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While several other organizations have done seemingly promising Java work in the SLP area, none of them was interested in taking over the spec lead position to progress a JSR on it. The IETF protocol SDP specifies messages that describe multi-media sessions and are included within other protocol messages as payload. This provides consistency by reducing duplication of common interfaces and classes among these JSRs. This will provide consistency by reducing duplication of common interfaces and classes among these JSRs.
Read the Spec Lead's explanation of the withdrawal. Workspace Versioning and Configuration Management. Workspace Versioning and Configuration Management provides client support for creating and manipulating sets of version-controlled files and web resources. The Work Area Service allows J2EE developers to set properties as application context that is implicitly attached to and made available anywhere during the processing of remote requests.
JSR had shown slow progress for several years and had not generated significant industry interest or participation. Internationalization Service for J2EE. The Internationalization Service enables distributed localization within Enterprise Java applications by transparently propagating and managing localization information within relevant J2EE application components. JSR had been idle for several years and the existing draft had not kept pace with changes in J2EE.
None of the EG members objected. JavaServer Pages TM 2. The Enterprise JavaBeans 2. This specification will build on servlet specification version 2. Web Services Security Assertions. Java TM Stream Assembly. TheJava TM Stream Assembly API specifies classes and interfaces for the creation, management, and processing of broadcast and interactive stream multiplexes.
JPC would have defined a loosely coupled, event based process component model that would simplify the development of composable, customizable services. It includes portlets, portlet container behavior, portlet windows, events, persistent storage and portlet services. We have now reached a point where we feel that we have a mutually acceptable new combined JSR proposal, which we now wish to seek endorsement of from the existing supporters of JSR and The JSR proposes a set of medium-level utilities that provide functionality commonly needed in concurrent programs.
Java TM Portlet Specification. This specification would have defined a set of APIs for Portal computing addressing the areas of aggregation, personalization, presentation and security. They reached a point where they felt that they had a mutually acceptable new combined JSR proposal, which they then sought endorsement of from the existing supporters of JSR and To enable interoperability between Portlets and Portals, this specification will define a set of APIs for Portal computing addressing the areas of aggregation, personalization, presentation and security.
The purpose of this specification is to define an optional package that provides standard access from J2ME to web services. Since version 1. In accordance with JCP 2. The service provider interfaces are the same except that they will then be directly specified in the Platform JSR. A metadata facility for the Java TM Programming Language would allow classes, interfaces, fields, and methods to be marked as having particular attributes.
J2SE TM 5. Defines an optional package that will facilitate the emergence of the market for the development of compelling games on mobile phones. An Optional Package that enables developers to write mobile location-based applications for resource-limited devices. It enables SIP applications to be executed in memory limited terminals, especially targeting to mobile phones. Web services security WS-Security has become the defacto standard to secure web services messages.
Lack of a standard in Java to write to these APIs, hasn't caused any interoperability or integration issues across vendor platforms. So, this JSR was withdrawn. Presence is a generic and protocol-agnostic API for Presence, providing a standard portable and secure interface to control, manage and manipulate Presence information between Presence clients and servers.
A protocol-agnostic API for Instant Messaging, this provides a standard portable and secure interface to control, manage and manipulate instant messages between clients through the use of presence servers.
Possible technologies for inclusion include programmable shading and advanced rendering techniques. Due to changes in the nature of the project development, this effort was discontinued in , a few months after the JSR was filed.
JSR was left open as a place-holder, but it no longer makes sense to leave it open. They were actively developing the 1. This release was a much more modest undertaking than was originally planned, and all new API changes are being designed with public input.
This defines an optional code package that standardizes application event tracking on a mobile device and the submission of these event records to an event-tracking server via a standard protocol. There had not been much progress on this JSR since quite some time. The draft was floated for Expert Group review but there was no response even after several reminders. The access to J2EE-components would be fully abstracted and encapsulated. This was intended to allow software developers to rapidly develop external Service Provider-type applications to interrogate the location and status of a user's mobile device.
This JSR will define a J2ME profile targeting embedded networked devices that wish to support a Java runtime environment, but that do not have graphical display capabilities. This JSR seeks to define a standard interface by which authentication modules may be integrated with containers and such that these modules may establish the authentication identities used by containers. Active Latest Stage:.
It is expected that this format can achieve considerable size savings over compressed JAR files. This JSR proposes four new Java TM programming language features: enumerations, autoboxing, enhanced for loops and static import. This will principally consist of increasing certain class file size limits and adding support for split verification.
Unicode Supplementary Character Support. The proposed specification will define a mechanism to support Supplementary Characters as defined in the Unicode 3. JAXP 1. Process Definition for Java TM. Enabling J2ME TM applications to handle multi-media and web content can give developers and users a seamless and integrated user environment on mobile phones and wireless devices. This specification defines a protocol agnostic messaging API for composing, sending and receiving short messages and multimedia messages.
Effort to define another layer of the J2ME Web Service stack, implementing the 'observable' behavior of a choreographed Web Service on the Device, relative to the message exchange requiring support.
Java Community Process SM version 2. Enterprise JavaBeans TM 3. This specification seeks to improve Java application access to SQL data stores by the provision of ease-of-development focused features and improvements at both the utility and API level. JAXB 2. This JSR proposes additional functionality while retaining ease of development as a key goal. Scripting for the Java TM Platform. The specification will describe mechanisms allowing scripting language programs to access information developed in the Java Platform and allowing scripting language pages to be used in Java Server-side Applications.
Mobile Operational Management. Create a predictable management environment for mobile devices capable of installing, executing, profiling, updating, and removing Java TM and associated native components in the J2ME TM Connected Device Configuration. This JSR defines an extension of the J2EE platform for the purposes of remotely monitoring and managing the software on mobile devices.
Advanced Multimedia Supplements. Defines core infrastructure APIs for heterogeneous data access that supports common application design patterns and supports higher-level tools and frameworks. Concurrency Utilities for Java EE provides a simple, standardized API for using concurrency from application components without compromising container integrity while still preserving the Java EE platform's fundamental benefits.
Work Manager for Application Servers. A work manager API providing for execution of concurrent work items within managed environments. Mobile Internationalization API. The Groovy Programming Language. Groovy is an agile, dynamic programming language for the Java Virtual Machine.
Groovy includes features found in Python, Ruby, and Smalltalk, but uses syntax similar to the Java programming language. Java TM Data Objects 2. JavaServer TM Pages 2. The purpose of JSP 2. JDM 2. Mobile Service Architecture. This JSR creates a mobile service architecture and platform definition for the high volume wireless handsets continuing the work started in JSR and enhancing the definition with new technologies. Mobile Service Architecture 2. Standard for defining and using complex pricing data and business rules, enabling integration, allowing business differentiating extensions.
Addresses usage-based business model needs, for communications and entertainment industries and utilities. This JSR is an update to the 1. Its scope goes beyond a JCP maintenance release, but is short of a new feature release. This JSR creates a mobile telephony API and platform definition which utilizes common telephony features and is small and simple to suite to high volume devices with limited resources.
It will improve usability of existing features and add new functionality. JSR specified changes to javax. The changes were distinct from other changes to javax. It defines generic sensor functionality optimized for the resource-constrained devices like mobile devices. Contactless Communication API. This specification will define J2ME Optional Packages for contactless communication, one package for bi-directional communication and the other for accessing read-only information.
The Mobile User Interface Customization API provides a way to query and modify the user interface customization properties of a mobile device or platform. The purpose of this JSR is to define an API that enables communication between mobile devices in a peer-to-peer ad-hoc network environment. Defines new tags and generated Javadoc document representation aimed to increase readability, information richness, and make the Javadoc more approachable to developers learning and using the APIs.
This was conveyed to the EG at earlier instances as well and nobody objected. Clients do not have to be Java applications, but can be. JSR specified the javax. The Fault Management API provides for the monitoring and management of alarms within an information or a telecommunications network.
This JSR defines an interface to an Order Management component, with specific extensions for activation and work order management use-cases. This specification aims to standardize a basic framework in Java for utilizing the Web services constraints and capabilities. The standardization of policy-based metadata will continue as part of the Service Component Architecture SCA , which will eventually provide Java-based language bindings as part of separate JSRs.
The purpose of this JSR is to define an API to access and manage the message boxes of the mobile device and their content. Provide an API to allow the processing of JSR annotations metadata ; this will require modeling elements of the Java TM programming language as well as processing-specific functionality.
Mobile Information Device Profile 3. This JSR will specify the 3rd generation Mobile Information Device Profile, expanding upon the functionality in all areas as well as improving interoperability across devices. The BeanShell Scripting Language. This specification will standardize BeanShell, a Java syntax compatible scripting language for the Java platform.
This JSR specifies Java packages for modeling and working with standard measures known as units. Defines a standard mechanism for associating design-time information with JavaServer TM Faces components. These included 86, putative enhancers and 10, genes Fig. To investigate how the putative enhancers may direct cell-type-specific gene expression, we further classified them into 38 modules, by applying non-negative matrix factorization to the matrix of normalized chromatin accessibility across the RNA—ATAC joint cell clusters Supplementary Table The putative enhancers in each module had a similar pattern of chromatin accessibility across cell clusters to the expression of putative target genes Fig.
This analysis revealed a large group of 12, putative enhancers that were linked to 6, genes expressed at a higher level in all neuronal cell clusters than in all non-neuronal cell types module M1 Fig. It also uncovered modules of enhancer—gene pairs that were active in a more restricted manner modules M2—M38 Fig. Genes are shown for each putative enhancer separately. UMI, unique molecular identifier.
Genes associated with module M1 are preferentially expressed in both glutamatergic and GABAergic neurons, but not in glial cell types Fig. CTCF is a ubiquitously expressed DNA-binding protein with a well-established role in transcriptional insulation and chromatin organization CTCF has also been shown to promote neurogenesis by binding to promoters and enhancers of the proto-cadherin alpha gene cluster and facilitating enhancer—promoter contacts 40 , We found putative enhancers with one or more CTCF-binding motifs linked to 2, genes that were broadly expressed in both inhibitory and excitatory neurons Fig.
Neurogenesis in the adult mouse brain is spatially restricted to the subgranular zone SGZ in the dentate gyrus of the hippocampus where excitatory neurons are generated, and the subventricular zone SVZ of the lateral ventricles that give rise to GABAergic neurons The NIPCs that are involved in adult neurogenesis 42 , 43 could be identified as the cells lined up in trajectories between radial glia-like cells and neuroblasts in both brain regions Fig.
We predicted potential transcription factors that contribute to NIPCs as well as other cell types by integrating RNA expression and motif enrichment analysis using the Taiji pipeline 45 Fig. An active enhancer previously validated by mouse transgenics 48 was predicted to target the nearby Trappc9 gene, which encodes a protein that is involved in nerve growth factor-induced neuronal differentiation 49 Fig.
Heat map showing the results of linkage disequilibrium score regression 52 analysis of the noncoding variants associated with the indicated traits or diseases in the human orthologues of cCREs identified from 43 subclasses of mouse cerebral cell. Genome-wide association studies GWASs have identified genetic variants that are associated with many neurological diseases and traits Supplementary Table 25 , but interpreting the results has been challenging because most variants are located in noncoding parts of the genome that often lack functional annotations To test whether our maps of cCREs in different mouse brain cell types could assist the interpretation of noncoding risk variants of neurological diseases, we identified orthologues of the mouse cCREs in the human genome by performing reciprocal homology searches 51 Methods.
For this analysis, we found that for Supporting the function of these human orthologues of the mouse brain cCREs, We performed linkage disequilibrium score regression 52 analysis and found significant associations between 20 neurological traits Supplementary Table 25 and the open chromatin landscapes in one or more subclasses of the brain cells we identified Fig.
In particular, we observed widespread and strong enrichment of genetic variants linked to psychiatric and cognitive traits such as major depressive disorder, bipolar disorder and schizophrenia SCZ within accessible cCREs across various neuronal cell types Fig.
Other neurological traits—such as attention deficit hyperactivity disorder and autism spectrum disorder—were associated with specific neuronal cell types in cerebral nuclei and the hippocampus Fig. Risk variants for schizophrenia were not only enriched in cCREs in all excitatory neurons, but also enriched in certain inhibitory neuron cell types 53 Fig. The strongest enrichment of heritability for bipolar disorder was found in cCREs that mapped in the excitatory neurons from the isocortex Fig.
Risk variants of tobacco use disorder showed significant enrichment in the cell types from the striatum—a cerebral nucleus previously implicated in addiction 54 Fig. Understanding the cellular and molecular basis of brain circuits is one of the grand challenges of the twenty-first century 55 , In-depth knowledge of the transcriptional regulatory program in brain cells would not only improve our understanding of the molecular inner workings of neurons and non-neuronal cells, but could also shed light on the pathogenesis of a spectrum of neuropsychiatric diseases Here, we report a comprehensive profiling of chromatin accessibility at single-cell resolution in the mouse cerebrum.
The chromatin accessibility maps of , cCREs, probed in , nuclei and cell types, span several cerebral cortical areas and subcortical structures. Taking advantage of our high-resolution brain dissections, we examined the regional specificity in chromatin accessibility of cell types in the mouse cerebrum and showed that most brain cell types exhibit strong regional specificity.
Regions were pooled from 3—31 of the same sex to obtain enough nuclei for snATAC-seq for each biological replica, and two biological replicas were processed for each dissection region. Using a SH Sony , 20 nuclei were sorted per well into eight well plates total of wells, 30, nuclei total, 15, nuclei per sample containing If processing two samples per day, tagmentation was performed with different sets of barcodes in separate 96 well plates. After tagmentation nuclei from individual plates were pooled together.
Next, After PCR, all wells were combined. Indexing primers and sequencing primers are in Supplementary Table The nuclei indexing version v1 or v2 used for each library is indicated in Supplementary Table To generate snATAC-seq libraries we used initially an indexing scheme as previously described version 1 22 , Here, 16 p5 and 24 p7 indexes were combined to generate an array of indexes for tagmentation and 16 i5 as well as 48 i7 indexes were combined for an array of PCR indexes.
Owing to this library design, it is required to sequence all four indexes to assign a read to a specific nucleus with long reads and a constant base sequence for both indices reads between i and p barcodes. To generate libraries compatible with other sequencers and not requiring spike-in libraries or custom sequencing recipes, we modified the library scheme Version 2. For this, we used individual indices for T7 and combined with one T5 with a universal index sequence for tagmentation for a total of tagmentation indexes.
For PCR, we used different i5 indexes and combined with a universal i7 primer index sequence. Tagmentation indexes were 10 bp and PCR indexes bp long. Nuclei were counted using a haemocytometer, and 15, nuclei were used for tagmentation. Final libraries were quantified using a Qubit fluorimeter Life Technologies and the nucleosomal pattern was verified using a Tapestation High Sensitivity D, Agilent.
After demultiplexing, the Index2 cell index was transferred to the read name, in order to keep the same fastq format for downstream processing. Paired-end sequencing reads were demultiplexed and the cell index transferred to the read name. Sequencing reads were aligned to mm10 reference genome using bwa Reads were sorted on the basis of the cell barcode in the read name.
The method for calculating enrichment at TSS was adapted from previously described. The max of the smoothed profile was taken as the TSS enrichment. Peaks were called using MACS2 scores for aggregate accessibility profiles on each sample. Next, cell-by-peak count matrices were calculated and used as input, with default parameters.
The lower component contained most embedded doublet types, and the other component contained majority of neo-typic doublets collision between nuclei from different clusters.
This value suggested that the nuclei have same chance of belonging to both classes. For round 1 clustering, we clustered and finally merged single nuclei to three main cell classes: non-neurons, GABAergic neurons, and glutamatergic neurons. For each main cell class, we performed another round of clustering to identify major cell subclasses. Last, for each subclass, we performed a third round of clustering to find cell types.
Detailed description for every step is as follows. Second, potential barcode collisions were also removed for individual datasets. First, we calculated a cell-by-bin matrix at 5-kb resolution for every dataset independently and subsequently merged the matrices.
Second, we converted the cell-by-bin count matrix to a binary matrix. Fourth, we focused on bins on chromosomes 1—19, X and Y. SnapATAC applies a nonlinear dimensionality reduction method called diffusion maps, which is highly robust to noise and perturbation However, the computational time of the diffusion maps algorithm scales exponentially with the increase of number of cells. We projected the remaining N — K cells onto the low-dimensional embedding as learned from the landmarks to create a joint embedding space for all cells.
Having more than , single nuclei at the beginning, we decided to apply this strategy on round 1 and 2 clustering. A total of 10, cells were sampled as landmarks and the remaining query cells were projected onto the diffusion maps embedding of landmarks.
Later for the round 3 clustering, diffusion map embeddings were directly calculated from all nuclei. For each round of clustering, we selected the top 10—20 eigenvectors that captured most of the variance.
Using the selected significant eigenvectors, we next construct a k -nearest neighbour graph. Each cell is a node and the k -nearest neighbours of each cell were identified according to the Euclidian distance and edges were drawn between neighbours in the graph. For each resolution value, we tested whether there was clear separation between nuclei. To do so, we generated a cell-by-cell consensus matrix in which each element represents the fraction of observations two nuclei are part of the same cluster.
A perfectly stable matrix would consist entirely of zeros and ones, meaning that two nuclei either cluster together or not in every iteration. The relative stability of the consensus matrices can be used to infer the optimal resolution. To this end, we generated a consensus matrix based on rounds of Leiden clustering with randomized starting seed s. The entries of M s are defined as follows:. Then, the consensus matrix C is defined as the normalized sum of all connectivity matrices of all the perturbed D s.
The entry i,j in the consensus matrix is the number of times single nucleus i and j were clustered together divided by the total number of times they were selected together.
We examined the cumulative distribution function CDF curve and calculated proportion of ambiguous clustering PAC score to quantify stability at each resolution. The resolution with a local minimum of the PAC scores denotes the parameters for the optimal clusters. In the case these were multiple local minimal PACs, we picked the one with higher resolution. Another measurement is dispersion coefficient, which reflects the dispersion ranges from 0 to 1 of the consensus matrix M from the value 0.
The closer to 1 is the dispersion coefficient, the more perfect is consensus matrix, and thus the more stable is the clustering. In a perfect consensus matrix, all entries are 0 or 1, meaning that all connectivity matrices are identical.
The dispersion coefficient is defined as:. For visualization, we applied UMAP Then, we performed cell clustering on 25, nuclei for 5 cell types: astrocytes; subventricular zone radial glia-like cells; neuronal intermediate progenitor cells; neuroblasts OBNBL ; and inhibitory neurons in olfactory OBGA1.
First, we calculated for cCRE the median accessibility per cluster and used this value as cluster centroid. Next, we calculated the coefficient of variant for the cluster centroid of each element across major cell types. Finally, we only kept variable elements with coefficient of variants that were larger than 1. We used the set of variable features defined above to calculate a correlation-based distance matrix. Next, we performed linkage hierarchical clustering using the R package pvclust v.
The confidence for each branch of the tree was estimated by the bootstrap resampling approach with 1, rounds. The specificity score is defined as Jensen—Shannon divergence, which measures the similarity between two probability distributions. For each cell type, the contribution of different brain regions was first calculated. Then, we compared this distribution with the contribution of brain regions calculated from all sampled cells.
For every cell cluster, we combined all properly paired reads to generate a pseudo-bulk ATAC-seq dataset for individual biological replicates. In addition, we generated two pseudo-replicates that comprise half of the reads from each biological replicate. We called peak for each of the four datasets and a pool of both replicates independently. Peak calling was performed on the Tn5-corrected single-base insertions using the MACS2 score 30 with these parameters:—shift —extsize —nomodel—call-summits—SPMR -q 0.
Finally, we extended peak summits by bp on either side to a final width of bp for merging and downstream analysis. We found that when cell population varied in read depth or the number of nuclei, the MACS2 score varied proportionally owing to the nature of the Poisson distribution test in MACS2 scores Ideally, we would perform a reads-in-peaks normalization, but in practice, this type of normalization was not possible because we did not know how many peaks we would get.
We filtered reproducible peaks by choosing a score-per-million cut-off of 2 to filter reproducible peaks. Lastly, because snATAC-seq data are very sparse, we selected only elements that were identified as open chromatin in a significant fraction of the cells in each cluster.
To this end, we first randomly selected the same number of non-DHS regions approximately , elements from the genome as background and calculated the fraction of nuclei for each cell type that showed a signal at these sites. Accessibility of cCREs in individual clusters was quantified by counting the fragments in individual clusters normalized by read depth CPM. The gene activity score were used for integrative analysis with scRNA-seq. For better visualization, we smoothed the gene activity score to 50 nearest neighbour nuclei using Markov affinity-based graph imputation of cells MAGIC To directly compare our single-nucleus chromatin accessibility-derived cell clusters with the single-cell transcriptomics defined taxonomy of the mouse brain 2 , we first used the snATAC-seq data to impute RNA expression levels gene activity scores according to the chromatin accessibility of gene promoter and gene body as previously described We performed integrative analysis with scRNA-seq using Seurat 3.
We randomly selected nuclei and used all nuclei for cell cluster with fewer than nuclei from each cell cluster for integrative analysis. We first generated a Seurat object in R by using previously calculated gene activity scores, diffusion map embeddings and cell cluster labels from snATAC-seq. Then, variable genes were identified from scRNA-seq and used for identifying anchors between these two modalities.
To quantify the similarity between cell clusters from two modalities, we calculated an overlapping score as the sum of the minimum proportion of cells or nuclei in each cluster that overlapped within each co-embedding cluster 5. Cluster overlaps varied from 0 to 1 and were visualized as a heat map with snATAC-seq clusters in rows and scRNA-seq clusters in columns.
We found strong correspondence between the two modalities, which was evidenced by co-embedding of both transcriptomic T-type and chromatin accessibility A-type cells in the same RNA—ATAC joint clusters Extended Data Fig. We used non-negative matrix factorization 76 to group cCREs into cis -regulatory modules on the basis of their relative accessibility across major clusters.
The basis matrix defines module related accessible cCREs, and the coefficient matrix defines the cell cluster components and their weights in each module. The key issue to decompose the occupancy profile matrix was to find a reasonable value for the rank R that is, the number of modules. Several criteria have been proposed to decide whether a given rank R decomposes the occupancy profile matrix into meaningful clusters.
Average values were calculated from times for non-negative matrix factorization runs at each given rank with random seed, which will ensure the measurements are stable.
Next, we used the coefficient matrix to associate modules with distinct cell clusters. In the coefficient matrix, each row represents a module and each column represents a cell cluster. The values in the matrix indicate the weights of clusters in their corresponding module.
The coefficient matrix was then scaled by column cluster from 0 to 1. In addition, we associated each module with accessible elements using the basis matrix. For each element and each module, we derived a basis coefficient score, which represents the accessible signal contributed by all cluster in the defined module. The feature score ranges from 0 to 1.
A high feature score means that a distinct element is specifically associated with a specific module. We designed the full model as. For each set of testing, between subtypes or between regions in cell type, we kept only cCREs that overlapped with peaks identified in corresponding cell types.
A likelihood ratio test was then used to determine whether the full model including cell cluster or region membership provided a significantly better fit of the data than the reduced model. The log 2 -transfomed fold change is used for two-group comparison, for multiple groups, we calculated a Jensen—Shannon divergence-based specificity score previously described 22 to better assign differential cCREs to cell type or brain region.
The fraction of accessibility of each cluster f was first calculated for each i th site. We normalized these scores by multiplying by corresponding scaling factors, which are considering different overall complexity across groups.
To do so, median number of sites accessible c in individual cells for each cluster was calculated and followed with log 10 -transforming. Then, we took the ratio of the average of c across all clusters over the median accessibility in each cluster as scaling factor. These corrected fraction of accessibility for each cCRE was then converted to probability by scaling by groups.
Then, we calculated Jensen—Shannon divergence between two probability distributions. For example, the probability distribution for the first cCRE as d 1, to test whether this cCRE is specific in group 1, we assumed another probability distribution:.
To find a reasonable cut-off to determine restricted or general cCREs, we consider JSS scores from all cCREs that are not identified as differential accessible from likelihood ratio test as a background distribution, and JSS scores from cCREs that passed our likelihood ratio test threshold and had positive values to be true positives.
Finally, the differential cCREs could be aligned to several cell types or brain regions based on the JSS score, we named the one can be assigned to only one type or region as region-specific or cell-type-specific cCREs. To validate the regional specificity of cell types, we took advantage of the spatially mapped quantified ISH expression from ABA 44 in five matched major brain structures, isocortex, olfactory areas, hippocampal formation HPF , striatum STR , pallidum.
For each cell type, we calculated the regional specificity score see previous section: regional specificity of cell types on the basis of the relative contribution from five brain regions estimated from snATAC-seq datasets, and also a coefficient of variation based on averaged normalized ISH signals of cell-type-specific marker genes.
For each cell-type-specific gene, we calculated the PCC between cell composition in five brain structures and spatial expression levels across the five brain structures derived from ISH.
Because the astrocyte subtypes identified in our study were not resolved in scRNA-seq studies, we identified subtype-specific genes for astrocyte subtypes using chromatin accessibility from snATAC-seq using a likelihood ratio test.
The cell-type-specific genes were filtered by FDR less than 0. Then, we calculated the fraction of overlap between spatially mapped ISH genes from different brain structures and genes with astrocyte subtype-specific accessibility. To find an optimal co-accessibility threshold for each cluster, we generated a random shuffled cCRE-by-cell matrix as background and identified co-accessible regions from this shuffled matrix.
We fitted the distribution of co-accessibility scores from random shuffled background into a normal distribution model by using the R package fitdistrplus Next, we assigned co-accessibility pairs to three groups: proximal-to-proximal, distal-to-distal, and distal-to-proximal. In this study, we focus only on distal-to-proximal pairs. We also generated a set of background pairs by randomly selecting regions from different chromosomes and shuffling of cluster labels.
We used the Taiji pipeline 45 to identify candidate driver transcription factors in cell clusters. In brief, for each cell type cluster, we constructed the transcription factor regulatory network by scanning transcription factor motifs at the accessible chromatin regions and linking them to the nearest genes.
The network is directed with edges from transcription factors to target genes. The weights of the edges were calculated by the relative accessibility of the promoters of the source transcription factors. We then used the personalized PageRank algorithm to rank the transcription factors in the network. The output of Taiji pipeline is transcription-factor-by-cell type matrix with PageRank scores.
From the output matrix, we calculated coefficient of variation across cell types. To identify driver transcription factors, we used following criteria: 1 transcription factors have FDR less than 0. We performed both de novo and known motif enrichment analysis using Homer v. Randomly selected background regions are used for motif discovery.
To identify motif enriched in different cell types or brain regions, we use variable cCREs as input and invariable cCREs as background. Gene Ontology biological process was used for annotations. Next, we reciprocal lifted the elements back to mm10 and only kept the regions that mapped to original loci. We further removed converted regions with lengths greater than 1 kb.
We prepared summary statistics to the standard format for linkage disequilibrium score regression. We used homologous sequences for each major cell types as a binary annotation, and the superset of all candidate regulatory peaks as the background control. CTCF-binding sites from the cortex and olfactory bulb were used in this study. No statistical methods were used to predetermine sample sizes.
There was no randomization of the samples, and investigators were not blinded to the specimens being investigated. However, the clustering of single nuclei on the basis of chromatin accessibility was performed in an unbiased manner, and cell types were assigned after clustering.
Low-quality nuclei and potential barcode collisions were excluded from downstream analysis as outlined above. Further information on research design is available in the Nature Research Reporting Summary linked to this paper. Paxinos, G. The Rat Nervous System 4th edn Zeisel, A. Molecular architecture of the mouse nervous system.
Cell , — Saunders, A. Molecular diversity and specializations among the cells of the adult mouse brain. Tasic, B. Shared and distinct transcriptomic cell types across neocortical areas.
Nature , 72—78 Hodge, R. Conserved cell types with divergent features in human versus mouse cortex. Nature , 61—68 Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Moffitt, J. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science , eaau Eng, C. Nature , — Herculano-Houzel, S. Cellular scaling rules for rodent brains. Natl Acad. USA , — Harris, K. The neocortical circuit: themes and variations.
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