Ex vivo impedance measurements of gastrocnemius (GA) were obtained using a dielectric measuring cell in 30-week-old female Pompe (N = 10) and WT (N = 10) mice. Longitudinal and transverse conductivity, σ, and the relative permittivity, εr, and Cole–Cole complex resistivity parameters at 0 Hz and infinite frequency, ρo and ρ∞, respectively, and the intracellular resistivity, ρintracellular were determined from the impedance data. Glycogen content (GC) was visualized histologically and quantified biochemically. At frequencies > 1 MHz, Pompe mice demonstrated significantly decreased longitudinal and transverse conductivity, increased Cole–Cole parameters, ρo and ρo-ρ∞, and decreased ρintracellular. Changes in longitudinal conductivity and ρintracellular correlated with increased GC in Pompe animals. Ex vivo high frequency impedance measures are sensitive to alterations in intracellular myofiber features considered characteristic of GSDII, making them potentially useful measures of disease status.Electrical impedance methods, including electrical impedance myography, are increasingly being used as biomarkers of muscle health since they measure passive electrical properties of muscle that alter in disease. One disorder, Pompe Disease (Glycogen storage disease type II (GSDII)), remains relatively unstudied. This disease is marked by dramatic accumulation of intracellular myofiber glycogen. Here we assessed the electrical properties of skeletal muscle in a model of GSDII, the Pompe In EIM, a low-amplitude, high-frequency electrical current is passed through a muscle of interest and the resultant voltage is measured. Disease-induced alterations in muscle volume electrical conduction properties will impact the measured electrical impedance values associated with ionic and polarization alterations due to the presence of free water, connective tissue, and changes in cellular integrity. Major advantages of EIM over the other technologies include its relatively low cost, convenience, and high reproducibility5.Quantitative techniques are needed to serve as biomarkers for the assessment of disease progression and therapeutic efficacy in a variety of disorders affecting skeletal muscle. One approach that has offered potential value for this purpose is electrical impedance myography (EIM)6. Our own impedance measurements on excised mouse muscle have confirmed the ability of ex vivo EIM to detect disease-induced alterations in myofiber cross sectional area (CSA), 7 to distinguish different neuromuscular disorders8, and with the application of very high frequency current, to differentiate slow- versus fast-twitch muscle fibers9. More recently, we have focused on obtaining similar information such as myofiber CSA by using an in vivo non-invasive surface impedance approach coupled with statistical analysis to establish a relationship between impedance values and muscle fibers of varying size, including those from immature animals10, and in a murine neuromuscular disease model associated with prominent muscle atrophy11. We have also assessed changes in the extracellular space by the analysis of impedance data in several murine models including: inflammatory infiltrates in carrageenan-induced inflammation12; increased fat deposition in the db/db obese diabetic mouse13; and increased fibrosis in the D2-mdx model of Duchenne Muscular Dystrophy (DMD)14.Earlier studies on isolated muscle fibers using impedance techniques provided data on the dielectric properties of skeletal muscle tissueintracellular pathological abnormalities on electrical impedance by studying the effects of glycogen accumulation, using a mouse model of Pompe disease16. Pompe6neo6neo/  mice exhibit muscular features typical of patients with Glycogen storage disease type II (GSDII), a recessively inherited deficiency of the enzyme acid α-glucosidase . Mice homozygous for disruption of the acid α-glucosidase gene begin to accumulate glycogen in cardiac and skeletal muscle lysosomes by 3 weeks of age and develop a substantial accumulation of glycogen in both intra-lysosomal and intra-cytoplasmic compartments by 6 weeks of age. By 1 month of age, Pompe mice exhibit markedly reduced mobility and strength, and by 8–9 months of age they develop obvious muscle wasting and a weak, waddling gait. Of note, female Pompe mice are considerably more affected than males in this model16.Here, we begin to address the effects of We hypothesized that intracellular myofiber abnormalities associated with Pompe disease would produce alterations in the electrical conduction properties of muscle. A total of 20 female mice (10 Pompe and 10 WT) at 30 weeks of age were assessed using ex vivo EIM measurements on the excised gastrocnemius (GA) muscle after animal euthanasia. Intracellular glycogen content (GC) in the GA muscle was visualized histologically by Periodic Acid Schiff (PAS) staining and quantified biochemically. We had two goals: 1. To evaluate differences in electrical properties between Pompe and wild-type (WT) muscle, and 2. To determine if electrical properties and/or modelled impedance parameters were associated with intramuscular GC. We hypothesized that increased GC would lead to increased intracellular water leading to reduced intracellular resistivity values and that ex vivo EIM would detect such a change.Figure 2 in WT mice to 1139 ± 41 µm2 in Pompe animals).Figures Figure The multifrequency relative permittivity and conductivity values, calculated using the calibrated ex vivo resistance and reactance data (provided in the Supplementary Data file), are shown in Fig. o and ρ∞, respectively, for the low (range 10 kHz–1 MHz) and high frequency (range 1–10 MHz) arcs are listed in Table 2, and the reactivity anisotropy ratio, β2, versus frequency for Pompe and WT mice are shown in Fig. 2 ≠ β2, over the entire frequency range. Specifically, the muscle is more anisotropic at low frequencies and becomes more isotropic at higher frequencies when the anisotropy ratios get closer to 1. At high frequencies, it becomes anisotropic again, but now the direction of anisotropy  changes.The resistivity versus reactivity plots and their estimated fitted model parameters are shown in Fig. ∞ and a significant increase in both ρo and ρo-ρ∞, in the Pompe versus the WT animals in both directions. This pattern is consistent with the presence of increasing poorly conductive intracellular material in the GA of the Pompe mice. Of note, no significant differences were observed for the model parameters fc and α in either direction or in either frequency range.Significant differences were found in several modelled resistivity parameters for Pompe versus WT mice, in both longitudinal and transverse directions, particularly at high frequency (range from 1 to 10 MHz) . Glycogen accumulation is typically observed at 3 weeks of age, and lysosomes increase in size and number thereafter. Disease progression, reported to occur slightly faster in the female versus male Pompe mice16, has been categorized by specific clinical signs16. Pompe disease progression can also be divided into distinct histological stages17: Stage 1—Glycogen-filled lysosomes located between intact myofibrils; Stage 2 – Enlarged glycogen-filled lysosomes; some leakage of glycogen into the cytoplasm; fragmentation of myofibrils; Stage 3 – Ruptured glycogen-filled lysosome membranes; definite glycogen in the cytoplasm; abnormal mitochondrial structure, few remaining myofibrils; Stage 4 – Contractile elements replaced by cytoplasmic glycogen; Stage 5 – Bloated cells, due to an influx of water, resulting in dissolution of glycogen. The female Pompe mice used in the present study were 30 weeks of age, and displayed no clinical manifestations of disease, i.e., had not yet reached clinical Grade 1. Nevertheless, both histological and biochemical data confirmed a significant increase in GC in the GA muscles of these animals, and, based on light microscopic analysis, the female Pompe mice used in this study can be categorized as histological Stage 1.The aim of the present work was to determine whether the electrical properties of skeletal muscle are different in a mouse model of glycogen storage disease (GSD) and assess the possible correlation between increased glycogen accumulation and any observed alterations in the electrical properties and/or modelled electrical impedance parameters. For this study we chose to use Pompe mice, the most widely utilized preclinical model of GSDII. Pompe mice, generated by insertion of a 18.We hypothesized that intracellular myofiber abnormalities associated with early stage Pompe disease would produce alterations in the electrical properties of skeletal muscle. Using an ex vivo EIM approach, we were able to detect significant changes in the intrinsic volume electrical conduction properties of muscle at this relatively early stage in Pompe disease progression. Whereas both longitudinal and transverse relative permittivity remained unchanged at all frequencies, conductivity values in both directions showed a significant reduction in the Pompe animals, particularly at frequencies > 1 MHz. The data collected here can now be added to the database of permittivity properties of healthy and diseased skeletal muscle in murine models of SMA, DMD, diabetes, ALS and myofiber hypertrophyo-ρ∞, is slightly, but not significantly, smaller in the Pompe mice as compared to WT; however, the base of the longitudinal high-frequency arc, ρo-ρ∞, is significantly greater for the Pompe versus the WT mice. Similarly, in the transverse direction, the bases of the low-frequency arcs are not significantly different in the Pompe and WT animals, but once again, the base of the transverse high-frequency arc is significantly higher for the Pompe versus the WT mice. Finally, although no significant differences in ρextracellular were detected between the WT and Pompe animals, the significant decrease in the intracellular resistivity, ρintracellular, detected in the Pompe mice, correlates with the increase in GC and might be attributed to an increase in intracellular water associated with the accumulating glycogen molecules21.Conductivity and relative permittivity data were then fitted to the complex resistivity version of the Cole–Cole model to provide the standard Cole–Cole resistivity parameters for the Pompe and WT mice. Differences in the longitudinal and transverse Cole–Cole resistivity parameters between Pompe and WT mice are apparent at both low (< 1 MHz) and high (> 1 MHz) frequencies. The base of the longitudinal low-frequency arc, defined by ρ2, and the reactivity anisotropy ratio, β2, versus frequency indicate that the anisotropy ratios are unequal, i.e., α2 ≠ β2 for both Pompe and WT mice. The results also indicate that at low frequencies the muscle in both Pompe and WT mice is more anisotropic than at high frequencies, since both α2 and β2 increase towards 1 at high frequencies. These findings extend the set of anisotropy values calculated for the GA in several murine disease models22.Additional anisotropic differences also merit discussion. Plots of the resistivity anisotropy ratio, α15, would result in enhanced alterations in electrical properties and stronger correlation results, if muscles from older Pompe animals had been studied. Second, our total sample numbers were relatively small (N = 10 per group). Increasing the sample size would increase the statistical power of our analysis. Third, although unavoidable in these ex vivo measurements, since muscle tissue was measured shortly after death, post-mortem ischemia could have altered the electrical properties data as compared to living in situ muscle. Fourth, inconsistencies in accurately positioning the excised muscle in the dielectric cell inevitably causes some degree of inaccuracy in the directional dependence of the resultant conductivity and relative permittivity values. Fifth, we did not evaluate changes in mitochondrial content or the presence of increased autophagy in the Pompe mice that we studied at 30 weeks of age since these factors have been reported to occur in the Pompe mice only at much later times25. Nevertheless, such additional intracellular changes, if present, could also contribute to the observed alterations in the conductivity and the high frequency modelled parameters that we observed9. Finally, we acknowledge that our findings, especially the high frequency data, could be subject to a variety of errors  inherent to the technique used26.The present study has a number of limitations. First, only one age of Pompe mice was evaluated. It is possible that more severe pathological changes , that have been reported to occur at later stages in disease progression27. In addition, the technique could serve as a useful measure of drug efficacy in diseases attempting to restore normal intracellular architecture such as enzyme replacement therapy in Pompe disease28. Further investigations are thus warranted to evaluate if the ex vivo EIM findings presented here have translation using in vivo surface and needle EIM approaches for the assessment of this and other diseases exhibiting intracellular abnormalities including vacuolar and mitochondrial myopathies. Such information would provide additional support for the use of EIM as a non-invasive biomarker of muscle health. To this end, one very recent pilot study has correlated EIM outcomes with measures of muscle strength and function in several patients with late-onset Pompe disease29, suggesting that EIM could serve as a potential biomarker of clinical change and response to interventions in this disease.Despite these limitations, our ex vivo EIM study reveals differences in the electrical properties of Pompe muscle and their relationship to increased GC. Being able to evaluate alterations in the internal structures of the muscle cell would be valuable in a variety of pathological settings since such a strategy could serve as a tool to assess conditions affecting mainly sub-cellular muscle components such as lysosomes, the mitochondria, the t-tubule system or the sarcomeres themselvesGuide for the Care and Use of Laboratory Animal, 8th edition, 2011 of the National Institutes of Health. All authors complied with the ARRIVE guidelines 2.0. Female mice homozygous (N = 10) or wild type (N = 10) for Gaatm1Rabn ); Strain #004154) were obtained from Jackson Labs (Bar Harbor ME) and aged to 30 weeks in order to ensure elevated glycogen content, which occurs naturally as these homozygous animals age. All animals were fed standard chow ad libitum.All experimental procedures were approved by the Institutional Animal Care and Use Committee at Beth Israel Deaconess Medical Center (Protocol Number 031–2019) and performed in accordance with guidelines set forth in The 7 and Compound Muscle Action Potential (CMAP) amplitudes30 were each measured as previously described.Forelimb and hindlimb grip strength2 from gas cylinder. After excision of the entire GA muscle, wet mass was determined using a standard analytical balance and height was recorded with a micrometer. GA was cut to approximately 5 × 5  mm2 (with variable height) to fit into the dielectric cell used for ex vivo impedance measurements, described below.Mice were euthanized by CO8. The excised GA was first placed in the cell with the fibers oriented perpendicularly to the metal plates , then removed and placed with the fibers parallel to the plates (for transverse myofiber measurements).All electrical impedance measurements were made with the mView impedance spectroscopy system  sweeping the frequency of the electrical current applied. In total, 41 logarithmically spaced frequencies were measured from 1 kHz to 10 MHz. Data was collected via an ex vivo approach using a Plexiglass dielectric measuring cell7. Sections were imaged at 20X with an epifluorescence microscope  and myofiber area was measured using muscle morphometry plug-in (developed by A. Sinadinos using Eclipse IDE) in FIJI, the open source image processing software . On average, 249 myofibers per WT muscle) and 406 myofibers (per Pompe muscle) were counted per animal, for total number of 2678 myofibers for WT mice and 4097 myofibers for Pompe mice at 30 weeks.Following ex vivo impedance measurements, left GA muscles were placed in 10% buffered formalin and fixed for at least 48 h. Samples were then embedded in paraffin blocks, sectioned into 10-μm slices, and stained with either Hematoxylin & Eosin (H&E) or Periodic Acid-Schiff (PAS) by standard methods and imaged at 20X and 40X by light microscopy. Additional paraffin sections were stained with anti–collagen VI antibody (Abcam ab6588) to identify the myocyte cell membranes and 4',6-diamidino-2-phenylindole (DAPI) to detect nuclei. The myofiber CSA was determined as previously describedFollowing ex vivo impedance measurements, the entire right GA muscles were immediately snap frozen and a small portion of the muscle (~ 10–15 mg) was later analyzed for GC, using the Glycogen Assay Kit II Colorimetric Kit  and a microplate reader  according to the manufacturer’s instructions including a background control. Results are reported as µg glycogen/mg wet weight (ww) muscle.r), a dimensionless value were calculated for each individual frequency of applied current, as previously described18.Resistance (R) and reactance (X) values were collected across the entire frequency range. Prior to the formal analysis, we removed any spurious EIM data  yielding a sample size of N = 10 (WT) and N = 9 (Pompe). Next, we calibrated the impedance measuring setup to compensate for potential high frequency measurement errors due to stray capacitances affecting the wires connecting the device to the electrodes. For this, impedance measurements were made with normal saline solution placed into the same dielectric cell. From the calibrated impedance values, the constituent volume conduction properties of muscle, i.e., the conductivity (σ), in Siemens/m, and the relative permittivity  to determine the resistivity anisotropy ratio, α2. Similarly, at each frequency the longitudinal reactivity was divided by the transverse reactivity (τL /τT) to determine the reactivity anisotropy ratio, β2.Obtained conductivity and relative permittivity data were converted into their equivalent resistivity, ρ, in Ω.m, and reactivity, τ , in Ω.m, values31 using MATLAB  as described9, with a weighted nonlinear least squares method32 to provide the standard Cole–Cole parameters, including resistivity at 0 Hz and infinite frequency, ρo and ρ∞ respectively, (both in Ω.m); center frequency fc (in MHz), and α (dimensionless). These parameters are associated with cell density (ρo and ρ∞), myocyte size (fc) and cell size distribution (α).Longitudinal and transverse resistivity and reactivity data were then fitted to the Cole–Cole resistivity modelo, the resistivity at 0 Hz, assumed to be equivalent to the resistivity of the extracellular fluid, ρextracellular, and ρ∞, the resistivity at infinite frequency, assumed to be the resistivity of the whole volume when current is flowing through both intracellular and extracellular compartments, were used to calculate the longitudinal and transverse resistivity of the intracellular compartment, ρintracellular, (in Ω.m), based on Eq.  using the most appropriate tests . Unless otherwise noted, all data were reported as mean ± SEM. Multiple group comparisons were performed by one-way ANOVA with Tukey’s multiple comparisons test. Multifrequency EIM values were compared using the two-way ANOVA followed by Sidak’s multiple comparison test. For correlation analyses, the Spearman correlation coefficient, r, was calculated. Statistical significance was based on Supplementary Information 1.Supplementary Information 2.


Compression molded films of LCRBTs containing chromonic molecules display multilevel hierarchical structure in which cholesteric mesophase co-exists with π–π stacking of the chromonic mesophase along with PEG microphase segregated domains. This is unexpected as conventional LCBCPs and LCBBCs that lack chromonic molecules do not form cholesteric mesophases. The presence of π–π interactions modifies the interface at the IMDS so that both chromonic and cholesteric mesophases coexist leading to the manifestation of cholesteric phase for the first time within block architecture and is very reminiscent of previously published LCRBCs without chromonic molecules. The key to the observed hierarchical assembly in these LCBBTs containing chromonic molecules lies in the interplay of LC order, chromonic π–π stacking, PEG side chain microphase segregation, and their supramolecular cooperative motion. This unique “single component” polymer scaffold transforms our capacity to attain nanoscale hierarchies and optical properties from block architecture similar to nanoscale mesophases resulting in random architecture.We describe the influence of competing self-organizing phenomena on the formation of cholesteric mesophase in liquid crystalline brush block terpolymers (LCBBTs) and liquid crystalline random brush terpolymers (LCRBTs) containing chromonic molecules. A library of LCBBTs and LCRBTs are synthesized using ring-opening metathesis polymerization (ROMP) of norbornene side-chain functionalized monomers comprising cholesteryl mesogen  and liquid crystalline random brush terpolymers (LCRBTs) containing chromonic molecules. The LCBBCs show interesting features due to (i) supramolecular cooperative assembly of liquid crystalline mesophase and block copolymer microphase segregation,16,17 (ii) low entanglement and fast assembly kinetics of brush molecules,18,19 and (iii) ability to tailor block copolymer order-disorder temperature and liquid crystalline transition temperature.18,19 This LCBBC platform self-assembles into a hierarchical structure with LC order in 3–10 nm range, brush BCP ordering due to phase segregation of brush molecules in the 10–200 nm range. Despite the ease of synthesis, processing and self-assembly of BCP, LCBCP and LCBBC scaffolds, periodicities corresponding to wavelengths in the visible range with the cholesteric mesophases has been particularly elusive.Block copolymers undergo microphase separation due to unfavorable mixing enthalpy and weak mixing entropy.16 One may envision that the development of LCBBC with additional supramolecular interactions including H-bonding or π–π stacking to produce hierarchical mesophases and co-exiting morphologies that are not generally observed in conventional platforms. We question if molecular interactions, especially resulting from π–π stacked aromatic cores of chromonic molecules,20 could potentially serve as a method to enhance cooperative LC–LC interactions between the chromonic molecules and LC molecules within a block scaffold architecture? Furthermore, we also question whether LCBBCs containing these chromonic molecules will form functional, stimuli-responsive, cholesteric mesophases that are easily produced from linear and branched liquid crystalline random copolymers?The role of microphase segregation of the non-LC block on LC mesophase behavior in chiral and non-chiral LCBCPs and LCBBCs has been investigated. The microphase segregation even in chiral LCBCPs and LCBBCs unwinds pitch of helical mesophases due to presence of preferred anchoring condition at the IMDS of microphase segregated domains. Thus, cholesteric mesophases are not observed in these non-chiral and chiral platforms.21,22 These molecules tend to stack face to face into aggregates aided by strong π–π interactions between aromatic cores and nanophase separation with hydrophobic cores surrounding groups.23 These molecules include a variety of drugs and dye molecules self-organize into chromonic π–π mesophases in water.24,25 Upon removal of water, dried films of π–π stacked chromonic liquid crystals are produced for production of masks, templates, biosensing and optical applications.26Lyotropic chromonic liquid crystals (LCLCs) are a unique subset of lyotropic LCs possessing rigid, plank-shaped molecules with aromatic cores and often functionalized at the periphery with ionic groups to aid solubility in aqueous solutions.Herein, we report the design of polynorbornene scaffold comprising ABC type block architecture wherein A block contains side liquid crystalline cholesteryl molecules, B block contain chromonic xanthenone molecules, and C block containing brush-like PEG molecules that serves an internal plasticizer. This new liquid crystalline brush block terpolymers (LCBBTs) scaffold comprising chromonic xanthenone self-assembles into co-existing cholesteric mesophase, π–π stacked chromonic mesophase, and microphase segregated PEG domains. Although helical multilevel hierarchical materials has been demonstrated using chiral polypeptides, BCPs and chiral PS-PLLA block copolymers, to the best of our knowledge cholesteric mesophase in conjunction with microphase segregated domain has never been observed from BCP architecture. The key to the observed hierarchical assembly and controlled structure formation lies in the interplay of LC order, chromonic π–π stacking, PEG side chain microphase segregation, and their supramolecular cooperative motion. This unique “single component” polymer scaffold transforms our capacity to attain nanoscale hierarchies from block architecture similar to nanoscale mesophases resulting random architecture.9) is synthesized according to previously reported procedure. Poly(ethylene glycol)monomethyl ether functionalized norbornene  is synthesized from modified literature procedure.27 The monomer bearing xanthenone (NBXan) is synthesized nonyloxycarbonyl}bicyclo[2.2.1]hept-2-ene  has been previously synthesized by established protocols. Polynorbornenes containing xanthenones, P(NBXan), are synthesized by adding monomer NBXan (500 mg) into a 100 mL round bottomed flask, capped and purged with nitrogen gas. In a separate vial, modified Grubbs catalyst is dissolved in CH2Cl2, purged with nitrogen, transferred into the flask containing the monomer. The flask is purged again with nitrogen. The rate of homopolymerization for the new monomer is monitored via1H NMR  and P(pyr)2(Cl)2RuCHPh) with CH2Cl2 as a solvent. In a typical example block terpolymers (TPX75B) is prepared by dissolving 522.8 mg of NBCh9 in CH2Cl2, followed by purging with nitrogen gas, followed by addition of 9.9 mg of modified Grubbs catalyst dissolved in CH2Cl2 is added and purged with nitrogen. In the next step, NBXan (70.0 mg) in CH2Cl2 and NBMPEG (102.5 mg) in CH2Cl2 respectively, are each allowed to polymerize for 30 minutes before being terminated by addition of ethyl vinyl ether and precipitated into methanol before being dried overnight. The random terpolymers are made in a similar manner except that all the monomers are mixed in one step before adding the catalyst  are established this way and are tabulated in Table S1.†The thermal transitions of the terpolymers are investigated by differential scanning calorimetry (DSC). Samples are initially heated to 150 °C, cooled to −50 °C and reheated to 150 °C at a scan rate of 10 °C min9) shows two LC transitions at 80.7 °C and 100.9 °C and PEG crystallization temperature at −34.5 °C. TPX85B (block terpolymer with 85 wt% of NBCh9) shows a similar trend, 28 This is in contrast to LCBBC78,27 which is a diblock copolymer of NBCh9 (78 wt%) and PEG brush unit (22 wt%), and shows two LC transitions and backbone polynorbornene glass transition temperature at 85.2 °C and 105.7 °C, and 23.5 °C, respectively, but does not present any nanoconfined PEG crystallization temperatures. In sharp contrast to TPX75B, only one LC transition at 83.5 °C for terpolymer with random architecture and 75 wt% of Ch9, TPX75R, and 87.7 °C for random terpolymer and 85 wt% of Ch9, TPX85R, is observed. Both these random terpolymers show PEG crystallization temperatures ranging from −21 and −43 °C due to nanoconfinement effects.TPX75B (block terpolymer with 75 wt% of NBChTc) and associated enthalpy of cooling are lower than polynorbornene containing PEG brushes (PNBPEG) due to the dilution of PEG chains within polynorbornene matrix  in this system may be overshadowed by the majority cholesteric mesophases from cholesteric monomers.Liquid crystalline mesophases depend on molecular shape of the mesogens but are also associated with the strength and position of the polar or polarizable groups within the molecule, polarizability of the molecule, and the presence of chiral centers.32 Both random and block terpolymers show this peak confirming presence of π–π stacking in both cases. In contrast, previous studies involving LCBBC78 (block copolymers) and LCRBC (random copolymers) that do not contain chromonic molecules did not show corresponding to π–π stacked aromatic cores as expected.27To fully understand the molecular packing in chromonic molecules incorporated in our terpolymer systems, the presence and distance of π–π chromonic stacking in compression molded films is examined using WAXS  which re15 From temperature controlled SAXS, the LC transition to isotropic transition occurs at ∼85 °C  and there are different levels of intermolecular packing and LC order: PEG domains at ∼14.97 nm are comparable to previous studies while the three LC reflections with domain spacings at 6.10 nm, 3.45 nm and 2.25 nm corresponding to mesophases emanating from NBCh9  are employed. Three types of reflections in TPX75B are determined from SAXS are pertinent to: (i) microphase segregation as indicated by q* (ii) PEG crystallization and (iii) LC order. Presence of uncorrelated reflections imply smectic polymorphism of LC mesogens.om NBCh9 . The SmAThe hierarchical arrangement of smectic layers, microphase segregated PEG domains, and morphological evolution of the hierarchical structure are determined using both room and temperature controlled SAXS and the domain sizes of co-existing structures are calculated and tabulated in Table S2.†16,33 From room temperature SAXS, both TPX75B and TPX85B present microphase segregated domains at ∼28 nm. However, the first q* reflection in the random polymers of TPX75R appears ∼13.2 nm indicating PEG segregated domains. This is in agreement to previously studied systems where random copolymer, LCRBC85, showed PEG segregated domains at ∼12 nm. In TPX75B, TPX75R, TPX85B, PEG domains (i) lack long range order so higher order reflections are not present and (ii) show order-disorder transition that is higher than the LC clearing temperature.All block terpolymers show confined microphase segregated amorphous PEG domain within LC matrix at room temperature. Presence of microphase segregated domains from PEG is noted as q* reflections and higher order reflections are not observed indicating the absence of long-range order.9 and NBMPEG show microphase segregation with higher order peaks where PEG in this system crystallizes into lamella structure.16 In contrast, block terpolymers lack higher order peaks and PEG microphase segregation does not order into any familiar morphology. We can attribute this phenomena to the introduction of π–π stacked chromonic units which disrupts the formation of ordered microphase segregated PEG domains. More importantly, the block terpolymers show photonic properties which are absent in the diblock copolymers.Block copolymers bearing NBCh34,35 In both cases the block and random terpolymers both form cholesteric mesophases where microphase segregation of amorphous PEG exists. Biopolymers such as chitin, cellulose and chitosan are especially known to form cholesteric spherulites.36–38 A case in point is an observation made by Zhiyu and coworkers where TiO2 nanorods when dissolved in solution form lyotropic solutions exhibit cholesteric spherulites.39SEM samples are prepared by drop casting a thin film of 30% w/v of TPX75B and TPX75R dissolved in THF on a glass slide and allowing the solvent to evaporate. The samples are then annealed in an oven at 84 °C in the cholesteric mesophase for 24 hours. The films are rapidly cooled with cold air once removed from the hot oven to lock mesogens once annealed. Using a tiny blade, the thin film is gently removed from the glass slide and transferred to an SEM stub with the top side of the film facing down on the SEM stub. This sample is then coated with 2.5 nm Au–Pd (80/20) conductive layer before imaging the sample. From SEM images we find that there is a fundamental difference between the block and random terpolymer systems. TPX75B shows conventional fingerprint structures of cholesteric mesophases while TPX75R shows spherulites .34,35 In9 and NBMPEG do not show any cholesteric mesophase or resultant 1D photonic properties. These LCBBCs incorporate microphase segregation of PEG, PEG crystallization and LC order where PEG cylinders form within the cholesteric majority matrix and effectively prevents 1D photonic features from being expressed. For example, in LCBBC78, PEG cylinders form in a majority LC matrix and as such disrupt the expression of cholesteric optical properties. Additionally, within these cylinders semicrystalline PEG domains are present as confirmed by SAXS, DSC and WAXS studies. In contrast, when a chromonic block bearing a xanthenone unit is introduced, the interface between the domains in TPX75B is modified and allows the cholesteric features to be expressed by disrupting formation of PEG crystalline domains. This is the first time, confined microphase segregated PEG domains co-existing with cholesteric LC matrix in the block systems has been observed.The morphological evolution of TPX75B with temperature shows that at room temperature π–π chromonic stacking, smectic mesophases and PEG microphase segregation co-exist and evolves into π–π chromonic stacking with cholesteric mesophase and PEG microphase segregation. This enables the expression of 1D photonic properties that will be described in the next section. In previous studies, block copolymers bearing only NBCh27,33 Furthermore, incorporation of other LC monomers in the polymer framework particularly azobenzene and other achiral LCs has an influence on the optical properties and these materials show red or blue shifts depending on LC–LC interactions. In this work azobenzene is substituted for a chromonic monomer and the cholesteric–chromonic mesophase interactions disrupts the cholesteric pitch length of these terpolymers. Interestingly, all the random and block terpolymer systems prepared here composed of chromonic–cholesteric interactions show blue, green or blue green reflections with maximum wavelength of around 475 nm.Cholesteric liquid crystalline polymers are characterized with continuous twisted arrangements of chiral mesogens  featuring 1D photonic properties that can be tuned by use of a stimulus such as heat, light or temperature. In our previous publications, we showed that cholesteric polymers can be tuned to reflect different wavelengths (colors) by use of temperature.T1 or T2 for UV-vis measurements. The samples are then rapidly quenched to room temperature to kinetically trap the cholesteric mesophase using cold air to a temperature below Tg to preserve the cholesteric mesophase while retaining the reflected color.40,41 Cholesteric materials are known as 1D bandgap materials with selective light reflection at wavelengths corresponding to the product of the helical pitch and the average refractive index of the medium.42 Existence of a cholesteric mesophase above ∼81 °C is supported by UV-vis studies under reflectance mode. In both block terpolymers and random terpolymer films, we observe a peak at 371 nm which is a further indication that π–π* transition that occurs in this system from stacked chromonic molecules, 43,44 Thus in all these samples π–π chromonic samples co-exist with cholesteric mesophase and in three samples, π–π stacking, PEG microphase segregation and cholesteric mesophase co-exist.Solid polymer powders are compression molded and annealed between two Kapton films at with reference to 16 However, in the current LC block terpolymers containing chromonic molecules, microphase segregated PEG do not form semicrystalline lamella/cylinders within the cholesteric matrix as supported by SAXS and as such hinders such smectic to smectic to LC clearing transitions. We hypothesize that this in effect can change the anchoring at the IMDS, a modification promoted by chromonic xanthenone. This modification of the interface using π–π using chromonic molecules allows for smectic to cholesteric to LC clearings transitions can be confirmed by thermal, X-ray and UV characterization. Furthermore, xanthenone molecules in random and block terpolymer system acts to impede formation of longer pitch of the cholesteric mesophase and red-shift in the visible spectra. Finally, π–π* transition arising from π–π stacking of chromonic molecules (xanthenone) manifests when UV-vis reflectance measurements are made.43In block copolymers containing chiral LCs, microphase segregated domains and high surface to volume ratio at the intermaterial dividing surface (IMDS) induce cholesteryl mesogens to homogeneously anchor at the interface. This generally promotes smectic to smectic transitions leading up to LC clearing transition and cholesteric mesophase cannot be produced or stabilized.9 We also do not see any impact of microphase segregation on blue shift that has been observed in other LCRBCs. Thus, by introducing chromonic molecules and π–π interactions, block copolymer systems lose the ability to form highly ordered microphase segregated structures without curved interfaces45 and instead are destined to self-assemble into microphase segregated structures without long-range order in conjunction with curved mesophases similar to the random systems and thus show optical properties reminiscent of random systems, for the first time.Due to the presence of π–π stacking, the block and random terpolymer show similar morphological features for 75% composition and this manifests into similar optical behavior, that is, π–π stacking manifesting as π–π* reflections and the reflection from the cholesteric phase. We do not see any major shift in reflection that is expected from convention LC–LC interactions, in this case chromonic–cholesteric interactions.46–48 While the homopolymer containing xanthenone does not form chromonic mesophases, both random and block terpolymers self-assemble into chromonic mesophases in the presence of cholesteryl LC molecules. The ability to produce chromonic mesophases from within polymer films, as opposed to just small molecular solution assembly, is advantageous as the chemical, thermal and mechanical stability of these systems can be tailored for a variety of applications.46,49,50This study presents the first examples of homopolymers and terpolymers containing chromonic molecules that can be dissolved in organic solvents, form good films and can overcome some of the surface effects observed in chromonic systems that are soluble in aqueous media.9, NBXan, and NBMPEG. The impact of chromonic molecules on chromonic–LC interactions, hierarchical self-assembly, and thermal properties in block and random terpolymers are investigated. The interface between the blocks in block terpolymers is modified and manifests into the co-existence of microphase segregated PEG domains, chromonic π–π interactions and smectic polymorphism at room temperature. This evolves into microphase segregated PEG domains, chromonic π–π interactions and cholesteric mesophase at 75 °C and above. This observation in block terpolymers is contrary to previous LCBCPs and LCBBC where cholesteric mesophase has never been noted. The presence of π–π chromonic interactions co-existing with cholesteric mesophases in the block and random terpolymers manifests as visible reflections from π–π* transitions and cholesteric mesophase in the optical spectra of these polymers. In this work, we show that using supramolecular chromonic π–π interactions single component liquid crystalline block copolymers will present morphology and optical properties similar to nanoscale mesophases resulting in liquid crystalline random copolymer and terpolymer architectures.We report the synthesis and characterization of random and block terpolymers by ROMP of NBChThere are no conflicts of interest to declare.RA-011-D1RA00899D-s001


We recently identified a kidney risk inflammatory signature (KRIS), comprising 6 TNF receptors (including TNFR1 and TNFR2) and 11 inflammatory proteins. Elevated levels of these proteins in circulation were strongly associated with risk of the development of end-stage kidney disease (ESKD) during 10-year follow-up. It has been hypothesized that elevated levels of these proteins in circulation might reflect (be markers of) systemic exposure to TNFα. In this in vitro study, we examined intracellular and extracellular levels of these proteins in human umbilical vein endothelial cells (HUVECs) exposed to TNFα in the presence of hyperglycemia. KRIS proteins as well as 1300 other proteins were measured using the SOMAscan proteomics platform. Four KRIS proteins (including TNFR1) were down-regulated and only 1 protein (IL18R1) was up-regulated in the extracellular fraction of TNFα-stimulated HUVECs. In the intracellular fraction, one KRIS protein was down-regulated (CCL14) and 1 protein was up-regulated (IL18R1). The levels of other KRIS proteins were not affected by exposure to TNFα. HUVECs exposed to a hyperglycemic and inflammatory environment also showed significant up-regulation of a distinct set of 53 proteins (mainly in extracellular fraction). In our previous study, circulating levels of these proteins were not associated with progression to ESKD in diabetes. TNFα is predominantly produced by activated macrophages and monocytes3, although other cells are capable of producing it. TNFα mediates its biological activities through its two membrane receptors; TNF receptor 1  and TNF receptor 2 5.Tumor necrosis factor alpha (TNFα) is a potent pro-inflammatory cytokine that exerts its pleiotropic effects on a wide variety of cell types and plays a vital role in the pathogenesis of inflammatory diseases7. A comprehensive and large-scale proteomic analysis of human endothelial cells by Gautier et al. identified 207 proteins that exhibited a significant variation following TNFα-interferon-gamma (IFNγ) stimulation. That study also deciphered at the proteomic level the biological networks involved in endothelial cell response to TNFα-IFNγ8. It is worthwhile to note that prior studies mainly examined the intracellular content of TNFα-induced endothelial cells and targeted only a few genes and/or proteins, and importantly matrices such as supernatants (medium that the cells were grown in) have not been investigated.Endothelial cells represent key effectors in inflammation and short-term treatment of TNFα-induced endothelial cells resulted in the up-regulation of inflammatory cytokines, including interleukins 6 (IL-6) and 8 (IL-8), and adhesion molecules such as intercellular adhesion molecule-1 (ICAM-1), vascular cell adhesion molecule-1 (VCAM-1) and selectins (e.g. E-selectin)9. In addition, AGEs bind to their receptor RAGE, and the activation of RAGE by AGEs increases endothelial permeability and causes the release and upregulation of TNFα and other cytokines such IL-6 and IL-8, which consequently induces the production of reactive oxygen species leading to glomerular injury and tubular damage, and ultimately leads to DKD9.The pro-inflammatory cytokine TNFα is believed to be a key inducer and driver of inflammation and plays a central role in the network of pro-inflammatory cytokines contributing to the pathogenesis of diabetic kidney disease (DKD) progression. Many factors including high glucose (hyperglycemia), angiotensin II and advanced glycation end-products (AGEs) serve as potent inducers of TNFα, which upregulates the expression of cell adhesion molecules (ICAM-1 and VCAM-1), monocyte chemoattractant protein 1 and colony-stimulating factor-1 in various kidney compartments, thereby promoting the recruitment of monocytes and macrophages to sites of inflammation10 were the first to suggest TNFα may participate in the pathogenesis of DKD. Following this initial report, other experimental works have consistently reported TNFα as a critical mediator in the development of DKD12 and the roles of TNF pathway in the pathogenesis of DKD and other kidney diseases have also been reviewed15. Recently, attention has been drawn to determine whether TNFα is a potential target for therapeutic intervention to ameliorate the progression of DKD. One study reported the effectiveness of anti-TNFα antibody in the amelioration of DKD in Ins2Akita diabetic mice and protection from streptozocin-treated hyperglycemic kidney injury in another macrophage-specific TNFα-deficient mice model. In both models, the authors observed significant reductions in albuminuria, improvement in kidney morphology and down-regulation of inflammatory cytokines, in addition to reductions in TNFα levels16.Hasegawa et al.17. We identified a robust kidney risk inflammatory signature (KRIS), comprising 17 circulating inflammatory proteins, including previously identified TNFR1 and TNFR2 receptors, interleukins and chemokines. The KRIS proteins were strongly associated with the 10-year risk of end-stage kidney disease (ESKD). It has been interpreted that elevated levels of these proteins in circulation, particularly TNFR1 and TNFR2 might be secondary to increased level of their TNFα ligand in circulation. Elevated levels of TNFα have been observed in individuals at risk of DKD, although inconsistency still existed with some studies reported TNFα levels had no significant change in diabetic individuals20. These findings, however, may be qualified. First, TNFα is a low abundance protein, hence, very low concentration in circulation, and the currently available methods for detection of TNFα are not sensitive enough to tackle the challenges of TNFα measurement at lower concentrations. Second, if TNFα levels in circulation are changing over a short period of time, therefore, a single measurement of this cytokine may not be a reliable predictor of progression to ESKD during 10-year follow-up. Finally, TNFα may exert its action without being cleaved off or shed from the cell membrane surface by a disintegrin and metalloproteinase 17 (ADAM17), and subsequently released into the extracellular space and into circulation22. Considering the above hypotheses, elevated level of TNFα might be a driver of the disease process that underlies progression to ESKD and may be responsible for elevated levels of KRIS proteins in circulation.Recently, we comprehensively examined 194 circulating inflammatory proteins in DKD24. As a model system, we selected HUVECs because they are a widely used cellular approach to study biological mechanisms under controlled conditions25. We could, therefore, compare the activities of both endogenous and exogenous protein profiles in hyperglycemia and TNFα-induced HUVECs. In addition to the KRIS proteins, the SOMAscan assay also allowed us to quantify 1305 unique proteins, providing an in-depth proteomic analysis of hyperglycemia and TNFα-stimulated HUVECs and resulting in a much more detailed picture of the proteomic variations associated with the inflammatory response.To investigate whether TNFα may regulate the expression levels of the KRIS proteins, we performed an in vitro study using endothelial cells as a target for TNFα under high glucose (hyperglycemia) condition. In addition to KRIS proteins, we sought to investigate whether TNFα is involved or regulates the expression of other proteins in both the cell lysate (intracellular) and the supernatant (extracellular) from human umbilical vein endothelial cells (HUVECs), in response to TNFα stimulation and hyperglycemia. Toward these aims, we utilized a global proteomic approach based on an aptamer-based SOMAscan proteomic assay that uses single-stranded DNA aptamersUsing the custom-made Olink proteomics platform, we quantified the expression levels (pg/ml) of select KRIS proteins  in the cell lysates and supernatants of 3 human cell lines; HUVECs, renal proximal tubule epithelial cells (RPTECs) and fibroblasts. Of the 3 human cell lines, we detected a considerable amount of KRIS proteins in both the cell lysate and supernatant from HUVECs  and (2) α = 3.8 × 10−5 (Bonferroni’s correction for 1305 proteins measured on the SOMAscan platform). The fold change is a ratio of a mean RFU value of a protein in HUVECs cultured in TNFα in hyperglycemia condition to a mean RFU value of a protein in HUVECs cultured in hyperglycemia alone. Interestingly, a comparison of expression profiles of 1305 proteins from HUVECs treated with high glucose (4.5 g/L) versus low glucose (1 g/L) condition showed no substantial impact on the expression protein levels in either the cell lysate or supernatant  for TNFα-stimulated HUVECs in hyperglycemia (y-axis) versus hyperglycemia (x-axis) alone groups. The color of each point indicates the p values intensity  from highly significant proteins (red dots) to non-significant proteins (blue dots). Fourteen proteins were found to be differentially expressed (Bonferroni’s corrected α = 3.8 × 10−5 for 1305 proteins measured) in intracellular HUVECs, whereas 48 proteins were found to exhibit a significant variation following TNFα stimulation in the presence of hyperglycemia in the extracellular HUVECs. These significant proteins are marked on the scatterplots . In the extracellular space, the RFU signal was massively elevated, mainly due to the addition of TNFα into the culture media, compared with that of control HUVECs samples .TNFα, used as our internal control, is one of the 1305 proteins measured on the SOMAscan assay. In the intracellular fraction of TNFα-stimulated HUVECs in the presence of hyperglycemia, the mean RFU value was 934 compared to 790 in the control HUVECs  versus extracellular (n = 48) proteins (excluding significant KRIS proteins) subjected to hyperglycemia and TNFα treatment is shown in Fig. p < 0.0001), whereas there were significant depletions of receptors (p = 0.0003) and membrane (p = 0.01) proteins. We observed no significant enrichment or depletion for intracellular proteins , secreted proteins 25%), membrane proteins (31%) or intracellular proteins (25%) . Circulating levels of these proteins were not associated with progression to ESKD in diabetes in our previous study17.It has been hypothesized that elevated levels TNFα in presence of hyperglycemia might be important mechanisms that underlie the development of DKDThe present study is a first report to provide a complete global proteomic profile of TNFα-stimulated HUVECs in the presence of hyperglycemia with special emphasis on the investigation of the extracellular/secreted matrix proteome, considered of high importance in DKD. In parallel to the supernatants, we also examined the intracellular/cell lysate fraction, corresponding to the path of proteins on their way to be secreted into circulation. In addition, this study presents the first comparative global analysis of intracellular versus extracellular proteomes of TNFα-stimulated HUVECs in the presence of hyperglycemia fractionated by cellular location (intracellular and extracellular spaces).27. We recently reported circulating plasma levels of TNF receptors  and other inflammatory proteins as an extremely robust and independent predictors of risk of ESKD in DKD17. The current study demonstrated that although TNFα protein level was extremely high in the extracellular HUVECs fraction, elevated TNFα level did not up-regulate extracellular levels of the KRIS proteins. Surprisingly, TNFR1 was the most significantly down-regulated protein in extracellular HUVECs, whereas TNFR2 protein levels were unaffected following TNFα treatment and hyperglycemia.Inflammatory processes play an essential role in the pathophysiology of DKD and other diabetes complications28. Another mechanism for the generation of the soluble TNF receptors included the constitutive production of TNFR1 within exosome-like vesicles29. Hawari et al. reported that the major soluble form of TNFR1 is a full-length 55-kDa protein in human serum and lung epithelial lining fluid, whereas supernatants from human vascular endothelial cells contain only the full-length 55-kDa TNFR129. In the present study, we have not determined whether circulating TNFR proteins measured in HUVECs were either fully cleaved or uncleaved or a combination of both forms. Regardless of the postulated mechanisms, the results of our study demonstrated that exposure to TNFα and hyperglycemia did not impact any of these mechanisms in HUVECs.There is limited evidence regarding regulation of intra- and extra-cellular levels of TNF receptors and the other KRIS proteins. Several explanations were proposed. One postulates the role of TNF-converting enzyme  sheddase. This is a primary sheddase and/or activator of TNFα and TNF family receptors, leading to the proteolytic cleavage and release of ectodomains into the extracellular space31. Interestingly, although we cannot exclude the impact of TNFa signaling through TNFR1 and TNFR2 on other proteins, our study showed that it did not result in increased production (intracellular levels) and secretion (extracellular levels) of KRIS proteins. Furthermore, the biological processes that were enriched with proteins stimulated by TNFa do not include apoptotic processes, one of the major pathways that is activated through TNFR1/2.TNFα and its receptors, TNFR1 and TNFR2, constitute a complex signaling network, with both TNFα receptors differently activated by membrane and soluble TNFα. Their biological interactions are complex. TNFα signaling through these receptors induces cellular responses ranging from the production of pro-inflammatory cytokines to the stimulation of cellular proliferation, differentiation and cell migration as well as the initiation of cell death or apoptosis8. Chemokines/cytokines are known to be pro-inflammatory and can be triggered during immune response to attract immune cells to the site of inflammation, whereas others are involved during normal tissue growth and development32. Taken together, our present findings reveal that HUVECs exposed to a hyperglycemic and TNFα environment regulates a distinct set of proteins, not associated with DKD and progression to ESKD.The present study also aimed to determine whether there are better protein signatures that distinguish between TNFα-stimulated HUVECs in the presence of high glucose versus high glucose alone conditions. Our global proteomic analysis indicated that TNFα induces/regulates the expression of many other proteins involved in immune response, chemokine and cytokine activities, and inflammatory processes. Among differentially expressed proteins, we observed several well-known cell membrane proteins that were involved in leukocyte recognition and recruitment including ICAM-1, and E-selectin, which were previously identified to be up-regulated upon inflammatory response of endothelial cells after TNFα stimulation34. This signal peptide along with other signals decide the final destinations, including residence in the ER , lysosomes or the plasma membrane  or getting exported to the cell exterior35. Other possible pathways of protein secretion were also proposed. First, proteins might be directly transported to the plasma membrane; second, accumulation of proteins underneath specific regions of the plasma membrane might be secreted to the cell exterior as a result of membrane blebbing; and third, the formation of tiny vesicles inside the cell, called exosomes, but are then released and broken in the extracellular space36.Different mechanisms of regulation by TNFa of the distinct set of 53 proteins in HUVECs may be considered. Interestingly, several proteins that were intracellularly down-regulated in response to elevated glucose and TNFα environment were found to be up-regulated or in excess in extracellular HUVECs. How do the right proteins get to the right places or, more specifically, how do cells decide which proteins to retain and which ones to secrete to the cell exterior? This may be simply based on the protein abundance in the cells or perhaps other specific sorting signals direct proteins to different parts of the cell or they get exported out of the cell and into the extracellular space. When a protein is made, it will either remain in the cytosol or enter the endoplasmic reticulum (ER) during translation if they have an amino sequence called a signal peptide, which is a series of hydrophobic regions generally found near the N-terminus of the protein that facilitates the penetration and transport through the ER membraneSeveral limitations should be considered when interpreting our study findings. The present study is limited by a relatively low number of replicates and the reliance on a single cell type (HUVECs) and the results of this study need to be replicated and expanded using other cells as a target for TNFα. This study is, however, novel in several aspects. Firstly, we further confirmed our previous observations in DKD that TNFα ligand effects were negligible on TNF receptors, and secondly, the use of highly multiplexed SOMAscan platform, and thirdly, we performed the first global proteomic analysis directly comparing the intracellular and the extracellular/secreted matrix proteome, in TNFα-stimulated HUVECs exposed to a hyperglycemic condition, allowing for a more detailed picture of the inflammatory processes in fraction-specific components.38, RPTECs (CRL-4031) were purchased from the American Type Culture Collection (USA) and cultured according to the manufacturer’s protocol, and skin fibroblasts were obtained from a patient with type 1 diabetes and cultured as previously described39. The expression levels of KRIS in the cell lysates and supernatants from the 3 human cell lines were determined using the custom-made Olink proteomics assay. This high-throughput proteomic platform relies on two specific probes  through Olink’s proprietary Proximity Extension Assay (PEA) technology40. The cell lysates and supernatants from 3 human cell lines were processed at the Olink Bioscience laboratory of Olink Bioscience .To determine the best model system to study the effect of TNFα stimulation under high glucose conditions, we assessed the expression levels of KRIS in the cell lysate and supernatant in 3 human cell lines; umbilical vein endothelial cells (HUVECs), renal proximal tubule epithelial cells (RPTECs), and fibroblasts. HUVEC cell strain was randomly selected from among 62 cell strains recently used in our study37. The umbilical cords were obtained at the time of delivery when a cesarean section was performed. Due to privacy issues, we do not have any detailed information regarding the pregnant women’s healthy history except that they were healthy with no history of diabetes or any cardiovascular complications. A study that used these cell strains was recently published38. For the present study, one HUVEC cell strain out of 62 HUVEC strains was selected randomly.Human Umbilical Vein Endothelial Cells (HUVECs) were isolated from umbilical cords of 62 newborns delivered by healthy Caucasian mothers between the 36th and the 40th gestational week at the Hospital of Chieti and Pescara in ItalyProtocols to collect HUVEC strain were in agreement with the ethical standards of the local Institutional Committee on Human Experimentation (Reference Number: 1879/09COET) and with the Declaration of Helsinki Principles. The protocol was approved by the Institutional Review Board and the participating subjects signed the informed consent. The Joslin Diabetes Center Committee on Human Studies approved the experimental procedures for this study.38. For this study, HUVECs were grown to sub-confluence, then 150,000 cells/well were plated in 6 well plates. Following 24 hours (h), cells were serum-starved (0.5% FBS) and cultured in 4.5 g/L D-glucose (high glucose) or incubated for 24 h with TNFα (10 ng/mL) in high glucose. TNFα at a concentration of 10 ng/mL was selected in this study because it is considered the optimal functional concentration of TNFα in many cell types and in experimental studies42. It was shown that after the first 24 h, TNFα was active at the start of the experiments and its activity started to decline after 24 h. Then, supernatants were collected in tubes, centrifuged a 14,000 rpm for 5 min (min) to clarify before stored a − 80 °C. Cells were trypsinized, transferred to a 50 mL polypropylene tube, and centrifuged at 1200 rpm for 10 min. Supernatants were discarded and cell pellets gently re-suspended in phosphate buffer saline (PBS) and transferred to pre-labeled 2.0 mL polypropylene tubes. Cell suspensions were centrifuged and all supernatants carefully removed. Cell pellets were quickly snap frozen in liquid nitrogen and immediately stored at − 80 °C.The details of the HUVECs culture preparation are described elsewhereCell pellet was re-suspended in 200 μL Mammalian Protein Extraction Reagent  and 1X HALT protease inhibitor (ThermoFisher), per kit instructions. The lysed cells were centrifuged at 14,000× g for 5 min and the clarified lysate was collected. The total protein amount was quantified using the BCA Protein Assay kit (ThermoFisher) and 2.4 μg of proteins were used in the SOMAscan assay.44. The SOMAscan platform offers a remarkably dynamic range, and this large dynamic range results from the detection range of each SOMAmer reagent in combination with three serial dilutions of the sample of interest: the 40% (the most concentrated sample), 1% and 0.005% (the least concentrated sample) dilution groups to detect low, medium and high abundant proteins, respectively. The assay readout is reported in relative fluorescent units (RFU) and is directly proportional to the target protein amount in the original sample. The details of the SOMAscan proteomics platform are described elsewhere24.The SOMAscan proteomic platform uses single-stranded DNA aptamers and the platform is facilitated by a new generation of the Slow Off-rate Modified Aptamer (SOMAmer) reagents that benefit from the aptamer technology developed over the past 20 years10-transformed to stabilize the variance prior to analysis. Fold change is a ratio of a mean RFU concentration of a protein in HUVECs cultured in TNFα in high glucose condition to a mean RFU concentration of a protein in HUVECs cultured in high glucose. Statistical significance of protein expression level changes between TNFα-stimulated and control HUVECs was determined by the paired two-tailed Student’s t-test and differentially expressed proteins at the Bonferroni’s correction α = 2.9 × 10−3 (17 KRIS proteins) and α = 3.8 × 10−5 (1305 proteins measured on the SOMAscan platform) were considered statistically significant. Enrichment or depletion of certain protein classes was conducted using two-sided Fisher’s exact tests over a background of 1305 proteins. Functional enrichment analyses were performed using DAVID Bioinformatics database46.All statistical analyses were performed using SAS for Windows, version 9.4 . SOMAscan RFU values were logSupplementary Information.


The cerebellar cortex encodes sensorimotor adaptation during skilled locomotor behaviors, however the precise relationship between synaptic connectivity and behavior is unclear. We studied synaptic connectivity between granule cells (GCs) and Purkinje cells (PCs) in murine acute cerebellar slices using photostimulation of caged glutamate combined with patch-clamp in developing or after mice adapted to different locomotor contexts. By translating individual maps into graph network entities, we found that synaptic maps in juvenile animals undergo critical period characterized by dissolution of their structure followed by the re-establishment of a patchy functional organization in adults. Although, in adapted mice, subdivisions in anatomical microzones do not fully account for the observed spatial map organization in relation to behavior, we can discriminate locomotor contexts with high accuracy. We also demonstrate that the variability observed in connectivity maps directly accounts for motor behavior traits at the individual level. Our findings suggest that, beyond general motor contexts, GC-PC networks also encode internal models underlying individual-specific motor adaptation. The variability in synaptic connectivity observed at the cerebellar granule cell - Purkinje cell connection in mice accounts for motor behavior traits at the individual level, suggesting that cerebellar networks encode internal models underlying individual-specific motor adaptation. Population dynamics in cortical networks encoding stimulus features route information to different parts of the brain dedicated to motor planning and motor control. If these neuronal dynamics eventually succeed in producing an adapted behavior, they are stabilized through synaptic plasticity yielding to a mutual structuring of circuit connectivity and activity patterns across the brain11. Recent experiments described how different types of neurons in the cerebral cortex are selectively and functionally connected depending on stimulus features they have to encode15. Similarly, in the cerebellar cortex, structured synaptic maps across individuals have been described in identified modules17. However, how they are related to adaptive behavioral conditions is not well understood.Sensorimotor adaptation and motor learning rely on a combination of neuronal computations performed in different brain areas which communicate through cerebello-thalamo-cortical loops21. By computing the actual sensory feedback, cerebellar networks estimate prediction errors, which are then used to adapt the ongoing motor sequence and internal models. Indeed, many studies demonstrated that the cerebellum can learn and store complex adaptive behavior23, such as walking on a split-belt treadmill25. However, how locomotor adaptation is encoded in cerebellar cortical synapses is unknown. One hypothesis would be that synaptic connectivity maps encode movement features in each specific context . Connectivity maps would then represent individual-specific engrams of adaptive behaviors established throughout motor learning and such feature-based maps would be animal and context specific21. To address this hypothesis, we investigated whether functional synaptic maps between granule cells (GC) and Purkinje cells (PC) in the anterior cerebellar vermis, an area involved in adaptation of locomotion30, are modified in different locomotor contexts (after training in a wheel or following impairment of locomotion) and during development. Many studies suggest that GC-PC synapses are a major site of sensorimotor information storage in the cerebellar cortex32. GCs integrate sensorimotor information conveyed by mossy fibers (MFs) and carry motor commands or current body sensory state from many precerebellar nuclei36. GCs compute and distribute this information to many different PCs via their long axons, the parallel fibers (PFs), leading to specific excitatory, functional connectivity maps17. The second major excitatory input to the cerebellar cortex, the climbing fibers (CF), target PCs, controls plasticity at the GC-PC synapses and its topographical organization defines an array of anatomo-functional modules called microzones43 organization of functional connectivity maps, biased but not fully determined by somatotopic hardwiring.17, Fig. 39 in subsets of PCs arranged in parasagittal stripes. These markers, which are highly conserved between individuals, delimit zebrin positive (e.g. P1+ and P2+) and negative  bands of PCs matching with the topographical arrangement of the CF inputs44, outlining cerebellar microzones in the cortex  were recorded in PCs . We therefore treated the GC layer either as a grid or as a series of GC columns of 40 or 20 µm width corresponding to either low or high-resolution photostimulation, respectively. Each functional connectivity map was then represented either as a map or a projected synaptic profile of the maximal synaptic weight in each GC column and aligned to the zebrin bands  and selected a group of medial PCs  and Cx the column at coordinate x of a stimulation grid  for every pair of columns x and x′ along the mediolateral axis of the functional connectivity maps. Cxx′ entries become the weights of edges between graph nodes associated to the positions x and x′. Cxx′ values were compiled in a correlation matrix C(M)  than average. This metric can detect degree heterogeneity within modules, possibly reflecting the existence of patches in the associated map with simpler or more complex shapes. (3) The average participation coefficient measures the probability that a node in a module is also connected to nodes in other modules. This metric may capture the tendency of patches in the map to partially overlap. (4) The average local assortativity measures the tendency of nodes to connect to other nodes with similar strength . Strong assortativity in a map may correspond to the co-existence of narrower and broader patches. Together these abstract metrics convey concrete and complementary information about the geometry of the connectivity maps. A detailed illustration of how synaptic map traits are translated into graph-level features is given by Supplementary Fig. We then quantified the topology of these graph representations of the synaptic maps using 4 standard metrics  and PND1856. At this age, they are hyperactive with bouts of vigorous jumping called ‘hoppy’ or ‘popcorn’ stage resulting from synchronous contraction of fore and hind limb extensors57. To assess whether these behavioral features correlate with specific synaptic map adaptation, we recorded GC-PC functional connectivity maps at low resolution  at different time points during mouse development: from PND9-10, before proper quadrupedal locomotor activity, to young adults (PND > 30) when locomotion is well adapted  were significantly larger in all but early cuff groups when compared to control group  may limit the condition-specificity of GC-PC connectivity maps. To verify whether locomotor contexts could still be discriminated even at the level of individual maps, beyond group-level comparisons, we resorted to a supervised machine-learning approach, in which a random forest classifier was trained to infer the underlying locomotor condition based on alternative characterizations of the spatial organization of each individual map.gweights_zonewise(M), zone-wise synaptic weights in Fig. In a first attempt, we described each map’s organization through eight-dimensional vectors whose entries were given by synaptic weights averaged over the eight structural microzones ) with the following entries: the modularity index for the whole map, and graph participation, degree z-score, and assortativity averaged separately over nodes associated to the ipsi- or the contralateral map sections ), thus fully ignoring anatomy; and a second, finer one, in which graph features were averaged by microzones (gzonewise(M), Supplementary Fig. In a second attempt, we described the fine structuration of connectivity maps in terms of a set of graph-based metrics Fig. . We provAs shown by Supplementary Fig. gglobal(M)) and behavioral performance either on the wheel (short/long training) or on the force pressure corridor (adapted sham/cuff). We again used multi-dimensional vectors of graph-based map ) descriptors as input and trained generalized linear models (GLMs) to predict individual-level behavior from spatial organization of their maps. The workflow of this analysis is described in Fig. The large variability between maps may be due to experimental “noise” or reflect on the contrary fine levels of behavioral differences across individuals in normal or adaptive conditions. To investigate this hypothesis, we tried predicting individual-level specificities in adaptive locomotor behavior from graph descriptions of the global synaptic maps  vs 0.02 ± 0.08  vs 5.30 × 107 ± 7.5 × 106 . BIday15 is the behavioral feature with the larger dispersion of individual values. Indeed, we were able to significantly predict the evolution of locomotor unbalance monitored by the BIday15 component from graph description characterizations of functional connectivity maps  was also marginally significant  or ponto-cerebellar MFs (P1+ and P2+)69 . This part of the P2− microzone receives information from both hindlimbs and forelimbs and targets hindlimb extensor muscles through the lateral vestibular nucleus72. We therefore postulate that in cuffed and wheel-trained animals, an intense reorganization of the coordination between forelimbs and hindlimbs is required for gait adaptation.We identified patchy GC functional synaptic connectivity maps to medial PCs as observed in our previous study 17 Fig. . Althoug20. A change in the relationships between muscles or a modification of limb alternation during locomotion as we have induced  in our experiments should affect the workflow of computation involving internal models as also suggested in refs. 24. We provide strong evidence that PCs learn to adapt locomotor behavior by adjusting GC-PC connectivity maps in a context-dependent manner. In medial PCs, each context  leads to functional connections with a specific set of microzones through GCs. Moreover, multi-dimensional analysis of graph parameters  with respect to global graph metric averages but averaging to the level of individual microzones does not yield additional improvement  male mice (Mus musculus) from PND9 to PND100 under CD1 background were used for this study. Mice were housed with all littermates and parents from birth to weaning age (PND21). Older mice from all experimental groups were housed by 3–5 littermates per cage, in conditions required to fulfill their ethogram, with nesting material, as well as food and water ad libitum in a 12/12 h light/dark cycle with constant hygrometry (45–50%) and temperature  under the agreement no A67-2018-38 . 83 AldoC-venus59. For sham mice, the exact same procedure was followed except that no cuff was implanted . After surgeries mice received a unique intraperitoneal injection of non-steroidal anti-inflammatory drug  and were left at rest for 24 h minimum before behavioral assessment. In cuffed animals, the plastic cuff remained around the sciatic nerve until the killing of the animals.Mice were anesthetized by inhalation of isoflurane , then laid at rest on the left side of the body to expose the right hindlimb. A mix of lidocaïn/bupivacaïne (2 mg/kg each) was subcutaneously injected at the level of the upcoming incision. A 0.5 cm incision was made parallel to the femur to expose leg muscles. Muscles were gently separated using sterilized wooden sticks to expose the main branch of the sciatic nerve. The nerve was pulled out of the limb and a sterile 2 mm section of split PE-20 polyethylene tubing (cuff), 0.38 mm ID/1.09 mm OD, was wrapped around the nerve with the help of a pointed steel stick and a bulldog clamp. The nerve was then pushed back under the muscle fascia, and skin was suturedTo assess and quantify cuff-induced gait and balance impairments during locomotion, we built a force-sensor device. Mice were trained to voluntarily walk along an 80-cm-long corridor covered with two parallel strip-shaped force-sensors on each side of the corridor. The width of the corridor and the space between the two strips were adjusted to ensure that left and right limbs of CD1 adult mice are monitored by the corresponding strip . Force sensors  are two-wire devices with a resistance that depends on applied force according to a logarithmic law, allowing simple force-to-voltage conversion when tied to a measuring resistor in a voltage divider. As mice enter the corridor, an IR-barrier connected to a Raspberry Pi microcomputer  triggers force and video acquisition. Force signals from the voltage-divider output were digitized at 15–20 kHz . Force signals of the left and right side of the body were acquired simultaneously using WinWCP 4.2.2 freeware .https://github.com/ludo67100/cerebellarMaps/tree/main/Balance_supplementary.The balance index BI; Fig.  correspoearly for the area under the curve; see Fig. late), and maximum imbalance (BI at day 15th after surgery).In cuffed, sham, and control animals, the BI was determined before and after surgeries every 2–3 days for 1 month. A time-course was established, and we estimated the cumulative gait imbalance during the early days (AUCTrained mice had access to a running wheel for 1 h/day during 7 (short training) or 19 (long training) consecutive days. For each session, mice were placed in an individual cage equipped with a vertical, access-free running wheel. Locomotor activity was monitored using a piezoelectric sensor on the cage coupled to a magnet on the wheel to count the number of wheel turns during each session. We measured the total distance covered during individual sessions and during all the training periods of 7 or 19 days, and the slope of the training trajectory using linear regression analysis.PND8-9 CD1 pups were placed in crushed ice for 2–3 min to be anesthetized. A small incision was rapidly made over the cerebellum and 0.5 to 1 µL fluorescent dye  was injected at the midline of lobules IV/V of the cerebellar cortex using a glass pipette and a pressure pump . Location and depth of the injection were determined by eye using visual cues. After injection, the opened skin was closed with a drop of a biocompatible glue  and pups were placed on a heat pad a few minutes, then they returned to their homecage.PND30 CD1 mice were anesthetized by inhalation of isoflurane  and mounted on a stereotaxic frame . Body temperature was monitored using a rectal probe and maintained with a heating pad. A mix of lidocaïn/bupivacaïne (2 mg/kg each) was subcutaneously injected over the skull prior to incision, followed by an intraperitoneal injection of a non-steroidal anti-inflammatory drug . A parasagittal incision was made over the skull to expose lambda and bregma landmarks. The skull was cleaned using cotton sticks soaked in sterile 0.9% NaCl solution . A 0.5 mm diameter hole was drilled at AP = −2 mm, ML = 0 (from Lambda) to expose vermal lobules IV/V. DiI was injected as described for pups. The skin was sutured after injection and animals were put back in their home cage2), containing (in mM): NaCl (120), KCl (3), NaHCO3 (26), NaH2PO4 (1.25), CaCl2 (2.5), MgCl2 (2), glucose (10), and minocycline (0.00005) . In all, 300-µm-thick transverse acute cerebellar slices were then prepared with a vibratome  in ice-cold (≤4 °C) N-methyl-d-glucamine (NMDG) based solution containing (in mM): NMDG (93), KCl (2.5), NaH2PO4 , NaHCO3 (30), HEPES (20), Glucose (25), sodium ascorbate (5), Thiourea (2), sodium pyruvate (3), N-acetylcysteine (1), Kynurenic acid (1), MgSO47H2O (10), and CaCl2.2H2O (0.5). After cutting, slices were maintained at 34 °C in bubbled ACSF for at least 45 min, then kept at room temperature until use (1–5 h).Acute cerebellar slices were prepared from PND9–PND90 male CD1 ALDOC mice. PND12 to PND90 Mice were anesthetized by inhalation of isoflurane 4%  and then killed by decapitation. PND9 and PND10 pups were sedated by hypothermia prior to decapitation. The cerebellum was rapidly dissected out and placed in ice-cold (≤4 °C) artificial cerebrospinal fluid (ACSF) continuously bubbled with carbogen : CsMeSO4 (135), NaCl (6), HEPES (10), MgATP (4), and Na2GTP (0.4). pH was adjusted to 7.3 with NaOH and osmolarity was set at 295–300 mOsm. Biocytin (Sigma Aldrich) or neurobiotin  were added (1 mg/ml each) for cell reconstruction. Voltages were not corrected for the liquid junction potential, which was calculated to be 9.8 mV . We accepted recordings for which the inward current at −60 mV did not exceed 1 nA. Synaptic currents in PCs were low pass filtered at 2.4–2.6 kHz, then sampled at 20–50 kHz. All cells were recorded in vermal lobules III, IV, or V, up to 130 μm from the midline. All experiments were performed at room temperature using the same bubbled ACSF than for dissection with inhibition blocked. We systematically blocked NMDA, adenosine, CB1, GABAA, GABAB, and mGluR1 receptors to limit the modulation of EPSCs amplitude by activity-dependent activation of these receptors. They were respectively blocked using (in mM): D-AP5 (0.05) , DPCPX (0.0005), AM-251 (0.001), picrotoxin (0.1), CGP-52432 (0.001), and JNJ-16259685 (0.002) .Whole-cell patch-clamp recordings in voltage-clamp mode were obtained using a Multiclamp 700B amplifier  and acquired with WinWCP 4.2.2 freeware . Patch pipettes (3–4 MΩ) were pulled from borosilicate capillaries using a gravitational puller . Series resistance was monitored and compensated  in all experiments, and cells were held at −60 mV to isolate excitatory postsynaptic currents (EPSCs). The internal pipette solution contained  with a density of 1.92 × 106 GCs per mm3 (as described in Harvey and Napper87), the photo stimulation of this area activates at least 4 × 10−5 × 1.92 × 106 = 76.8 GCs. Conversely, the stimulation of a 40 × 40 × 100 μm area activates 2 × 10−4 × 1.92 × 106 = 384 GCs. These values might be underestimated since dendrites from GCs located in adjacent sites can be activated.Uncaging  experiments were performed using RuBi-Glutamate  perfused in the recording chamber in a closed circuit. In ALDOC micen = 100): P2- contralateral 416.6 ± 70.72; P2 + contralateral 71.56 ± 24.59; P1- contralateral 320.516 ± 62.94; P1 + 34.63 ± 16.18; P1- ipsilateral 320.46 ± 60.54; P2 + ipsilateral 69.75 ± 22.53; and P2- ipsilateral 438.04 ± 64.25. Recorded PCs were located at 52.42 ± 29.8 µm from the midline of lobules III/IV/V, corresponding to the cluster 1 defined in Valera et al.17.After recordings, the patch pipette was gently pulled out from the soma to close the membrane and the slice was immediately transferred from the recording chamber to a fixation solution composed of 4% paraformaldehyde (Electron Microscopy Sciences) in ACSF for a maximum of 24 h at 4 °C. Zebrin bands were identified using intrinsic Venus fluorescence. Recorded cells were labeled using Alexa 555-Streptavidin . Ipsi- and contralateral P1+, P1−, P2+, and P2− Zebrin bands and distance between the recorded PC and the midline (P1+) were measured in each experiment. In adult CD1 mice, Zebrin band lengths in lobule III/IV/V were . Median profiles and average maps were therefore obtained from spatially aligned maps within a global referential defined by the Zebrin band patterning, highly conserved between individuals  were interpolated and convolved with a triangular kernel  and the maps from control locomotion data set (high resolution) using graph-parameters (see below), high-resolution control maps were downsampled. This was done by convolving the high-resolution maps with a 2 × 2 box kernel and then downsampling it by .M, whose Mxy entry denotes the EPSC amplitude recorded at the position coordinates . We then focused on the column vectors Mx of this matrix, giving the EPSC amplitude profile for a specific column at position x, and for every pair of positions x and x′ along the scanned mediolateral axis we computed the normalized Pearson correlations Cxx′ = corr between the connectivity profiles at the two considered positions. Cxx′ values were then compiled in a correlation matrix C(M)  can be considered as the adjacency matrix of a weighted undirected graph. The entries Cxx′ thus become the strengths of links between graph nodes associated to the positions x and x′. The more similar are the connectivity profiles between two positions x and x′ and the stronger will be the weight Cxx′ of the connection between them. A standard Louvain algorithm52 is then used to optimally partition graph nodes into non-overlapping communities such that total weight of within group edges are maximized and weight of between-group edges are minimized. A resorting of node labels according to the extracted community labels leads to the “Rearranged Graph Matrix” in Fig. To quantitatively parameterize and therefore compare the spatial structure of synaptic maps, we introduced a mathematical translation into graph representations see Fig. . The conhttps://pypi.org/project/bctpy/) designed by Rubinov and Sporns90.All the absolute graph properties were calculated using the Brain Connectivity Toolbox for python  Modularity index - modularity index maximizes the number of within-group edges and minimizes the number of between-group edges using the Louvain community detection algorithm. It is a measure of how modular or “patchy” a graph is, i.e. higher the modularity index, the more distinct “patches” are in a graph. See Supplementary Fig. z-score (weighted) – is a measure of how relatively stronger or weaker is the connectivity of a node within a given module. It can be seen, in this sense, as a form of “degree centrality”, restricted to individual modules. A high average module degree z-score for the graph representation of a map indicates that patches in the map tend to be organized around a center, fading in a graded manner into the background which does not elicit stimulation responses in the considered PC. On the contrary, maps with lower average module degree z-score have internally homogeneous patches with sharper edges  Module degree 90:M = set of modules; i and all nodes in module m; i and all nodes.(c) Participation coefficient (weighted) - measures how strongly does a node in a module connects to nodes of the other modules. The participation coefficient is close to 1 if the connections received by the node are uniformly distributed among all modules and close to 0 if it favors connections to the nodes within its own modules. A high participation coefficient for a graph indicates that patches in the map tend to be partially overlapping in their range or imperfectly separated, as islands in an archipelago linked by narrow “piers”  Local assortativity (weighted) - is a measure of the tendency of a node to connect to other nodes with the similar degree. A positive local assortativity coefficient indicates that nodes tend to link to other nodes with similar degrees . Note that the node degree of a site within a map patch measures how many other sites the response profile of the considered site is similar to and that it is thus an indirect measure of the patch size scale. Thus, a map displaying both smaller and large patches well separated between them would result in a graph with a large assortativity  for each map); two medians, evaluated separately over nodes on the ipsilateral and the contralateral sides of the maps  for each map); and, finally, different median values for nodes within each distinct anatomical microzone ).Negative entries in the adjacency matrix were interpreted as an absence of similarity between the response profiles of two columns. Since our graph representations intend to describe relations of similarity, we thus ignored negative entries, treating them as zero for the computation of all graph features. All these graph metrics are then summarized into multi-dimensional vectors providing a parameterization of the spatial structure of each individual map. Modularity index is a graph centric measure and hence we get one value per map/graph. Module degree Absolute values of graph features do not have a straightforward meaning. Furthermore for small graphs as the ones considered here may strongly fluctuate in value depending on graph size. However, values can be reported in a more informative way, by expressing them as percent differences with respect to chance level expectations for their corresponding null graphs. We then computed normalized percent differences:gweights_zonewise(M)) and (2) lateralized absolute values of graph properties ). The sklearn.manifold.TSNE function in python was used. Centroids of point clouds corresponding to maps obtained in similar locomotor adaptation conditions have been obtained by computing the median projected coordinates of all the maps in the considered point cloud.t-SNE is a non-linear dimensionality reduction technique that transforms vectors in a high dimensional metric space into vectors within a lower dimension metric space  while ensuring that close points in the higher dimensional space are mapped to points which are still close in the lower dimensional space, whereas dissimilar points are mapped farther away. t-SNE projections were used to reduce the dimensionality for two kinds of data: (1) the zone wise distribution of synaptic weights , gbilateral(M), and gzonewise(M)) and zone wise synaptic weights (gweights_zonewise(M)) were used to train and test four random forest classifiers in order to classify the different locomotion contexts . A random forest classifier uses multiple decision trees and combines their results to enhance the classification performance. The classifier used 150 individual tree estimators per tree ensemble, each with a maximum depth of 30. The data was divided into 100 stratified trials, with training:testing ratio of 80:20%. Proportion of correctly and misclassified samples  were quantified in a confusion matrix and the accuracy was calculated as a percentage of samples that were correctly classified. To compare the accuracy score with chance level scores, random forests were also trained on shuffled data, using identical hyper parameters but by shuffling target prediction labels. The sklearn.ensemble.RandomForestClassifier function from the sklearn package in python was used. We also used an analogous procedure to train a classifier to discriminate maps from different developmental stages.The absolute graph features of three different resolutions  is encoded as a bias variable  that is multiplied with the graph features:I = trial number; Yi = behavioral feature for trial i; β0 = common intercept; N = set of all graph-based features; βj = slope for graph feature j; Gji = value for graph property j in trial i; γj = group specific slope for graph feature j; Ti = ±1, for the two groups adapted cuff/sham & short/long training); ϵi = error. The statsmodels.api.GLM routine from the python statsmodels package was used.In order to check how well the behavioral features of individual mice are predicted from the graph features of individual maps, we trained and cross-validated generalized linear models for 100 trials with training:testing split of 75:25%. The accuracy of the predictions on testing data  was quantified by calculating x in the following way . Each greek letter is associated to a different connectivity module in the modular partition of the graph representation. If the same module includes nodes associated to distant, non-contiguous positions then multiple functional zones are generated out of the same module, one for each spatially connected range (numbered by the progressive index). The second definition of functional zones was similar to the first one , however, we dropped the criterion of spatial connectedness of the resulting range. In this way the partition in functional zones mirrored exactly the one of the graph into modules, however, some of the generated functional zones could be made of spatially disconnected ranges. In this second definition, functional zones were simply labeled by the greek letters of the corresponding graph module, without index . In all cases, we computed MI between the structural and functional labelings of column positions as: X and Y respectively represent the strings of structural and functional zone labels for each map. p, where x ∈ X, y ∈ Y, is joint probability distribution of X and Y, that quantifies the overlap between structural and functional zone labels, whereas p(x) and p(y) represent the marginal probability distribution of X and Y. Chance-level values of MI could be estimated as well, by shuffling structural microzone labels and re-evaluating MI. MI values, for both actual and shuffled zoning, were normalized by the largest entropy value, among the entropies of structural and functional zone labeling:A mutual information analysis was used to quantify the degree of similarity between subdivisions of maps into zones based on anatomy  or based on graph representations .We thus first assigned structural and functional labels to each map column position U, MWU; Kolmogorov–Smirnov, KS; Levenes; Shapiro-Wilk, SW and paired or independent t-tests) were performed with the corresponding functions from the Scipy package. Unless reported differently, alternative and method arguments were set to ‘two-tailed’ and ‘auto’. Correction of Kolmogorov–Smirnov p-values with Holm method was performed with statsmodels.stats.multitest.multipletests function. Repeated Measure ANOVA on activity in the wheel and multivariate analysis of the BI time course (one-way repeated MANOVA and post hoc ANOVA) were performed using the Real Statistics Resource Pack (release 6.8), Copyright (2013–2021), Charles Zaiontz, www.real-statistics.com. Permutation testing was done by multiple bootstraps92 where a permutation of indices was used to calculate r-values and MSE for actual and shuffled data.Only biological replicates were used for statistics. Statistics were performed on independent experiments  except in Figs. Further information on research design is available in the Supplementary InformationReporting Summary