dataesr/scientific-paragraphs-categorization huggingface dataset:
Terminal differentiation of proximal tubule and loop of Henle cells was strongly linked to nuclear receptors that regulate metabolism, such as Esrra and Ppara in proximal tubules and Esrra and Ppargc1a in the loop of Henle segment, once more indicating the critical role of metabolism of driving gene expression and differentiation  45. Stromal-to-epithelial communication is critical in the developing and adult kidneys. Previous studies indicated that the survival, renewal, and differentiation of nephron progenitors is largely regulated through its cross-talk with the adjacent ureteric bud  46. To investigate the complex cellular communication network, we used CellPhoneDB  47  to systematically infer potential cell-cell communication in the developing and adult kidney. CellPhoneDB provides a comprehensive database and a statistical method for the identification of ligand-receptor interactions in scRNA-seq data.
Analysis of our scRNA-seq dataset indicated that the number of cell-cell interaction pairs was larger in developing kidney compared to the adult kidney (Fig. 5a ). In the developing kidney, the stroma showed the greatest number of interactions among all cell types, coinciding with the well-known role of epithelial-stromal interactions in driving kidney development  44. Of the identified interactions, many were related to stroma-secreted molecules such as collagen 1, 3, 4, 6, and 14 (Fig. 5b ). Interestingly, the nephron progenitor cluster showed important ligand-receptor interaction between Fgf1, Fgf8 as well as Fgf9 and the corresponding receptor Fgfr1, which is consistent with the well-known role of FGF signaling in kidney development  48. Of the manifold identified interactions in the fetal kidney, stromal interaction and the VEGF-involving interaction remained significant in the adult data set, underscoring the importance of endothelial-to-epithelial communication.
6 ). First, we examined the region around Uncx, for which reproducible association with kidney function was shown in multiple kidney function GWAS  53, 54. Interestingly, the orthologous GWAS locus demonstrated a strong open chromatin region in nephron progenitors but not in any other differentiated cell types of the murine (Fig. 6a ) or human (Fig. 6b ) kidney. Consistently, by examine epigenome data (including H3K27ac, H3K4me1, H3K4me3, and ChIP-seq data across multiple stages), we observed enrichment for H3K27ac, H3Kme1, and Six2 binding at this locus. We also detected Uncx gene expression in the fetal kidney samples, but not in the adult kidney (Fig. 6c ). Next, we analyzed the chromosome 15 GWAS region around Dab2, where we identified open chromatin regions in multiple kidney cells. Dab2 expression, on the other hand, strongly correlated with open distal enhancer regions in proximal tubule cells (Fig.
Discussion In summary, here we present the first cellular resolution open chromatin map with both developing and adult mouse kidney. Using this dataset, we identified key cell type-specific regulatory networks for kidney cells, defined the cellular differentiation trajectory, characterized regulatory dynamics and identified key driving TFs for nephron development, especially for the terminal differentiation of epithelial cells. Furthermore, our results shed light on potential cell types and target genes for genetic variants associated with kidney disease. By performing massively parallel single cell profiling of chromatin state, we were able to define the key regulatory logic for each kidney cell type by investigating cis-regulatory elements, motif enrichment, and TF-target gene co-expression.
These analyses helped the identification of key TFs for different kidney cell types. TF identification is challenging in scRNA-seq data, since the expression of several cell type-specific TFs is low  59. We showed that by extracting motif information, snATAC-seq data provided critical complementary information for TF identification. In our dataset, we also observed that the single cell open chromatin atlas was able to define more distinct cell types even in the developing kidney compared to scRNA-seq analysis. Given the continuous nature of RNA expression, it has been exceedingly difficult to dissect specific cell types in the developing kidney  9, 10, 13. In addition, it has been difficult to resolve the cell type origin of transcripts expressed at low levels in scRNA-seq data. However, in our snATAC-seq data, the low dimensional embedding showed distinct clusters.
snATAC-seq data analysis Data processing and quality control. Raw fastq files were aligned to the mm10 (GRCm38) reference genome and quantified using Cell Ranger ATAC (v. 1. 1. 0). We only kept valid barcodes with number of fragments ranging from 1000 to 40,000 and mitochondria ratio less than 10%. One of the important indicators for ATAC-seq data quality is the fraction of peaks in promoter regions, so we did further filtration based on promoter ratio. We noticed the promoter ratio seemed to follow a binary distribution, with most of cells either having a promoter ratio around 5% (background) or more than 20% (valid cells) (Supplementary Fig. 2a ). We therefore filtered out cells with a promoter ratio <20%. After this stringent quality control, we obtained 11,429 P0 single cells (5993 in P0_batch_1 and 5436 in P0_batch_2) and 16,887 adult single cells (7129 in P56_batch_3, 6397 in P56_batch_4, and 3361 in P21_batch_5).
Preprocessing. Since snATAC-seq data are very sparse, previous methods either conducted peak calling or binarization before clustering. Here, we chose to do binarization instead of peak calling for two reasons: 1) Peak calling is time consuming; 2) Many peaks are cell type-specific, open chromatin regions in rare populations are more likely to be treated as background. After binning fragments into 5 kb windows and removing the fragments not matched to chromosomes or aligned to the mitochondria, we binarized the cell-bin matrix. In order to keep only bins that were informative for clustering, we removed the top 5% most accessible bins and bins overlapping with ENCODE blacklist. The 484,606 remaining bins were used as input for clustering. Dimension reduction, batch effect correction, and clustering.
Clustering was conducted using snapATAC  19  , a single-cell ATAC-seq algorithm scalable to large dataset. Previous benchmarking evaluation has shown that snapATAC was one of the best-performing methods for snATAC-seq clustering  68. Diffusion map was applied as a dimension reduction method using function runDiffusionMaps. To remove batch effect, we used Harmony  20  , in which the low dimensional embeddings obtained from the diffusion map were used as an input. Harmony iteratively pulled batch-specific centroid to cluster centroid until convergence to remove the variability across batches. After batch correction, a graph was constructed using k-Nearest Neighbor (kNN) algorithm with k = 15, which was then used as input for Louvain clustering. We used the first 20 dimensions for the Louvain algorithm.
The number of dimensions was chosen using a method recommended by snapATAC, although we noticed that the clustering results were similar among a series of dimensions from 18 to 30. Cell type annotation. We used a published list of marker genes  9, 21  to annotate kidney cell types. In order to infer gene expression of each cell type, we built a cellgene activity score matrix by integrating all fragments that overlapped with gene transcript. We used GENCODE Mouse release VM16 27 as reference annotation. Comparison with mouse ATAC Atlas dataset. We compared our cell type annotations and QC procedures with previously published sciATAC-seq data  17  with mouse adult kidney samples included. As for the QC procedure, in the published mouse ATAC atlas, cells were kept with at least 500 UMIs and less than 10% mitochondria reads, while in our analysis, we kept cells with at least 1000 UMIs and less than 10% mitochondria reads.
In addition, we also filtered out cells with promoter ratio below 20%. Despite the more stringent QC step, the median number of accessible peaks per cell is comparable, with 4413 in our dataset and 4333 in the Mouse ATAC Atlas dataset. Our dataset contains larger number of cells (28,346 in our dataset vs. 6299 in the mouse atlas dataset), allowing us to improve the cell type resolution of the prior map. For example, we were able to identify immune and CNT cells and separate the PCT and PST segments of the proximal tubules and IC and PC cells in the collecting duct. Thus, our dataset includes 13 annotated cell types while the previous dataset has 6. In addition, the different resolution may also result in the difference in experimental technologies. Among the annotated cells, the cell type proportion is mostly consistent.
By investigating the cell type-specific marker genes reported in the Mouse ATAC Atlas dataset, we observe good consistency between the cell type-specific peaks, indicating a consistent cell type annotation. Peak calling and visualization. Peak calling was conducted for each cell type separately using MACS2  22. We aggregated all fragments obtained from the same cell types to build a pseudo-bulk ATAC data and conducted peak calling with parameters "-nomodel-keep-dup all-shift 100-ext 200-qval 1e-2 -B-SPMR-callsummits". By specifying "-SPMR", MACS2 generated "fragment pileup per million reads" pileup files, which were converted to bigwig format for visualization using UCSC bedGraphToBigWig tool. We also visualized public chromatin ChIP-seq data and RNA-seq data obtained from ENCODE Encyclopedia with the following identifiers: ENCFF338WZP, ENCFF872MVE, ENCFF455HPY, ENCFF049LRQ, ENCFF179NTO, ENCFF071PID, ENCFF746MFH, ENCFF563LOO, ENCFF184AYF, ENCFF107NQP, ENCFF465THI, ENCFF769XWI, and ENCFF591DAX.
The Six2 ChIP-seq data were obtained from the ref. 69 and the WGBS data were obtained from ref. 70. Genomic elements stratification. Mouse mm10 genome annotation files were download from UCSC Table Browser (https://genome. ucsc. edu/cgi-bin/hgTables) using GENCODE VM23. TSS upstream 5 kb regions were included as promoter regions, but the results were similar when using 2 kb upstream regions as promoters. We then studied the number of overlapped regions between open chromatin regions identified from the snATAC-seq and bulk ATAC-seq dataset and genome annotations. Since one open chromatin region could overlap with multiple genomic elements, we defined an order of genomic elements as exon > 5′-UTR > 3′-UTR > intron > promoter > distal elements. To be more specific, if one peak overlapped with both exon and 5′-UTR, the algorithm would count it as an exon-region peak.
Peaks with corrected p values (Benjamini-Hochberg approach) below significance level (0. 05) in all pairwise tests were defined as cell type-specific peaks. This approach was inspired by our previous experience on scRNA-seq data differential expression analysis  21  , and similar to another published snATAC-seq paper  73. In total, we obtained 60,683 highly specific DAPs, which were used for motif enrichment analysis. Motif enrichment analysis. Motif enrichment analysis was conducted using DAPs by HOMER  34  with hypergeometric test for enrichment (one-sided). We used the parameters background = "automatic" and scan. size = 300. We noticed that de novo motif identification only generated few significant results, so we focused on known motifs for our following study. We used the significance level of 0.
05 for Benjamini-Hochberg (BH) corrected p-value to determine the enriched results. The motif enrichment results are provided in Supplementary Data 6. Peak-peak correlation analysis. Peak-peak correlation analysis was conducted using Cicero  33. In order to find developmental stage-specific peak-peak correlations, the analysis was conducted for P0 and adult separately. Cicero uses Graphic Lasso with distance penalty to assess the co-accessibility between different peaks. Cicero analysis was conducted using the run_cicero function with default parameters. A heuristic cutoff of 0. 25 score of co-accessibility was used to determine the connections between two peaks. snATAC-seq trajectory analysis. snATAC-seq trajectory was conducted using Cicero, which extended Monocle3 to the snATAC-seq analysis.
We obtained the preprocessed P0 snATAC-seq cell-peak matrix from snapATAC as input for Cicero and conducted dimension reduction using latent semantic indexing (LSI) and visualized using UMAP. Trajectory graph was built using the function learn_graph. Batch effect was not observed between the two P0 batches, and the trajectory graph was consistent with cell type assignment with clustering analysis (Fig. S17a, b ). In order to study how open chromatin changes are associated with the cell fate decision, we first binned the cells into 15 groups based on their pseudotime and cell type assignment. Next, we studied the DAPs between each group and its ancestral group using the same methods described above. The number of newly open and closed chromatins were reported using pie charts. The exact peak locations are provided in the Supplementary Data 11.
Thus, we used a more stringent score of 0. 4 for the cutoff and retained 232,380 and 206,701 CRE links in the P0 and adult data, respectively. Bulk ATAC sequencing analysis. Bulk ATAC-seq raw fastq files were processed using the end-to-end tool ENCODE ATAC-seq pipeline (Software and Algorithms). This tool provided a standard workflow for ATAC-seq data quality control, adapter removal, alignment, and peak calling. To obtain high quality ATACseq peaks, peak calling results from two biological replicates were compared and only those peaks that were present in both replicates were kept, which were further used to compare with snATAC-seq peaks. Correlation of bulk and single nuclei ATAC sequencing data. snATAC-seq reads were aggregated to a pseudo-bulk data for the comparison purpose. To prevent the effect of sex chromosome and mitochondria chromosome, reads from chromosome X, Y and M were excluded from our analysis.
We used multi-BigwigSummary tool from deeptools  75  to study the correlation between different samples. Specifically, the whole genome was binned into equally sized (10 kb) windows, and the reads in each bin were aggregated, generating a bin-read count vector for each of the sample. The correlation of these vectors was computed as a measure of pairwise similarity between samples. To compare the number of peaks in these two datasets, we used as input the narrowpeak files from the snATAC-seq and bulk ATAC-seq analysis. We filtered out bulk ATAC-seq peaks with q value > 0. 01 to be consistent with the snATACseq setting. Since the snATAC peaks were called after merging different time points, we also took the union set of bulk ATAC-seq peaks from different time points. We then used findoverlap function in GenomicRanges package  76  to identify overlapping peaks.
Comparison between single nuclei ATAC sequencing data and single cell RNA sequencing data. In order to compare the cluster assignment between snATACseq data and scRNA-seq data, we obtained the average gene expression values and peak accessibility in each cluster for P0 and adult samples separately. We next transformed snATAC-seq data by summing up the reads within gene body and 2 kb upstream regions to build gene activity score matrix, as suggested in Seurat  23. Then, we normalized the data and computed the mean expression and mean gene activity scores in each cell type, and calculated z-scores of each gene. Pearson's correlation coefficient was then calculated among top 3000 highly variable genes between snATAC-seq data and scRNA-seq data. We found high concordance between these two datasets in terms of cell type assignment (Fig.
Single cell RNA sequencing data analysis Alignment and quality control. Raw fastq files were aligned to the mm10 (Ensembl GRCm38. 93) reference genome and quantified using CellRanger v3. 1. 0. Seurat R package v3. 0 was used for data quality control, preprocessing, and dimensional reduction analysis. After gene-cell data matrix generation of both P0 and adult datasets, matrices were merged and poor-quality cells with <200 or >3000 expressed genes and mitochondrial gene percentages >50 were excluded, leaving 25,138 P0 and 18,498 adult cells for further analytical processing, respectively (Supplementary Fig. 4a, b ). Pre-processing, batch effect correction, and dimension reduction. Data were normalized by RPM following log transformation and 3000 highly variable genes were selected for scaling and principal component analysis (PCA).
1. 2. 2  35  , which infers the gene regulatory network based on co-expression and DNA motif analysis. The network activity is then analyzed in each cell to identify recurrent cellular states. In short, TFs were identified using GENIE3 and compiled into modules (regulons), which were subsequently subjected to cis-regulatory motif analysis using RcisTarget with two gene-motif rankings: 10 kb around the TSS and 500 bp upstream. Regulon activity in every cell was then scored using AUCell. Finally, binarized regulon activity was projected onto Monocle3-created UMAP trajectories. Ligand-receptor interactions. To assess cellular crosstalk between different cell types, we used the CellPhoneDB repository to infer cell-cell communication networks from single cell transcriptome data  47. We used the Python package Cell-PhoneDB v2.
1. 2 with the database v2. 0. 0 to predict cell type-specific ligand-receptor interactions as per the authors' instructions. Only receptors and ligands expressed in more than 5% of the cells in the specific cluster were considered. Thousand iterations of permutation test were conducted and p values were corrected using FDR methods. Human kidney tissue processing. Ten adult human kidney samples were obtained from the non-tumor tissue of six partial or radical nephrectomy patients from the Hospital of the University of Pennsylvania. Institutional Review Boards at the University of Pennsylvania reviewed this study. This project utilized deidentified kidney biospecimens collected via CHTN (Cooperative Human Tissue Network), and therefore was considered "exempt" by the local IRB. The work was completed in compliance with all relevant ethical regulations.
Acknowledgements This work in the  Susztak lab  is supported by the  NIH   DK076077 ,  DK087635  and  DK105821. M. S. B. is supported by a  German Research Foundation  grant ( BA 6205/2-1 ). We thank the  University of Pennsylvania Diabetes Research Center (DRC)  for the use of the next generation sequencing Core ( P30-DK19525 ). We thank  Dr. Pablo G. Cámara ,  Dr. Wenliang Wang ,  Qin Zhu ,  Feiya Ou ,  Siling Du , and  Lulu Liu  for constructive suggestions that improve the manuscript.
Data availability Raw data, processed data, and metadata from mouse samples have been deposited in GEO with the accession codes GSE157079. The processed data and metadata can also be viewed, analyzed, and downloaded at susztaklab. com/developing_adult_kidney/ snATAC/, susztaklab. com/developing_adult_kidney/scRNA/, and susztaklab. com/ developing_adult_kidney/igv/. The raw data, processed data, and metadata from human samples are available at https://www. diabetesepigenome. org. In this study, we downloaded public data from the following database with accession numbers: GUDMAP (RID:Q-Y4CY); ENCODE (ENCFF338WZP, ENCFF872MVE, ENCFF455HPY, ENCFF049LRQ, ENCFF179NTO, ENCFF071PID, ENCFF746MFH, ENCFF563LOO, ENCFF184AYF, ENCFF107NQP, ENCFF465THI, ENCFF769XWI, ENCFF591DAX); GEO (GSM1051156, GSM3716711, GSM3716714, GSM1586397).
Consequently, digital documents cannot be directly tracked by Blockchains (for which a single bit change in the digital representation corresponds to a new document) and solutions related to visual fingerprinting should also be investigated. Visual fingerprints are compact and salient visual content features computed from the content itself, which can uniquely identify duplicated and/or replicated versions of it in a reference database. It is also referred to as content-based copy detection (CBCD) or near duplicate detection  [9]. In a nutshell, the main contributions of the paper relate to the methodological, experimental and applicative levels, as follows: • methodological level: conception, specification and implementation of an on-chain/offchain load-balancing solution, thus making it possible for the intimately constrained computing, storage and software resources of any Blockchain to be abstractly extended by general-purpose computing machine resources; • experimental level: specifying an experimental testbed and carrying out the underlying experiments for achieving the proof-of-concepts for complex Blockchain application execution (namely visual fingerprinting) on lightweight computing resources (namely, a multiprocessor ARM embedded platform, integrated into a Raspberry Pi); • applicative level: the methodological framework developed in this study makes it possible for a Blockchain of an arbitrarily large number of nodes to be deployed over any combination of computing resources, from cloud servers and PCs to Raspberry Pi; in this way, even low resource devices (Raspberry Pi) can host up to nine nodes executing complex applications (namely visual fingerprinting).
). According to  [10, 11] , the Blockchain is an information storage and distribution technology, which is transparent, secure, and operates without a central control body. By extension, a Blockchain is a database that contains the history of all the exchanges made among its users, since its creation. This database is secure and distributed; it is shared by its different users, without intermediaries, thus allowing any authorized user to check the validity of the information chain. Transactions between users are grouped in blocks. Each block is validated by the nodes of the network (e. g. , the so-called miners or bakers). Once validated, the block is timestamped and added to the Blockchain. Then the transaction becomes visible not only to the receiver, but also to the entire network. Blockchains consider the so-called consensus algorithms to preserve their security side.
Video Fingerprinting Basic Concepts Video fingerprinting methods are meant to identify duplicated and slightly replicated versions of a given video sequence (query) in a reference video database  [17] [18] [19]. They are also commonly referred to as content-based copy detection (CBCD) or near duplicated content detection. Any fingerprinting method encompasses two main steps. Firstly, some visual contentbased features (fingerprints) that are able to concisely and accurately represent that query are computed. Secondly, such features should be matched with the reference database, according to a similarity metric and a threshold pre-set using functional and/or theoretical criteria. Fingerprinting systems are characterized by three main properties. Firstly, the uniqueness property requires for two video sequences with different content to have two different fingerprints (in the sense of the similarity measure and of its underlying threshold).
Secondly, the robustness property requires that any query video sequence and its replicas obtained through different distortions have identical fingerprints (here again, in the sense of the similarity measure and of its underlying threshold). Finally, the scalability property relates to the system's ability to deal with large databases. From the methodological point of view, fingerprinting computation can be achieved by various means  [9] ; we mention here-after only a few: 2D-DWT (Discrete Wavelet Transform) coefficients, 3D-DCT (Discrete Cosine Transform) coefficients, pixel differences between consecutive frames, visual attention regions, quantized block motion vectors, MPEG-7 features, such as the color-layout descriptor, invariant moments of frames edge representation, dominant-edge orientation of keyframes.