dataesr/scientific-paragraphs-categorization huggingface dataset:
Enrichment for gene sets related to brain disorders To further address how disruption of the CCC signaling may be related to ASD, we intersected the 165 genes with lists of genes associated to ASD, schizophrenia (SCZ), intellectual disability (ID), bipolar disorder (BD), and attention deficit hyperactivity disorder (ADHD) and tested for significance of over representation (Figure  7b ). Among the genes overlapping with ASD lists, CNTNAP2, LRRC4C, NLGN2, NLGN3, NRXN1, NRXN2, NRXN3, and PLXNA4 are considered as ASD risk genes with "high confidence" in SFARI database, due to multiple studies linked them to Autism and other neurodevelopmental disorders 13,46-50. Also, significant enrichment was found with bipolar disorder (odds ratio (OR)=2. 7) and ADHD (OR=2. 4) gene lists, similar to previous findings  12, 51.
The data indicate that most of the randomly paired L-R had correlation coefficients near "0" but the interacting pairs identified by CellChat showed significantly larger correlations, using spatial data across all brains or the subset in defined layers (Figure  4Sa/b ). The correlation shifted to both positive and negative perhaps because CellChat interaction includes soluble agonists, antagonist, and co-stimulatory and coinhibitory co-factors. (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted October 15, 2021. ; https://doi. org/10. 1101/2021. 10. 15. 464577 doi: bioRxiv preprint 14.
Discussion ASD is a neurodevelopmental disorder involving genetic, epigenetic, and environmental factors through various processes  3, 58. At the cellular level, cell-cell signaling is critical. Using CellChat, we have performed a CCC analysis using previously published single cell datasets  23. The availability of data from both ASD and controls allowed us for the first time to systematically compare cell-cell communications in ASD and control brains. Results from our multiple-level CCC analyses suggest that several cell-cell signaling pathways are potentially disrupted in the ASD brains, involving mostly neurons, but glial cells also contribute. Our findings are in line with a large body of literature that support the importance of cell-cell interactions in neurodevelopment and brain disorders.
For example, studies suggested that genes dysregulated in ASD were enriched in cell-cell communication, nervous system development, or cilia regulators or components terms  10, 11. Our computational analysis is based on the combined expression of ligands, receptors, and their cofactors in any pair of cell types. It is conceptually different from differential expression analysis based on individual genes. This can be seen in the results shown in Figure  5 , where many of the disrupted signaling pathways contain only few or no DEGs that were determined from the same datasets. Also different from many other cell-cell communication studies, we applied a combination of global approaches and sample-by-sample approaches. While the former potentially has higher sensitivity (due to more cells included), the latter likely has better precision as it requires independent statistical supports from many samples.
Genes in these differential CCC pathways were enriched for molecular pathways and cellular processes that play important roles in neurogenesis and neuron functions, as well as risk genes for ASD. For reproducibility, we performed a subsampling analysis by randomly selecting 50% of the cells (repeating 10 times). Analyzing the data by our global approach showed that the changes in L-R strengths between ASD and controls were highly correlated to what were obtained using all cells (Figure  S8 ). In addition, we tested two other software (NATMI and CellPhoneDB)  29, 31  and obtained significantly overlapping but not identical results, with the difference largely due to differences in both the collection of ligand-receptors and how interactions are quantified, as discussed by others 33. To put our results in a broader literature content, below we discuss key signaling pathways with strong literature support for their association with ASD or abnormal neurodevelopment: NRXN, FGF, CNTN, NCAM, NGL, NRG and PTN pathways (Figure  5 ).
The neurexin pathway is composed of NRXN1, NRXN2, NRXN3, NLGN1, NLGN2 and NLGN3 genes, all of which except NLGN1 are identified as "high confident" ASD genes in SFARI database. Many studies have also linked these genes closely to ASD and other neurodevelopmental disorders. Wiśniowiecka-Kowalnik et al. 59  described three CNV within NRXN1 in subjects from three families showing ASD, anxiety and depression, developmental delay, and speech delay. Furthermore, Pinto et al. 60  detected an excess of exonic NRNX1 CNVs in 996 cases compared to 4,964 controls. However, Wang et al. 47  did not find an association between NRXN1 and ASD, suggesting heterogeneity of the disorder, but their study associated NRXN2 and NRXN3 to ASD and specific alleles to ASD severity. On the receptor side, a nonsense variant in NLGN2 was reported in a patient with severe anxiety, obsessive-compulsive behaviors, autism, short attention span, global develop-mental delays, hyperphagia, obesity, macrocephaly and dysmorphic features 48.
However, no data suggests a link between NGL-3 gene and ASD. The LAR complex, on the other hand, is part of (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. The copyright holder for this preprint this version posted October 15, 2021. ; https://doi. org/10. 1101/2021. 10. 15. 464577 doi: bioRxiv preprint 17 the RPTPs large family known to be involved in broader functions in the central nervous system  86. Even though, other RPTP complexes have been associated with ASD 60 , no direct information exists for LAR complex. Neuregulin (NRG) complexes, NRG1/2/3/4, play important roles in many neurological disorders such as brain trauma, spinal cord injury, and SCZ  87, 88. All isoforms contain a fragment encoding epidermal growth factor (EGF)like domain that enables the interaction with ErbB receptor tyrosine kinases  89.
Another limitation of the study, as in other cell-cell interaction studies based on scRNA-seq or snRNA-seq, lies in the lack of spatial information, as juxtracrine and paracrine signaling operate from 0 to 200 μm  104. The application of spatial transcriptomic data will help, but that technology by itself is currently not able to provide single cell resolution data. However, one study applied spatial transcriptomics to investigate alterations occurring in tissue domains within a 100-μm diameter around amyloid plaques in Alzheimer's disease and found alterations consistent with disease pathogenesis 105. As the technology advances and more data become available for ASD, we should be able to improve the methods in this study, as discussed in a recent review  104. In addition, since the snRNA-seq data were from postmortem brains, our findings by themselves cannot distinguish if the CCC signaling disruption contributes directly to ASD pathogenesis or is a result of the brain's response to ASD conditions.
Performance analysis The signal to noise ratio (SNR) at the receiver side is the ratio between the received signal P r and the noise N 0. Assuming a channel of bandwidth B, the SNR γ can be expressed as: γ = P r N 0 B. ( 1 ) If E b and E s are the signal's energy per bit and the signal's energy per symbol, for an M-ary signal with modulation order k = log 2 (M) bits per symbol, the SNR per symbol γ s can be expressed as: γ s = E s N 0 = k • E b N 0 = k • γ b , ( 2 ) where γ s is SNR per symbol, and γ b is SNR per bit. The use of cyclic prefix in the OFDM data transmission system can lead to power loss. If N is FFT size or the total number of subcarriers and Ncp is the number of symbols in the cyclic prefix, the power loss a g can be expressed as: a g = N N + Ncp. ( 3 ) Using the power loss a g , we can calculate the average SNR per symbol γ s as: γ s = a g • γ s.
By comparing the source symbol with the estimated source symbol, we can evaluate our simulation's performance in terms of PER and compare them with the theoretical calculation. Fig. 2  shows the system model to evaluate theoretical performance analysis. The theoretical performance analysis using the Rayleigh frequency-selective channel is not very realistic to model V2X communication. However, it allows for a simple analytical performance comparison of the OFDM technique with different modulation schemes, i. e. , BPSK, QPSK, 16-QAM, and 64-QAM of the 802. 11p/bd standard. To build a more realistic simulation for V2X communication, we extend the simulation model based on Fig. 2  using MATLAB. The transmitter and receiver part is built using the WLAN Toolbox, and the frequency-selective Rayleigh fading channel is replaced by the V2V channel model proposed by TGbd.
Acknowledgements This research is supported by the  Indonesia Endowment Fund for Education (LPDP) , with grant number:  PRJ-3752/LPDP. 3/2016 , and the  Directorate General of Higher Education (DITJEN DIKTI) of Ministry of Education and Culture of Republic of Indonesia. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.
Acknowledgements. We thank the anonymous referee for providing useful suggestions and comments, which helped to improve the manuscript. This research made use of Astropy (http://www. astropy. org),a communitydeveloped core Python package for Astronomy (Astropy Collaboration 2013, 2018), and related packages such as Spectral-Cube and Reproject, as well as of  APLpy (Robitaille & Bressert 2012 ), an open-source plotting package for Python. Early work on the project described here was supported by the  European Research Council (ERC)  via the  ERC Advanced Investigator Grant   247078  ( GLOSTAR: A Global View of Star Formation in the Milky Way ) awarded to K. M. M, and a  Marie Curie International Outgoing Fellowship  (No. 275596 ) awarded to A. B. , while H. B. acknowledges support from the  Horizon 2020 Framework Programme  via the  ERC Consolidator Grant   CSF-648505.
Further understanding of the terminal differentiation program could aid the understanding of kidney disease development. While single cell RNA sequencing (scRNA-seq) has improved our understanding of kidney development in mice and humans  9, 10, 13, 14  , it provides limited information of TFs, which are usually expressed at low levels. Equally difficult is to understand how genes are regulated from scRNA-seq data alone. Chromatin state profiles, on the other hand, determine the gene expression potential and can pinpoint the availability of TF binding sites. Together with gene expression, open chromatin profiles can define the gene regulatory logic, which is the fundamental element of cell identity. However, there is a scarcity of open chromatin information by Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) or chromatin immunoprecipitation (ChIP) data by ChIP-seq related to kidney postnatal development.
In addition, epigenetic changes observed in bulk analyses mostly represent changes in cell composition, rather than cell type-specific changes  15  , making it challenging to interpret bulk ATAC-seq data. The publicly available mouse kidney ATAC-seq data contain limited number of cells from adult tissues, and therefore do not provide insight into chromatin changes during development  16, 17. Here, we generate a single cell open chromatin and corresponding expression survey for the developing and adult mouse kidney, which is available to the community via searchable websites (susztaklab. com/developing_adult_kidney/snATAC/ for snATAC-seq data, susztaklab. com/developing_adult_kidney/scRNA/ for scRNA-seq data, and susztaklab. com/developing_adult_kidney/igv/ for IGV view of peak tracks). Using this atlas, we produce an epigenome-based classification of developing and mature cells and defined cell type-specific regulatory networks.
Results Single cell accessible chromatin landscape of the developing and adult mouse kidneys. To characterize the accessible chromatin landscape of the developing and adult mouse kidneys at single cell resolution, we performed single nuclei ATAC-seq (snATAC-seq) on kidneys of mice on postnatal day 0 (P0), at 3 and 8 weeks (P21 and P56) of age (Fig. 1a ). In mice at birth, the nephron progenitors are still present and nephrons are formed until around day 21  13, 18. In parallel, we also performed bulk (whole kidney) ATAC-seq analysis at matched developmental stages. Following sequencing, we aggregated all high-quality mapped reads in each sample irrespective of barcode. The combined snATAC-seq dataset from all samples showed the expected insert size periodicity (Supplementary Fig. 1a ) with a strong enrichment of signal at Transcription Start Sites (TSS), indicating high data quality (Supplementary Fig.
1b ). The snATAC-seq data showed high concordance with the bulk ATAC data (Spearman correlation coefficient >0. 84 between matched stages, see "Methods" section, Supplementary Fig. 1c ). We next revealed cell type annotations from the open chromatin information. After conducting stringent filtering of the number of unique fragments, promoter ratio and mitochondria ratio (see "Methods" section, Supplementary Fig. 2a ), we kept 28,316 cells across the samples (Fig. 1b ). Cells were then clustered using SnapATAC  19  , which binned the whole genome into 5 kb regions to address the sparsity of the data (see "Methods" section). Prior to clustering, we used Harmony  20  , an iterative batch correction method, to correct for variability across samples (Supplementary Fig. 2b ). Using batch-corrected low dimensional embeddings, we retained 13 clusters, all of which had consistent representation across the number of peaks, samples and read depth profiles (Figs.
Correcting gene expression matrices for ambient RNA with SoupX 24 yielded very similar clusters. Average gene expression was highly correlated before and after ambient RNA correction (Supplementary Figs. 6  and 7 , see "Methods" section). DE genes for cell types retrieved after correction for ambient RNA are accessible in Supplementary Data 3. To compare the consistency between cluster assignment in the snATAC-seq data and the scRNA-seq data, we next pooled each snATAC cluster and derived gene activity scores for the top 3000 highly variable genes and computed the Pearson's correlation coefficient between each snATAC cluster and scRNA cluster (see "Methods" section). This analysis indicated good concordance between the two datasets (correlation for P0 samples see Fig. 1g , for adult samples see Supplementary Fig.
8 ). While the correlation between gene expression and inferred gene activity score was high, we noted some differences in cell proportions, which was likely related to the sample preparation-induced cell drop-out (Supplementary Figs. 2d  and 5a ). Consistent with previous observations that single cell preparations are more biased towards immune cells than single nuclear preparations  25  , we noted that the immune cell repertoire was limited in the snATAC-seq dataset; on the other hand, stromal cells were better captured by the nuclear preparation. Further sub-analysis of the stromal cluster indicated a heterogenous stromal cell population in both P0 and adult samples (Supplementary Figs. 9 and 10). Our data are in keeping with recent findings highlighting considerable heterogeneity in the developing kidney interstitium  26.
DE genes for stroma subclusters are shown in Supplementary Data 4. Additional experimental validation is needed to confirm and further delineate stromal cells. To allow the interactive use of this dataset by the community, we not only made the raw data available but also the processed dataset via our searchable website (susztaklab. com/developin-g_adult_kidney/snATAC/ for snATAC-seq data, susztaklab. com/ developing_adult_kidney/scRNA/ for scRNA-seq data, and susztaklab. com/developing_adult_kidney/igv/ for IGV view of peak tracks). Supplementary Fig. 11  shows an example of the Ace2 locus in the website. Characterization of the cell type-specific regulatory landscape. To characterize different genomic elements captured by snATAC-seq data, we first stratified the genome into promoters, exons, 5′ and 3′ untranslated regions, introns, and distal regions using the GENCODE annotation  27  (see "Methods" section).
Other genes, however, had accessible chromatin at their TSS in multiple cell types. For example, Umod, a marker gene for loop of Henle, showed accessible chromatin at TSS in multiple tubule cell types (Supplementary Fig. 12e ). Rather than with its TSS, cell type-specific chromatin accessibility of Umod strongly correlated with an upstream peak, which is likely an enhancer region, as indicated by the H3K27Ac ChIP-seq signal. In addition, we noticed enrichment of intronic regions and distal elements (Supplementary Fig. 13a, b ) in cell type-specific DAPs, indicating their role in cell type-specific gene regulation. These observations motivated us to study cis-regulatory elements using the snATAC-seq data and scRNA-seq data. We reasoned that a subset of the cell type-specific regulatory elements should correlate with cell type-specific gene expression.
Inspired by Zhu et al. 30  , we aligned DAPs and DEGs from our snATACseq and scRNA-seq datasets, and inferred the putative regulatory peak-gene pairs by their proximity (see "Methods" section). Such cis-regulatory elements predictions were confirmed by comparing with cis-regulatory elements inferred previously  31  , as we recapitulated roughly 20% of elements from their analysis. In addition, our analysis was able to identify several known enhancers such as for Six2 and Slc6a18  31, 32  (Supplementary Fig. 13c ). To quantify the contribution of cis-regulatory elements, we analyzed peak co-accessibility patterns using Cicero  33. Using a heuristic co-accessible score 0. 4 as a cutoff, we identified 232,380 and 206,701 cis-regulatory element links in the P0 and adult data, respectively. However, it is worth-noting that the interaction between genomic elements is complex and is not limited to coaccessible peaks.
Given the complex interaction between genomic regions, we next looked into identifying key TFs that occupy the cell typespecific open chromatin regions. Until now, information on cell type-specific TFs in the kidney has been scarce. Therefore, we performed motif enrichment analysis on the cell type-specific       open chromatin regions using HOMER (see "Methods" section)  34. The full list of cell type-specific TF binding motifs is shown in Supplementary Data 6. Since several TFs have identical or similar binding sequences, we next correlated motif enrichment with scRNA-seq TF expression. Using this combined motif enrichment and gene expression approach, we have defined the mouse kidney cell type-specific TF landscape. Examples include Six2 and Hoxc9 in nephron progenitors, Wt1 and Mafb in podocytes, Hnf4a, Ppara, and Bhle41 in proximal tubules, Esrrb and Foxa1 in loop of Henle, Vdr in distal convoluted tubule, Elf5 in principal cells, Tcfcp2l1 in intercalated cells, Erg and Sox17 in endothelial cells, Spi1 and Batf in immune cells, and Twist1 and Nr2f2 in stromal cells (Fig.
2a  and Supplementary Fig. 14 ). In order to study the putative target genes of TFs, we examined TF regulon activity using Single-Cell rEgulatory Network Inference and Clustering (SCENIC)  35. SCENIC was designed to reveal TF-centered gene co-expression networks. By inferring a gene correlation network followed by motif-based filtration, SCENIC keeps only potential direct targets of each TF as modules (regulons). The activity of each regulon in each cell was quantified and then binarized to "on" or "off" based on activity distribution across cells (see "Methods" section). SCENIC was also able to conduct clustering based on the regulon states of each cell. SCENIC results (Fig. 2b-d ) indicated strong enrichment in Trps1, Hnf1b, Maf, Hnf1a, and Hnf4a regulon activity in proximal tubules, Hmga2, Hoxc6, Hoxd11, Meox1, Six2, Tcf4, and Uncx in nephron progenitors, Esrrg, and Ppargc1a in loop of Henle, Hmgb3 in proliferating cells and Foxc1, Foxc2, Foxd1, Lef1, and Mafb in podocytes, respectively.
Corresponding snATAC-seq tracks for these predicted target genes along with the TF motif are depicted in Supplementary Fig. 15b. By comparing the number of cell type-specific TFs reported by HOMER and SCENIC to that from DEGs in RNA expression data, it became evident that our integrative cis-regulatory analysis with snATAC-seq and scRNA-seq datasets yielded significant benefits in discovering the TF-regulatory network over analyzing transcript data alone (Supplementary Fig. 16a, b ). The regulatory trajectory of nephron progenitor differentiation. All cells in the body differentiate from the same genetic template. Cell type-specific chromatin opening and closing events associated with TF binding changes set up the cell type-specific regulatory landscape resulting in cell type specification and development.
We found that closing of open chromatin regions was the predominant event during the nephron progenitor differentiation (Supplementary Fig. 12d ). We then evaluated the cellular differentiation trajectory in the snATAC-seq and scRNA-seq datasets (see "Methods" section). We identified the multiple nephron progenitor sub-groups (Fig. 3a, b ), which will need to be carefully mapped to prior gene expression-driven and anatomical locationdriven nephron progenitor sub-classification. Consistently, across both data modalities, we identified that the podocyte precursors differentiated early from the nephron progenitor pool (Fig. 3a, b ). The tubule cell trajectory was more complex with a shared intermediate stage and later differentiation into proximal tubules and distal tubules/loop of Henle (Fig.
3a, b  and Supplementary Fig. 17a-c ). We also integrated snATAC-seq and scRNA-seq data to obtain a single trajectory (see "Methods" section). The cell types in this dataset were correctly mapped and the trajectory resembled the path observed in individual analyses (Supplementary Fig. 17e-g ). The robustness of developmental trajectories was further supported by RNA velocity analysis using Velocyto 37 (Supplementary Fig. 17d ), and by comparing with previous human and mouse kidney developmental studies  9, 13, 14. Building on both the SCENIC-generated gene regulatory network and the robust differentiation trajectories of the snATAC-seq and scRNA-seq datasets, we next aimed to understand chromatin dynamics, identify TFs and driver pathways for cell type specification. To this end, we first determined variation in chromatin accessibility along the three differentiation trajectories using ChromVAR  38.
To this end, we investigated TFs and target genes differentially expressed over scRNA-seq pseudotime (Supplementary Data 10). We noticed a good concordance of time-dependent changes of TF and predicted target gene expression along with motif enrichment, including the lineages for podocytes (e. g. , Foxc2, Foxl1, Mafb, Magi2, Nphs1, Nphs2, Plat, Synpo, Thsd7a, Wt1, and Zbtb7c), proximal tubule (e. g. , Ace2, Atp1a1, Dab2, Hnf1a, Hnf4a, Hsd17b2, Lrp2, Maf, Slc12a3, Slc22a12, Slc34a1, and Wnt9b), loop of Henle (e. g. ,Cyfip2, Cytip, Esrrb, Esrrg, Irx1, Irx2, Mecom, Pla2g4a, Pou3f3, Ppargc1a, Stat3, Sytl2, Tfap2b, Thsd4, and Umod), as well as for both proximal tubule and loop of Henle (e. g. , Bhlhe40, Hnf1b, and Tmprss2), respectively (Fig. 3c  and Supplementary Fig. 18b ). Most interestingly, we noticed two distinct patterns of how gene expression was related to chromatin accessibility.