Intrinsically disordered proteins rich in cationic amino acid groups can undergo Liquid-Liquid Phase Separation (LLPS) in the presence of charge-balancing anionic counterparts. Arginine and Lysine are the two most prevalent cationic amino acids in proteins that undergo LLPS, with arginine-rich proteins observed to undergo LLPS more readily than lysine-rich proteins, a feature commonly attributed to arginine’s ability to form stronger cation-π interactions with aromatic groups. Here, we show that arginine’s ability to promote LLPS is independent of the presence of aromatic partners, and that arginine-rich peptides, but not lysine-rich peptides, display re-entrant phase behavior at high salt concentrations. We further demonstrate that the hydrophobicity of arginine is the determining factor giving rise to the reentrant phase behavior and tunable viscoelastic properties of the dense LLPS phase. Controlling arginine-induced reentrant LLPS behavior using temperature and salt concentration opens avenues for the bioengineering of stress-triggered biological phenomena and drug delivery systems. Arginine-rich proteins display reentrant LLPS behavior with changes in salt and temperature due to the hydrophobicity of arginine. Controlling the reentrant LLPS behavior opens avenues for the bioengineering and drug delivery fields. This phenomenon involves the phase separation of macromolecules into two immiscible liquid phases — a dense phase rich in macromolecules (the coacervate phase) and a dilute phase in which the macromolecules are depleted (the supernatant phase)  have the ability to form LLPS, but IDPs account for a large proportion of biological LLPS7. IDPs are commonly enriched in charged, aromatic, and polar amino acids, while their structural flexibility makes IDPs more likely to form multivalent interactions9. The amino acid composition of IDPs tunes the physical property of the dense coacervate phase, such as diffusivity, interfacial tension, density, viscoelasticity, and exchange dynamics in the resulting dense coacervate phase11. These properties of LLPS are key to the bioengineering application of LLPS, including as delivery agents for protein-based drugs and mRNA vaccines14. Thus, understanding the role of amino acid composition in tuning the pertinent physical properties of the coacervate phase is important.Liquid−liquid phase separation (LLPS) is an important organizing principle for biomolecular condensates that compartmentalize specific metabolites without a biological membranese) Fig. . The sim19. Considering that the release of mRNA-based cargo is an important application outlook of LLPS, it is noteworthy that arginine is abundant in RNA-binding proteins (RBPs) that interact with proteins/RNAs to form phase-separated ribonucleoproteins (RNPs)22. Interestingly, arginine is a much more effective driver than lysine, despite both carrying the same single positive charge per residue19. For instance, in FUS, LAF-1, and Ddx4, substitutions of arginine-to-lysine has significantly reduced the phase-separation propensity, and vice versa, an increase in arginine content has been shown to lead to more favorable LLPS19. These observations have been rationalized to be due to arginine forming stronger cation−π interactions with proteins or RNAs containing aromatic rings than lysine24. Another recent study highlighted the role of arginine in stabilizing the coacervate phase by showing that poly-arginine peptides form condensates with much greater viscosity compared to poly-lysine (poly-Lys) peptides when mixed with polyU25.Among the different amino acids, arginine is considered an important driver for LLPS2-hybridized quasi-aromatic26. It acts as a hydrogen bond donor, interacting strongly with water only in the molecular plane of the guanidinium group, whereas the top and bottom surfaces of the guanidinium group tend to dehydrate readily , and contains methionine, serine, valine, proline, and glycine  in the primary sequence  as lysine-rich, positively charged, protein models and hyaluronic acid as the negatively charged biopolymer to induce LLPS by complex coacervation. This choice is further motivated by protamine and hyaluronic acids (HA) being both injectable biopolymers, already employed for biomedical applications, while protamine coacervation is considered a promising delivery mechanism for mRNA vaccinesnce Fig. . Using pOur studies reveal dramatic differences in LLPS by complex coacervation as a function of salt concentration and temperature, with re-entrant phase behavior observed for the arginine-rich, but not for the lysine-rich, peptide. Importantly, we demonstrate that the hydrophobicity of the arginine tunes the LLPS propensities at low and at high salt concentrations, as well as give rise to the emergent materials properties reflected in viscoelastic behavior of the dense LLPS phase. Our results can serve to guide the design of a phase-separating platform for drug and gene delivery systems controlled under conditions  that give rise to reentrant LLPS.Arginine-rich protamine, lysine-rich ε-poly-lysine (εPL) and hyaluronic acid (HA) were chosen as model constituents for investigating the effect of the guanidinium and ammonium side chains on the liquid-liquid phase separation (LLPS) of protein constituents. Protamine and εPL have similar molecular weights (~5 kDa). The LLPS of both HA-Protamine and HA-εPL coacervates is expected to be induced by complex coacervation mediated by multivalent electrostatic attraction of the oppositely charged side chains of the model polyelectrolytes. We examined the response of the LLPS of these model systems to ionic strength spanning 0 to 4 M in order to examine whether reentrant behavior is seen at high salt concentration, at which condition hydrophobic interactions should dominate. Under each condition examined, the yields of HA-Protamine and HA-εPL coacervates were determined by measuring the optical density of the suspension at 600 nm and the volume of the macroscopically separated dense phase Fig. . The yie41. When 10 wt% of 1,6-hexanediol was added, only HA-Protamine coacervate formed at high salt concentration was entirely dissolved  induce phase separation of protamine at lower salt concentration (2.75 M) by strengthening hydrophobic interactions. In contrast, salts later in the series known to induce “salting in” effects  drive phase separation of protamine at higher salt concentration , as these salts tend to be less effective in strengthening hydrophobic interactions  and lower critical solution temperature (LCST) behavior, whereas the HA-Protamine coacervation at high salt concentration (here tested at [NaCl] = 3.5 M) exhibits only UCST behavior Fig. . The iniion Fig. . The obsion Fig. . Commonl45 to characterize the residues hydrophobicity in all atom representation in explicit solvent, using the amber03ws force field47. We compute the dehydration free energy of Arg and Lys in protamine and poly-Lys by employing an unfavorable harmonic biasing potential to sample over the range of water densities (by expelling or dragging water) in the dynamical hydration volume of the target residue. The dynamical hydration volume of each residue under study (Lys and Arg) is defined as the union of pinned spherical sub-volumes with radius Rv to the center of every heavy atom  of the amino acid, whose position can evolve during MD simulation; we include only the first hydration shell waters in the free energy calculation by choosing Rv = 0.55 nm. The unbiased free energy from INDUS is then obtained by using the Unbinned Weighted Histogram Analysis Method (UWHAM)49. Representative MD snapshots of poly-Lys and protamine are shown in Fig. 2, Lys16 and Lys33. Similarly in the protamine chain, the dehydration free energy of Arg is obtained in the hydration volumes of Arg4, Arg16 and Arg32, where the subscript indicates the residue index in sequence  simulations31.We experimentally determined the net biopolymer concentration by quantitative amino acid analysis in the dense phase of HA-Protamine formed under low salt and high salt (4 M NaCl) conditions, which is a proxy for inducing dehydration conditions Fig. . The res−5 N/m) 1.4 times higher than of low salt HA-εPL (2.34 × 10−5 N/m). The viscosity (28.5 Pa·s) and interfacial tension (4.20 × 10−3 N/m) of the high salt HA-Protamine coacervate phase are two orders of magnitude greater compared to that of the low salt HA-Protamine and the HA-εPL coacervate phase. However, high salt HA-Protamine coacervates are still considered to have liquid-like properties as shown in Supplementary Fig. To investigate the effect of hydrophobic interactions on the physical properties of the coacervate phase, we compared the polyelectrolyte concentration, viscosity, and interfacial tension of low salt HA-εPL, low salt HA-Protamine coacervate phase without additional NaCl, and high salt HA-Protamine coacervate phase formed at high salt ([NaCl] = 4 M) condition Table . The low−1. Thus, low salt HA-Protamine coacervates predominantly exhibit viscous, liquid behavior. In contrast, high salt HA-Protamine coacervates exhibit viscoelastic fluid behavior with a crossover frequency  at lower frequencies compared to low salt HA-Protamine coacervates. These observations show that inter- and intra- molecular hydrophobic interactions between protamine in the HA-Protamine coacervate phase not only render the coacervate phase denser, but also impart viscoelastic property to the coacervate phase. Changes in the viscoelastic properties of the coacervate are expected to play important roles in regulating the mechanical response (fragile or resilient) of the dense coacervate phase against stress induced by the chromatin constituents, which in turn will affect many cellular processes, such as, signaling and gene regulation.The next question we ask is whether the HA-protamine coacervate phase display viscoelastic, i.e. materials, properties. To do so, we determined the storage modulus (G′) and loss modulus (G″) of low salt and high salt HA-Protamine coacervates Fig. . The lostcorr) was derived from cw-EPR lineshape analysis assuming isotropic rotational motion of the spin label. On SL-εPL in the HA-εPL dense phase tcorr was found to be 454 ps, representing dynamic liquid state behavior  for SL-protamine despite the higher polyelectrolyte concentration in the HA-Protamine, compared to the HA-εPL, coacervate phase. The absolute value for tcorr of SL-protamine may be lower than for SL-εPL because the spin label of SL-protamine is exclusively tethered to the protamine chain ends that are more dynamic than the middle of a biopolymer chain. Overall, SL-protamine and SL-εPL are highly mobile, representing behavior of a liquid coacervate phase formed under low salt conditions. In contrast, the rotational motion of SL-protamine in high salt HA-Protamine coacervates is significantly hindered. This is reflected in the cw-EPR lineshape whose major population  presents a spectral feature in the “rigid limit” for the spin labels of SL-protamine, despite their positions at protamine chain ends  of the entire droplet. The results show that in both HA-Protamine and HA-εPL coacervates, protamine and εPL molecules can be exchanged with the surrounding protamine and εPL molecules in the dilute phase Fig. , bottom.ior Fig.  top. Con ps Fig.  middle. nds Fig.  bottom. nds Fig. .Fig. 6Ch50. To determine the conformational properties of poly-Lys and protamine chains and gain molecular insight into their collective behavior, we performed atomistic molecular dynamics (MD) simulations in explicit solvent. To do so, we study the conformational landscape of poly-Lys and protamine by monitoring the radius of gyration (Rg) and the end-to-end distance (dee) of the chains in a large ensemble of structures obtained from 600 ns long MD simulations in the NPT ensemble at T = 300 K and P = 1 bar. Each chain was simulated without salt and in the presence of 3.5 M [NaCl] . As shown in Fig. Rg and dee indicating that the poly-Lys is mainly in extended conformational states with rare intramolecular contacts between the nonadjacent residues along the sequence . At high salt concentration, the electrostatic interactions are screened, and the lower net charge of protamine than poly-Lys suggests greater conformational fluctuations of protamine that can lead to effective association with other chains. This conformational property of protamine is similar to that of poly-Arg  are mostly similar to that of the poly-Lys chain  that measures the molecular diffusivity of water by tracking displacement of water over micrometer distances, and Overhauser DNP (ODNP) that captures instantaneous movement of water with sub-nanosecond range correlation times across nanometer distances, again yielding molecular diffusivity of water54. If ODNP is performed using spin labels tethered to the protamine chain ends or statistically labeled at the ɛPL side chains  of deionized water (DW), while the reduced value of the diffusion coefficient of water in the dense phase is consistent with the higher viscosity of the dense phase. Overall, the interstitial water molecules diffuse relatively unhindered compared to the diffusion of the polymer constituents in the highly concentrated dense phase in both systems. Interestingly, the self-diffusion coefficient of interstitial water in the dense phase of HA-Protamine (1.34 × 10−9 m2/s) is similar to that in the dense phase of HA-εPL (1.52 × 10−9 m2/s), despite the 1.7 times higher polyelectrolyte concentration and an order of magnitude higher viscosity in the dense HA-protamine coacervate, than the HA-εPL coacervate, phase.First, phase-separated dilute and dense coacervate phase of the HA-Protamine and HA-εPL coacervate system were prepared by centrifugation, and the self-diffusion coefficients of water in these phases determined by PFG-NMR. In the low salt HA-εPL coacervate system, the interstitial water diffusivity is found to be 2.35 × 10ase Fig. , consistely Fig. . In bothDcoupling factor) that includes the effect of bound and freely exchanging water with nanosecond scale correlation times, and the diffusion coefficient of freely exchanging water (Dksigma) with picosecond scale correlation times, as discussed in previous studies55. The results show that Dcoupling factor within the dense phase of low salt HA-εPL (8.69 × 10−10 m2/s) is slightly faster than that in the dilute phase (8.27 × 10−10 m2/s), as reported in Ref. coupling factor in the dense phase of the low salt HA-Protamine (7.09 × 10−10 m2/s) is by a factor of ~1.3 slower than in the dilute phase (9.41 × 10−10 m2/s)  and due to favorable interaction between the biopolymer and water that may be stronger than water-water hydrogen bond interactions. Still, the observed dynamics of average hydration water according to ODNP is consistent with that measured by PFG-NMR, in that the interstitial water moves freely in both low salt HA-εPL and low salt HA-Protamine coacervation. In contrast, the mobility of average hydration water is an order of magnitude slower in the dense phase of high salt HA-Protamine, with Dcoupling factor ~ 0.52 × 10−10 m2/s  Fig.  middle. 2/s Fig.  right. IDksigma) with picosecond range correlation times. Under low salt conditions, the diffusivity of water in the dense phase of HA-εPL (1.68 × 10−10 m2/s) and HA-Protamine (1.54 × 10−10 m2/s) systems is faster than in the dilute phase of HA-εPL (1.02 × 10−10 m2/s) and HA-Protamine (1.24 × 10−10 m2/s) systems, respectively  compared to the low salt coacervate phase, suggesting that even surface water is highly immobilized in the high salt HA-Protamine system , suggesting that electrostatic interactions alone do not fully account for LLPS propensity of cationic polymers. We investigated two model cationic-rich systems that could undergo LLPS: poly-lysine and protamine, the latter being an arginine-rich, clinically relevant, highly biocompatible and injectable biomolecules used in cardiac surgery to neutralize the anticoagulant effects of heparin36. We find that both protamine and poly-lysine undergo LLPS at low salt concentration, but that only protamine exhibits salt and temperature-dependent reentrant phase behavior. Furthermore, we show that the viscoelastic physical properties of the protamine and poly-lysine coacervate phases differ. Our combined experimental and computational studies reveal that the re-entrant LLPS behavior of protamine at high salt concentrations and the more viscous nature of the protamine-rich coacervate phase compared to that of poly-lysine, is due to the greater hydrophobicity of arginine over lysine, in particular, its ability to form π−π stacking interactions. The use of very high salt concentration in this study well exceeds physiological ionic strength, but is serving as a tool to study hydrophobic properties of arginine. High salt conditions mimic dehydrating conditions frequently found in the crowded cellular environment. Similar dehydration condition as modeled in this study can be achieved by using molecular crowders or by using high concentrations of multi-charged metabolites found under cellular conditions57. Given the abundance of multi-charged metabolites and the fact that their levels are altered by cellular conditions, such as stress and cell division, the high salt concentration and the reentrant phase behavior in this study are expected to account for cellular protein phase separation under cellular condition with enhanced hydrophobic interactions.Arginine-rich peptides or proteins are an important class of biomolecules that have applications ranging from carriers for genes, drug, and even adhesive molecules, due to their cationic nature, nucleic acid binding ability, and cell-penetrating properties, as well as biocompatibility. These proteins have the ability to undergo LLPS, and the resulting protein droplets have controllable cargo recruitment and release properties in response to external triggers, making them one of the most promising development options for delivery platformsThe ability of protamine to undergo LLPS with HA at low salt concentrations, dissolves with increasing salt concentration and then shows reentrant LLPS formation at high salt concentrations, in a salt type and temperature-dependent fashion showcases the potential application of arginine-rich systems for salt triggered cargo (a nucleic acid or drug) uptake and release Fig. . Additio59. At physiological salt concentrations in the mussel foot, mfp-3 coacervates would be fluidic to facilitate secretion and delivery to the substrate. Then, the high ionic strength of the seawater would induce the hardening of mfp-3 coacervates, which concentrates the adhesive proteins, imposes viscoelasticity and promotes a more robust adhesion at the plaque-substrate interface. Our results suggest that arginine might be the most potent mechanisms for regulating the physical properties of mfp-3 coacervates in this series of processes. Specifically, we demonstrated that low salt HA-Protamine coacervates exhibit viscous and highly dynamic liquid-like character with low interfacial tension, while high salt HA-Protamine coacervates driven by hydrophobic interactions exhibit viscoelastic and immobile solid-like character with high interfacial tension. Importantly, the prominent role of arginine in giving rise to high viscosity and viscoelastic properties of the dense coacervate phase, as well as salt-dependent reentrant behavior and temperature induced phase transitions through hydrophobic interactions could all be recapitulated using purposefully simplified model systems consisting of protamine and HA. It should be possible to use other arginine-rich biopolymers with additional bioactive properties for the design and improvement of biological adhesion, as well as for mimicking the biological wet-adhesion for developing surgical glues, or antifouling surface.Understanding the role of arginine in tuning the physical properties of the dense LLPS phase is also highly relevant for developing underwater adhesives. Coacervates composed of mfp-3 and mfp-5, key adhesive primer proteins for the mussel underwater adhesion, are rich in arginine, and not in lysine, unlike other mussel foot proteins 22. Under physiological conditions, SGs have been understood to be involved in storing untranslated mRNAs, resulting in translational arrest60. Based on previous observations, SG-related RBPs with disordered arginine-rich motifs undergo phase separation in vitro and in vivo62. However, SGs induced and co-localized by arginine-rich peptides display reduced dynamic properties63 and sometimes, gelation or aggregation of SGs is known to be associated with neurodegenerative diseases60. In agreement with this, our results show that the high salt HA-Protamine coacervates driven by hydrophobic interactions have solid-like properties of elasticity and high immobility of protamine and water, along with an increase of protamine concentration within the coacervate phase. Additionally, at physiological low salt concentrations, arginine-rich protein coacervates are much denser and exhibit viscoelastic properties, but still contain dynamic protein chains and water comparable to lysine-rich protein coacervates. This appears to indicate that arginine-rich domains/proteins of SGs might function to retain and protect mRNA by generating tight binding and viscoelastic environment for mRNA. We hence speculate that the activity and function of biomolecular constituents can be maintained in engineered arginine-rich membraneless organelles or biomolecular condensates.Last, but not least, arginine is also prevalent in RNA-binding proteins (RBPs), which are one of the main components of phase-separated stress granule (SGs) , Poly(ethylene glycol) (10 kDa), Sigmacote (SL2), Particles with a diameter of 2 μm (L3030), Fluorescein-5(6)-isothiocyanate (F3651) and 4-Carboxy-TEMPO (382000) were purchased from Sigma-Aldrich . Hyaluronic acid (5 kDa) was purchased from SK-bioland  5(6)-SFX (6-(Fluorescein-5-(and-6)-Carboxamido) Hexanoic Acid, Succinimidyl Ester) (F2181) was purchased from Thermo Fisher . 2% PFPE-PEG-PFPE tri-block copolymer surfactant (E2K0660) from RAN Biotechnologies, Inc.  were used.Protamine 10 mg/mL), εPL (10 mg/mL) and HA (10 mg/mL) were dissolved in 0.1 M sodium acetate buffer (pH 5.0). By varying the concentrations of NaCl, the LLPS of the HA-Protamine and HA-εPL was quantified by turbidity at room temperature after mixing for 1 min. Turbidity measurements were carried out at 600 nm using UV–Vis spectrometer , where absorbance interference by HA, protamine, and εPL was negligible. Each experiment was performed three times. The formation of droplets was confirmed with optical microscopy . If not stated otherwise, all samples for HA-Protamine and HA-εPL coacervation were prepared under charge-balanced optimal phase separation conditions of 7:3 and 8:2 ratios, respectively  spectrometer  at 600 nm. The temperature was changed from 4 °C to 70 °C at a rate of 2 °C/min. The experiment was conducted with heating and cooling cycles.64. Specifically, protamine and hyaluronic acid were mixed at a ratio of 3:7 and then centrifuged at 920 × g for 1 h to obtain the dilute phase and sedimented droplet phase. Each phase was then evaporated, and the dried pellets dissolved in 6 N HCl with 5% water-saturated phenol, and transferred to glass vials. The glass vials with sample were purged with argon and then sealed with flame. The acid hydrolysis reaction was carried out for 24 h in a 110 °C heating block. Afterward, the samples were evaporated and sequentially washed with DW and methanol. The samples were reconstituted in SYKAM sampled dilution buffer. To generate a standard curve using known concentrations of each polyelectrolyte, the same procedures were performed with four different concentrations of protamine solution, hyaluronic solution, and blank water. The peak areas of arginine and glucosamine for protamine and hyaluronic acid minus the mean corresponding peak area of the hydrolyzed water blank were calculated. Linear regression was applied to this adjusted peak area, correlated with the corresponding concentration of the four standard concentrations injected. Slopes were based on four-point calibration. These slopes were used to calculate the concentrations of each polyelectrolyte in the droplets and the dilute phase.To determine the protamine and hyaluronic acid concentrations in the droplets, a quantitative amino acid analysis was performed with a SYKAM System S4300 Amino Acid Analyzer , according to the previously reported methodFRAP experiments were conducted using a Leica TCS SP5 microscope . HA-protamine droplets containing 2% FITC-protamine and HA-εPL droplets containing 2% TxRed-εPL were prepared for this measurement. Samples of 512 × 512 pixels were imaged with a 25× water-immersion objective. Full droplets were bleached with a 488 nm laser at 100% power. Recovery was monitored every 0.523 s for 400 frames. Images were processed by ImageJ. All imaging was performed with the same acquisition settings for optical zoom, scan speed, gain, resolution, offset, magnification, and laser intensity.1H nuclei1.H diffusion was measured on dense phase and dilute phase at room temperature. The diffusion coefficients of interstitial water are determined using a pulse sequence of stimulated echoes with bipolar pulses. The echo attenuation Ψ was fit to the Stejskal-Tanner equation, γ [s−1G−1] is the gyromagnetic ratio of protons, g [G/cm] is the magnitude of the applied field gradient, δ = 1 ms denotes the duration of the effective gradient pulse, Δ = 20 ms is the total diffusion time, and D [m2 s−1] is the diffusion coefficient. A total of 16 echoes were acquired using a range of gradient amplitudes for each measurement and measured attenuations were fitted with single-exponential decays. The self-diffusion coefficient of pure water (DW) is in good agreement with the literature value of 2.40 × 10−9 m2/s65.Pulsed-field gradient NMR were carried out using a Bruker  ULTRASHIELD 300 SWB PLUS spectrometer and a DIFF50 diffusion probe at a proton NMR frequency of 300.15 MHz. The experiments were operated by tunning the diffusion probe to N,N-dimethylpropan-1-amine-N-hydroxysuccinimide (EDC-NHS) chemistry with a 10-fold molar excess of the 4-carboxy-TEMPO spin label. Excess label was removed with extensive dialysis (3500 Da MWCO) for 24 h. Since 4-carboxy-TEMPO was labeled on the amine group, protamine has a spin only at the N-terminal, and spins are located throughout the εPL chain. Non-labeled protamine was used to achieve spin dilution. Spin-labeled (SL)-protamine was mixed with non-labeled protamine at a ratio of 1:3 to achieve 25% spin labeling. Then, the samples were prepared by adding hyaluronic acid.Protamine and εPL were labeled by 3-(ethyliminomethylideneamino)-66 operating in MATLAB . The spectra were analyzed using the ‘chili’ function by first setting the g values and hyperfine coupling tensor values according to known literature values with minor adjustments to achieve an exact match of the positions of the three features of the nitroxide spectrum on the magnetic field axis. Next, the spectra were fit using the ‘esfit’ graphical user interface by varying Gaussian and Lorentzian line broadening parameters and the ‘logtcorr’ parameter that is the log10 of the isotropic rotational correlation time (τcorr). To first set the line broadening parameters the spectrum that was clearly indicative of the least restricted spin label motion  was fit by varying the line broadening parameters alone. Since the simple line broadening parameters do not affect the spectrum in the same fashion as changes to τcorr this results in a less than ideal fit but a good match to the intrinsic line broadening of the spectra due to experimental factors. This spectrum along with the others were then fit in series by varying only the ‘logtcorr’; with the g values, hyperfine coupling tensor values, and line broadening parameters held constant. With this, the trend in τcorr between the various samples is captured. All EasySpin operations were executed within the cw-EPR application available for free at the MathWorks File Exchange.Electron paramagnetic resonance (EPR) experiments were performed using an X-band  Bruker EMXPlus spectrometer equipped with a Bruker ER4119HS-W1 high sensitivity microwave cavity (Bruker) at room temperature. Samples of 4.0 μL was loaded into a quartz capillary of 0.6 mm ID × 0.84 mm OD , which were then sealed at both ends with Critoseal, and placed into 4 mm diameter open-ended quartz EPR tubes. EPR spectra were acquired using a microwave power of 4 mW, a modulation frequency of 100 kHz, a modulation amplitude of 1G, and a sweep width of 150G. EPR spectra were analyzed to determine the isotropic rotational correlation time of the spin label attached to the polyelectrolytes using the EasySpin software package69.For Overhauser dynamic nuclear polarization (ODNP) experiments, 3.5 μL of dilute and dense-phase spin-labeled protamine samples were loaded into quartz capillaries of 0.6 mm ID × 0.84 mm OD , and both ends of the tubes were sealed with Critoseal. The capillary tube was mounted in a home-built NMR probe consisting of a tuning/matching circuit and a handmade U-shaped NMR coil. The coil was positioned in the center of a Bruker ER4119HS-W1 EPR resonator. A Bruker EMXplus EPR spectrometer was used to tune and match the EPR resonator and collect spectra as described above, but with the sample loaded into the NMR coil to account for the shift in tuning frequency and the resulting shift in magnetic field position of the EPR resonances. For the ODNP experiments, the magnetic field was set to the field value at the center of this EPR spectrum (~0.3484 T), and high-power microwaves for DNP were supplied by a custom microwave source at the tuning frequency of the resonator (~9.78 GHz). Dry nitrogen was passed over the sample to maintain a sample temperature of ~295 K. NMR spectra were collected using a Bruker Avance III NMR console (Bruker). ODNP data were analyzed using an open-source python-based software package called DNPLab designed for processing and analyzing NMR data. In particular, data were processed and fit using the ‘hydrationGUI’ module. This module applies the analysis procedures pioneered in the Han lab that have been previously described and applied in several similar studies69. The fluorescent probe particles with a diameter of 2 μm were imaged with a confocal microscope at 561 nm excitation every 20 ms for low salt conditions and every 1 s for high salt conditions at room temperature. Mean-squared displacements (MSD) of the particles in dense phase was obtained using MATLAB software. The averaged MSD vs. lag time plot fit to MSD (τ) ~ 4Dτα, where α is the diffusive exponent, to estimate the diffusion coefficient Dprobe. The viscosity η was calculated using the Stokes-Einstein equation, Dprobe = kBT/6πηr, where kB is the Boltzmann constant, T = 295.15 K is temperature, and r = 1 μm is the probe radius.Microrheology was performed by tracking the motions of probe particles embedded in coacervate phaseL) and width (W) of a droplet during the relaxation, The interfacial tension of the HA-protamine coacervates was measured by observing coalescence events of two droplets over time. A solution containing droplets of FITC-tagged protamine-HA was added into a coverslip-sandwiched fluid chamber with a flat oil/water interface to minimize friction from the surface during droplet coalescence. Coalescence events were recorded with confocal microscopy with specific time intervals at 488 nm excitation. The decay time (τ) was determined with a ratio of the difference and sum of the length  algorithm75. The simulations were performed in the isobaric isothermal ensemble (NPT); the temperature and pressure were maintained at T = 300 K and P = 1 bar, using the stochastic velocity-rescale thermostat and Parrinello-Rahman Barostat76. In INDUS, the GROMACS package is modified to bias the coarse-grained water number, Nv, in hydration volume using the Indirect Umbrella Sampling (INDUS) method77. The number of water molecules in the hydration shell of each residue can be conformation dependent, and it can also change by the definition of the hydration volume itself. Here, we chose the size of the spherical probe volume of every heavy atom in such a way that we have at least two-three layers of water molecules in the hydration shell (the first coordination shell). We note that a careful selection of the probe volume is important; indeed, if the spherical probe volumes are very large, the resulting hydration volume would be less relevant to the surface of the target residue and the dewetting free energy can be largely affected by the errors due to the hydration water fluctuation. The Gaussian coarse-graining function used in INDUS is parameterized with a standard deviation of σ = 0.01 nm and a truncation length rc = 0.02 nm. The water dynamics is governed by the Hamiltonian: H = H0 + 1/2 κ (Nv − N*)2, where H0 is the unbiased Hamiltonian, and the second term represents the harmonic biasing potential with strength κ = 10 kBT. The biased ensemble averages 〈Nv〉 was obtained by sampling directly from biased ensembles with different N* values. All biased simulations were run for a total of 5 ns; the first 1 ns were discarded for equilibration phase. The overlapping successive windows of the Nv (the instantaneous water molecules in the hydration volume) that were obtained from performing biased simulations at different N* (i.e. “for N* in {−9..3..54}”)  = −lnPv(N). The Pv(N) is calculated using the Unbinned Weighted Histogram Analysis Method (UWHAM).The peptides were simulated using version 4.5.3 of the GROMACS molecular dynamics packageFurther information on research design is available in the Supplementary informationPeer Review FileReporting Summary


This review article has summarized the recent advancement in the measurement pHi and extracellular pH (pHe) in cancer cells, and the effect of pHi and pHe on proliferation, migration and biological functions of cancer cells. This paper has also elaborated recent treatment strategies to manipulate pHi and pHe for cancer treatment. Based on the recent progress in pHi and pHe manipulation in cancer treatment, we have proposed potential nanoparticle-based strategies to manipulate pHi and pHe to effectively treat cancer.The pH in tumour cells and the tumour microenvironment has played important roles in cancer development and treatment. It was thought that both the extracellular and intracellular pH values in tumours are acidic and lower than in normal cells. However, recent progress in the measurement of pH in tumour tissue has disclosed that the intracellular pH (pH The pH in tumour cells and the tumour microenvironment has played important roles in cancer development and treatment. As known, extracellular and intracellular pHs in tissues affect the function of the cells and play an important role in cancer development and treatment. As reported, the extracellular pH (pHe) affects the proliferation of human T cells and the expression of the interleukin-2 receptor.3Cancer is one of the most severe diseases in the world. According to statistics, in total 8.8 million people died from cancer in 2015, accounting for 17% of the total deaths.e of cancer cells is more acidic than normal cells.4–6 Generally, pHe values of the normal tissues  are in the rage of 7.2–7.5. However, pHe of tumour cells is mildly acidic in the range of 6.4–7.0. Since Warburg et al. first reported the abnormal anaerobic glycolysis in tumour cells, they measured the glucose and lactic acid in tumor veins and found more lactic acid and less glucose on the tumour tissue than on the normal tissue due to the fermentation process in the tumour side, which may affect pHe and pHi in tumour.7 Consequently, it has been assumed that pHe and pHi in cancer cells should be more acidic than those in normal cells during 1930s to 1980s.7,8It is widely accepted that the pHi and pHe in cancer cells including pH-sensitive nuclear magnetic resonance spectroscopy (MRS), positron emission tomography (PET) radiotracers, magnetic resonance imaging (MRI) and optical imaging (Optics).9 It has been found that pHi in cancer cells is actually mildly alkaline or near neutral, similar to normal cells.10,11 These new findings subvert the traditional assumption that the pHi in cancer cells is more acidic than normal cells. More extracellular acidity and more intracellular alkalinity means a smaller ratio of pHe/pHi.With the progress on sensing technologies, several techniques have been developed to measure pHe and pHi.2,12–17 These novel strategies have been developed to control the pHe/pHi ratio in cancer microenvironments and cells to induce apoptosis of cancer cells, improving the treatment efficiency.18Subsequently, researchers have investigated the mechanisms of pH controls in cancer cells and microenvironments. Numerous membrane transporters across tumour cells have been found for pH hemeostasis in cancer cells, and further been used to manipulate pHe and pHi in tumour tissues and their corresponding normal tissues. Then, we have further outlined the mechanisms of pHe/pHi maintenance in cancer cells and the developed therapeutics to manipulate the pHe/pHi in cancer tissues. In the outlook, the potentials of new strategies using state-of-art nanotechnology to manipulate the pHe/pHi in cancer tissues have been proposed for cancer treatment.In this review, we have summarized the recent progress on the studies of pH2.2.1.e and pHi in tumour have been developed including pH-sensitive electrodes (POT), chemical exchange saturation transfer MRI (CEST-MRI), MRS, PET, MRI, and Optics.4,6,19–33in vivo pH measurement.Several approaches for the measurement of pHin vivo pH measurement, there is not adequate data of the pH values measured by the same MRI or Optics method for comparable analysis. Thus, for the consistency of the comparison, the pH measured by POT or MRS were collected and compared in Section 2.2 and 2.3.Although several novel MRI and optical imaging agents (probes) have been developed and applied for 2.2.e of eight types of tumour tissues and the corresponding normal tissues has been summarized in 4,6,34–39According to the literature reports, pHe of cancer cells is 0.3–0.7 pH unit lower than that of corresponding normal cells. For example, malignant melanoma tissues have an average pHe of 6.96 while the average pHe in normal skin cells is 7.39,4 which is 0.43 difference. The average pHe in vulvar tumours is 7.26, 0.7 pH unit less than in normal vulvar tissues (with an average pHe of 7.96).6 Uterine tumour tissues also have a lower average pHe (6.92) than normal uterus, whose average pHe is 7.64.6 Although the average pHe of two kinds of brain tumours is slightly different, they are both more acidic than normal brain tissues.38 Similar results have also been observed in other tissues, such as lung,34 breast,39 and skeletal muscle.35–37 Thus, it is very clear that most cancer cells usually have a more acidic pHe than their corresponding normal cells, and the differences vary from 0.3–0.7.As shown in et al. proposed that tumour cells used glycolysis rather than oxidative phosphorylation to acquire energy, even in the presence of oxygen.7 Excess anaerobic glycolysis has been considered as the major reason for the extracellular acidity of tumour tissues.10,40 For most animal cells, there are two different pathways for glucose metabolism, i.e. aerobic and anaerobic glycolysis. The detailed processes of glucose metabolism in the cells have been briefly outlined in 2. Subsequently, the produced acetyl-CoA undergoes the citric acid cycle, finally degrading into CO2 and producing 30 ATP molecules. In the anaerobic process, two pyruvate molecules transfer into two lactate molecules with the assistance of lactate dehydrogenase, but this transfer only produce 2 ATP. The overall reactions of these two ways are briefly expressed as follows:Warburg via the aerobic pathway. However, in the tumour cells, the glucose is mostly metabolized through the anaerobic pathway, which produces a large amount of lactate and releases limited energy due to a high level of pyruvate and hypoxia in the tumour environment. During the process, the tumour growth requires a large amount of energy compared to the normal tissue, which produces more CO2 and lactic ions in tumour. The produced CO2 was excreted extracellularly, resulting in the acidic condition in the tumour microenvironment, i.e. 0.3–0.7 pH units lower than the average pHe of normal tissues.In the normal cells, most glucose is fully metabolized to produce carbon dioxide, water and the energy 2.3.i of cancer cells is not acidic, not as postulated previously. Since the 1980's, more research outcomes have demonstrated that pHi of cancer cells is around neutral and even mildly alkaline.10,11i of six kinds of tumour tissues and their corresponding normal tissues collected from MRS method.11,41–45Interestingly, pHi of these tumour cells is slightly higher than that in their corresponding normal cells, although the difference is less than 0.1 pH unit and not significant. For example, the average pHi of brain tumours is 7.31, slightly higher than normal brain cells (7.24).11,42,46 Redmond et al. reported that the intracellular environment of osteosarcoma cells is also mildly more alkaline than in normal cells.43 Furthermore, this weak alkalinity of the intracellular environment in tumour cells has also been discovered in many other types of tumours, such as hepatoblastoma11 and squamous cell carcinoma.44,45 These evidences thus clearly indicate that pHi of tumour cells is near neutron or even more alkaline. Thus, the discrepancy of pHe and pHi in tumour cells is much larger than in normal tissues.Very surprisingly, the average pH2.4.i is achieved by transporting respiratory end-products (such as CO2 and lactate) across the cell membrane. When the extracellular concentration of acidic respiratory end-products is lower than intracellular, the excess CO2 can passively across the cell membrane by diffusion.47 However, in most cases, the CO2 and lactate generated from glucose metabolise is accumulated in extracellular tumour site due to low blood flow rate, resulting in development of acidic microenvironments in tumour.48,49 In this situation, the release of CO2 and lactate in microenvironments mainly relies on numerous special membrane proteins, such as carbonic anhydrase enzymes . More relevant pH regulators are listed in e and pHi is based on passive diffusion and active membrane transporters. +/H+ exchanger 1 (SLC9A1).50–59For most cells, the maintenance of neutral  pHi and acidic pHe.60–65 Among them, the mechanism for the import of weak bases (e.g. bicarbonate) and the extrusion of weak acids  with the assistance of proteins in tumour cell membrane has been clearly demonstrated.66 Apart from this, the intracellular protons have been pumped out of tumour cells in three different ways, including direct discharge from the cells, exchange with other extracellular cations (e.g. Na+), and extrusion by the vacuolar ATPase.56,67In the last two decades, several complicated mechanisms have been revealed about how cancer cells maintain the alkaline pH3.e and pHi in tumour cells is much larger than in normal cells. The maintenance of pHe and pHi in the tumour mainly relies on some specific proton pumps and intracellular buffer systems.10,68,69 For instance, the balance of HCO3−/CO32− buffer system in tumour is administrated by carbonic anhydrase enzymes CA2, CA9 and CA12.12,70,71 Besides, the Na+/H+ buffer system is manipulated by Na+/H+ exchangers, such as SLC9A1.72 The regulation of pHe and pHi depends on the synergic effect of all of these pumps and buffer systems.As discussed above, the difference of pHe/pHi ratio may severely affect many biological and chemical processes in the cells, and eventually result in the proliferation and aggressiveness of cancer cells.60 For example, the incubation of melanoma in the acidic environment can significantly enhance its metastasis, aggressiveness and migratory activity in vitro.73 Martinez-Zaguilan reported that C8161 and A375P cells were cultured in acidic medium (pH 6.8) for 3 weeks and then transferred to the membrane invasion culture system (MICS) chambers.73 They found that C8161 cells and A375P cells treated in acidic medium have significantly enhanced migration and invasion, as shown in et al. also reported that acidic-treated C8161 cells cultured in normal medium (pH 7.4) showed higher aggressiveness than those cultured in acidic environment (low pH group) and control (native group), as shown in 74 The C8161 cells cultured in lower pH medium (6.7) has shown the inhibition of the cell invasion, indicating less aggressiveness. These results have demonstrated that the regulation of pHe and pHi ratio in the tumour is highly important for metastasis, aggressiveness and migratory activity. Fine control of pHe and pHi in tumour may improve the cancer treatment.It is known that even the little change of pHe and pHi may also disorder the function of some proteins (such as tenascin and fibronectin), particularly in cancer cells.75,76 For example, mild change of environmental pH by 0.7 pH unit dramatically affected the RNA alternative slicing. The major expression of tenascin-C (TN-C) isoforms was 8 kb TN mRNA in human skin fibroblasts at pH 7.4, while 6 kb TN mRNA isoform was the majority of TN-C expression at pH 6.7 . It can be achieved by oral administration of alkaline agents or even by simple adjustment of diet habit.The drugs for direct manipulation are mainly small molecular substances (such as bicarbonates). This approach is to directly increase pH77 In practice, it seems difficult to maintain the mildly alkaline microenvironment near tumour tissues via oral administration, as a high dose and continuous intake of the alkaline substrate is required. Based on the breast cancer study, White et al. investigated the exact daily dose of sodium bicarbonate needed for breast cancer treatment.78 The calculated daily dose for a normal human (with 70 kg weight) would be 31.75 g sodium carbonate or 32.5 g trisodium citrate.79 Another example is the Tris–base buffer to inhibit tumour progression and metastasis.80 The size of the pancreatic tumour in the mouse model was significantly decreased after 200 mM of Tris–buffer treatment. Based on their data, the daily dose for the mice can be calculated as 18.2 g of Tris–base buffer per kg, equivalents to 31 g of Tris–base intake per day for an adult (70 kg). Although it is possible for a cancer patient to intake more than 30 g alkaline agents (such as sodium carbonate or trisodium citrate) with daily drinking water, it would be more efficient to deliver alkaline agent to the tumour tissues rather than to the whole body. A recent non-randomized controlled study investigated the efficacy of local infusion of alkaline agent.81 Researchers found that there was a 6.4-fold difference of geometric mean of viable tumour residues (VTR) when the hepatocellular carcinoma patients were treated with transarterial chemoembolization (TACE) accompanied with or without locally infusing bicarbonate (LIB) into tumour , the risk of suffering from pancreatic cancer decreased by 18% for each 100 mg increase of magnesium intake per day by men on the continuous scale.86 These results may provide a diet-based way to manipulate the pH environment in vivo and assist cancer treatment.The adjusted diet could be low in protein but high in potassium and/or magnesium.4.2.e/pHi ratio is to inhibit the functional proton pumps. It is well known that the maintenance of high pHe/pHi ratio in tumour tissues relies on many proton regulators (pumps) on the cell membrane. Most of these proton pumps on the tumour cell membrane have a few specific isoforms that do not exist on the normal cell surface. Thus these isoforms may provide some specific target sites for cancer therapy. Once these functional proton pumps are inhibited, the pH balancing system of tumour cells may be disordered and the pHe/pHi ratio may increase. The abnormal proton transportation and change of the pHe/pHi ratio may affect the behaviour of tumour cells. Recent research reports have demonstrated that the inhibition of proton regulators have suppressed the proliferation and promoted the programmed cell death in some tumour cell lines.87–92 For example, treatment with proton pump inhibitors led to the induction of apoptosis in many types of gastric cancer cells, which involves in the regulation of tumour pH.89 Besides, the inhibition of proton extrusion by Na+/H+ exchanger inhibitors72 or V-ATPase inhibitors93 may make cancer cells susceptible or vulnerable. Now a few proton pump inhibitor drugs have been used in the clinical stage. 90,94–102 As seen in The second alternative strategy to administrate the pHi may increase hyperthermia efficacy (over 42 °C) and the programming cell death response to TNF (tumour necrosis factor) induced by apoptosis ligand, also known as TRAIL.96,103–105 For example, both bafilomycine A1 (an inhibitor of V-ATPase) and EIPA (an inhibitor of the Na+/H+ exchanger) increased the thermo-sensitivity of the AsPC-1 tumours (grown in nude mice) by individually mildly decreasing pHi, and the thermo-sensitivity was markedly enhanced by the sharp decrease in pHi, resulting from the synergetic effect of the combination of these two therapies.96Interestingly, decreasing pH3− and cellular exhaust of CO2 and Cl−, which both lead to pHe decrease and pHi increase. Proton pumps (or more exactly Na+/H+ exchangers) directly exchange intracellular H+ with extracellular Na+.e/pHi in tumour cells for potential and effective cancer therapy.To conclude, more targeting sites and relevant inhibitors need to be explored in order to more efficiently manipulate pH4.3.i.87–92 One way to achieve the reduction of pHi in tumour cells is to promote cancer glycolysis to the utmost extent by maximizing the glucose supplement. The extremely high rate of glycolysis may break the capacity of proton pumps in tumour cells, which means that tumour cells cannot timely transport acidic metabolites  outside and hence decreases pHi. For example, a very high glycolysis rate was observed in human melanoma cells (cultured in the medium containing high amount of glucose) when DNP, an uncoupling agent, was added.106Several research reports have showed that the apoptosis of tumour cells can be boosted by the adequately large decrease of their pHi, finally leading to cell death.102 This may result from several different mechanisms, one of which is the reduction of glycolysis metabolism.107 For example, the enzymatic functions of hexokinase, one of the vital enzymes for the maintenance of the high level of glycolysis metabolism in tumours cells, was strongly inhibited (activity decreased from 82 ± 3.2% to 31.2 ± 5.7) with the sharp decrease (from 8 to 6 respectively) of pHi in SNB-19 glioma cells.108The programmed cell death can also be activated by the sharp reduction of pHmeta-Iodobenzylguanidine is one of the inhibitors of mitochondrial complex 1, acting as proton extrusion inhibitors (or hyperglycemia) and then decreasing pHi in cancer cells.100,109–111 However, this drug is normally used as a radioiodine therapy agent, and the dose used for radioiodine therapy is not high enough to perform a strong inhibition on proton transportation. Dinitrophenol (DNP), a new type of chemotherapeutic drugs, has also shown a remarkable enhancement in glycolysis with the increase of blood pressure at a low dose. It has been reported that mM-level DNP can inhibit the proliferation of cancer cells and lead to apoptosis in the human pulmonary adenocarcinoma Calu-6 cell line.112The tumour glycolysis can be promoted by inhibiting the production of mitochondrial ATP, which requires some specific inhibitors. meta-iodobenzylguanidine and DNP) that have shown their ability to decrease pHi by boosting the glycolysis rate in tumour cells, the hyperglycemia-reliable mechanism restricts the feasibility of this cancer therapy strategy.Overall, even though there are some drugs (such as 5.e and pHi in tumour cells have been summarized and the ways to manipulate cellular pH in cancer cells have been discussed. It is clear that tumour cells have a more acidic pHe (0.3–0.7 lower) than normal cells, and pHi in tumour cells is neutral or even more alkaline than that in normal cells. The abnormally high ratio of pHe/pHi in tumour cells is due to the high rate of glycolysis in tumour cells, which produces numerous acidic products (such as H2CO3 and CO2). The maintenance of pHe/pHi relies on several special proton pumps on tumour cell membranes, such as SLC9A1 and V-ATPase. Then the mechanisms of these proton pumps are discussed and two potential pH manipulating strategies are presented, including direct manipulation by delivering small molecule drugs and indirect manipulation sing proton pump inhibitors.In this review, pHe/pHi ratio in tumour. A future potential method is to combine 2 or 3 inhibitors so that pHe/pHi can be well controlled, which may significantly enhance the efficacy of the cancer treatment.It has been demonstrated that treatment of cancers  is very possible by manipulating pHe/pHi ratio for cancer treatment is to use functional nanoparticle delivery systems to efficiently transport the known inhibitors. Compared to small molecular inhibitors, nanoparticles could have more advantages. For example, nanoparticles can be accumulated around tumour tissues through enhanced permeability and retention effect (EPR effect).113 Of course, inhibitor-loaded nanoparticles can be further functionalized with target ligands, which may enhance the accumulation in the tumour tissues and manipulate the pHe/pHi ratio.The other future approach to manipulating the pHe. Moreover, some alkaline nanoparticles can be modified as a carrier for delivering anticancer drugs to more efficiently treat cancers.114,115Another potential way to direct pH manipulation can be achieved by target delivery of alkaline nanoparticles to the tumour tissues by virtue of EPR effect. Thus, accumulated alkaline nanoparticles neutralize the extracellular acids and efficiently increase pHThere are no conflicts to declare.


This research note introduces the EPINetz Twitter Politicians Dataset, a comprehensive dataset of 2449 Twitter accounts of German parliamentarians, minsters, state secretaries, parties, and ministries on a state, federal, and European Union level for the year 2021. This hand-curated dataset not only provides up-to-date information on elected officials, but it also includes additional variables such as their party affiliation, age, and gender. Furthermore, it provides linkages to additional data sources by providing the accounts’ Wikidata and Abgeordnetenwatch (Parliamentwatch) IDs. While it does not provide actual tweet data, the dataset will be a valuable resource for researchers by providing easy access to elected German politicians. We demonstrate some of the dataset’s uses with an analysis of the 2021 German Federal Elections. The full dataset can be accessed via 10.7802/2415.The online version of this article (10.1007/s11615-022-00405-7) contains supplementary material, which is available to authorized users. This research note presents the EPINetz Project’s Twitter Politicians Dataset, a comprehensive overview of institutionalised German politics on Twitter. Over the years, digital media have been recognised as playing an increasingly relevant role in political communication. By now, it has become a widely accepted truth that social media have taken their place among the arbiters of public opinion Margetts . While aWhile use of the platform among the general population is relatively sparse in most non–Anglo-Saxon countries, including Germany  ID for parliamentarians. When these variables were unavailable on Wikidata, we manually extracted this information from the Abgeordnetenwatch website instead.8For the initial data acquisition, we built on a number of Twitter lists of German politicians in parliament that were curated and made publicly available by Martin Fuchs, aka “Wahlbeobachter” (election observer).The EPINetz Twitter Politicians Dataset comes with a number of variables, allowing for a more fine-grained analysis. Table In this section, we present an exemplary study on the Twitter communication of German politicians during the federal election campaign 2021. Therefore, we collected the timelines  of all accounts in our dataset for the period between 19 April and 26 September. We thus let our research period start with the day of the nomination of Annalena Baerbock of the Green Party, the second nomination of a lead candidate after the Social Democratic Party (SPD) had already announced that Olaf Scholz was running for Chancellor. The research period “naturally” ends with the closing of ballot boxes at election day, 6 p.m. We further restricted our sample to accounts related to the seven largest parties represented in the 19th German Bundestag and thus to the parties with a realistic outlook to win mandates in the 20th election. These were the Alternative for Germany , the Green Party (Bündnis 90/Die Grünen), the Christian Democratic Party (CDU), the Christian Social Union in Bavaria , the Liberals (FDP), the Left Party (DIE LINKE), and the SPD. Accounts with no party affiliation, i.e., ministries, were dropped for this analysis. To collect the data, we used Twitter’s academic V2 API access, allowing for full archive searching.Previous research has richly documented the activity differences between parties with respect to certain social media (Jürgens and Jungherr These imbalances in party representation are even more pronounced when analysing Twitter activity during the campaign period. As shown in Table Social media like Twitter provide rich opportunities for the study of relational linkages between users. Among other applications, research has made great use of it for empirical inquiry into networks of right-wing extremism (Knüpfer et al. Political communication research has shown great interest in the so-called echo chamber phenomenon. Empirical indicators might be derived from the network analyses presented above, building on theoretical assumptions of homophily regarding party affiliation. Partisan selective media exposure is an alternative conception of the echo chamber (Heft et al. Populist communication on social media has been an increasing concern for political science research, as direct communication via these platforms is suspected to circumvent the power of traditional moderators and gatekeepers, allowing more extremist messages to spread freely (Engesser et al. With this research note, we gave an overview of the EPINetz Twitter Politicians Dataset, the variables contained, and the accounts tracked. To illustrate potential use cases for the dataset in political communication research, we presented an analysis of the 2021 German federal elections, providing exploratory insights into the Twitter use of politicians during the election period. We would like to encourage researchers to use the dataset and conduct research beyond the context of election campaigns and the various methods showcased here. Potential use cases are longitudinal studies on politicians’ tweet behaviour, additional uses for the sociodemographic variables provided, and utilisation of the linkages to additional data sources, such as Abgeordnetenwatch for the tracking of voting behaviour in parliament or Wikidata for the collection and analysis of additional data. Current limitations of the dataset are that it does not contain comprehensive tracking of parliamentarians’ mandates during legislative periods and that we provide data only for the legislative periods of 2021. We intend, however, to annually update the EPINetz Twitter Politicians Dataset to continually provide an up-to-date resource for researchers. In this regard, we are looking forward to seeing exciting new applications of the dataset both within and outside the field of political science.Appendix


Specific nutritional composition might support skeletal muscle and enhance treatment support. In this Materials and Methods: Using C2C12-myotubes, the effects of chemotherapy  on protein synthesis, cell-viability, caspase-3/7-activity and LDH-activity were assessed. Addition of EPA, DHA, L-leucine and vitamin D3 and their combination (SNCi) were studied in presence of above chemotherapies. Tumor cell-viability was assessed in oxaliplatin-treated C26 and MC38 CRC cells, and in murine and patient-derived CRC-organoids.Results: While chemotherapy treatment of C2C12-myotubes decreased protein synthesis, cell-viability and increased caspase-3/7 and LDH-activity, SNCi showed improved protein synthesis and cell viability and lowered LDH activity. The nutrient combination SNCi showed a better overall performance compared to the single nutrients. Treatment response of tumor models was not significantly affected by addition of nutrients.in vitro study shows protective effect with specific nutrition composition of C2C12-myotubes against chemotherapy toxicity, which is superior to the single nutrients, while treatment response of tumor cells remained.Conclusions: This  Colorectal cancer is the fifth most common type of cancer and leads to ~10% of cancer deaths , 3. Cancin vitro analysis , while C2C12 myotube cell viability, and protein synthesis significantly improved.Several therapeutic approaches to target the multifactorial nature of cachexia are suggested in literature , 11–14 iin vitro models were used to provide better understanding and additional data on a beneficial effect of individual SNC ingredients, EPA, DHA, L-leucine and vitamin D3 or the combination (SNCi). C2C12 myotubes, C26 and MC38 colorectal cancer cells and CRC-organoids were used, in the presence or absence of chemotherapy treatment. As a marker for muscle wasting, muscle protein synthesis in C2C12 myotubes was measured. To determine chemotherapy associated toxicity, cell viability, caspase 3/7 activity (a measure of apoptosis) and LDH activity (a measure of necrotic cell death) were measured in C2C12 myotubes, and in C26 and MC38 colorectal cancer cells and CRC-organoids, cell viability was measured. While we already showed improved protein synthesis and cell viability with SNCi, we hypothesize that SNCi can counteract muscle wasting and chemotherapy-induced muscle toxicity more effectively compared to the individual SNCi ingredients EPA, DHA, L-leucine and vitamin D3 supplementation alone, providing better muscle protection, while it does not interfere with tumor organoid response to chemotherapy treatment.In the present study, three different p < 0.05) and OX , myotubes were able to induce a significant anabolic response to insulin and leucine stimulation compared to basal protein synthesis resembling a fasting state (p < 0.05) (p < 0.001) (p < 0.005) (p < 0.05) , 5-fluorouracil (5FU), a combination of oxaliplatin and 5-fluorouracil (OXF) and irinotecan (IR) of C2C12 myotubes showed a dose-dependent decline in protein synthesis , 1G, 1J,ng state , 1D, 1J.ng state , 1D, 1J.ng state , 1E, 1K, < 0.05) . In line< 0.001) , 1F, 1L.< 0.001) . LDH rel< 0.005) , 1F, 1L  < 0.05) . Comparip < 0.0001, p < 0.0001, p = 0.0043, p < 0.0001 respectively) (p < 0.0001) (p < 0.05)  . Protein 0.0001) . However < 0.05) –2F.p = 0.0386, p < 0.0001, p < 0.0001, p < 0.0001 respectively). SNCi in IR treated myotubes showed a significant increase in cell viability at all IR concentrations except 1500 μM . Myotube cell viability significantly increased with EPA supplementation at 8 and 31 μM OXF  and vitamin D3 supplementation at 8, 31 and 125 μM OXF compared to OXF alone   , 3F. No  0.0001) , 3E compp = 0.0454, p < 0.0001 respectively)  and with vitamin D3 at 31 μM OXF (p = 0.0041) compared to OXF alone . LDH activity significantly decreased with SNCi in OXF treated myotubes compared to OXF alone at all four concentrations (p < 0.0001)  (p = 0.0007), DHA at 8 and 125 μM OXF  L-leucine and vitamin D3 at 8 μM OXF showed a significant decrease in LDH activity (p = 0.0266) compared to control.To determine potential reduction of chemotherapy toxic effects of individual nutrients and SNCi, caspase 3/7 activity and LDH activity were assessed. Caspase 3/7 activity was used as a measure of apoptotic cell death, while LDH activity refers more to necrotic cell death. OXF with SNCi shows significant decreased caspase 3/7 activity at OXF 8 and 31 μM compared to OXF alone (ctively) . IR withctively) . Single XF alone , 1F. An XF alone , 4E (p = 0.0001) , howeverctively) . Single activity –5F . Although relative IC50 values of PDO1 and PDO4 were significantly increased upon supplementation with vitamin D3 and SNCi, relative IC50 values of 9 out of 11 models were not significantly affected by nutritional supplementation . PDO2 and PDO3 showed non-significantly increased cell viability (p = 0.075 and p = 0.123). The generalized effect of SNCi on all in vitro tumor models combined showed a Δ-viability of −9.4% with a standard deviation of 11.2%. When combining OX treatment with single nutrient supplementation of EPA, DHA, L-leucine and vitamin D3, Δ-viability values remained unaffected or even decreased .To provide insights in the potential effects of SNCi on downstream proliferation and metabolic pathways of in vitro study, supplementation of single nutrients EPA, DHA, L-leucine and vitamin D3 attenuated chemotherapy-associated toxicity of C2C12 myotubes as shown by an enhanced basal protein synthesis, increased cell viability with EPA and vitamin D3, with varying effects on caspase 3/7 activity and LDH activity. These effects were further strengthened upon supplementation with SNCi, indicating that the combination of supporting nutrients is essential to deliver maximum benefit to chemotherapy treated C2C12 myotubes. Neither supplementation with single nutrients nor SNCi interfered with chemotherapy treatment efficacy in a large series of CRC tumor cells and organoids. Moreover, SNCi did not alter RNA expression of molecular gene set pathways in CRC patient-derived organoids. This study focusses on the in vitro effects of nutrients and the nutritional combination SNCi in muscle and tumor cells, not capturing the complexity of the whole-organ or whole-organism. Though, this in vitro study was followed up by a cachexia mouse-model where we tested the same nutritional combination  − log([chemotherapy]))/slope))) + bottom. The fixed-effect bottom parameter estimates were constrained at 0% , non-treated wells received normalisation with DMSO. After exposure to SNCi (96 h) and chemotherapy (72 h), tumor organoid viability was quantified using CellTiter-Glo 2.0 Luminescence AssayTumor organoids were expanded for three days, plated in BME droplets of 10μL, pre-treated with SNCi (24 h) and treated with 5 μM oxaliplatin (72 h). After 96 h, organoids were harvested using dispase (ThermoFisher Scientific) and spun down  in a pellet, and snap frozen in liquid nitrogen. RNA was isolated using RNeasy Mini Kit (Qiagen) and RNA quality was checked using 2100 BioAnalyzer (Agilent). Briefly, sequencing libraries were made from poly-adenylated RNA using the Rapid Directional RNA-Seq Kit (NEXTflex) and sequenced on Illumina NextSeq500 to produce single-end 75 base long reads (Utrecht Sequencing Facility). Reads were aligned to human reference genome GRCh37 and pathway analysis (Geneset Maps) was performed using R2 software .t-test or mixed model ANOVA followed by post hoc LSD analysis were used to compare different treatments. Statistical difference was reached with P < 0.05. Figures were created using BioRender (https://biorender.com), Illustrator (Version 25.2.1) and GraphPad (Version 8.3.4).All data are expressed as mean ± SEM. Statistical analysis was performed using IBM SPSS Statistics . A Student’s


Systems Thinking (ST) has become essential for practitioners and experts when dealing with turbulent and complex environments. Twitter medium harbors social capital including systems thinkers, however there are limited studies available in the extant literature that investigate how experts' systems thinking skills, if possible at all, can be revealed within Twitter analysis. This study aims to reveal systems thinking levels of experts from their Twitter accounts represented as a network. Unraveling of latent Twitter network clusters ensues the centrality analysis of their follower networks inferred in terms of systems thinking dimensions. COVID-19 emerges as a relevant case study to investigate the relationship between COVID-19 experts’ Twitter network and their systems thinking capabilities. A sample of 55 trusted expert Twitter accounts related to COVID-19 has been selected for the current study based on the lists from Forbes, Fortune, and Bustle. The Twitter network has been constructed based on the features extracted from their Twitter accounts. Community detection reveals three distinct groups of experts. In order to relate system thinking qualities to each group, systems thinking dimensions are matched with the follower network characteristics such as node-level metrics and centrality measures including degree, betweenness, closeness and Eigen centrality. Comparison of the 55 expert follower network characteristics elucidates three clusters with significant differences in centrality scores and node-level metrics. The clusters with a higher, medium, lower scores can be classified as Twitter accounts of Holistic thinkers, Middle thinkers, and Reductionist thinkers, respectfully. In conclusion, systems thinking capabilities are traced through unique network patterns in relation to the follower network characteristics associated with systems thinking dimensions. Experts and practitioners need to address the increasing challenges of today’s sociotechnical systems while maintaining and elevating their performances under increasing complexities. These challenges include (1) requirement of high level systems integration to serve overarching goals, (2) lack of clarity to allow for decision support and commitment to alternative courses of action, (3) uncertainty caused by incomplete knowledge of systems and the unintended consequences, and (4) the effects of interdependence where there is mutual influence among systems and their related elements making analysis difficult  pandemic started to spread around the globe originally from China. In March 2020, the virus influenced almost most of the countries on every continent worldwide; consequently, the World Health Organization (WHO) declared the COVID-19 outbreak a worldwide pandemic. This virus is considered a danger to human health, resulting in worldwide fear and governmental quarantines. The side effect of this virus was somehow as dangerous as the virus itself for society. “As reported by the BBC, false information ranging from suggested medical care to conspiracy theories have been seen widely around the web, shared, and reposted by thousands of people on social media” . After a few rounds of review, Twitter grants us team access to collect data. Twitter, by default, has some restrictions for tweets. The first restriction is it gives a maximum of 5000 tweets for each individual account. The second restriction is that we can access the information of 15 followers of each account every 15 min, which makes the follower analysis time-consuming and difficult. These two restrictions were the main limitations of data collection for the current research. R version 4.0 and “rtweet” library have been used to collect the data from Twitter. To start tweet collection, the “create_token” function using four API credentials from Twitter, including consumer key, secret consumer key, access token, and access secret, have been utilized, followed by the usage of “get_timeline” function to extract tweets of 55 identified Twitter accounts. First, the potentially important information from each Twitter account was extracted, such as the name of the account, id, screen name, location, description, follower counts, friends count, listed count, favorites count, status counts, and some other information. Then, all the tweets for each account were categorized into (1) organic tweets, (2) replies, and (3) retweets. The ratios for each class calculated for every Twitter account, as shown in Table R version 4.0 and rtweet, tidyverse libraries were used to collect and analyze the followers’ data of 55 identified Twitter accounts. The follower analysis was performed based on the modified code from Bellman . Due to The two following networks are a one-step neighborhood network meaning only two layers of followers were considered. For example, the University of Washington Virology’s followers and followers of those followers were collected with the condition that followers of followers that were not connected with first-level followers of UWV were removed from the network.Nodes were defined as users, and edges connected two users based on the “following relationship”. The intensity of a node was quantified by the centrality of that node.VOSviewer software was used to cluster the network. VOSviewer’s clustering algorithm has two hyperparameters, such as the minimum size of a cluster, a threshold for the minimum size of a cluster, and a resolution parameter that helps handle the number of clusters. VOSviewer uses a local moving algorithm introduced by Van Eck et al.  to solveThe Twitter network is constructed, making use of 104 features extracted from 55 Twitter accounts. These features are summarized in Table Normalized Gower distance is used to quantify pairwise relationships among 55 accounts. Distance values are converted to a similarity matrix to construct the weighted network. Running a community structure finding (CSF) algorithm using the weighted network results in low modularity score (i.e. 0.072) detecting two clusters. CSF based on the modularity has resolution problem that it fails to find clusters at a finer resolution. In order to find clusters that result in better modularity of the network, the weighted network is binarized, retaining relationships stronger than 1.1 times the mean. Then, the community structure finding algorithm by Blondel et al.  is appliFollower networks of 55 Twitter accounts, the followers’ features dataset are analyzed summarize statistics related to centrality and node-level metrics of each follower network see Fig.  are extrMoreover, the node-level metrics, such as out-degree (this account follows how many accounts), in-degree (number of this account’s friends), number of nodes in the network of this account, and number of edges in the network of this account were added to the five extracted composite centrality variables, as mentioned above. Finally, the five composite centrality variables and four node-level metrics were employed to interpret the followers’ characteristics of each of 55 Twitter accounts.In this study, the Systems thinking (ST) skills instrument developed by Jaradat  is utiliThe eigen centrality is an ideal ‘all-round’ social network analysis score, well suited for comprehending human social networks Bhasin  that calEigen centrality has a hypothetical positive correlation with all seven ST skills dimensions. For example, the Twitter account that scores high eigen centrality is well-connected with its followers and has well-linked followers; the individual (who owns the Twitter account) might have a high level of Interaction and Autonomy skills based on the operational definitions in Table The account with a high score in the eigen centrality influences the entire network effectively and proficiently with a good amount of information and knowledge it has. Thus, the individual owner of Twitter might possess good systems thinking skills such as level of Hierarchical View and Complexity. Finally, the account with a high Eigen score is more responsive to explaining and solving unexpected and emergent social problems discussed in the Twitter network. Therefore, the Twitter account owner might have high systemic skills regarding the level of Change, Uncertainty, and Flexibility. Overall, the eigen centrality measure can be a good representative of the level of systemic thinking skills of the Twitter account owner.The degree centrality score is similar to the eigen centrality score in the sense that measures consider the well-connectivity of a node in a network system Bhasin . InteresThe closeness centrality assigns a score to a node according to its “closeness” to all other nodes  in the network system Disney . closeneThe betweenness centrality computes the frequency of a node poses on the shortest path among other nodes of the network  Disney . This meBased on foregoing discussion, the four measures of centrality can serve as good indicators for assessing the level of seven ST skills for Twitter accounts with respect to the analysis of their followers’ network, as depicted in Fig. Twitter features dataset (introduced in Sect.  4.A.) has been utilized to cluster 55 Twitter accounts. Since clustering is an unsupervised learning method that needs validation, the follower analysis results (introduced in Sect. 4.B.) are used to elucidate clusters. The emerged clusters of accounts shown in Fig. Interestingly, Twitter accounts in cluster 3 have relatively higher follower network centrality measures than the other two clusters. Additionally, Twitter accounts in cluster 1 have relatively lower follower network centrality metrics than those in cluster 2. According to mapping the relationships between ST dimensions and Twitter accounts' centrality measures, we infer high scores in centrality measures of follower analysis associated with high ST capability of individuals. As a result, we conclude since Twitter accounts in cluster 3 have relatively higher centrality measures, the individuals corresponding to these Twitter accounts might have higher ST capability skills than others. Consequently, we call Twitter accounts in cluster 3 holistic thinker cluster. Similarly, the Twitter accounts in cluster 1 are reductionist thinkers due to low scores pertaining to their follower network analysis's centrality scores. Since Twitter accounts in cluster 2 have centrality scores almost between clusters 1 and 3, they are called middle thinker Twitter accounts. Results indicate the follower network analysis might be able to quantify systemic thinking capabilities of individuals based on their tweets in the case of the COVID-19 pandemic.Twitter is a crucial medium in circulating information, and unfortunately, sometimes erroneous or inadequate messages are propagated in a network by users. For example, in the case of the worldwide outbreak of the COVID-19 pandemic, we observed many unsupported claims about the origin of the virus, virus transmission, possible prevention, and likely treatment. In this confusing situation, knowing reliable sources and the level of holistic systems thinking of these sources greatly assist the public in making correct decisions. Thus, COVID-19 stands as a potential use case to investigate the relationship between COVID-19 experts’ Twitter network and their systems thinking capabilities.The interdisciplinary literature review revealed that there was a literature gap connecting COVID-19 experts’ system thinking level and their Twitter network profiles. Therefore, we identified 55 trusted expert Twitter accounts based on the lists of Forbes, Fortune, and Bustle.We constructed the Twitter network of these experts based on features extracted from their Twitter accounts, including organic metrics , tweets’ measures , sentimental analysis, source of tweets as well as their tweets like most frequent words and their frequencies and frequency of using COVID-19 synonyms. Clustering the network based on tweets and Twitter features identified three distinct groups of experts emerged. The follower networks of experts were generated to elucidate clusters. The system thinking dimensions were mapped to follower network measures such as betweenness, closeness, degree, eigen centralities, and node-level metrics. By comparing the follower network measures of 55 experts, we inferred that three identified clusters had meaningful differences in centrality scores and node-level metrics. The cluster with a higher, medium, lower score can be classified as Holistic thinkers, Middle thinkers, and Reductionist thinkers, respectively.Briefly, this research tested that the capabilities of individuals as system thinkers can reveal unique network patterns and distinct communities associated with the level of system thinking of COVID-19 experts.Twitter restrictions related to the search of the past tweets and access to followers’ information of each Twitter account was one of the limitations of the study. Another limitation was the validity of mapping systems thinking dimensions to centrality measures of follower network analysis; further investigation with other samples of the population is needed to validate the current mapping. Future studies will be directed into investigating the relationship between Twitter users’ systems thinking and their sentiment analysis. Analyzing the organic tweets of expert Twitter accounts related to COVID-19 using text mining and natural language processing to better understand the impact of tweets on followers and spread knowledge and information within the Twitter network. The new research finding can be compared and combined with the current research results to analyze these studies' validity and consistency.Using follower analysis and Twitter analysis to understand systems thinking of people, especially the influencers and celebrities, and how their role is important to spread the true news and knowledge to the community.How systems thinking is related to having a better network of followers promotes the more efficient and effective transformation of information and knowledge to the community.As an individual’s level of systems thinking skills can be enhanced, the social media activity of the individuals can be improved. This is very important since the research shows there is a necessity to create a safe and healthy virtual environment for everybody, so everyone can express their opinions and beliefs in the direction of the community’s values.The results of the current study have some practical implications as follows:


First published: 10 June 2021https://onlinelibrary.wiley.com/doi/10.1111/jvim.16197Volume 35, Issue 4Correction to the grant/award number for the funder as follows.Funding information: Rural Development Administration, Grant/Award Number: PJ01404502.


Similarly, the core unit CAH was functionalised with the nitroreductase (NTR) reactive p-nitrobenzyl unit to produce probe JEG-CAN. Both, JEG-CAB and JEG-CAN exhibited a significant fluorescence increase when exposed to either HCys and ONOO− (JEG-CAB) or HCys and NTR (JEG-CAN) thus demonstrating their effectiveness to function as AND logic gates for HCys and a chosen analyte.With this research we set out to develop a number of coumarin-based ‘AND’ logic fluorescence probes that were capable of detecting a chosen analyte in the presence of HCys. Probe JEG-CAB was constructed by attaching the ONOO With this research we set out to develop of a number of coumarin-based ‘AND’ logic fluorescence probes that were capable of detecting a chosen analyte in the presence of HCys. Typical physiological concentrations of HCys range between 5–15 μmol L−1.2 However, elevated levels of HCys (>15 μmol L−1), which is known as hyperhomocysteinemia (hHCys),3 have been associated with pregnancy disorders, Alzheimer's disease, cardiovascular disease and neurodegenerative diseases (NDs).4–6 It is believed that the main cause of HCys induced toxicity is through the non-enzymatic modification of proteins. This is achieved through irreversible covalent attachment of the predominant metabolite of HCys, homocysteine thiolactone (HTL), to lysine residues; a phenomenon known as ‘protein N-homocysteinylation’ that results in the loss of a proteins structural integrity leading to loss of enzymatic function and aggregation.7Homocysteine (HCys) is a non-proteinogenic amino acid, formed from the de-methylation of methionine,8–11 However, these fluorescent probes have focused on the detection of a single biomarker (HCys), however, processes associated with HCys induced toxicity often involve more than one biochemical species. For example, it has been reported that peroxynitrite (ONOO−) and nitric oxide (NO˙) play a significant role in HCys-mediated apoptosis in trigeminal sensory neurons12 and HCys has been reported to induce cardiomyocytes cell death through the generation of ONOO−.13 The production of ONOO− is believed to be the result of an increased production of superoxide (O2˙−) by HCys activating the enzyme NADPH oxidase.14–16 This increased production of O2˙− leads to a reduction in the bioavailability of NO˙ by increasing the formation of ONOO− (NO˙ + O2˙− → ONOO−).17 The reported ONOO− concentrations in vivo are believed to be approximately 50 μM but, higher concentrations of 500 μM have been found in macrophages.18,19 Furthermore, hypoxia has been reported to facilitate HCys production in vitamin-deficient diets20 where hypoxia leads to an upregulation of nitroreductase (NTR) – a reductive enzyme upregulated in cells under hypoxic stress.21,22 Therefore, the development of tools that enable an understanding of the relationship of HCys with these biologically important species would be highly desirable.A number of fluorescent sensors have been developed for the detection of HCys to help improve our understanding of its role in biological systems.23 These include AND logic gate based-fluorescence probes, which require both analytes to work in tandem to produce a measurable optical output.24–28 In our group, we have developed several ‘AND’ reaction-based probes for the detection of ROS/RNS and a second analyte.29–32 These ‘AND’ logic scaffolds have been used to detect two analytes within the same biological system.24,33To achieve this, a number of fluorescent probes have been developed that are capable of detecting two or more analytes.et al.CAH, with a salicylaldehyde  or nitroreductase (NTR), respectively . The mixture was heated to reflux overnight and the solvent was then removed. The intermediate was then hydrolyzed in H9% yield .− ‘AND’ HCys in PBS buffer solution . The maximum absorption of JEG-CAB at 336 nm shifted to 323 nm with the addition of HCys and then slightly shifted to 328 nm following the addition of ONOO−  led to a significant increase in fluorescence intensity . However, in a similar manner to that shown in − or vice versa   led to a large time dependant increase in fluorescence intensity. To ensure both analytes were required, NTR and NADPH was kept constant (4 μg mL−1 and 400 μM respectively) resulting in a 3.4 fold fluorescence increase  led to a 2 fold increase in fluorescence intensity. Again, LC-MS experiments confirmed the proposed reaction mechanism . As shown in increase . We attrScheme S3.38,42 Su Fig. S22.Kinetic studies for JEG-CAN with both NTR and HCys were carried out . We are now pursuing the development ‘AND’ logic fluorescence probes with longer excitation and emission wavelengths suitable for − or NTR, respectively. CAH is a useful platform that enables easy modification for the development of ‘AND’-based fluorescent probes for the detection of HCys/Cys and a second analyte. Both JEG-CAB and JEG-CAN were shown to be ‘AND’-based fluorescent probes.In summary, we have developed two coumarin-based ‘AND’ logic fluorescence probes (JEG-CAB and JEG-CAN) for the detection of HCys/Cys and ONOONo conflicts of interest.RA-009-C9RA04908H-s001