50) in triplicate, and turbidity was measured at 600 nm using a microplate reader (Agilent, BioTek Synergy H1). The turbidity of mCherry-NPM1/PEG mixtures was analyzed across a range of mCherry-NPM1 concentrations, from 0.1 μM to 1 mM. To evaluate condensate growth over time, we prepared a mixture of 20 μM mCherry-NPM1 with 10% (w/v) PEG and monitored turbidity changes over a 30-min time course.

Condensate imaging for colocalization assays
To assess the interaction and exchange properties of protein condensates with macromolecules in the surrounding solution, colocalization assays were performed using both confocal microscopy and flow cytometry. Colocalization was evaluated in three categories: (a) antibodies, (b) lipids, and (c) cancer drugs. For antibody-based assays, a positive control was established using a 6xHisTag monoclonal antibody conjugated to Alexa Fluor 488 (Thermo Fisher, MAI-21315-A488). A biotin monoclonal antibody labeled with Alexa Fluor 488 (Thermo Fisher, 53-9895-82) was used as a negative control. Recombinant mCherry-NPM1 protein, containing a 6xHisTag, was incubated with either anti-HisTag (1:800 dilution) or anti-biotin antibody (1:800 dilution) for 10 min at room temperature. The antibody-protein mixture was then incubated with PEG to facilitate condensate formation for 30 min, after which the samples were imaged. For lipid-based colocalization, 2 μM of Oregon Green 488 carboxylic acid (AAT Bioquest, 5-OG488) or Oregon Green phosphatidylethanolamine (Thermo Fisher, O12650) was incubated with 20 μM mCherry-NPM1 at room temperature for 10 min. The lipid-protein mixture was then combined with PEG for an additional 30-min incubation to allow condensate formation. As an additional control, 20 μM mCherry-NPM1 was pre-incubated with 2 mg/mL neomycin trisulfate salt hydrate (Millipore Sigma, N1876) for 10 min, followed by incubation with 2 μM Oregon Green phosphatidylethanolamine for 10 min. PEG was then added for another 30-min incubation prior to imaging. For cancer drug-based assays, 20 μM Coumarin-labeled NPM1-Halo protein was first incubated with PEG to form condensates. Following this, the samples were incubated with either 50 μM mitoxantrone (AmBeed, A207951) or 100 μM FLTX1 (MedChemExpress, HY-119437) for 10 min at room temperature. The samples were subsequently pipetted for imaging. For confocal acquisition of drug colocalization images (Fig. 5e), samples were imaged on a Zeiss LSM 980 using 488-nm excitation with a 505–550-nm emission window for FLTX1 (green channel), and 561-nm excitation with a 590–630-nm emission window for mitoxantrone (red channel). These non-overlapping spectral windows minimized bleed-through, and single-color controls confirmed negligible cross-excitation and emission cross-talk.

Condensate analysis by flow cytometry
To assess condensate formation and dynamics, flow cytometry was performed in parallel with confocal imaging using the same batch of protein samples, all analyzed on the same day as described above. Briefly, 35 μL of each 40 μM scaffolding protein (mCherry-NPM1, mCherry-DDX4, mCherry-HP1α, or EGFP-HMGB1) was mixed with 35 μL of either 1X storage buffer or 20% (w/v) PEG in a 96-well clear, untreated round-bottom plate (VWR, 82050-622). The mixtures were incubated at room temperature for 30 min, after which samples were analyzed on an AttuneTM NxT Acoustic Focusing Cytometer. For the dissolution assay, pre-formed 20 μM condensates were treated with 10% (w/v) 1,6-hexanediol for an additional 10 min prior to flow cytometry analysis. Each condition was tested in triplicate, and the mean fluorescence intensity from over 10,000 events was used for quantification. To evaluate concentration-dependent condensate formation, varying concentrations of mCherry-NPM1 (2–80 μM) were prepared by mixing protein stock solutions with either 1X storage buffer or 20% (w/v) PEG. Samples were incubated for 30 min at room temperature before flow cytometry analysis. For the salt titration experiments, mCherry-NPM1 was diluted in 1X storage buffer supplemented with either 0 or 2 M NaCl, yielding final NaCl concentrations ranging from 100 mM to 1 M and a constant final protein concentration of 20 μM. Equal volumes of 20% (w/v) PEG were added, and condensates were allowed to form for 30 min before analysis. For colocalization assays, antibodies, lipids, and small-molecule drugs were added to mCherry-NPM1 condensates at the same concentrations used in confocal microscopy experiments. These samples were similarly incubated and analyzed by flow cytometry. For flow-cytometry acquisition of drug partitioning assays (Fig. 5f), FLTX1 fluorescence (green channel) was excited with the 488-nm laser and collected in the BL2 detector (525/50-nm bandpass), while mitoxantrone fluorescence (red channel) was excited with the 561-nm laser and collected in the YL2 detector (610/20-nm bandpass). Single-color controls were used to generate a compensation matrix, enabling correction of minor spectral spillover between the green and red channels.

To investigate condensate dynamics, a time-resolved assay was performed using NPM1-Halo. Unlike the high-throughput format, these experiments were carried out in individual tubes. Specifically, 10 μM NPM1-Halo-TAMRA was incubated with 10% (w/v) PEG for 1 to 12 h at room temperature to allow condensate formation and aging. A baseline measurement was collected at - 5 min, and soluble NPM1-Halo-Coumarin (without PEG) was added to the pre-aged NPM1-Halo-TAMRA condensates, followed by immediate vortexing for 2 s. The samples were then monitored by flow cytometry at defined time intervals to track incorporation of the coumarin-labeled protein over time.

Imaging Flow Cytometry (IFC)
To directly visualize protein condensates and compare their morphological features across different concentrations, imaging flow cytometry (IFC) was performed using the Amnis ImageStreamX Mark II system (Luminex Corporation). mCherry-NPM1 protein samples were prepared at concentrations ranging from 1 to 40 μM and mixed with an equal volume of 20% (w/v) PEG in a 96-well plate. Following a 30-min incubation at room temperature to allow condensate formation, samples were subjected to IFC analysis. The instrument was equipped with a 60× objective and a 561 nm excitation laser for mCherry detection. Brightfield and fluorescent images of individual condensates were acquired simultaneously. A minimum of 10,000 events were collected per condition, and IDEAS® software (version 6.2, Luminex Corporation) was used for image analysis, including quantitative extraction of morphological parameters (e.g., area, circularity) and fluorescence intensity. Representative images of single mCherry-NPM1 condensates were selected across the concentration range to illustrate size and intensity changes. Surface area measurements derived from brightfield images were plotted against fluorescence intensity from the mCherry channel to assess concentration-dependent condensate growth. The combination of high-content imaging and quantitative flow analysis confirmed that condensates remain intact under flow and are amenable to both morphological and fluorescence-based characterization.

Data processing and statistical analyses
All statistical analyses were performed using GraphPad Prism 9.5 (GraphPad Software). Bar graphs represent the mean±standard deviation (SD) unless otherwise noted.

For conventional flow cytometry, over 10,000 events were collected per sample using the Attune™ NxT Acoustic Focusing Cytometer in volumetric mode (40 μL total acquisition volume at 200 μL/min). No gating was applied; all events were included in the analysis. The X-mean fluorescence intensity was used for quantification and plotting. Forward and side scatter (FSC/SSC) were recorded to monitor event quality, but not used for gating.

For imaging flow cytometry (IFC), >10,000 individual condensates were analyzed using the Amnis ImageStreamX Mark II system with IDEAS® software. Representative condensate images and quantitative data—such as surface area and mCherry fluorescence intensity—were extracted and displayed using scatter plots and violin plots. Condensates were identified using automated segmentation masks without manual gating or filtering. An aspect ratio versus area plot was used to identify events characteristic of condensates. Events with an aspect ratio close to 1 were interpreted as exhibiting the rounded, spherical morphology typical of biomolecular condensates.

To exclude small, spherical debris that also appeared within this region, an additional gate was defined in the top-right quadrant of the aspect ratio versus area plot, symmetrically distributed along the diagonal. This gating strategy selectively captured events with an aspect ratio of 1 and higher area values, thereby enriching for larger, condensate-like particles.

To validate this gating approach, condensates were prepared at increasing concentrations of mCherry–NPM1, and imaging flow cytometry data were acquired using an Amnis ImageStreamX Mark II. Statistical analysis of the gated population (top-right quadrant of the aspect ratio vs area plot) showed a progressive increase in mCherry fluorescence intensity with rising mCherry–NPM1 concentrations. This trend was consistent with confocal microscopy results, which demonstrated a corresponding increase in condensate size at higher protein concentrations. Notably, the fluorescence intensity increase observed within the gated population was more pronounced than that of the broader “All Events” population obtained from the fluorescence intensity versus area scatter plot.

Image-based analysis of the imaging flow cytometry data further confirmed an increase in apparent condensate size with higher mCherry–NPM1 concentrations, supporting the conclusion that the applied gating strategy effectively distinguished true condensates from background debris.

For condensate dynamic exchange assays, quadrant analysis was used to monitor the incorporation of incoming blue-labeled proteins into pre-formed red-labeled condensates. Events were plotted based on red (TAMRA) and blue (coumarin) fluorescence, and the percentage of double-positive condensates was used to quantify molecular exchange over time. Flow cytometry settings were optimized to detect biomolecular condensates and are as follows: Acquisition volume: 40 μL (200 μL/min); Stopping option: 10,000 events; Laser voltages: FSC=100, SSC=240, BL1=220, YL1=230, YL2=230, all others=200; Threshold: SSC=500 (0.5×10^3).

Coarse-Grained (CG) simulation details
We employed coarse-grained (CG) molecular dynamics simulations to investigate the phase behavior and intermolecular interactions of full-length NPM1 proteins. Each protein was modeled using a Cα-level representation, wherein every amino acid residue is mapped to a single bead centered on its α-carbon (Cα) position. The interactions between residues were described by the HPS-Urry model57, which has been shown to accurately reproduce LLPS in silico36,80. To account for the structural organization of NPM1, we applied rigid-body motion to its folded domains—namely, the N-terminal oligomerization domain (OD; residues 14–118) and the C-terminal DNA-binding domain (CTD; residues 243–294), based on high-confidence structure predictions (pLDDT>90). The remaining segments, including the N-terminal residues (1–13) and the central intrinsically disordered region (IDR; residues 119–242), were treated as fully flexible. In these simulations, the OD was represented as a monomeric folded unit rather than enforcing its native pentameric state, allowing us to probe domain contributions to self-association without imposing oligomeric constraints. Future work will extend these studies to explicitly include OD pentamerization.

Simulations were conducted in a rectangular slab geometry of dimensions 175×175×1225 Å^3 containing 100 NPM1 chains, with periodic boundary conditions applied in all three directions. We performed a 5 μs long Langevin dynamics simulations at a fixed temperature of 300 K, with the friction coefficient \gamma = m_{AA} / \tau. Here m_{AA} is the mass of each amino acid bead and \tau is the damping factor set to 1000 ps. The equations of motion were integrated using a velocity-Verlet algorithm with a time step of 10 fs. All simulations were performed using the HOOMD-blue molecular dynamics engine (version 4.7) with additional features provided by the azplugins package81. We conducted the CG simulation at 100 mM salt concentration.

For analysis, we excluded the initial 1 μs of the trajectory to allow for equilibration. Density profiles and intermolecular contact maps were computed from the remaining 4 μs of production data. Error bars were calculated using block averaging by dividing the production trajectory into 4 equal blocks and computing the standard error of the mean.

Theoretical details of first-passage calculations
Let us define Fdil(t) as the first-passage probability density function of entering a blue protein into the dense phase of the red condensate for the first time at time t if initially at t=0, the system started in the dilute phase. Similarly, one can define the first-passage probability density function for the interphase as Fint(t). Then the temporal evolution of these probability functions is governed by a set of backward master equations73:

dFdil(t)/dt = kon Fint(t) - kon Fdil(t) (6)

dFint(t)/dt = kon Fdil(t) + kbounce Fdil(t) - (kbounce + kentry) Fint(t) (7)

In this equation, Fden(t) is the probability density of a blue protein to be found in the dense phase immediately after leaving the interphase, and we can assume that Fden(t) = \delta(t). This means that if the system is in this state at t=0, the process is immediately accomplished.

Applying Laplace transformations Fe(s) = \int_0^\infty e^{-st} F(t) dt, we obtain

(s + kon) Fgdil(s) = kon Fgint(s) (8)

(s + kbounce + kentry) Fgint(s) = kbounce Fgdil(s) + kentry (9)

Solving Eq. (8–9) simultaneously, we obtain

Fgdil(s) = (kon kentry) / (s^2 + s(kon + kbounce + kentry) + kon kentry) (10)

After performing inverse Laplace transformation of the expression (10), we obtain the first-passage distribution function of exchange times from dilute to dense phase and it is given by

F(t) = Fdil(t) = (e^{- 2 t} [kon + kbounce + kentry + \sqrt{(kon + kbounce + kentry)^2 - 4 kon kentry}] - e^{t \sqrt{(kon + kbounce + kentry)^2 - 4 kon kentry} - 1} [kon + kbounce + kentry + \sqrt{(kon + kbounce + kentry)^2 - 4 kon kentry}]) / \sqrt{(kon + kbounce + kentry)^2 - 4 kon kentry} * kon kentry (11)

Then we can compute the cumulative distribution function (CDF) from this FPT distribution:

CDF(t) = \int_0^t F(\tau) d\tau (12)

Finally, the MFPT of protein exchange from dilute to dense phase can be obtained as

Texchange = \int_0^\infty t Fdil(t) dt = - (\partial Fgdil(s) / \partial s)_{s=0} = (kon + kbounce + kentry) / (kon kentry) (13)

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This work provides a powerful framework to study dynamical behaviors of biomolecular condensates utilizing a flow\ncytometry. The manuscript is written clearly and the results are well-supported. The supplemental materials with the actual\npictures of each step procedure would be also very valuable. However, there are several points that require further\nassessment before acceptance in the Nature Communications.\n1. The methodology of flow cytometry for the condensates was not clear to me. For example, the buffer used in the flow\ncytometry was the same as the buffer for preparing condensates? Also, would the flow speed and liquid viscosity affect the\nmorphology and dynamics of condensates?\n2. The results obtained by flow cytometry need to be discussed more rigorously. In Fig. 2c, authors state that PEG treatment\ncauses a clear shift in SSA-C versus FSC-A profiles. However, without the (overlaid) plot for the condition without PEG, it is\nhard to understand what kind of the shift is occurring by PEG treatment.\n3. Similar line to the 2nd comment. In Fig. 3e, the authors imply that larger condensates exhibit higher light scattering (FSC-\nA and SSC-A) based on the scatter plot. But the distribution is not showing a simple positive correlation. Please add an\nexplanation on the distribution pattern. Would it be helpful to look at the scatter plots of mCherry Emission versus FSC-A,\nfor example?\n4. The results of CG-MD simulation seem floating from the entire story. For example, it is difficult to find the link between the\nintramolecular interaction in NPM and the molecular exchange in NPM condensates. Considering the results and\ndiscussions in Fig. 6, the MD simulation would be helpful in explaining how the \"aging\" of the condensates mechanistically\noccurs at molecular level.", "reviewer_2": "In the manuscript “A High-Throughput, Flow Cytometry Approach to Measure Phase Behavior and Exchange in\nBiomolecular Condensates”, He and colleagues present data from analyses of phase separation behavior using flow\ncytometry and compare in places to other approaches. While we already know a lot about how sequences and solution\nconditions affect phase behavior, it will always be relevant to have complementary techniques-in if they can be scaled to a\nlarger number of sequences and conditions.\nOverall, the paper is clearly presented, though I think the manuscript could be strengthened by putting the work in a better\ncontext (for example, what are the quantities that they are aiming to measure and what are the alternative approaches) and\nby quantifying the results better.\nSome comments\nMajor:\n1.\nAs a new method, I think it would be essential that the results are compared quantitively to current methods including state of\nthe art biochemical methods. How/where can I see that the results from the flow cytometry method is quantitatively the same\nas other quantitative measurements?\nFor example, statements such as “Parallel flow cytometry analysis showed an analogous increase in mCherry fluorescence\nintensity with increases in protein concentration (Fig. 3d), thus mirroring what was observed using confocal microscopy and\nconsistent with a larger association of fluorescently tagged proteins per event.” should really be made more quantitative.\nAgain, I am not sure that confocal microscopy is the state of the art for quantifying composition of condensates\n2.\nWhat is the size range of the “droplets” that are seen by the flow cytometry approach; presumably things smaller than the\ndiffraction limit could be missed?\n3.\nThe section “Molecular dynamics studies of NPM1” contains nice work, but I find it quite detached from the rest of the paper.\nThe authors try to connect them in a “platform” but I don’t really see how they complement or strengthen each other. Also,\nthere are not really any quantitative comparisons between the experiments and simulations. Maybe the authors could link\nthe two sections better?\n4.\nThe authors conclude “Together, these findings demonstrate that in vitro condensate formation can be reliably assessed\nusing both traditional methods and newer approaches such as flow cytometry.”\nWhat is the data and quantifications that support this? “Reliably assessed” is a somewhat vague statement as is “traditional\nmethods”. Could the authors be more specific about what can be quantified, and how closely the results agree with state-of-\nthe-art approaches. These validations are essential before the approach can be scaled to study, for example, large libraries\nof small molecules.\nMinor:\n1.\nI think the authors could better explain which properties they are aiming to measure and what the alternatives might be. Also,\nthere are a number of higher-throughput approaches available both in vitro and in vivo that might be worth mentioning.\n2.\nOn page 5, the authors three times state that flow cytometry is superior (maybe once would be enough?), and it would be\nuseful to be clearer in what way it is superior relative to what. The authors mostly compare to confocal microscopy, but there\nare many other approaches and so it would be useful to be clearer about what compromises are being made in terms of\nwhat can be measured and how accurately and rapidly.\n3.\nWhat does the sentence (p. 5) “flow cytometry can be a superior method for mimicking freedom of condensate” mean? (I am\nconfused by the word “freedom” here)\n4.\nOn page 8, what is meant by complexity in “consistent with increased size and complexity (Fig. 2c).”?\n5.\nConsider changing the word volume in “paired with high volume small molecule libraries” on p. 8 since volume here\n(presumably) means large number, but could also be mis-interpreted to mean a large physical volume.\n6.\np. 9 “One potential concern with flow cytometry is whether shear stress or buffer conditions\nmay compromise condensate integrity.”\nYes, or strengthen it (DOI: 10.1126/sciadv.adv7875)\n7.\nOn p. 11, the authors write “Using the Sequence Charge Decoration (SCD) parameter, which quantifies local charge\nclustering in disordered regions,48 we found that the IDR region has the most charge segregation (SCD = –1.473), with\nnegative and positive charges clustered within the IDR segment (Fig. S4a-b).”\nDoes it make sense to compare the values of SCD of the IDR with those of the OD and CTD?\n8.\nIn the simulations of the full length NPM1, is the OD kept “monomeric” and if so why?\n9.\nIn the sentence (p. 15) “These findings support the idea that biomolecular condensates can selectively incorporate lipid-like\nmolecules, potentially modulating their biochemical properties.” it is unclear whether “their” refers to the condensates, lipids\nor both.\n10.\nOn page 16, the authors write “they were noticeably smaller and less spherical compared to those formed with PEG (Fig.\n5h)” and “the resulting structures are substantially smaller, highlighting the role of crowding agents like PEG in enhancing\ncondensate growth and detectability in vitro.” Can these observations be quantified?\n11.\nPage 21 “fibrillation” -> fibril formation\n12.\nIn the equations on p. 22 (bottom) and p. 23 (top), is there a “d” missing on the left-hand side?\n13.\nFigure 1 “Macromoleclues” -> “Macromolecules”", "reviewer_3": "The authors present a flow cytometry–based framework for high-throughput, quantitative analysis of biomolecular\ncondensate formation, composition, and molecular exchange. In general, they confirm that assay readouts can be obtained\nby FCM with statistical robustness due to high throughput. I like the approach of repeating high-throughput measurements at\nhigh frequency to increase temporal sampling. However, the claims and wording need to be much more precise.\nThe interesting demonstration is limited in Figure 6. Other parts, while methodologically sound, are overstated or conflate\nconcepts such as resolution, temporal resolution, and signal-to-noise ratio. For example, the manuscript repeatedly uses\n“real-time,” “the first assay,” “temporal resolution,” and “superior signal-to-noise ratio” without adequate evidence or careful\nqualification. Microscopy and microfluidic approaches have been used to measure condensate exchange in real time\n(though at lower throughput), so the “first assay” claim is overreaching. The actual advantage here is high-throughput,\nrepeated sampling across many droplets, not tracking the same objects over time.\nI cannot recommend this manuscript for publication. The method has merit for high-throughput screening of condensate\nproperties, but claims must be moderated, missing controls and gating information added, and the text revised for conceptual\naccuracy. Also, since the demonstration does not prove its significance, it is appreciated if further demonstrations which\ncannot be done microscopes.\nSpecific Comments:\n“The first assay” in the Abstract is an overclaim. Real-time exchange measurements have been reported using microscopy\nand microfluidic devices, albeit at lower throughput. Reflect the specific novelty of your FCM-based approach.\nFig. 2c and Supp. Fig. S1: The claimed SSC-A vs FSC-A shift for PEG treatment is not clearly visible. Proper controls must\nbe shown.\nFig. 2d: The claim that such resolution is difficult to achieve by microscopy is misleading; microscopy offers fundamentally\nhigher spatial resolution, and methods such as 3D quantitative phase imaging can provide more detailed scattering and\nmorphology. Do not conflate spatial resolution (microscopy) with statistical resolution (FCM). FCM excels in sample size and\nscalability; microscopy excels in spatial detail and single-object tracking. In addition, FSC vs fluorescence plots would be\nmore informative than the violin plot.\nFig. 4a,b: The rationale for using image FCM data in the main text is unclear; they could be moved to Supplementary.\nFig. 6 caption: The statement about confocal lacking temporal resolution is misleading. The process observed is not\ninherently too fast; rather, throughput limits prevent measurement of large droplet populations at 1 min intervals. This should\nbe stated accurately.\nThe “superior signal-to-noise” claim (p. 5) is unsupported and likely incorrect without quantitative comparison.\nIs “Thousands to millions of droplets within minutes” true for all the instruments used in this study, including imaging FCM;\nclarify. FCM analysis lacks presentation of gating strategies; these should be included. For transparency, raw FCS data\nshould be deposited in an open repository."}, "author_response": "Comment 1: The methodology of flow cytometry for the condensates was not clear to me. For\nexample, the buffer used in the flow cytometry was the same as the buffer for preparing\ncondensates? Also, would the flow speed and liquid viscosity affect the morphology and dynamics\nof condensates?\nResponse: We thank the reviewer for raising these important methodological points. The\ncondensates were prepared in storage buffer (50 mM Tris-Cl, pH 7.5 at 25°C; 1 M NaCl; 1 mM\nDTT) supplemented with 10 % PEG8000. During flow cytometry analysis, the sample started in\nthis same buffer, while the instrument sheath fluid (a standard proprietary solution provided by the\nmanufacturer but we have found it to be equivalent to PBS) is designed to serve for hydrodynamic\nfocusing and does not mix with the sample stream. Therefore, the buffer environment of the\ncondensates is likely to remain unchanged throughout the analysis. Regarding the potential\neffects of flow speed and liquid viscosity, we used standard flow rates to facilitate widespread\nadoption of this method for condensate studies. The hydrodynamic focusing employed by the flow\ncytometer ensures laminar flow, and the transit time through the laser interrogation point is very\nbrief. Under these conditions, shear forces are minimal and we projected that they would not\ndisrupt condensate morphology (imaging flow cytometry confirmed this). While we acknowledge\nthat extreme flow rates could potentially compromise condensate integrity, our imaging flow\ncytometry (IFC) analysis demonstrates that consistent condensate morphology, scatter, and\nfluorescence signals are maintained under standard operating conditions (Fig.4).\nComment 2: The results obtained by flow cytometry need to be discussed more rigorously. In Fig.\n2c, authors state that PEG treatment causes a clear shift in SSA-C versus FSC-A profiles.\nHowever, without the (overlaid) plot for the condition without PEG, it is hard to understand what\nkind of the shift is occurring by PEG treatment.\nResponse: We thank the reviewer for highlighting the need for clearer presentation of our flow\ncytometry data. To address this concern, we have revised both our figures and the main text\ndiscussion. Specifically, we now present side scatter (SSC) versus forward scatter (FSC) plots for\nmCherry-NPM1 under both no-PEG and +PEG conditions (Fig. 2c), which reveal a dramatic\nincrease in event counts (9,567 vs. 765) and a clear redistribution of particle populations upon\nPEG treatment. Because light scattering depends on size, refractive index, and internal structure,\nFSC and SSC are best regarded as optical proxies rather than absolute measures of dimension,\nand we have explicitly included this clarification in the revised manuscript. We further\ncomplemented this analysis with histogram plots of mCherry fluorescence intensity (Fig. 2d),\nwhich show a distinct rightward shift under PEG conditions. Additional scatter and histogram data\nfor other proteins are now included in Fig. S1. Together, these revisions provide a more rigorous\nand transparent presentation of the cytometry data, highlighting its statistical power to capture\nPEG-induced condensate formation.\nComment 3: Similar line to the 2nd comment. In Fig. 3e, the authors imply that larger\ncondensates exhibit higher light scattering (FSC-A and SSC-A) based on the scatter plot. But the\ndistribution is not showing a simple positive correlation. Please add an explanation on the\ndistribution pattern. Would it be helpful to look at the scatter plots of mCherry Emission versus\nFSC-A, for example?\nResponse: We agree with the reviewer that the relationship between condensate size and light\nscattering (FSC-A and SSC-A) is not strictly linear nor do we claim this to be the case. As shown\nin Fig. 3e, the distribution pattern indicates that while larger condensates generally exhibit higher\nscattering signals, the data also reflect heterogeneity in condensate morphology, internal structure,\nand refractive index. This non-linear relationship is consistent with Mie scattering theory, which\npredicts that scattering intensity depends on both particle size and refractive index contrast in a\ncomplex, non-monotonic manner rather than scaling linearly with size (Cytometry A 97, 569, 2020;\nUltrasound Med. Biol. 40, 138, 2014). The spread observed in the distribution likely reflects\ncontributions from condensate shape irregularities and packing density differences, which can\nmodulate scattering signals even at similar sizes. In response to the reviewer's suggestion, we\nfurther analyzed scatter plots of mCherry emission versus FSC-A at different protein\nconcentrations (Figure R1). While the shifts in these plots are less pronounced than those in Fig.\n3e, we still observed a modest trend toward higher FSC-A and fluorescence signals at elevated\nconcentrations. We attribute the weaker separation to the fact that fluorescence intensity reports\non protein content within condensates, whereas scattering signals are additionally influenced by\nshape irregularities, packing density, and local refractive index variations. These factors\ncollectively blur a strict one-to-one correlation but remain consistent with the heterogeneous\nnature of biomolecular condensates. We have incorporated this explanation and the new analyses\ninto the revised manuscript.\nFigure R1. Scatter plots of mCherry fluorescence intensity versus forward scatter area (FSC-A)\nfor mCherry-NPM1 at increasing protein concentrations (1, 5, 10, 20, and 40 μM). A modest trend\ntoward higher FSC-A and fluorescence intensity is observed at elevated concentrations,\nconsistent with increased condensate size and protein content per particle. The distributions do\nnot follow a strict linear shift, reflecting heterogeneity in condensate morphology, internal packing\ndensity, and refractive index, which influence scattering signals in addition to size.\nComment 4: The results of CG-MD simulation seem floating from the entire story. For example,\nit is difficult to find the link between the intramolecular interaction in NPM and the molecular\nexchange in NPM condensates. Considering the results and discussions in Fig. 6, the MD\nsimulation would be helpful in explaining how the \"aging\" of the condensates mechanistically\noccurs at molecular level.\nResponse: We appreciate the reviewer’s comment and understand the concern. We would like\nto clarify that in our study, the coarse-grained molecular dynamics (CG-MD) simulations and the\ntheoretical model address complementary but distinct aspects of condensate behavior. The CG-\nMD simulations were designed to reveal how NPM1 alone can organize into phase-separated\nassemblies, with a specific focus on the contribution of its structural domains (OD, IDR, CTD) to\nself-association and condensate stability in the absence of nucleic acids or other binding partners.\nIn contrast, the stochastic theoretical framework in Fig. 6 was developed to rationalize the\nexperimentally observed exchange kinetics and their modulation by aging. In particular, the\ntheoretical model allowed us to explicitly test how interfacial resistance and altered entry rates\ncan slow down exchange, thereby providing a mechanistic rationale for the observed\nexperimental delays.\nThus, while both approaches provide mechanistic insight into condensate behavior, they were not\nintended to be directly linked at the level of molecular exchange. Instead, they collectively broaden\nthe scope of our study: the simulations highlight the architectural and sequence-level\ndeterminants of NPM1 self-assembly, while the theory accounts for the dynamic exchange\nproperties observed experimentally. Together, these complementary perspectives contribute to a\nmore complete understanding of condensate formation and dynamics.\nWith respect to the reviewer’s suggestion, we agree that a detailed molecular-level description of\nhow condensate aging occurs mechanistically would be highly valuable. However, probing the\nliquid-to-solid transitions that underlie condensate maturation (such as fibril-like structural\nconversions observed experimentally; Nat. Chem. Biol., 20, 1044, 2024) requires simulation\ntimescales that are not feasible with our current CG-MD approach. As highlighted in a recent\nreview (Curr. Opin. Chem. Biol., 75, 102333, 2023), capturing such slow, nonequilibrium\ntransitions by molecular simulation remains a fundamental challenge. For this reason, we instead\nemployed a theoretical framework to model aging at the kinetic level. Molecular-level mechanisms\nof condensate aging are the subject of ongoing investigations in our laboratory and will be pursued\nin future work.\nMajor Comment 1: As a new method, I think it would be essential that the results are compared\nquantitively to current methods including state of the art biochemical methods. How/where can I\nsee that the results from the flow cytometry method is quantitatively the same as other quantitative\nmeasurements? For example, statements such as “Parallel flow cytometry analysis showed an\nanalogous increase in mCherry fluorescence intensity with increases in protein concentration (Fig.\n3d), thus mirroring what was observed using confocal microscopy and consistent with a larger\nassociation of fluorescently tagged proteins per event.” should really be made more quantitative.\nAgain, I am not sure that confocal microscopy is the state of the art for quantifying composition of\ncondensates\nResponse: We thank the reviewer for this important suggestion. We agree that providing\nquantitative comparisons between our flow cytometry approach and established methods is\ncritical for validating our methodology. We quantified confocal images of mCherry-NPM1\ncondensates (n = 3 independent fields per concentration; >10 droplets per field) in ImageJ by\nsegmenting spherical droplets and measuring mean fluorescence intensity (MFI) per droplet. To\navoid pseudoreplication, we computed the image-level observations for statistical comparisons.\nMFI increased systematically with protein concentration, and the confocal-derived image medians\ncorrelated strongly with flow-cytometry median fluorescence measured on the same samples\n(Pearson's r = 0.9653). These results are shown in the revised manuscript, along with more\nrepresentative confocal images (new Fig. S2). Additionally, we note that the same concentration-\ndependent trend was independently observed in turbidity measurements of mCherry-NPM1\ncondensates (Fig. 3a), further confirming that all three orthogonal methods (confocal imaging,\nflow cytometry, and turbidity) consistently report the increase in condensate abundance and\nfluorescence intensity with protein concentration. Beyond performing these new quantitative\ncomparisons, we have carefully revised the main text to address the reviewer's concerns about\noverstatement. The revised text now highlights that confocal microscopy remains valuable for\nassessing morphology and spatial distribution, while flow cytometry could potentially rapid,\npopulation-level quantification across a larger throughput of individual events as a complementary\ntool.\nNew Figure S2. Quantitative comparison of confocal imaging and flow cytometry analysis of\nmCherry-NPM1 condensates. a) Representative confocal fluorescence images of mCherry-\nNPM1 condensates at 1, 5, 10, 20, and 40 μM protein concentration. For each concentration,\nthree independent fields of view are shown. Scale bar = 5 μm. b) Quantification of mean\nfluorescence intensity (MFI) per condensate droplet from confocal images using ImageJ\nsegmentation. Data was pooled from three independent fields of view per concentration (n > 30\ncondensates in total; >10 condensates per field). c) Correlation between confocal-derived image\nmedians (panel b) and median fluorescence measured by flow cytometry on the same samples\n(Fig. 3d). Pearson’s r = 0.9653, p = 0.0077 (**), indicating a strong and statistically significant\npositive correlation between the two methods.\nMajor Comment 2: What is the size range of the “droplets” that are seen by the flow cytometry\napproach; presumably things smaller than the diffraction limit could be missed?\nResponse: We thank the reviewer for raising this important question. Indeed, conventional flow\ncytometers have a practical lower detection limit of approximately 300–500 nm for polystyrene\nbeads, depending on optical settings (Cytometry B Clin. Cytom., 90, 326, 2016). Because\nbiomolecular condensates typically have lower refractive index contrast than polystyrene, the\neffective threshold for reliable detection is likely somewhat higher. However, in our assays, the\ncondensates visualized by confocal microscopy are predominantly several micrometers in\ndiameter (Fig. 2 and 3), which is well above this detection range. Thus, the droplets analyzed by\nflow cytometry correspond to the main condensate population observed by microscopy methods.\nFurthermore, we note that FSC and SSC signals are not direct readouts of particle size. Scatter\nintensity is reported in arbitrary units and depends not only on diameter but also on refractive\nindex, morphology, and instrument optics. Light scatter sensitivity is typically described only in\nterms of the smallest detectable polystyrene bead, which specifies the detection threshold but\nprovides no absolute size calibration (Cytometry 99, 671, 2020). For this reason, we rely on\northogonal imaging to confirm condensate sizes rather than inferring absolute diameters from\nFSC/SSC alone. We have revised the main text to include this discussion.\nMajor Comment 3: The section “Molecular dynamics studies of NPM1” contains nice work, but I\nfind it quite detached from the rest of the paper. The authors try to connect them in a “platform”\nbut I don’t really see how they complement or strengthen each other. Also, there are not really\nany quantitative comparisons between the experiments and simulations. Maybe the authors could\nlink the two sections better?\nResponse: We thank the reviewer for this thoughtful comment. We want to highlight here that\nour molecular dynamics (MD) section is not intended to provide direct quantitative comparisons\nwith the experiments. Rather, our aim in including the MD simulations was to uncover how the\nmodular domain organization of NPM1 contributes to its self-association and assembly into\ncondensates. Specifically, the MD simulations provide structural and mechanistic insights into the\nrelative roles of the N-terminal oligomerization domain, intrinsically disordered regions, and DNA\nrecognition motifs in driving multivalent interactions. These molecular-level insights are not\ndirectly accessible from the flow cytometry-based experimental measurements, but they establish\na complementary perspective that explains why NPM1 forms condensates and how its distinct\ndomains contribute to phase separation. To make this intent clearer, we have revised the MD\nsection to explicitly highlight its complementary role and to avoid the impression that a direct\nquantitative experiment–simulation comparison was intended (see page 13).\nTo further strengthen the connection between simulations and experiments, we additionally\nanalyzed the electrostatic surface potentials of the OD and CTD domains (new Fig. S6d). These\nsurfaces revealed complementary charge distributions: the OD and CTD harboring positive and\nnegative patches, respectively, while the IDR contains extended negative regions. Such\nelectrostatic complementarity provides a molecular rationale for the domain-specific contacts\nobserved in our simulations and directly links to our experimental findings that condensates are\ndisrupted by increased salt concentration (Fig. 3f–g). By linking domain-resolved electrostatics\nwith salt-dependent condensate stability, the revised text now integrates the experimental and\nsimulation sections into a unified framework (see page 15).\nMajor Comment 4: The authors conclude “Together, these findings demonstrate that in vitro\ncondensate formation can be reliably assessed using both traditional methods and newer\napproaches such as flow cytometry.” What is the data and quantifications that support this?\n“Reliably assessed” is a somewhat vague statement as is “traditional methods”. Could the authors\nbe more specific about what can be quantified, and how closely the results agree with state-of-\nthe-art approaches. These validations are essential before the approach can be scaled to study,\nfor example, large libraries of small molecules.\nResponse: We thank the reviewer for pointing out this vague phrasing. We agree that the\nstatements \"reliably assessed\" and \"traditional methods\" lacked specificity. To address this, we\nhave revised the sentence to directly reflect our data. In our study, turbidity measurements\ndemonstrate increased OD₆₀₀ with higher protein concentrations, while confocal microscopy\nshows a corresponding increase in condensate size (Fig. 3a-d). To further validate the flow\ncytometry approach, we quantified mean fluorescence intensity (MFI) from >30 condensates\nimaged by confocal microscopy (using ImageJ) and compared these values with MFI obtained by\nflow cytometry. This analysis revealed a strong correlation between the two methods (Pearson's\nr = 0.9653, new Fig. S2). Together, these results provide quantitative evidence that flow cytometry\nyields measurements consistent with confocal microscopy, supporting its use for scalable studies.\nWe have revised the main text to clarify these quantifications and added Fig. S2 to present the\ncorrelation analysis.\nMinor Comment 1: I think the authors could better explain which properties they are aiming to\nmeasure and what the alternatives might be. Also, there are a number of higher-throughput\napproaches available both in vitro and in vivo that might be worth mentioning.\nResponse: We thank the reviewer for this helpful suggestion. Our study focuses on measuring\ncondensate size, morphology, and fluorescence intensity, as these properties reflect both the\nphysical state and molecular composition of condensates. To clarify this point, we have revised\nthe Introduction to better define the properties being measured in this work. Additionally, while our\noriginal manuscript discussed several in vitro approaches (e.g., FRAP, turbidity, and\nsedimentation assays), we now expand the discussion to include emerging in vivo tools.\nSpecifically, we highlight live-cell imaging and optogenetic systems that enable controlled\ncondensate assembly and dynamic tracking, while also noting their current limitations in\nthroughput and quantitative precision. These revisions provide clearer context for our choice of\nflow cytometry and situate our approach among available experimental strategies.\nMinor Comment 2: On page 5, the authors three times state that flow cytometry is superior\n(maybe once would be enough?), and it would be useful to be clearer in what way it is superior\nrelative to what. The authors mostly compare to confocal microscopy, but there are many other\napproaches and so it would be useful to be clearer about what compromises are being made in\nterms of what can be measured and how accurately and rapidly.\nResponse: We thank the reviewer for this thoughtful feedback. We agree that our original\nphrasing overstated the point by repeatedly claiming that flow cytometry is \"superior.\" We have\nrevised the text to clarify the specific advantages that flow cytometry provides. Specifically, we\nnow emphasize that flow cytometry offers higher throughput, better statistical power, and reduced\nphotobleaching compared to confocal microscopy. At the same time, we acknowledge that\nconfocal imaging retains advantages in spatial resolution and direct visualization of condensate\nmorphology, while other assays (e.g., FRAP) provide complementary insights into dynamics and\ncomposition. These revisions make the trade-offs clearer and better situate flow cytometry among\nexisting methods.\nMinor Comment 3: What does the sentence (p. 5) “flow cytometry can be a superior method for\nmimicking freedom of condensate” mean? (I am confused by the word “freedom” here)\nResponse: We thank the reviewer for pointing out the confusing phrasing. The original phrase\n“freedom of condensate” was intended to convey that flow cytometry allows condensates to\nremain freely suspended in solution rather than being constrained on a surface or in a small\nimaging volume. We have revised the text to clarify this point and now explicitly state that flow\ncytometry enables high-throughput and quantitative analysis while maintaining condensates in\ntheir native, unconfined state.\nMinor Comment 4: On page 8, what is meant by complexity in “consistent with increased size\nand complexity (Fig. 2c).”?\nResponse: We thank the reviewer for highlighting the ambiguous use of “complexity.” Here, we\nrefer to the internal granularity or structural heterogeneity of condensates, which is captured by\nside scatter (SSC) in flow cytometry. We have revised the text to replace “complexity” with\n“internal granularity” and clarified how SSC and FSC relate to particle properties, while\nmaintaining the caution that these are optical proxies rather than direct size measurements.\nMinor Comment 5: Consider changing the word volume in “paired with high volume small\nmolecule libraries” on p. 8 since volume here (presumably) means large number, but could also\nbe mis-interpreted to mean a large physical volume.\nResponse: We thank the reviewer for this suggestion. To avoid potential confusion, we have\nreplaced “high volume” with “large-scale” to clearly indicate a large number of small molecules\nrather than physical volume.\nMinor Comment 6: p. 9 “One potential concern with flow cytometry is whether shear stress or\nbuffer conditions may compromise condensate integrity.”\nYes, or strengthen it (DOI: 10.1126/sciadv.adv7875)\nResponse: We thank the reviewer for this suggestion. We have added a sentence citing Chauhan\net al., 2025, to highlight that active transport and dynamic cellular processes can reinforce\ncondensate stability, indicating that physical stresses such as shear or buffer conditions do not\nnecessarily compromise integrity. While this may be the case in a situation of a longer time scale,\ngiven the brevity of the transit from the sample solution to the analysis we do not believe this will\nlead to general alteration of the output observed.\nMinor Comment 7: On p. 11, the authors write “Using the Sequence Charge Decoration (SCD)\nparameter, which quantifies local charge clustering in disordered regions,48 we found that the\nIDR region has the most charge segregation (SCD = –1.473), with negative and positive charges\nclustered within the IDR segment (Fig. S4a-b).”\nDoes it make sense to compare the values of SCD of the IDR with those of the OD and CTD?\nResponse: We understand the reviewer’s concern. We agree that the SCD parameter is most\nmeaningful when applied to intrinsically disordered regions, as it was originally developed to\ncapture charge clustering in flexible segments rather than well-folded domains. Our goal in\npresenting the SCD values for the OD, IDR, and CTD was to provide a comparative view of charge\ndistribution across the full NPM1 sequence. While the OD and CTD, being largely folded domains,\nare not ideally suited for SCD-based analysis, their values were included as qualitative indicators\nto highlight relative differences in charge patterning among the three domains. In this context, the\ncomparison serves to emphasize that NPM1 exhibits distinct sequence-level asymmetries, with\nthe IDR displaying the strongest charge clustering which is likely to influence condensate\nformation.\nMinor Comment 8: In the simulations of the full length NPM1, is the OD kept “monomeric” and if\nso why?\nResponse: We thank the reviewer for raising this important point. In our coarse-grained\nsimulations, the OD was modeled as a rigid folded domain but treated as monomeric rather than\nenforcing its pentameric state. The OD was kept monomeric to evaluate how the different domains\nof NPM1 contribute to self-association in the absence of predefined oligomeric constraints. By not\nimposing pentamerization, the simulations could probe how the OD interacts with other domains\nand contributes to condensate formation in a more unbiased manner. Notably, even under this\nsetup, the OD exhibited strong intermolecular contacts consistent with its known propensity to\noligomerize, underscoring its dominant role as a driver of NPM1 self-association. We also note\nthat exploring the effect of OD pentamerization on condensate behavior is an important next step,\nand ongoing work in our laboratory is directed toward such pentamer-based simulations. For\nclarity, this modeling choice has now been explicitly noted in the Supporting Information (CG\nSimulation Details section).\nMinor Comment 9: In the sentence (p. 15) “These findings support the idea that biomolecular\ncondensates can selectively incorporate lipid-like molecules, potentially modulating their\nbiochemical properties.” it is unclear whether “their” refers to the condensates, lipids\nor both.\nResponse: We thank the reviewer for this suggestion and have clarified the sentence to specify\nthat “their” refers to the condensates.\nMinor Comment 10: On page 16, the authors write “they were noticeably smaller and less\nspherical compared to those formed with PEG (Fig. 5h)” and “the resulting structures are\nsubstantially smaller, highlighting the role of crowding agents like PEG in enhancing\ncondensate growth and detectability in vitro.” Can these observations be quantified?\nResponse: We thank the reviewer for this suggestion. In response, we have now included RNA\ntitration experiments where increasing rRNA concentrations were added to mCherry-NPM1 in the\nabsence of PEG, and the resulting condensate formation was quantified using flow cytometry\n(new Fig. S7b). These data show a clear, concentration-dependent increase in median mCherry\nfluorescence intensity, providing quantitative evidence that RNA promotes condensate formation.\nWe have revised the text to highlight this trend while avoiding direct comparisons of condensate\nsize and shape between PEG- and RNA-driven conditions. This allows us to present a quantitative\nreadout of RNA-induced condensate assembly without overinterpreting differences in morphology,\nconsistent with the original intent of using these experiments as a proof of principle for flow\ncytometry–based detection of condensate composition.\nMinor Comment 11: Page 21 “fibrillation” -> fibril formation\nResponse: We thank the reviewer for the suggestion and have replaced “fibrillation” with “fibril\nformation” in the manuscript.\nMinor Comment 12: In the equations on p. 22 (bottom) and p. 23 (top), is there a “d” missing on\nthe left-hand side?\nResponse: We thank the reviewer for identifying this mistake. Yes, there is a missing “d” on the\nleft-hand side of both the equations which we corrected now in both the revised manuscript and\nthe supporting information.\nMinor Comment 13: Figure 1 “Macromoleclues” -> “Macromolecules”\nResponse: We thank the reviewer for noticing this typographical error and have corrected\n“Macromoleclues” to “Macromolecules” in Fig. 1.\nComment 1: “The first assay” in the Abstract is an overclaim. Real-time exchange measurements\nhave been reported using microscopy and microfluidic devices, albeit at lower throughput. Reflect\nthe specific novelty of your FCM-based approach. Fig. 2c and Supp. Fig. S1: The claimed SSC-\nA vs FSC-A shift for PEG treatment is not clearly visible. Proper controls must be shown.\nResponse: We thank the reviewer for this comment. We have removed the phrase “first assay”\nfrom the Abstract to avoid overclaiming and now describe our method as a high-throughput flow\ncytometry approach to measure real-time exchange of biomolecules in condensates. Regarding\nthe SSC-A vs FSC-A analysis, we have revised both our figures and the main text to improve\nclarity. Specifically, Fig. 2c now displays side-by-side scatter plots for mCherry-NPM1 with and\nwithout PEG treatment, showing a marked increase in event counts and a clear redistribution of\npopulations under PEG conditions. In addition, we included complementary histogram plots of\nfluorescence intensity (Fig. 2d) and updated Supplementary Fig. S1 with overlaid scatter and\nfluorescence distribution plots for the other proteins. Together, these revisions provide the\nappropriate controls and better highlight the PEG-induced shifts in the flow cytometry data.\nComment 2: Fig. 2d: The claim that such resolution is difficult to achieve by microscopy is\nmisleading; microscopy offers fundamentally higher spatial resolution, and methods such as 3D\nquantitative phase imaging can provide more detailed scattering and morphology. Do not conflate\nspatial resolution (microscopy) with statistical resolution (FCM). FCM excels in sample size and\nscalability; microscopy excels in spatial detail and single-object tracking. In addition, FSC vs\nfluorescence plots would be more informative than the violin plot.\nResponse: We thank the reviewer for this comment. We have revised the text to clarify that flow\ncytometry provides high-throughput quantitative measurements and statistical resolution across\nlarge numbers of condensates, whereas microscopy offers higher spatial resolution and single-\nobject tracking. In addition, we have supplemented the analysis with FSC vs fluorescence scatter\nplots to better visualize population-level trends, as now shown in new Fig. S1.\nComment 3: Fig. 4a,b: The rationale for using image FCM data in the main text is unclear; they\ncould be moved to Supplementary.\nResponse: We thank the reviewer for this suggestion. We have moved the imaging flow\ncytometry (IFC) panels (originally Fig. 4a-b) to Supplementary Information (new Fig. S3), while\nretaining the quantitative analysis of condensate size (Fig. 4c) in the main text. The main text\nnow refers to the representative IFC images and scatter plots in the Supplementary Information.\nComment 4: Fig. 6 caption: The statement about confocal lacking temporal resolution is\nmisleading. The process observed is not inherently too fast; rather, throughput limits prevent\nmeasurement of large droplet populations at 1 min intervals. This should be stated accurately.\nResponse: We thank the reviewer for this comment. We have revised the caption of Fig. 6 and\nthe corresponding main text to clarify that confocal microscopy readily captures fusion events, but\nits throughput limits make it less suited for monitoring real-time exchange across large droplet\npopulations.\nComment 5: The “superior signal-to-noise” claim (p. 5) is unsupported and likely incorrect without\nquantitative comparison.\nResponse: We thank the reviewer for this comment. We have removed the signal-to-noise ratio\nstatement and revised the text (p. 5) to highlight other demonstrated advantages of flow cytometry,\nincluding reduced photobleaching, minimized user bias, reproducibility, and high throughput.\nComment 6: Is “Thousands to millions of droplets within minutes” true for all the instruments used\nin this study, including imaging FCM; clarify. FCM analysis lacks presentation of gating strategies;\nthese should be included. For transparency, raw FCS data should be deposited in an open\nrepository.\nResponse: We thank the reviewer for this important point. We have clarified in the main text that\nthe statement “thousands to millions of condensates within minutes” specifically refers to\nconventional flow cytometry, whereas imaging flow cytometry operates at lower throughput (see\np. 11). We have also included representative gating strategies for our analysis (see SI p. 26) to\nensure clarity and reproducibility. In addition, all raw FCS data have been deposited in Zenodo\nand are publicly available under DOI: 10.5281/zenodo.17279422, as indicated in the Data\nProcessing and Statistical Analyses section.\nWe thank the reviewers for their careful evaluation of this manuscript and attention to detail. We\nfeel that the suggested revisions have helped us craft an improved manuscript that will better\nserve the community.\nSincerely,\nMarcos Pires\nProfessor of Chemistry\nDepartment of Chemistry\nUniversity of Virginia\nCharlottesville, VA 22904\nEmail: mpires@virginia.edu"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors responded well to the 3 reviewers' comments. I agree that the paper is accepted.", "reviewer_2": "In the revised version of the manuscript “A High-Throughput, Flow Cytometry Approach to Measure Phase Behavior and Exchange in Biomolecular Condensates”, He and colleagues address many of my comments. Further, their answers should — together with my original comments — be sufficient for the reader to form their own opinion about the robustness of the work.", "reviewer_3": "The authors have substantively addressed prior concerns by tempering novelty claims, clarifying the complementary roles of microscopy and FCM, improving figure clarity (Fig. 2 and Fig. 6), and enhancing transparency (gating; data deposition). The study will deliver a robust, scalable platform for population‑level condensate kinetics. Accept after the following minor revision:\n1. How much “real-time” it is. can authors report the effective dead time from mixing to the first FCM acquisition (e.g., mixing, transfer, start of read). In Fig. 6c–e (pp. 47–48), the earliest point already shows >40% double‑positive events, implying that sub‑minute exchange may be under‑sampled.\n2. For multicolor/drug assays in Fig. 5e–f, specify the filters/compensation matrix (briefly is enough) so readers can assess potential color leaks and reproduce the settings.\n3. In Fig. 6e, state replicate number (n) and error‑bar definition (mean ± SD or SEM) in the caption or Methods?"}, "author_response": "RESPONSE: We thank the reviewer for their time and thoughtful consideration of\nour revisions.\nRESPONSE: We thank the reviewer for their time and thoughtful consideration of\nour revisions.\nRESPONSE: We thank the reviewer for their time and thoughtful consideration of\nour revisions, and we are sorry for making the omission.\nFor effective dead time (Fig. 6), a line was added on the figure caption for Fig. 6c\nto read, \" Effective dead time from mixing to the first data acquisition is about 10-\n15 seconds; this dead time is not included in the times shown.\" There is also a\nchange in the caption for Fig. 6e; it reads, “The 10-15 seconds effective dead\ntime is not included in the times shown. “A similar line was added in the figure\ncaption for Fig. S11 to read, “Effective dead time from mixing to the first data\nacquisition is about 10-15 seconds; this dead time is not included in the times\nshown”.\nFurther, in the Main Text on page 21, text was added to highlight the dead time\nas follows, “We note that the earliest timepoint reflects an operational dead time\nof approximately 10–15 seconds between addition of NPM1-Halo-Coumarin,\nrapid mixing, tube loading, and the initiation of flow-cytometry acquisition. All\nkinetic assays in this study use identical rapid-handling procedures, ensuring\ninternal consistency across all experiments. Because this mixing dead time\nalready captures a portion of the rapid NPM1 exchange process, the earliest\nmeasurement represents droplets that have begun to equilibrate.”\n2. For multicolor/drug assays in Fig. 5e–f, specify the filters/compensation matrix\n(briefly is enough) so readers can assess potential color leaks and reproduce the\nsettings.\nRESPONSE: We thank the reviewer for their time and thoughtful consideration of\nour revisions, and we are sorry for making the omission.\nFor confocal acquisition of drug colocalization images (Fig. 5e), samples were\nimaged on a Zeiss LSM 980 using 488-nm excitation with a 505–550-nm\nemission window for FLTX1 (green channel), and 561-nm excitation with a 590–\n630-nm emission window for mitoxantrone (red channel). These non-overlapping\nspectral windows minimized bleed-through, and single-color controls confirmed\nnegligible cross-excitation and emission crosstalk.\nFor flow-cytometry acquisition of drug partitioning assays (Fig. 5f), FLTX1\nfluorescence (green channel) was excited with the 488-nm laser and collected in\nthe BL2 detector (525/50-nm bandpass), while mitoxantrone fluorescence (red\nchannel) was excited with the 561-nm laser and collected in the YL2 detector\n(610/20-nm bandpass). Single-color controls were used to generate a\ncompensation matrix, enabling correction of minor spectral spillover between the\ngreen and red channels.\n3. In Fig. 6e, state replicate number (n) and error-bar definition (mean ± SD or\nSEM) in the caption or Methods?\nRESPONSE: We thank the reviewers for their time and thoughtful consideration\nof our revisions, and we are sorry for making the omission.\nIn thew caption for Fig. 6e, a line has been added to indicate the replicates and\nerror bars as follows, “Each kinetic trace represents a single continuous time-\ncourse experiment (n = 1) for the respective aging condition. Each time point\nreflects 10,000 condensate events collected by flow cytometry; no error bars are\nshown.”"}]

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