dataesr/scientific-paragraphs-categorization huggingface dataset:
This leads to the paradoxical observation that despite the essential role of BRD9/ncBAF in driving the transformed state, it is not enriched at MYC-bound sites, whereas depletion studies in RT cells  (Weissmiller et al. , 2019)  and our current data revealed MYC as the essential oncogenic driver. Given the strong association of ncBAF with CTCF and not MYC, it is unlikely that the BRG1 seen at the MYC promoters in RMC cells corresponds to ncBAF. Moreover, CTCF motifs were not enriched at the promoter sites where BRG1 was evicted, but were present at distal sites, where no BRG1 was detected. Thus, the role of ncBAF in RMC remains to be determined. In RT cells, SMARCB1 re-expression led to SWI/SNF re-localization to what have been described as lineage-specific enhancers  (Nakayama et al. , 2017; Wang et al.
Tumour Samples The two RMC samples subjected to scRNAseq were collected from Strasbourg University Hospital and Curie Institute, according to institutional guidelines. Sample collection for further research analysis was approved ethical Committees of participating institutions and all patients provided an informed written consent for the use of material for further research. Regarding bulk RNAseq, beside the RNAseq of 11 patients recently reported  (Msaouel et al. , 2020a) , we generated an additional dataset of multi-region RNAseq of a cohort of 4 RMC patients, including multiple sections and lymph nodes metastasis (Dataset S2).
scRNA-seq analysis of human primary RMC tumours the different renal tubule clusters were computed by Clustifyr v1. 0. 0  (Fu et al. , 2020). Trajectory analyses were plotted and visualized using Similarity Weighted Nonnegative Embedding (SWNE)  (Wu et al. , 2018). Gene set variation analysis were performed using the rpackage GSVA  (Hänzelmann et al. , 2013). For the "bulk RMC signature", the upregulated genes from the differential analysis of the MDACC RMC cohort (11 tumours versus 6 NAT) were selected using log2FC > 2 and FDR < 0. 01  (Msaouel et al. , 2020a). For all signatures, gene sets were retrieved from either Hallmarks MSigDB or KEGG pathways. Gene signatures were computed and visualized on UMAPs using the R package VISION (https://github. com/YosefLab/VISION).
Boyden Chamber Invasion assays Before seeding, 100ul of diluted Matrigel (1:20, 356234, Corning) was added in each insert (24-well 8um inserts, Corning) and left to dry for 2hrs at 37°C before being washed twice with PBS. Subsequently, RMC cells were harvested and 2 x 10 5 cells and seeded in the Boyden chambers in corresponding media without serum. 24hrs later, migrated cells were fixed using PFA 4% for 10 min before being stained using Crystal violet for 10 min. Excess stain was washed 3 times in PBS before images were captured on phase contrast microscope. Quantification of migrated cells was done by resuspension of staining using 100mM acetic acid for 15min before luminescence was measured on a BioTek Luminescence microplate reader (using Gen5 software).
RNA preparation and quantitative PCR RNA isolation was performed according to standard procedures (Macherey Nagel RNA Plus kit). RT-qPCR was carried out with SYBR Green I (Roche) and SuperScript IV Reverse Transcriptase (Invitrogen) and monitored using a LightCycler 480 (Roche). The mean of ACTB, TBP, RPL13A and GAPDH gene expressions was used to normalize the results. Primer sequences for each cDNA were designed using Primer3 Software and are available in Table  2.
Public data correlation analysis using TGCA and CCLE database Spearman correlation for all selected genes were retrieved from co-expression studies using the Cancer Cell Line Encyclopaedia  (Broad, 2019)  and the TCGA chromophobe renal cell carcinoma (KICH) databases. All transcription factors were extracted using the "Full Human TFs" list from  (Lambert et al. , 2018). Scatter plots were made using Prism5. For the correlation with TFCP2L1, the epithelial and mesenchymal genes were retrieved from  (Watanabe et al. , 2019).
Comparisons of interest were performed using the Wald test for differential expression and implemented in the Bioconductor package DESeq2 version 1. 16. 1. Genes with high Cook's distance were filtered out and independent filtering based on the mean of normalized counts was performed. P-values were adjusted for multiple testing using the Benjamini and Hochberg method. Deregulated genes were defined as genes with log2(foldchange) >1 or <-1 and adjusted p-value <0. 05.
Analysis of bulk RNA-seq of patient samples For RMC cohorts, raw counts were retrieved in excel format and normalized first by sequencing depth using DESeq2 sizefactors and then divided by median of gene length. Samples were clustered using the hclust function with "ward. D2" linkage function and visualized as heatmaps using pheatmap package v1. 0. 12. The deconvolution of immune and stromal cells was done using MCP-counter v1. 2. 0  (Becht et al. , 2016). Sample compositions were also estimated by deconvolution from our single-cell data using the CIBERSORTx algorithm  (Newman et al. , 2019). Volcano plots were generated with ggplot2 v3. 3. 2. Gene set enrichment analyses were done with the GSEA software v3. 0 using the hallmark gene sets of Molecular Signature Database v6. 2. Gene Ontology analysis was done using DAVID (http://david.
ChIP-seq analysis After sequencing, peak detection was performed using the MACS software  (Zhang et al. , 2008). Peaks were annotated with Homer using the GTF from ENSEMBL v75. Global clustering analysis and quantitative comparisons were performed using seqMINER  (Ye et al. , 2011). Super-enhancers were called with the python package Ranking Of Super Enhancers (ROSE) by ranking H3K27ac peaks either by H3K27ac, BRG1 or MYC signal. De novo motif discovery on FASTA sequences corresponding to windowed peaks was performed using MEME suite (meme-suite. org). Motif correlation matrix was calculated with in-house algorithms using JASPAR database as described in  (Joshi et al. , 2017). Motif discovery from ChIP-seq peaks was performed using the RSAT peak-motifs algorithm (http://rsat. sb-roscoff. fr/peak-motifs_form.
cgi). Motif analysis Searching of known TF motifs from the Jaspar 2014 motif database at BRG1bound sites was made using FIMO  (Grant et al. , 2011)  within regions of 200 bp around peak summits, FIMO results were then processed by a custom Perl script which computed the frequency of occurrence of each motif. To assess the enrichment of motifs within the regions of interest, the same analysis was done 100 times on randomly selected regions of the same length as the BRG1 bound regions and the results used to compute an expected distribution of motif occurrence. The significance of the motif occurrence at the BRG1-occupied regions was estimated through the computation of a Z-score (z) with z = (x -μ)/σ, where: -x is the observed value (number of motif occurrence), -μ is the mean of the number of occurrences (computed on randomly selected data), -σ is the standard deviation of the number of occurrences of motifs (computed on randomly selected data).
Acknowledgements We thank all the staff of the IGBMC common facilities in particular  Betty Heller  and  Patricia Wagner  from  Cell Culture ,  Claudine Ebel  and  Muriel Philipps  from  Flow Cytometry  and  Dr. Paola Rossolillo  and  Karim Essabri  of the  molecular biology facility. We also would like to thank the  Institut Curie  facilities and in particular  Dr Pascale Philippe-Chomette ,  Pr Michel.
Then, we used cluster analysis to sort regions based on their patterns of covariation across principal gradients calculated at different connectivity strengths. To establish the implications of connectivity strength-specific gradients and their utility in characterizing individual differences, we: (i) measured inter-and intra-bin agreement (i. e. , reliability) of individual differences  (vertex-level, cortex-level) , and (ii) assessed the relative predictive value of the strength-specific gradients for variables such as age, sex, and full-scale intelligence quotient (FSIQ). To accomplish these objectives, we leveraged two independent datasets. The analytic workflow is outlined in Table  1.
Datasets Resting-state fMRI data from two openly available datasets were used: the Human Connectome Project (HCP)  ( Van Essen et al. , 2013 ) , and the enhanced Nathan Kline Institute-Rockland Sample (NKI-RS)  ( Nooner et al. , 2012 ). Both group-and individual-level functional connectomes were examined to probe the functional organization across a full range of connectivity thresholds. The HCP data was acquired at Washington University at St. Louis on a customized Connectome Skyra scanner (Siemens 3 Tesla) using a multiband sequence  ( Smith et al. , 2013 ; U ğurbil et al. , 2013 ; Van Essen et al. , 2013 ). Resting-state fMRI was acquired with a multiband factor of 8, 2 mm isotropic resolution, and a repetition time of 0. 72 s for a duration of 14. 4 min, which resulted in 1200 vol per run.
Participants were asked to relax, keep eyes open and fixated on a crosshair, and not to fall asleep. Four resting-state runs were collected in different sessions across two days (REST1 and REST2), where each session comprised two runs with different phase encoding directions (LR and RL). We used the minimally preprocessed fMRI data that was already provided by the HCP  ( Glasser et al. , 2013 ). The resting-state data has been motion corrected, minimally spatially smoothed (2 mm), high-pass filtered (2000s cutoff), denoised for motion-related confounds and artifacts using independent component analysis  ( Salimi-Khorshidi et al. , 2014 ; Smith et al. , 2013 ) , and aligned with MSMAll  ( Robinson et al. , 2014 ). At the group level, we used the dense group-average functional connectome provided by the HCP S1200 data release  ( HCP 1200 Subjects Data Release Reference Manual , 2017 ).
, 2021 ; Li et al. , 2019 ) , and both GSR and non-GSR data were used to build vertex-wise connectivity matrices calculated with Pearson's correlation  ( Murphy and Fox, 2017 ). The fsaverage4 surface space (2562 vertices per hemisphere) was used to balance the vertex resolution and computational cost at individual level. For each participant, we mapped the measurements of cortical thickness and estimates of myelination (T1w/T2w ratio), as provided by the HCP's minimally preprocessing pipeline  ( Glasser et al. , 2013 ) , to the same surface space. Details regarding the HCP dataset and minimal preprocessing pipeline are published in prior studies  ( Glasser et al. , 2013 ; Smith et al. , 2013 ). We also generated the group-averaged connectivity matrix, via the arithmetic mean of the individual connectivity matrices, using this subset to replicate the group-level results for both GSR and non-GSR data.
In addition, we replicated our findings in a cross-sectional lifespan sample with 313 healthy participants (214 female, age: 6-85 years, 42. 2 ± 22. 4 years) selected from the NKI-RS dataset, who have no diagnosis of any mental or neurological disorders and passed quality control of a head motion criteria (mean framewise displacement < 0. 25 mm). The NKI-RS data was acquired at the Nathan Kline Institute on a Siemens TrioTim 3 Tesla scanner using a multiband sequence. Resting-state fMRI was acquired with a multiband factor of 4, 3 mm isotropic resolution, and a repetition time of 0. 645 s for a duration of 9. 7 min, which resulted in 900 vol per run. Preprocessing was performed with the Connectome Computational System  ( Xu et al. , 2015 ) , and included discarding the first five timepoints, compressing temporal spikes (AFNI 3dDespike), slice timing correction, motion correction, 4D global mean intensity normalization, nuisance regression (Friston's 24 model, cerebrospinal fluid and white matter), linear and quadratic detrending, as well as bandpass filtering (0.
01-0. 1 Hz). Preprocessing steps were repeated to include GSR and non-GSR respectively. The preprocessed data were then projected on the fsaverage surface, and similarly to the HCP dataset downsampled to the fsaverage4 surface. Details of the dataset and preprocessing were described in our prior studies  ( Nenning et al. , 2020 ; Nooner et al. , 2012 ). We used Pearson's correlation to establish the individual connectivity matrices for both GSR and non-GSR data.
Intermediate connections relate to individual microstructure To characterize the gradient profiles across connectivity bins at the network level, we averaged the gradient scores within canonical restingstate networks. Across connectivity thresholds, the principal gradients reflected the difference between sensory and association cortices, suggesting that this is a relatively stable phenomenon ( Fig. 4 D ). Notably, at intermediate connectivity strengths the gradient scores for the attention networks increased towards gradient scores of high-order networks (i. e. , DMN), while the limbic network showed the opposite pattern ( Fig. 4 D ). This finding was also replicated for data processed with and without global signal regression (Fig. S9 ). At the individual level, we also measured the association between principal gradients and brain microstructure.
The principal gradients of intermediate connections capture individual differences Next, we used the HCP sample to evaluate the test-retest reliability of the principal gradients at each connectivity bin with discriminability and ICC, which can also reflect the ability to identify individuals. High discriminability was observed for the principal gradients at the top-ranked and intermediate connectivity strengths ( Fig. 5  A left). ICC also exhibited higher scores at the top-ranked and the relative lowerranked connectivity levels ( Fig. 5  A right). Analysis based on data with global signal regression showed similar results, with an overall higher discriminability across all thresholds, but a drop in the ICC at intermediate levels (Fig. S12A ). In addition, we also evaluated the cross-threshold agreement of the principal gradients using ICC.
Acknowledgements Parts of this work were funded by the  National Institutes of Health NIH  Grants  R01AG047596  : Mapping interindividual variation in the aging connectome,  U01MH099059 :  Longitudinal Discovery of Brain Developmental Trajectories ,  R01MH094639 :  Discovery Science of Human Brain Function Across the Lifespan , and  R01MH101555 :  Real-time fMRI Neurofeedback Based Stratification of Default Network Regulation. RF1MH128696 :  An Alignment Framework For Mapping Brain Dynamics  and  Substrates of Human Cognition Across Species.
Data and code availability The dataset used in this work are publicly available at the Human Connectome Project ( https://www. humanconnectome. org ) and the enhanced Nathan Kline Institute-Rockland Sample data repository ( http:// fcon _ 1000. projects. nitrc. org/indi/enhanced/ ). The preprocessing code for the NKI data is available at https://github. com/zuoxinian/CCS , the code for discriminability is available at https://github. com/neurodata/ discriminability , and for intraclass coefficient at https://github. com/ TingsterX/Reliability _ Explorer. Glmnet is available at https://glmnet. stanford. edu/index. html.
Cases and sample selection Tissue samples from patients analyzed for care purpose in the department of pathology of CHU Brest were used, following our national and institutional guidelines, in compliance with the Helsinki Declaration and after approval by our institutional review board. All samples were included in the registered tissue collection part of the Brest Biological Resources Center BB-0033-00037, NF S 96-900 certificated Brest, CHRU Brest AC-2019-3642 -DC -2008 -214). No clinical, epidemiological or evolution data about the patients was collected during this study. Groups of patients with gastric samples were constituted as follows: (A) control patients with gastric weight-reduction surgery with lymphoid islet with healthy mucosa; (B) patients with autoimmune gastritis (Biermer disease); (C) patients with Helicobacter pylori-related gastritis; (D) patients with MSI gastric adenocarcinoma, differentiating those without nodal metastasis (D1) from those with nodal metastasis (D2); (E) patients with MSS gastric adenocarcinoma without nodal metastasis (E1) or with nodal metastasis (E2).
Tissue labeling before imaging mass cytometry acquisition For downstream analyses, all samples were cut at 3 mm and placed on Superfrost ® Plus slides (Thermo Scientific, Saint-Herblain, France). All slides were stained with alcian blue to record the microscopic morphology of the tissue. Slides were scanned at 20X magnification on the 3DHistech Panoramic Midi slide scanner (3DHISTECH, Budapest, Hungary), and visualized using CaseViewer software (v. 2. 4/32bits, 3DHistech) for the selection of ROIs (Supplementary Figure  1 ). With successive xylene baths, coverslips were removed and the mounting medium was washed. The same IHC-protocol was followed except for the peroxidase blockade step, before the incubation of slides with the 39 metal-conjugated antibodies panel and the cell intercalator.
Image analysis pipeline based on QuPath Generated data were visualized as MCD files using the Fluidigm MCDTM viewer. To better separate antibodies signals and noises, each marker was separately visualized and a minimum signal threshold was set in the Fluidigm MCDTM viewer. For each recorded ROI, a stack of 16-bit single-channel OME-TIFF files was exported from MCD binary files using MCDTM Viewer 1. 0 (Fluidigm). OME-TIFF files of each marker were then stacked in one TIFF file with ImageJ (v. 1. 8. 0_172). The TIFF files were opened with QuPath (v. 0. 3. 2), setting the image type on fluorescence, for running the segmentation with the best parameters for each ROI, based on the nuclei-staining Iridium channel and peri-nuclear cell expansion  (19). Measurements (mean of the marker signal in each cell) were then exported as a CSV table for analysis.
7 were considered as high positive. Statistics and heat maps were generated in R (v4. 0. 5) with the ggplot2 package. In addition, TLS were classed according to the 3 stages classification in terms of LA (CD20+CD21-CD23-TLS), Non-GC TLS (CD20+CD21+CD23-) and GC-like TLS (CD20+CD21+CD23 +). Distributions between groups were compared using the Kruskal-Wallis non-parametric test performed on Medcalc software.
Funding The authors would like to thank "La Reǵion Bretagne", the 'Ligue Contre le Cancer", and the "Canceŕopole Grand Ouest" for their financial support in this project (INCEPTION grant), as well as the European grant program FEDERProgos RU 000950. LBAI was supported by the Agence Nationale de la Recherche under the "Investissement d'Avenir" program with the Reference ANR-11-LABX-0016-001 (Labex IGO).
These effects were larger for autism-associated genes than in other LoF-intolerant genes. Using brain imaging data from 21,040 individuals from the UK Biobank, we could not detect significant differences in the overall brain anatomy between LoF carriers and non-carriers. Our results highlight the importance of studying the effect of the genetic variants beyond categorical diagnosis and the need for more research to understand the association between these variants and sociodemographic factors, to best support individuals carrying these variants. Autism is a heterogeneous condition characterized by atypical social communication, as well as unusually restricted or stereotyped interests  1. Its genetic architecture is highly complex, with contributions from monogenic factors, for example caused by a de novo variant with large effect and polygenic factors, which is attributable to the cumulative effect of multiple common variants, each having a small effect  2.
org/10. 1038/s41591-023-02408-2 than in undiagnosed individuals (Supplementary Table  1  and Methods)  8. We refer to these genes as 'autism-associated genes' despite no evidence linking these genes specifically to autism compared to other neurodevelopmental conditions (Extended Data Fig. 1 )  5, 6, 12  and recent evidence for association of rare de novo variants in autism-associated genes with autism and co-occurring cognitive impairment  7. In addition, we analyzed 2,492 genes not considered as autism-associated genes, but with evidence for intolerance to LoF variants in reference populations (hereafter referred to as 'constrained genes'; Supplementary Table  1  and Methods)  13. Second, we identified high-confidence rare LoF variants (frequency <1% in each study) that were absent from the reference European population in the Genome Aggregation Database (gnomAD; https://gnomad.
5b ). Altogether   6 ). Traits were classified according to the broad category defined in the UK Biobank database. FVC, forced vital capacity; FEV 1 , forced expiratory volume in 1 s; PEF, peak expiratory flow. b, OR (logistic regressions) and standardized β values (linear regressions) associated with variant presence and autism PGS from multivariable regression analyses of socioeconomic traits and fluid intelligence, stratified by gene type and autism OR of genes carrying the variants (Methods). The Townsend index measures were reversed so that higher material deprivation was indicated with a negative sign. The β values associated with autism PGS when S-LoFs in constrained genes with autism OR > 10 are considered in the regression analysis are shown (Supplementary Tables  4  and 5  show complete results).
Online content Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at https://doi. org/10. 1038/s41591-023-02408-2. https://doi. org/10. 1038/s41591-023-02408-2 indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons. org/licenses/by/4. 0/. © The Author(s) 2023  1  Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, IUF, Université Paris Cité, Paris, France.
Ethical approval Informed consents from all individuals were obtained according to following ethics clearances. The SSC is a multisite effort gathering 12 recruitment sites and informed consents were obtained from all participants included in each site at the time of their initial enrollment and centralized by the Columbia University Institutional Review Board (IRB) under the protocol AAAC6306(M00Y17). All SPARK participants were recruited under a centralized IRB protocol (WCG IRB protocol no. 20151664) and provided written informed consent to take part in the study. Participants of the UK Biobank study provided informed consent and ethical approval was provided by the UK's National Health Service, National Research Ethics Service (Ethics Committee reference no. 11/NW/0382). Data analyses were conducted in accordance with the following research projects that have been deemed exempt under 45 CFR 46.
A note on terminology Throughout the manuscript, we use the term 'individuals with autism' to refer to individuals who have a diagnosis of autism. This person first terminology is preferred by many but not all individuals with autism. We use the term 'undiagnosed individuals' to refer to parents and siblings of individuals with autism who do not have a diagnosis and individuals from the UK Biobank who also have not indicated that they have an autism diagnosis. We note that some of these individuals may have an autism diagnosis that is not recorded in the datasets used. We further note that some of these individuals may be autistic but may not have received a formal diagnosis.