Forty-five meniscus samples of variable degeneration were harvested from the lateral meniscus body region of 45 patients during total knee arthroplasties. Samples underwent serial mapping on a 3.0-T MRI scanner  using a force-controlled and torque-inducing compressive loading device. Samples were measured at three loading positions, i.e. unloaded, loaded to 2 bar (compression force 37 N) and 4 bar (69 N). Histology (Pauli classification) and biomechanics (Elastic Modulus) served as references. Based on histology, samples were trichotomized as grossly intact (n = 14), mildly degenerative (n = 16), and moderate-to-severely degenerative (n = 15) and analyzed using appropriate parametric and non-parametric tests. For T1, we found loading-induced decreases in all samples, irrespective of degeneration. For T1ρ, zonal increases in intact (apex) and decreases in degenerative samples (base) were found, while for T2, changes were ambiguous. In conclusion, force-controlled loading and serial MR imaging reveal response-to-loading patterns in meniscus. Zonal T1ρ response-to-loading patterns are most promising in differentiating degeneration, while T1 and T2 aren’t clearly related to degeneration.and may provide an imaging-based indication of functional tissue properties.Meniscus pathology may promote early osteoarthritis. This study assessed human meniscus functionality (i.e. its response to loading)  Due to its decisive role in load bearing, load transmission, load dissipation and in providing joint stability, congruity and lubrication2, meniscus integrity in form and function is of utmost importance to the knee joint’s long-term health3. Meniscus functionality, which is the tissue’s ability to function properly, i.e. to disperse loads and reduce friction, is heavily dependent on its extracellular matrix properties. Collagen fibers, primarily type-I, define the tissue’s tensile strength and shock-absorbing properties, while proteoglycans contribute to compressive strength by upholding swelling pressure5. Lately, therapeutic efforts have been aimed at preserving and restoring the damaged meniscus secondary to trauma or degeneration7. This is of particular relevance as meniscus and cartilage pathologies are closely interrelated. Consequently, meniscus damage and loss are key features of and risk factors for developing osteoarthritis (OA)8. The discussion on whether meniscus pathologies are a cause or consequence of knee OA is ongoing10.Acute and chronic meniscus pathologies are frequent clinical entities13, and contribute to the evolution of OA by altering load distribution and transmission to the adjacent articular cartilage. Hence, detecting such degenerative changes early is necessary in therapeutic efforts to preserve the meniscus and prevent or delay the onset of early OA15.Yet common consensus prevails that morphological meniscus defects, i.e. surface breakdown and tissue tearing, are the consequence of degenerative changes of the extracellular matrix16. However, evaluation is subjective and solely based on morphological aspects such as surface integrity and intra-tissue signal intensities. MRI is also limited when detecting smaller lesions and changes prior to surface breakdown18. Quantitative MRI  techniques such as T2, T1 and T1ρ mapping provide spatially resolved measures of tissue (ultra)structure and composition beyond mere morphology and have been applied to assess meniscus in health and disease20. T1ρ and T2 characteristics in meniscus have been studied extensively, whereas data on T1 characteristics are sparse. While increases in T1ρ and T2 in the presence of OA and meniscus lesions have been reported21, these parameters’ substantial intra- and inter-individual variability only allows differentiation of histological extremes, i.e. intact versus severely degenerated tissue13. In imaging studies, meniscus functionality refers to the loading-induced intra-tissue adaptations and their imaging correlates and has been evaluated by simultaneous qMRI mapping and axial loading23. Studying the posterior horn of the medial meniscus, Calixto et al. found significant loading-induced decreases in T1ρ and T2 in OA knees and no such changes in control knees22. For the meniscus body, the same group observed significantly larger changes in T1ρ and T2 in the medial meniscus in OA versus non-OA knees23. Quantifying the tissue’s response to loading by measuring the changes in T1ρ (ΔT1ρ), T2 (ΔT2) and -possibly- T1 (ΔT1) as surrogate parameters of functionality therefore provides innovative imaging biomarkers of load transmission and, possibly, its failure in (early) OA.Due to its non-invasiveness, superior soft tissue contrast and absence of ionizing radiation, Magnetic Resonance Imaging (MRI) is clearly the most powerful and versatile imaging method of contemporary clinical medicine. Clinical-standard morphological MRI is the modality of choice in the evaluation of pathologies in and around the knee joint with high diagnostic accuracy in the assessment of gross meniscus pathologiesT1ρ, ΔT2, and ΔT1 in human meniscus are lacking, the present study aimed i) to assess the response to standardized loading of human lateral meniscus samples (body region) on the basis of qMRI mapping techniques, ii) to correlate the loading-induced changes to histological and biomechanical reference measures, and iii) to subsequently define the response-to-loading patterns of human meniscus in health and disease. The underlying research question was: What is the physiological and pathological response to loading of human meniscus ? Our hypotheses were that i) zonal changes in ΔT1ρ, ΔT2, and ΔT1 are demonstrated in response to standardized loading of meniscus as an indication of adaptive intra-tissue changes, and ii) the responses to loading thus discernible are distinctly different in grossly intact vs. early degenerative vs. moderate-to-severely degenerative meniscus.As standard values of ΔAll 45 samples underwent complete MRI measurements and subsequent histological and biomechanical reference characterization. In all samples, sufficient tissue thickness was available for these analyses.Histologically, samples displayed a wide range of different manifestations with Pauli sum scores ranging from 1 to 16 Fig. . AccordiElastic Modulus at strains 20% and 80% was not significantly different between the subgroups: strain 20%: 15.5 ± 10.1 MPa (Pauli Grade-I), 17.8 ± 12.5 MPa (Pauli Grade-II), 16.2 ± 9.9 MPa (Pauli Grades ≥ III) (p = 0.893); strain 80%: 351.4 ± 74.3 MPa (Pauli Grade-I), 343.4 ± 70.4 MPa (Pauli Grade-II), 353.5 ± 67.1 MPa (Pauli Grades ≥ III) (p = 0.919).Unloaded, we did not find significant degeneration-dependent differences in the various regions-of-interest (ROIs) for T1 or T1ρ. For T2, however, we determined significant differences in the apical zone with significantly lower T2 medians in Pauli grade-I than in Pauli grades-II (and higher) samples (p = 0.004). Overall, qMRI parameter values tended to be highest in Pauli Grades ≥ III samples and in the apical zone. Unloaded absolute qMRI parameters are given in Table Qualitatively, loading-induced changes were variable across the samples. Meniscus samples were subject to substantial compression and deformation under loading. Consequently, the physiological wedge shape underwent considerable flattening with increased loading.In Pauli Grade-I samples, only the apical and intermediate zones underwent slight changes, i.e. decreases in T1, and increases in T1ρ and T2, while the base zone was largely unaltered Fig. , while i0) to 718 ± 263 , and 604 ± 236  . Second, we found distinct changes in T1, T1ρ and T2 in response to loading. Table Quantitatively, loading-induced adaptations as detailed above were reflected by several changes: First, mean pixel numbers decreased significantly from 801 ± 237  ms, δ1: 605 (560–642) ms, δ2: 554 (525–586) ms, p < 0.001). Decreases were larger at the meniscus base and related to loading intensity, i.e. larger loading induced larger decreases . Significantly larger relative changes were found for the intermediate zone and the entire sample.For T1, we found significant decreases in all samples, irrespective of the histological Pauli Grade , they were distinctly different in the meniscus zones. First, we found -by trend- loading-induced decreases at the base zone. These were significant in Pauli Grades ≥ III samples (base ROI: δ0: 49.7 (42.5–58.9) ms, δ1: 41.9 (39.7–48.2) ms, δ2: 41.0 (38.5–49.0) ms, p < 0.001). Second, we observed discrepant and significantly different changes at the apical zone. Here, T1ρ values increased in Pauli Grade-I and -II samples  ms, δ1: 58.6 (55.2–63.2) ms, δ2: 55.0 (48.3–63.3) ms, p = 0.001), while they decreased non-significantly in Pauli Grades ≥ III samples  ms, δ1: 56.9 (44.5–67.4) ms, δ2: 51.3 (45.0–69.2) ms, p = 0.819). Changes in the intermediate zone were similar. Third, the amplitude of changes was not clearly associated with loading intensity.For T1ρ, we made numerous distinctly different observations. Even though mean relative changes were not significantly different for the entire samples  ms, δ1: 24.2 (22.5–26.0) ms, δ2: 23.0 (21.9–24.5) ms, p = 0.001), further zonal analysis revealed that these significant changes were exclusive to the apical zone with differences being significant in all samples (p < 0.001), Pauli Grade-II samples (p = 0.002) and Pauli Grades ≥ III samples (p < 0.001).For the other zones, however, changes were not significant. By trend, relative changes tended to be larger at higher loading intensity, even though significant differences were only found for the apical zone of all samples .For T2, we found ambiguous zonal loading responses, even though overall, decreases were more prevalent than increases. Even though in all samples, significant decreases were found with increasing loading . While these findings were confirmed for δ1 , correlations were not significant anymore for δ2 . Please see Supplementary Table We did not find significant correlations between histological measures and qMRI parameters, while the correlations between biomechanical parameters and qMRI parameters, i.e. between the Elastic Modulus at strains 20% and at 80% and T1, T1ρ, and T2 at δin-vivo and in-vitro studies that reported regional and zonal variability in human meniscus due to tissue heterogeneity in structure, composition and biomechanical properties24.The most important findings of this study are 1) lateral human meniscus body samples undergo distinctly different zonal changes in T1, T1ρ and T2 in response to loading and 2) these changes are associated with histological degeneration but not with biomechanical properties, i.e. Elastic Modulus. More specifically, in histologically grossly intact meniscus, the physiological response to loading is defined by decreases in T1, divergent changes in T1ρ with increases at the apex and decreases at the base, and ambiguous changes in T2. In severely degenerative meniscus however, the pathological response to loading is characterized by homogenous decreases in T1ρ throughout the tissue, while for T1 and T2, similar changes as for the intact samples were found. Across all samples and histological subgroups, substantial inter-individual variability could be determined. Even though we employed statistical measures and tests that decrease sensitivity to outliers, the substantial variability certainly prevented clearer statistical inferences and biological conclusions. Except for T2 in the apical zone, we could not find any significant degeneration-related differences for any ROI in the unloaded configuration. In all samples, we found that the apical zone (by trend) had the highest and the intermediate zone the lowest T1, T1ρ, and T2 values. This finding is well in line with recent in-vitro design, the loading intensity may be considered grossly representative of the in-vivo situation. The applied compressive forces of 38 N (δ1) and 69 N (δ2) were actuated on meniscus samples of standard length (15 mm) and variable width (mean: 8.4 mm)25, resulting in a mean sample surface area of 126 mm² and mean pressure levels of 0.30 MPa (δ1) and 0.55 MPa (δ2), respectively. For the lateral meniscus body region, such pressures are well in line with those experienced during level walking (ca. 0.5 MPa)26, even though peak pressures may be considerably higher during unlevel walking, running or stair climbing. Thus, our loading regime is representative of static loading configurations as in prolonged one- or two-leg standing. Within the premise of this study’s in-vitro setting, we consider the inclusion of two different loading regimens  to provide a thorough understanding of the loading-induced intra-tissue adaptations and to indicate if (and to what extent) variations in loading intensities are related to variations in tissue functionality as assessed by qMRI parameters.In our study, loading induced morphological deformation and flattening of the wedge shape and, correspondingly, significant decreases in the compressed samples’ pixel numbers, which is indicative of sufficient sample pressurization. Within the confines of our study’s 27, while for meniscus, the exact structural and/or compositional correlate is yet unknown. The loading-induced decreases are most likely due to water redistribution within the tissue, its partial loss and relative increases in the solid matrix constituents. Decreases in T1 were larger with more intense loading, in particular in the intermediate zone, which may be explained by the fact that this zone is biomechanically most compliant28. Mean overall decreases in T1 tended to be larger at the meniscus base, which is plausible, too, given the higher water content4 and the abundant amount of loose vascularized connective tissue29. Under loading, T1 values decreased in all samples, irrespective of degeneration, which indicates the dominant contribution of water to T1 relaxation that most likely overpowers the contribution of the biochemical variations in collagen and/or proteoglycans30. Also, MRI measurements were performed after extended sample storage in medium which may have increased tissue hydration beyond the constitutively high water content.The observed changes in the T1, T1ρ, and T2 maps are grossly reflective of the morphological changes. Overall, the samples’ responses to loading were variable. For T1, we found consistent loading-induced decreases throughout all ROIs of all histological Pauli Grades. For cartilage, T1 relaxation has been linked to tissue hydration4. Consequently, degeneration-related changes in the a-priori heterogeneous nature of the tissue are responsible for the discrepant changes at the apical (and intermediate) zones. Under loading, the tight collagen network is compressed and condensed. Intuitively, one would speculate that T1ρ values are decreased because of more restricted spin motion. In fact, T1ρ values were increased in Pauli Grade-I samples, which is in line with previous in-vivo data23. As T1ρ probes the low-frequency interactions of the tissue’s macromolecules and bulk water31, physiological adaptations of the structurally intact matrix, i.e. changes in collagen fibre anisotropy, restrictions in water redistribution and upheld swelling pressure, may have led to these increases. Nonetheless, the significantly different loading responses may indicate sufficient load transmission (in Pauli Grade-I samples) and its failure (in Pauli Grades ≥ III samples), thereby corroborating earlier findings23, even though the exact biophysical correlates of these changes remain unclear.Loading-induced changes in T1ρ were less homogeneous. While we found decreases at the meniscus base in all samples, changes at the meniscus apex and -to a lesser extent- the intermediate zone were significantly different in the distinct Pauli Grade subgroups. In Pauli Grade-I samples, T1ρ values were increased, while in Pauli Grades ≥ III samples, they were decreased. Similar to T1, the consistent decreases in T1ρ at the meniscus base are most likely secondary to the fact that T1ρ relaxation is dominated by extracellular water, especially in advanced degeneration19. Loading-induced decreases were more prevalent than increases, which is primarily indicative of changes in water distribution and content4. Consequently, stronger loading was associated with larger changes in T2, at least for the apical zone. Here, loading induced significant decreases in T2 in all samples except for Pauli grade-I samples, which is an indication of more pronounced degeneration-dependent changes in the apical zone. Due to its lack of intrinsic ability for self-repair, the inner zone is considered particularly prone to permanent tissue damage32, and consequently, degenerative tears usually extend from the inner zone to the periphery33. This clinical observation was confirmed in our study as tissue fraying and tearing in more degenerative samples usually originated from the apical zone.For T2, the response-to-loading patterns were ambiguous and not related to histological degeneration. In meniscus, T2 is widely considered to be sensitive to water interactions and related to content, orientation and anisotropy of the collagen fibers35, this finding is only partially in line with the pertinent literature. Potential reasons for this discrepancy relate to differences in study design, reference evaluation, sample numbers, degeneration severity, harvesting locations and biomechanical testing protocols. In their comparative evaluation of medial and lateral menisci, Katsuragawa et al. included six meniscus pairs each from OA and non-OA knee joints34. OA menisci were harvested from TKAs because of OA involving (at least) the medial compartment, while non-OA menisci came from knee joints without a previous history of OA as confirmed macroscopically. For biomechanical evaluation, standardized cylindrical specimens were prepared from the anterior horn and subjected to confined compression testing to determine aggregate modulus and permeability . Although histological work-up was performed, degeneration severity was only assessed qualitatively. The authors did not find any significant differences in the biomechanical parameters for lateral menisci from OA versus non-OA knees, while for medial menisci, they determined large differences indicating considerable softening in degenerative medial menisci. In contrast, Fischenich et al. found progressive decreases in biomechanical properties with increasing tissue degeneration35. The authors included 24 medial and lateral meniscus pairs from TKAs, sectioned them into anterior and posterior regions and performed i) indentation relaxation tests to determine the instantaneous and equilibrium compressive moduli and ii) tension testing to determine tensile modulus. Meniscal degeneration was assessed macroscopically based on inspection of gross tissue morphology, while histology was not performed. While tensile modulus was largely retained throughout degeneration, significant degeneration-dependent decreases in instantaneous and equilibrium compressive moduli were found. Whatever the exact relation of meniscal degeneration and biomechanical properties, fluid flow seems to predominate over matrix composition in determining the compressive properties of meniscus7, which suggests that even  moderate-to-severely degenerative meniscus upholds viscoelastic capacities to withstand compressive loading, at least when quantified by qMRI mapping techniques. This is in line with our findings that suggest that the structural and/or compositional correlates of T1 and T2 (and their loading-induced changes) are not significantly different across the different grades of histological degeneration. In contrast, zonally discrepant changes in T1ρ, i.e. significant increases at the apex in grossly intact samples and significant decreases at the base in moderate-to-severely degenerative samples, indicate that the correlates of T1ρ undergo degenerative processes that may be detected by functional imaging techniques. Moreover, the underlying tissue model/theory used to determine the mechanical properties of the meniscus samples and their assumptions are highly relevant. In the present study, we used a traditional two-parameter exponential model36 that is common in modelling approaches of nonlinear properties of fibril-reinforced tissues in tension such as meniscus39. Nonetheless, alternative models such as the linear biphasic theory40 have also been applied before to quantify mechanical properties of meniscus41.Surprisingly, we did not find any significant differences in the biomechanical properties of the histological subgroups. As earlier reports on the relationship of meniscal degeneration and biomechanical properties are conflictingex-vivo setting limits overall transferability to the in-vivo setting. Alongside differences in tissue hydration due to sample storage, additional factors such as temperature, culture conditions, preparation procedure, and sample position and orientation as well as the fact that we performed the MRI measurements in standard saline solution may alter relaxation characteristics and consecutive response-to-loading patterns42. Nonetheless, in a clinically relevant basic research context, this study comprehensively assessed degeneration-related response-to-loading patterns in human meniscus. For once, measurements were performed on a clinical MRI scanner  and on samples from a clinical population representative of the entire continuum of health and disease. Meanwhile, the increasing interest in the association of joint imaging and joint mechanics  has produced a number of MRI-compatible loading devices that are intended to realize the clinical translation of stress MRI techniques. Loading of patients’ knee joints (and menisci) is based on suspending weights via dedicated pulley systems47 or direct compressive loading along the leg axis50. In practical terms, further in-situ and in-vivo studies assessing meniscus functionality on the basis of such loading devices and quantitative MRI techniques are necessary to corroborate our study’s findings of the partial relatedness of loading-induced qMRI parameter changes and  degeneration.This study has several limitations. First, our study’s in vivo (that involves tensile and shear stresses51) is limited. Secondarily, the meniscus undergoes complex motion and adaptations, thereby deforming and displacing the perimeniscal soft tissues52. Another related aspect involves dynamic contact mechanics across the joint that change during activities of daily living, e.g. gait and stair climbing, and has considerable effects on resultant peak contact stresses of meniscus and cartilage26. Despite these limitations, our study’s implementation of standardized loading of meniscus samples in vitro is a basic requirement to realize reproducible and comparable loading regimes across sample cohorts at optimized image resolution and signal-to-noise ratio. Nonetheless, for more complex biomechanical evaluations, in-situ and in-vivo configurations are critical. Moreover, we deliberately chose an elastic polyvinylsiloxane inlay to circumferentially confine the meniscus samples. Under loading, the inlay was deformed as a sign of effective load transmission. This, however, increases the complexity of the subsequent biomechanical analyses, e.g. in finite element simulations. Third, even though sample size allowed for sound statistical analyses and valid inferences, it was limited, not least due to the study’s single-institution design. At this stage, it remains speculative -in particular when considering the qMRI parameters’ substantial statistical variability- if larger sample sizes might have brought about less equivocal findings. Fourth, as meniscus properties and their imaging correlates demonstrate considerable zonal and regional variability23, samples were harvested from the lateral meniscus body region only for the sake of topoanatomic consistency. Accordingly, our findings are only reflective of this region and may be different for the anterior and posterior regions of the lateral meniscus, let alone the medial meniscus. Fifth, samples were only harvested from total knee arthroplasties, which raises the question of our sample cohort’s representativity when it comes to truly healthy samples. In this regard, future studies should include alternative sample sources with a focus on young non-OA donors to be more representative of the entire spectrum of health and disease. Another related aspect concerns the semiquantitative histological scoring used as reference standard. Histological signs of degeneration across one sample may be variable, thereby rendering mean (subcomponent) scores potentially unrepresentative of focal alterations in matrix structure and composition that, however, may substantially affect qMRI characteristics. Sixth, even though our findings indicate an exciting scientific approach to further differentiate meniscus in health and disease, statistically significant differences between histological subgroups do not imply the ability to diagnose on qMRI parameters alone. With reproducibility of research being an increasingly important cornerstone of modern biomedical research, additional translational studies need to be conducted to confirm that the statistically significant differences observed in this study are of scientific and diagnostic relevance. In this regard, the apical meniscus zone should be primarily considered in efforts to differentiate the tissue’s functional status. Seventh, for biomechanical evaluation in terms of unconfined compression testing, an invasive preparation procedure was chosen that was characterized by resection of the top and bottom of the specimen cylinder. Even though biomechanical properties were certainly altered, the methodology is scientifically sound and biomechanically valid because the innermost third of the specimen, which largely determines compressive properties53, was entirely included in all specimens. Eighth, the use of a standard 3.0 T MRI device and the choice of sequence parameters was intended to facilitate eventual translation of our findings into clinical practice. While a scanner with higher field strength (e.g. 7.0 T or 9.4 T) would have provided better signal-to-noise ratio, such scanners have not (yet) seen widespread clinical application and relaxation times are different from those at 3.0 T54. We deliberately chose sequence parameters similar to those in earlier studies59 to render generalizability and transferability of the findings less related to actual sequence parameter settings.Second, the compressive loading device does only partially emulate physiological loading. While the experimental setup provides indications of the intra-tissue changes secondary to compressive loading that goes through the meniscus, its reflection of the complex biomechanical environment during loadbearing in-vitro study, which warrants further in-vivo studies.In conclusion, this study is the first to systematically assess human meniscus functionality based on advanced quantitative MRI techniques and in relation to histological and biomechanical reference measures. Under standardized compressive loading, we determined different response-to-loading patterns across the meniscus zones that are related to histological degeneration. Changes in T1ρ relaxation and in the apical zone seem to indicate distinctly different loading-induced intra-tissue adaptations and may help to identify potential load transmission failure in degenerative joint disease. However, tissue hydration likely overpowered the contribution of biochemical alterations in this Philips Healthcare  supported this study by providing the T1ρ sequence. The authors had and have full control over the data and information submitted for publication.ex-vivo observational imaging study on human lateral meniscus body samples obtained from patients undergoing total knee arthroplasty (TKA) at our institution. Approval from the relevant local Institutional Review Board  and individual written informed consent were obtained from all patients beforehand. All experiments were performed in accordance with relevant local guidelines and regulations.This study was designed as a prospective, 60 was the inclusion criterion, while all forms of secondary OA, previous trauma to and/or surgery of the index joint and other bone and joint pathologies were the exclusion criteria. Overall, we included 23 left and 22 right knees of 24 male and 21 female patients with a mean age of 67.4 ± 10.9 years [range: 40–94 years].We harvested lateral meniscus body samples (n = 45) from patients undergoing elective total knee arthroplasty at our institution . To avoid sample pooling, we only included one sample from each patient. Primary OA as determined radiographically (defined as Kellgren-Lawrence grades ≥ 2)http://www.psychologie.hhu.de/arbeitsgruppen/allgemeine-psychologie-und-arbeitspsychologie/gpower.html)61 to determine minimum sample sizes. Based on earlier studies23, we estimated a minimum sample size of 39 for a statistical power of 0.9, a type-I error of 0.05, an effect size of 0.5, and three groups undergoing three measurements.We used dedicated software for statistical power analyses . To this end, we connected the pneumatic mechanism via pressure lines (Festo) to the standard hospital pressure outlet providing pressure levels of up to 4.69 bar. The control components were located outside the MRI scanning room. During MRI measurements we set target pressures to three different levels, i.e. p0 = 0 bar , p1 = 2 bar (δ1), and p2 = 4 bar (δ2). Earlier pressure-force calibration studies based on digital sensors  indicated that set pressure levels resulted in highly reproducible compressive forces of f1 = 37.1 ± 0.8 N (δ1) and f2 = 69.1 ± 1.1 N (δ2) on the half-spherical piston and resultant torques of t1 = 0.67 Nm (δ1) and t2 = 1.24 Nm (δ2) on the compression lever62.We applied standardized compressive loading on the meniscus samples using an MRI-compatible compressive loading device, which loads meniscus samples by means of torqueray Fig. 4. By conray Fig. . The upw0, to be followed by two consecutive pressure levels, i.e. δ1 and δ2. After each change in pressure level and before initiating the MRI measurements, we observed an equilibration period of 5 min. This time period was based on an earlier study that investigated stress relaxation as a function of physiological strains. Chia and Hull found equilibration in human meniscus to be complete after three to four minutes39. In other studies, considerably longer equilibration periods of up to 20 min were observed35.Within 24 h after preparation, the meniscus samples underwent standardized MRI measurements in standard saline solution using a clinical 3.0 T MRI system  and the MRI-compatible compressive loading device. We selected appropriately sized polyvinylsiloxane inlays and PMMA plates to confine the sample circumferentially Fig. . Next, w2. Zonal ROIs were thus defined by these vertical thirds (as inner borders) and the segmented sample outline (as outer borders)  carried out the manual segmentations of the meniscus samples. Using the morphological PDw images obtained along the mediolateral plane as references, segmentation masks of sample outlines were generated while excluding boundary pixels to eliminate partial volume effects. SN  validated these masks against the processed T1, T1ρ, and T2 images. In addition to the entire sample outlines, further zonal ROIs were defined by automatic subdivision of segmented outlines using a custom-made routine implemented in MATLAB . First, this routine determined the sample’s maximum mediolateral diameter by projecting the segmented outline’s most peripheral pixels onto a horizontal line parallel to the lever device’s bottom. Second, after dividing this line into thirds, the division was back-projected onto the segmented outline to obtain the apical zone (I), intermediate zone (II), and outer zone (III) rs) Fig. .64, we applied a displacement rate of 0.0083 mm/sec and a strain of 100%.For biomechanical assessment, we harvested two cylindrical samples of 4 mm diameter from the red-red to the red-white transition, bilaterally adjacent to the mediolateral plane, using a 4mm-diameter biopsy punch  that was oriented perpendicular to the tibial surface. After resection of the femoral and tibial meniscus surfaces, we cut the punched-out core cylinders to standard height of 3 mm and carried out unconfined compression testing on a materials testing machine  equipped with a 20 mm wide rigid and impermeable piston. A tare load of 0.2 N was applied to maintain standardized interaction of the interfaces and mechanical equilibrium throughout the measurements. No additional preconditioning loads were applied. As in previous studies37. Here, the strain energy Ψ is given byThe resultant stress-strain data obtained were fitted to a two-parameter exponential modelFor each sample, non-linear optimizations were run in MATLAB to specifically fit the material parameters. Eventually, we determined the Elastic Modulus EM for the strains of 20% and 80% to obtain representative data from high and low strains and to limit the amount of data for subsequent multiple comparisons. Representative samples’ measured stress-strain curves and modelling predictions are included as supplementary material  to visualize the samples and merged six individual micrographs into one image per sample. Two investigators  graded the tissue status semi-quantitatively according to the Pauli classification30 in consensus. On a per-sample basis, final scores were discussed until consensus was reached. No measures of inter- or intra-rater variability were calculated. The Pauli classification assesses surface integrity for the femoral, tibial and inner surfaces , cellularity (score 0–3), collagen organization and alignment (score 0–3), and matrix staining intensity (score 0–3). Accordingly, the Pauli sum score  indicates either the absence of any signs of degeneration (score 0) or most severe degeneration (score 18). Based on Pauli sums scores, samples were grouped into Pauli grades, i.e. grossly intact , early degenerative , moderately degenerative , and severely degenerative . Due to limited sample sizes, Pauli Grade-III and -IV were collapsed as the Pauli Grades ≥ III subgroup.For histological assessment, samples were fixed in paraformaldehyde (4%) for 7 days, embedded in paraffin, sectioned along the mediolateral plane, cut to 5-µm thick slices and stained with hematoxylin-eosin and Safranin O0 refers to the unloaded absolute qMRI parameter values, while δ1 and δ2 refer to the values under loading of 2 bar (δ1) and 4 bar (δ2). Consequently, relative changes in absolute qMRI parameter values at δ1 or δ2 versus δ0 are referred to as Δ1 or Δ2. For example, the relative change in T1ρ at δ2 vs. δ0 (connoted as T1ρΔ2) was calculated as T1ρΔ2 = (((T1ρ(δ2)/T1ρ(δ0)) − 1) * 100) [%]. After sample allocation, we performed group-wise comparisons using one-way ANOVA  or the Kruskal-Wallis tests . Normality was tested using the D’Agostino-Pearson test. Longitudinal differences were assessed using the Friedman test followed by Dunn’s post-hoc tests. We evaluated differences in pixel numbers using repeated measures ANOVA, while we used unpaired t-tests for the ROI-specific relative changes. Correlations were quantified using Spearman’s correlation coefficient ρ. Normally distributed data are given as mean ± standard deviation and non-normally distributed data as median (interquartile range). Level of significance was set to p ≤ 0.005 to contain the number of statistically significant, yet scientifically (most likely) irrelevant findings.We performed the statistical analyses using GraphpadPrism . δSupplementary Information.


Grafting is the main means of propagation for watermelon crops. The aim of the present study was to evaluate whether light quality during graft healing variably affects different scion × rootstock genotype combinations. Two watermelon hybrid scions (Sunny Florida F1 and Celine F1) and two interspecific squash rootstocks (Radik and TZ-148) were used, and four scion × rootstock genotype combinations derived. After grafting, we tested seven light-emitting diodes (LEDs), which provided narrow-band red (R) and blue (B); R-B with 36% (36B), 24% (24B), and 12% (12B) blue; 12B with additional far-red (12B+FR); and white (W), in a healing chamber. In three genotype combinations, shoot length, leaf area, and shoot biomass were mainly enhanced under red-blue LEDs, while stem diameter was greater under R. In contrast, dry weight of roots, Dickson’s quality index, and ratio of shoot dry weight/length were variably affected in each genotype combination. From the results, it is concluded that light treatments differentially affected each genotype combination, but some parameters involving biomass production show genotypic dependency. Cucurbita moschata × Cucurbita maxima). Watermelon is valuable crop with 1.38 billion euros export value in 2019 throughout the world, with Europe having the highest export share of 54% . WatermeProduction of grafted watermelon seedlings mainly takes place in modern nurseries in order to achieve high quality and lower cost. The whole procedure involves a critical period of healing in which environmental conditions such as relative humidity, temperature, and light must be controlled in a sensitive manner. Technological advancements offer the opportunity to fully control the abovementioned conditions in a growth chamber instead of greenhouse benches. Light in particular has an important role as an energy source as well as a signal perceived by pigments and protein photoreceptors . Solely Until today, only a few published research articles ,10 are rThe experiment was executed in the facilities of a commercial nursery , and all measurements were conducted at the Laboratory of Vegetable Crops of the Aristotle University of Thessaloniki, Greece.Citrullus lanatus L.) hybrids were used as scion materials, “Celine F1” and “Sunny Florida F1”, while two interspecific squash hybrids were used as rootstock materials, “TZ-148” and “Radik”. Seed material was provided by HM.Clause SA, Portes-Les-Valence, France. Four genotype combinations derived from the abovementioned scion and rootstock hybrids: Celine F1 × TZ-148 (Cel × TZ), Sunny Florida F1 × TZ-148 (Flor × TZ), Celine F1 × Radik (Cel × Rad), and Sunny Florida F1 × Radik (Flor × Rad). In order to produce seedlings to be grafted, seeds from rootstock and scion hybrids were sowed in 128-cell and 171-cell plug trays , respectively. The substrate consisted of a 5:1:2 mixture of peat, perlite, and vermiculite. According to commercial practices, rootstock seeds were sown one day later in order for the scion and rootstock seedlings to develop proper stem diameter before grafting. Two watermelon (−2 s−1) provided by high-pressure sodium lamps , while the rootstock hybrids were grown at 20 °C minimum temperature, but no supplemental artificial lighting was required (according to unpublished data of our group). Afterward, sowed trays remained at 95–98% relative humidity and 25 °C temperature for 2 (rootstocks) or 3 days (scions), until germination. Upon germination, the trays were placed for 10 days in a Venlo-type greenhouse until grafting. Specifically, the scion hybrids were grown under 21.5 °C minimum temperature and 18 h supplemental artificial lighting . At the same time, the entire root system was also removed from the rootstocks, which is common for increasing grafting efficiency of cucurbits . ImmediaUpon grafting, seedlings were placed in a healing chamber with precisely set conditions for 6 days. Specifically, the temperature was 25 °C, relative humidity initially was 98% and gradually dropped to 89%, and air was recirculating. Conditions were controlled by a climate control system .−2 s−1. The LEDs were mounted on shelves (L: 2.00 m × W: 1.66 m × H: 0.76 m) at a distance of 30 cm between LED and plant top.Sole artificial lighting was provided by seven LEDs whose light distributions, yield photon fluxes (YPF), and phytochrome photostationary states (PPS) are presented in Upon exiting the healing chamber, 10 randomized grafted seedlings per genotype combination and light treatment were sampled, while quality parameter evaluations followed. According to a study of our group , the evap = 0.05, while mean comparisons were conducted using Tukey test at a = 0.05.The experiment was conducted twice, with similar conclusions reached in each replication. Statistical analysis was performed using IBM SPSS software . Data were compared by one-way and two-way analysis of variance (ANOVA) at significance level In the present study, we report that results from some parameters are consistent in most scion × rootstock genotype combinations, while results from a few parameters are attributed to genotypic dependency. According to two-way ANOVA, almost all tested parameters (11 out of 13) were significantly affected by the different light treatments . MorpholColorimetry showed significant differences in three genotype combinations, except for Cel × TZ. Specifically, in Flor × TZ narrow-band B developed a lighter color  compared to R, 36B, 24B, and 12B; more intense color  compared to 36B, 24B, and 12B; and significantly different hue angle and a*/b* values compared to 36B, 24B, and 12B . MoreoveLeaf color parameters are strongly correlated to relative chlorophyll content . HoweverShoot length did not show significant differences among the light treatments in Flor × Rad . HoweverRegarding stem diameter development, Cel × TZ, narrow-band R induced the development of greater values compared to B, while in Flor × TZ, narrow-band R promoted its development compared to the rest of the treatments except for 36B . MoreoveLeaf area exhibited the lowest numerical  values under B in three genotype combinations, except for Cel × Rad . SpecifiTwo genotype combinations involving Sunny Florida F1 scion (Flor × TZ and Flor × Rad) developed greater shoot dry weight under 24B compared to B, 12B, and W . The samChlorophylls are responsible for photon capture and electron delivery in the photosystems, leading to photosynthesis. Their main light absorption spectral regions coincide with the red and blue wavelengths; thus, photosynthesis is mainly driven by red and blue light . PreviouRoot dry weight showed variable results in each genotype combination . SpecifiR/S ratio is an indicator of biomass allocation (underground and aboveground) within a plant and potentially demonstrates the plant’s ability to successfully establish in new ground after transplanting. This ratio is highly reliant on a possible genotype-dependent parameter, root dry weight, and a genotype- and light-dependent parameter, shoot dry weight. In our case, R/S ratio was not significantly affected by the different light treatments in three genotype combinations . HoweverTwo seedling quality indicators, DW/L and DQI, were also calculated from the obtained data . The twoParameters involving biomass production  clearly revealed genotypic dependencies, since the different scion × rootstock combinations variably responded to the light treatments. For morphological parameters such as stem diameter, shoot length, and leaf area, it is concluded that light treatment differentially affected each genotype combination. Moreover, it is proven that small interplays in the light spectra such as the ones demonstrated between 12B and 12B+FR, or between 12B and W, do not variably affect the status of grafted watermelon seedlings, possibly due to the short time of exposure (only six days) under each wavelength. Regardless of our results, a point that has to be made is that W is a positive treatment for practical applications due to the high color rendering index (over 50 units), thus providing a favorable working environment compared to the rest of the light treatments, and this feature should also be taken into consideration during light quality selection.


Loading-induced changes in T1ρ and T2* were not related. In conclusion, proteoglycan depletion alters cartilage functionality and may be assessed using serial T1 and T2 mapping under loading.Water, collagen, and proteoglycans determine articular cartilage functionality. If altered, susceptibility to premature degeneration is increased. This study investigated the effects of enzymatic proteoglycan depletion on cartilage functionality as assessed by advanced Magnetic Resonance Imaging (MRI) techniques under standardized loading. Lateral femoral condylar cartilage-bone samples from patients undergoing knee replacement (n = 29) were serially imaged by Proton Density-weighted and T1, T1ρ, T2, and T2* mapping sequences on a clinical 3.0 T MRI scanner . Using pressure-controlled indentation loading, samples were imaged unloaded and quasi-statically loaded to 15.1 N and 28.6 N, and both before and after exposure to low-concentrated  or high-concentrated trypsin . Controls were not treated (n = 9). Responses to loading were assessed for the entire sample and regionally, i.e. sub- and peri-pistonally, and zonally, i.e. upper and lower sample halves. Trypsin effects were quantified as relative changes (Δ), analysed using appropriate statistical tests, and referenced histologically. Histological proteoglycan depletion was reflected by significant sub-pistonal decreases in T1 ( By and large, these findings have been confirmed for Germany, too2. Beyond the considerable impact on individual patients, OA has considerable societal and economic impact, which led to its declaration as a "priority disease" by the World Health Organisation3. Its medical and economic burden is projected to increase further as the principal risk factors, i.e. age and obesity, are projected to rise in incidence4.Cartilage degeneration is the hallmark change of osteoarthritis (OA), a potentially devastating and permanently debilitating condition that affects hundreds of millions of people globally, i.e. 3.3–3.6% of the world’s population. In the United States, radiographic evidence of OA is present in approximately 80% of the senior population, i.e. > 65 years of age, even though only 60% of these individuals have symptoms5. On a structural and compositional level, the tissue undergoes progressive changes of its key constituents, i.e. collagen and proteoglycans, that comprise the extracellular matrix (ECM). During early degeneration, proteoglycan content is lost, while the collagen network is increasingly disoriented and disrupted, which leads to increased water uptake, tissue swelling, and softening6. If progressive, the degenerative changes result in advanced tissue disintegration and, eventually, tissue loss.Pathomechanistically, cartilage degeneration is the result of abnormal tissue remodelling secondary to predisposing tissue susceptibility, unfavourable biomechanical conditions, and persistent inflammation9, quantitative MRI (qMRI) techniques and alternative biophysical methodologies such as solid-state nuclear magnetic resonance (NMR) have been studied in clinical and research contexts to assess the tissue beyond mere morphology10. While solid-state NMR spectroscopy provides atomic-level insight into the structure, dynamics, and functionality of cartilage and bone14, qMRI techniques spatially map the tissue’s distinct biophysical properties such as T2 or T1ρ relaxation characteristics in a pixel-wise manner, thereby providing markers of (ultra)structure and/or composition. Recently, qMRI techniques have been combined with mechanical loading to study the tissue in more functional contexts and beyond the unloaded configuration. For additional details, the reader is referred to related comprehensive review articles16. The rationale behind these approaches is to assess the tissue in more functional contexts by mapping its imaging features in both the unloaded and loaded configurations to determine changes as surrogate parameters of functionality. In a variety of in-vitro, in-situ, and in-vivo studies, physiological and pathological responses to loading could be discerned as promising indicators of the cartilage status in health and disease22. Hence, a solid body of scientific and clinical evidence indicates that aberrant response-to-loading patterns are associated with altered tissue integrity and functionality. Nonetheless, the interrelatedness of the tissue’s structural and compositional properties and resultant functionality remains to be fully understood as it determines the joint’s susceptibility to OA and OA progression.In consideration of the deficits of clinical-standard morphological Magnetic Resonance Imaging (MRI) techniques in the detection of early, potentially reversible, cartilage degeneration6 in a well-established pathomimetic model23. Nine samples served as controls. The samples’ response to loading as a marker of tissue functionality was assessed before and after incubation without (control study arm) or with trypsin (trypsin exposure study arms) using serial T1, T1ρ, T2, and T2* mapping under standardized indentation loading in three different configurations, i.e. unloaded, moderate loading, and strong loading. Based on this imaging-based basic research framework, we aimed to bring together compositional changes induced by controlled in-vitro degradation of cartilage and the imaging correlates of physiological and pathological functionality as assessed by the relative changes under loading of T1, T1ρ, T2, and T2*. Our hypotheses are i) that trypsin exposure alters cartilage composition and cartilage functionality as a function of concentration and ii) that T1, T1ρ, T2, and T2* are reflective of these alterations and thereby provided imaging features of physiological and pathological functionality.Therefore, the objective of this study was to systematically investigate the role of proteoglycan depletion in load bearing of human articular cartilage. To this end, 20 human articular cartilage samples were exposed to trypsin in two different concentrations to simulate one of the earliest changes in cartilage degeneration and OAAll 29 cartilage samples completed evaluation by qMRI and histology.Histological evaluation of cartilage tissue adjacent to the cartilage sample revealed gross tissue integrity throughout as signified by mean Mankin sum scores of 1.9 ± 1.1 , equalling Mankin Grade (MG) 0 in all tissues. Of note, most adjacent tissues had Mankin sum scores of 1 or 2, i.e. slight pre-existent signs of degeneration such as focal hypercellularity, surface fibrillation or Safranin-O de-staining. After incubation without trypsin exposure control samples did not demonstrate any structural or compositional changes on post-exposure evaluation, as indicated by intact tissue surface, regular cellularity, and no Safranin-O de-staining Fig. .Figure 10 refers to the absolute qMRI parameter values in the unloaded reference configuration, while δ1 and δ2 refer to the respective values under loading of 15.1 N (δ1) and 28.6 N (δ2). Consequently, relative changes in the values at δ1 or δ2 versus δ0 are referred to as Δ1 or Δ2.In the following, δ0post) as compared to the unloaded reference configuration pre-exposure (δ0pre). Table 1 and δ2, as compared to the unloaded configuration δ0, both before and after trypsin exposure. Supplementary Table 0, δ1, and δ2.In response to loading, T1, T1ρ, T2, and T2* underwent variable zonal and regional changes as a function of trypsin exposure and loading intensity. Table Generally, we observed substantial variability in response to loading in terms of absolute values and relative changes as indicated by high measures of statistical spread such as standard deviations and interquartile ranges throughout the study.T1 at δ0 was consistently decreased in controls after incubation without enzyme exposure. After LT exposure, however, changes were variable with negligible decreases in the sub-pistonal area (SPA) and slight increases in the peri-pistonal area (PPA). In contrast, after HT exposure, we observed moderate, yet significant increases in the entire samples’ upper halves, both sub-pistonally  and peri-pistonally  as compared to slight and non-significant decreases in the lower halves  and ambiguous changes in the lower sample halves (PPAlower). In all samples, regardless of trypsin exposure, loading-induced changes in T1 demonstrated the following pattern of characteristic changes: Relative changes in the SPA were more pronounced than in the PPA and relative changes in the upper halves more pronounced than in the lower halves. Of note, these patterns were found for T1ρ and T2, too. Consequently, SPAupper and PPAlower displayed largest and smallest loading-induced decreases in T1, respectively. Overall, no significant differences between pre- and post-exposure relative changes in T1 were found in controls and after LT exposure. However, decreases in T1 were significantly larger after HT exposure in the SPAupper, both in response to moderate  and strong loading . In other regions-of-interest (ROIs), decreases in T1 were not significantly different  [controls], Δ0 = − 12.1 ± 8.1%, p = 0.002), while hardly any changes were found after LT and HT exposure , while after LT and HT exposure, T2 at δ0 was non-significantly increased (Table 1 < Δ2), that was not the case for the largely ambiguous peri-pistonal changes. No significant differences in the pre- vs. post exposure loading responses were found in controls or after LT exposure, while after HT exposure, sub-pistonal decreases were significantly larger . The significant sub-pistonal decreases were primarily driven by changes in the upper sample halves . In contrast, T2* at δ0 was substantially increased after LT and HT exposure, e.g.  , while controls and HT exposed samples did not demonstrate significant differences. After HT exposure, we observed divergent increases in T2* under moderate loading alongside decreases under strong loading  and after LT exposure , while they were marginally increased after HT exposure . Additionally, serial measurements of sample heights at δ0, δ1, and δ2 indicated significant decreases in response to loading, irrespective of trypsin exposure (p < 0.001) (Supplementary Table upper), though significant only after trypsin exposure. Otherwise, not directly loaded regions, i.e. the entire peri-pistonal area (PPA), did not demonstrate significant pre- and post-exposure differences in pixel numbers (Supplementary Table Cartilage sample heights at δThe most important findings of this study are that (1) proteoglycan depletion secondary to trypsin exposure affects human articular cartilage functionality and its imaging features and (2) the altered response-to-loading patterns demonstrate regional and zonal variability and may be best assessed using T2 and, to a lesser extent, T1 mapping.25. As a serine protease, trypsin cleaves peptide bonds of proteoglycans and brings about their selective degradation as a function of concentration and exposure duration. Notably, trypsin-induced proteoglycan depletion is largely inconsistent and affected by the initial proteoglycan distribution and concentration, location within the joint, cartilage thickness, and surrounding medium. In bovine retropatellar cartilage, Moody et al. determined approximately 46% and 71% proteoglycan loss following exposure to 0.1 and 1.0 mg/mL trypsin for 2 h23. The trypsin concentrations in the present study were informed by these data and confirmed by other studies that used Safranin-O staining for reference purposes and demonstrated proteoglycan loss of 76% after exposure to 2.5 mg/mL trypsin over 3 h26 and of 51% after exposure to 0.1 mg/mL trypsin for 2 h27. Despite these studies’ differences in terms of species, trypsin type, and cartilage location, topography, and integrity, these studies are largely in line with one another and with our findings. Histologically, we found slight-to-moderate Safranin O de-staining of the superficial and transitional zones, confirming effective proteoglycan depletion as a function of concentration. Yet, at this point we can only speculate whether trypsin-induced degradation is specific (and exclusive) to proteoglycans. In contrast to the commonly held belief that -apart from specific collagenases- collagen fibrils are resistant to enzymatic cleavage29, previous studies have provided anecdotical evidence that human trypsin-2 is indeed capable of cleaving collagen-type II in human cartilage, while animal trypsin is not30. Consequently, in the absence of advanced biochemical reference methodology to prove or disprove trypsin specificity, the following discussion is based on the premise that porcine trypsin (as used in this study) did not affect other ECM constituents.In this pathomimetic in vitro model, trypsin was selected to induce depletion of proteoglycans and non-collagenous macromolecules because a solid body of scientific evidence details its effects on articular cartilage, thereby allowing comparative analyses33. These response-to-loading mechanisms may be reflected by the imaging characteristics of pre-exposure samples. For once, sample height decreased with loading. For another, the qMRI parameters underwent distinct changes. In this context, it is important to note that -with the exception of dGEMRIC (delayed gadolinium-enhanced MRI of cartilage) no qMRI parameter has exclusive specificity to any particular cartilage constituent. T1 decreased considerably and as a function of loading intensity with most pronounced decreases in the sub-pistonal upper sample halves. This is plausible in light of this marker’s primary predisposition to intra-tissue water34, resulting in longer T1 relaxation with higher fluid fractions35, while collagen or proteoglycans seem to contribute less36. T1ρ, however, increased under loading though not clearly related to strain. Interpretation is not straightforward as this parameter’s specificity profile remains unclear with water, collagen and proteoglycan content as well as collagen fibre orientation assumed to contribute to T1ρ characteristics38. Under loading, the deeper radial collagen fibers display fiber crimping39, which increases the percentage of fibers oriented at magic angle and reduces residual dipolar interaction, thereby increasing T1ρ (as well as T2) values. Moreover, recent literature reports have disproven the previously assumed proteoglycan specificity of T1ρ and qualified this association as weak41 so that loading-induced compositional changes may only be secondary. Consequently, T1ρ nowadays is considered a marker of the tissue’s macromolecular constitution with sensitivity to its solid and fluid constituents. Recently, positive correlations of T1ρ with fluid fraction and collagen fiber orientation, and negative correlations with collagen and proteoglycan content were reported35. Moreover, earlier studies by us and others have reported this parameter’s exquisite mechanosensitivity and loading-induced changes in T1ρ in a range of pre-clinical and clinical contexts43. T2 underwent divergent changes in response to loading, i.e. sub-pistonal decreases and peri-pistonal increases. While the former was associated with strain, the latter was not. As T2 is widely considered a marker of collagen fibre orientation and density, collagen content and network organisation, and intra-tissue water44, its discrepant changes most likely reflect the complex interplay of these constituents during loading and are largely in line with earlier literature data46. However, the relation between T2 and proteoglycan content remains unclear. Studying suspensions of chondroitin sulphate in experimental contexts, which is the most abundant sulphated glycosaminoglycan of cartilage proteoglycans47, Menezes et al. found an exponential association with higher T2 values indicating lower proteoglycan concentrations37. In the same study, a similar association was determined for T2 and collagen concentration. In light of the constitution of the ECM which is primarily made up of collagens (70%) and proteoglycans (20%)47, collagen is dominating -by far- over proteoglycan in both constituents’ contributions to T2 relaxation. These findings were later confirmed by other studies49. T2* was subject to ambiguous changes without a clear pattern or any obvious relation to strain. Susceptible to the magnetic susceptibility effects of inherently inhomogeneous cartilage, T2* supposedly reflects collagen microstructure rather than collagen content51. Like T2, T2* is sensitive towards water and its interactions with collagen, while its association with proteoglycans is still debated50. Earlier reports have reported an inverse relation, i.e. higher T2* values indicating lower proteoglycan and collagen contents35. However, the ambiguity of loading-induced changes in T2* does not allow solid conclusions to be made.During physiological loading, intact articular cartilage is subjected to water redistribution, both within the tissue from loaded to less loaded areas and into and out of the tissue. Concomitantly, the ECM experiences condensation and deformation, relative increases in proteoglycan and collagen contents, and collagen fibre re-orientation52 and these three qMRI parameters are closely associated with hydration53. Moreover, the lower proteoglycan concentration itself probably contributes to these increase48. Further, the above-mentioned response-to-loading patterns were altered. First, significantly larger decreases in T1 and T2 were found after HT exposure, but only in the SPAupper (T1) or SPA (T2), respectively. It is important to note that the significant differences in T2 were not present in the unloaded configuration, thereby highlighting the potential value of cartilage functionality assessment and rendering T2 particularly suitable for assessing altered functionality after proteoglycan depletion. Otherwise, pre- and post-exposure changes in T1, T2 or T2* were not significantly different. This finding is plausible as, physiologically, proteoglycans pre-stress the collagen network by osmotic pressure54. Proteoglycan depletion, however, removes this pre-stress, thereby reducing mechanical properties, which renders the tissue more compliant and viscous, and subsequently facilitates water flow 29. These are the most likely imaging correlate of the pronounced decreases in T1 and T2. Therefore, proteoglycan depletion -one of the earliest signs of cartilage degeneration and OA6—compromises the load-bearing capacity of the superficial tissue (and surface), thereby promoting susceptibility to further damage to other tissue regions. Notably, the mechanical effects of trypsin are far more widespread than suggested by histological Safranin O de-staining. Griffin et al. determined trypsin-induced histological proteoglycan loss to a depth of up to 200 μm, while mechanical effects were noted to a depth of 500 μm29. Even when supplemented by more advanced reference measures such as optical absorbance measurements, proteoglycan concentration is variably affected by trypsin23, so that absolute quantification of proteoglycan content, let alone assessment of its integrity and functionality, may be challenging to realize.Substantial increases in T1, T2, and T2* were after HT exposure, mostly driven by changes in the upper sample halves. This is not surprising as proteoglycan depletion causes an influx of waterSecond, trypsin supposedly left the underlying collagen network’s microstructural architecture intact. Beyond histological referencing, this is evidenced by the fact that, overall, T2* and T1ρ, both parameters with sensitivity towards functional collagen properties44, were otherwise not significantly altered after trypsin exposure. Herein, it is worth realizing that T1ρ was not significantly affected by proteoglycan depletion, which is surprising, given this parameter’s sensitivity towards intra-tissue changes as detailed above. Most likely, these observations are due to the overpowering dominance of collagen as compared to proteoglycans in both constituents’ contribution to T1ρ relaxation37. Hence, proteoglycan depletion of the superficial and upper transitional tissue zones, i.e. those zones that display the lowest proteoglycan content anyway55, seems largely irrelevant to T1ρ characteristics, lending more evidence to the theory that proteoglycan content and T1ρ are largely unrelated.56. Consequently, full recovery may necessitate hours to days57 and may not have been completed. This, of course, limits repeatability and may be only partially remedied by intra-sample referencing. Incomplete ultrastructural recovery could also be a reason for the substantial standard deviations observed throughout the study. Regardless of the exact underlying cause, the tissue’s loading and cultivation history, if relevant, needs to be taken into account when interpreting these changes.Pre-exposure measurements under loading and incubation without enzyme exposure had significant effects on T1ρ, T2, and T2* that underwent significant decreases in controls. Probably, these decreases are related to the tissue’s stress relaxation after loading and an indication of ongoing ultrastructural recovery. The rate of stress relaxation is associated with strain and proceeds significantly slower after higher strainsin-vitro design limits generalizability to the in-vivo setting. Even though trypsin-2 has been detected in the synovial fluid of rheumatoid arthritis-affected human joints30, unphysiological trypsin concentrations as well as culture and loading conditions limit the in-vivo correspondence of trypsin-induced proteoglycan depletion as studied in vitro. Moreover, cartilage degeneration as the hallmark change of OA is characterized by numerous structural and compositional changes beyond proteoglycan depletion, i.e. disruption and disorganisation of the collagen network amongst others6, which are not emulated in our trypsin-induced experimental setup of isolated proteoglycan depletion. This again limits the generalizability to the in vivo setting. Beyond, proteoglycan depletion may not have been consistent throughout the samples. Following preparation, the bare sample shoulders and periphery might have been more susceptible to trypsin, which might have affected the PPA more than the SPA. Second, the study’s reference framework only included histology, which may be too coarse to detect finer compositional tissue alterations23 and could be supplemented by more advanced techniques such as micro-spectroscopy, biochemical assaying techniques, polarized light microscopy, dGEMRIC or solid-state NMR in the future. In particular, solid-state NMR allows the quantitative evaluation of cartilage and its organic constituents at the molecular level with potential prospects as an additional ultrastructural reference11. Alongside larger sample sizes, more refined references are a prerequisite for more in-depth functional analysis of proteoglycan depletion, for example as a function of gender or age. In addition, very-high-resolution scanning probe methodologies, i.e. atomic force microscopy, scanning electron microscopy or transmission electron microscopy, could provide further insights into cartilage structure, composition, and biomechanical properties for reference purposes to complement quantitative MRI measurements58. Third, despite histological quality checks, the tissue source of total joint replacements is a potential confounder. Beyond the extent appreciable histologically, samples may have been pre-degenerated and pre-inflamed, thereby potentially contributing to sample heterogeneity as signified by considerable measures of spread such as standard deviations. Nonetheless, given the stark differences between animal and human cartilage59, we intended to obtain clinically meaningful results and deliberately included human articular cartilage to study its functionality in reference to the sample’s histological tissue status, which partially remedies this situation. Our experimental imaging setup, i.e. the clinical 3.0 T MRI scanner and the ready-to-use mapping sequences with clinically applicable parameter settings are also reflective of this intention. Nonetheless, truly healthy cartilage from amputations, organ donor networks or body donors ought to be included in future studies. Fourth, histological mismatching of the cartilage sample and adjacent tissue may have decreased validity and representatively of histological results, in particular with regards to the variable degeneration in joints, compartments, and tissue regions. Fifth, trypsin exposure was only terminated by continuous washing with PBS, while no dedicated trypsin inhibitors were used. Consequently, traces of trypsin may have remained in the samples, bringing about stronger-than-intended proteoglycan-depletive effects. Yet, this source of variability was systematic after LT and HT exposure, thereby not affecting this study’s main outcomes. Sixth, pre-exposure response-to-loading patterns were quite heterogeneous, in particular for T1ρ and T2, which has been observed before18. Even though principally attributable to the small sample size, it is important to realize that the tissue loading response is not just related to cartilage sample integrity and its primary or secondary degradation, but also to other person-level factors such as age, gender, obesity, and genetics, as well as joint-level factors such as axis alignment, muscle strength and other factors that are reflective of abnormal loading60. These variables are beyond our experimental control and may affect the tissue’s loading responses in health and disease to a much larger extent than previously thought. As detailed above, larger sample sizes with alternative tissue sources and stricter inclusion criteria may help clarify the contributions of these factors to cartilage functionality. Seventh, for the sake of comparability with literature data, we only performed conventional mono-exponential fitting. Alternative fitting techniques such as bi-exponential relaxation of T261 help to quantify different water compartments in cartilage and may thus increase the specificity for altered hydration and functionality.This study has several limitations. First, its In conclusion, this study found that trypsin-induced proteoglycan depletion affects cartilage functionality as assessed by serial qMRI mapping and standardized pressure-controlled indentation loading. Dose-dependently, proteoglycan-depleted cartilage areas undergo substantially larger changes in T2  as an indication of the altered micromechanical environment secondary to deficient pre-stress of the collagen network. Simulating one of the earliest changes in cartilage degeneration, the close association between compositional deficit and altered functionality was not discernible from quantitative MR images in the unloaded configuration, thereby corroborating the diagnostic potential of imaging-based cartilage functionality assessment. Biomechanical imaging may provide a non-invasive means to study matrix changes in functional contexts and beyond mere composition and may perspectively advance joint and tissue diagnostics in patients.ex-vivo imaging study using human cartilage samples from the lateral femoral condyle that were harvested from total knee arthroplasties performed at the University Hospital Aachen. Prior to the study, local Institutional Review Board approval  and individual written informed consent had been obtained. All methods were carried out in accordance with relevant guidelines and regulations.The present study was carried out as an intra-individual Following resection during surgery, the surgical material was transferred to the laboratory in sterile cell culture medium, i.e. Dulbecco’s modified Eagle’s medium  that contained 10% fetal calf serum , 100 μg/mL Gentamycin , 100 U/mL penicillin and streptomycin , and 1.25 μg/mL amphotericin B (GIBCO-BRL). Surgical material from a total of 29 patients  was included. Patients were only included if they had primary OA of the knee joint that involved at least the medial compartment, but excluded if they had other bone and joint conditions such as rheumatoid arthritis, secondary OA, and historical findings suggestive of previous trauma or surgery to the index knee. To avoid sample pooling, only one sample was obtained from each patient.62, the central lateral femoral condyle was identified, and only intact joint regions without any gross macroscopic damage, i.e. Outerbridge grade 0, were prepared. Second, the material was cut to osteochondral samples of standard square shape and size of 1.2 × 1.2 cm (width × length) using dedicated rongeurs and scalpels. Third, we prepared the samples to be as even and plane as possible by preserving the subchondral lamella and by removing the cancellous bone underneath. By doing so, we intended to prevent loading-induced compaction and heterogeneity in load distribution despite the considerable curvature of the femoral condyles. Fourth, for reference purposes, two notches at opposing sample sides were created via rongeur to define the mid-sagittal plane, while an additional third notch was used to define the sample centre as the intersection of the mid-sagittal plane and its perpendicular along the third notch . Fifth, directly adjacent cartilage tissue with similarly grossly intact macroscopic appearance was prepared to determine the histological status of the sampled cartilage region before any intervention . Based on earlier literature and our own experimental data63, we estimated a minimum sample size of 28 with a statistical power of 0.9, a probability of type-I error (α) of 0.05, a maximum inter-group difference of 0.6, an intra-group standard deviation of 0.5, and Cohen’s effect size model f2. Accordingly, we included a total of 29 macroscopically intact cartilage samples.Prior to this study, sample size had been estimated by use of a dedicated online tool  attached to the device’s upper frame. The centrally positioned cartilage sample thus experiences standardized indentation loading by control of pressure.The device has been described and validated earlierFollowing preparation, pre-exposure MRI measurements were performed on a clinical 3.0 T MRI scanner . A modified single channel receive-only prostate coil  without the inflatable balloon tip that circumferentially comprised the transparent sample box at the height of the cartilage layer was used for imaging. The scanner’s in-built body coil was used for the application of the radiofrequency pulses. Cartilage samples were centred underneath the indentor piston and attention was paid to align the piston’s undersurface with the sample’s surface.0) followed by two consecutive loading positions, i.e. at 0.75 bar (δ1) and at 1.5 bar (δ2). After scout views, Proton Density-weighted (PDw) sequences in all three planes . Imaging was performed at room temperature which was monitored during one measurement series (20.1 ± 0.6 °C).Imaging of the cartilage samples was performed individually and serially, i.e. in the unloaded configuration  trypsin at high concentration (1.0 mg/mL)—high trypsin (HT) exposure (n = 10), #2) trypsin at low concentration (0.1 mg/mL)—low trypsin (LT) exposure (n = 10), and #3) controls without any trypsin exposure (n = 9).for study arm #1 1.6 mL trypsin-Ethylenediaminetetraacetic acid (EDTA) solution  and 6.4 mL sterile PBS to obtain the high trypsin concentration (1.0 mg/mL trypsin);for study arm #2 0.16 mL trypsin–EDTA solution and 7.84 mL sterile PBS to obtain the low trypsin concentration (0.1 mg/mL trypsin);for study arm #3 8 ml medium plus additives as detailed above to obtain controls.Practically, samples were individually suspended in 50 mL volume tubes  that contained.25. Even though these concentrations are unphysiological, they are commonly used to realize rapid proteoglycan degradation. By means of a tube rotator , tubes were continuously rotated for 2 h while being incubated in a standard laboratory incubation unit at 37 °C, 5% CO2, and 21% O2. After incubation, proteolysis was halted by discarding the trypsin–EDTA solutions and by continuous washing with sterile PBS for 1 h, after which samples were replaced in medium + additives as above.Trypsin was obtained as a 10 × solution containing 5.0 g/L porcine trypsin (from porcine pancreas cells) and 2.0 g/L EDTA. Trypsin is commonly used in pathomimetic models of articular cartilage to induce proteoglycan depletion and the chosen concentrations of 0.1 mg/mL and 1.0 mg/mL are widely consented in the literatureWithin 24 h after the pre-exposure MRI measurements, cartilage samples were imaged again in strict analogy to the first measurement conditions. While trying to align pre- and post-exposure framework conditions, we were particularly careful to realize identical sample orientation and position.Sample height was determined on the mid-sagittal PDw images for each cartilage sample and loading position individually. The first author  measured cartilage sample height at the sample centre and at an equidistance of 2 mm to both sides by use of the digital caliper tool of the in-house PACS . With a step size of a single pixel, the caliper tool has a resolution of 0.24 × 0.24 mm.20. Fit quality was checked using R2 statistics adjusted to the degrees of freedom and only pixel with R2 values > 0.95 were included. For T2 and T2* fitting, we only included echo times < 60 ms to decrease the effects of noise. Second, cartilage tissue was segmented by manual definition of the sample outlines. While considering the corresponding mid-sagittal PDw image, T2-weighted morphological image (of echo time 41.9 ms), and histological sections as reference, the first author (TH) performed the segmentations of each cartilage sample at each loading position. Segmentation was performed conservatively to decrease partial volume effects by only including pixels that safely lay inside the cartilage tissue and by excluding boundary pixels at the topmost (towards piston or medium) and lowermost layer . Third, the sample- and loading position-specific outlines were validated against the corresponding T1, T1ρ, T2 and T2* maps and quality checked for morphological correspondence by visual inspection  , (5) SPA deep half (SPAlower), (6) PPA superficial (PPAupper), and (7) PPA deep (PPAlower)  to generate spatially resolved quantitative T1, T1ρ, T2 and T2* maps as beforeIn line with standard procedures, the cartilage samples and the adjacent cartilage tissue were subject to histological processing and analysis. After simultaneous decalcification and fixation in Ossa fixona , cartilage samples were sectioned along the mid-sagittal plane, while the adjacent tissue was sectioned parallel to it. Following sectioning, the material was embedded in paraffin, cut to 5-µm sections, stained with hematoxylin/eosin and Safranin O, and imaged using a standard light microscope  , cellularity (score 0–3), proteoglycan staining intensity (score 0–4), and tidemark integrity (score 0–1). Based on the resultant Mankin sum score , the adjacent cartilage tissue was grouped into Mankin grades, i.e. MG 0 (Mankin sum scores 0–4), MG I (scores 5–8), MG II (scores 9–10), and MG III (scores 11–14)67. Only if initial scores were different between the investigators were individual sections discussed until consensus was established.Histological sections were analysed by three blinded investigators experienced in musculoskeletal histopathology  who performed the histological evaluation individually. Adjacent cartilage tissue was assessed in terms of histological degeneration to quality check the included material. To this end, semi-quantitative grading of degeneration was performed according to the Mankin classificationCartilage samples following incubation with or without additional trypsin exposure were evaluated qualitatively with a focus on structural and compositional changes as compared to the adjacent tissue.0 refers to the unloaded reference configuration, while δ1 and δ2 refer to moderate and strong loading configurations, while the relative changes at δ1 or δ2 versus δ0 are referred to as Δ1 or Δ2. For a given qMRI parameter Tx, Δx was calculated as Δx = ((Txδx/Txδ0) − 1) * 100 [%]. Analogously, relative changes between the unloaded pre- and post-exposure configurations δ0post vs. δ0pre were defined as Δ0 and calculated as Δ0 = ((Txδ0post/Txδ0pre) − 1) * 100 [%]. Throughout, ROI-specific analyses were performed as a function of loading intensity and study arm. To evaluate loading-induced changes in the samples prior to as compared to after incubation with or without additional trypsin exposure, pair-wise comparisons of pre- and post-exposure measures, i.e. Δ1pre vs. Δ1post, and Δ2pre vs. Δ2post, were performed by paired Student's t tests. As absolute T1, T1ρ, T2, and T2* values were not assumed to be normally distributed, they were analysed using Friedman's test followed by Dunn's post-hoc test. To assess changes in the unloaded reference configuration because of incubation with or without additional trypsin exposure, δ0pre and δ0post were comparatively evaluated using the Wilcoxon matched pairs test. To assess loading-induced changes in the samples’ cross-sectional areas, sample heights and pixel numbers were analysed by repeated measures ANOVA. Supplementary Figure The first and last authors  carried out the statistical analyses using GraphPad Prism . As detailed above, δp ≤ 0.01 to contain the number of statistically significant, yet scientifically (likely) insignificant findings.Data are presented as median and interquartile range  or mean ± standard deviation . To account for the multiple comparisons performed in this exploratory study and control the family-wise error rate, Bonferroni-Holm correction was used. À priori, the level of significance was set to Supplementary Information.Supplementary Legends.


Ustilago maydis suggest that the amphipathic fungicide dodine disrupts the fungal plasma membrane (PM), thereby killing this corn smut pathogen. However, a recent study in the wheat pathogen Zymoseptoria tritici does not support such mode of action (MoA). Instead, dodine inhibits mitochondrial ATP-synthesis, both in Z. tritici and U. maydis. This casts doubt on an fungicidal activity of dodine at the PM. Here, we use a cell biological approach and investigate further the effect of dodine on the plasma membrane in both fungi. We show that dodine indeed breaks the integrity of the PM in U. maydis, indicated by a concentration-dependent cell depolarization. In addition, the fungicide reduces PM fluidity and arrests endocytosis by inhibiting the internalization of endocytic vesicles at the PM. This is likely due to impaired recruitment of the actin-crosslinker fimbrin to endocytic actin patches. However, quantitative data reveal that the effect on mitochondria represents the primary MoA in U. maydis. None of these plasma membrane-associated effects were found in dodine-treated Z. tritici cells. Thus, the physiological effect of an anti-fungal chemistry can differ between pathogens. This merits consideration when characterizing a given fungicide.Early reports in the fungus  As this can expose both consumers and the environment to significant amounts of anti-fungal chemistries, a detailed understanding of their cellular activity is paramount . The amphipathic nature of dodine suggests that the molecule inserts into the fungal plasma membrane (PM). Indeed, investigation of the physiological effect of dodine in various fungi conclude that dodine disrupts the integrity of the fungal PM , attack the pathogen by inhibiting more than one cellular process. However, dodine's MoA is not understood, and, consequently, the fungicide is listed in the FRAC code© list 2020 as having an “unknown MoA”  experiments. Finally, the dynamics of PM-associated actin patches are impaired and endocytosis is inhibited. This is possibly due to abolished targeting of the cross-linking protein fimbrin to the site of endocytioc internalization. While dodine also alters the PM appearance in Z. tritici, none of these U. maydis phenotypes were found in this wheat pathogen. Thus, the two fungi react differently to dodine, suggesting that the physiological effect of a fungicide could vary between fungal pathogens.Here, we exploit established live cell imaging tools and techniques for dis e.g.  and Z. t tritici  and comp2U. maydis  -positive U. maydis cells  staining did not reveal an increase in stained cells . Co-staining of propidium iodide and DiBAC4(3) in dodine-treated cells demonstrated that the voltage-sensitive dye is only entering dead cells  . We usedis cells C, with ain alive D, 1E. Thve cells G. Howeveed cells F, and evU. maydis and Z. tritici that express the membrane-bound reporter GFP-Sso1. We photo-bleached the fluorescent marker and monitored the recovery of fluorescence due to lateral diffusion of unbleached GFP-Sso1. In control experiments, photo-bleached areas in the PM showed partial recovery of GFP fluorescence within several minutes (−1), recovery of fluorescence was significantly impaired  in dodine-treated cells of  minutes . Howeverimpaired , suggestf the PM D. This rU. maydis to be crucial for cell morphogenesis . Lifeact-labeled patches appeared at the PM and stand stationary, while increasing the amount of F-actin  differs significantly between both fungi .Fungal endocytosis is initiated by the assembly of “actin patches” at sites of endocytic vesicle formation . A cruci Lifeact , which w Lifeact . This pr F-actin . This is F-actin , suggestU. maydis and Z. tritici. We tested the effect of the fungicide over a range of concentrations in the two fungi. In Z. tritici, actin patch showed the characteristic dynamic behavior in the presence of dodine, although the number of motile patches declined at high concentrations , with almost no motility of actin patches at 20 µg ml−1  demonstrated that fimbrin is not required for actin assembly at the PM, but is crucial for patch motility , and showed the same dynamic behavior as Lifeact-labelled actin patches (not shown). However, in U. maydis cells that were treated with 20 µg ml−1 dodine no peripheral fimbrin signals were seen . Such effect at low dodine concentrations was also found in Z. tritici , but its MoA is unknown . We report here that dodine acts at the PM of U. maydis, where the fungicide has multiple effects: it (i) inserts in the membrane and causes PM invaginations, (ii) reduces the fluidity of the PM, (iii) alters the ion permeability, thereby depolarizing the cell and (iv) blocks endocytosis by arresting actin patches at sites of endocytic internalization. We found that the plasma membrane of Z. tritici reacts differently to dodine, with no obvious defect in its fluidity or integrity, and only mild effects on actin patch formation. Plasma membrane invaginations, which most likely indicate considerable insertions of dodine, occur at lower concentrations in U. maydis than in Z. tritici. Thus, we consider it possible that the lipophilic cation inserts more easily into the U. maydis plasma membrane. Alternatively, the cationic dodine may be held longer in the basidiomycete plasma membrane before it diffuses into the cell. Such extended residency would increase the dodine concentration in the membrane and so underpin the difference between both fungi. But why does the plasma membranes of both fungi show differential affinity for dodine? The answer may lie in their phylogenetic relationship. U. maydis belongs to the phyllum basidiomycota (Ustilaginomycotina), whereas Z. tritici is an ascomycete (Pezizomycotina). Both phyla diverged ∼ 500 million years ago , and two basidiomycetes  reveals that the basidiomycete fungi contain ∼ 2.1–2.6-times more phosphotidylserine in their cell membranes  compromises the permeability of the plasma membrane to ions and (ii) inhibits endocytosis. However, the effects at the plasma membrane occur at much higher concentrations  MoA can differ between fungi, in particular if they are phylogenetically distant, and (ii) quantitative and standardized studies are required to distinguish between primary and secondary effects. Clearly, the analysis of fungicide effects in living pathogen cells provides a unique and powerful approach to the study of the MoA of a given fungicide.In this paper, we show a differential effect of dodine on the plasma membrane of the basidiomycete fungus epletion , the fun33.1U. maydis strains FB1, FB1GSso1, AB33GLifeact and FB2Fim2G were published previously . For daily use, U. maydis strains were maintained on agar plates (1% agar (w v−1), 1% glucose (w v−1) in complete medium; CM, −1) glucose (CMglucose), at 28 °C for 12 h, 200 rpm. Frozen Z. tritici NSY glycerol stocks were streaked onto YPD agar plates  and grown at 18 °C for 5 days. From here, YG cultures  were inoculated and grown at 18 °C for 48 h, shaking at 200 rpm.Long-term storage of all fungal strains was at −80 °C in NSY glycerol  agar cushions and observed using an IX83 motorized inverted microscope , equipped with a PlanApo × 100/1.45 oil TIRF objective (Olympus) and a VS-LMS4 Laser-Merge-System with solid-state lasers . For photo-bleaching experiments, a 405 nm/ 60 mW diode laser was used, which was coupled into the light path by an OSI-IX 71 adaptor (Visitron System) and controlled by a UGA-40 controller  and VisiFRAP 2D FRAP control software. Z stacks were generated by using an objective piezo . Images were acquired using a Photometrics CoolSNAP HQ2 camera . All parts of the system were under the control of the software package VisiView (Visitron System). Samples were observed for no longer than 10 min, to prevent oxygen depletion. All image processing was performed in MetaMorph 7.8.x .Cells were placed onto 2% (w v3.4−1 in methanol) or the respective amount of methanol alone (solvent control), for 30–45 min at room temperature, rotating on a SB2 Rotator .For all experiments, one milliliter of overnight cell cultures were treated with different concentrations of dodine  tri, methine oxonol; Thermo Fisher Scientific, Loughborough, UK; stock: 1 mg ml−1) at a final concentration 20 µg ml−1. Subsequently, cells were washed twice with CM or YG media by centrifugation using a Micro Star 17R cooled centrifuge  at 3500 or 5000 rpm for 5 min, followed by immediate microscopic analysis using the 488 nm laser at 20% intensity and exposure time of 150 ms. The total number of cells and the number of stained cells was recorded and the percentage of stained cells was determined. The mean ± SEM from 3 experiments was calculated using Prism 5.Cell survival: To assess cell death, 100 μl of dodine-treated cell cultures were incubated with 1 µl propidium iodide . After 5 min incubation at room temperature, 1 µl of cell suspension was placed onto a 2% (w v−1) agar cushion and analyzed using the 488 nm and 561 nm lasers, both set at 20% intensity and exposure time of 150 ms. The total number of cells and the number of stained cells was recorded and the percentage of stained cells was determined. The mean ± SEM from 3 experiments was calculated using Prism 5.Endocytic vacuolar sorting: To investigate the effect of dodine on endocytic uptake into the vacuole, 1 ml of the dodine-treated U. maydis cells was incubated with 1 μl of the endocytosis reporter dye FM4-64  for 5 min in the dark at room temperature. Cells were washed twice with CM media by centrifugation using a Micro Star 17R cooled centrifuge  at 3500 rpm for 5 min. This was followed by resuspension of the cell pellet 1 ml CMglucose and incubation on a SB2 Rotator in the dark at room temperature for 45 min. 1 µl cell suspension was placed onto a 2% (w v−1) agar cushion and microscopically images were taken  as described previously  agar cushion and TMRM was imaged using 561 nm laser, at 20% intensity, whereas the GFP-Sso1-carrying PM was visualized with a 488 nm laser at 20% intensity and an exposure time of 150 ms. To quantitatively determine the mitochondrial potential, a region of interest, covering the entire cell within the confinement of the PM, labelled with GFP-Sso1, was generated. This region was transferred to the image of TMRM fluorescence and the integrated signal intensity was measured. The value was corrected by the integrated signal intensity within the same region in the image background. The mean ± SEM from 131 to 211 measurements, done in 2–3 experiments was plotted in Prism 6.eviously . In brie3.5−1) agar cushion and z-stacks were taken at a z step size of 0.2 μm with the 488 nm laser at 20% and an exposure time of 150 ms. The total number of cells and the number cells showing PM alterations was recorded and the percentage of stained cells was calculated and a graph with the mean of 3 experiments was generated.The effect of dodine on plasma membrane appearance was analyzed in strains FB1GSso1 and IPO323_eGFP-Sso1. Treated cells were paced onto a 2%  agar cushion and a reference image was taken. A central area of 2–3 μm was photo bleached, using a 405 nm laser at 80% output power, followed by immediate image acquisition at 30 sec intervals for 5 min. Fluorescence recovery was measured as average intensity in the bleached area of the PM using MetaMorph. In parallel, the signal intensity in an unbleached area was measured. All fluorescent intensity values were corrected for adjacent image background, and signal intensities in photo-bleached regions were compared to those in unbleached parts of the PM.The effect of dodine treatment on the fluidity of the fungal PM fluidity was investigated by fluorescent recovery experiments. To this end, 1 ml of overnight 3.7U. maydis and Z. tritici cultures were treated with various concentrations of dodine or the solvent control, containing equivalent amounts of methanol, for 30 min at room temperature, rotating on a SB2 Rotator. 1 μl of the cell suspensions was placed onto a 2% (w v−1) agar cushion and movies of 100–150 plains with an exposure time of 150 ms and the 488 nm laser at 10% was acquired. From those movies, kymographs of single cells were generated. The total number of actin patches and the number of actin patches showing normal dynamic behavior was recorded and the percentage of dynamic patches was calculated. Only patches which showed a full circle of assembly, plateau and scission within 15 s were considered as dynamic. Patches which were visible in plane 1 and did not show any sign of scission within 15 s were considered as non-dynamic. Patched which got assembled within the 15 s observation window but plateaued till the end of the 15 s were not considered at all.The effect of dodine treatment on actin patch dynamics was visualized in strains AB33GLifeact and IPO323_Lifeact-ZtGFP. To this end, 1 ml of overnight 3.8t-test with Welch’s correction. Data sets that did not passed a Shapiro-Wilk test for normality were compered using a nonparametric two-tailed Mann-Whitney test. If more than two data sets were statistically compared a one-sided ANOVA test was used for normal distributed data sets. All data sets which did not passed a Shapiro-Wilk test for normality (P > 0.05) were compared using a Kruskal-Wallis test. Non-linear regression curves were calculated as dose response inhibition (four parameters) in Prism6; EC50 values were determined graphically.All images were adjusted for brightness, contrast and gamma values using MetaMorph 7.8.x or Adobe Photoshop CS6. All statistical were done using the software GraphPad Prism 5 or 6 . Data sets were tested firstly for normal distribution using the Shapiro-Wilk test for normality. In case only two data sets were compared, all data sets which passed that test were statistically compared using a Student’s 3.9U. maydis cells, treated with 50 µg ml−1 dodine, was done as described (−1) glutaraldehyde and 2% (v v−1) formaldehyde in 0.1 M PIPES buffer pH 7.2. Samples were post-fixed with 2% (w v−1) potassium permanganate in dH2O, then dehydrated and embedded in Durcupan resin (Sigma-Aldrich). 60 nm ultrathin sections were collected on pioloform-coated 100-mesh copper EM grids , contrasted with lead citrate and analyzed using the JEOL JEM 1400 transmission electron microscope.Transmission electron microscopy in escribed  using a Martin Schuster: Data curation, Investigation, Formal analysis. Gero Steinberg: Conceptualization, Visualization, Formal analysis, Writing - review & editing, Supervision, Project administration, Funding acquisition.