Recent advances in multivariate fMRI analysis stress the importance of information inherent to voxel patterns. Key to interpreting these patterns is estimating the underlying dimensionality of neural representations. Dimensions may correspond to psychological dimensions, such as length and orientation, or involve other coding schemes. Unfortunately, the noise structure of fMRI data inflates dimensionality estimates and thus makes it difficult to assess the true underlying dimensionality of a pattern. To address this challenge, we developed a novel approach to identify brain regions that carry reliable task-modulated signal and to derive an estimate of the signal's functional dimensionality. We combined singular value decomposition with cross-validation to find the best low-dimensional projection of a pattern of voxel-responses at a single-subject level. Goodness of the low-dimensional reconstruction is measured as Pearson correlation with a test set, which allows to test for significance of the low-dimensional reconstruction across participants. Using hierarchical Bayesian modeling, we derive the best estimate and associated uncertainty of underlying dimensionality across participants. We validated our method on simulated data of varying underlying dimensionality, showing that recovered dimensionalities match closely true dimensionalities. We then applied our method to three published fMRI data sets all involving processing of visual stimuli. The results highlight three possible applications of estimating the functional dimensionality of neural data. Firstly, it can aid evaluation of model-based analyses by revealing which areas express reliable, task-modulated signal that could be missed by specific models. Secondly, it can reveal functional differences across brain regions. Thirdly, knowing the functional dimensionality allows assessing task-related differences in the complexity of neural patterns. •Representational spaces in the brain have a functional (non-noise) dimensionality.•We provide a statistical test for dimensionality that applies to noisy data.•The best low-rank matrix approximations provide estimates of dimensionality.•Bayesian hierarchical modelling quantifies uncertainty in dimensionality estimates.•In 3 fMRI datasets, the form and extant of neural representations is clarified. A growing number of fMRI studies are investigating the representational geometry of voxel response patterns. For example, using representational similarity analysis RSA; , researcInterpreting representational geometry in neural responses can be difficult. For example, RSA tests for a hypothesized representational pattern, but an important and more fundamental question should be addressed first, namely whether there is any dimensionality to the underlying neural pattern and, if so, what that dimensionality is.Knowing whether a pattern has dimensionality should be prerequisite for RSA and other multivariate representational analyses because a particular similarity structure can only be found when there is sufficient dimensionality to represent the proposed relations. For example, searching for a flavor space with dimensions sweet, sour, bitter, salty and umami would be a fool's errand in brain areas that contain little or no dimensionality.Although previous studies have made substantial progress in identifying whether any dimensionality underlies an observed pattern , a straiThere are many methods of dimensionality reduction and estimation, most of which involve low-rank matrix approximation and aim to maximize the correspondence between the original and the approximated matrix. For example, two common approaches to estimate the dimensionality of an observed neural or behavioral pattern are principal component analysis (PCA) or relatedly, multidimensional scaling (MDS).PCA, or the closely related factor analysis and singular value decomposition (SVD) , is wideSimilarly, MDS finds the best representation of original distances in a low-dimensional space . For exaEstimating the dimensionality of neural data brings its own unique challenges. In a noise-free scenario, dimensionality can be defined as the number of linear orthogonal components  underlying a matrix that are larger than zero , indicatOne criterion, which we adopt in the work reported here, is to choose the number of components that should maximize reconstruction accuracy (measured by correlation) on new data . While even for data with low or moderate true dimensionality more components will always increase fit for existing data , performance on test data  will usually be best for a moderate number of components because these components largely reflect true signal as opposed to noise in the observed training sample.The problem of distinguishing between signal and noise in a neural pattern is related to the bias-variance trade-off in supervised learning and model-selection. Overly simple models (few components) are highly biased, fitting training data poorly and not performing well on test data. These overly simple models cannot pick-up on nuances in the signal. Conversely, overly complex models (many components) are too sensitive to the variance in the training data . Although they fit the training data very well, overly complex models treat noise in the training data as signal and, therefore, generalize poorly. Thus, the sweet spot for test performance should be at some moderate number of components that largely reflect true signal see  A. Thus,One naive way to navigate this trade-off between simple and complex models is to use some arbitrary cut-off, such as including the number of components that captures some amount of variance in the training data or deciding based on visual inspection which components may carry signal  and cross-validation to identify areas across the brain with underlying dimensionality. We derive which of all possible low-dimensional reconstructions of the fMRI signal is the best dimensionality estimate of a held-out test run, and quantify the goodness of the low-dimensional reconstruction via Pearson correlation.We use a cross-validation procedure to identify the appropriate dimensionality, which retains the components that carry signal and are therefore most likely to generalize to new data. By assessing the significance of the correlation, we can distinguish between areas that show reliable signal with underlying dimensionality vs. areas that do not show a reliable task-modulation. We will refer to this task-dependent dimensionality as functional dimensionality. After establishing significant functional dimensionality, we use Bayesian modeling to derive a population estimate and associated uncertainty of the degree of dimensionality.We define functional dimensionality as reliable task-dependent changes in a neural pattern that generalize across runs within a subject, though the representational geometry does not need to be common across subjects. A prerequisite for functional dimensionality is that neural patterns are reliable within subjects. As we show below see also B, our apThrough simulations and evaluation of three (published) fMRI datasets, we find that our method successfully identifies areas with significant functional dimensionality and provides reasonable estimates of the underlying dimensionality. In the first fMRI dataset, participants performed a categorization task which required differential attention to various stimulus features . The secAcross all three studies, we were able to identify areas carrying functional dimensionality in a manner that supported and extended the original findings. Focusing on wholebrain effects in the first two studies, we identified a consistent network of areas showing functional dimensionality during visual stimulus processing. This network encompassed areas that were reported by the original authors as being task-relevant, identified through representational similarity analysis and cognitive model fitting . Furthern voxels, neurons, or sensors m conditions. For example, BOLD response patterns in the fusiform face area (FFA) to 3 different stimulus conditions can be expressed as a matrix Y of the size n (number of voxels) n and m, which in this example would be 3, assuming many voxels in FFA were included in the analysis. As fully explained below, the maximum possible dimensionality is Neuroimaging data, such as fMRI, M/EEG, or single-cell recordings, can be represented as a matrix of However, functional dimensionality could be lower. For example, dimensionality would be lower if the region only responded to face stimuli and showed the same lower response to house and tool stimuli.The approach to dimensional estimation we present here is modular and estimates a matrix's dimensionality by combining low-rank approximation with cross-validation and significance testing. This modularity allows to flexibly choose the dimensionality reduction technique which best fits with ones requirements. Here, we used SVD (which is often used to compute PCA solutions) because it is a well-understood, easy to implement, and a computationally efficient low-rank matrix approximation.The choice of SVD, as well as how the data matrix is normalized is informed by our understanding of the underlying neural signal. Because voxels differ greatly from one another in their overall activity level and activity levels can drift over runs, we mean-center each row  of the data matrix by run. In contrast, we do not mean-center each column, as would typically be done with approaches that focus on the covariance of the column vectors . The reason we do not normalize by column  is that we are open to the possibility that different stimuli may be partially coded by overall activity levels of a population of voxels. For example, imagine a brain area only responds strongly to faces, but not to other stimuli. An SVD with demeaned voxels  would be sensitive to this dimension of representation, whereas a procedure that effectively worked with demeaned columns would not be sensitive to this task-driven difference in neural activity , but the precise nature of the preprocessing and implemented GLM is not critical to our method. Functional data were motion corrected, co-registered and spatially normalized to the Montreal Neurological Institute (MNI) space.Y was then multiplied by To reduce the impact of the structured noise, which is correlated across voxels, on the dimensionality estimation and to improve the reliability of multivariate voxel response patterns , we appldiagonal . Each n×noise−12 .In fMRI data, the baseline activation can differ across functional runs. This has important implications for our approach presented here, as it can bias the correlation between neural patterns across runs. To account for this, we demeaned the pre-whitened beta estimates across conditions, resulting in an average estimate of zero for each voxel. This demeaning reduces the possible maximum dimensionality of the data to M of the form U and V are matrices of size M. A k-dimensional reconstruction of the matrix M can be achieved by only keeping the k largest singular values in The dimensionality of a matrix is defined as its number of non-zero singular values, identified via singular value decomposition (SVD). SVD is the factorization of an observed  theorem , leadingj(number of runs) matrices Y of n(number of voxel) m(number of beta estimates), with the restriction of To estimate the dimensionality of fMRI data, we applied SVD to Critically, fMRI beta estimates are noisy estimates of the true signal. In the presence of noise, all singular values of a matrix will be non-zero, requiring the definition of a cut-off criterion to assess the number of singular values reflecting signal. Removing noise-carrying components from a matrix is beneficial, as it avoids overfitting to the noise and thus, improves the generalizability of the low-dimensional reconstruction to another sample see . To thatk highest singular values and setting all others to zero. Each low-dimensional reconstruction of matrix k of the underlying dimensionality. As keeping components that reflect noise rather than signal lowers the correlation with an independent data set, the highest correlation is not necessarily achieved by keeping more components. This procedure thus avoids inflated dimensionality estimates.Data are partitioned k for run j, the training and validation runs from 1 to k-dimensional reconstruction of the averaged data. The quality of this final low-dimensional reconstruction is measured as Pearson correlation with After identifying the best dimensionality estimate j estimates of the underlying dimensionality and j corresponding test correlations per participant. Under the null-hypothesis of no dimensionality, and thus, only noise present in the matrix, reconstruction correlations averaged across runs are distributed around zero. Thus, across-participants significance of the averaged reconstruction correlations can be assessed using one-sample t-tests or non-parametric alternatives, as for instance permutation tests  for each participant. In a noise-free scenario, all j estimates reflect the true dimensionality and thus, direct inference could be made solely based on these estimates. Under noise, these estimates could over- or underestimate the true dimensionality. The less reliable the j dimensionality estimates, the higher the variance across them. Mere averaging of the j estimates across participants would discard this information, weighting all participants equally, irrespective of their reliability. Down-weighting the influence of less reliable dimensionality estimates on the population estimate leads to a better population estimate . On the population level, we chose a truncated normal distribution with mean of μ and a standard deviation of σ, limited to the range of the possible dimensionality estimates. This was chosen to reflect the assumption that participants from the same population should have similar, though not necessarily identical functional dimensionalities. The combination of a normal distribution on the population level and a truncated t-distribution on the single subject level ensured that participants with largely dividing dimensionality estimates are shrunk towards the mean of the overall sample in an optimal way.As can be seen in As we did not have strong priors regarding the dimensionality of the neural patterns, we implemented a uniform prior over the population dimensionality estimates, reflecting that the dimensionality could be anything from 1 to Notably, this does not imply that all participants need to show an estimated functional dimensionality larger than zero, but rather reflects the assumption that a significant second-level functional dimensionality suggests a non-zero functional dimensionality in the population.The prior distribution can be adapted to be informative for studies estimating the functional dimensionality of neural patterns with stronger priors . The model is formally expressed in Equation j dimensionality estimates.The maximum population variance was defined as the expected variance of this uniform distribution j estimates of a participant's dimensionality were not independent, since the training data overlapped. Thus, the standard deviation of the estimates will be underestimated. The degree of this underestimation will be the same for all participants though, which allows us to rely on the observed standard deviation as a proxy for the estimation noise without correcting.The Before applying our method to real fMRI data, we tested the validity of our method through dimensionality-recovery studies on simulated fMRI data. Estimating the dimensionality for simulated cases where the true underlying dimensionality is known allowed us to assess whether our procedure results in a reliable dimensionality estimate.Simulated data were created using the RSA toolbox  and custWe simulated data with a dimensionality of 4, 8, and 12 and ten steps of exponentially increasing noise levels to investigate how noise affects dimensionality estimates, and how this effect interacts with the ground-truth dimensionality. The lowest noise level was set as to match the reliability we observed in the original study by k, we first generated true, i.e. noise-free, n(voxel) m(conditions) matrices with underlying pre-defined dimensionality. This was achieved by applying PCA to a random 16 k-dimensional reconstruction of it. All eigenvalues of this initial k-dimensional matrix had the same value. Rows of this matrix were added to an To generate data with varying ground-truth dimensionality In the next step, we calculated the dot-product of the generated beta matrices and generated design matrices, which were HRF convolved. This resulted in noise-free fMRI time series.n(voxel) t(timesteps) matrix was then spatially smoothed and temporally smoothed with a Gaussian kernel of 4 FWHM. Finally, this temporally and spatially smoothed noise matrix was added to the noise-free time-series and the design matrix was fit to the resulting data using a GLM. This resulted in a (noisy) voxel A noise matrix was generated by randomly sampling from a Gaussian distribution. The To gather a reliable estimate of the performance of our procedure, we ran a total of 100 of these simulations for each combination of ground-truth dimensionality and noise-level.We then estimated the dimensionality for each simulated participant as described above and passed each participant's average estimated dimensionality and the standard deviation of this estimate to the described hierarchical Bayesian model. To assess the goodness of the estimated dimensionalities, we combined all posterior estimates of the single simulated participants' dimensionalities  The posterior estimate of the degree of underlying dimensionality should be close to the ground-truth dimensionality when the signal-to-noise ratio is high. 2) The uncertainty of the posterior estimate should increase with increasing noise-levels. 3) Estimates should gracefully degrade such that as noise increases the relative order of ground-truth dimensionalities should still be reflected in the estimated dimensionalities and the posteriors still contain the ground-truth values. 4) With increasing noise, the relative importance of the prior should increase and in the limit all ground-truth dimensionalities should converge to the mean of the prior.As can be seen in An additional observation from the simulation results is that moderate levels of noise can lead to a small inflation of estimated dimensionalities for higher ground-truth dimensionality levels, as seen here for the case of a dimensionality of 8. This effect is due to the correlational structure of noise in fMRI data. The SVD is sensitive to this correlational structure and as a result, singular values that reflect noise could surpass singular values that reflect signal. This would then cause an overestimation of the underlying dimensionality, since keeping a noise-carrying singular component would not improve the correlation with the held-out validation data, but adding the next, signal-carrying singular value to the reconstruction would. However, this inflation is only minor. It does not violate the rank order of the posterior dimensionality estimates and the distribution of the posterior dimensionality estimates reflects the increased uncertainty in the estimate.Together, these simulations show that our procedure is suitable to provide an accurate estimate of the degree of underlying functional dimensionality for good signal-to-noise ratios. Moreover, the access to the whole distribution of dimensionality estimates allows to draw valid inferences on the relative degree of functional dimensionality even under high noise, and the width of the distribution of these estimates reflects the uncertainty of these estimates. Thus, the combination of cross-validated SVD and hierarchical Bayesian modeling can provide a robust and interpretable estimated distribution of the degree of underlying functional dimensionality, which reflects the certainty in the estimate.Following the successful tests of our procedure with simulated data, we applied our method to three different, previously published fMRI datasets, all employing visual stimuli and testing healthy populations. We tested three core aims of our method: 1) Identifying areas carrying functional dimensionality, 2) Using functional dimensionality to assess sensitivity to stimulus features, and 3) Measuring task-dependent differences in dimensionality.Using data from a category learning study by Participants were trained on categorizing nine objects that differed on four binary dimensions: shape (circle/triangle), color (red/green), size , and position (left/right). During the fMRI session, participants were presented with the set of all 16 possible stimuli and had to perform the same categorization task. Out of 23 participants, 20 were included in the final analysis presented here, with 19 participants completing 6 runs composed of 48 trials and one participant completing 5 runs.Standard pre-processing steps were carried out using SPM12  and betaj correlations and dimensionality estimates were ascribed to the center of the searchlight. The code for the searchlight was based on the RSA toolbox  in an extended network of occipital, parietal and prefrontal areas see . In thesAs can be seen in Within the first dataset, we showed that by identifying areas with significant functional dimensionality, it is possible to reveal areas that can plausibly be tested for correspondence with a hypothesized representational similarity structure, as for instance derived from a cognitive model. More specifically, we were able to identify all areas that have been reported in the original analysis by Using data from a study with real-world categories and photographic stimuli by During the experiment, participants were presented repeatedly with 54 different natural images that were of nine different shapes and belonged to six different categories , allowing the authors to dissociate between neural responses reflecting shape or category information.Standard pre-processing of the data was carried out using SPM12 . In line with the authors original analysis, we tested for differences depending on whether the stimuli were averaged to emphasize their category or shape information. To that end, we constructed two separate GLMs. The first GLM (catGLM) was composed of one regressor per category , thus averaging across objects shapes. The second GLM (shapeGLM) consisted of nine different regressors, one for each shape, averaging neural responses across object categories. In both GLMs, regressors were convolved with the HRF and six motion-regressors as covariates of no interest were included.Dimensionality was estimated separately for both GLMs. We ran a wholebrain searchlight with a 7 mm sphere  into two contrasting categories based on trial-and-error learning. Over the course of the experiment, participants completed two learning problems . Correct classification in type I problem required attending to only one of the bug's features, whereas classification in type II problem required combining information of two features in an exclusive-or manner.Previous research has shown that neural dimensionality appropriate for the problem at hand is linked to successful task performance . Thus, wIn order to infer the degree of functional dimensionality, we estimated it across ROIs encompassing LOC in the left and right hemisphere separately for the two categorization tasks. Because the relevant stimulus dimensions were learned through trial-and-error learning, we excluded the first functional run (early learning) of each problem and analyzed the remaining three runs for each problem.t-tests. To derive the best population estimate for the underlying dimensionality for each ROI and problem, we implemented the above described hierarchical Bayesian model. To that end, we calculated mean and standard deviation of each participant's dimensionality estimate per ROI and problem and used those summary statistics to estimate the degree of underlying dimensionality for each ROI and problem.Prior to estimating the dimensionality, data were pre-whitened and mean-centered. Dimensionality was estimated across all voxels for each ROI and problem, resulting in 3 (runs) Estimating dimensionality across two different ROIs in LOC and two different tasks allowed us to test whether the estimated dimensionality differs across problems with different task-demands. As participants had to pay attention to one stimulus feature in the type I problem and two stimulus features in the type II problem, we hypothesized that dimensionality of the neural response would be higher for type II compared to type I in an LOC ROI.Both ROIs showed significant reconstruction correlations across both tasks  dataset in which the ground-truth dimensionalities were known, we applied our method to three published fMRI datasets. In each case, the procedure confirmed and extended the authors' original findings, advancing our understanding of the function of the brain regions considered. Each of three datasets highlighted a potential use of estimating functional dimensionality.In the first study, working with data from These additional areas invite further analysis as they might implement processes and representations outside the scope of the tested model. Functional dimensionality can indicate interesting unexplained signal. For example, in the first dataset examined, functional dimensionality was found in all the areas identified by In the second study, working with data from Our method may have been more sensitive to signal because it makes fewer assumptions about the underlying representational structure and allows for individual differences in the underlying dimensions. In this sense, assessing functional complexity complements existing analysis procedures. Indeed, our approach could be used to evaluate multiple stimulus groupings to inform feature selection in encoding models .In a third study, working with data from In summary, assessing functional dimensionality across these three studies complemented the original analyses and revealed additional nuances in the data. In each case, our understanding of the neural function was further constrained. Moreover, comparing the results to those from model-based and other multivariate approaches was informative in terms of understanding underlying assumptions and their importance.Of course, as touched upon in the Introduction, there are many possible ways to assess dimensional structure in brain measures and progress has been made on this challenge . Here, oWe hope our contribution is useful to researches interested in further exploring their data, whether it be fMRI, MEG, EEG, or single-cell recordings. Researchers may consider variants of our method. For example, as mentioned in the Introduction, the SVD could be substituted with another procedure depending on the needs and assumptions of the researchers. There is no magic bullet to the difficult problems of estimating the underlying dimensionality of noisy neural data, but we have made progress on this issue both theoretically and practically. In doing so, we have also provided additional insights into the brain basis of visual categorization. We hope that by demonstrating the merits of estimating the functional dimensionality of neural data that we motivate others to take advantage of this additional and complementary viewpoint on neural function.A Matlab toolbox for estimating functional dimensionality of fMRI data as well as data needed to replicate the analyses presented here will be made available after publication. Nifti files and code for the analyses presented here are available from the authors upon request.


Together, our results indicate that high-dose GluOC triggers necroptosis through upregulation of FasL at the plasma membrane in a manner dependent of activation of CREB-p300, followed by the activation of Fas signaling in neighboring adipocytes.The uncarboxylated form of osteocalcin (GluOC) regulates glucose and lipid metabolism in mice. We previously showed that low-dose (≤10 ng/ml) GluOC induces the expression of adiponectin and peroxisome proliferator-activated receptor γ (PPARγ) via a cAMP–PKA–ERK–CREB signaling pathway in 3T3-L1 adipocytes. We also noticed that high-dose (≥20 ng/ml) GluOC inhibits the expression of adiponectin and PPARγ in these cells. We have here explored the mechanism underlying these effects of high-dose GluOC. High-dose GluOC triggered morphological changes in 3T3-L1 adipocytes suggestive of the induction of cell death. It activated the putative GluOC receptor GPRC6A and thereby induced the production of cAMP and activation of protein kinase A (PKA), similar to signaling by low-dose GluOC with the exception that the catalytic subunit of PKA also entered the nucleus. Cytosolic PKA induced phosphorylation of cAMP response element-binding protein (CREB) at serine-133 via extracellular signal-regulated kinase (ERK). Nuclear PKA appeared to mediate the inhibitory phosphorylation of salt-inducible kinase 2 (SIK2) at serine-358 and thereby to alleviate the inhibitory phosphorylation of the CREB co-activator p300 at serine-89. The activation of CREB and p300 resulted in increased expression of the transcription factor FoxO1 and consequent upregulation of Fas ligand (FasL) at the plasma membrane. The interaction of FasL with Fas on neighboring adipocytes triggered the phosphorylation at threonine-357/serine-358 and homotrimerization of mixed-lineage kinase domain-like protein (MLKL), a key regulator of necroptosis, as well as Ca These metabolic effects of GluOC are achieved primarily through promotion of both insulin secretion from and the proliferation of pancreatic β-cells, with these latter actions being mediated either directly on the pancreas7 or indirectly via the stimulation of glucagon-like peptide-1 (GLP-1) secretion from intestinal endocrine cells9. In addition, daily intraperitoneal injection of GluOC resulted in full recovery of the liver in mice with steatosis induced by a high-fat diet7. We also showed that oral administration of GluOC was as effective as intraperitoneal administration in reducing the size of adipocytes in adipose tissue of mice12.Osteocalcin, a noncollagenous bone matrix protein, regulates glucose and energy metabolism in its uncarboxylated form (GluOC)13. Activation of GPRC6A by GluOC induced the intracellular accumulation of cAMP and the consequent activation of protein kinase A (PKA) in these cells. It also induced phosphorylation of cAMP response element-binding protein (CREB), but this effect appeared to be mediated indirectly by extracellular signal-regulated kinase (ERK) rather than directly by PKA.We recently characterized the signaling pathway by which GluOC increases the expression of both peroxisome proliferator-activated receptor γ (PPARγ), a master regulator of adipogenesis, and adiponectin, an insulin-sensitizing adipokine, by acting at G protein-coupled receptor family C group 6 subtype A (GPRC6A) in 3T3-L1 adipocytesDuring the course of these experiments, we also found that GluOC at concentrations of ≥20 ng/ml inhibited the expression of adiponectin in 3T3-L1 adipocytes, in contrast to the stimulation apparent at concentrations of ≤10 ng/ml. We have now explored the mechanism of this effect, and we found that GluOC at such high concentrations increases the expression of forkhead box protein O1 (FoxO1) via activation of p300, a CREB co-activator, and the consequent expression of Fas ligand (FasL). FasL at the plasma membrane then activates Fas signaling in neighboring cells via direct cell–cell interaction and thereby triggers necroptosis.We cultured 3T3-L1 adipocytes or preadipocytes for 48 or 96 h with GluOC at 5 or 40 ng/ml or with 1 µM staurosporine, an inducer of apoptosis. The cells were then examined by phase-contrast microscopy Fig.  and counWe next observed 3T3-L1 adipocytes incubated for 48 h at higher magnification by phase-contrast and fluorescence microscopy Fig. . Miniatu522) level in response to treatment with low-dose or high-dose GluOC, or an increase in expression level in response to only high-dose GluOC (FoxO1 and FasL). ATGL, FoxO1, and FasL genes are all targets of the transcription factor CREB and related downstream transcriptional factors16.To shed light on the molecular mechanism underlying the observed effects of high-dose GluOC on 3T3-L1 adipocytes, we examined the expression or phosphorylation of various molecules associated with lipid hydrolysis or cell death. The cells were cultured with GluOC at 5, 20, 40, or 80 ng/ml or with 1 µM staurosporine for 6 h and were then subjected to immunoblot analysis Fig. . Culture17.Among the molecules examined, staurosporine increased the expression of adiponectin and, to a small extent, that of FasL. The upregulation of adiponectin by staurosporine might be mediated by PPARγ in response to the inhibition of cyclin-dependent kinase 5, given that staurosporine is a relatively nonspecific inhibitor of protein kinasess and the consequent production of cAMP13, we measured cAMP production and consequent phosphorylation of CREB in 3T3-L1 adipocytes exposed to high-dose GluOC  thought to mediate the effects of low-dose GluOC. However, knockdown of GPRC6A inhibited the upregulation of FoxO1 by high-dose GluOC to a similar extent as it did that of adiponectin by low-dose GluOC Fig. . We nextuOC Fig. . The pro13. Immunoblot analysis revealed that FoxO1 expression induced by high-dose GluOC was attenuated by both inhibitors. Together, these various results thus showed that, similar to low-dose GluOC, high-dose GluOC activates the GPRC6A–cAMP–PKA–ERK–CREB pathway.We further examined the effects of PKA and MEK  inhibitors on the expression of FoxO1 induced by high-dose GluOC in 3T3-L1 adipocytes Fig. , given tGiven that GluOC increased both the expression of ATGL and the phosphorylation of perilipin in 3T3-L1 adipocytes, we investigated whether it also increased the release of free fatty acids (FFAs) as a result of triglyceride hydrolysis. Indeed, GluOC induced the release of FFAs into the culture medium in a dose-dependent manner Fig. . We also18. We therefore examined the possible roles of these co-activators in high-dose GluOC action in 3T3-L1 adipocytes. We first investigated the distribution of these proteins in cytoplasmic and nuclear fractions of 3T3-L1 adipocytes  and regulatory (PKA RI-α) subunits of PKA. Phosphorylation of nuclear p300 at Ser89, which is mediated by SIK2 and inhibits p300 activity, was markedly attenuated by high-dose GluOC. On the other hand, the phosphorylation of cytoplasmic CRTC2 at Ser171, which is also mediated by SIK2 and inhibits CRTC2 activity, was increased by GluOC at both low and high doses. PKA RI-α was essentially restricted to the cytoplasmic fraction. In contrast, the subcellular localization of PKA C-α was shifted to the nucleus by high-dose GluOC, indicative of increased PKA activity in the nucleus. SIK2 was localized largely to the cytoplasm, and the amount of cytoplasmic SIK2 was increased by stimulation with GluOC in a dose-dependent manner. SIK2 is phosphorylated at Ser358 and thereby inhibited by PKA, and such phosphorylation of nuclear SIK2 was increased by high-dose GluOC, consistent with the upregulation of active PKA (PKA C-α) in the nucleus. Cytoplasmic SIK2 was phosphorylated in response to stimulation with either low- or high-dose GluOC. Together, these results indicated that high-dose GluOC activates p300 in the nucleus of 3T3-L1 adipocytes as a result of PKA-mediated inhibitory phosphorylation of SIK2 and consequent attenuation of SIK2-mediated inhibitory phosphorylation of p300.CREB often requires a transcriptional co-activator such as p300 or cAMP-regulated transcriptional co-activator 2 (CRTC2) for its functiontes Fig. . ImmunobWe then examined the possible effect of GluOC on the binding of p300 to CREB with the use of homogeneous time-resolved fluorescence resonance energy transfer (HTRF) technology Fig. . The HTRWe found that high-dose GluOC induced cell death in 3T3-L1 adipocytes. To investigate further the type of cell death induced by high-dose GluOC, we first examined the cleavage of caspase-8 and caspase-3, which contributes to apoptosis Fig. . ImmunobWe next attempted to discriminate among live cells, necrotic cells, and apoptotic cells by fluorescence microscopy Fig. . High-doGiven that FasL located in the plasma membrane activates its receptor, Fas, in neighboring cells to induce necroptosis, we examined the localization of FasL induced by high-dose GluOC in 3T3-L1 adipocytes. Immunoblot analysis revealed that high-dose GluOC increased the amount of FasL in the plasma membrane fraction of these cells in a dose-dependent manner Fig. . Confoca21. Given that high-dose GluOC did not appear to induce either apoptosis  occur downstream of MLKL activation in necroptosis24, we examined whether high-dose GluOC might induce such effects in 3T3-L1 adipocytes. Indeed, we found that GluOC at 40 ng/ml increased the intracellular Ca2+ concentration in a manner dependent on the presence of extracellular Ca2+, as well as increased ROS accumulation in these cells , a mediator of mitochondrial dynamics, given that mitochondrial fission occurs during necroptosistes Fig. . Furthertes Fig. .Fig. 7Ac26, given that an unidentified receptor other than GPRC6A has been proposed to mediate an action of GluOC in neuronal development27. However, this possibility was excluded by the experiments of knockdown of GPRC6A. We then examined the possibility that different G proteins might couple to GPRC6A to mediate the effects of high dose31. We previously showed that GluOC at concentrations ≤10 ng/ml increased the intracellular cAMP concentration, likely through activation of Gs. Given that GluOC at concentrations of 40–160 ng/ml induced cAMP accumulation in a dose-dependent manner, we concluded that high-dose GluOC also activates Gs via GPRC6A.The present study was undertaken in response to our finding that, whereas GluOC increases the expression of adiponectin and PPARγ in 3T3-L1 adipocytes at concentrations up to 10 ng/ml, at concentrations of ≥20 ng/ml it actually inhibits such expression. We first assumed that high-dose GluOC binds to a receptor with an affinity lower than that of the putative receptor GPRC6A32, we examined the possible role of these molecules in high-dose GluOC action. Our results suggested that high-dose GluOC activates both CREB and p300 and thereby induces the upregulation of FoxO1. These findings are consistent with the previous observation that activation of p300 is essential for the CREB-dependent expression of FoxO116. Our previous13 and present studies have examined the effects of GluOC on the expression of adiponectin, PPARγ, SIK2, ATGL, FoxO1, and FasL with reference to CREB activation in 3T3-L1 adipocytes, with adiponectin and ATGL genes being targets of PPARγ34 and ATGL and FasL genes being targets of FoxO135. Among the direct targets of CREB, PPARγ and SIK2 were expressed in response to stimulation with both low-dose and high-dose GluOC, whereas FoxO1 was expressed only in response to high-dose GluOC. The expression of PPARγ and SIK2 in cells exposed to low-dose GluOC likely does not require the activation of p300. On the other hand, the expression of FoxO1 requires exposure of the CRE-containing region of the gene promoter to the histone acetyltransferase activity of p300, which occurs only in response to high-dose GluOC stimulation, as revealed by our ChIP-qPCR data and the dephosphorylation of p300 at Ser89. Our HTRF analysis also suggests that the binding of p300 to CREB is dependent on the phosphorylation of CREB at Ser133 induced by GluOC36.Given that CREB interacts with transcriptional co-activators such as p300 and CRTC237. We found that SIK2 was present in the cytoplasm and nucleus at a ratio of ~9:1, and that the amount of SIK2 in the cytoplasm was increased by GluOC stimulation, probably because the SIK2 gene is a target of CREB38. The increase in the amount of SIK2 likely accounted for the GluOC-induced increase in the inhibitory phosphorylation of CRTC2 at Ser171 in the cytoplasm, with the upregulation of SIK2 expression more than compensating for the increase in SIK2 phosphorylation by PKA. An eightfold increase in the nuclear abundance of PKA C-α induced by only high-dose GluOC appears to result in inhibition of nuclear SIK2 activity by PKA-mediated phosphorylation at Ser358, leading to a reduced phosphorylation level at Ser89 and consequent activation of p300, which likely explains why only high-dose GluOC induces necroptosis in 3T3-L1 adipocytes.SIK2 mediates the phosphorylation of both p300 and CRTC2, and its activity is inhibited by PKA21. High-dose GluOC appears to induce necroptosis via GPRC6A in 3T3-L1 adipocytes. The extent of cell death induced by high-dose GluOC was relatively modest, being ~33% after 96 h. This limited extent of cell death likely reflects the fact that direct cell–cell contact between neighboring cells is absolutely required for initiation of necroptosis, rather than the promotion of cell proliferation or toxicity induced by high-dose GluOC.Our data further suggested that FoxO1 in turn increases expression of FasL, which then triggers cell death signaling via engagement of Fas in neighboring cells. The interaction between FasL and Fas expressed on neighboring cells has been found to trigger apoptosis, an inflammatory response, or necroptosis8. However, the serum GluOC level declines with age35, so that it is <1 ng/ml in mice at 24 weeks of age and would be expected to increase to ~5–7 ng/ml after oral GluOC administration if the same amount of absorption occurs as in younger animals. On the other hand, the serum GluOC concentration increases to ~50 ng/ml at 30 min after intraperitoneal injection of GluOC at 10 µg/kg (unpublished observation), a concentration that would be expected to trigger necroptosis in adipocytes. Adipose tissue of young mice is thus likely exposed to circulating concentrations of GluOC similar to our definition of low-dose GluOC that contribute to maintenance of tissue homeostasis, whereas potentially beneficial effects of GluOC on triglyceride hydrolysis might be expected to occur after oral or intraperitoneal administration of GluOC. Middle-aged or older mice would not be expected to experience a physiological benefit of endogenous GluOC, but they might still experience a pharmacological benefit.We defined low- and high-dose GluOC as concentrations of up to 10 ng/ml and of ≥20 ng/ml, respectively. With regard to the physiological and pharmacological relevance of these concentrations, the serum concentration of GluOC in mice at 6–8 weeks of age is ~4 ng/ml and increases to ~8–10 ng/ml at 20 min after the oral administration of GluOC at a dose of 10 µg/kgIn conclusion, our results provide insight into the mechanism by which high-dose GluOC induces necroptosis in 3T3-L1 adipocytes. High-dose GluOC triggers activation of GPRC6A and thereby upregulates the expression first of FoxO1 via activation of the CREB-p300 complex and then of FasL—in addition to increasing the expression of adiponectin, PPARγ, and ATGL, as well as the phosphorylation of perilipin that are also induced by stimulation with low-dose GluOC. The interaction of FasL at the cell surface with Fas expressed on neighboring cells finally triggers necroptosis in the latter cells Fig. .13. Such differentiation was apparent from morphological changes such as the formation of lipid droplets, and the proportion of differentiated cells was consistently >90%.3T3-L1 preadipocytes obtained from American Type Culture Collection  were induced to differentiate into adipocytes as described previously13. Immunoblot analysis was also performed as described13 with primary antibodies listed in Supplementary material (Table). Where indicated, cell lysates were separated into cytoplasmic and nuclear fractions with the use of a Minute Cytoplasmic and Nuclear Extraction Kit .Measurement of cellular cAMP and RNA interference for GPRC6A were performed as described previously3T3-L1 adipocytes were cultured in Dulbecco’s modified Eagle’s medium (DMEM) containing GluOC for 24 h, after which the amount of FFAs released from the cells into the culture supernatants was determined with the use of a Free Fatty Acid Quantification Colorimetric/Fluorometric Kit .3+ cryptate-conjugated antibodies to p300 as a fluorescence resonance energy transfer (FRET) donor and with d2-conjugated antibodies to CREB as the acceptor. After incubation for 2 h at room temperature in an HTRF 96-well low-volume white plate  covered with an ultraviolet-transparent protective seal  to prevent evaporation, the HTRF signal was captured with an HTRF-compatible microplate reader . Conjugation of antibodies to CREB and p300 with Eu3+ cryptate or d2 was performed by CisBio. The HTRF ratio was calculated as  × 104, and Delta F% was calculated as ([Ratiosample – Rationegative control]/Rationegative control) × 100.3T3-L1 adipocytes were lysed with a lysis buffer containing 0.2 M KF  as a fluorescence booster. The samples were mixed with EuChIP was performed with the use of a SimpleChIP Plus Kit . In brief, cells were fixed with formaldehyde for protein-DNA cross-linking and lysed, after which chromatin was fragmented by partial digestion with micrococcal nuclease. Chromatin immunoprecipitates were prepared with protein G-conjugated magnetic beads and with either antibodies to CREB, p300 or control mouse immunoglobulin G . After reversal of protein-DNA cross-links, the DNA was purified and subjected to qPCR analysis with the use of KOD-SYBR qPCR Mix  and a Thermal Cycler Dice Real Time SystemII  and with the primers 5′-TACCCCACCGCCCCCCA-3′ (sense) and 5′-GACTGACAGGCTGCGCGG-3′ (antisense) specific for the CRE-containing region of the mouse FoxO1 gene promoter.3T3-L1 adipocytes plated on cover glasses were cultured in DMEM containing GluOC for 12 or 24 h and, where indicated, then loaded with the MitoTracker probe  for 45 min at 37 °C. The cells were then fixed with 4% paraformaldehyde for 30 min and permeabilized with 0.2% Triton X-100 for 5 min at room temperature. After exposure to Blocking One histo  for 5 min at room temperature, the cells were incubated with antibodies to FasL (Abcam) or with those to N-cadherin or to DRP1  for 1 h and then with Alexa Fluor 488- or Alexa Flour 546-conjugated secondary antibodies (Thermo Fisher Scientific) for 30 min at room temperature. The cover glasses were then inverted onto glass slides, to which Vectashield antifade mounting medium containing 4′,6-diamidino-2-phenylindole (DAPI)  was applied for 30 min at room temperature. The cells were then observed with an LSM710 laser-scanning confocal microscope . All confocal images are representative of at least three independent experiments.3T3-L1 adipocytes in a six-well plate were incubated in DMEM containing the indicated concentrations of GluOC or 1 μM staurosporine for 48 h, whereas 3T3-L1 preadipocytes were incubated with 1 μM staurosporine for 1 h. The cells were then incubated with FITC-conjugated Annexin V, EthD-III, and Hoechst 33342 for 15 min in the dark with the use of an Apoptotic/Necrotic/Healthy Cells Detection Kit (Abcam). The cells were then observed with a fluorescence microscope . All images are representative of five independent experiments.2+-free DMEM containing 1 mM EGTA (Thermo Fisher Scientific). The cells were loaded with Fluo-4 acetoxymethyl ester  for 30 min at 37 °C, incubated in the absence of the dye for 30 min at room temperature, and then subjected to live-cell imaging with a fluorescence microscope . All images are representative of three independent experiments.3T3-L1 adipocytes were exposed for 24 h to the indicated concentrations of GluOC either in DMEM or in Ca2O2, or 200 µM tert-butyl hydroperoxide (Sigma). The cells were exposed for 30 min at 37 °C either to CellROX Oxidative Stress Reagent (Thermo Fisher Scientific) and Hoechst 33342 or to Liperfluo , a perylene derivative for detection of lipid peroxides, and they were then observed with a fluorescence microscope . All images are representative of three independent experiments.3T3-L1 adipocytes were incubated for 12 or 24 h in DMEM supplemented with the indicated concentrations of GluOC, 1 mM H8. Myristoylated PKI 14-22 amide (PKA inhibitor) was obtained from Enzo Life Sciences , U0126 (MEK inhibitor) and AS1842856 (FoxO1 inhibitor) from Merck-Millipore , garcinol (p300 inhibitor) and necrostatin-1 (RIP1 inhibitor) from Abcam, staurosporine (inducer of apoptosis) and carvacrol (TRPM7 inhibitor) from Wako , and neutralizing antibodies to FasL from R&D Systems .Recombinant mouse GluOC was prepared as describedt-test or by one-way or two-way analysis of variance (ANOVA). Post hoc comparisons were performed with the Tukey–Kramer HSD (honestly significant difference) test.Quantitative data are presented as means + SEM and were compared between or among groups with Student’s Supplementary Table


This is a case report of a challenging diagnosis of IgE monoclonal gammopathy of undetermined significance, which transformed into myeloma, then transformed into IgE-producing plasma cell leukaemia in a 71-year-old male who was followed in Brest, France, from 2015 to 2019. The IgE-producing variant is the rarest sub-type of multiple myeloma, and plasma cell leukaemia is considered to be the rarest and the most aggressive of human monoclonal gammopathies. In November 2015, hypogammaglobulinemia was detected during a systematic check-up. A kappa light chain monoclonal gammopathy was first diagnosed due to an increase of the free kappa/lambda light chains ratio. No monoclonal immunoglobulin was detected by either serum protein electrophoresis  or immunofixation . In June 2018, a blood smear led to the diagnosis of plasma cell leukaemia. A monoclonal peak was detected and identified as IgE-kappa. Analysis of an archival sample taken three years earlier, revealed the presence of a monoclonal IgE, which had been missed at diagnosis. Chemotherapy with bortezomib and dexamethasone was introduced. The patient survived 10 months after the diagnosis of leukaemia. This case shows that an abnormal free light chain ratio should be considered as a possible marker of IgE monoclonal gammopathy even in the absence of a solitary light chain revealed by immunofixation. In addition, the use of an undiluted serum may increase the sensitivity of the immunofixation for the detection of IgE monoclonal gammopathies compared to the 1:3 dilution recommended by the manufacturer. The International Myeloma Working Group provides guidelines that recommend performing serum protein electrophoresis (SPE), serum quantitation of free light chains (FLC) and serum immunofixation electrophoresis (IFE) testing for IgA, IgG, IgM, kappa and lambda light chains in the screening of MG  after the systematic discovery of hypogammaglobulinemia. A time line summarizing patient follow-up from 2015 to 2019 is presented in in-situ hybridization (FISH) study demonstrated cytogenetic abnormalities including an immunoglobulin heavy locus  rearrangement in 88% of the nuclei and three copies of cyclin-dependent kinases regulatory subunit 1 (CKS1B), a chromosome 1q marker, in 46% of the nuclei analysed. The patient was diagnosed with light-chain MM stage II according to the revised International Staging System for Myeloma classification . No bone marrow biopsy was performed due to the refusal of the patient. Plasma cells fluorescence 9/L. He was then admitted to intensive care for renal failure management. The blood smear performed with Sysmex SP1000-I  showed 74% of plasma cells  and dexamethasone  was introduced (patient weight: 65 kg), decreasing the white-blood cell to 13.8 x109/L after one week (The patient received intravenous Pamidronate (60 mg/day) and adapted hydration to correct hypercalcemia. A renal replacement therapy resulted in a decrease of plasma potassium to 3.6 mmol/L, calcium to 1.95 mmol/L and creatinine to 342 µmol/L. As recommended, chemotherapy with the proteasome inhibitor bortezomib , cyclophosphamide (300 mg/m2/week) and dexamethasone (40 mg/week); and the fourth chemotherapy cycle: bortezomib , doxorubicin (36 mg/m2) and dexamethasone (40 mg/week). At the end of the four chemotherapy cycles in November 2018, laboratory results showed a leukocyte count of 3.1 x109/L without circulating plasma cells. Serum protein electrophoresis showed complete disappearance of the monoclonal peak in the gamma-globulin region but IFE still detected the IgE-kappa monoclonal component. Free kappa light chains and FLLC were 3.8 and 1.4 mg/L, respectively, with a FKLC/FLLC ratio of 2.75. Due to an alteration of the general state and the development of peripheral neuropathy secondary to chemotherapy, the patient was admitted to a follow-up and rehabilitative care unit for palliative care and no further chemotherapy was performed. An informed consent form for the publication of a case report was signed by the patient during his last hospitalization in February 2019. He died in March 2019, 10 months after the diagnosis of sPCL.A few days later, the patient was transferred to the haematology ward to receive the second and third chemotherapy cycles: bortezomib  and after each series with the Sebia hypergamma control serum (PN 4787). Quality controls for IFE (IT/IF control PN 4788) were performed once a week. No quality control of IgD and IgE immunofixation has been performed. No standard has been used. Dilutions of sera before immunofixation were 1:6 for IgG and 1:3 for IgA, IgM; IgD, IgE, FKLC and FLLC excepted with the 2015 sample performed retrospectively in June 2018 without diluting IgD and IgE.The absence of urinary immunofixation at the time of screening followed the recommendations of the International Myeloma Working Group: “In the context of screening, the serum FLC assay in combination with serum protein electrophoresis and immunofixation yields high sensitivity, and negates the need for 24h urine studies for diagnoses other than light chain amyloidosis” (et al. did not observe any significant difference in IgG measurement before and after 25 years of storage at - 25°C (French legislation requires that samples be stored frozen as soon as tumour markers have been tested for further analysis if necessary. In this case, the sample used for the measurement of β2-microglobulin in 2015 was stored at - 20°C and used in 2018 to perform the IFE. The literature on the storage stability of immunoglobulins is quite poor and no data are available to our knowledge on IgE stability. Glislefoss et al. observed that IgE MM has a more aggressive clinical course than others MM subtypes (In a recent literature review of 63 IgE MM cases, Hejl et al. to develop sPCL from MM (et al. in his review: 7 of the 63 IgE MM patients reported have developed a sPCL (Secondary PCL is a rare late-stage complication of relapsed/refractory myeloma (et al. reported the translocation t (Although Avet-Loiseau The International Myeloma Working Group provides recommendations for primary PCL treatment (Several limitations to the conclusions of this case report must be considered. This is a rare case not chosen from representative population samples, so we cannot generate information on rates, incidences or prevalence. This case report could strengthen the hypothesis that the IgE myeloma subtype may expose the patient to an increased risk of developing aggressive leukaemia compared to other subtypes but this generalization must be taken with caution.IgE myeloma is the rarest subtype of myeloma and it has probably the worst prognosis. The medical laboratory plays an important role in the proper diagnosis and quality of management of MM patients. This case revealed that an abnormal FLC ratio should be considered as a warning signal suggesting the possibility of IgD or IgE myeloma even in the absence of a solitary light chain in IFE. In addition, the use of an undiluted serum could increase the sensitivity of the immunofixation for the detection of IgE and IgD monoclonal gammopathies compared to the 1:3 dilution recommended by the manufacturer. Further investigation is warranted to evaluate the diagnostic strategy of systematically testing IgD and IgE with IFE in the presence of an abnormal FLC ratio.


Dendrimeric Antigens (DeAns) consist of dendrimers decorated with multiple units of drug antigenic determinants. These conjugates have been shown to be a powerful tool for diagnosing penicillin allergy using in vitro immunoassays, in which they are recognized by specific IgE from allergic patients. Here we propose a new diagnostic approach using DeAns in cellular tests, in which recognition occurs through IgE bound to the basophil surface. Both IgE molecular recognition and subsequent cell activation may be influenced by the tridimensional architecture and size of the immunogens. Structural features of benzylpenicilloyl-DeAn and amoxicilloyl-DeAn (G2 and G4 PAMAM) were studied by diffusion Nuclear Magnetic Resonance (NMR) experiments and are discussed in relation to molecular dynamics simulation (MDS) observations. IgE recognition was clinically evaluated using the basophil activation test (BAT) for allergic patients and tolerant subjects. Diffusion NMR experiments, MDS and cellular studies provide evidence that the size of the DeAn, its antigen composition and tridimensional distribution play key roles in IgE-antigen recognition at the effector cell surface. These results indicate that the fourth generation DeAns induce a higher level of basophil activation in allergic patients. This approach can be considered as a potential complementary diagnostic method for evaluating penicillin allergy. Dendrimers are highly branched and regular macromolecules with well-defined structures that attract considerable interest due to their potential applications in many fields of science. The three-dimensional architecture of dendrimeric systems confers them various intrinsic features such as structural homogeneity, integrity, controlled composition and high-density multidentate homogeneous terminal groups, ready for conjugation. These characteristics, added to their stability and versatility, mean that dendrimers have been used for many applications, such as sensing, catalysis, molecular electronics and photonics . MoreoveAllergic drug reactions are one of the most important health problems nowadays. Betalactam (BL) allergy is self-reported by approximately 10% of adverse drug reaction sufferers. A high proportion of these cases are mediated by immunoglobulin E (IgE), leading to a range of symptoms, from simple skin involvement to anaphylactic shock ,5. HowevIn addition, BL allergy has a complex diagnosis which is still not correctly addressed. Many BLs with different chemical structures exist and each patient has a unique IgE response. An individual can be allergic to one BL only, or cross-reactive, being allergic to BLs with the same or similar side chains, or to multiple, potentially structurally diverse BLs from different families ,10,11. AIn vitro testing represents a more rational alternative, as it can potentially identify the drug responsible, allowing the physician to find a safe alternative and reducing the need to perform drug provocation testing. However, it is necessary to confirm the sensitivity, specificity and negative and positive predictive values for these in vitro tests in order to enable their implementation in clinical practice. The most common in vitro tests are based on the detection of specific IgE, either in serum (immunoassays) or bound to receptors on the surface of effector cells (basophil activation tests) . BLs are haptens that need to be bound covalently to a carrier protein to induce an immune response. This hapten-carrier conjugate is used in immunoassays to quantify drug specific IgE (sIgE) in serum. The immunoassay is the most widely used in vitro test for the diagnosis of drug allergy. However, it has certain disadvantages, such as low sensitivity and the limited availability of commercial kits for a small range of BLs. Among non-commercial ones, Radio Allergo Sorbent Test (RAST) is the most commonly employed in research laboratories.l-Lysine is the most widely used carrier molecule for the RAST, due to its accessibility, multivalency and ease of chemical functionalization for different haptens [The nature of the carrier molecule is important for the development of an in vitro test for the detection of drug sIgE antibodies, as the conjugate it forms can influence IgE recognition. Poly- haptens ,15. Howe haptens  as carri haptens . The cou haptens ,21,22,23Although the nanotechnological advances achieved in immunoassay design are very promising ,25, RASTLike other functional assays, BAT tries to mimic in vivo IgE-mediated cell activation and mediator release. This test is useful for evaluating IgE-mediated reactions for a variety of injectable drugs since there is no need to use drug-carrier conjugates. BAT is also valuable for the identification of the drug responsible for a reaction. However, its sensitivity depends on the drug involved, with values of around 55% reported for BLs ,27,28.BAT is based on the determination of activation markers expressed on basophil surface after the interaction of the drug with sIgE . HoweverIn this paper, we analyze BAT results in a group of patients with immediate allergic reactions to BLs using various DeAns as immunogens. We compare the results to those obtained using the free drug or hapten. We further analyze these results taking into account the structural features of the DeAns, using diffusion Nuclear Magnetic Resonance (NMR) and molecular dynamics simulation (MDS).In the penicillin allergy scenario, the formation of the antigenic determinant structures (or epitopes) is based on the nucleophilic attack by the amino groups of lysine residues in proteins on the electrophilic β-lactam ring. The antigenic determinants of benzylpenicillin and amoxicillin, BPO and AXO, respectively, have been previously characterized ,21. Thei1H and 13C NMR [PAMAM dendrimers of different generations (G2 and G4) were peripherally functionalized with either BPO or AXO units, generating four DeAns, namely BPO-DeAn-G2, BPO-DeAn-G4, AXO-DeAn-G2 and AXO-DeAn-G4 a. All co 13C NMR ,21. The MDS study provided information on the size and shape of the DeAn conjugates. Both DeAn-G2 conjugates were found to be similar in size, as quantified by the radius of gyration (Rg), H) using the Stokes-Einstein equation  were determined with DOSY (difussion-ordered spectrocopy) eperiments and used to estimate size in Å by calculating the hydrodynamic radius  usThe values calculated by MDS are in broad agreement with those obtained from the DOSY experiments, showing values of ~14 Å for DeAn-G2 and ~20–22 Å for DeAn-G4.In order to study the potential of the different DeAns to be used as antigen in the BAT for the diagnosis of benzylpenicillin and amoxicillin allergy, we evaluated basophil activation through specific IgE recognition at the cell surface. This was achieved using BAT with blood samples from three amoxicillin-allergic patients and two individuals tolerant to this drug , accordiBAT results b were coWhen analyzing the data for tolerant individuals , a positive BAT result is observed in control 2 for the free molecule of AX, yielding a false positive result. However, it is important to note that negative test results were obtained using DeAns for both control individuals, even though they are allergic to other BLs . Similarly, patient 1 only showed a positive BAT for the AX-derived molecules with no activation of any BP-derived conjugates. In terms of the chemical structure, additional amino and hydroxyl groups on the benzyl side chain of AX compared to the side chain of BP may explain the differential activation through IgE recognition at basophil surfaces. These preliminary results indicate a high specificity of the BAT when using DeAns as immunogens. The high specificity of sIgE recognition for BPO-DeAn and AXO-DeAn has already been demonstrated in immunoassays , howeverWe evaluated whether the inclusion of these DeAns can improve the potential of BAT for diagnosing penicillin allergic patients. Employing the current available approach using the free drug alone, of the three patients allergic to AX  basophil activation occurred in two of them (patients 1 and 3), and of the two patients allergic to BP (patients 2 and 3) only patient 2 showed positive results. These ratios increase to three of three of patients using AXO-DeAn and two of two using BPO-DeAn, respectively. Thus, all patients were correctly diagnosed as allergic, demonstrating that the inclusion of DeAns improves test sensitivity. Moreover, sensitivity was also better than that obtained by RAST, which could only diagnose two of the three patients correctly .The diagnosis of penicillin allergy is complex and there is a large and unmet need from health-care professionals for better in vitro methods. The sensitivity of the tests can be influenced by the structure of the immunogen and is related to the underlying mechanisms involved in the allergic process. Activation of effector cells, mast cells and basophils, requires antigens of a certain size  and may In the BAT scenario, the free drugs (AX or BP) are assumed to bind proteins present in blood covalently through β-lactam reactivity, forming a big enough conjugate to achieve cross-linking. This approach attempts to emulate in vivo conditions, however it lacks information about the chemical composition of the conjugate inducing the activation. By using DeAns, one has more control over conjugate size, multivalence and the structure of peripheral antigenic determinants, allowing more reproducible assays. The use of the appropriate DeAn structure ensure the optimal interaction between the drug moieties and sIgE on the basophil surface, inducing more potent basophil degranulation and improving BAT sensitivity. 16-dendrimers (64 Å) trigger mast cell degranulation by cross-linking IgE-receptor complexes, whereas smaller DNP-dendrimers are inhibitory [These relationships between cell activation and immunogen structure have been recently explored in in vitro studies performed in animal models using 2,4-dinitrophenyl (DNP) as a model hapten. It was found that the number of epitopes and the distance between them in synthetic nanostructures have different effects on mast cell degranulation ,37,38,39hibitory ,40,41, fThese studies have provided much needed information about the requirements necessary to activate effector cells, which cannot be deduced using the free drug alone. In fact, little is known about the nature of the adduct that activates basophils in the assay. In this context, the size of the actual carrier protein, the number of reactive sites, and the proximity between them are considered key factors that influence the cross-linking process, and their evaluation would be very complex. The use of well-defined nanostructures with consistent sizes and epitope density will provide a valuable tool to study the structural parameters required for these cell processes. We evaluated the ability of these nanostructures to stimulate basophils and we found that DeAns were able to induce activation in a selective and specific way. Interestingly, basophils from allergic patients follow similar patterns regarding generation: DeAn-G4 produces higher SI than DeAn-G2; this could be due to the size, valence or proximity between epitopes in the immunogen: compared to DeAn-G2, DeAn-G4 have a higher size (~20 Å vs. ~14 Å), an increased density of epitopes (64 vs. 16) and a higher proximity between them, favoring the IgE cross-linking on cell surface, and enhancing activation. This preliminary data suggests that using a size of approximately 20 Å as well as a higher density of epitopes may better resemble the IgE molecular recognition that occurs with penicillin-protein conjugates formed in vivo.Dendrimer Building. AXO-DeAn-G2 was produced using the previously described procedure [rocedure . Simulation Details. Full atomistic simulation was performed in water as explicit solvent at neutral pH. We used the AMBER 12 MD software package for all calculations [+ and Cl− counterions were added and the molecules hydrated, using the TIP3P water model [ulations . To preser model , in a tr2. This was followed by another five periods of minimization while decreasing the harmonic restraint force constant from 20 kcal/mol-Å2 to zero in steps of 5 kcal/mol-Å2. To allow a slow relaxation of the assembled dendrimer-solvent system, the minimized structure was heated slowly from 0 to 300 K with three steps of 40 ps of MD, the first of them under conditions of constant volume-constant temperature (NVT) and the rest under constant pressure-constant temperature (NPT) conditions. Initially, we applied a weak 20 kcal/mol-Å2 harmonic constraint to the solute starting structure and slowly decreased it to zero in 5 kcal/mol-Å2 steps. At this point the dendrimer relaxation was determined from the autocorrelation function of the squared radius-of-gyration.Solvated structures were minimized as described previously , using s−4 Å.Finally, we carried out 2 ns of unconstrained MD simulation in NPT ensemble to equilibrate the system at 300 K. To solve the motion equation we used the Verlet leapfrog algorithm , with anRg, dendrimer shape, etc.), the last 1 ns equilibrated trajectory was used. Amber modules ptraj and cpptraj were used to accomplish these analyses. VMD software was used for the calculation of molecular surfaces [Finally, starting from the configurations generated by the above procedure, production runs of 20 ns trajectories were performed under an NPT ensemble. Temperature regulation was achieved using the Berendsen weak coupling method (1 ps time constant for heat bath coupling and 0.5 ps for pressure relaxation time) . The parsurfaces . TM 400 MHz spectrometer, equipped with a 5 mm BBFOPLUS probe with 2H “lock” channel and z gradient. The spectrometer is also equipped with a control temperature unit prepared to work at temperatures ranging from 0 °C to +50 °C. Gradient strength was calibrated by measuring the diffusion rate of pure water of residual protons in D2O. All experiments were conducted at 300 K. The samples were allowed to equilibrate for no fewer than 15 min.The samples were prepared in deuterium oxide at a concentration of.0.5 mM (within the infinite dilution range for similar samples at 0.1–2.1 mM) . The expTo determine the diffusion rates, a 2D sequence using double stimulated echo for convection compensation and LED using bipolar gradient pulses for diffusion was used.BK is the Boltzmann constant, T is the temperature and η is the viscosity of the solution (1.0963 cP for D2O viscosity) [The diffusion coefficients determined were used to calculate the hydrodynamic radius via the Stokes-Einstein equation: scosity) .Subjects, 2 tolerant and 3 with an immediate allergic reaction to penicillins, were included in the study. Diagnosis was confirmed following the European Academy of Allergy and Clinical Immunology (EAACI)/European Network for Drug Allergy (ENDA) guidelines . PatientThe study was conducted according to the Declaration of Helsinki principles and was approved by the Provincial Ethics Committee of Malaga. All subjects included in the study were informed orally and signed the corresponding informed consent.N-Formyl-Met-Leu-Phe) to the positive control tube. Samples were incubated with two immunogen concentrations: BP at 2 and 0.5 mg/mL; AX at 1.2 and 0.25 mg/mL and DeAns at 1 and 0.1 mg/mL (chosen on the basis of previous dose–response curves and cytotoxicity studies). The samples were incubated for 20 min at 37 °C in a water bath. The degranulation was stopped by incubating the samples on ice for 5 min and then 20 µL of staining reagent containing two monoclonal antibodies, anti-IgE PE and antigp53 FITC (gp53 is a glycoprotein expressed on activated basophils), was added to each tube and incubated for 20 min in an ice bath covered to prevent exposure to light. The cells were analyzed in a flow cytometer by acquiring at least 1000 basophils per sample. Results were considered as positive when the SI, calculated as the ratio between the percentage of degranulated basophils with the different haptens and the negative control, was greater than two to at least one of the dilutions mentioned above. When the percentage of spontaneously activated basophils was less than 2.5%, the percentage of basophils activated after contact with the antigen should be equal to or greater than 5%. The BAT selection strategy of representative samples for a patient and a control is shown in Basotest  was used following the manufacturer’s recommendations and previous procedures . BrieflyThe study of the structural requirements of conjugates for basophil activation can define the suitable molecules needed to perform BAT with increased sensitivity. Structural properties of DeAn have shown to be appropriate for intra-molecular cross-linking of sIgE bound to FcεRI on basophils. This preliminary study has allowed, for the first time, the performance of BAT with molecules other than the free drug, which enhances the response by way of a better control of the method in terms of reproducibility regarding the immunogen size and density of epitopes. Nothing similar designed at the nanoscale focused on basophils has been undertaken, and potential application to diagnosis are foreseeing, in terms of sensitivity of the test.


Observing sea ice by very high-resolution (VHR) images not only improves the quality of lower-resolution remote sensing products  by providing details on the ground truth of sea ice, but also assists sea ice fieldwork. In this study, two fieldwork-based methods are proposed, one for the practical acquisition of VHR images over drifting Arctic sea ice using low-cost commercial off-the-shelf (COTS) sensors equipped on a helicopter, and the other for quantifying the compensating effect from continuously drifting sea ice that reduces geolocation uncertainty in the image mosaicking procedure. The drifting trajectory of the target ice was yielded from that recorded by an icebreaker that was tightly anchored to the floe and was then used to reversely compensate the locations of acquired VHR images. After applying the compensation, three-dimensional geolocation errors of the VHR images were decreased by 79.3% and 24.2% for two pre-defined image groups, respectively. The enhanced accuracy of the imaging locations was affected by imaging duration causing variable drifting distances of individual images. Further applicability of the mosaicked VHR image was discussed by comparing it with a TerraSAR-X synthetic aperture radar image containing the target ice, suggesting that the proposed methods can be used for precise comparison with satellite remote sensing products. Observing the state and variation of properties of sea ice by satellite remote sensing is crucial for monitoring the effects of a changing climate. Extensive techniques have been applied to extract various physical properties of sea ice  from remote sensing capabilities for multi-temporal data acquisition covering the entire polar regions. To validate those sea ice properties retrieved from satellite imageries, very high-resolution (VHR) on-site images acquired by imaging instruments on various platforms  have been used as the ground truth for sea ice concentration ,2,3,4, tSatellite, airborne and terrestrial lidars have also been used to provide the sea ice surface properties, such as sea ice surface types and surface topography, as reliable day and night measurement techniques through thin clouds . The seaVHR images are also useful for assisting fieldwork by recording the detailed sea ice condition or the locations of on-site measurements during the fieldwork period e.g., ,15). Add. Add15])On-site acquisition of VHR sea ice images can be implemented by various remote sensing platforms such as airplane, ship, helicopter, unmanned aerial vehicle (UAV), and so on. These platforms help image acquisition at lower altitude; therefore, optical images that are less affected by clouds can be obtained.Airplanes have been used for image acquisition over a relatively wider area  and enabShip-borne imaging sensors have been installed on icebreakers cruising polar oceans, e.g., ,8,20,22.Recently, UAV-based image acquisition has been applied to sea ice observation e.g., ,24) due  due 24])Helicopters are carried onboard icebreakers for logistics and scientific experiments e.g., ,4,5,6). ,5,6. 4,5To validate the sea ice characteristics including surface topography, floe size distribution, ridge concentration, melt pond fraction, etc., aerial ground truth data  can be used because the mosaicked images have enough coverage and sufficiently fine spatial resolution for the validation purposes. However, continuous sea ice drift occurring during image acquisition causes distorted imaging locations with time, which eventually becomes an obstacle for precise image mosaicking and geographic matching with the lower-resolution remote sensing products acquired over larger areas at once. Moreover, it is not possible to select static ground-control points for conventional georeferencing and quality assessment. Previous studies have incorporated sea ice drift correction for the reconstruction of surface topography using aerial photographs from assembled stereo imaging sensor packages  or the cThis study aims to propose methods for the practical and cost-effective acquisition of helicopter-borne VHR images over drifting Arctic sea ice using commercial off-the-shelf (COTS) sensors equipped on a helicopter, carried onboard an icebreaker, and for the compensation of the effect from continuous sea ice drift from each opportunistically acquired image to reduce geolocation uncertainty during the image mosaicking procedure. The applicability of the mosaicked VHR image was discussed by direct comparison with a TerraSAR-X (TSX) synthetic aperture radar (SAR) satellite image including overlapped region.The study area is located in the Arctic Ocean, northwest of the Chukchi Sea around 77°36′ N/179°20′ E , where aProcedures of the VHR image acquisition, mosaicking, and accuracy assessment consist of: (i) helicopter-borne image acquisition with GPS logging; (ii) compensating for the effect from continuous sea ice drift using a time interpolated trajectory of the IBRV Araon; (iii) mosaicking the VHR images and error estimation; and (iv) testing the feasibility of the mosaicked VHR image. Detailed processes in each step are described in following sections.Two helicopters were carried onboard the IBRV Araon during the expedition. A commercial digital camera—a Canon M6 with 22 mm lens—was attached to the bottom of a BELL 206L-3 helicopter, with nadir viewing geometry. A GPS logger—a VBOX Sport —capable of 20 Hz logging  was placed inside the cockpit, 0.8 m higher than the position of the digital camera. Photographs, hereafter called images, were taken every second using an intervalometer in aperture priority mode with an f-number of F11 and ISO 400 setup . The f-nBefore acquiring the helicopter-borne VHR images, the time of the camera was synchronized with GPS time. For the acquired images, the acquisition time was matched with the time of 20 Hz GPS logs, and then geographic coordinates, latitude, longitude, and the altitude of the GPS log corresponding to each image were selected if the time difference between the time stamp of the VHR image and the GPS log was less than 0.02 s. Altitude was revised by subtracting 0.8 m to compensate for the position of the GPS logger relative to the camera.Although geographic coordinates were assigned to each image using high-precision records from the 20 Hz GPS logger , the continuous drift of ice floes during the image acquisition causes uncertainties in the geolocations which are used as imaging locations during the mosaicking procedure. Generally, the systematic acquisition of gridded images over a static object, e.g., land, is required for a conventional and precise image mosaicking procedure . HoweverPrior to compensating for the effect from sea ice drift, two image subsets, i.e., Subsets I and II, were selected, from relatively lower altitudes of 200 m to 400 m and from altitudes higher than 1000 m, respectively. First acquired images in each subset were designated as reference images for the compensation of the effect of sea ice drift.To compensate for the effect from continuous sea ice drift, the drift record of the main ice was assessed. The drift trajectory was recorded from the IBRV Araon using an internal 1 Hz GPS logger. After the IBRV Araon’s departure from the main ice on 15 August 2017 around 9:00 UTC, the trajectory of an ice mass balance buoy (IMB) with 1-h GPS logging interval was consecutively used for further comparison of the mosaicked image with the lower-resolution satellite image. The IMB was deployed on 14 August 2017 around 21:00 UTC on the main ice.The acquisition interval of the imaging location synchronized with the helicopter-borne image was 0.05 s denser than the sea ice drift records from the IBRV Araon, i.e., 1 Hz interval. Therefore, time interpolation was applied to the lower-interval ship data to match the time intervals of the two GPS logs. Before applying time interpolation, the linearity of the drift trajectory was assessed using the heading and trajectory of the IBRV Araon by calculating studentized residuals, i.e., residuals divided by estimated standard errors, from linear regression line  to selecDifferences between geographic coordinates of the main ice at the acquisition time of the reference image and at the acquisition time of the other images were calculated based on the time interpolated trajectory of sea ice drift. The differences, i.e., the ice floe motion which occurred during the time gap corresponding to image pairs consisting of the reference image and each other image, were then subtracted from each image except for the reference image. From this compensation of the sea ice drift, the acquisition locations of the images were corrected to be located over the study area, as to be acquired at the same time as the reference image acquisition while maintaining an identical imaging footprint.The images with initial and drift compensated geographic coordinates were mosaicked using PhotoScan software  based on the structure-from-motion (SfM) technique . The SfMAccuracy assessment was fulfilled by comparing estimated imaging locations before and after compensation for the effect of sea ice drift for the two image subsets. The estimated imaging location was calculated by determining the relative orientation and location between cameras using the SfM technique  during tThe mosaicked image was compared with the SAR image from the TSX satellite to test further feasibility . The TSXTo test the feasibility of the helicopter-borne mosaicked VHR image for the calibration or validation of lower-resolution remote sensing data, consistency in shape and geographic matching of the main ice included in the mosaicked VHR image with the TSX SAR image was assessed using similarity transformation . The simAbout 4000 helicopter-borne VHR images were acquired in a single flight of 1 h 14 min 22.45 s during 13–14 August with simultaneous GPS logging . The purAs stated in the methods section, two image subsets, i.e., Subsets I and II, were designated from the relatively lower altitude range between 200 m and 400 m and from altitudes higher than 1000 m, respectively . Subset The drift record from the IBRV Araon was investigated to compensate for the effect of continuously drifting sea ice . Before Additionally, the linearity of the ship trajectory during the period when the image subsets were obtained, from 13 August 23:50:39 to 14 August 00:46:19, was assessed using the studentized residuals of each trajectory record. All of the studentized residuals were located within the range between ±3 ; therefoAmong the images comprising Subsets I and II, the first two acquired images of each subset were designated as the reference images for compensation of the effect from sea ice drift with consideration of the linear motion of the main ice without rotation. Differences between geographical coordinates of the main ice at the acquisition time of the reference image and at the acquisition time of the other images were calculated based on the linear time interpolated trajectory of sea ice drift. The location differences were then subtracted from the geographic coordinates of each imaging location; therefore, the imaging locations, except for that of the reference image  or to deformation from collisions with adjacent free-drifting ice floes a. In FigThe distribution of ice floes around the main sea ice was different in the SAR image acquired on 16 August 18:49:52 compared with the mosaicked VHR image comprising images acquired during 14 August 00:27:47.15–00:38:47.15, since the time passage of more than two days caused different drifts and rotations of the adjacent ice floes, and might have caused deformation in boundary regions from collisions between the ice floes.About 4000 VHR images were opportunistically acquired by using the helicopter carried onboard the icebreaker , with thTo compensate for the effect of sea ice drift on the imaging locations, the drift trajectory of the main ice was investigated. Ice floe drift is a complex movement combining translation and rotation; however, during the helicopter-borne VHR image acquisition, the main ice maintained a heading direction with a low variation of ±0.0004° d and traAfter compensating for the effect of sea ice drift, the imaging locations of the two image subsets, excluding the pre-defined reference images, were adjusted as the retrospective locations at the acquisition time of the reference images. This enabled a reduction in the uncertainty in the geolocation of each image without utilizing conventional fixed ground control points e.g., ,49), whi, whi49])The initial errors from the images of Subset I  were larger than the errors from the images of Subset II . The multiple discrete flight routes comprising the images of Subset I, which were selected from the altitude range between 200 m and 400 m within the confined area, showed time gaps between each flight route and longer imaging duration, and brought the larger initial errors. Using the images from multiple discrete flight routes like the Subset I images is possibly applicable to generate a mosaicked image in a situation that systematic image acquisition is not available. The errors evaluated using the estimated imaging locations from both image subsets (Subsets I and II) were reduced after the compensation for the effect of sea ice drift ; the decFor practical uses, e.g., the calibration or validation of lower-resolution remote sensing data or sea ice products retrieved from the lower-resolution remote sensing data, the mosaicked VHR image needs to be compared directly with the lower-resolution data. The mosaicking processes using the sea ice drift compensated images were fulfilled by using popular commercial SfM software, PhotoScan, and other commercial or open source SfM softwares can be easily applied for generating mosaicked VHR image with well-established image mosaicking routines. The SAR image applied for the feasibility test of direct comparison was acquired on 16 August, after the departure of the IBRV Araon from the main ice. Based on the heading information provided by the IBRV Araon while anchored to the main ice, the ice floe was assumed to be only very lightly rotating during the image acquisition; however, the ice floe was identified to rotate by about 7.05° when compared with the SAR image using the similarity transformation. From this, it can be determined that the rotation occurred after the helicopter-borne VHR image acquisition from continuous drift of the ice floe. More precise direct matching and comparison with the SAR image could be possible if the rotation of the target ice was obtained continuously along the drift using in situ measuring sensors, e.g., multiple sea ice buoys deployed on a single ice floe. Although the rotation of the ice floe after the imaging period was not considered, precise registration was fulfilled using a few control points and the similarity transformation with the mosaicked image which was proximally located to the same ice floe in the SAR image by the translation using the combined trajectories of the IBRV Araon and the IMB. In the zoomed area around the IBRV Araon not showing gaps in the mosaicked VHR image c,d, detaAdditionally, the sea ice drift compensated helicopter-borne images can be used for detailed sea ice deformation studies using the DEM or point cloud extracted from the multi-view images. The small-scale topographic features of sea ice  can also be extracted by the shadows cast away from the features in the VHR images .This study proposed two fieldwork-based methods for the practical acquisition of VHR images over drifting Arctic sea ice using low-cost COTS sensors attaching to a helicopter carried onboard an icebreaker, and for compensating for the effect of continuous sea ice drift, which reduces geolocation uncertainty during the image mosaicking procedure. The time interpolated trajectory of the ice floe recorded from an icebreaker was used to compensate for the effect of sea ice drift in each imaging location. After the compensation for the continuous drift, the total errors of the imaging locations were reduced by 79.3% and 24.2% for the two pre-defined image groups, respectively, depending on the imaging duration causing variable drifting distances of individual images. While the ship trajectory was applied to compensate for the effect of sea ice drift in this study, sea ice buoy or other tracking data can be utilized in this purpose.From the feasibility test of the mosaicked VHR image, performed by comparison with the TSX SAR image, the proposed methods were considered to be suitable for application for direct comparison between lower-resolution satellite remote sensing data and sea ice information from the precisely geolocated VHR ground truth images, from reduced geolocation discrepancy with a simple geographic registration. Under the condition that information of both sea ice motion and rotation are obtained, the geolocation discrepancy will be further reduced. Although helicopter-borne images were used in this study, the proposed methods can also be applicable to mosaic sea ice images obtained from airplane or UAV platforms.In further study, various properties of sea ice—e.g., surface roughness, distribution of pressure ridge, and surface composition—extracted from precisely georeferenced mosaicked VHR images can be utilized for direct comparison with SAR images and sea ice products extracted from SAR images based on the proposed methods.