dataesr/scientific-paragraphs-categorization huggingface dataset:
T. gondii parasites show differential gene expression linked to intracellular protein localization across all identified host clusters We sought to provide further detail on how the parasite modulates its gene expression in response to the host investigating exclusively T. gondii gene expression. In contrast to the infected host cells, T. gondii parasites were actively replicating. Therefore, we assigned cell cycle stages (S/M or G1) across all cells, using previously described subtranscriptomes  (64)  and regressed the cell cycle out of our dataset, resulting in 5 clusters (Tg1-5) across infection conditions (Figure  4A , B; Supplementary Figure  9 ). When comparing the infection conditions with the identified clusters, we show that cluster Tg 1 is characterized by PTG specific expression.
Normalization, dimensionality reduction and clustering Main computational analysis of read-count matrices was performed using the Seurat package (v 4. 0. 3) (106) in R (v 4. 2. 0). The complete R workflow can be assessed in an R markdown (see code availability section). First, count matrices and metadata were loaded and split by the respective species. Ensembl IDs of genes were translated to gene symbols and cells with a mitochondrial gene count above 10% were filtered. Subsequently reads were normalized for sequencing depth using the "SCTransform" function in Seurat, selecting the top 3000 variable genes (107). Thereafter, dimensionality reduction was performed using PCA, computing the first 50 PCs. The first 15 PCs from the analysis were then subjected to shared-nearest-neighbor (SNN) inspired graph-based clustering via the "FindNeighbors" and "FindClusters" functions.
Differential gene expression analysis Differential gene expression analysis (DGEA) of genes in identified clusters was performed using the function "FindAllMarkers" from the Seurat package (v. 4. 0. 5). Following the default option of the method, differentially expressed genes for each cluster were identified using a non-parametric Wilcoxon rank sum test. Differentially expressed genes in a cluster were defined by setting initial thresholds above a logarithmic fold-change of 0. 5 and being present in at least 25% of the cells belonging to the same cluster. Representative marker genes with an adjusted p-value below 0. 05 for each cluster were further selected. p-values were adjusted using a Bonferroni correction including all genes in the dataset. To find representative marker genes with elevated expression in comparison to the remaining clusters, only positive log foldchanges were considered.
Cell type classification and annotation We classified cell types in our data using the clustifyr package (v. 1. 8. 0) (108) and the sorted microarray expression data presented in (9) as reference. In brief, clustifyr adopts correlation-based methods to find reference transcriptomes with the highest similarity to query cluster expression profiles. After converting the microarray data in the correct format for automated cell type annotation, we used the default settings (Spearman rank correlation) to estimate correlation coefficients between the single cells in our clusters and the reference cell types.
Cell cycle scoring To chart the cell cycle phases of individual cells, cell cycle scoring was performed using the "CellCycleScoring" function in Seurat. Scoring was performed based on the host (M. musculus) expression data as well as on the T. gondii data. Here the scoring function was performed using the default parameters. In brief, the function assigns each cell a score based on its expression of G2/M and S phase markers, which are assumed to be anticorrelated in their expression levels. Thus, cells expressing neither are assumed to be in G1 phase. Cell cycle associated marker gene lists for mouse were retrieved from: https://raw. githubusercontent. com/hbc/ tinyatlas/master/cell_cycle/Mus_musculus. csv (20210707). These gene lists are based on orthology analyses of human cell cycle genes presented in (109).
Gene set enrichment analysis (GSEA) To interpret gene expression data and differentially expressed genes further, we performed a gene set enrichment analysis for clusters of interest. This was done using the gseGO function of the clusterProfiler package in R (v. 4. 0. 5). To identify significantly enriched genes, we set the ontology parameter to use gene set enrichment for biological processes and set the gene set parameters to include a minimum of three genes and a maximum of 800 genes in one set. The reference dataset for M. musculus was used, and the resulting p-value from the fast preranked gene set enrichment analysis (fgsea) was adjusted using a Benjamini-Hochberg correction. For differentially expressed genes between cluster M6 and cluster M7, correction of gene set enrichment resulted in nonsignificant p-values, which is why we don't report significant differences for gene set enrichment but only fold changes of genes of interest with their respective biological process (Figure  2A ).
To identify T. gondii specific gene set enrichment we used the ToxoDB database to annotate all T. gondii genes present in our dataset and included computed GO terms for I) function and II) process generated by VeuPathDB utilizing InterPro-to-GO (110). Then the counts for each computed GO-term within the gene list of interest were determined. After the fraction of genes of the geneset of interest was determined and divided by the fraction of the reference (all data) to determine the enrichment for each term and geneset. We termed the resulting value "enrichment score" in our data.
Comparative analysis of gene expression and reference data For reference data integration and comparison we included three publicly available datasets, to I) perform cell type annotations (cell type classification and annotation) (9) II) to compare the response in our data to data investigating PRR agonists  (71)  and III) to compare our results to identified essential genes for T. gondii during INFg stress  (72). For II) we first identified differentially expressed genes for each single condition (PAMP) using DESeq2 v. 1. 36. 0 (111). We investigated expression patterns for II) by intersecting genes identified to be upregulated in BMDCs during stimulation with each agonist. For simplicity we generated bimodal scores resulting in two modes: expression (1) and no expression (0). We further show gene expression of PRR agonists by investigating aggregated expression of individual genes across our experimental conditions, visualized in a heatmap.
Generation of extended T. gondii co-expression To investigate gene expression correlation between genes resulting from the co-expression network analysis and other genes in the dataset, we first performed an asymmetric bicor correlation analysis between all genes present in the co-expression network cluster of interest and all remaining genes to determine which genes exhibit positive or negative expression correlation across cells. Then, we decided for an appropriate cut-off, only considering genes to be positively or negatively correlated based on the distribution of correlation values, resulting in thresholds between 0. 15/-0. 15 and 0. 25/-0. 25. After identifying a subset of genes exhibiting correlation above or below the determined threshold, we calculated pearson correlations between genes of interest and the subset of correlated genes and visualized them using the corrplot package (v.
Acknowledgments We thank the  Microbial Single Cell Genomics and the Eukaryotic Single Cell Genomics facilities at Science for Life Laboratory (SciLifeLab), Sweden , for cell sorting as well as smart-seq2 library preparations, respectively, and the  National Genomics Infrastructure, SciLifeLab  for sequencing. The computations were performed using resources provided by SNIC through  Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX). We thank  Moritz Treeck  and the  Treeck group  for their scientific advice on Toxoplasma biology.
Funding JA is supported through grants from the  Swedish Research Council  ( VR 2021-05057 ), and the  Swedish Foundation for Medical Research (SSMF). JH and MIMS are supported by  Vetenskapsrådet  ( VR 2021-06602 ). ABh was supported by a grant from the  Swedish Research Council  ( VR 2018-0241  to ABa). ABa was funded by  VR 2022-00520. FH is in part funded by the  Sven and Lily Lawski foundation.
In obese animals, CD64 + Macs appeared heterogenous with only a subset acquiring CD11c expression (Figure  1A ) as previously described  9. Macs recruited to the obese vWAT most likely fall into this CD11c-expressing fraction, although CD11c did not seem to highlight a clearly definite population. This prompted us to find markers unequivocally discriminating trMacs from those recruited during obesity. Our past experience incited us to look for markers highly expressed on trMacs, rather than recruited macrophages, as trMacs usually express specific markers that makes them more easily recognizable  7. Using the Immunological Genome (Immgen) database, we found CD209a, LYVE1, SIGNR1, MRC1, CD301 and CD38 as candidate markers. Among them, CD38, MRC1, CD301 and CD209 were uniformly expressed by trMacs in the vWAT (Figure  1B ) and combining those markers distinctly identified trMacs in this depot (Figure  1C ).
Then, we found lysosomes (Figure  1F ) and lipid droplets (Figure  1G ) were more abundant in CD38 hi MRC1 hi Macs as compared to their CD38 lo MRC1 lo counterparts, suggesting a stronger lipid catabolic activity. A similar increased lysosomal content was observed in CD38 hi MRC1 hi Macs of Ob/Ob mice as compared to CD38 lo MRC1 lo cells (Figure  S1F ). Next, we set out to know more about the molecular signature of those Macs subsets, especially the CD38 lo MRC1 lo population that arise during obesity. Our analysis of a publicly available transcriptomic dataset of the mouse obese vWAT revealed that 30% of the genes induced at the whole tissue level are genes we previously found induced during monocyte to macrophage differentiation in vivo 10 (Figure  S2 ). Strikingly, more than half of the top 20 induced genes, which include Mmp12, Gpnmb, Il7r, Rnf128, Trem2 and Itgax, were associated with monocyte to macrophage differentiation (Figure  S2 ).
Macrophage subsets kinetics and metabolic dysfunctions onset over the course of diet-induced obesity in C57BL6/J mice. We then assessed the dynamics of the two Mac subsets defined above during the course of dietinduced obesity. For that purpose, male mice were fed a high-fat diet (HFD) over a 16-week period, and cohorts were analyzed every 4 weeks. Body mass increased overtime due to increased fat mass while lean mass remained stable (Figure  2A ). Interestingly, vWAT mass peaked after 8 weeks of HFD, and then lost its ability to further expand over the next 8 weeks of HFD (Figure  2B ). Fasting glucose levels slightly increased overtime while insulinemia raised dramatically (data not shown), leading to a progressive increase in the HOMA-IR, an index of insulin resistance (Figure  2C ). Notably, we observed the HOMA-IR raised at the time vWAT lost its expandability potential.
Human subjects. Omental WAT samples were obtained from subjects with obesity enrolled in the bariatric surgery program at the Nutrition Department of Pitié-Salpétrière hospital in Paris. Ethical approval was obtained from the Research Ethics Committee of Hôtel-Dieu Hospital (CPP Ile-de-France N°1). Informed written consent was obtained from all subjects and the protocol was registered on http://www. clinicaltrials. gov (NCT01655017, NCT00476658). The clinical characteristics of the subjects used for Figure  4I  are described in Table  S1. Before BS, height and weight were measured by standard procedures. Blood samples were collected after a 12 hours overnight fast and clinical variables were measured as described  28. Glucose intolerance and type 2 diabetes were diagnosed in accordance with the American Diabetes Association definition.
Draq7 or propidium iodide were used to exclude dead cells. Intracellular Ki-67 staining was performed using the Foxp3 staining kit from Thermo Fisher Scientific. Bodipy staining was performed on cells fixed using the Cytofix/Cytoperm TM kit from BD Biosciences. Lysotracker Red staining was performed accordingly to the manufacturer specifications. When absolute cell counts are provided, a fixed number of nonfluorescent beads (10000, 10-µm polybead carboxylate microspheres from Polysciences) was added to each tube. The formula number of cells = (number of acquired cells × 10,000) / (number of acquired beads) was used. Cell counts were finally expressed as a number of cells per milligram of tissue. Data were acquired on a BD LSRFortessa™ flow cytometer (BD Biosciences) and analyzed with FlowJo software (Tree Star).
mRNA extraction, reverse transcription and quantitative PCR. RNAs from epidydimal adipose tissue samples were prepared using the RNeasy Lipid Tissue kit (Qiagen) and cDNAs were synthesized using the Transcriptor First Strand cDNA Synthesis Kit (Roche). RNAs from sorted cells were prepared using the RNeasy Plus micro kit (Qiagen). RNA was reverse transcribed using the SuperScript VILO cDNA synthesis kit (Thermo Fisher Scientific). Quantitative PCR analyses were performed using a LightCycler® 480 real-time PCR system and dedicated software (Roche). Gene expression was normalized to a minimum of 2 housekeeping genes using the Roche LightCycler® 480 software. All primer sequences are available upon request.
Histological analysis of the liver and adipose tissue. Epidydimal tissue and liver samples are fixed in 10% formalin buffer and then embedded in paraffin to produce tissue sections (5 µm). Liver sections were stained with hematoxylin and eosin to identify steatotic areas. Epidydimal adipose tissue collagen content was determined by staining sections with picrosirius red (Sigma-Aldrich) and photographs are taken under polarized light to reveal crosslinked collagens. For quantitation of adipocyte size, adipose tissue sections were stained with hematoxylin and eosin, and the Calopix software was used to measure average adipocyte area as well as the distribution of adipocyte sizes.
Publicly available microarray and single-cell mRNA sequencing (scRNAseq) datasets. Epidydimal adipose tissue microarray data of lean and obese male mice (GSE36033, in which mice were fed with the same high-fat diet than in our current study)  30  were used to obtain a signature of the genes induced in the vWAT during obesity. We also used our previous microarray profiling of sorted mouse blood monocytes and thioglycolate-elicited peritoneal monocyte-derived macrophages (GSE15907)  10  to obtain a gene signature typical of monocyte to macrophage differentiation in vivo. Finally, microarray profiling of omental adipose tissue samples from patients with obesity and lean controls (GSE55200)  31  were used to determine the expression levels of several genes of interest. Human omental adipose tissue scRNAseq data were obtained from a published study  32  and analyzed using the Seurat R package.
mRNA sequencing, data preprocessing and analysis. Total RNA preparation was performed from 20000 sorted cells (macrophage subsets or adipose tissue CD9 hi progenitors) using the RNeasy Plus Micro Kit (Qiagen). cDNA libraries were generated using the KAPA mRNA HyperPrep kit (Roche). RNA-Seq libraries were sequenced on an Illumina NovaSeq 6000 (30 million reads per sample). RNAseq analysis was completed using the Eoulsan 2. 5 pipeline. The minimap2 version 2. 18 index was used to map raw reads to the genome and data normalization was performed with DESeq2. A linear model for microarray and RNAseq analysis (LIMMA) was conducted to select differentially expressed genes with a 1. 5-fold change cutoff between at least two conditions. Annotated genes with a count mean over 10 in at least one condition and a coefficient of variation of more than 0.
L-R pairs targets are integral to the algorithm and consist of a prebuiltprior model that integrates existing knowledge on ligand-to-target signaling pathways. Here, the "geneset" comprised genes that were downregulated (with a minimum of 25% decrease in gene expression) in CD9 hi progenitors of Ccr2 -/-mice that present a marked decrease in CD38 lo MRC1 lo Macs accumulation in their vWAT. Once run, Nichenet returns a list of prioritized ligands with the target genes they are predicted to control. All analyses were conducted according to the NicheNet tutorial (https://github. com/saeyslab/nichenetr).
Statistical analysis. Statistical significance of differences was performed using GraphPad Prism (GraphPad Software). Twotailed Student's t-test was used to assess the statistical significance of the difference between means of two groups. When more than 2 groups were studied, analysis of variance was performed followed by Newman-Keuls as post-hoc test. Graphs depicted the mean ± SEM. Statistical significance is represented as follows: *P<0. 05, **P<0. 01, ***P<0. 001 and ****P<0. 0001. (E) Expression of the proliferation marker Ki-67 by CD38 hi MRC1 hi and CD38 lo MRC1 lo macrophages in the vWAT of obese mice (n=5 mice). (F) Quantification of lysosomes with Lysotracker staining of CD38 hi MRC1 hi and CD38 lo MRC1 lo macrophages in the vWAT of obese mice (n=3 mice). (G) Intracellular lipid levels were determined by Bodipy staining of CD38 hi MRC1 hi and CD38 lo MRC1 lo macrophages in the vWAT of obese mice (n=3 mice).
ACKNOWLEDGMENTS This work was supported by grants to ELG from the  Agence Nationale pour la Recherche  ( ANR-17-CE14-0023 ,  ANR-17-CE14-0009 ,  ANR-21-CE14-0023 ). GM and KC received funding from the  Agence Nationale pour la Recherche  ( ANR-17-CE14-0009 ). GM received funding from the  Agence Nationale pour la Recherche  ( ANR-21-CE14-0023 ) and the  European Foundation for the Study of Diabetes (EFSD). Melissa Ouhachi  received a one-year doctoral fellowship from the  Nouvelle Société Française d'Athérosclérose (NSFA). Marina Chaves de Oliveira  and  Amanda Oliveira  were supported by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES, Brazil).
To acquire samples, we used a BD FACSCanto II, and the results were analyzed by FlowJo 10. 6 software. Cells. 293T and TZM-bl cells were maintained in culture in Dulbecco's modified Eagle medium (DMEM) (Invitrogen) supplemented with 10% FBS and Pen-Strep. Primary CD4 1 T cells were purified from human peripheral blood by density gradient centrifugation (lymphocyte separation medium 1077; PAA Laboratories, Merck) followed by immunomagnetic selection (Miltenyi). More than 98% of the cells were CD4 1 CD3 1. For activation, T cells were treated either with phytohemagglutinin (PHA) or with anti-CD3/CD28/CD2-coated beads (Miltenyi). To stimulate T cells with PHA, purified CD4 1 T cells maintained at 5 Â 10 6 million/mL in Roswell Park Memorial Institute (RPMI) medium were treated overnight with PHA (1 mg/mL; Oxoid, Thermo Fisher).
Apoptotic or dividing cell nuclei were manually excluded from the analysis due to aberrant nuclear staining. Image manipulation was conducted using Fiji software. The mean fluorescence intensity for the NF-k B staining inside the nuclei was then quantified in the different conditions, and statistical differences were analyzed using GraphPad Prism 9 software. Virological synapse formation and visualization. Activated T cells were infected with NL4-3 as described before and left in culture untreated or in the presence of E. coli cells at a 50:1 ratio of bacteria to T cells. Three or 4 days after infection, cells were collected and mixed at a 1:1 ratio with uninfected target cells that had also been kept in culture without or with bacteria. Mixed donor and target cells were seeded onto a poly-lysine-coated glass coverslip before being cultured at 37°C for 1 to 2 h to let the virological synapses form.
Virological synapses were visualized using a confocal microscope (LSM700; Zeiss), and images were processed and analyzed using Fiji and Imaris software. Statistical analysis. Pearson correlation matrix and interactions between HIV-1 replication and ICAM-1 and CD25 expression were calculated with RStudio 2021. 09. 1 using R version 4. 1. 0. The following packages were used: readxl (https://CRAN. R-project. org/package=readxl), pheatmap (https://CRAN. R-project. org/package=pheatmap), corrplot (https://github. com/taiyun/corrplot), caret (https://CRAN. R-project. org/ package=caret), and interactions (https://CRAN. R-project. org/package=interactions). Expressions and HIV-1 replication data were normalized on the "no bacteria" condition. The Pearson correlation matrix was plotted with the function rquery.
ACKNOWLEDGMENTS We thank  J. Dufloo ,  F. Mammano , and  F. Roesch  for critical readings and editing of the manuscript. We thank members of the  Virus and Immunity Unit , especially  T. Bruel  and  N. Saunders , for helpful discussions and assistance. We thank the  Imagopole (Institut Pasteur)  and the  IAH (Image Analysis Hub)  for help in image acquisition and analysis. We especially thank  J. Elis  for his continuous support. Work in the O. S. laboratory is supported by  Institut Pasteur ;  Urgence   COVID-19 Fundraising Campaign of Institut Pasteur ;  Fondation pour la Recherche Médicale  (FRM); ANRS; the  Vaccine Research Institute  ( ANR-10-LABX-77 );  Labex IBEID  ( ANR-10-LABX-62-IBEID );  ANR/ FRM   Flash Covid PROTEO-SARS-CoV-2 ;  ANR Coronamito ; IDISCOVR; the  INCEPTION program  ( Investissements d'Avenir grant   ANR-16-CONV-0005 ); the  French Government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence ,  Integrative Biology of Emerging Infectious Diseases  (grant no.
ANR-10-LABX-62-IBEID ); HERA European funding; and the  NIH PICREID  (grant no. U01AI151758 ). M. Z. is supported by the  ANRS. Y. d. S. -A. is supported by  Calmette & Yersin funding  ( Y-SU11007 [PA/MLH/N 70/12 ]). J. M. G. and S. L. were supported by a grant from the  French government 's  Investissement d'Avenir Program, Laboratoire d'Excellence ,  Integrative Biology of Emerging Infectious Diseases  (grant  ANR-10-LABX-62-IBEID ) and the  Fondation pour la Recherche Médicale  (grant  DEQ20180339185 ).
Total internal reflection fluorescence microscopy (TIRFM) TIRFM was performed using a Till PHOTONICS iMIC microscope equipped with an oil immersion objective (Apo N 100×, NA1. 49 Olympus America Inc. ), a heating chamber maintained at 37 • C and two cameras: a cooled iXonEM camera and an iXon3 897 Single Photon Detection EMCCD Camera (Andor Technology). The critical angle was determined prior to imaging by scanning through incident angles from 0 • to 5 • to maximize evanescent wave induced fluorescence  (Mari-Anais et al. , 2016a , 2016b). Excitation was performed with 491 and 561 nm lasers. For the phagosome closure assay, images were acquired at 50 ms per frame in bright field illumination, TIRF mode, and in epifluorescence mode with polychrome illumination at 3 μm increment. TIRFM image stacks were processed using ImageJ Color Profiler software (NIH).
Microscopy was performed on a Leica SR GSD (DMI 6000B) equipped with an oil immersion objective (HCX PL APO 160x, NA1. 43, Leica), 532 and 642 nm lasers and an EMCCD Andor iXon Ultra 897. Protein enrichment and localization were quantified using ThunderSTORM  (Ovesny et al. , 2014)  and SR-Tesseler  (Levet et al. , 2015). Tesselation based colocalization analysis was performed using Coloc-Tesseler software  (Levet et al. , 2015).
ACKNOWLEDGEMENTS The authors thank the IMAG'IC facility members and in particular  Beatrice Durel  for their help with dSTORM, and  Christophe Le Clainche  ( Institute for Integrative Biology, Gif-sur-Yvette, France ) for discussions on the project. IMAG'IC is a member of the  National Infrastructure France BioImaging  ( ANR-10-INBS-04 ). The authors thank  Sophie Echène  for the illustrations in this article. Work in the F. N. Laboratory was supported by  CNRS ,  Inserm ,  Université Paris Cité , and grants from  Agence Nationale de la Recherche  ( ANR 16-CE13-0007-01 ,  ANR 20-CE13-0017-01 ) that included AM's salary and the gratifications to support FA and RW. MD is supported by a PhD fellowship from the  French Ministry for Higher Education, Research and Innovation.