The increasing availability of large-scale single-cell atlases has enabled the detailed description of cell states. In parallel, advances in deep learning allow rapid analysis of newly generated query datasets by mapping them into reference atlases. However, existing data transformations learned to map query data are not easily explainable using biologically known concepts such as genes or pathways. Here we propose expiMap, a biologically informed deep-learning architecture that enables single-cell reference mapping. ExpiMap learns to map cells into biologically understandable components representing known ‘gene programs’. The activity of each cell for a gene program is learned while simultaneously refining them and learning de novo programs. We show that expiMap compares favourably to existing methods while bringing an additional layer of interpretability to integrative single-cell analysis. Furthermore, we demonstrate its applicability to analyse single-cell perturbation responses in different tissues and species and resolve responses of patients who have coronavirus disease 2019 to different treatments across cell types. Lotfollahi et al. present ExpiMap, a deep-learning model enabling interpretable reference mapping of RNA sequencing data using biologically defined entities, offering end-to-end analysis from dataset integration to functional interpretation. However, leveraging existing atlases poses a computational challenge known as reference mapping enabling rapid integration of newly generated datasets, denoted as a query. The transfer of knowledge from the reference to the query allows the rapid annotation of the query data7, imputation of missing modalities in the query15 and the discovery of novel populations15.The progress and development of experimental technologies18 to map query datasets by minimal modification of the reference atlas19. Existing reference mapping methods embed new query data into a reference latent space by removing technical differences, such as batch effects between the reference and the query, without access to reference data. However, the implicitly used latent dimensions for joint data representation are not directly interpretable.Single-cell reference mapping is growing in popularity20. As the former disentanglement approaches have not yielded sufficiently useful latent spaces in our context23, we hypothesize that using prior information may help identifiability. In particular, we aim to leverage known or newly learned gene programs (GPs) to contextualize query data by answering various questions, including ‘which GPs are disturbed in a disease query data compared with the healthy reference?’ and ‘which biological programs explain a novel population in the query?’ By thus making reference mapping interpretable, it can move beyond mere data alignment between query and reference and be used for further interpretation of query data for example, in the case of disease perturbation versus a healthy atlas. Currently, the standard approach for identifying biological programs in query cells compared with a reference atlas is to test for differentially expressed genes and downstream gene set enrichment. However, the differential expression on an atlas consisting of cells from an arbitrary number of studies with variable degrees of biological and technical heterogeneity represents a challenge for statistical analysis. The currently accepted best practices25 suggest that differential expression should be performed on non-integrated expression data and not on the corrected expression values after integration; hence, statistical models should account for complex experimental designs and adjust for batch effects, which is further hampered by modelling constraints such as parameter identifiability. Instead of using simpler non-parametric statistical tests, both biologically relevant and irrelevant genes may be captured, which may compromise the accuracy of enriched gene set terms.An important trend in machine learning is the development of interpretable models, for example, by adding statistical assumptions to learned latent spaces or including prior information from validated mechanisms or other data20. Interpretable reference mapping requires incorporating domain knowledge20, such as curated GPs, into the representation learning model to guide interpretation and exploration. Including domain knowledge to design ‘domain-informed’ deep learning architectures has been shown to improve the performance on challenging prediction tasks, from tumour type26 to protein structure27. Earlier works proposed incorporating regularized linear decodes to include domain knowledge into autoencoders for single-cell data28, with scalable and expressive embeddings compared with existing factor models, such as f-scLVM29. Recent approaches such as VEGA30, scETM31 and pmVAE32 also feature variational autoencoders with linear decoders or training separate VAEs for each GP yet connected via a global loss in the case of pmVAE. Yet, accounting for the incompleteness of domain knowledge and learning new knowledge de novo from the data, rather than being locked into prior-based feature design, are not fully addressed by existing methods. Finally, going beyond single dataset analysis towards large-scale data integration and reference mapping while injecting domain knowledge remains challenging.Collectively, it may be useful to have interpretable embeddings directly associated with validatable GPs in the context of atlas-wide comparisons to capture the relevant biological signals while accounting for nonlinear batch effects. This end-to-end approach is common in deep learning and has been shown to outperform classical approaches that use sequential regularization and analysis20 recently). We construct an ‘explainable programmable mapper’ (expiMap) as an interpretable conditional variational autoencoder34 that allows the incorporation of domain knowledge by performing ‘architecture programming’, that is, constraining the network architecture to ensure that each latent dimension captures the variability of known GPs. We apply an attention-like mechanism35 to select the relevant GPs for each reference dataset. This helps with the prioritization of essential gene sets but also allows the inclusion of genes not initially included in annotated GPs, thereby addressing the incomplete nature of the knowledge database. To identify new variations unique to the query data, such as disease effects, we identify de novo GPs in addition to the known GPs in knowledge bases by learning disentangled latent representations. The framework can be used to automatically identify and explore biological processes in normal and disease states when mapping new query datasets to the atlas while maintaining comparable integration performance to existing data integration methods.In this Technical Report, to address these challenges, we propose to build a machine learning system that exploits the knowledge of the underlying biological phenomenon for single-cell representation learning  to fit the data. In contrast, nonlinear methods using deep neural networks41 come with a larger capacity at the expense of reduced model interpretability.Linear methods, such as principal component analysis (PCA)N) reference single-cell datasets and custom GPs. These can be gene lists from existing curated databases43, lists extracted from literature44 or individually curated gene sets  and an additional vector for corresponding one-hot encoded study labels (SN1:) for each cell, for example, the experimental laboratories or sequencing technologies  to output are programmed according to the GP matrix so that a latent GP can only contribute to the reconstruction of genes in a particular GP 47, a kernel-based measure of latent variable independence47, to further enforce independence between old and new unconstrained GPs learned during query optimization 51. We then mapped a query PBMC dataset of samples from eight patients diagnosed with systemic lupus erythematosus whose cells were either untreated (control) or treated with interferon (IFN)-β, a potent cytokine inducing a strong transcriptional response in immune cells52. Successful mapping should align untreated cells to matching cell types in the healthy reference while preserving the strong effect of IFN-β. The expiMap model trained with GPs extracted from the Reactome43 pathway knowledgebase successfully mapped the query untreated cells to the healthy reference while forming clusters indicative of the IFN-β-treated cells  and contextualizing it within a given healthy reference. To demonstrate the applicability of our model in this scenario, we constructed a human immune cell atlas from four studies of bone marrowlls Fig. .Fig. 2Ex54 including IFN-related pathways , and active amino acid metabolism in CD14+ monocytes after IFN-β stimulation  is a classical approach for inferring the activity of GPs and involves the sequential pipeline of differential expression analysis and gene set enrichment test. To evaluate the robustness of expiMap’s integrated GP test, we hence compared it with the classical GSEA via limma-fryTo further analyse the contribution of individual genes in each GP, we introduce the gene importance score: the absolute value of decoder weights for genes in GPs see also , which c7, Seurat v48 and Symphony18. Although expiMap and scVI both leverage scArches for reference mapping, scVI did not mix the untreated monocytes from the query data with healthy monocytes in the reference . Thus, the ability to learn new GPs to analyse query data containing new variations, such as new states or cell populations, is pivotal. We address this by allowing expiMap to learn novel GPs associated with the query data that exist in the knowledge base but are not detected previously in the reference model, as well as de novo programs that are not described in the knowledge base .αβ (and GPs containing a superset of those) and cytokine signalling in the immune system, from Reactome. We also removed B cells in the reference and the top two B-cell GPs containing information about B-cell receptor signalling and antigen presentation, as shown in Fig. 61) owing to the soft membership features in the model that were not initially in the pre-defined GP, demonstrating the ability of the model to incorporate extra information and enrich incomplete domain knowledge  and the effect of immunosuppressive interventions. We leveraged the integrated immune PBMC atlas to map IFN-β dataset . These findings suggest that a complex cellular communication circuit may be differentially activated in both patients and may be related to the differences in treatment response at the cellular level.Our analyses pointed us towards CD8tes Fig.  in both lls Fig.  and CD14tes Fig.  in sever66 and compared them at different clinical stages in our integrated Immune atlas. This analysis revealed that the annexin pathway displayed differential transcriptional behaviour in the severe and remission stages of P1 and P2, involving CD14+ and CD16+ monocytes, natural killer (NK) cells and CD8+ T cells . In this circuit, CD14+ and CD16+ monocytes show the potential to receive signals from NK cells and CD8+ T cells for P1D1. In P1D5, the annexin pathway switches completely to signalling between CD16+ monocytes and CD4+ T cells. In stark contrast, P2D1 is characterized by the annexin pathway focusing on CD14+ monocytes, which continues throughout the remission stage (P2D5), with the addition of CD16+ monocytes persisting towards D7 of remission (P2D7) , NK cells and T cells in COVID. The differences observed between patients in the expression of annexin-related interaction circuits may be related to the capability of viral clearance in each patient62 and the early expression of FPR1 by CD16+ monocytes, which is associated with the early detection of pathogenic molecules and tissue damage70. Interestingly, our analysis shows the expression of IFNG for P2D7 by NK and CD8+ T cells  that included healthy and T2D cells into this reference  pancreatic datasets75  differinnce Fig. . On the nce Fig. , a separnce Fig.  and assence Fig. , showing76, while expiMap scores are directly comparable. Furthermore, when specific cell types are missing from the reference, the annotation transfer cannot be performed, such as for the immune cells that were present only in the query . However, they lacked sufficient specificity and sensitivity, while expiMap score based on all PanglaoDB markers correctly annotated B-cell lineage, corresponding to the B-cell lineage marker Cd79a81 as well as non-activated B cell (Cd19 and Ms4a1) and activated plasma B cell (Jchain) markers  across all datasets with extreme GP scores in T2D-model cells , which would be influenced by all genes, might be misleading and could obscure such commonalities. As we demonstrated when mapping COVID-19 patient data, CD8MAP Fig. . At the MAP Fig. . Overall88 of curated domain knowledge, expiMap can be trained on multiple databases while pruning irrelevant information. However, selecting the relevant knowledge to include in the model can affect the model’s performance. As we demonstrated, including IFN-related knowledge can improve the performance in reference mapping  could also be a softplus or exponential function. Further, X is a random variable representing gene expression, C indicates conditions  and X given the latent variable Z.Our model builds upon the framework of  variational autoencodersW and L are matrix parameters for latent variables Z and one-hot encoded conditions C, respectively; and D is a matrix of condition-specific dispersion parameters for each gene. W is a n × m matrix with n corresponding to the number of genes and m corresponding to the number of GPs provided as an input.entclass1pt{minimap(w) in equation  is a part of our total loss to train the model. During the model training, the posterior distribution W (see the previous section). The first column is reserved for the name of the gene sets and the other columns should contain the names of genes. Gene matrix transposed files (.gmt file format) could be directly used in our API as an input.We use tab-delimited text files where the rows represent gene sets as an input to construct masks for B with columns corresponding to GPs and rows corresponding to genes, with ith gene is in the jth GP and 0 otherwise. Such a matrix is actually always constructed from the files described above before passing to the model. We refer to matrix B as the GP matrix.A database could be also passed to the model in the form of a binary matrix g(H). The GP matrix B specifies GPs and the gene memberships for these programs. The matrix B is used as a mask for the matrix of the decoder weights W, where the parameters for inactive genes in each GP are set to zero and do not change during training if the hard mask is used.The decoder network in equation  consistsIn the case of a soft mask, we add a regularization term that forces gene weights for genes that are not originally part of a GP to become zero, but also allows them to become active (non-zero) if they contribute to the reconstruction:Some columns of M can be set to a vector of ones Z to correspond to the GPs encoded in W.Both variants (hard and soft masks) force the elements of Znew; in the decoder, the additional matrix Wnew is concatenated to W (now denoted by Wold), resulting in:To allow new GPs to be learned, the model can be extended with additional nodes in reference training or query projection. For this, the last layer of the encoder is expanded with additional nodes connected to the existing nodes from the previous layer and producing the new vector Wnew, which is equivalent to the Laplace before this matrix. In addition, for each element of the vector Znew the sample estimate of HSIC between the element and the other elements of Zold and Znew is added as a regularization term to the loss22. Also Wnew can be constrained with hard gene membership or regularized with soft gene membership (see the previous section) as Wold using an additional GP database. In this case we do not use HSIC regularization for these new constrained nodes.In addition, L1 regularization is added to β. Excluding the group lasso Rγ(W) that appear in the proximal update step (discussed further), the loss function of the model can be written as:Znew and Zold are the old (existing in reference model) and new (learned in query training) unconstrained programs, respectively.We use the stochastic proximal gradient descent to optimize the ELBO loss (equation ) with adt is the step in the gradient descent algorithm and η is the learning rate.Then, to minimize the objective function we use the update schemeequation ) over a γ = 0. The proximal operator above has a closed-form expression (see the next section for the derivation), so it is easy to apply it after the stochastic gradient descent update. The gradient for the expectation terms is obtained with the reparametrization trick, as is common in the VAE framework96.The hard mask variant implies To derive the closed form of the proximal operator described in the previous section, we need two theorems.Suppose that Ei denotes a vector space and × is a Cartesian product. The proof of this theorem can be found in ref. 98.Then for any f and g are closed , convex functions; H denotes a Hilbert space; and g. The proof of the theorem can be found in ref. 99.A sufficient condition for We use the two theorems above to find the closed form of the proximal operator (equation ). The exW; thus, this function is clearly separable in columns, and the theorem 1 is applicable here. This means that we only need to calculate the proximal operator for a column, as we can find the full proximal operator as a Cartesian product of the proximal operators for different columns. This is the same as using its own proximal operator for each column of W separately.The sums in the regularization function are made over columns of k of W can be written asThe regularization summand for a separate column The regularization summand (equation ) has the98) asFor the group lasso part 98 asIt should be noted that in the case when the mask’s column equals a vector of ones equation  \documen98, rewritten) isIn addition, the subgradient η). By direct calculation for The proximal operator mentclasspt{minimaThis basically means that equation ) that\doUsing this and the theorem 2 we can conclude thatTherefore, the closed form of the proximal operator for the case Moreover, the closed forms of mentclasspt{minimaquations , respectk can be written asFor the case equation .The subgradient equation .Using the same reasoning as in the derivation of the proximal operator for sparse unannotated factors, we see thatη) for the column k of W, which corresponds to the annotated factor kThis means that we again can use the theorem 2 and obtain the closed form of the proximal operator ))\documen15. After training a conditional VAE model for multiple batches of the reference dataset, the trained weights are transferred to a new model with additional conditional nodes used to map new query batches to the reference. Further, additional nodes for new learnable GPs can be added at this stage (see the learning new GP section). During the training of the expanded model for query projection, only the conditional weights connecting new batches and the weights for new GPs (if any) in both encoder and decoder are tuned; the rest of the weights are frozen. Projecting with scArches preserves the latent representation of the reference and projects the query data to the same latent space while correcting for batch effects between the query and data.The projection of a query dataset to a reference dataset is performed using the single-cell architectural surgery (scArches) approachi of the latent variables for the cells from the groups a and b respectively, we use the logarithm of the Bayes factor:H0 and H1, respectively.To test the hypothesis G1 and G2 denote the independent group variables for X1 and X2, respectively.We can compute The probability equation  can be e40, to obtain the approximate value for equation (The probabilities (equation ) can be equation .Through the examples in the paper, we refer to the results obtained as ‘expiMap test results’ and at the threshold W in the decoder . If most of the gene weights in the column of jth GP, the direction Dj of predominant upregulation (negative or positive) can be calculated heuristically by several methods. We use two methods:For the Then, we can multiply the latent score of the GP by this direction 60. The overall score was computed as the average of all scores.Integrations were evaluated with methods implemented in scIB. We evaluated biological conservation through graph cLISI, normalized mutual information (NMI), adjusted Rand index (ARI) and average silhouette width (ASW) for cell type; and batch correction through principal component regression, ASW for batch, kBET, graph connectivity and graph iLISI. All metrics are further described in the scIB paperWe assessed the dominance of genes in a GP for Extended Data Fig. alpha_kl, the value of which is multiplied by the kl divergence term in the total loss. If the visualized latent space looks like a single blob after the reference training, we recommend to decrease the value of alpha_kl. If the visualized latent space shows bad integration quality, we recommend to increase the value of alpha_kl. The good default value in most cases is alpha_kl = 0.5. The required strength of group lasso regularization  depends on the number of used GPs and the size of the dataset. For 300–500 GPs, we recommend to use alpha = 0.7 and increase for larger numbers of GPs.The main hyperparameter that affects the quality of integration for the reference training is early_stopping = True for the query to reference mapping. Smaller datasets tend to require more epochs of training to map the query into the reference well. If you observe that the query is not integrated into the reference, we recommend to try longer training for the query mapping.We recommend to use 200 epochs and gamma_ext parameter), it will make them sparser, and thus more interpretable, and also can de-activate redundant new GPs completely, which is important when the number of new unconstrained GPs is high.When using new unconstrained GPs, we recommend to start with ten of them or more. This ensures that all new significant sources of variation in the query would be covered by the new GPs. We also recommend to use L1 regularization for the new GPs 59 in limma58 R/Bioconductor package was applied to log counts per million (logCPM) values obtained by voom transformation102 to test for the enrichment of the gene set terms in the Reactome pathway database78. The Reactome database was obtained from the Molecular Signature Database (MSigDB)104.Read counts were normalized using the trimmed mean of M-values (TMM)All the cell type labels and metadata were obtained from original publications unless specifically stated below.48 and PBMC samples were obtained from 10x Genomics (https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.0.0/pbmc_10k_v3), Freytag et al.49 and Sun et al.50. The detail of the retrieval path and the pre-processing can be found in Luecken et al.60 and Lotfollahi et al.15 We used the Reactome pathway database for annotations78 from MsigDB104; we also removed all pathways with fewer than 12 genes. The genes that were not in the GPs database were filtered out, reducing the total number of genes from approximately 11,000 to 3,690. Then 2,000 highly variable genes were selected for training.The immune dataset includes samples from bone marrow cells and peripheral blood cells from different human samples. The bone marrow data were collected from Oetjen et al.105). The pools from the IFN-β and control cells were mixed together and loaded to a 10x kit. The dataset was obtained from the Seurat tutorial . We have used the same genes as in the reference (Immune atlas).This dataset contains cells from eight patients with Lupus treated with IFN-β or left untreated for 6 h . The dataset is available on Gene Expression Omnibus (GEO); the accession number is GSE150861.The dataset66. For this analysis, we used the non-integrated shared gene space between all the integrated datasets after removing those genes supported by fewer than five counts, for a total of 10,851 genes ready for analysis. We then ran CellChat on each subset using the curated database for interactions in human samples. The gene expression of each ligand–receptor pair was visualized using a dotplot generated using Scanpy 1.8.1 (ref. 61) and anndata 0.7.6 (ref. 106). The scripts for the analyses, as well as the package version used in the analysis, can be found in the ‘covid’ section of the repository.We used the integrated dataset to analyse cell–cell interactions using the CellChat package6 total counts, excluding highly expressed genes, and subsequently applied a log transformation. We merged datasets across studies using Ensembl IDs and retaining the genes expressed in all studies. We used merged data across studies, followed by the identification of highly variable genes, z-normalization and the computation of top PCA components. We clustered the data and plotted known pancreatic islet cell type markers to annotate cell types cluster-wise.The datasets are publicly available on GEO and further described in Supplementary Table 107 release from March 2020 and Reactome78 v4.0 and mapped them to mouse genes using Ensembl108 V103 orthologues. We used only gene sets with at least three genes and at most 200 genes. We excluded genes that were not present in these gene sets. With expiMap, we integrated the reference datasets using samples as batches and projected query samples. We also performed matched integrations with Seurat109, Symphony18 and scVI40. We evaluated different integrations, as described in the integration evaluation section. We used reference query split as batches and excluded non-healthy query samples as they were not expected to be integrated into the healthy reference owning to biological differences. For the downstream interpretation analysis, we used directed expiMap scores.For integration, we separated the datasets into reference and query, as described in Supplementary Table k-nearest neighbour (kNN), as described in ref. 15. We evaluated the annotation transfer with F1 score and by visual evaluation of prediction accuracy and certainty on UMAP.We used multiple methods to evaluate PanglaoDB cell type scores. We plotted the PanglaoDB cell type scores of expected pancreatic cell types on query UMAPs and visually compared the results to cell type annotation. We used the PanglaoDB gene set scores as features for the annotation transfer from reference to query with weighted For gene-level analyses on integrated data we normalized expression with Scanpy using functions normalize_total and log1p.n = 161,764) (ref. 109), heart  (ref. 11), lung  (ref. 13), colon  (ref. 110) and liver  (ref. 111). All datasets, except heart, were obtained from the Sfaira database112, which includes cell type labels. Heart was obtained from the scVI package. For the expiMap training for each dataset, we used the Reactome pathway database, selected only pathways that contain more than 12 genes and filtered out all genes that are not present in any pathway, and then we selected 2,000 HVG for training. For the other models, we used the same lists of genes.We leverage datasets from five different tissues including PBMCs  (ref. 86). The raw dataset is available at the GEO under accession number GSE132188. Cell type labels were obtained from an adata object provided by the authors of scVelo113. We used the Reactome pathway database version 7.5.1 for annotations78 from MsigDB104. Days 14.5 and 15.5 were used as a reference, and days 12.5 and 13.5 as a query. For the reference dataset, we removed all pathways with fewer than 13 genes. The genes that were not in the GPs database were filtered out, reducing the total number of genes from approximately 28,000 to 10,000. Then 4,000 highly variable genes were selected for training. For the query, we used the genes obtained after pre-processing the reference dataset. RNA velocities were calculated using scVelo.We used the developmental dataset from mouse endocrinogenesis (https://scarches.readthedocs.io/en/latest/scvi_surgery_pipeline.html).scVI: we used the setup from the scarches tutorial for query to reference mapping with scVI (https://github.com/immunogenomics/symphony).Symphony: we used the parameters recommended in the repository , but used supervised principal component analysis(sPCA) instead of PCA.Seurat: we adapted the Seurat reference mapping tutorial (The details for pre-processing of the datasets used for the model training are provided in the section ‘Datasets and pre-processing’. If not indicated otherwise in that section or in the legends, no data were excluded from training and analysis. The hyperparameters chosen for model training for all experiments are listed in Supplementary Note 114.A step-by-step protocol for installing the software, training the model and downstream analysis can be found on Nature Protocol ExchangeFurther information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41556-022-01072-x.Supplementary InformationSupplementary Notes 1–12 and Figs. 1–10.Reporting SummarySupplementary TablesSupplementary Tables 1–15. Each tab is a Supplementary table, and descriptions and captions are provided in the tabs.Supplementary Data 1expiMap latent scores.Supplementary Data 2expiMap latent scores for cells of each cell type listed in the figure; Bayes factors for IFN-β stimulated versus rest for each cell type in the figure.Supplementary Data 3expiMap latent scores for cells of each cell type listed in the figure; Bayes factors for IFN-β stimulated versus rest for each cell type in the figure.Supplementary Data 4UMAP coordinates for plots in the figure.Supplementary Data 5UMAP coordinates of the full model and the model with removed top IFN-β GP with condition, study and cell type labels.Supplementary Data 6Results from CellChat.Supplementary Data 7UMAP coordinates of expiMap latent space, annotations and annotation transfer scores and uncertainty.Supplementary Data 8UMAP coordinates of the expiMap latent space, cell type markers and expiMap latent scores for cell types.Supplementary Data 9expiMap latent scores of GPs listed in the figure.Supplementary Data 10Bayes factors for testing each cell type versus rest.


All images were elastically registered to the ARA atlas and then averaged. High-resolution MR images were used to validate a CT-based registration pipeline. The resulting method was applied to a mouse model of Parkinson’s disease subjected to a test–retest study (n = 6) with the TSPO-specific radioligand [18F]VC701. The identification of regions of microglia/macrophage activation was performed in comparison to the Ma and Mirrione template. The new toolbox identified 11  brain sub-areas of significant [18F]VC701 uptake increase versus the 4 (3 after FDR) macro-regions identified by the Ma and Mirrione template. Moreover, these 11 areas are functionally connected as found by applying the Mouse Connectivity tool of ARA. In conclusion, we developed a mouse brain atlas tool optimized for PET–CT imaging analysis that does not require MR. This tool conforms to the CCFv3 of ARA and could be applied to the analysis of mouse brain disease models.Automatic analysis toolboxes are popular in brain image analysis, both in clinical and in preclinical practices. In this regard, we proposed a new toolbox for mouse PET–CT brain image analysis including a new Statistical Parametric Mapping-based template and a pipeline for image registration of PET–CT images based on CT images. The new templates is compatible with the common coordinate framework (CCFv3) of the Allen Reference Atlas (ARA) while the CT based registration step allows to facilitate the analysis of mouse PET–CT brain images. From the ARA template, we identified 27 volumes of interest that are relevant for in vivo imaging studies and provided binary atlas to describe them. We acquired 20 C57BL/6 mice with [ The most representative and popular atlases are those developed by Franklin, Paxinos and Watson for mouse and rat4 and by the Allen Institute for Brain Sciences with the more recent Allen Reference Atlas (ARA) for the mouse brain (https://atlas.brain-map.org/). Besides the differences in anatomical structures borders and ontology5, the major difference between the Franklin-Paxinos and the ARA atlases is the definition of the 3D coordinates within the brain volume. The former is considered the reference atlas for stereotaxic surgery and is based on a classical neuroanatomical approach with Bregma as zero point. The latter has been developed more recently by integrating different types of information from in vivo imaging (i.e. Positron Emission Tomography-PET), MR and post-mortem imaging  with genome-wide image database and gene expression data7. The ARA has higher spatial resolution compared to other atlases (10 µm isotropic voxel) but the relation of the template with anatomical landmarks as Bregma is unknown. Therefore, a spatial alignment with high resolution images as MR is necessary to validate stereotaxic coordinates8.The definition of the brain anatomical spaces, where structures and regions are precisely localized, is at the basis of the study of brain functionality and connectivity across different species. Brain atlases define the organization of anatomical and functional regions in a three-dimensional (3D) volume. Stereotaxic brain atlases are mostly used in neurological studies and are drawn on Magnetic Resonance (MR) images where voxels are grouped into anatomical structures. Mouse and rat represent mammalian models of brain development and several rodent atlases are available based on different strategies, i.e. haematoxylin and eosin, Nissl staining9. Anatomical information is obtained through the integration of PET imaging with different modalities as Computed Tomography (CT) and MR. PET-MR hybrid devices are a recent development for both clinical and preclinical purposes. Technical considerations and the high costs limit the diffusion of this equipment and to date the most common solution is represented by PET–CT systems10. This combination allows the localization of the molecular target in the anatomical space, even if the reference technique for the brain anatomy is MR imaging11. Therefore, the identification and quantification of radioligand uptake areas within PET image volume is obtained by applying structural MR-derived templates that segmented the brain into anatomical/functional regions. Typically, the workflow consists in a sequence of image alignment and deformations to a reference space that make it possible to compare different subjects or the same subject in different conditions as in longitudinal studies12.PET is a functional molecular imaging technique that leverages on specific radiotracers to provide information about the distribution of a specific target, e.g. a receptor, enzyme or process, within brain or peripheral areas in pathological compared to physiological conditionshttps://www.pmod.com/web/; https://www.fil.ion.ucl.ac.uk/spm/; https://mipav.cit.nih.gov/) account for non-deformable and pre-defined macro-regions. This entails some limitations in the identification of functional sub-areas that could be characterized by ligand uptake modification, also influencing the correlation analysis across the different areas. Another limitation of MR-based templates is that they work most efficiently when a high-resolution 3D scan is present, which is not always the case in murine studies.The MR templates included in most software used for PET images analysis  software, which exploit the ARA volume for the identification of brain structures using the same ontology and coordinate framework. In this way, the areas where a modification of a biomarker is highlighted by PET could be matched with the additional information provided by ARA, such as in situ hybridization, cell projection maps and in vitro cell characterization. The gold standard procedure to achieve optimal spatial registration is based on high resolution isotropic 3D MR. The acquisition of such an image however is lengthy, costly, and it might not even be always feasible, as performing two long procedures requiring anaesthesia in the same day on the same animal can be unethical. Therefore, to better exploit this template, we also introduced and validated a pipeline that allows transforming individual images to this standard space using a CT image and not an MR sequence. This makes the analysis more versatile, applicable to a wide range of PET tracers and independent on the acquisition, on a different scanner, of a high-resolution 3D MR image. As the analysis does not depend on the availability of a tracer-specific template, it is immediately widely applicable. Furthermore, its performance does not depend on the contrast in the PET image, which can vary depending on the tracer target. Indeed, compared to the PET-template based one, the CT-based spatial normalization is unbiasedIn this work, after a detailed description and validation of the proposed method, we provided an example application to the analysis of a Parkinson’s disease (PD) model.Experimental procedures involving the use of animals complied with the EU Directive 2010/63/EU for animal experiments and were approved by the Ethical Committees of the San Raffaele Research Institute (Milan) and the Italian Ministry of Health (license n. 237/2019-PR). The study was carried out in compliance with the ARRIVE guidelines.18F]-fluoroglucose ([18F]FDG), and CT. Twelve of them were also acquired with a 3D T2-weighted high resolution MR sequence.To optimize and build the pipeline of PET–CT template creation, we acquired twenty adult C57BL/6 male mice with PET and the radioligand for glucose metabolism 2-Deoxy-2-[14. Six mice were acquired with the TSPO specific radiotracer [18F]VC70115 before and after neurotoxin treatment and analysed using the new brain atlas template in comparison to PMOD 4.1  VOI template12.The template analysis was then applied to the study of microglia/macrophage activation in a PD mouse model obtained with the subacute administration of the neurotoxin 1-methyl-4-phenyl-1,2,5,6-tetrahydropyridine (MPTP)18F]FDG was prepared for clinical use following European Pharmacopeia VIII Edition and i.v. injected in animals in fasting conditions (mean injected activity: 4.5 ± 0.3 MBq).Three-month-old C57BL/6JRj male mice (n = 20) were purchased from Janvier Labs . The radiotracer VC701 was prepared as previously described15 and injected i.v. under feeding conditions (mean injected activity: 4.7 ± 0.3 MBq). CT and PET acquisitions were performed 100 min post injection using X-Cube and β-Cube, respectively. Each animal was anesthetized with 2% isoflurane in medical air and then positioned prone on the scanner bed for the CT scan centered on the brain . At the end of CT acquisition, the bed with the immobilized animal was removed and inserted in the PET scanner for a 20 min static acquisition. During the exam, mice were maintained under anesthesia, body temperature and respiratory rate were constantly monitored. CT and PET data were reconstructed and co-registered as described above. Images were transformed voxel by voxel in Standard Uptake Values (SUVs) and scaled to the global mean uptake value to measure modifications in radioactivity concentration between conditions. PMOD VOIs template was applied as previously described16.The radiotracer VC701  that allows a correlation of neuropathology with behaviour or phenotype. PD is characterized by loss of dopaminergic neurons and α-synuclein aggregation in the Substantia nigra pars compacta, a small region within the midbrain. A key and recently recognized event in PD pathogenesis is the neuroinflammatory process triggered by α-synuclein aggregation19. The fact that this process is a very early event during PD development is confirmed by different in vivo PET studies in prodromal PD patients21. The inflammatory response in PD is carried out by microglia and, to a minor extent, astrocytes. Activated microglia produce detrimental cytokines and inflammatory factors, and overexpress a variety of receptors as the translocator protein (TSPO). Using radiotracers specific for TSPO, as [11C]PK11195, it was possible to follow microglial activation over PD progression and find a negative correlation between the increase of neuroinflammation and dopamine content in midbrain22. Therefore, while MR imaging is applied to the study of neuroanatomical changes, which are particularly evident in advanced stage of PD, PET imaging is able to detect early biochemical modifications that are already present in pre-symptomatic/premotor phases. Image analysis plays a fundamental role in identifying dysfunctional regions and integrating information deriving from different sources. Different PD rodent models have been developed and studied using PET and radiotracers that allow the identification of biomarkers of disease development and progression23. In the preclinical setting, a major limitation is represented by the lack of standardization of image acquisition and tools for the analysis.The development of brain templates enables the precise identification of the elements (sub-regions) that are fundamental for the study of pathology progression and response to treatment in neurodegeneration and neuroinflammation. Three-dimensional brain atlases for small rodents are available to study neuroanatomy in relation to gene expression, cell types and functional pathways. One of the more advanced and complete for mouse is the Allen Brain Atlas  and visualization of the cortical bone, should perform identically. In addition, this pipeline was not validated against animals with uncommon anatomies, such as after major surgeries to the skull or extreme degrees of atrophy.The present work has some limitations. A full study on the effect of the CT image quality on the image registration precision was not performed. However, it can be expected that any acquisition protocol with a good spatial resolution  or are involved in PD motor dysfunction 25. In fact, we found a significant radiotracer uptake increase also in somatomotor, somatosensory, anterior cingulate and orbital cortex areas, which undergo structural and functional alteration during PD, as measured by MR and PET26.When applied to the PD model, the template allowed the recognition of the sub-regions involved in response to a neuronal insult, such as subacute MPTP. In particular, we found that after 7 days of MPTP treatment, the uptake of the TSPO-specific radiotracer VC701, which has been already used by our group for the study of different neurodegeneration models31. The two areas with maximal elevation of [18F]VC701 uptake were striatum and thalamus, and were consistent between the two templates, even after FDR adjustment. The application of PMOD template failed to reveal significant changes of radiotracer uptake in cortical regions sampled as a whole, although microglial activation was observed in selected cortical areas such as orbital or cingulate cortex in PD32. In addition, the adaptation of the PET–CT volume to the ARA volume and the definition of anatomical regions that are superimposable offer the possibility to apply all the advanced tools developed for ARA, such as the connectivity tool (https://connectivity.brain-map.org/projection) that allows to reconstruct the different neuronal circuits within the brain. Therefore, when we selected the dorsal part of the striatum using this tool, we observed that this region is connected to cortical and subcortical areas in which we observed [18F]VC701 uptake increase, suggesting that the whole circuit was affected by the neurotoxin.Subacute MPTP33. The A9 region that projects to the dorsal part of the striatum is more susceptible to MPTP administration in comparison with A10 that, instead, projects to the ventral part of striatum34. In line with these findings, we found no neuro-inflammatory response in the ventral part of the striatum and other limbic cortical and subcortical regions.In mouse brain, cell body of dopaminergic neurons are mainly localized in three distinct subcortical regions: substantia nigra (A9 region), ventral tegmental area (A10) and retrorubral fieldAs expected, when the connectivity tool was applied to the ventral part of the striatum we failed to find any association with brain regions with increased levels of radioligand. This example shows the value of the ARA association with Allen connectivity tools in the evaluation of the regional distribution of PET radiopharmaceuticals or interpretation of brain metabolic studies.35.Moreover, different tools for gene expression have been developed and adapted to the ARA, increasing the possibility of integrating omics information for a given phenomenonIn conclusion, the brain template developed here represents an efficient tool for mouse PET–CT image quantification, making MR acquisition unnecessary and offering the unique advantage to merge molecular or structural data deriving from in vivo preclinical imaging with genetic, molecular and cell information present in ARA tools.16.The same animals and their PET–CT datasets were used for the preparation of the following articles: “In vivo susceptibility to energy failure parkinsonism and LRRK2 kinase activity” by Salvatore Novello and colleaguesSupplementary Information.


Epithelial tissues acquire their integrity and function through the apico-basal polarization of their constituent cells. Proteins of the PAR and Crumbs complexes are pivotal to epithelial polarization, but the mechanistic understanding of polarization is challenging to reach, largely because numerous potential interactions between these proteins and others have been found, without a clear hierarchy in importance. We identify the regionalized and segregated organization of members of the PAR and Crumbs complexes at epithelial apical junctions by imaging endogenous proteins using stimulated‐emission‐depletion microscopy on Caco-2 cells, and human and murine intestinal samples. Proteins organize in submicrometric clusters, with PAR3 overlapping with the tight junction (TJ) while PALS1-PATJ and aPKC-PAR6β form segregated clusters that are apical of the TJ and present in an alternated pattern related to actin organization. CRB3A is also apical of the TJ and partially overlaps with other polarity proteins. Of the numerous potential interactions identified between polarity proteins, only PALS1-PATJ and aPKC-PAR6β are spatially relevant in the junctional area of mature epithelial cells, simplifying our view of how polarity proteins could cooperate to drive and maintain cell polarity. Many of our organs, including the lungs and the intestine, are lined with a single layer of cells that separate the inside of the organ from the surrounding environment inside the body. These so-called epithelial cells form a tightly packed barrier and have a very characteristic organization.The apical surface faces the outside world, while the basal surface faces the inner tissues. These different interfaces are reflected in the organization of the cells themselves. The shape, composition, and role of the apical cell surface are distinct from those of the basal surface, and they also contain different proteins. In some epithelial cells, the apical surface specializes and forms protruding structures called microvilli. Thus, epithelial cells are said to be polarized along this apical–basal axis. Over the last 30 years, many labs have identified and studied which proteins help epithelial cells become and stay polarized.Previous biochemical experiments showed that these so-called polarity proteins interact with each other in many different ways. But it remains unclear whether some of these interactions are more important than others, and where exactly in the apical or basal membranes these interactions take place.Mangeol et al. used super-resolution microscopy to observe the polarity of proteins at the apical membranes of both human and mouse cells from the small intestine to answer these questions. They focused on areas called tight junctions, where the intestinal cells connect with each other to form the barrier between the outside and the inside. First, all the polarity proteins clustered together in various formations, they were not distributed uniformly. For example, one protein called PAR3 was at the level of the tight junctions, whereas other proteins were closer to the apical surface and the outside world. Only two pairs of proteins – PAR6 and aPKC, and PALS1 and PATJ – formed stable clusters with each other. This finding was unexpected because previous biochemical experiments had predicted multiple interactions. Third, the PALS1/PATJ complexes stayed at the bottom of the microvilli protrusions, whereas PAR6/aPKC were inside the protrusions.Taken together, these experiments reveal a detailed snapshot of how the polarity proteins themselves are organized at the apical surface of epithelial cells. Future work will be able to address how these protein complexes behave over time. In epithelial tissues, cells coordinate their organization into a polarized sheet of cells. Each cell acquires an apico-basal organization and specialized lateral junctions, namely, tight junctions , adherens junctions, and desmosomes . This orOver the past two decades, several proteins have been discovered to be pivotal to epithelial polarization, such as PAR3, PAR6, aPKC (PAR complex), Crumbs, PATJ, PALS1 (Crumbs complex), Scribble, LGL, and DLG (Scribble complex) in mammals  microscopy the polarity proteins that are key to the establishment of the apical pole of epithelia: PAR3, aPKC, PAR6β, PATJ, PALS1, and CRB3A. These proteins localize at the apical junction region of epithelial cells. Because how proteins interact and localize is likely to depend on cell differentiation, we decided to focus here on mature epithelia, a state where we hypothesize that protein interactions and localization are stationary. Using human and murine intestine and Caco-2 cells, we first imaged endogenous polarity proteins with respect to the TJ to appreciate their overall organization in the region. Second, we localized these proteins two-by-two to uncover relevant apical polarity protein subcellular associations. Finally, we focused on the organization of polarity proteins with respect to the actin cytoskeleton. We find that polarity proteins localize in distinct subregions that do not reflect the canonical definition of polarity protein complexes. In addition, their localization with respect to the cytoskeleton emphasizes some emerging roles of polarity proteins as regulators of actin organization.To obtain a first estimate of polarity protein localization in the TJ region, we systematically imaged each polarity protein with respect to a marker of the TJ. To this end, each apical polarity protein and a TJ marker (ZO-1 or Occludin) were immunostained and imaged together using STED microscopy . STED imWe found that the localization of each polarity protein was conserved across all samples and species  and 2. ACaenorhabditis elegans . To compare how proteins localized when imaged with their fluorescent tag or with antibody staining, we achieved the following: we first imaged live PAR6β tagged with Citrine with STED, then fixed, permeabilized, and labeled the cells with antibodies against PAR6β. We finally came back to the same cells observed live and imaged them with STED, this time using Alexa Fluor-568 conjugated antibody. We find a very good agreement between images observed live with Citrine or fixed with Alexa Fluor-568 . In partSecond, to further examine whether permeabilization with detergent can generate clusters, we chose to permeabilize fixed cells by freezing them in liquid nitrogen and thawing them at room temperature. Such treatment is known to destabilize cell membranes . After tWe conclude that the clustered organization of proteins we observe is not due to methodological artifacts as we can find the same clusters of PAR6β proteins in live and fixed CRISPR-Cas9 knock-in Caco-2 cells, and the absence of detergent when staining cells with antibodies against PATJ does lead to the same organization as the one observed with detergent.The organization of proteins in separate subdomains led us to investigate how polarity proteins were organized within these subdomains, and more specifically how clusters of polarity proteins were localized with respect to each other. To tackle this question, we imaged polarity proteins two-by-two in Caco-2 cells and quantified the extent of their colocalization using the protein–protein proximity index developed in First, we found that some of the proteins colocalize strongly: PALS1 with PATJ and aPKC with PAR6β, presumably in both cases forming a complex, as the literature suggests . SurprisIn the generally accepted description of the canonical Crumbs complex, PALS1 binds to the transmembrane protein CRB3A and PATJ binds to PALS1 . TherefoConsistent with what we observed with STED, we often found PATJ organized in clusters apical of the TJ . We starCRB3A is thought to anchor PALS1 and PATJ to the plasma membrane. However, given our results showing a minor colocalization of PATJ and PALS1 with CRB3A, it is unlikely to be the case for most PALS1 and PATJ molecules. Therefore, the localization of PATJ close to the apical membrane led us to wonder whether PATJ together with PALS1 could be associated with the apical plasma membrane via interactors that remain to be discovered. Thus, we measured the distance of the immunogold label of PATJ to the plasma membrane  and founBecause polarity proteins play a key role in the epithelial organization, we wondered how these proteins were organized with respect to the actin cytoskeleton. Actin is very densely packed in the TJ region, which makes challenging the identification of where polarity proteins are localized with respect to the actin structure, if not impossible, with 2D STED. Indeed, the method leads to overlaps between actin structures that are then very difficult to extricate. Therefore, we chose to use 3D STED, which has a planar resolution lower than 2D STED (about 120 nm) but a much higher axial one (about 140 nm), making it a tool of choice to decipher how polarity proteins organize together with actin in three dimensions.We find that in the region where PATJ, PALS1, PAR6β, and aPKC are located, the actin cytoskeleton identified with 3D STED consists mostly of microvilli structures . PATJ anIn this study, we have systematically localized polarity proteins with super-resolution microscopy in epithelial cells. We observed endogenous PAR3, aPKC, PAR6β, PATJ, PALS1, and CRB3A in the human intestine and Caco-2 cells, and PAR3 and PALS1 in the mouse intestine. We found the following : (1) allPrevious studies were largely based on biochemical approaches. The first interactions found defined canonical polarity protein complexes, while subsequent studies highlighted the numerous potential interactions that can be found with such approaches, between polarity proteins of different complexes , as wellDrosophila melanogaster embryos during cellularization . Cells were maintained in Dulbecco’s modified Eagle’s minimum essential medium supplemented with 20% heat-inactivated fetal bovine serum and 1% non-essential amino acids , and cultured in 10% CO2/90% air. The medium was changed every 48 hr.A clone of Caco-2 cells, TC7, was used in this study because differentiated TC7 cells form a regular epithelial monolayer . Caco-2/PARD6B locus was prepared by genome editing with the CRISPR-Cas9 method (Streptococcus pyogenes Cas9 protein were purchased from Synthego. This gRNA targeted the stop codon region of the PARD6B exon 3 with this sequence: ATCATAACATTATGAAACCG (TGG). The donor plasmid was constructed with Citrine coding sequences inserted at the position of the PAR6DB STOP codon and flanked by the 5′ homology arm chr20: 50749657–50750486 (PARD6B) and 3′ homology arm chr20: 50750962–50750487 (PARD6B) (see Appendix 1). The donor plasmid was purchased from Twist Bioscience. The gRNA was diluted following the manufacturer’s instruction and ribonucleoprotein complexes were formed with 30 pmoles of gRNA and 12 pmoles of spCas9. 106 Caco-2 cells were transfected with ribonucleoprotein (RNP) and 5 µg donor plasmid using Amaxa Nucleofector . Transfected cells were seeded in Petri dishes. After 15 days, fluorescent cells were sorted by a cytometer FACSAriaII and cloned in 96-well plates. After the expansion of the fluorescent clones, the screening of PAR6β Citrine KI cells was performed by Western blot analysis with anti-PAR6β and anti-GFP antibodies (see Appendix 1). The targeted insertion was confirmed by PCR amplification of the expected junction fragments followed by Sanger sequencing (Eurofins). A clone (A6) that expresses PAR6β Citrine but no longer expresses PAR6β  was used for further experiments (see Appendix 1).The Caco-2 PAR6β Citrine knock-in cell line expressing PAR6β Citrine from the human 9 method . ChemicaHuman intestine biopsies were obtained under the agreement IPC-CNRS-AMU 154736/MB. Intestinal samples were fixed in paraformaldehyde  4% in phosphate-buffered saline  for 1 hr at 20°C. Biopsies were embedded in optimal cutting temperature compound  and frozen in liquid nitrogen.Mouse intestine samples were obtained following local ethical guidelines. After washing with PBS, intestinal samples were fixed in PFA 4% in PBS for 20 min at room temperature. Samples were then embedded in OCT compound and frozen in liquid nitrogen.Cells were washed in PBS and then fixed in PFA 4% in PBS for 20 min at room temperature. When apico-basal orientation observations were needed, cells were sectioned along the apico-basal axis. Before sectioning, cells were embedded in OCT compound and frozen in liquid nitrogen.l-lysine solution .When needed, samples were sectioned with a cryostat . 10 µm sections were transferred to high-precision 1.5H coverslips  previously incubated with poly-Intestinal sections and cultured cells were prepared similarly. Intestinal sections were permeabilized in 1% SDS (Sigma-Aldrich) in PBS for 10 min. In cultured cells, 10 min of permeabilization was achieved with 1% SDS in PBS for CRB3A antibody, as well as PAR6β and aPKC antibodies when used in combination with TJ markers; otherwise, all other protein labeling protocols included 1% Triton X100 (Sigma-Aldrich) in PBS at permeabilization for 10 min. After washing with PBS, samples were saturated with 10% fetal bovine sera (Gibco) in PBS (‘saturation buffer’) over an hour at room temperature. Primary antibodies were diluted in the saturation buffer and incubated overnight at 4°C. In more details: rabbit anti-ZO-1 , mouse anti-Occludin , mouse anti-E-cadherin , rabbit anti-PAR3 , rabbit anti-PAR6β , rabbit anti-PKCζ , mouse anti-PKCζ , chicken anti-PALS1 , using a ×100 oil immersion objective . Two‐color STED was performed with Alexa Fluor 532 excited at 522 nm (fluorescence detection in the 532–555 nm window), and Alexa Fluor 568 excited at 585 nm (fluorescence detection in the 595–646 nm window). To minimize the effect of drifts on imaging, both dyes were imaged sequentially on each line of an image and depleted using the same 660 nm laser. Detection was gated to improve STED signal specificity. The parameter used to generate 3D STED was set to 100% in LASX. To ease the analysis, when imaging with 3D STED, we picked junctions where microvilli were vertical, while in other imaging analyses the orientation of microvilli was unknown because of the lack of actin labeling.Citrine fluorescent proteins were imaged similarly to organic dyes. Citrine was excited at 507 nm, depleted at 592 nm, and fluorescence was detected in the 517–582 nm range. Special care was taken to limit cell damage as we found that too high STED laser excitation led to major cell damage.Cells were washed in PBS and then fixed in PFA 4% in PBS for 20 min at room temperature. After rinsing with PBS, cells were put into a sucrose gradient to reach 30% sucrose overnight. Cells were then frozen in liquid nitrogen and immediately thawed at room temperature. Immunostaining was carried out without permeabilization step, directly with primary antibodies . After the washing steps, cells were incubated with a secondary antibody carrying 6 nm gold particles . A tertiary antibody was used to observe where gold particles were localized on a macroscopic level .Cells were then prepared specifically for electron microscopy. They were fixed in 2.5% glutaraldehyde, 2% PFA, and 0.1% tannic acid in sodium cacodylate 0.1 M solution for 30 min at room temperature. After washing steps, cells were post-fixed in 1% osmium in sodium cacodylate 0.1 M solution for 30 min at room temperature and contrasted in 2% uranyl acetate in water solution for 30 min at room temperature. Cells were then dehydrated in ethanol and embedded in Epon epoxy resin.Cells were observed with a transmission electron microscope, FEI Tecnai G2 200 kV , in an electron tomography mode. Tomograms were reconstructed using the Etomo tool of the IMOD software.To quantify the density and positions of polarity proteins with respect to TJ markers, we used custom-made ImageJ macros and Python programs. In each case, the reference protein was a TJ protein (ZO-1 or Occludin) that was localized precisely, defining a reference position along the junction from which intensity measurement was done. For planar orientations, the reference was the maximum intensity of the TJ marker along the junction; intensity measurements consisted in getting the intensity profiles of proteins perpendicular to the junction, all along the junction. For apico-basal orientations, we measured intensity profiles on the apico-basal axis, all along the junction. On a given profile, the reference was taken at the most apical point where the TJ marker intensity was a third of its maximum intensity; the reason for this choice is that TJs spread along the apico-basal axis tended to vary up to threefold from one cell to another and this definition of the reference allowed us to define a reproducible apical edge of the TJ. In the process, we used bilinear interpolation to obtain subpixel quantification. The results of analyses were then normalized for intensity for each junction to avoid junction-to-junction intensity variation. Because we used a reference protein for each junction, we could then align all results based on the reference position of the reference protein and pool all results into a single protein density plot. Since we used either ZO-1 or Occludin as references, we controlled that these proteins are localized similarly in The principle of quantification of protein–protein proximity was introduced by 1 and A2 from the autocorrelation of images of protein 1 and protein 2, respectively, and the amplitude C12 from the cross-correlation analysis. One evaluates the fraction of protein 1 colocalizing with protein 2 with the protein–protein proximity index P1 = C12/A2, and the fraction of protein 2 colocalizing with protein 1 with the protein–protein proximity index P2 = C12/A1.In practice, we extracted junctions from two-color images, restricting the analysis to a band of 400 nm centered on the reference given by the TJ (as defined earlier). As we found the analysis to be dependent on orientation, when planar orientation was used, we excluded junctions that were not straight. All extracted junctions of a given protein pair to be examined were then concatenated into one large two-channel image on which we achieved autocorrelation and cross-correlation analysis . We extracted the amplitude of peaks obtained in each of the autocorrelated and cross-correlated images as proposed in In  This important work advances our understanding of how apical polarity proteins are organized in epithelia using advanced, super-resolution imaging. The microscopy performed in this study is skillful and beautifully presented and clarifies the view of how polarity proteins may interact in mature epithelia. This article will be of interest to cell biologists, especially those interested in cell polarity and tissue architecture. In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Decision letter after peer review:eLife. Your article has been reviewed by 3 peer reviewers, including Danelle Devenport as Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Anna Akhmanova as the Senior Editor.Thank you for submitting your article "Super-resolution imaging uncovers the nanoscopic segregation of polarity proteins in epithelia" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.Summary:The manuscript addresses a fundamental problem: the organisation of epithelial polarity determinants at the apical domain of human epithelial cells. Mangeol et al. investigate this question using super-resolution microscopy approaches (STED) in polarised intestinal epithelial cells. Using immunofluorescence labeling of endogenous proteins, they provide a careful characterization of Par3-Par6-aPKC and Patj-Pals1-Crb3a localization relative to tight junctions. They find that each protein localizes in the near vicinity of the tight junction, in a clustered organization. Through pairwise colocalization analyses, they observe significant separation of polarity proteins that are generally considered to be part of the same molecular complex based on biochemical assays. Specifically, PAR3 is not associated with aPKC or PAR6, and CRB3a colocalizes poorly with all other polarity proteins, raising interesting questions for future analyses.The imaging performed in this study is skillful and beautifully presented and, achieving an isotropic resolution of about 80nm, is impressive. However, because of this great gain in resolution compared to other studies of similar components, the major concern of all three reviewers is that the process of fixation and immunostaining may introduce artefacts that are causing the segregated dots to appear. Variable antibody quality and insufficient validation of antibody specificity raise additional concerns about the observed patterns of localization.Essential Revisions:The issues with fixation and antibodies would be best addressed by generating endogenously tagged, CRISPR-knockin GFP versions of some of the proteins studied, which is technically straightforward to perform these days, and would allow the conclusions to be drawn with full confidence. As such experiments would constitute major experimental revisions and will take some time to complete, we recommend confirming the most novel and possibly contentious protein pairs: for example PALS1 versus aPKC, and perhaps CRB3a compared to others.Major points:Related to fixation and immunofluorescence:1. All of the results depend on antibody quality, specificity and antigenicity, but no antibody validation provided (with the exception of PATJ). If one primary antibody is less specific than the others, the colocalisation data will be heavily skewed, appearing not to be colocalised. Perhaps this can explain why Crb3a fails to colocalise with the other proteins? Validating the results with a second primary antibody or an endogenously tagged GFP-fusion protein would alleviate this concern.2. Antibodies are in the range of 10-15nm in length, so with an isotropic resolution of 80 nm, this might have to be taken into account when using primary and secondary antibodies to reveal proteins. In particular, monoclonal versus polyclonal antibodies might have differing effects on localisation precision. I would very much appreciate some comments or thoughts on the fact that polarity proteins were revealed using antibodies.3. The authors use rather high concentrations of detergent (1% SDS or 1% Triton X-100) for permeabilisation according to their protocols. Are they not worried that this might affect tissue integrity and protein distribution? Couldn't these conditions cause segregated clusters to appear?Other major points:1. Figure 1. It would be good to show and demonstrate that Occludin and ZO-1 labeling are completely interchangeable in terms of localisation precision.2. Figure 3. I do understand the authors' rational for analysing the localisation in the orientation  that reveals the largest distance, but it would be good to nonetheless show the other orientation for completeness (maybe as supplementary).3. Figure 5. The final figure shows overlapping PATJ and aPKC at TJs, with the aPKC lost from TJs upon knockdown of PATJ. This result seems to suggest that the CRB-PALS-PATJ complex must directly bind/recruit aPKC to TJs, which seems to conflict with their earlier conclusions that these complexes are completely separated in punctae and do not truly overlap at the nanoscale.4. Figure 5. The super-resolution characterization of actin organization is not as extensive or convincing as the description of polarity protein localization. A closer examination of actin organization relative to PATJ and aPKC at junctional, apical, and villi positions would strengthen the findings in Figure 5.5. I am also a bit confused by the analysis presented in Figure 5 with regards to colocalisation of components with apical F-actin structures and the deduction from these and the EM data that some components, aPKC/Par6, localise to 'the first row of' microvilli near junctions whilst PALS1-PATJ localise near the base of said microvilli. How would localisation to the apical plasma membrane outside of or within microvilli be restricted to only the ones near junctions? There is not only F-actin in microvilli but also all over and near the apical cortex, so what distinguished the ability of aPKC/PAR6 to bind to actin in microvilli? The PATJ knock-down results are interesting, and I agree suggestive of some interaction between the complexes and actin organisation. But without further analyses as to what other components might be affected in their localisation in this situation, it is hard to judge whether the effect on actin is a direct or rather indirect one, so I am unsure as to what these images add without more in depth follow-up.6. In some cases the number of biological replicates is small. Only one mouse sample was used, and the quantifications of junctions are performed across just 1 or 2 cell culture replicates . Therefore, the data reflect the variability across junctions  but they don't reflect the variability across biological replicates. This also means the p-value in Figure 5 was calculated using n=number of junctions rather than n=experimental replicates, which would be a more appropriate comparison of means. Quantifying the data across 3 biological replicates to show the variability across experiments would greatly strengthen the results and conclusions.7. The authors rightly point out where their study fits within what has been attempted by other labs previously in order to understand and dissect apical polarity complex function. They clearly define interesting aspects, such as PALS1-PATJ and aPKC-PAR6 forming independent clusters, and the lack of colocalisation and thus maybe association with Crumbs3. In contrast to the last sentence statement of their abstract 'This organization at the nanoscale level significantly simplifies our view on how polarity proteins could cooperate to drive and maintain cell polarity.' I cannot yet see what these results simplify about our understanding of apical polarity complexes and even more so what the authors' new model is of how the complexes work. This needs to be spelt out more clearly, please.Drosophila epithelia , and should maybe be introduced already at an earlier point of the paper. Furthermore, papers by Wang et al. and Dickinson et al., that also analyse PAR complex clustering should be cited and mentioned in the introduction/discussion . Cortical forces and CDC-42 control clustering of PAR proteins for Caenorhabditis elegans embryonic polarization. Nature Cell Biology, 19(8), 988-995. http://doi.org/10.1016/S0960-9822(99)80042-6; Dickinson, D. J., Schwager, F., Pintard, L., Gotta, M., and Goldstein, B. (2017). A Single-Cell Biochemistry Approach Reveals PAR Complex Dynamics during Cell Polarization, 1-42. http://doi.org/10.1016/j.devcel.2017.07.024).8. I would also point out that, in part, other studies have pointed in the same direction. The recent paper by the Ludwig lab  points in part in a similar direction, identifying a vertebrate 'marginal zone' similar to the one already known from invertebrate epithelia, as well as identifying basal to this an apical and basal tight junction area. Furthermore, as the authors themselves discuss in the discussion, the 'splitting away' of Par3 has been observed in  Essential Revisions:The issues with fixation and antibodies would be best addressed by generating endogenously tagged, CRISPR-knockin GFP versions of some of the proteins studied, which is technically straightforward to perform these days, and would allow the conclusions to be drawn with full confidence. As such experiments would constitute major experimental revisions and will take some time to complete, we recommend confirming the most novel and possibly contentious protein pairs: for example PALS1 versus aPKC, and perhaps CRB3a compared to others.We agree with the reviewers’ comment that labeling proteins with antibodies can lead to artifacts. As this was reported many times, such artifacts can be due to a lack of specificity of antibodies, or because of fixation or permeabilization. The use of CRISPR knock-in fluorescent versions of the proteins studied can alleviate these artifacts.We tried to prepare PAR6β, PATJ, and CRB3A with fluorescent tags; PALS1 and aPKC could not be used because they come in several isoforms . The CRISPR knock-in of the fluorescent protein Citrine in PAR6β in Caco-2 cells was successful. The GFPtagged version of PATJ we tried resulted in the diffused localization of the protein in the cytoplasm in Caco-2 cells, suggesting artifacts of tagging. Our lab has tried to obtain CRISPR knock-ins of CRB3A in Caco-2 cells without, so far, any success. CRISPR knock-ins are powerful tools, but we can witness as many other labs working on human cell lines that successful knock-ins are strongly protein-dependent and lead to many failures.To best address the reviewers’ concerns, we designed the following experiment using the CRISPR knock-in PAR6β-Citrine in Caco-2 cells. Since we observed the organization of polarity proteins in fixed samples thanks to superresolution microscopy, the imaging of the fluorescent tag had to be achieved on a live sample with superresolution microscopy to allow for comparison. Moreover, to ensure that we observe the same organization in fixed and live samples, we first imaged with STED the fluorescent protein Citrine in live Caco-2 cells, then fixed, permeabilized, and labeled the cells with antibodies and finally came back to the exact same cells observed in live and imaged them with STED, this time thanks to an AlexaFluor-568 labeled antibody. We find the same clusters of PAR6β when observed live with Citrine or fixed with AlexaFluor-568. This comparison rules out the possibility that the observed clusters are artifactual and confirms our results using superresolution.How we modified our manuscript:Examples of these experiments are reported in Figure 3A and we described these results in the main text .Major points:Related to fixation and immunofluorescence:1. All of the results depend on antibody quality, specificity and antigenicity, but no antibody validation provided (with the exception of PATJ). If one primary antibody is less specific than the others, the colocalisation data will be heavily skewed, appearing not to be colocalised. Perhaps this can explain why Crb3a fails to colocalise with the other proteins? Validating the results with a second primary antibody or an endogenously tagged GFP-fusion protein would alleviate this concern.Of all the antibodies we used, only the characterization of the rat antibody -CRB3A has not been published.How we modified our manuscript:A section of the supplementary material is dedicated to the validation of the -CRB3A antibody.2. Antibodies are in the range of 10-15nm in length, so with an isotropic resolution of 80 nm, this might have to be taken into account when using primary and secondary antibodies to reveal proteins. In particular, monoclonal versus polyclonal antibodies might have differing effects on localisation precision. I would very much appreciate some comments or thoughts on the fact that polarity proteins were revealed using antibodies.We agree with the reviewers’ comment that when trying to describe how polarity proteins are organized, it is important to consider how antibodies are located with respect to their epitope, how their size can affect the localizations we witness, and how the spatial resolution of the microscope can affect the perceived distribution of polarity proteins.How we modified our manuscript:To discuss these points, which question how polarity proteins are organized, we added the following text in the discussion part of our manuscript .“We have shown that our observations were not the consequence of methodological artifacts, as live imaging on PAR6β-Citrine CRISPR-Cas9 knock-in and immunolabelling gave very similar results on the same cells, and permeabilization with or without detergent allowed us to observe the same organization of PATJ . Nevertheless, because of the size of antibodies, the combination of primary and secondary antibodies displaces the fluorescent signal from the epitope it targets by about 25nm. Can this displacement alter our conclusions regarding protein organization? The fluorescent densities observed with STED are very likely to be the result of the clustering of tens of fluorophores, therefore tens of antibodies recognizing tens of polarity proteins. Because these densities are created by tens of antibodies, we speculate that these antibodies are oriented isotropically in space. As a result, the use of antibodies might slightly increase the size of polarity protein clusters, but the fluorescence created by antibodies in images is well centered on polarity protein clusters, making antibodies a tool of choice for this study.”versus polyclonal antibodies here, the epitopes used to obtain polyclonal antibodies are very small in fully folded proteins, domains encompassing epitopes being in the order of a few nanometers. Therefore, while monoclonal antibodies will always reach the same location in a protein, polyclonal will reach positions that will differ by a few nanometers at most. We believe this effect is negligible in comparison to the use of primary and secondary antibodies.To keep the discussion on monoclonal 3. The authors use rather high concentrations of detergent (1% SDS or 1% Triton X-100) for permeabilisation according to their protocols. Are they not worried that this might affect tissue integrity and protein distribution? Couldn't these conditions cause segregated clusters to appear?We agree with the reviewers’ comment that detergent used for permeabilization could lead to artifacts. To the best of our knowledge, the concentrations used in our manuscript are commonly reported in the field, and we adjusted the concentration to their minimum to obtain reproducible results. To verify that the detergents we use are not responsible for the clustered organization of proteins, we created a protocol where permeabilization with detergents is replaced by freezing and thawing cells once in liquid nitrogen, which partially cracks cell membranes. We already used this protocol in the initial version of the manuscript Figure 4 when deciphering the location of PATJ with electron microscopy. The morphology of cells was preserved in these conditions.Using this protocol for STED microscopy on PATJ, we could not witness any significant difference with the images obtained with detergent. It is to be noted that only labeling with PATJ worked in these conditions. Freezing and thawing cells was not sufficient to detect any antibody of the other polarity proteins.The results come in addition to what we observed with the CRISPR knock-in (See our response above 2.) and confirm that our observations are not due to artifacts.How we modified our manuscript:Examples of these experiments are reported in Figure 3B and we described these results in the main text .Other major points:1. Figure 1. It would be good to show and demonstrate that Occludin and ZO-1 labeling are completely interchangeable in terms of localisation precision.We agree with the reviewers’ comment that this is important to show. We find that the maxima of Occludin and ZO-1 along the apico-basal axis are distant by at most 15 nm, which is sufficient to be used as interchangeable references.How we modified our manuscript:The revised Figures 1 and 2 and their supplemental Figure 1 now display examples of the co-labeling of Occludin and ZO-1 as well as their analyses.2. Figure 3. I do understand the authors' rational for analysing the localisation in the orientation  that reveals the largest distance, but it would be good to nonetheless show the other orientation for completeness (maybe as supplementary).We agree with the reviewers’ comment that showing both orientations will strengthen our analysis.How we modified our manuscript:The corresponding figure  comes with a supplementary figure displaying perpendicular orientations.3. Figure 5. The final figure shows overlapping PATJ and aPKC at TJs, with the aPKC lost from TJs upon knockdown of PATJ. This result seems to suggest that the CRB-PALS-PATJ complex must directly bind/recruit aPKC to TJs, which seems to conflict with their earlier conclusions that these complexes are completely separated in punctae and do not truly overlap at the nanoscale.We now have deleted this part of the figure because we think it is too preliminary and does not bring enough to the current manuscript. A much more extended study would be required to tackle how polarity proteins recruit one another and how they affect the cytoskeleton.4. Figure 5. The super-resolution characterization of actin organization is not as extensive or convincing as the description of polarity protein localization. A closer examination of actin organization relative to PATJ and aPKC at junctional, apical, and villi positions would strengthen the findings in Figure 5.We agree with the reviewers’ comment that the characterization of actin organization was not as extensive as the description of polarity protein localization and that the data and analysis provided were insufficient. We devised to describe as precisely as possible how PATJ, PALS1, aPKC, and PAR6β proteins are organized versus actin. As actin is almost entirely organized into microvilli in the region, we described how these proteins are organized versus microvilli. This analysis allowed us to find that PATJ and PALS1 are mostly located at the base and in between microvilli whereas aPKC and PAR6β are mostly located all along microvilli.How we modified our manuscript:These experiments are reported in the new Figure 6 and we described these results in the main text .5. I am also a bit confused by the analysis presented in Figure 5 with regards to colocalisation of components with apical F-actin structures and the deduction from these and the EM data that some components, aPKC/Par6, localise to 'the first row of' microvilli near junctions whilst PALS1-PATJ localise near the base of said microvilli. How would localisation to the apical plasma membrane outside of or within microvilli be restricted to only the ones near junctions? There is not only F-actin in microvilli but also all over and near the apical cortex, so what distinguished the ability of aPKC/PAR6 to bind to actin in microvilli? The PATJ knock-down results are interesting, and I agree suggestive of some interaction between the complexes and actin organisation. But without further analyses as to what other components might be affected in their localisation in this situation, it is hard to judge whether the effect on actin is a direct or rather indirect one, so I am unsure as to what these images add without more in depth follow-up.We believe these concerns are now answered in the new version of Figure 6 as we provide much stronger data and analysis to prove these affirmations. We are also surprised that polarity proteins cluster at the base or in the microvilli closest to the junction. “Why are these microvilli special” remains an observation that will interesting to dissect in future studies.6. In some cases the number of biological replicates is small. Only one mouse sample was used, and the quantifications of junctions are performed across just 1 or 2 cell culture replicates . Therefore, the data reflect the variability across junctions  but they don't reflect the variability across biological replicates. This also means the p-value in Figure 5 was calculated using n=number of junctions rather than n=experimental replicates, which would be a more appropriate comparison of means. Quantifying the data across 3 biological replicates to show the variability across experiments would greatly strengthen the results and conclusions.We agree with the reviewers’ comment that this needed to be addressed. Data presented in the main figures 1, 2, 4, and 6 are now the result of three replicates, either from cell culture replicates or subjects. The proximity analysis presented in Figure supplement 1 of Figure 4 is obtained from single cell culture as these are very difficult experiments that are confirmations of results obtained in Figures 1 and 2. The results obtained with electron tomography are also obtained with one cell culture replicates, although on several cells.7. The authors rightly point out where their study fits within what has been attempted by other labs previously in order to understand and dissect apical polarity complex function. They clearly define interesting aspects, such as PALS1-PATJ and aPKC-PAR6 forming independent clusters, and the lack of colocalisation and thus maybe association with Crumbs3. In contrast to the last sentence statement of their abstract 'This organization at the nanoscale level significantly simplifies our view on how polarity proteins could cooperate to drive and maintain cell polarity.' I cannot yet see what these results simplify about our understanding of apical polarity complexes and even more so what the authors' new model is of how the complexes work. This needs to be spelt out more clearly, please.The main question many previous studies focused on was “what interactions between polarity proteins and other proteins exist”. If we focus only on proteins observed in this manuscript, these studies concluded that PAR6β can interact with PAR3, aPKC, and CRB3A, aPKC can interact with PAR3 and PAR6, PALS1 can interact with CRB3A, PATJ, and PAR6. This has led to multiple reviews and articles, including ours, presenting polarity proteins as a block of many interacting proteins, without any hierarchy in what could be the most relevant interactions.We show in our manuscript that of these 8 potential interactions between key proteins, only two, namely the pairs PAR6β/aPKC and PATJ/PALS1, are likely to be described as prominent or stable, discarding the other 6 as stable in this area of the cell.How we modified our manuscript in an attempt to better explain these facts:– We rephrase the last sentence of the abstract: “Of the numerous potential interactions identified between polarity proteins, only PALS1-PATJ and aPKC-PAR6β are spatially relevant in the junctional area of mature epithelial cells, simplifying our view of how polarity proteins could cooperate to drive and maintain cell polarity.”– We revised the paragraph addressing this point in the previous version of the manuscript .Caenorhabditis elegans embryonic polarization. Nature Cell Biology, 19(8), 988-995. http://doi.org/10.1016/S0960-9822(99)80042-6; Dickinson, D. J., Schwager, F., Pintard, L., Gotta, M., and Goldstein, B. (2017). A Single-Cell Biochemistry Approach Reveals PAR Complex Dynamics during Cell Polarization, 1-42. http://doi.org/10.1016/j.devcel.2017.07.024).8. I would also point out that, in part, other studies have pointed in the same direction. The recent paper by the Ludwig lab  points in part in a similar direction, identifying a vertebrate 'marginal zone' similar to the one already known from invertebrate epithelia, as well as identifying basal to this an apical and basal tight junction area. Furthermore, as the authors themselves discuss in the discussion, the 'splitting away' of Par3 has been observed in Drosophila epithelia , and should maybe be introduced already at an earlier point of the paper. Furthermore, papers by Wang et al. and Dickinson et al., that also analyse PAR complex clustering should be cited and mentioned in the introduction/discussion . Cortical forces and CDC-42 control clustering of PAR proteins for We agree with the reviewers’ comment that these references are needed. They are now included in the manuscript.Drosophila epithelia earlier in the text (or others) as it felt more straightforward to us to introduce results in the literature directly relating to our models, then describe our findings and finally focus on what relates to our findings in other model systems, potentially pointing at conserved mechanisms.We did not move the reference to