dataesr/scientific-paragraphs-categorization huggingface dataset:
Staining -FFPE-based multiplex immunofluorescence (mIF). mIF staining of FFPE slides was performed using Ventana Discovery Ultra automated tissue stainer (Roche Tissue Diagnostics, Tucson AZ USA). Slides were baked first at 60°C for 8 min and subsequently further heated up to 69°C for 8 min for subsequent deparaffinization. This cycle was repeated twice. Heat-induced antigen retrieval was performed with Tris-EDTA buffer pH 7. 8 (Ventana) at 92°C for 32 min. After each blocking step with Discovery Inhibitor (Ventana) for 16 min, the Discovery Inhibitor was neutralized. Primary antibodies were diluted in 1X Plus Automation Amplification Diluent (Akoya Biosciences). Primaries were detected using according anti-species secondary antibodies conjugated to HRP (OmniMap Ventana) (Extended Data Table  3 ).
Next, unmixing of the channels and tiling of the images was performed with PhenoChart (v1. 0. 12) and inForm (v2. 4). Tiles were fused in HALO (Indica labs, v3. 2. 1851. 328). High-resolution mIF were obtained using a STELLARIS 8 microscope (Leica) with a 40X1. 1 NA waterimmersion objective (HC FLUOTAR L VISIR 25x/0. 95 WA-TER) and 1. 0 zoom. The white light laser (WLL: 440 nm-790 nm) allowed to image all opal dyes mentioned above, channels were acquired sequentially to reduce crosstalk. Images were obtained in bidirectional mode with 2048 x 2048 pixels (pixel size: 273. 8nm x 273. 83 nm) at 600 Hz. Where indicated, images were acquired with z-stacks between 10-15 pm (z-steps: 1 pm) and 3D-reconstructed and displayed in 'Maximum'-mode using the Leica Application Suite X software (Leica). Analysis -FFPE-based mIF.
CellRanger (v6. 0. 2, 10x Genomics) was used to extract unique molecular identifiers, cell barcodes, and genomic reads from the sequencing results of 10x Chromium experiments. Then, count matrices, including both protein coding and non-coding transcripts, were constructed aligning against the annotated human reference genome (GRCh38, v3. 0. 0, 10x Genomics). In order to remove potentially damaged or unhealthy cells and improve data quality, the following filtering steps were performed in addition to the built-in CellRanger filtering pipeline. Cells associated with over 50,000 transcripts, usually less than 1% of the total number of samples, were removed. Cells associated with a low number of unique transcripts, less than 500 unique transcripts detected (1% of the total number of samples), were removed.
Cells with over 20% of mitochondrial transcripts were removed. Transcripts mapping to ribosomal protein coding genes as well mitochondrial genes were removed together with transcripts detected in less than 10 samples. Normalization with SCTransform. For normalization and variance stabilization of each scRNA-seq experiment's molecular count data, we employed the modeling framework of SCTransform in Seurat v3  (65). In brief, a model of technical noise in scRNA-seq data is computed using 'generalized gamma poisson regression'  (66). The residuals for this model are normalized values that indicate divergence from the expected number of observed UMIs for a gene in a cell given the gene's average expression in the population and cellular sequencing depth. Additionally, a curated list of cell cycle associated genes, available within Seurat, was used to estimate the contribution of cell cycle and remove this source of biological variation from each dataset in order to increase the signal deriving from more interesting processes.
In brief, this tool generates artificial doublets from existing scRNA-seq data by merging randomly selected cells which are then pre-processed together with real data and jointly embedded on a PCA space that serves as basis to find each cell's proportion of artificial k nearest neighbors (pANN). For this step we restricted the dimension space to the top 50 PCs. Finally, pANN values are rank ordered according to the expected number of doublets and optimal cutoff is selected through ROC analysis across pN-pK parameter sweeps for each scRNA-seq dataset; pN describes the proportion of generated artificial doublets while pK defines the PC neighborhood size. In order to achieve maximal doublet prediction accuracy, mean-variance normalized bimodality coefficient (BCmvn) was leveraged. This provides a ground-truth-agnostic metric that coincides with pK values that maximize AUC in the data.
To overcome DoubletFinder's limited sensitivity to homotypic doublets, we consider doublet number estimates based on Poisson statistics with homotypic doublet proportion adjustment assuming 1/50,000 doublet formation rate the 10x Chromium droplet microfluidic cell loading. Ambient mRNA signal removal. After doublet prediction and removal, we analyzed each scRNA-seq dataset in order to estimate the extent of ambient mRNA contamination in every single cell and correct it. We used the R package Cellular Latent Dirichlet Allocation (CELDA) (68) which contains DecontX, a method based on Bayesian statistical framework to computationally estimate and remove RNA contamination in individual cells without empty droplet information. We applied the DecontX() function in CELDA to the raw count matrices with default parameters.
Subsequently, we removed all cells with contamination values above 0. 5 and we used the decontaminated count matrices resulting from DecontX() for downstream analysis. Data integration. Individual datasets, after preprocessing, doublet removal and ambient mRNA regression, were aggregated according to specific criterias (e. g. tissue of origin, profiling time, culture condition) and went through a joint normalization step with SCTransform in order to mitigate technical confounding factors, which also served as means for selection of a set of meaningful 2,000 most variable global genes prior to data integration. Integration of different conditions (culture model, treatment and timepoints) was performed using the log-normalized corrected UMI count data in two steps. First, the residuals for the top 2,000 global variable genes were used as input to computing the top 100 Principal Components (PCs) through the RunPCA() function in Seurat.
The leading 30 PCs and 50 nearest neighbors were then used to define the shared neighborhood graph with the FindNeighbors() function in Seurat. Subsequently, datasets were clustered according to the shared neighborhood graph using the Louvain algorithm (69) through the Seurat function FindClusters() with resolution 0. 8. Finally, we used these high-resolution clusters to define a restricted, noise reduced and cell state specific set of genes through differential expression analysis (see Methods section below). In the second step of the integration process, we compiled a list consisting of the ensemble of the top 30 DEGs for each cluster and used it to focus and repeat PCA dimensionality reduction. The first 30 PC vectors of the new PCA space served as basis to obtain a two-dimensional (2D) representation of the data through Uniform Manifold Approximation and Projection (UMAP)  (70)  implemented in RunUMAP() with 50 nearest neighbors.
Intercellular communication analysis. To investigate ligand-receptor (LR) mediated cell-cell communications during immune cell activation in our IIO models, we focused on the signals exchanged between Th1 cells, activated t-bet B cells and CD8+ CTLs. For this analysis we extracted genes labeled as either ligands or receptors from curated databases  (58)  and required that genes were differentially expressed between the three populations under investigation, which facilitated retrieval of directional information about the signal exchange. To gain insights into functional cell-cell communication, we used the NicheNet pipeline which considers the influence of sender-cell ligands on receiver¬cell gene expression  (58). NicheNet's analysis pipeline provided us with a ranking of predicted ligands that most likely affect gene expression in activated t-bet B cells and CD8+ CTLs highlight-ing the role of critical Thl-secreted factors in driving immune cell phenotypes within IlOs.
Acknowledgements The authors would like to thank  Derek Wong ,  Dr. Simona Notova ,  Maddie DeWinter , and  Dr. Andrew Hunt  for helpful scientific discussions, and  Dr. Ashty Karim  for help with manuscript preparation. The authors also thank  Hugues Bonnet  for assistance with the BLI equipment. K. F. W. thanks the  Chateaubriand Fellowship  program: this material is based upon research supported by the  Chateaubriand Fellowship of the Office for Science & Technology of the Embassy of France in the United States. A. I. acknowledges support from  Glyco@Alps  ( ANR-15-IDEX-0002 ) and  Labex Arcane/CBH-EUR-GS  ( ANR-17-EURE-0003 ). M. C. J. acknowledges  DTRA  ( HDTRA1-21-1-0038 ) and the  National Science Foundation  ( MCB 1936789 ). The authors also acknowledge support from  ICMG UAR   2607  for BLI facilities.
sanguinis by purified membrane component (LPS) is mediated by TLR2. GF and KB cells were pre-treated with either TLR2 or TLR4 neutralizing antibodies (anti-TLR2 and anti-TLR4 antibodies) or an isotype IgG control antibody for 30 min prior to the stimulation with heat-inactivated gram -or heat-inactivated gram + bacterial pathogens for 48 h. Cells were harvested for the extraction of total RNAs, and the S100A15 expression was investigated using qRT-PCR. Data obtained from qRT-PCR analysis of S100A15 expression in GF and KB cells following the stimulation with heatinactivated P. gingivalis or heat-inactivated S. sanguinis LTA (Figure  2A ) demonstrate an inhibition of P. gingivalis-induced S100A15 expression in both GF and KB cells in response to pre-treatment with anti-TLR4 antibody, and the inhibition of S.
gingivalis or by purified membrane component (LTA) is mediated by TLR4 and if the induction of S100A15 by gram + S. sanguinis by purified membrane component (LPS) is mediated by TLR2. GF and KB cells were pre-treated with either TLR2 or TLR4 neutralizing antibodies (anti-TLR2 and anti-TLR4 antibodies) or an isotype IgG control antibody for 30 min prior to the stimulation with heat-inactivated gram -or heat-inactivated gram + bacterial pathogens for 48h. Cells were harvested for the extraction of total RNAs, and the S100A15 expression was investigated using qRT-PCR. Data obtained from qRT-PCR analysis of S100A15 expression in GF and KB cells following the stimulation with heat-inactivated P. gingivalis or heat-inactivated S. sanguinis LTA (Figure  2A ) demonstrate an inhibition of P. gingivalis-induced S100A15 expression in both GF and KB cells in response to pre-treatment with anti-TLR4 antibody, and the inhibition of S.
Taken together, these data demonstrate that the induction of the antimicrobial protein, S100A15, by gram -bacterial pathogens is a TLR4-dependent mechanism, while the induction of the S100A15 protein by gram + bacterial pathogens is a TLR2-dependent mechanism. GF and KB cells were pre-treated with anti-TLR2, anti-TLR4, or control IgG 1 h prior to the stimulation with either heat-inactivated P. gingivalis or heat-inactivated S. sanguinis for 48 h. Total RNA was extracted from treated and control cells and subsequently subjected to qRT-PCR analysis to assess the relative transcription levels of S100A15. (B) GF and KB cells were pre-treated with anti-TLR2, anti-TLR4, or control IgG 1 h prior to stimulation with either LPS (100 ng/mL) or LTA and KB cells were pre-treated with anti-TLR2, anti-TLR4, or control IgG 1 h prior to the stimulation with either heat-inactivated P.
gingivalis or heat-inactivated S. sanguinis for 48 h. Total RNA was extracted from treated and control cells and subsequently subjected to qRT-PCR analysis to assess the relative transcription levels of S100A15. (B) GF and KB cells were pre-treated with anti-TLR2, anti-TLR4, or control IgG 1 h prior to stimulation with either LPS (100 ng/mL) or LTA (50 ng/mL) for 48 h. Total RNAs were extracted from treated and control cells and subsequently subjected to qRT-PCR analysis to assess the relative transcription levels of S100A15. Densitometric quantification of S100A15 mRNA over actin mRNA transcript. The data were normalized to the level of treated and untreated control cells in each sample. Each bar represents the mean ± SD of three independent experiments performed in duplicate, * p < 0. 05.
The role of LPS-induced ASK1 activity in the activation of mitogen-activated protein kinases, including c-Jun N-terminal kinase (JNK) and p38 pathways, has been reported in several studies  [24, 25]. We, therefore, decided to investigate whether bacterial pathogeninduced ASK1 activity is involved in the activation of MAP kinase signaling pathways, JNK, and/or p38. Both GF and KB cells were stimulated with either heat-inactivated gram - (P. gingivalis) or gram + (S. sanguinis) bacterial pathogens for 48 h. Then the total cell lysates were prepared. Next, we analyzed the expression and the phosphorylation of JNK and p38 proteins by Western blot using specific antibodies. Although data obtained from Western blot analysis (Figure  3 ) did not reveal any marked alteration at the expression levels of either JNK or p38 following stimulation of both GF and KB cells with either heat-inactivated P.
gingivalis (A) or heat-inactivated S. sanguinis(B), when compared with control cells. Actin was used as an internal control for loading and transfer. (C) Analysis of band intensity on films is presented as the relative ratio of TRAF6 expression to the expression of actin, the relative ratio of phospho-ASK1, Phospho-JNK, and Phospho-p38 to the expression of ASK1, JNK, and p38 expression. The data were normalized to the level of Actin expression in treated and untreated control cells in each sample. Each bar represents the mean ± SD of three independent experiments performed separately, * p < 0. 05. (D,E) The DNA-binding activity of the transcription factors AP-1 and ATF-2 were investigated by EMSA using nuclear extracts of treated and control cells. The stimulation of both GF and KB cells with either heat-inactivated P.
05. Next, we decided to address the role of the NF-κB pathway in the modulation of gram - and gram + bacterial pathogens-induced effects in both GF and KB cells. Both GF and KB cell lines were stimulated with either heat-inactivated P. gingivalis or heat-inactivated S. sanguinis for 48 h. The total cell lysate was collected for Western blot and kinase assay, as well as nuclear and cytoplasmic protein extraction for EMSA and Western blot analysis. Data obtained from IκBα kinase assays show that inhibition of nuclear factor kappa-B kinase subunit alpha (IKKα) impacts IκBα's ability to phosphorylate in vitro the NF-κB inhibitor in both GF and KB cells following stimulation with either heat-inactivated P. gingivalis (Figure  5A ) or heat-inactivated S. sanguinis (Figure  5B ), suggesting an essential role for the NF-κB pathway in the modulation of both gram -and gram + bacterial pathogens-induced effects in the oral mucosa.
Both GF and KB cells were stimulated with either heat-inactivated P. gingivalis (2 × 10 4 cells/mL) or heat-inactivated S. sanguinis (2 × 10 4 cells/mL) microbial pathogens (MPs) for 48 h. Then, total protein lysate and nuclear extract were prepared to perform kinase assays, Western blot, and EMSA. Kinase assay demonstrates the activation of IKK by both in vitro phosphorylation of the IκBα substrate and NF-κB inhibition in both GF and KB cells by stimulation with either P. gingivalis (A) or S. sanguinis(B) when compared to control cells. Data are representative of three independent experiments performed separately. Western blot analysis of cytoplasmic protein of treated and control cells using anti-IκBα, IKKα, and actin antibodies. Western blot analysis of the protein stability of IκBα in the cytoplasmic protein of both cells demonstrates degradation of IκBα protein, evidence NF-κB activation in both GF and KB cells when stimulated with either heat-inactivated P.
Sequencing process Clustering was performed on a cBot and sequencing on a HiSeq2000 (Illumina). After quantification, the libraries were equimolarly pooled by 12, leading to 10 multiplexes of 12 samples each. Assignment to the multiplexes was done by random blocking on the time point and the breed, in order for each multiplex to contain the four time points (3 samples per time point) and the five breeds. Each pool was denatured, diluted and clustered on three lanes using the Cluster Generation kit v3 (Illumina). Sequencing was performed using SBS kit v3 (Illumina) in single read 50 nt mode. Raw sequencing data were saved as FASTQ files. The quality of the data was assessed using FastQC from the Babraham Institute. Potential contaminants were investigated with the FastQ Screen software (the Babraham Institute).
Transcript quantification and data normalization Quantification of gene expression was performed for each library using FeatureCounts (Subread package 1. 4. 6-p4, Liao et al. , 2014). Bovine gene annotation was downloaded from Ensembl 79 (sequence UMD 3. 1). Reads were assigned at the exon level and counts were summarized at the gene level, using default parameters (-t exon -g gene_id), corresponding to unambiguously assigning uniquely aligned singleend reads and reversely stranded reads (-s 2). Following the advised workflow of the Bioconductor package EdgeR 3. 18. 3 (Robinson et al. , 2010; R Core Team, 2018) , we first removed lowly expressed genes, and kept genes that had more than one count per million in at least two libraries. Out of the 24,616 bovine genes annotated in Ensembl 79, 13,107 genes went through the filter.
Normalization of count data was performed using the Trimmed Mean of M-values normalization  (Robinson et al. , 2010)  and dispersion was estimated using a Cox-Reid profile-adjusted likelihood  (Chen et al. , 2014) , using the Bioconductor package EdgeR 3. 18. 3 under R 3. 4. 0 environment  (Robinson et al. , 2010; R Core Team, 2018).
Differential expression analyses of bovine genes The differential expression analysis of bovine genes was carried out using the Bioconductor package EdgeR 3. 18. 3 under R 3. 4. 0 environment  (Robinson et al. , 2010; R Core Team, 2018) , which models gene count data according to a negative binomial distribution and moderates the degree of over-dispersion across genes. A generalized linear model (GLM) was fitted for each gene, and tests for determining differential expression were done using a likelihood ratio test  (McCarthy et al. , 2012). We considered nineteen contrasts to assess differential expressions between two conditions using GLM (Fig  1). Berthier. First, to study baseline differential expression between breeds before infection, we chose ZFU as breed reference, the unique indicine trypanosusceptible breed whose samples were placed at the extremity of the second axis in the PCA (S2 Because animals were repeatedly sampled, the design matrix was constructed according to a nested factorial formula, with animals and time points nested within the breed.
Functional analysis The web-based software application Ingenuity® Pathway Analysis (IPA®, Version 43605602, 2018-04-04) was used to perform the functional analysis of the DEGs, based on the content of the Ingenuity® Knowledge Base (IKB). For all contrasts detailed above, among the 13,107 bovine Ensembl identifiers (with their associated logFC and FDR) that defined the background gene list used in IPA® analyses and that were uploaded into the software application, 11,316 identifiers were mapped to their corresponding object in IKB®. We then checked via Ensembl Biomart the existence of human orthologues for the 1,791 bovine identifiers that were not recognized in IKB® and found 577 human orthologues with a high confidence level and a one-toone match. Human Ensembl identifiers for these 577 genes were then used instead of bovine identifiers in files uploaded into IPA®.
The most upregulated genes during infection were MARCO (macrophage receptor with collagenous structure, significantly upregulated in all breeds except ZFU during infection), and ENSBTAG00000022715 (unannotated gene in Ensembl). Conversely, IGF2 (Insulin-like Growth Factor 2) was downregulated in all breeds, but especially in NDA, whereas ANKEF1 was significantly downregulated in NDA only. Among these genes, 55 were involved in canonical pathways or biological functions that were enriched during infection (S10 Table ) and, among them, 25 were present in at least ten functions. Top represented DEGs were IL2RA, which was involved in 276 functions, IGF2 in 142, GATA1 in 95, GPB2 in 52, and MARCO in 58 functions. Thirty-three canonical pathways were concerned, among which oxidative phosphorylation, mitochondrial dysfunction, sirtuin pathway, and Th1 and Th2 pathways.
Modulation of gene expression involved in metabolism during infection Among the main functions and biological processes identified as significantly enriched in DEGs detected in all breeds during infection, functions related to metabolism are clearly highlighted. First, regulation of lipid metabolism was highly impacted during infection, and could suggest a direct effect of trypanosomes or a modulation of the host immune response  (Kay et al. , 2006; Fessler, 2015). Indeed, several up-regulated genes in all breeds were involved in cholesterol synthesis (e. g. , MVD, IDI1, FDFT1, SQLE, LSS, CYP51A1), and accordingly, lipid metabolism functions (e. g. , synthesis of cholesterol and sterol), cholesterol biosynthesis and mevalonate pathways, and key upstream regulators of cholesterol synthesis (SREBF1 and SREBF2, INSR) were strongly enriched and activated in the common DEGs data set, while cholesterol, as an upstream regulator, was considered inhibited, thus promoting its own synthesis.
Funding This work was supported by the  ANR  grant  n°2011 JSV6 001 01  (http://anr. fr/)and by the  LabEx ParaFrap  ( ANR-11-LABX-0024 ). MGX acknowledges financial support from  France Génomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). The PhD grant of  Moana Peylhard  was supported by  CIRAD  PhD grant program and  IRD.
Data, script and code availability Raw sequences data (fastq, count matrix, experimental design) will be publicly available at GEO (https://www. ncbi. nlm. nih. gov/geo/) under accession number GSE197108 upon publication. Scripts and intermediate tables (DEG analysis, input tables for IPA® analyses, output tables from IPA® analyses) are publicly available in Cirad Dataverse under https://doi. org/10. 18167/DVN1/L9SHAX. SNP genotypes of the experimental cattle are publicly available in Cirad Dataverse under https://doi. org/10. 18167/DVN1/APTZOC and in WIDDE (http://widde. toulouse. inra. fr/widde/).
Research subjects The study, registered on the "clinicaltrials. gov" website under the number NCT02768935, was conducted according to the guidelines of the 1975 Declaration of Helsinki, and approved by the "Comité de Protection des Personnes Sud-Méditerranée", Ethics Committee (protocol 2016-A00793-48, approved on the 16th September 2016). Informed consent was obtained from all subjects enrolled. Male subjects were hospitalized for MI occurrence in the Department of cardiology, University Hospital of Nice, France, from October 2017 to December 2019. MI was defined by clinical symptoms of ischemia associated with ECG changes indicative of new ischemia (new ST-T changes or new left bundle branch block or development of pathological Q waves) and significant troponin rise (High Sensitivity Troponin I, Siemens-Healthineers).
None of the patients received reperfusion therapy but all of them were subjected to coronary angioplasty. T2D was defined based on the glycated hemoglobin (HbA1c) values and the medical records. 16 male subjects without T2D (ND) and 11 with T2D (D) were included (Fig. 1 ). None of the patients had any clinical symptoms of systemic inflammation. For each patient, clinical informations were collected. Biological data were collected at the day of the MI event. Biological analyses were performed in the Laboratories of Clinical Biochemistry and of Haematology at the University Hospital of Nice which are certified by the National French Committee of Accreditation. For all assays, quality controls fell within predefined limits.