dataesr/scientific-paragraphs-categorization huggingface dataset:
Functional enrichment analysis To study the biological mechanisms and gene ontology of the selected genes, we used the g:Profiler software (https:// biit. cs. ut. ee/ gprofi ler/ gost). The Gene Ontology (GO)-biological processes associated with the selected genes were listed; nodes (GO-biological process) with adjusted p-value less than 5% were reported as important. To avoid overly specific and general processes, only GO-biological processes with a size between 30 and 300 were considered. When the large number of GO-terms are affected, redundancy between them is high making it difficult to read graphs. To visualize and interpret those results, we used the EnrichmentMap and AutoAnnotate apps in Cytoscape to visualize the GO-terms network  [39]. When too few biological processes were detected, larger GO-biological processes (up to 500 genes) were also considered.
The number of DEGs is also indicated in or at proximity of the dot. C, D Cytoscape representation of deregulated GO terms ("Biological Process" category) in the turquoise (C) and blue (D) gene modules. The full data are available in Additional file 17: Table  S5B , C. Each dot represents a deregulated GO-Term; their size and color are, respectively, proportional to the number of genes in the GO-term and the enrichment adjusted p-value. GO-Terms are grouped into categories using the Autoannotate Cytoscape app. Insets: Boxplots of eigengene values for each sample in the respective modules (see also Additional file 4: Fig. S4 ) samples with overt Tau pathology. Figure  2B  shows that, compared to control microglia, PAM DEGs were significantly enriched for these different pathological microglia gene signatures.
The sensome gene signature represents a set of membrane-associated proteins and receptors that are selectively expressed in microglia and that help them sense changes in their environment  [36]  (refined in  [37] ). This signature was also significantly affected in PAM. Using a spatial transcriptomic approach, Chen et al. recently identified a plaque-induced gene (PIG) network mainly involving microglial and astroglial genes  [21]. As expected, PAM signature was strongly enriched with PIGs genes, as 52 of the 57 PIGs genes were deregulated in PAM (Fig. 2B ). Genes with similar expression patterns (e. g. , co-expression) are likely to have similar functions and can be grouped into modules by WGCNA  [34]. We performed WGCNA analysis to identify gene modules among the DEGs, and GO-based enrichment analyses to extract the hypothetical biological functions for each of these modules (see Additional file 3: Fig.
2D ; Additional file 17: Table  S5C ). In gene networks or subnetworks, hub genes (i. e. , most highly connected genes) represent master regulators that are likely to play essential roles in controlling the biological response. We used specific applications in Cytoscape (see Materials and methods) to first construct the genes' network of the two WGCNA modules, and second to identify the most connected subnetworks and their potential hub genes. In the turquoise module, the 1074 most highly connected genes were separated into subnetworks using the MCC cluster tool. The ten larger clusters are detailed in Additional file 18: Table  S6 , with hub genes highlighted in dark green. The three larger subnetworks are also shown in Additional file 4: Fig. S4B-D , with hub genes in yellow. In the largest subnetwork, App and Penk which are, respectively, up-and down-regulated in PAM appeared to play orchestrating and redundant roles for controlling chemotaxis and endopeptidase activities (Additional file 4: Fig.
Next, we addressed the question of the extent to which age affected the transcriptomic changes observed in PAM. To meet this goal, RNA-seq data from the 8-mo and 12-mo samples were reanalyzed using a Generalized Linear Model (GLM) model, with Microglia-subtype (PAM vs CM) and Age (8-mo vs 12-mo) as between samples' factors (Additional file 5: Fig. S5 ). Thus, we identified 723 DEG in the Microglia-subtype:Age interaction (raw p-value < 0. 05). Among them, we restricted our analysis to the 179 genes that were significantly deregulated in PAM vs CM (Additional file 5: Fig. S5A ). WGCNA analysis further identified three genes modules (turquoise, blue and brown) that were significantly correlated with the Microglia-subtype trait. The largest module (i. e. , turquoise module) included 96 genes that related to i) Wound healing, Cell projection organization and PKB signaling biological processes; ii) Bacterial invasion and Phagosome KEGG pathways and iii) Ephrin signaling KEGG pathways (Additional file 5: Fig.
3C ; Additional file 19: Table  S7B ). Cellular reaction in this microglia subtype was also demonstrated by deregulation of functions linked to Cell differentiation and Myeloid activation. Among those DEGs, WGCNA analysis identified 2 distinct modules of co-deregulated genes (Table  S7A ). The largest one contained the vast majority of the DEGs (91/102) and corresponded to genes that showed an age-dependent up-regulation in PCM (Fig. 3D ). The second module was limited to only 7 genes, including App and could not be related to a specific biological function (data not shown). On the other hand, gene network analysis identified a cluster of 49 highly connected genes that are strongly associated with the Lysosome (p = 4. 1 × 10 -11 ), the Antigen processing and presentation (p = 2. 4 × 10 -10 ), and the Phagosome (p = 7.
Each dot represents a deregulated GO-Term; their size and color are, respectively, proportional to the number of genes in the GO-term and the enrichment adjusted p-value. GO-Terms are grouped into categories using the Auto-annotate Cytoscape app. (D) Boxplots of eigengene values for each sample in the most abundant WGCNA module of PCM versus CM DEGs (91 genes). Statistical analyses: 2-ways ANOVA with Microglia subtype and Age as between subjects' factors. Subtype p < 0. 001; Subtype:Age p < 0. 01. FDR corrected post hoc tests; **p < 0. 01, ***p < 0. 001. (E) Cytoscape network visualization of highly connected genes in PCM versus CM DEGs. Log 2 expression ratio between PCM and CM are mapped to the nodes using a blue-white-red gradient with red indicating higher expression in PCM, and blue lower expression.
The inner, middle and outer rings represent log-ratio in, respectively, the 4-, 8-and 12-mo samples. Yellow nodes depict hub genes within the network (See figure on next page. ) changed in PCM in an age-dependent manner. WGCNA analysis further identified two gene modules. The largest one (turquoise module) was significantly correlated with the Microglia-subtype factor. Genes of this module were up-regulated in the intermediate and late stage of the disease (Additional file 6: Fig. S6D , upper panel) and related to inflammatory processes, notably Cytokine production, Antigen presentation, Myeloid cell activation and the Phagosome KEGG pathway (Additional file 6: Fig. S6E ). Interestingly, these genes showed opposite regulation in CM being less expressed in 12-mo compared to younger cells. The second module (blue module) was significantly correlated with the Age factor and contained genes whose expression were down-regulated in an age-dependent manner specifically in PCM (Additional file 6: Fig.
To what extent do PAM and PCM differ? To further investigate the extent to which PAM and PCM differ at the transcriptomic level, we searched for genes significantly deregulated between the two microglia subtypes. Considering both the 8-mo and 12-mo samples, we identified 551 DEGs (FDR < 0. 05), of which 80% (446/551) were up-regulated in both PAM vs PCM (Fig. 4A ). WGCNA analysis identified a single module of 497 genes, which was significantly correlated with the Microglia-subtype trait (r = 0. 95; p < 2. 10 -8 ) and more highly expressed in PAM (Fig. 4B , Additional file 20: Table  S8A ). These 497 genes were associated with inflammation related biological processes (Fig. 4C ; Additional file 20: Table  S8B ) and, at least in part, overlapped with those deregulated in PCM vs CM, indicating that PCM present an intermediate reactive state between CM and PAM.
These cell-type specific data also reveal different age-dependent regulations in (See figure on next page. ) Fig. 4  Microglial gene expression remodeling between plaques-associated and plaques-distant AD microglia. A Heatmap of differentially expressed genes in PAM (violet) versus PCM (orange) along aging. The scaled expression value (row Z score) is shown in a blue-red color scheme with red indicating higher expression, and blue lower expression. The full data are available in Additional file 20: Table  S8A. B Boxplots of Eigengene values for each sample in the most abundant WGCNA module of PAM versus PCM DEGs (497 genes). Statistical analyses: 2-ways ANOVA with Microglia subtype and Age as between subjects' factors. Subtype p < 0. 001; Subtype:Age p < 0. 05. FDR corrected post hoc tests; ***p < 0.
001. C Cytoscape representation of deregulated GO terms ("Biological Process" category) in the most abundant module of PAM versus PCM DEGs (497 genes). The full data are available in Table  S8B. D Venn diagram showing the overlap between DEGs in PAM versus CM (FDR < 0. 05, violet circle), PCM versus CM (FDR < 0. 05, orange circle), and PAM versus PCM (FDR < 0. 05, green circle). (E) Bar plot showing the significance (-log10(p-value)) of enrichment of AD GWAS genes in PAM and PCM DEGs. Different AD GWAS sets corresponding to different p-value cutoffs  (Marioni al. , 2018) , were tested. The numbers under brackets indicate the number of ortholog genes at the cutoff, and the numbers in columns, the numbers of PAM or PCM DEG GWAS genes PCM and PAM. Further, in CM, the expression profiles of the 551 DEGs reflected a similarity with PCM (Additional file 8: Fig.
Laser capture microdissection: cells selection and microisolation 1. After staining and dehydration, preparation strips (Arcturus) were applied onto the glass slides containing the cryosections to remove destroyed tissues and/or non-specifically stuck materials; 2. Glass slides containing the sections were placed on the microscope stage over the illuminated area; 3. Images of the zone of interest were first acquired under red-fluorescence microscopy; virtual circles of 70 µm (PAMs selection) or 100 µm (PCMs selection) radius, centered on each TR + plaques, were drawn on the visualizing monitor using the Arcturus software. 4. Fluorescence illumination was then switched to the green channel to visualize GFP+ cells (i. e. , microglial cells), keeping on screen the circles drawn under the red fluorescence channel; 5.
Funding This work was supported by  France-Alzheimer  [Grant AAP 2013 -Micro-Malzh]. The PhD of  AL Hemonnot-Girard  was partly supported by the  Labex ICST  [ ANR 11-LABX-0015 ]. Dr C. Meersseman  was supported by the  FRM  [ ING20140129397]. SDG is supported by  NIH  grants  AG014449 ,  AG017617 , and  AG072599. FJ is supported by  France-Alzheimer  [Grant AAP 2017 -StressAlz].
Our data set collected a total of 1,263 cells, including 674 cells from the LP DC sample and 589 cells from the epithelial one. The t-distributed stochastic neighbor embedding (tSNE) analysis showed that the transcriptional profiles of these two samples were unambiguously distinct (Figure  1C ). Clustering (see STAR Methods) defined a total of 5 clusters for the two samples (Figure  1D ; Table  S1 ): clusters 0 and 1 corresponded almost exclusively to the epithelial DC sample, whereas clusters 2, 3, and 4 were constituted by >95% of cDCs from the LP (Figure  1E ). CD11c (Itgax), CD103 (Itgae), and CD11b (Itgam) were expressed similarly in all clusters (Figure  1F ). These results suggest that epithelial colonization might transcriptionally shape gut cDC2s. Lamina propria cDC2s display a proinflammatory mature-like gene signature Analysis of the clusters corresponding to LP CD103 + CD11b + cDC2s (clusters 2-4) showed that cluster 2 was enriched for cytokine signaling genes (Reactome 2016, p value 2,29e-5), including genes from the interleukin-1 (IL-1) pathway (Wiki Pathway 2015, p value 1,24e-3) (Figure  2A ).
The immature phenotype of intraepithelial cDC2s relies on both ATRA and Muc2 To gain an insight into the epithelial cues involved, we prepared gut organoids from the duodenum (Figure  6D ). Incubation of sorted LP cDC2s with these organoids strongly downregulated their CCR7 surface expression (Figures  6E  and S5B ). In contrast, the expression of CD86 did not decrease. Of note, LP cDC2s were recruited and allowed to physically interact with these duodenal 3D structures embedded into Matrigel. However, they did not penetrate their lumen (Figure  6D ), suggesting that transmigration was not very effective in this experimental system. These data suggest that while CCR7 might be downregulated by soluble cues that diffuse out from organoids, CD86 might rather respond to apical cues present in their lumen and to which sorted cDC2s cannot access in the context of organoids.
These results indicate that diverse epithelial cues might be acting together to imprint the immature phenotype of intraepithelial cDC2s. We turned to the literature to identify potential cues involved. Among the soluble cues described was transforming growth (C) Violin plot showing the expression of Spi1 gene among clusters of single-cell RNA-seq analysis of CD103 + CD11b + DCs. Cluster 0 versus 3: p value 3,11E-11; cluster 1 versus 3: p value 2,84E-18. (D) Retinoic acid receptor alpha expression measured by intracellular staining of CD103 + CD11b + DCs comparing epithelium and lamina propria. Mean ± SEM, data are pooled from 3 independent experiments with n = 2-3 per experiment and compared using t test, *p < 0. 05. (E) Adult SPF C57BL/6J mice were gavaged with olive oil or bisdiamine for 2 days.
Plots represent the number of CD103 + CD11b + cells in the epithelium (left) and lamina propria (middle) from the entire small intestine determined by flow cytometry. Mean ± SEM, data are pooled from 2 independent experiments and representative of 3 independent experiments. Each symbol represents one mouse. Data were compared using t test, **p < 0. 01, ****p < 0. 0001. Right panel: percentage of intraepithelial DCs among total CD103 + CD11b + dendritic cells in control or bisdiamine-treated mice. (F) Flow cytometry analysis of CD103 + CD11b + DC numbers from the small intestinal epithelium analyzed in duodenum, jejunum, and ileum of SPF C57BL/6J mice fed with vitamin A-deficient, excess vitamin A, or control diet for 3 months. Mean ± SEM, data are pooled from 2 independent experiments and compared using two-way ANOVA, *p < 0.
In (G) and (H), mean ± SEM, data are pooled from 2 independent experiments. Total number of cells analyzed: 157 cells (control), 316 (ATRA). Data were compared using Mann-Whitney test, **p < 0. 01, ****p < 0. 0001. (I) Upper panels: zoom-in of CD103 + CD11b + LP DCs migrating intro microchannels and passing through constrictions of between 1. 5 and 2mm for control and ATRA conditions. Scale bar, 40 mm. Lower panels: montage of CD103 + CD11b + LP DCs migrating into microchannels. White stars show CD103 + CD11b + DCs zoomed-in on the upper panels. Scale bar, 100 mm. (J) Heatmap of myosin IIA distribution of LP CD103 + CD11b + DCs passing through the constrictions. Please also see Figure  S4. (legend continued on next page) factor b (TGF-b), which can be secreted by epithelial cells and endow mouse bone marrow DCs with anti-inflammatory properties ex vivo  (Iliev et al.
These precursor cells move into the small intestine from blood vessels that are located within the LP. This implies that the precursor of intraepithelial cDC2s must maneuver through the LP before reaching their niche inside the epithelium  (Farache et al. , 2013) , in agreement with our pre-DC transfer experiments and cell trajectory (E) Geometric mean fluorescence intensity of CCR7 and CD86 comparing LP CD103 + CD11b + DCs incubated with organoids derived from duodenum or control LP cDC2s. Mean ± SEM, data are pooled from 3 independent experiments and compared using t test, ****p < 0. 0001. (F) Sorted LP CD103 + CD11b + DCs were incubated alone or with freshly obtained gut epithelial cells overnight, with either ATRA 1 nM, bisdiamine 45 mM, or anti-TGF-b 10 mg/ml. Differentially expressed markers were analyzed by flow cytometry, and geometric mean fluorescence intensity of CCR7 and CD86 is represented.
Flow cytometry analysis Cells were stained in 2mM EDTA, 5%FBS in PBS. RALDH activity in individual cells was measured using an Aldefluor kit according to the manufacturer's protocol. 7. 5 mM of DEAB was added in different tubes at 37 C for 15 min as Aldefluor fluorescent baseline control. Intracellular staining of cytokines was performed with BD Cytofix/Cytoperm TM Solution Kit, after obtention of cells and incubation with PMA (10ng/ml), Ionomycin (1mg/ml) and Brefeldin A (1mg/ml), during 4h at 37 C/ 5% CO 2. Flow cytometry was performed on Fortessa (BD), LSR II (BD) and FACSAria (BD), and analyzed using FlowJo software version 10. Percentage values were charted with Graphpad Prism version 8.
Single-cell RNA sequencing Single-cell RNAseq library preparation and sequencing CD103 + CD11b + dendritic cells were sorted from the lamina propria and epithelium of 2 pooled small intestines of C57BL/6J mice. Cellular suspensions were loaded on a 10X Chromium instrument (10X Genomics) according to the manufacturer's protocol based on the 10X GEMCode proprietary technology. Single-cell RNA-Seq libraries were prepared using Chromium Single Cell 3' v2 Reagent Kit (10X Genomics) according to manufacturer's protocol as described in  Goudot et al. , 2017. The sequencing was performed using a Rapid Run flowcell of HiSeq 2500 (Illumina) in Paired-end 26/98 to target 100M reads per sample. With an average of 1000 cells per sample, the obtained coverage corresponds to 100,000 reads/cell. Single-cell RNAseq data processing and analysis Cell Ranger (version 2.
0. 1) (from 10x genomics) was used to process Chromium single cell 3' v2 RNA-seq output files. First, cellranger mkfastq (with default parameter) was applied to generate fastq files for the Read1 for cell barcode and UMI and Read2 for transcript. After, cellranger count (with default parameters) aligned the Read2 to the mouse reference genome mm10 using STAR (version 2. 5. 1) (Dobin et al. , 2013). Further analysis was performed using Seurat package (version 2. 3. 4) in R (version 3. 4. 0) (Stuart et al. , 2019). For the filtering step, we excluded the poor-quality cells such as cells with less than 200 unique genes and more than 3600 unique genes per cell (as they are potentially cells doublets). Only genes expressed in 3 or more cells have been preserved. Finally, after filtering step about 1% of cells were discarded and for the rest of the analysis, we used an expression matrix resulting in 13316 genes across 2318 cells (among 2341 cells).
The matrix was normalized using gene expression values for each cell, was divided by the total number of transcripts and multiplied by 10,000. Then, these values were natural log-transformed before downstream analysis. For dimensionality reduction analysis, we first identified 2634 genes as highly variable genes across the single cells (cutoff value for dispersion = 0. 5; cutoff value for average expression = 0). Then we performed PCA using the variable genes as input and determined 20 PCs as significant PCs. These principal components were used as input for t-Distributed Stochastic Neighbor Embedding (tSNE)  (Van Der Maaten and Hinton, 2008). Clusters were identified using the shared nearest neighbor (SNN) modularity optimization based clustering algorithm from the Seurat package (FindClusters function with the following parameters: dims.
QUANTIFICATION AND STATISTICAL ANALYSIS Number of mice and experiments, and statistical tests are reported in each figure legend. Analyses were performed using GraphPad Prism 8 software. Statistical significance was calculated using t test (paired or unpaired) or Mann-Whitney, one-way ANOVA or Kruskal-Wallis test, or two-way ANOVA according to test requirements. Error bars represent SEM and p values <. 05 were considered statistically significant (* p <. 05, ** p <. 01, *** p <. 001, **** p < ,0001).
ACKNOWLEDGMENTS We thank  Institut Curie  for access to the flow cytometry, animal, and the cell and tissue imaging facilities (PICT-IBiSA). We thank  Emmanuel Gautier  for kindly providing Ccr2 À/À mice. C. A. R. thanks  Gehenna Guerrero-Serrano  for teaching her how to prepare gut organoids and the  ARC foundation  for financial support. This project has received funding from  INSERM, Institut Curie , and the  DCBIOL Labex  ( ANR-10-IDEX-0001-02-PSL  and  ANR-11-LABX-0043 ). High-throughput sequencing was performed by the  ICGex NGS platform of the Institut Curie  supported by the grants  ANR-10-EQPX-03  ( Equipex ) and  ANR-10-INBS-09-08  ( France Genomique Consortium ) from the  Agence Nationale de la Recherche  (" Investissements d'Avenir" program ), by the  ITMO-Cancer Aviesan (Plan Cancer III) , and by the  SiRIC-Curie program  ( SiRIC grant INCa-DGOS-4654 ).
RESULTS AND DISCUSSION We previously analyzed whole-blood transcriptomic data from 32 patients with COVID-19 with various disease severity and 13 healthy controls. 4 The main characteristics of these patients are described in Table  I. To uncover the role of complement in disease severity, we determined the RNA levels of 28 complement genes with expression above the lower limit of quantification (see Fig  E1  in the Online Repository at www. jacionline. org). Of those, 19 were differentially expressed depending on disease stage (Fig  1, A ). Hierarchical clustering identified 2 main gene groups (showing high intragroup correlation and including 17 of the 19 genes) displaying distinct patterns of expression: group 1 contained genes whose expressions peaked in moderate disease, whereas the group 2 genes showed increased expression in patients with severe disease and to a greater extent in patients with critical disease, whereas patients with moderate disease had expression levels that were comparable to those in the healthy controls (Fig 1  , B ).
Gene expression analysis Detailed methods were previously reported. E1  Briefly, we analyzed 100 ng (5 mL) of total RNA from each sample by using the NanoString Human Immunology kit v2, following the manufacturer's instructions. Raw RNA counts were adjusted by using 5 housekeeping genes selected from the 15 candidate control genes provided by NanoString, using the geNorm method. Normalized counts were log 10 -transformed for all subsequent analyses. A total of 35 genes belonging to the complement system were identified, of which 28 were expressed above the lower limit of quantification in at least 1 sample. Among those, 19 were differentially expressed in 1 group, as determined by an uncorrected P value less than. 05 of 1-way ANOVA. A heatmap displaying these 19 genes (see Fig  1,  A in the print text) was obtained by using pheatmap (package pheatmap), with the data centered to 0 and scaled to unit variance for each gene.
Statistical analyses Continuous variables were compared among groups by using the Kruskal-Wallis test, whereas categoric variables were compared by using the Fisher exact test or the x 2 test of independence, where applicable. No exact a priori sample size was determined owing to lack of prior knowledge of the expected effect size in such an exploratory study. However, we calculated that a sample size of 10 in each group would allow detection of a Cohen effect size of 1. 5 (ie, a difference in means of 1. 5 times the SD) with a power of 90% and a 5% significance level. Correlation coefficients were determined by using Spearman method to detect monotonic relationships. Receiver operating characteristic area under the curve P values were calculated by using the default method for GraphPad Prism (GraphPad Software, San Diego, Calif).