Gradient mapping is an important technique to summarize high dimensional biological features as low dimensional manifold representations in exploring brain structure-function relationships at various levels of the cerebral cortex. While recent studies have characterized the major gradients of functional connectivity in several brain structures using this technique, very few have systematically examined the correspondence of such gradients across structures under a common systems-level framework. Using resting-state functional magnetic resonance imaging, here we show that the organizing principles of the isocortex, and those of the cerebellum and hippocampus in relation to the isocortex, can be described using two common functional gradients. We suggest that the similarity in functional connectivity gradients across these structures can be meaningfully interpreted within a common computational framework based on the principles of predictive processing. The present results, and the specific hypotheses that they suggest, represent an important step toward an integrative account of brain function. Analysis of functional MRI data from the Human Connectome Project and Brain Genomics Superstruct Project reveals common functional gradients among the human isocortex, cerebellum, and hippocampus. A key technique in elucidating such relationships involves mapping of high dimensional biological features to lower-dimensional manifold representations, also known as gradients19. Gradient mapping reduces the dimensionality of complex data through dimensional decomposition techniques . When applied to brain connectivity data, the resulting gradients can be interpreted as low-dimensional spatial representations of continuous transitions in connectivity profiles within or across brain structures.Research capitalizing on genetic, histological, and neuroimaging data has started to reveal brain structure-function relationships at various levels of analysis within the cerebral cortex21. Previous studies using various analytical approaches have consistently identified a few functional connectivity gradients within the isocortex25. One gradient is referred to as the association-sensorimotor gradient  that describes gradual changes in the similarity of connectivity profiles from heteromodal regions typically considered part of the default mode network28 to the primary sensory areas in the cerebral cortex. This gradient has been recently characterized as a domain-general axis along which several biological features are organized, including cortical thickness, cerebral metabolism, intracortical myelination, neuronal size and density, evolutionary cortical expansion, embryonic development, and allometric scaling30. We refer to another connectivity gradient that is consistently identified in the literature as the representation-modulation gradient  networks. This gradient distinguishes isocortical ensembles involved in the representation of higher-dimensional sensory signals  or their compressed, lower-dimensional multimodal summaries (in the default mode network)32 from those ensembles that are thought to modulate these representations via processes such as attention regulation, goal maintenance, and strategy selection36. Another gradient that is commonly reported in the literature on functional connectivity gradients distinguishes between the visual network and the somatosensory/motor network37. It is not immediately clear what this gradient represents: It may suggest a segregation of exteroceptive sensory systems, but it may also suggest a distinction in the properties of sensory signals as they arise from different sensory surfaces 25.Prior work computing connectivity gradients in humans has most often used blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signal collected when a person is not being deliberately probed with an external task—so-called resting state or intrinsic activity39 and the hippocampus42 in terms of their intrinsic connectivity within themselves or in relation to the isocortex. However, the degree of correspondence between these connectivity gradients, and their similarity to intrinsic connectivity gradients within the isocortex, has not been well characterized. The functional similarity of these gradients is suggested by numerous studies describing coordinated learning systems across the isocortex and cerebellum44, isocortex and hippocampus46, and cerebellum and hippocampus51. Despite this evidence, to our knowledge, no published study to date has systematically examined how the functional organization of the isocortex, the cerebellum, and the hippocampus relate to one another in terms of macroscale connectivity gradients defined based on BOLD fMRI data.Beyond the isocortex, research has also identified connectivity gradients that characterize the functional organization of the cerebellum52  as our primary sample, and in participants in the Brain Genomics Superstruct Project54  as our validation sample. In addition to group-level replication, we also assessed the degree to which the correspondence in functional connectivity gradients across structures was replicable at the level of individual participants (see Supplementary Information). Following prior published work on functional connectivity gradients, we derived functional connectivity gradients for each structure via diffusion map embedding, an established technique to nonlinearly reduce the dimensionality of large-scale connectivity data55. We chose to analyze these data from the perspective of the isocortex to consider how gradients of cerebellar and hippocampal connectivity to the isocortex would align with connectivity gradients within the isocortex, focusing on the three most dominant gradients in each structure (see Methods).In the present study, we investigated the correspondence between intrinsic functional connectivity gradients of the isocortex, and of the cerebellum and the hippocampus in relation to the isocortex, using BOLD fMRI data collected at wakeful rest from healthy young adult participants in the Human Connectome ProjectZ-transformed Pearson’s correlation coefficient) matrices between all cerebellar voxels and all isocortical surface vertices, as well as between all hippocampal voxels and all isocortical vertices, which were used as inputs for diffusion map embedding. The resulting connectivity gradients in this study, therefore, represented how similar a given pair of voxels are within each non-isocortical structure in terms of their patterns of connectivity with the isocortex. This approach is consistent with that of published work on the hippocampus and subcortical structures, in which functional connectivity gradients were defined based on the pattern of connectivity between these structures and the isocortex57.To characterize the functional organization of the cerebellum and the hippocampus in relation to the isocortex, we first constructed group average functional connectivity . Gradient 2 was anchored by the subiculum at one end and CA1–3 and CA4-DG at the other (p < 0.001), with no significant differences between CA1–3 and CA4-DG (p ≤ 0.16). Gradient 3 likewise distinguished between the three hippocampal subfield groups, with the CA4-DG showing higher values than CA1–3 (p ≤ 0.004) and the subiculum (p ≤ 0.001); CA1–3 also showed higher Gradient 3 values than the subiculum (p ≤ 0.02).For intrinsic hippocampal-isocortical connectivity, the top three gradients together explained >65% of the variance in the data within each hemisphere, with Gradient 1 accounting for >40%, Gradient 2 accounting for >15%, and Gradient 3 accounting for >8% of the variance in the data Fig. . Gradien58 Fig. . This an25  as well as salience networks, Gradient 2 distinguishing the visual network from the somatosensory/motor network, and Gradient 3 distinguishing the default mode and exteroceptive sensory networks from the frontoparietal and salience networks59. For each cerebellar and hippocampal gradient, this procedure yielded an isocortical map characterizing the variability in isocortical functional connectivity along a given non-isocortical gradient  and of hippocampal Gradient 2  compared with the other gradient-weighted connectivity maps of these non-isocortical structures. In contrast, isocortical Gradient 3 showed the strongest correspondence with the weighted connectivity maps of cerebellar Gradient 2  and of hippocampal Gradient 1  relative to the other weighted connectivity maps of these structures.Next, we calculated intrinsic functional connectivity maps for each of these structures weighted by their voxel-wise gradient values, using the same procedure as published workent Fig. . We then 60 Fig. . This anTo further clarify the relationships between functional connectivity gradients across structures, we additionally performed conventional seed-based analyses to estimate cerebello-isocortical and hippocampo-isocortical functional connectivity using discretized functional connectivity gradients of each structure as seed regions of interest. Here, each cerebellar, hippocampal, and isocortical functional connectivity gradient derived from the same group average connectome data was first split into 20 spatially-discontiguous bins based on the percentile ranks of vertex-/voxel-wise gradient values. We then calculated bin-to-bin functional connectivity for each pairwise combination of the three structures. Specifically, for each pair of non-isocortical and isocortical gradients, we calculated pairwise functional connectivity based on 20 non-isocortical bins and 20 isocortical bins and examined the magnitude of isocortical connectivity changes along a non-isocortical gradient. If there exists a one-to-one relationship in functional connectivity between a given pair of non-isocortical and isocortical gradients, we expect that the bins anchoring the corresponding ends of the same gradient would be maximally correlated with one another. To test this idea, we first quantified the magnitude of functional connectivity between each non-isocortical gradient bin and 20 isocortical gradient bins. We then computed a single Pearson’s correlation coefficient based on these 20 functional connectivity estimates and bin indices (1–20) along an isocortical gradient to represent the pattern of isocortical functional connectivity for each non-isocortical gradient bin. Once computed for each non-isocortical gradient bin, we calculated the correlation coefficient between the magnitude of isocortical functional connectivity and bin indices (1–20) along a non-isocortical gradient; this procedure yielded one Pearson’s correlation coefficient that quantifies the strength of the linear relationship between a given pair of non-isocortical and isocortical gradients based on functional connectivity. This analysis revealed results largely consistent with the analysis of gradient-weighted functional connectivity discussed above, demonstrating that differences in functional connectivity between positions along isocortical Gradient 1 are most strongly associated with differences in functional connectivity along cerebellar Gradient 1 and hippocampal Gradient 2, whereas connectivity differences along isocortical Gradient 3 are most strongly associated with connectivity differences along cerebellar Gradient 2 and hippocampal Gradient 1 Fig. .Following the same analytical procedure, we additionally investigated the relationships between cerebellar and hippocampal connectivity gradients. Our hypothesis was that a pair of cerebellar and hippocampal gradients that correspond to the same isocortical gradient would also show the strongest correspondence with each other. Confirming this hypothesis, we found that cerebellar Gradient 2 and hippocampal Gradient 1, both of which corresponded most strongly to isocortical Gradient 3, showed a stronger spatial correlation in gradient-weighted functional connectivity maps as well as stronger linear relationship in seed-based functional connectivity with each other; a similar pattern of results was observed with cerebellar Gradient 1 and hippocampal Gradient 2, which corresponded most strongly to isocortical Gradient 1 . Overall, the topography of functional connectivity gradients of the isocortex, the cerebellum, and the hippocampus was similarly observed across samples, although some differences in the distribution of gradient values were noted in higher-order gradients . Hippocampal Gradient 2 also distinguished the subiculum from CA1–3 (p ≤ 0.001), whereas the difference between the subiculum and CA4-DG was not observed (p ≤ 0.84). Importantly, the correspondence in functional connectivity gradients across structures as examined by gradient-weighted functional connectivity maps and conventional seed-based analyses of functional connectivity via gradient bins both demonstrated the pattern of results consistent with those obtained with the discovery sample above  replicated two gradients identified by published research describing cerebellar and hippocampal functional connectivity to the isocortex42, and additionally revealed the topography of the third gradient in each structure that, to our knowledge, has not previously reported in the literature. We first discuss the results in terms of prior published evidence that characterizes the functional organization of one  brain structure in terms of its connectivity profiles with the isocortex67, and then suggest that the corresponding gradients of functional connectivity might be understood as dominant functional organizing principles within a common computational framework. These results, and the specific hypotheses that they suggest, represent an important opportunity to build an integrative account of brain function.Accumulating evidence suggests that the organization of the isocortex38. When the organization of intrinsic functional network connectivity is considered68, cerebellar Gradient 1 is anchored at one end by the default mode and frontoparietal control networks and at the other end by the somatosensory/motor and salience networks, in a manner that is strongly similar to isocortical Gradient 1. Hippocampal Gradient 2, which also showed the strongest correspondence to this isocortical gradient, revealed a dissociation along the medial-lateral axis that differentiated the subiculum from the CA subfields. This result is in line with prior evidence characterizing distinct hippocampal subfields with variation in connectivity, computational roles, and myeloarchitectural maturation72. It has been shown that the subiculum exhibits stronger functional connectivity with the default mode network than the other subfields, whereas CA1–3 shows stronger connectivity with the somatosensory/motor and visual networks42, consistent with the observed correspondence in connectivity gradients.Isocortical Gradient 1 (association-sensorimotor) primarily distinguishes between heteromodal regions part of the default mode network and the primary sensory cortical areas. Cerebellar Gradient 1, which showed strong correspondence to this isocortical gradient, has been similarly characterized as a gradual transition from areas involved in non-motor  to those implicated in motor function38 that is required during tasks involving higher cognitive load73. This pattern is also consistent with prior work descriptively demonstrating that cerebellar Gradient 2 is associated with preferential connectivity with the default mode and somatosensory/motor networks at one end and the frontoparietal and salience networks at the other68. Hippocampal Gradient 1, which also showed the strongest correspondence to this isocortical gradient, has been described as capturing variation in functional connectivity along its longitudinal axis42, consistent with evidence identifying gradual changes in anatomical connectivity, gene expression, and electrophysiological response properties along this axis75. Prior work has identified the largest difference in intrinsic functional connectivity between the anterior and posterior subregions with respect to the frontoparietal and salience networks, with the posterior hippocampus showing stronger positive connectivity with these networks, and the default mode and somatosensory/motor networks at the anterior end42.Isocortical Gradient 3 (representation-modulation) distinguishes cortical areas as part of the default mode and primary sensory areas from functional networks such as the frontoparietal, salience, and dorsal attention networks. Cerebellar Gradient 2, which showed strong correspondence to this isocortical gradient, has been similarly interpreted as reflecting differences in attentional modulation, specifically in relation to task-focus77. It is, therefore, possible that this third gradient represents an axis of skeletomotor vs. visceromotor signals in the cerebellum, although these two signals are likely not independent of each other80. Hippocampal Gradient 3, which revealed a more complex topography along both the longitudinal and medial-lateral axes, also distinguishes between two of the hippocampal subfield groups. However, slight differences in the topography of this gradient were noted between the discovery and validation samples, suggesting that it may be sensitive to methodological variability. The two datasets examined in the current study, although similar in size, varied in numerous parameters associated with fMRI data acquisition , which may interact to influence the characteristics of BOLD signal and derivatives. In the context of overall similar results, prior studies of functional connectivity parcellation have also noted distinct topographical variability between the HCP and GSP datasets81. More research is needed to fully characterize hippocampal gradients; the use of high-field  functional imaging would be especially useful in clarifying the microstructural properties of the hippocampus that each hippocampal connectivity gradient may be related to.Isocortical Gradient 2, cerebellar Gradient 3, and hippocampal Gradient 3 require future research to clarify their possible functions. Cerebellar Gradient 3 appears to be anchored at one end by the frontoparietal and somatosensory/motor networks and at the other end by the default mode and salience networks. The default mode and salience networks are thought to constitute an intrinsic brain system for allostasis 82. Extending this line of work, future studies might investigate how variation in gradient correspondence relates to individual differences in cognition and behavior.Importantly, the correspondence of functional connectivity gradients across these structures was largely replicable at the level of single participants, although the extent of this replicability also varied across structure pairs and as a function of the gradient order. To our knowledge, the current study is among the first to examine individual differences in the degree of correspondence in connectivity gradients. However, several studies have identified individual variation in the organization of functional connectivity gradients across participants and that this variation was predictive of various aspects of cognition in both healthy and clinical populations59. Below, we sketch this interpretation of the observed correspondence in functional connectivity gradients across structures with links to a clear theoretical formulation of predictive processing as an overarching computational framework. It is important to acknowledge upfront that empirical findings reported herein were based on interregional similarity in low-frequency fluctuations of BOLD signal at rest. By definition, the observed pattern of correspondence in functional connectivity gradients by itself cannot be taken as direct evidence for the flow of signals within a structure or across structures. Prior work has shown, for example, that the topography of brain states captured by hidden Markov modeling of resting fMRI data resembles different ends of functional connectivity gradients, suggesting that these gradients may be driven in part by the states occurring at rest83. These states are associated with structural architecture84, so this finding is not inconsistent with our hypothesis. Moreover, it was precisely because of the structural and other biological features that characterize isocortical Gradient 1 that we originally hypothesized its role in predictive processing87. The evidence of coordinated gradients builds on these original ideas to suggest hypotheses about the domain-general computations that may be associated with functional connectivity gradients. Future research needs to directly test this computational hypothesis regarding information flow using techniques that allow for causal inference.The evidence of gradient correspondence may have important implications for clarifying the computational mechanisms of functional coordination among the isocortex, the cerebellum, and the hippocampus. In our recent work, we proposed that these two large-scale functional gradients can be interpreted as being an integral part of the intrinsic neural architecture supporting the implementation of predictive processing in the brain, including allostasis90. To date, predictive processing hypotheses have been offered to describe the computational capacities of several structures within the vertebrate brain, including the cerebral cortex in general93, the hippocampus97, and the cerebellum100. A variety of specific computational proposals abound, but they are generally united by three components that are thought to be implemented in a hierarchical arrangement in the brain’s architecture: (1) prediction signals that the brain generatively constructs using memory—or alternatively, an internal model101; (2) prediction errors that learn the differences between signals carrying sensory information predicted by the brain and sense data originating from the sensory surfaces of the body; and (3) precision signals that adjust the strength and durability of (1) and (2)103. Prediction errors are potential teaching signals, but their capacity to update predictions is thought to depend on how they are weighted by precision signals, which are interpreted as the predicted value of the allostatic information they will provide, or salience104. Prediction signals are also thought to be weighted by their estimated value to explain the incoming sense data, which may correspond to their estimated prior probabilities103. Predictive processing reduces uncertainty as an animal moves around in an ever-changing but only partly predictable world. Learning accumulates as an internal model of the animal’s body in the world by which the brain’s top-down predictions emerge and implement allostasis and from which movements and perceptions arise, all of which can be achieved in a metabolically efficient manner59.Predictive processing is emerging as a powerful neurocomputational hypothesis that accounts for diverse psychological and biological functions subserved by a brain105 as discussed in refs. 87) based on laminar development: Prediction signals are hypothesized to flow from deep layers of less granular areas  to the superficial layers of dysgranular cortices ; from the deep layers of dysgranular cortices to the superficial layers of granular (six-layered) cortices ; and from the deep layers of eulaminate cortices to the superficial layers of koniocortex . Conversely, prediction error signals are hypothesized to flow from superficial layers of more granular cortices to deep layers of less granular cortices. As prediction error signals flow from sensory regions  to limbic and other heteromodal regions in the frontal cortex , it is compressed and reduced in dimensionality30. A growing number of experimental studies have also verified signal flow or behavioral effects that are consistent with these hypotheses - e.g., evidence from electrophysiological recordings suggests prediction signal ensembles oscillate synchronously in the alpha and beta frequency bands, whereas prediction error signals oscillate in the higher-frequency gamma range107.It is hypothesized that the cytoarchitectural properties of the isocortex support the flow of prediction and prediction error signals  estimating the precision of prediction signals by suppressing those with weaker priors and (2) creating and maintaining prediction signals longer than it takes to process incoming prediction errors, when necessary90. This hypothesis is consistent with available evidence showing that one of the frontoparietal network’s subnetworks acts as an extension of the default mode network to regulate complex introspective processes111. It is also in line with current evidence that these two networks exhibit longer timescales of cortical processing than the other cortical networks112.The cytoarchitectural gradient described above is overall topographically consistent with the isocortical association-sensorimotor functional gradient, suggesting its role in supporting the flow of prediction and prediction error signals. At the association end, the regions within the default mode network are thought to construct highly compressed multimodal representations, which enable the initiation of prediction signals guiding motor actions and making perception possible113. The prediction errors at the sensorimotor end are closer in dimensionality to the sensory signals in the periphery, while the prediction errors at the association end constitute the compressed multimodal summaries of those lower-level signals.At the sensorimotor end, exteroceptive sensory networks process signals that correspond to low-level sensory predictions that have been decompressed (as probabilistic inferences) from the compressed multimodal summaries in the association regions. We hypothesize that all cortical neurons process signals that continually confirm or refine the prediction signals they receive 76. This hypothesis is in line with other views of salience network function that emphasize its role in both attention regulation116 and multisensory integration117. The representation-modulation gradient in the isocortex, then, might be interpreted as an axis of the functional organization that differentiates ensembles associated with the representation of prediction and prediction error signals  from those involved in the implementation of attentional modulation to set the precision of these signals .The salience network is thought to estimate the precision of prediction errors by tuning the gain on these signals as they propagate from the body’s sensory surfaces, representing confidence in the reliability and quality of incoming sense data and their predicted relevance for allostasis. It is, therefore, possible that this network helps the brain adjust its internal model to the body’s energetic conditions118 to expanded association cortices in humans when compared with other primates, including other great apes120. This expansion potentially allows for increased information compression and dimensionality reduction, suggesting the possibility that human brains are capable of multimodal summaries characterized by greater abstraction121. This perspective has been integrated into the predictive processing account76, but requires a further empirical test.There are also accounts of brain structure-function correspondence that are complementary to predictive processing hypotheses and consistent with the current findings. For example, research in comparative neuroscience has linked evolutionary changes in general brain-scaling functions59.The observed correspondence in functional connectivity gradients across the isocortex, cerebellum, and hippocampus can be used as a starting point for developing a unified, integrative view of brain function, building upon prior work investigating the computational capacities of these structures based on the principles of predictive processing. Although speculative, we hypothesize that the isocortex, cerebellum, and hippocampus might integrate over the same signals, but emphasize and/or construct different features to modulate one another as they construct prediction, prediction error, and precision signals in the service of allostasis, owing to their extensive connectivity with one another and with the subcortical structures implicated in the processing of these signals100, possibly as a means to compensate for delays in sensory feedback123. In visual perception, the cerebellum is thought to be critical for predicting incoming sensory information based on sequence detection and updating predictions based on the statistics of the sensory environment126. Extending these accounts, we hypothesize that the cerebellum computes sensory prediction error signals (and perhaps predicts the sensory prediction errors within the isocortex) to adjust signals of various cortical ensembles faster than isocortical sensory prediction error signals can be computed59.It has been traditionally thought the cerebellum estimates the sensory state of the body by predicting the consequences of motor commands128. This learning mechanism depends critically on the memory of errors that accumulate during training, which exists independently of two traditional forms of motor memory (memory of perturbations and of actions)127. Although motor learning can occur on different timescales with different error sensitivities129, the memory of errors is thought to exert its influence through the error sensitivity of the fast learning process127. Therefore, an additional possibility is that the cerebellum rapidly estimates the reliability of sensory prediction errors in the isocortex, conveying this information to parts of the isocortex  where it is further used to update precision estimates about sensory prediction error signals.The cerebellum may also be involved in estimating the precision of isocortical sensory prediction errors. During motor learning, the brain controls error sensitivity  by learning relatively more from small and consistent errors than from larger and variable ones122. Deep cerebellar nuclei, which are the gateway of cerebellar output, can also be modulated to fire up to 100+ Hz on average130. Despite the fact that the cerebral cortex and the cerebellum are connected to each other only by way of polysynaptic projections131, numerous nonprimary sensorimotor  areas of the cerebral cortex project to the cerebellar cortex via the cortico-ponto-cerebellar paths132. The isocortical areas that project to specific parts of the cerebellar cortex via the pons are also the target of efferent projections from the same cerebellar cortical areas via the thalamus135. These parallel, reciprocally-connected cerebello-isocortical circuits might provide an anatomical substrate for the correspondence in functional connectivity gradients between the isocortex and the cerebellum identified in this study, further supporting a domain-general view of cerebello-isocortical interaction. The cerebellum also exhibits monosynaptic or polysynaptic connections with several subcortical structures that are known to play an important role in allostasis, including the hypothalamus, periaqueductal gray (PAG), nucleus of the solitary tract, and amygdala137, suggesting a critical contribution of this structure to predictive regulation of the body.The hypothesis that the cerebellum can exert rapid modulation of signals in the isocortex via coordinated functional gradients is further supported by available microanatomical and connectivity evidence. The majority of cerebellar neurons are granule cells, which can generate action potentials that are relatively short-lived and at much higher frequencies than neurons in the cerebral cortex139 and facilitate reweighting of signals in the isocortex46. Specifically, it may help ensure that the subsequent prediction signals generated by the internal model of the isocortex are not slaves to the statistics of the external sensory environment and instead are more in line with the animal’s goals 46. This mechanism likely draws upon the functional loop between the hippocampus, the entorhinal cortex, and the isocortex. Isocortical afferents to the hippocampus carry highly compressed, multimodal summaries of sensory information via the entorhinal cortex140, whose Layer II and III project widely to the DG, CA1-CA4, and the subiculum via the perforant path141. Subcortical projections to the hippocampus include those from the medial septum, amygdala, anterior thalamic nuclei, supramammillary nucleus of the hypothalamus, and brainstem nuclei such as the ventral tegmental area, PAG, and locus coeruleus142. By interfacing with isocortical ensembles at many levels of the predictive hierarchy, while receiving rich low-level sensory information from subcortical structures, the hippocampus, too, may intervene at multiple points of this hierarchy to modulate cortical signaling. This hypothesis is consistent with anatomical evidence identifying projections of CA1 and subiculum to Layer V and VI of the entorhinal cortex140 as well as widespread multimodal association areas in the isocortex, including the medial frontal cortex, temporal pole, orbitofrontal cortex, anterior and posterior cingulate cortices, parietal and inferotemporal cortices144 and to some extent lateral frontal cortex143.The hippocampus is thought to generate prediction signals42. Specifically, at the posterior end of the hippocampus  implicated153. Future work should investigate the complementary contributions of cerebello-isocortical, hippocampo-isocortical, and cerebello-hippocampal interactions to the brain’s internal model, which might be characterized by their dissociable involvement in processing different types of information and/or on different timescales.Coordinated gradients of connectivity suggest signal exchange between the cerebellum and the hippocampus, whose interaction is understudied thus far. Emerging evidence suggests the existence of a cerebello-hippocampal learning system155. Future work might also consider expanding analysis space to the broader subcortex to more comprehensively take into account its functional organization157 and examine their contribution to predictive processing. If this framework bears fruit, it has the potential to offer a coherent, neurobiologically-inspired research program to unite the study of mind and behavior, collapsing the artificial boundaries between cognitive, perceptual, affective, motor, and even social phenomena. This evidence might also provide a common framework for understanding and treating the neurocomputational basis of mental disorders, neurodegenerative disorders, and physical disorders.The present results offer the opportunity to synthesize evidence across literatures into a common neurocomputational framework based on the principles of predictive processing. Our hypotheses, while speculative, illustrate the value of connectivity gradients in innovating specific questions about the computational aspects of brain function, with the association-sensorimotor and representation-modulation gradients as two common axes of information processing in the brain. Future work might specifically address these questions and probe modulation of connectivity gradient coordination across structures by explicit task demands or by clinical conditions in which neural mechanisms subserving predictive processing are hypothesized to be dysfunctional52  included in the HCP1200 2017 data release. All group-level analyses were conducted using the group average preprocessed whole-brain dense functional connectome (.dconn) data identifying temporal correlations between all cortical vertices and subcortical voxels. For individual-level analyses, we used preprocessed timeseries (.dtseries) data to derive estimates of functional connectivity, which were averaged across four runs. A full description of the data preprocessing pipelines implemented by the HCP is discussed elsewhere159. Briefly, each participant’s fMRI data underwent gradient distortion correction, EPI distortion correction, motion correction, spatial co-registration to structural reference, spatial normalization to template volumetric space, resampling to template surface space, volumetric and surface smoothing with a 2 mm Gaussian kernel, and were submitted to independent component analysis (ICA) for further artifact removal161. For inter-subject registration, feature-based alignment and the Multimodal Surface Matching Algorithm (MSMAII) were implemented163. Prior to the computation of the group average dense connectome, each dataset was temporally demeaned and had variance normalization applied164 and submitted to the group-level principal component analysis (PCA). The output of the group-PCA  are then renormalized, eigenvalue-reweighted, and correlated to form the group average dense connectome data . We did not perform any further preprocessing on these data beyond what had already been implemented by the HCP. Participant recruitment procedures and informed consent forms, including consent to share de-identified data, were previously approved by the Washington University Institutional Review Board as part of the HCP.We analyzed the fMRI data collected at wakeful rest from 1003 participants as part of the HCP WU-Minn Consortium54. Briefly, this dataset includes 1139 participants  who had undergone one structural scan  and two 6 min functional runs at wakeful rest  using 3 T Siemens Tim Trio Scanners. Participants provided written informed consent in accordance with guidelines established by the Partners Health Care Institutional Review Board and the Harvard University Committee on the Use of Human Subjects in Research.To validate the findings from the HCP dataset, we additionally analyzed structural and functional MRI data part of the Brain Genomics Superstruct Project (GSP). A comprehensive description of the GSP dataset is discussed elsewherehttp://surfer.nmr.mgh.harvard.edu/). Defects in the surface topology were corrected165, and the gray/white boundary was deformed outward using an algorithm designed to obtain an explicit representation of the pial surface. Each participant’s cortical surface mesh was registered to a common spherical coordinate system167. Preprocessing of functional data was performed using the surface-based pipeline developed by Yeo and colleagues168 using a combination of FreeSurfer169, FSL170, and Advanced Normalization Tools (ANTs)171 routines as well as additional MATLAB functions. This pipeline consisted of the following preprocessing steps: Removal of the first four frames, slice timing correction, motion correction with rigid body translation and rotation, motion outlier detection, functional-to-structural co-registration via boundary-based registration172, nuisance regression, interpolation of censored frames with Lomb-Scargle periodogram173, and band-pass filtering . Volumetric data were then projected onto the FreeSurfer fsaverage6 surface space (~2 mm vertex spacing) followed by surface-constrained smoothing with a 2 mm Gaussian kernel. Subcortical voxels were resampled to the MNI152 template space (2 mm isotropic resolution) and volumetrically smoothed with a 2 mm Gaussian kernel.Each participant’s structural data underwent intensity normalization, skull stripping, and an automated segmentation of cerebral white matter to locate the gray/white boundary via the FreeSurfer image analysis suite (v6.0), which is documented and freely available for download online 175 using fsl_motion_outliers176. Volumes with FD >0.2 mm or DVARS >50 were marked as outliers (censored frames), following the criteria used by previous studies168. One frame before and two frames after these outlier volumes, as well as uncensored segments of BOLD data lasting fewer than five contiguous volumes, were also flagged as censored frames177. BOLD runs with more than 50% of the volumes labeled as censored frames were discarded.We estimated framewise displacement (FD)173. To maintain consistency with the HCP dataset, we resampled the denoised BOLD timeseries data in the fsaverage6 space to fs_LR 32k space, after which these surface data were combined with the volumetric data to form a single whole-brain dense timeseries (.dtseries) file per run.To account for the effect of confounding variables, we performed linear regression separately for each BOLD run with multiple nuisance regressors, including (1) a vector of ones and linear trend, (2) six motion parameters, (3) averaged white matter signal, (4) averaged ventricular signal, along with the first-order temporal derivatives of (2), (3), and (4). The white matter mask for each participant was derived from FreeSurfer’s segmentation of their structural image, followed by three rounds of erosion before resampling to their native BOLD space. The ventricular mask was obtained similarly, but only with one round of erosion. In the event that there were fewer than 100 voxels after a round of erosion, no further erosion was performed. Regression coefficients were computed without censored framesFrom the original pool of 1139 participants with two BOLD runs, we discarded 12 participants who had at least one run with more than 50% of the volumes labeled as censored frames. We additionally discarded 25 participants for whom surface resampling resulted in fewer vertices/voxels in at least one of the runs than the rest of the participants. The final GSP dataset analyzed in the current study thus consisted of 1102 individuals. For each participant, we concatenated the two BOLD runs and computed Pearson’s correlation coefficient between every pair of vertices/voxels, which was standardized via Fisher’s r-to-z transformation. Individual-level Z maps were averaged across all 1102 participants to yield the group’s average dense connectome data.55. Diffusion map embedding is a nonlinear data dimensionality reduction technique that enables analysis of similarity structure in functional connectivity patterns in a large number of data points  by identifying a set of low-dimensional manifolds  capturing principal dimensions of spatial variation in connectivity. Based on the group average dense connectome data in each sample, we first derived functional connectivity matrices between (1) all isocortical vertices , (2) all cerebellar voxels and all isocortical vertices , and (3) all hippocampal voxels and all isocortical vertices . We defined the voxels belonging to the cerebellum and the hippocampus based on a probabilistic cerebellar atlas178 and the Harvard-Oxford subcortical structural atlas180, respectively, with both thresholded at 50%.We derived functional connectivity gradients of the isocortex, the cerebellum, and the hippocampus using diffusion map embeddingr values using a hyperbolic tangent function and applied row-wise thresholding to retain the top 10% connections, with all other connections set to zero. To characterize the relationship  in functional connectivity between a given pair of isocortical vertices or cerebellar/hippocampal voxels, we computed a non-negative square symmetric affinity matrix for each functional connectivity matrix. In keeping with the previous investigations, we opted to use cosine similarity to characterize the similarity structure in functional connectivity for the isocortico-isocortical and cerebello-isocortical matrices and normalized angle similarity for the hippocampo-isocortical matrices181. Finally, we used these affinity matrices as input to diffusion map embedding, which yielded ten gradients per affinity matrix identifying the dominant dimensions of spatial variation in isocortical functional connectivity as well as cerebello-isocortical and hippocampo-isocortical connectivity. In the present study, we focused on the three most dominant gradients in each structure as similar numbers of gradients have been emphasized in prior work42. Functional connectivity gradients were derived following the identical procedure for both the discovery and validation samples. To facilitate the comparison between samples , all connectivity gradients calculated for the validation sample were aligned to the discovery sample using Procrustes rotation (number of iterations = 10) prior to analyses.We converted each functional connectivity matrix back to Pearson’s 183. This procedure generated binary ROIs of the subiculum, CA1–3 and CA4-DG58, which were subsequently down-sampled to the resolution of functional data. For each of the isocortical gradients, we characterized the distribution of gradient values across the seven canonical functional networks of the isocortex54. Here, we adhere to the original and conventional use of these network labels and color schemes to avoid confusion, although it is important to realize that both the default mode and “limbic” networks contain agranular, limbic tissue86 and “limbic” network regions often appear within the default mode network81.We performed post hoc characterization of the functional gradients identified via diffusion map embedding at various levels to interpret their significance. For the hippocampal gradients, we characterized and compared the distribution of gradient values between major hippocampal subfields. To do this, we first performed automatic segmentation of hippocampal subfields on a T1-weighted structural image in the MNI152 space39. For example, to characterize how a given cerebellar gradient relates to the isocortex, we computed a cerebello-isocortical connectivity map for each cerebellar voxel and multiplied it by the corresponding gradient value for that particular voxel. In this way, the pattern of functional connectivity between each cerebellar voxel and all isocortical vertices was weighted by the position of the voxel on the cerebellar gradient. These voxel-wise, gradient-weighted cerebello-isocortical connectivity values were summed over all cerebellar voxels, resulting in a single isocortical projection of the cerebellar gradient. We repeated this procedure for each gradient derived for the cerebellum and the hippocampus. To statistically assess the correspondence between these gradient-weighted functional connectivity maps of the cerebellum and the hippocampus and isocortical functional connectivity gradients, we computed vertex-wise Spearman’s rank correlations. We conducted non-parametric spin tests to derive the statistical significance of each association while controlling for spatial autocorrelations60. This approach follows that of prior work investigating the relationships between gradients derived from histological and functional connectivity features7. Importantly, this method to compare gradient-weighted functional connectivity of non-isocortical structures with isocortical functional connectivity gradients is not biased or circular because these features were not computed based on overlapping connectivity data.To interpret the cerebellar and hippocampal gradients in terms of their relations to the isocortex, we calculated functional connectivity maps between these structures and the isocortex weighted by their voxel-wise gradient values following previous studies185. This procedure, therefore, resulted in 20 spatially-discontiguous bins for a given gradient, each of which consisted of the same number of vertices or voxels. We computed the mean timeseries of each gradient bin, which was used to compute Pearson’s correlation coefficient for a given pair of gradients across structures within each individual participant. This analysis yielded a 20 × 20 matrix that quantifies the magnitude of functional connectivity between any given combination of gradient bins for each pair of gradients. All individual-level functional connectivity estimates were averaged to yield group-level data for each gradient pair.We additionally performed a seed-based functional connectivity analysis to further clarify the correspondence between the isocortical, cerebellar, and hippocampal gradients. For this analysis, we first discretized each group-level functional connectivity gradient into 20 bins following prior approaches using percentile ranks of vertex-/voxel-wise gradient valuesr values representing how functional connectivity with the 20 isocortical gradient bins changes as a function of 20 non-isocortical gradient bins. In the final step, we calculated the Pearson’s r value between these 20 Pearson’s r values indexing connectivity changes along a non-isocortical gradient and non-isocortical gradient bin indices (bin 1–20). This procedure yielded one correlation coefficient for each pair of non-isocortical and isocortical gradients quantifying how these gradients relate to each other based on functional connectivity. The gradient-weighted connectivity approach described above reveals the degree of the spatial association at a global level; the seed-based connectivity analysis described here complements that approach by quantifying the magnitude of the linear relationship between a given pair of gradients by taking into account their connectivity structure at a more precise level.For a given pair of non-isocortical  and isocortical gradients, we computed a series of Pearson’s correlation coefficients using estimates of functional connectivity calculated above. For instance, for each gradient bin generated from a non-isocortical gradient, we computed the Pearson’s correlation coefficients between isocortical gradient bin indices (bin 1–20) and functional connectivity values between the non-isocortical gradient bin and all isocortical gradient bins. Repeating this step for all non-isocortical gradient bins, we obtained 20 Pearson’s Further information on research design is available in the Supplementary InformationReporting Summary


Male infertility has received vast attention in recent years and has no clear etiology in almost 40% of cases. Several methods have been suggested for preserving sperm and spermatogonial stem cells (SSCs) in both in vivo and in vitro conditions. The efficacy of these methods is related to their abilities, including providing an optimal environment for sperm preservation and long-term SSC culture for in vivo and in vitro differentiation of these cells. In this review article, a full MEDLINE/PubMed search was performed using the following search terms: “Spermatogonial Progenitor Cells, Stem Cells, Fertility Preservations, Sperm Freezing, Cell Differentiations, Tissue Scaffold, 3-Dimensional Cell Culture", which retrieved results from 1973-2022. Related articles were added to the bibliography of selected articles. Exclusion criteria included non-English language, abstract only, and unrelated articles. The production of functioning male germ cells is suggested by introducing modern bioengineered systems as a new hope for the maintenance of male fertility. Till now, few in vitro spermatogenesis investigations have provided appreciable amounts of mature gametes. Each method had benefits and disadvantages, but the 3-dimensional culture method had the greatest impact on the differentiation and preservation of SSCs. One of the critical elements of research is the preservation of sperm and the differentiation of SSCs. Several methods have been employed in this area. Various scaffolds providing an environment similar to an extracellular matrix and conditions for germ cell development and survival have been employed in recent research. Fertility in both men and women has recently attracted attention. Due to continuous low total fertility rates below replacement level (2.1 children per woman), several countries in Asia and the European Union are expected to soon see considerable population declines (1). Some studies revealed a significant decline in semen quality during the past 50 yr . Around 40-50% of infertility cases are caused by the “male factor", and up to 2% of males worldwide have sperm with poor quality. As a result, maintaining male fertility is crucial (4). Several disorders and diseases that impact male fertility are more prevalent now than they previously were. The fertility preservation procedure might be required for some systemic diseases or medical therapies that could impair fertility (5).Non-oncological factors, including genetic disorders, inflammation induced by trauma, infections, and autoimmune diseases, are considered risk factors for male infertility (6-8). Reduced sperm production and function; however, may result from the underlying illness (5). Serious trauma, such as spinal cord injuries linked to erectile and ejaculatory dysfunction, is on the opposite end of the non-oncological spectrum . Certain cancers that develop before or after puberty require preserving fertility issues because of chemotherapy and radiation treatments. 80% of cancer survivors are considering starting a family, but only 10% would use donor sperm or adoption.Vasectomy is one of the other conditions (11), up to 6% of men eventually ask for a reversal when their circumstances and objectives alter (12). In light of this, the patient might be advised about the possibility of sperm freezing before a vasectomy or sperm extraction from epididymis during surgery (13).Postpubertal males who produce completely developed sperm have the opportunity to have their semen cryopreserved, which allows them to have children later in life through in vitro fertilization (14). Since young boys are unable to produce sperm until puberty, it will be difficult for them to maintain fertility (15). Postpubertal males can be offered a testicular biopsy which contains spermatogonial stem cells (SSCs) (16). SSCs are a subpopulation of type A spermatogonia localized in the seminiferous tubules' basement membrane, and they are essential to the process of spermatogenesis, which produces millions of haploid spermatozoa every day (17). Potentially, isolated SSC or immature testicular tissue can be developed in vitro or returned to the natural physiological microenvironment. Developing SSC niches would make it easier to create a successful long-term SSC culture in vitro, which is the basis for further study and applications (16).The preservation and culture of germ cells are suggested methods for preserving fertility in these people. In this review, we will focus on the procedure for differentiating and maintaining germ cells and sperm both in vitro and in vivo.The MEDLINE/PubMed database were thoroughly searched and 6489 articles were found. The following MeSH phrase combinations were used to search “Spermatogonial Progenitor Cells" AND “Stem Cell" AND “Fertility Preservations" AND “Sperm Freezing" AND “Cell Differentiations" AND “Tissue Scaffold" AND “3-Dimensional Cell Culture".The exclusion criteria were as: abstract only, non-English language, and unrelated articles. Following the search, it was found that data spanned the time frame of 1973-2022. Relevant articles were added from the bibliographies of selected articles and finally, 82 articles related to the inclusion and exclusion criteria were selected .All studies, including data related to the development of culture systems, preservation, and differentiation of SSCs, were included in the article. Articles lacking these data were reserved for other qualitative aspects of the article. Finally, only tissue engineering data were extracted from manuscripts that included scaffolds and 3D printing, and SSCs.In the postnatal testis, SSCs develop from gonocytes, which grow from primordial germ cells (PGCs) during fetal development (18). In the embryo's epiblast stage, which takes place around 7-7.25 days post coitum (DPC), a tiny group of alkaline phosphatase-positive cells is the first sign of PGCs, a temporary cell population. The extra embryonic ectoderm's expression of bone morphogenetic protein 4 (BMP4) and BMP8b is necessary for PGC specification, and BMP8b is from the extraembryonic ectoderm. When the allantois develops in mice between 8.5 and 12.5 DPC, PGCs start migrating from the hindgut to the indifferent gonad (19).Due to PGCs proliferation at the migratory phase, approximately 3000 PGCs go through the genital ridges. At 13.5 DPC, PGCs in the male gonad develop into gonocytes, and these gonocytes are enclosed in testicular cords made of Sertoli precursor cells and peritubular myoid cells (20). The terms mitotic (M)-prospermatogonia, T1-prospermatogonia, and T2-prospermatogonia can be used to subcategorize gonocytes.M-prospermatogonia, located away from the basal membrane in the middle of the testicular cords, continues to expand until 16.5 DPC of mouse development. At this point, they change into T1-prospermatogonia and reach G0 mitotic arrest. During the 1For a long time, it was believed that producing sperm cells outside of the original testicular environment in a culture plate was impossible because of the incredibly complex process of spermatogenesis and sperm growth, which happens inside and out over 2 months . Modern techniques developed advanced 3-dimensional co-cell cultures, and differentiation of SSC and their precursor cells have, in any case, revolutionized this field. Numerous studies have used embryonic stem cells (ESC) and induced pluripotent stem cells (iPSC) to create male germ cells. Still, it has been found that embryos from studies that start with PGC are normally compared to those produced by studies using ESC or iPSC (24).Human ESCs and iPSCs have been recognized as significant sources of pluripotent cells for the in vitro production of germ cells. Human iPSCs produced from autologous cells such as skin biopsy-derived fibroblasts, blood cells, or hair keratinocytes have also been used to make germ cells or gonadal support cells (25).These methods have been used for Klinefelter and nonobstructive azoospermia patients. Besides legal and ethical limitations, an important limitation of human ESC and iPSC-based techniques is the high chance of gathering hereditary and epigenetic alterations during reconstructive procedures (26). Subsequently, human ESC or iPSC induction of germ cells that can transmit hereditary alterations to the sibling, may not be the safest method for preservation. Therefore, the safest method for maturity preservation may not involve human ESC or iPSC generation of germ cells that may pass on genetic material to a relative (27).The 2 main divisions of the mammalian testicular tissue are the seminiferous tubules and interstitial tissue. It has been demonstrated that the putative testis' tubular structures progressively divide into cords from an amorphous primordium. Seminiferous tubules in young men include various testis cell types and components, including SSCs, basement membrane (deposited by Sertoli cells), peritubular myoid cells, and Sertoli cells. However, in adult males, the SSCs undergo spermatogenesis to differentiate into mature spermatozoa. SSCs are responsible for maintaining the male germ cell pool and can self-renew (28).At the start of puberty, the Sertoli cells also go through major morphological and functional changes. At stages of development, these supporting cells generate certain types of proteins, cytokines, hormones, and growth factors that ultimately form the blood-testis barrier, playing a crucial role in spermatogenesis and testis formation. Like with all other germ cells, SSCs are in direct contact with Sertoli cells, SSCs and more mature germ cells cannot exist in vivo without Sertoli cells (29). Leydig cells, a distinct type of cell, are also found in the testicular interstitium and respond to luteinizing hormone by producing the steroid hormone (testosterone). This mechanism maintains postpubertal spermatogenesis and begins the androgenic consequences in the male fetus (30).In primates, there are 2 distinct spermatogonial subtypes. The A pale spermatogonia serves as progenitor cells that can divide to create both A pale and differentiated B spermatogonia, while the A dark spermatogonia are thought to be the testicular stem cell with strong proliferative activity. Incomplete mitosis and further differentiation in spermatogonia result in intermediate and B spermatogonia with cytoplasmic bridges. The maturation of B spermatogonia results in the production of primary and secondary spermatocytes, spermatids, and spermatozoa (29).After adolescence, additional mitotic and meiotic divisions occur in the second division, which leads to produce primary and secondary spermatocytes and, eventually, spermatozoa. Since 1960, several studies have focused on how to produce meiotic and postmeiotic alterations in male germ cells in vitro. For many years, a major area of study focused on the fundamental principles of male germ cell differentiation, which produces mature haploid spermatozoa inside structurally well-organized tissue. However, according to other investigations, spermatogenesis was stopped before it reached the meiotic phase (31). Another important characteristic of the testis is its dual functions as a glandular and gonad tissue, the in vitro production of sperm from male germline stem cells is still considered difficult because the entire spermatogenic process must occur in a cell culture plate. Also, differentiating SSCs' requires a varied set of paths. The molecular process is crucial for the maintenance and differentiation of testis reproductive cells, which should be considered in vitro culture (32).BMP4, Noggin, prevents RA-induced production of c-Kit and RA-8 (Stra8)-stimulated gene expression, but BMP4 and RA signaling cooperate. Neurogenin 3, whose function is not yet understood, is an intrinsic differentiation element (36). Sertoli cells release glial cell-derived neurotrophic factor (GDNF), and in mice with aging SSCs, GDNF expression is found to be decreased. Fibroblast growth factor 2 (FGF2) is, additionally, important for spermatogenesis (37). Through the extracellular signal-regulated kinase 1/2 and mitogen-activated protein kinase (MAPK) 1/3 pathways, FGF2 and GDNF cause the G1 to S transition. Some transcription factors, including an inhibitor of differentiation 4, LIM-homeodomain proteins, B-cell chronic lymphocytic leukemia/lymphoma 6B, and Ets variant 5, are activated by GDNF stimulation of GDNF family receptor 1 and attributed to spermatogenesis. This is accomplished by activating the rearranged during transfection receptor .Also, molecular pathways are crucial in these cells' differentiation ability. Retinoic acid (RA) and the RA receptor are both necessary for triggering meiosis (33). This stimulates differentiation and blocks factors like promyelocytic leukemia zinc finger (34). The Sertoli cell-secreted stem cell factor can bind to the proto-oncogene receptor tyrosine kinase, which starts a signaling cascade for differentiation (35). The antagonist of Over the past 10 yr, the major roles of the MAPK, transforming growth factor-/Smad, and MAPKs signaling pathways have been emphasized by increasing evidence in spermatogenesis. The MAPK signaling system controls the dynamics of tight and adherens junctions, meiosis and proliferation of germ cells, and proliferation and lactate production in Sertoli cells, although the transforming growth factor-/Smad and AMPK signaling pathways also have an impact on these dynamics. Together, these signaling pathways create a complicated regulatory system for spermatogenesis (40). A number of the factors that impact differentiation are summarized in figure 2.The most advanced treatments for preserving male germline or fertility that are now available include testicular tissue freezing, SSC transplantation, and in vitro spermatogenesis (41). The primary experimental tactics have been emphasized: SSCs transplantation or testicular tissue grafting into host animals and the differentiation of in vitro spermatogenesis. In vitro investigations have focused on preserving germ-cells and producing mature and functional spermatozoa. 2 methods have been suggested for the maintenance of SSCs. Generating viable male germ cells is a new hope for maintaining male fertility that has been made possible by modern bioengineered systems. Future chances for genome preservation and fertility therapy for cancer patients of adult, juvenile, and prepubertal ages, as well as noncancerous infertile males, may be made possible by these procedures (42).Organ culture systems are considered valuable methods for studying pathophysiological processes because they can effectively represent the functions of the organ in a variety of conditions and environments. The lack of the typical tissue microenvironment can change cellular response (43). The essential cell-cell and cell-matrix signals involved in spermatogenesis are supported by 3D-techniques, which provide all the necessary information about this organ for in vitro spermatogenesis or the maintenance of male fertility (44). By providing better mimics of the species- and age-specific structures of the testis, as well as the biomechanical properties, cell-cell interactions, and natural matrix of the mammalian reproductive tract, 3D cultures that place cells on synthetic matrices, organoids, or organs-on-a-chip cultures, may be able to overcome the limitations of in vitro 2D culture vessels (45). Furthermore, organ cultures allow researchers to control the paracrine environment and separately evaluate the effects of each growth factor on the spermatogenesis process. Many techniques have been developed for the in vitro maintenance and culture of intact tissues. The contact between the seminiferous tubules and the interstitial area may be maintained in the 3D testicular tissue culture systems, which are suitable for the progression of spermatogenesis. To encourage in vitro spermatogenesis; however, such media must be improved by including specific necessary and efficient elements  (46).Testicular organ cultures from newborn mice were used in the first study on in vitro spermatogenesis. The first attempts to develop a culture method for testicular tissues were made in 1920. The testicle pieces were preserved in a culture medium based on plasma clot. The plasma clotting method is a culture that may be used to examine the development of fetal organs and testicular tissue (47). The main benefit of this method was to enable the visual study of whole cells with an emphasis on their dynamic properties and shape modulations over time and in response to regularly controlled environmental changes (48).Another technique is the raft one in which the organ was set on a lens made of paper or rayon to help transfer the tissue. Excess fluid was filtered out, and the net containing the tissue was repositioned on a new pool of medium. A size of around 8 mm is the largest that may be properly maintained. To prevent ripping or crushing of the tissues, great care should be used when dissecting these explants. This approach was utilized to research testis culture and cancer in in vivo models. The target tissue was placed on Trowell's grid system, which was made of perforated steel sheet that is hydrated up to the grid's surface, before being placed in a culture chamber. There is a challenge with the raft floating method. Skeletal tissues are frequently placed directly on the grid due to their higher degree of rigidity, whereas softer tissues, such as the skin or glands, must first be stretched out on rafts to be sustained throughout the grids. This is because different tissues have varying degrees of stiffness. The testis of the adult rat was organs grown using this method. However, the results revealed that most of the tubules had degenerated, with just a tiny number surviving for up to 3 days (49).Based on a modified plasma clat approach, human epidermal cells were seeded on rafts made of collagen and fibroblasts, and an air-fluid was placed between the cells (50). In cultures of mammalian testicular tissue, degeneration of the germinal cells starts a few days after expansion, followed by complete necrosis of the remaining tissues. Although in some reported signs of seminiferous epithelium differentiation, cultures were not kept in acceptable condition for long enough to comprehensively analyze the outcomes. As a result, substantial efforts were made to create culture methods that would allow testicular germ cells to survive and differentiate (51). Since it is known that the process of in vivo spermatogenesis takes many days to complete, for example, 54 days in a rat, it is essential to choose conditions for long-term maintenance of testicular structure if the spermatogenic process is to be in vitro examined (45).Stukenborg et al. reported the first successful creation of elongated spermatids in mouse testicular cell aggregates in soft agarose and methylcellulose, and their findings are encouraging (52).Despite using this technique to produce elongated spermatozoa in mice, the spermatids remained in the pachytene stage. The culture of agars alone is the reason for this lack of development and may be responsible for the low effectiveness of tubulogenesis and spermatogenesis. It is interesting to note that germ cells in the center culture developed SCC aggregates, and were then finally killed due to the lack of support. In a different investigation, neonatal mouse testicles were cultured in agarose for 2 wk at the gas-liquid interphase. Cell aggregates were used to study tubulogenesis, and spermatogonia developed until meiosis (51). Immature mouse testicular tissue was cultured on agarose gels at a concentration of 1.5% (w/v) in 2016 and demonstrated for the first time that morphologically normal spermatozoa with intact acrosomes could be produced in vitro using agarose culture (16). They showed that in this method, in addition to the production of sperms, which are assessed morphologically, histologically, and immunohistochemically, in vitro spermatogenesis could be preserved for more than 2 months. Because of the important role of interaction between Sertoli and SSCs, a 2-layer agarose culture medium was used (53). A 2-layer agarose culture medium was employed by researchers. They demonstrated that colonies forming in the agarose culture's upper layer were free of somatic cells, which were seen at the bottom of the plate. This interaction between spermatogonia and Sertoli cells has also been shown to affect the differentiation of spermatogonia and tubular formation. However, Sertoli cells did not migrate into the bottom layer of the agarose culture in this case because more viscose agarose was present (1.5% vs. 0.5%), preventing Sertoli cells from doing so. Instead, Sertoli cells aggregated in the top layer. According to the immunocytochemistry data, Vimentin, a Sertoli cell marker, was found in aggregations (45).A study revealed strong modeling of in vitro human testicular organogenesis from the fetal genital ridge, which represents a significant advance for the study of male reproduction. Human gonads from aborted 12-19-wk fetuses were cut up and placed on agarose gel stands. Additionally, the meiotic phases were completed, and the differentiation of spermatogonia was seen morphologically and in terms of gene expression (54).In vitro-grown supplements containing growth factors or drugs for local or systemic distribution, such as vascular endothelial growth factor, antioxidant compounds, and testosterone, were investigated in recent studies on improving SSCs survival (55).Several antioxidant chemicals are being researched for their ability to enhance sperm survival, reduce apoptosis, and improve SCC survival (56). Several studies have employed substances like melatonin and n-acetyl cysteine, for example. In contrast to n-acetyl cysteine, which inhibits apoptosis and improves apoptosis and proliferation (57), melatonin affects survival before transplantation but does not affect apoptosis (58).The extracellular matrix (ECM) mostly comprises proteins and polysaccharides. Laminin is a crucial modulator of protein, paracrine, autocrine growth factor, and Sertoli cell secretion and transfer in the basement membrane, which directly affect the survival and differentiation of testicular cells throughout development (59). One of the fundamental signaling pathways, direct cell-to-cell contact, is crucial for the effectiveness of spermatogenesis and might be efficiently assisted by hydrogels (60). Cells might be implanted in a thick layer using collagen gel matrices, creating an extracellular environment that successfully mimics the activities of the seminiferous tubule basement membrane. Using 2 classes of natural-based polymers, including proteins (like collagen) and polysaccharides , testicular cells have been ordered spatially in several investigations. The survival of testicular cells is improved by including laminas in the structure of collagen-based gels. In addition to these advantages, such 3D culture microenvironments prevent cell ischemia, particularly in the long run (61). Some of the investigation on 3D scaffold is summarized in table I.Alginate, collagen, and acellular tissue matrices (produced by decellularizing different tissues or organs) have special characteristics, including the capacity to produce hydrogels with 98-99% aqueous media at physiologic conditions. Testicular cells can develop and migrate more easily on hydrogel testicular matrices. The essential elements of testicular ECM should still be present in these matrices, while cells, bioactive cellular proteins, DNA, and substances that may interfere with the efficiency of culture should be eliminated (49).FAS) gene increased, whereas that of the Bax and P53 genes decreased. Cell viability was significantly reduced, but spermatogenesis was restored after the freeze-thawed SSCs encapsulated in alginate hydrogel. Various studies show that alginate hydrogels had high vitality (74.08%) and low cytotoxicity (5%), and protect SSCs from damage (62).Alginate can also lessen the toxicity brought on by freezing when used in cryopreservation. Alginate hydrogels were tested in 2017 for their potential toxicity in the culture systems of SSCs. Surgery was used to extract the SSCs from immature mice that were 6 days old. SSCs were enclosed in alginate hydrogels. The cells were removed from the hydrogel and analyzed for survival, cell shape and structure, cytotoxicity, and expression of the apoptotic genes following encapsulation and cultivation for a month in Dulbecco's Modified Eagle Medium culture media with 10 ng/ml GDNF. Compared to the control group, the main group's expression of the Fas Cell Surface Death Receptor . Carbon nanotubes have shown a superb ability to bind serum proteins and encourage cell adhesion, spreading, and development in vitro, even for long-term applications (72). Despite nanocarbon's benefits, cytotoxicity has remained a major cause for worry (which might be reduced by removing serum proteins before cell culture or purifying scaffolds) (73).Graft technic is an additional method for sperm storage that obtained full spermatogenesis using tissue from all 3 species in 2002 by grafting tiny fresh biopsies from prepubertal mice, pigs, and goats onto the back skin of nude mice. The study claims that the replacement of segments was just fixed subcutaneously without creating synthetic vascular anastomosis (74).In the same nude mouse model, the spermatogonial survival rates after orthotopic immature testis tissue (ITT) xenotransplantation decreased over time  (75). Moreover, continuing vascularization is necessary for a successful grafting approach because germ cell loss occurs within the first 3 wk. Orthotopic grafting was the optimum choice since studies on germ cell development revealed a detrimental effect of higher temperatures on grafts. Rodent testicular tissue has been implanted in a number of locations, including the back skin, ear tip, the anterior chamber of the eye, and testis. The authors of these data speculate that eliminating the testicular parenchyma could result in a rupture of the integrity of the seminiferous tubules, making it easier for SSCs from the donor to recolonize the area. As a result, intratesticular graft has been suggested as a suitable substitute for intra-scrotal ITT grafting, although being technically more difficult (76).One study developed an undiscovered in vitro organ culture technique that enables the transplanting of mouse SSC lines which could differentiate into colonies and mature sperm. The haploid cells were recognized by cell morphology through histological and immunohistochemical analysis, and genetic markers  and to achieve fertility, they used micro-insemination, which led to a healthy pregnancy (77).3) experiencing a period of no blood supply, which is defined by hypoxia, even followed by a condition of reoxygenation, this tissue characterized by damage, as was previously shown for ovarian tissue transplantation (78). In culture-based research, platelet-derived growth factors, FGF, and vascular endothelial growth factors have been used in various studies to increase vascularization. Because of their short half-lives, the dangers of distant dispersion, such as enhanced vascular permeability with the risk of the encouragement of tumor neo-angiogenesis, hypotension, and vascular growth factors present substantial hurdles when employed in vivo. By employing encapsulating matrices and customized nanoparticles, these problems might be solved by the controlled release of physiologically active chemicals contained in the place of interest (79).A spontaneous circulatory connection between the host and the grafted tissue was developed as arteries sprouted from the transplanted component and joined the host's vascular system (49). Since every cell should be placed within at least 200 μm distance of a capillary, any tissue that has a longer distance must have a developed and sustained vascularization to survive, as results in extremely little quantities of testicular tissue . The procedure involves the collection of, preferably, 3 semen samples with an abstinence period of at least 48 hr in between samples, followed by the cryopreservation of the sperm samples (81). However, it is frequently necessary to collect more than one semen sample on the same day to prevent delay in oncological treatment. When ejaculation fails or there are no spermatozoa in the ejaculate, collection of the sperm from the epididymis through either percutaneous epididymal sperm aspiration or microsurgical epididymal sperm aspiration can be done (82).Spermatogenesis is a susceptible process influenced by endocrine hormones and micro-environmental conditions. Several major illnesses might affect male fertility. These conditions are divided into 2 categories before and after puberty. There is currently no option to preserve the testis tissue except by using various techniques and in vivo or in vitro inducing differentiation in pre-puberty situations when a person does not have mature sperm. In humans, when prepubescent boys face cancer treatment, freezing immature testicular tissue containing SSCs can be suggested. In adulthood, fertility can be restored by autologous tissue transplantation or its SSCs, or in vitro maturation. So far, animal studies have been conducted with good results, but extensive and acceptable clinical trials have not yet been performed. Transplantation of testicular pure cells is impossible due to the risk of reintroducing cancer cells.Therefore, tissue-engineered testis, can be helpful in achieving spermatogenesis for tissue transplantation or in vitro conditions. In the postpuberty period, sperm can be frozen and used for intracytoplasmic sperm injection under suitable conditions. Many hypotheses suggest that this maturation can be achieved through 2- and 3D tissue culture, and sperm can be obtained. But using this system in different laboratories and providing setup brings various problems. Standardized organoids and organoid scaffolds that can be developed for 3D cultures appear preferable.In this review, the details of developing various techniques for both mentioned groups have been discussed, we tried to recognize the advantages and disadvantages of various plans and, utilize the best technique for the intended individuals in different conditions. Of course, researchers are still working on determining the best and most efficient strategy, and one of the main issues in this area is how to preserve and differentiate germ cells.The authors declare no conflict of interest.


Establishing and Maintaining Cell Polarity,” we are pleased to present volume II of this Research Topic, which reflects the recent progress made in the field.Cell polarity plays a crucial role in spatially segregating signaling within cells, and it is vital for various cellular functions, including motility, barrier formation, and determining cell fate. Over several decades of research, numerous signaling circuits and network principles have been uncovered, contributing to our understanding of how polarity is established in various cell systems. However, ongoing research continues to reveal new mechanisms and factors that contribute to this process. Building on the success of the first volume, “Pawluchin and Galic revisit the questions of how cell motion is accomplished in a 3D microenvironment, and to what degree observations acquired in a planar system can be translated into 3D. They discuss the commonalities and key differences between 2D and 3D in the context of the molecular mechanisms by which cell motion is generated in a planar system, as well as how the motion patterns of the cell emerge at the macro scale. While focusing on the cell leading edge where polarity is developed, they also provide an in-depth discussion of membrane curvature-dependent regulation of actin dynamics and signaling in both 2D and 3D.Historically, much of our knowledge of cell migration has been gained from using 2D experimental systems. In this volume, in vivo ECM. Such an approach may allow us to identify the topological features that dictate cell migration patterns and determine the underlying mechanisms that regulate topography-sensing in health and disease. In the current volume, Loesel et al. contribute a method article that describes how they quantify cell migration dynamics, including speed, directionality, and the number of detached cells from cell spheroids seeded onto engineered randomly oriented and aligned ECM mimicking fibers.To recapitulate the complex fibrous architecture of the extracellular matrix (ECM), growing studies of cell migration are performed using synthetic fibrous networks designed to mimic Drosophila neural stem cells , ACD is accomplished by reorienting the mitotic spindle along the apical-basal polarity axis through a variety of conserved complexes, including the apical, mutually exclusive Partner of Inscuteable (Pins)-Mushroom body defect (Mud) and Pins-Inscuteable (Insc) complexes. Parra et al. identify Hu li tai shao  as a direct Mud-binding protein that may segregate Pins from Insc by inducing phase separation of the Pins-Mud complex during spindle positioning.An interesting example of cell polarity is asymmetric cell division (ACD), which allows stem cells to generate differentiating progeny while simultaneously maintaining their own pluripotent state. In Caenorhabditis elegans, UNC-6/Netrin is a conserved bi-functional guidance cue that regulates dorsal-ventral axon guidance. The UNC-5 receptor was thought to maintain dorsal protrusion polarity and inhibit ventral growth cone protrusion, resulting in net dorsal growth cone advance. Mahadik and Lundquist demonstrate a novel role for a short isoform of UNC-5 (UNC-5B), which promotes dorsal polarity of growth cone filopodial protrusion and stimulates growth cone protrusion, in contrast to the previously described role of UNC-5 long in inhibiting growth cone protrusion.Another notable function of cell polarity is in axon guidance. In the developing nervous system, axon guidance relies on a variety of extracellular cues interpreted by specific receptors. In While this volume is relatively brief in its coverage of this Research Topic, we would like to express our gratitude to the authors and reviewers for their dedicated contributions. We believe that these studies offer valuable insights into the field of cell polarity, particularly through the perspective of model organisms, and address less-explored aspects such as the regulation of phase separation and multi-cue interpretation.


Anthocyanins and proanthocyanidins are water soluble, bioactive, secondary metabolites of the flavonoid class of plant pigments. They are responsible for red, purple, blue colours of vegetative and reproductive parts of plants Figure 1dihydroflavonol reductase (DFR) and anthocyanidin synthase (ANS) define the committed steps involved in anthocyanin and proanthocyanidin biosynthesis. The structural genes, including DFR and ANS are co-ordinately regulated by a complex of myeloblastosis (MYB), basic helix-loop-helix (bHLH) and WD40-repeat proteins (MBW), which is conserved across diverse plant species. Transcription factors (TFs), belonging to other families, along with MBW complex fine tune the anthocyanin biosynthesis under different exogenous and endogenous cues  in autumn. They identified cyanidin glycosides as the pigments that increased in correlation with sepal colour. Moreover, they correlated gene expression of anthocyanin synthesis pathway enzymes with flower colour, and anthocyanin accumulation and with the expression of MYB, bHLH, WRKY, and NAC TF genes may control their transcription. Several of these TFs could drive gene expression from the HmCHS4  and HmDFR1 (dehydroflavone reductase) gene promoters when transiently expressed in tobacco leaves. This identification of the anthocyanins, biosynthetic enzymes and regulatory factors involved in sepal colour development may help to guide development of ornamental flowers with pigmented sepals.Targeted metabolomics and transcriptomics were also used by Chen et al. described the mechanism of the transition of leaf colour in Populus × euramericana ‘Zhonghuahongye’ at three different stages of leaf development through metabolic analysis. Ten core metabolites that were mostly flavonoids revealed substantial differences in all comparisons. The transition of red leaf colour from a bright-purplish red to a brownish green was due to the downregulation of flavonoid metabolites, including malvidin 3-O-glucoside and cyanidin, which provides a genetic basis for the improvement of poplar cultivars.Peng et al. analysed the molecular basis of anthocyanin biosynthesis regulation in different popular cultivars displaying varying contents of anthocyanin in their leaves. The authors identified 39 anthocyanin compounds, which showed differential profiles in the leaves of these different popular cultivars. Transcriptome analysis revealed several differentially regulated genes in the selected poplar cultivars. The expression patterns of certain differentially regulated genes correlated with the anthocyanin metabolites. Based on the correlation analysis, the authors identified one R2R3-MYB SG5 sub-group transcription factor gene, Podel.04G021100. The SG5 sub-group of R2R3-MYB TF regulates structural genes involved in the biosynthesis of flavonoids  varieties displaying contrasting grain pigmentation using an integrated transcriptomics and metabolomics approach. Metabolomic analysis at different developmental stages revealed differential accumulation of flavonoids including anthocyanin in the grains of different varieties. Transcriptome analysis provided information about several differentially regulated genes in the grains of three varieties. Integration of metabolomics and transcriptomics data revealed several structural genes of the flavonoid biosynthesis including genes that encode modifying enzymes  of anthocyanidins, which may account for variation in the quality and quantity of the anthocyanin pigmentation. A transcription factor gene HvMYC2 was found to be co-expressed with HvF3H, HvGT1, HvGT2, and HvMAT, suggesting that it might be involved in the regulation of anthocyanin biosynthesis and modifying genes. Promoter transactivation assay and yeast 1 hybrid system confirmed that HvMYC2 can transactivate the expression of HvF3H. Further, the study demonstrates that HvMYC2 might play a role in mediating UV-B and low temperature induced anthocyanin pigmentation.Likewise, Yao et al., who treated the developing plum fruit surface with humic acid, seaweed extract and amino acids to elucidate their influence on carbohydrates and anthocyanin biosynthesis during ripening. All biostimulants potentially enhanced the total soluble solids (TSS) and titratable acidity (TA), as well as anthocyanin accumulation in the red skinned plum (‘Yinhongli’) compared to green skinned plum. Moreover, the amino acids treatment significantly increased the expression of anthocyanin biosynthetic genes, particularly CHS and flavonoid 3-O-glucosyltransferase (UFGT), leading to early pigment accumulation compared to the control. This study also showed a significant correlation between total sugar and anthocyanin content during fruit ripening and provides an avenue for further investigation of the interactions between anthocyanin biosynthetic and sugar metabolic pathways in plum.Effects of biostimulants on the accumulation of carbohydrates and anthocyanin biosynthesis in ‘Yinhongli’ plum are described by These studies highlight the utility of integrated approaches involving transcriptomics and metabolomics for the identification of candidate regulators of anthocyanin biosynthesis and provide basic knowledge for anthocyanin production for health and ornamental applications. Understanding the molecular basis of the anthocyanin biosynthesis will be helpful in improving the content of these health promoting molecules in fruits and vegetables. However, in order to explore the full potential of health beneficial effects of anthocyanin, it is imperative to address bioavailability and stability issues associated with anthocyanins. These limitations might be tackled by approaches like their complexation with lipids and proteins, and nanoencapsulation . HoweverAll authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.