dataesr/scientific-paragraphs-categorization huggingface dataset:
The Quality Control (QC) module then assesses the quality of input data and applies user-defined cutoffs to exclude low quality data. The data normalization and denoising module prepares data for further analysis and identifies highly variable features. The dimension reduction and clustering module allow users to apply linear and non-linear dimension reduction approaches and identify clusters of cells. In the next step, various statistical tests can be applied on the identified clusters to highlight important genes. The next module allows marker-based cell type annotation of the clusters. Interactive visualizations are provided by the final visualization module. scRNAseq_KNIME workflow provides extensive and interactive visualization. For example, clusters of cells can be visualized in 2D and 3D interactive plots.
Many other data visualization options including VlnPlot, FeaturePlot, DotPlot, Heatmap, and interactive FeaturePlot are provided. Results of downstream analyses such as identification of differentially expressed genes are also provided through interactive data tables. To showcase the scRNAseq_KNIME workflow and its usage, we applied it to the analysis of the single-cell RNAseq dataset of Peripheral Blood Mononuclear Cells (PBMC), downloaded from 10x Genomics (Figure  1 B ). Overall, the scRNAseq_KNIME workflow implements the main steps required for scRNA-seq data analysis. In addition, each node of scRNAseq_KNIME workflow is interactive, and the users can test multiple parameters (Figure  1A  and supplementary figure  2 ). Importantly, the R nodes can further be customized by modifying the R scripts.
The main features of scRNAseq_KNIME workflow are: -Easy installation and execution: The workflow is developed with the KNIME platform, which comes with extensive documentation for its installation and execution. We further provide detailed documentation for running scRNAseq_KNIME workflow on Mac, Linux and Windows operating systems. -Completeness: scRNAseq_KNIME workflow covers the main steps of scRNA-seq data analysis, allowing users (biologists and bioinformaticians) to run complete scRNA-seq data analysis and generate publication-ready plots. -Possibility of customization: The scRNAseq_KNIME workflow modules are composed of R nodes containing R scripts. Users with expertise in R can customize R nodes by modifying the existing code. -Modularity: Like snakemake, scRNAseq_KNIME workflow modules are independent from each other, meaning a change in one module of workflow does not require processing of previous modules.
Images were acquired once per minute. Exemplary anatomic brain images collected prior to the administration of contrast agent (Ipre) are shown in Figure  6A , left column. After carefully evaluating brain size and structure across genotypes, there are no significant structural differences observed across the three genotypes (NoX Hom , WT, and AllX Hom ). We also examined the proton density across the brain as a measure of overall water content and did not see any differences in water content as a function of genotype (Supplementary Figure  8 ). ROIs were  then auto-segmented, and changes in intensity as a percentage of the pre-contrast image intensity were calculated for six of these: cortex, ventricles, hippocampus, thalamus, corpus callosum, and amygdala. Data were analyzed across all regions (excluding ventricles) in a single omnibus model to examine the influx of contrast over time.
Imaging and quantification Thirteen cortical images per mouse were obtained using a Zeiss LSM 700 confocal microscope and Zeiss Zen software. To ensure consistency, all images were taken at a depth of 6 µm below the surface of the section. A custom ImageJ script was written to step through each collected image, assist the user in drawing lines on the various blood vessels visible from the CD31 channel, and automatically export channel values associated with the coordinates along the line to a CSV file. Data associated with this ImageJ macro pipeline are referred to as localization data. One line was drawn per blood vessel in the image, starting from inside the lumen wall of the blood vessel and extending approximately 6-8 µm away from the vessel. Once the data were collected, they were imported into RStudio for analysis.
Quantified image statistical analysis The localization data were thresholded to exclude line values exceeding 30 pixels or 4. 7 µm. For each channel, a linear mixed model was used to analyze the genotype-by-channel relationship using the lmer command from the R package rstatix. In the following model, experiment represents primarily experimenters, and in channels where only one experiment was done (AQP4x and α-syntrophin), this factor is removed: image represents the slice, animal represents the mouse identification number, DOP stands for date of perfusion, Distance is the number of pixels away from the start of the vessel, and Channel represents either AQP4, CD31, AQP4x, or α-syntrophin. Channel∼Genotype * Distance + (1|experiment)+ (1|animal) + (1|DOP) Post-hoc analyses on perivascular and parenchymal regions were conducted using the Wilcox test.
Co-cultures To study astrocyte/endothelial interactions, the mouse brain endothelioma cell line bEND. 3 was used (American Type Culture Collection CRL-2299, RRID:CVCL_0170) due to the advantages of a homogeneous population of cells and to reduce the number of animals used. These cells were maintained in endothelial media [DMEM media containing 4. 5 g/L D-glucose (Gibco 11960-014) and supplemented with 10% defined heat-inactivated FBS (HyClone, SH30070. 3), 4 mM L-glutamine (Gibco 25030-081), and 1 mM sterile filtered sodium pyruvate at 37 • C and 5% CO 2 ]. This media was exchanged every 3-4 days. To limit genetic drift, cells were never used past passage 30, as per the vendor's recommendation. In astrocyte/endothelial co-cultures, astrocytes were cultured on top of ECs. From a confluent dish bEND.
OIS data processing and analysis Optical imaging data were processed using MATLAB following an analysis pipeline described elsewhere  (Wright et al. , 2017)  with modifications. Briefly, a binary brain mask was created for each mouse by manually tracing the mouse dorsal cortex within the field of view. Background light levels were subtracted, and each pixel's individual time trace was temporally and spatially detrended. Using the changes in reflectance in the 530, 590, and 625 nm LED channels, a modified Beer-Lambert Law was solved to generate fluctuations in oxy-and deoxyhemoglobin concentration, as described previously  (White et al. , 2011). Changes in total hemoglobin concentrations were the sum of changes in oxy-and deoxyhemoglobin concentrations. Global signal regression was performed by regressing the average of all time traces of pixels within the mask from each pixel's time trace.
MRI assessment of contrast agent accumulation in the brain MRI experiments were performed using a 4. 7-T small-animal MR scanner (Agilent/Varian, Santa Clara, CA). Data were collected with a laboratory-built, actively-decoupled transmit/receive coil pair: 7. 5-cm ID volume transmitter coil and 1. 6-cm (OD) surface receiver coil. Mice were placed in a prone position in a customized cradle and anesthetized with isoflurane (1. 2%/O 2 ). Mouse body temperature was controlled at 37. 0 ± 0. 5 • C with a physiologic monitoring unit (Small Animal Instruments, Stony Brook, NY) employing the combination of a warm water pad and warm air blown through the bore of the magnet. T2-weighted (T2W) transaxial anatomic images were collected before and after dynamic contrast-enhanced (DCE) MRI experiments (pre-contrast, Ipre; post-contrast, Ipost) with a 2D fast-spin-echo (fsems) sequence: matrix size, 128 × 128; FOV, 16 × 16 mm 2 ; slice thickness, 0.
MR image processing and segmentation Anatomic MRI images (I pre and I post ) were read into MATLAB, smoothed with a Gaussian filter (Sigma 0. 75), and converted into NIfTI format (. nii). The I pre (aka T2W) images were skull-stripped manually and registered to a published mouse brain atlas  (Dorr et al. , 2008)  using the Advanced Neuroimaging Tools (ANTs)  (Avants et al. , 2014). ROIs auto-segmented with ANTs based on the label map associated with the mouse brain atlas. Typical ROIs were combined in MATLAB as a NIfTI file. Brain ROIs considered in this work include the partial primary somatosensory area (GM, cortex), ventricles, hippocampus, thalamus, corpus callosum (CC), and amygdala (Figure  5 ). The ratio I ratio = 100(%) * (I post -I pre )/I pre was computed in MATLAB. DCE data were zero-padded to 128 × 128 and smoothed with a Gaussian filter (Sigma 0.
75), and the normalized DCE signal at each time point, S j ' (j' = 1, 20), was calculated as S j ' = (S j -S 0 )/S 0 , in which S 0 is the average of the first four-time frames before tail-vein injection of Dotarem R. DCE data (S j ') across each ROI were extracted for subsequent statistical analysis using MATLAB. All MRI images and DCE data ROIs were inspected and confirmed with ITK-SNAP  (Yushkevich et al. , 2006). Anatomic images collected before and after the DCE experiment were compared to ensure that the mouse did not move during the imaging pipeline. Two of the 28 datasets collected were discarded due to motion. that could be construed as a potential conflict of interest.
Acknowledgments We acknowledge the assistance of  Gregory Strout ,  Dr. Sanja Sviben , and  Dr. James Fitzpatrick  at the  Washington University Center for Cellular Imaging (WUCCI ) in ultrastructural imaging studies, which are supported by the  Washington University School of Medicine , the  Children's Discovery Institute of University and St. Louis Children's Hospital  ( CDI-CORE-2015-505  and  CDI-CORE-2019-813 ), the  Foundation for Barnes-Jewish Hospital  ( 3770  and 4642), and the  Intellectual and Developmental Disabilities Research Center  ( P50HD103525 ). MRI experiments were performed in the  Mallinckrodt Institute of Radiology's Small Animal Magnetic Resonance Facility. Finally, we thank  Dr. KyuHo Song  for assistance in performing co-registration of mouse-brain MR images,  Dr.
Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by the  NIH  grants  R01NS102272  and  R01MH116999  (JD),  NIH  grant  R00AG061231 , the  Rockford Draper Early-Career Faculty Development Award  (DS), and ZM-L was supported by the  Bridge to Faculty  fellowship by the  Children's Hospital of Philadelphia.
Characteristics of the super-enhancer landscape in ACC Following the standard pipeline to identify super-enhancers, we generated and analyzed ChIP-seq data of the histone modification marking active enhancers and promoters, histone H3 lysine 27 acetylation (H3K27ac)  [12] , in 15 ACC tumor samples and 2 samples of non-cancer tissue of the adrenal gland (Figure  1a -b and Methods). We identified a total of 3742 superenhancer regions to be active in at least two cancer or noncancer samples. For each super-enhancer region, we calculated a score in all analyzed samples by summing the normalized H3K27ac ChIP-seq signal in the respective region. This score was further used to assign super-enhancers to their gene targets resulting in 2836 regions with gene associations (Methods). Ranking the super-enhancer regions by their average activity score over all ACC samples revealed that super-enhancers with highest scores were largely associated with transcription factors (TFs) and/or oncogenes.
Indeed, the NMF signatures extracted from super-enhancer scores (3,742 regions) of 15 ACC tumors and two non-cancer adrenal grand samples reflected the two molecular subgroups (Fisher's exact test, p-value = 0. 007, Figure  2b ). We further examined whether subgroup specific differences were also present on the expression level of super-enhancer target genes. For this, we applied NMF on trascriptomics data from our cohort and in the TCGA ACC dataset (N = 76) using as input expression values 2,836 genes associated with ACC super-enhancers. The NMF signatures corresponded to the two molecular subgroups both in the TCGA ACC dataset (Fisher's exact test, p-value = 2. 6 • 10 11 , Figure  2c ) and in our cohort (Fisher's exact test, p-value = 0. 03, Supplementary Figure  S1 ). To characterize specific differences in super-enhancer activity between molecular subgroups, we performed differential analysis of super-enhancer scores (Methods).
We identified 76 super-enhancer regions in which the super-enhancer scores significantly differed between the C1A/C1B subgroups after FDR correction (Figure  2d ). In total, the 76 differential regions were associated with 161 genes, 31 of which we identified as TFs and/or oncogenes. Gene expression analysis indicated that the subgroup specific differences were also present on the gene expression level in both our patient cohort, as well as in the external TCGA ACC dataset (Figure  2e , Supplementary Table  4 ).
SE, super-enhancer; FDR, false discovery rate. differences were also present in the expression values of CRC transcription factors, both in our patient cohort and the TCGA ACC dataset (Figure  3d , Supplementary Table  4 ). Although the majority of CRC transcription factors are shared between C1A and C1B, our findings suggest the existence of several genes that may serve as potential master transcriptional regulators of these molecular subgroups. Next, we examined gene copy number status of NR5A1 and other potential drivers of transcriptional programs of C1A and C1B in the two molecular subgroups. We observed statistically significant correlations between molecular subgroups and gene copy numbers of 3 CRC transcription factors: in C1A, a frequent copy number gain of NR5A1 and, laying 55 Mb apart from it on chromosome 9, KLF9; in C1B, a frequent copy number gain of IRF1 (Figure  3e ) (NR5A1, Fisher's exact test qvalue for gain in C1A = 0.
Expression of CRC transcription factors predicts molecular subgroups We validated the connection between CRCs and the molecular subgroups C1A and C1B by analyzing the predictive performance of CRC gene expression values in a binary classification task on the TCGA ACC dataset. The expression level of the seven CRC transcription factors with differential activity scores between the subgroups were used to build a classification model to predict the molecular subgroups C1A and C1B for each sample. We observed a high classification performance in a leaveone-out cross-validation scheme using the seven transcription The super-enhancer landscape reflects molecular subgroups of ACC. a, PCA of all super-enhancer region scores (N=3,742) that were identified in ACC samples (N=16) and samples of non-cancer tissue (N=2).
The samples are labeled according to their sample name, the color reflects the molecular subgroup. b, Output of NMF on all super-enhancer scores (N=3,742) in all patient samples (N=16). The colors indicate the gene expression subgroup. c, Output of NMF on gene expression of super-enhancer targets (N=2836). Gene expression data are taken from the TCGA ACC dataset (N= 76). The colors indicate the molecular subgroups and steroid phenotype. The rows of the matrices in (b) and (c) correspond to the signatures identified by NMF, the activity score indicates the weight given to the respective signature. d, Volcano plot illustrating super-enhancer score differences between the two molecular subgroups. Colored in red are regions with significantly different super-enhancer scores (q-value <0. 05 from edgeR's likelihood ratio test  [22] ).
05. The color in the box signify in which subgroup the super-enhancer scores were higher. c, Predicted CRC membership for genes with differentially active super-enhancers identified in (b) in patient samples from the two molecular subgroups. d, Gene expression of transcription factors identified in (b). Gene expression values were taken from the TCGA ACC dataset (N= 90). q-values were derived from edgeR's exact test, **** q-value <0. 0001, ** q-value <0. 01, * q-value <0. 05 e, Copy number aberrations (CNA) of the transcription factor genes identified in (b). Data were taken from the TCGA ACC dataset (N= 90) and averaged across patients of the same molecular subgroup. Copy number profile: -1, genomic loss; 0, no change; 1, genomic gain. Indicated with (***) are CRC genes with significantly different copy number profiles between the molecular subgroups, i.
The super-silencer landscape of adrenocortical carcinoma As we observed that H3K27ac and super-enhancer landscape reflected ACC molecular subgroups, we set up to investigate whether landscapes of other chromatin marks showed differential patterns between C1A and C1B. Super-silencers, also called H3K27me3-rich regions (MRRs)  [32] , are large genomic regions characterized by enrichement of the repressive mark histone H3 lysine trimethylation (H3K27me3) deposited by PRC2 (Figure  5a  and Methods). To profile super-silencers in ACC, we generated and analyzed H3K27me3 ChIP-seq data for 17 ACC patient samples and 4 samples of non-cancer tissue of the adrenal gland of ACC patients. A total of 1,382 super-silencer regions were detected in at least two samples for which we calculated a super-silencer activity score (Supplementary Table  5 ).
Characterization of broad H3K4me3 domains in ACC at active and poised promoters To study the chromatin state of active and poised promoters, we generated and analyzed ChIP-seq data of trimethylation at the 4th lysine residue of the histone H3 protein (H3K4me3)  [35]. The analysis included 18 ACC patient samples and 4 samples of non-cancer tissue of the adrenal gland of ACC patients. We calculated an activity score for each promoter region by summing the ChIP-seq signal around the TSS of each gene. Unsupervised analysis using PCA of all 39,584 promoter scores showed no clear structure in our ACC cohort, no were principal components linked to the ACC molecular subtypes (Figure  5a , Supplementary table  9 ). Next, we defined broad H3K4me3 domains (BDs) that span over the TSS into the gene body and act as markers of essential genes  [11].
Our study has limitations due to the rarity of ACC, leading to a limited sample size. To overcome this, the TCGA database was used when appropriate. The study only considered bulk ChIP-seq data, limiting conclusions about intratumor heterogeneity. Despite these limitations, our study sheds light on the histone modification landscape of ACC, linking the super-enhancer landscape to molecular subgroups and identifying core transcriptional networks. Drug treatment experiments showed potential for certain super-enhancer-affecting molecules in treating ACC, but differences in treatment outcome were observed between subgroups demanding for further investigation. In sum, our study provides the groundwork for epigenetic characterization of ACC tumors and indicates the potential for novel combination treatment strategies for this aggressive cancer.
Patient samples Patient samples were obtained between 2008 and 2015 in the Cochin hospital (Paris, France), snap-frozen, and stored as described in Assié et al. [1]. An expert endocrine pathologist diagnosed the malignancy of the samples and characterized the samples according to the Weiss criteria  [43]. In addition, clinical information including age, sex, hormone secretion, mitotic count, Ki67 staining index, ENSAT tumor stage, disease-free survival, and overall survival was retrieved from the ENSAT database  [44].
Super-enhancers, super-silencers and broad H3K4me3 domains were detected using LILY  [9]  which is based on the Rank Ordering Of Super-Enhancers (ROSE) algorithm  [12]. For super-enhancers, LILY stitched enhancers within a region of 12. 5 kb together while excluding promoter regions (±2. 5 kb from the transcription start site (TSS)) to identify clusters of enhancers. Next, the clusters were ranked according to their ChIP signal score and a threshold determined by ROSE distinguished super-enhancer from other enhancers. For supersilencers and broad H3K4me3 domains we used a stitching size of 4kb and included promoter regions. In addition, we calculated an H3K4me3 activity score for each promoter region by summing the normalized ChIP-seq signal in a region from 1000 bp to +2000 bp around the TSS gene coordinates taken from the Ensembl database  [50].
34, Figure  1c ) and the 10% quantile of all values for super-silencers (threshold = 0. 31, Figure  3b ). Broad H3K4me3 domains (BDs) were assigned to genes whenever there was an overlap with a TSS. The TSS gene coordinates were taken from the Ensembl database  [50]. More than one BD region could be assigned to one gene and more than one gene to each BD region. Transcription factors (TFs) were identified using the list of human TFs by Lambert et al. [52]. For oncogenes, the list of COSMIC (including tier 1 and tier 2 data) was used  [53].
Non-negative matrix factorization Non-negative matrix factorization (NMF)  [21]  was calculated using the R package Bratwurst  [54]  and an adapted script based on publicly available code from Gartlgruber et al. [10]. NMF decomposes a matrix of observations V into an exposure matrix H and a signature matrix W. This was done for different ranks k and 40 random initializations. The ideal factorization rank was determined using metrics including the Frobenius error, mean Amari distance and cophenetic correlation coefficient. Based on this comparison rank k=2 was the optimal factorization (Supplementary Figure  S1 ). Since NMF is invariant to column scaling on W or row scaling on H, we normalized W such that each column summed up to 1 and then scaled H correspondingly. The H matrices were used to compare the k signatures with the molecular subgroups.
C1A/C1B classification of cell lines using PCA Cell line gene expression data were aligned to TCGA ACC data using joint quantile normalization of FPKM-UQ values. We then performed PCA on the TCGA gene expression data using genes with differentially active super-enhancer scores and subgroup specific CRC TFs; the gene filtering allowed us to discard transcriptional variability due to the potential variation in the amount and composition of tumor microenvironment in the ACC tumors. PCA on the gene expression data from the TCGA ACC database showed that the first principal component (PC1), explaining 18. 1 % of the variability in gene expression in the TCGA dataset, was a perfect indicator for the C1A/C1B subgroup allocation. To ensure the validity of this approach for the C1A/C1B group allocation, we projected the gene expression vectors of the in-house patient samples onto the TCGA principal component subspace (Supplementary figure  S6 ); C1A/C1B status of the in house samples was obtained using an alternative approach based on the expression of marker genes.
Super-enhancer landscape of the NCI-H295R cell line We analyzed H3K27ac ChIP-seq data of the ACC cell line NCI-295R and calculated super-enhancer scores for super-enhancer regions defined in samples of our ACC cohort. After projecting the activity scores on the PCA of the patient samples, we concluded that the super-enhancer landscape of the H295r cell line reflects the C1A subgroup (Supplementary Figure  S5a ). Gene expression analysis of CRC transcription factors gave further evidence of this allocation (Supplementary Figure  S5b ).
Data and code availability The code used in this study is available at https://github. com/BoevaLab/ACC_chromatin_ modifications. Gene expression (RNA-seq), histone modification (CHIPseq), and DNA methylation (RRBS) data for the in house ACC tumors can be accessed via EGA(in_process_for_EGA). Cell line data can be accessed via EGA(in_process_for_ EGA). (Comité de protection des personnes Ile de France 1, application #13311). Figure  S1 : Different metrics to determine the optimal NMF factorization rank for a, gene expression data of super-enhancer target genes in the TCGA ACC database (Figure  2b ), b, all super-enhancer scores (Figure  2c ). c, NMF of the unthresholded gene expression data of the TCGA ACC database (all genes) and d, the respective factorization metrics. e, NMF of the super-enhancer target gene expression data of the ACC patient cohort and f, the respective factorization metrics.