dataesr/scientific-paragraphs-categorization huggingface dataset:
Similar to the authors of this commentary, we emphasize, in scRNA-seq data analysis, the necessity for clearly distinguishing distinct DC types from different activation states of the same DC type. To achieve this aim, proper strategies are required for robust assignment of a cell-type identity to each individual cell, irrespective of its activation state, in a manner enabling resolution of the heterogeneity of mixed-cell clusters. This can be achieved by integrating phenotypic and transcriptomic data (as enabled with index sorting or CITE-seq  33  ) or by performing a gene set enrichment analysis at the level of individual cells, by using the single-cell CMap algorithm  56  with specific composite up/down gene modules carefully defined from an independent dataset. We used both strategies in the present study as a technical guide to help readers in their future analyses of scRNA-seq data.
A first Seurat analysis was performed only on cells from uninfected mice (345 cells after QC, 2 of which were removed due to lack of index sorting data). Clusters were identified based on either gene expression using Seurat (k-nearest neighbor = 5; resolution = 0. 2) or phenotypic marker expression using Rphenograph 57 (v. 0. 99. 1) (number of nearest neighbors k = 20), taking into account the protein expression for GFP, B220, Ly6D, XCR1, CX3CR1, BST2, SiglecH, CCR9, CD11b and CD11c (but not for tdTomato). A CMap analysis  56  was performed on this dataset using cell-type-specific signatures (tDCs, cDC2s, cDC1s and pDCs) established on reanalysis with the BubbleGUM GeneSign module  58  of a published  29  , independent, bulk RNA-seq dataset (Gene Expression Omnibus (GEO) accession no. GSE76132), and a relative pDC_vs_pDClike signature retrieved from a previously published study  4.
The GSE76132 dataset encompassed four cell types, tDCs, cDC2s, cDC1s and pDCs, analyzed through bulk RNA-seq. We retrieved and reanalyzed these data to generate signatures for each DC type compared with the three others. Hence, we ended up with four composite signatures, one for each DC type, with its 'UP' and 'DN' genes when compared with the three other DC types. The study by Rodrigues et al. 4  encompassed only two cell types, pDCs and pDC-like cells, analyzed through scRNA-seq. Hence, from this dataset, we retrieved one composite signature encompassing the genes expressed higher in pDCs above pDC-like cells ('UP') and reciprocally the genes less expressed in pDCs than in pDC-like cells ('DN'). The CMap algorithm has been made available on Github (https:// github. com/SIgN-Bioinformatics/sgCMAP_R_Scripts), as well as an example and recommendations on how to use it (https://github.
After several washings with PB, sections were then stained in PB, 0. 1% Triton X-100 and 2% BSA with secondary antibodies (Supplementary Table ) for 2 h at 4 °C. To analyze YFP signal, after the incubation with secondary antibodies, sections were washed and incubated with PB, 0. 1% Triton X-100, 2% BSA and 5% rabbit serum for 30 min at room temperature. Sections were then stained with anti-GFP antibodies directly labeled with Alexa 488 for 2 h at 4 °C. Finally, sections were washed with PB and mounted with a coverslip and Prolong Antifade Gold mounting medium (Life Technologies). Whole sections were acquired by spectral confocal microscope (Zeiss LSM 880) with ×20 or ×40 objectives. Pictures were then analyzed using ImageJ v. 1. 52p software, including through the development of specific macros (https://github.
b, c, The expression of CD11c (b), GFP (c) and of CD26 vs CD64 (d) was analyzed on Ly6C + tDC cells (orange), Ly6C neg tDC (purple) and CD26 neg Ly6C neg cells gated as shown in (a). e, CD11c high tDC (purple) and pDC-like cells (green) were gated from the spectral flow cytometry dataset according to the strategy used with the initial regular flow cytometry dataset (as shown in Fig. 3b), and then analyzed for CD26 vs Ly6C expression, for comparison with the gating strategy used to identify Ly6C neg tDC and Ly6C + tDC (panel a). f, Analysis of the expression of GFP vs the indicated cell markers upon gating on Ly6D neg CX 3 CR1 neg cells. g, Percentages (mean ± SD) of GFP + cells (top) or tdT + cells (bottom) in indicated cell populations gated as shown in (a). For (a) and (f), the analysis shown is representative one ZeST mouse out of 9 tested.
For (b-e) n = 2 for NI, n = 4 for MCMV 36 h and n = 3 for MCMV 48 h. https://doi. org/10. 1038/s41590-023-01454-Analysis of the FB5P-seq data for cells from uninfected mice to generate DC type-specific signatures for helping annotation of the total FB5P-seq dataset. a, UMAP dimensionality reduction of the gene expression data for DC types isolated from the spleen of 3 uninfected ZeST mice. Cells were index sorted into the 5 DC types studied (see Fig. 3b), and used for single cell RNA sequencing. After quality controls, 343 cells were kept for the analysis. The color code indicates belonging of the cells to the 7 Seurat clusters obtained. b, Projection onto the UMAP space of the phenotype of cells based on their belonging to the Rphenograph clusters (color code) obtained upon re-analysis of the fluorescent signals for 10 of the phenotypic markers acquired during index sorting as listed in panel c.
c, Annotation of the Rphenograph clusters for DC type identity based on mean fluorescent intensities per marker and cluster as shown on the heatmap. d, Number of cells belonging to the intersections between Seurat (rows) and Rphenograph (column) clusters. Seurat clusters were annotated for cell types based on analysis of their marker genes (see Supplementary Table). e, Heatmap showing sgCMAP scores of individual cells (rows) for DC type-specific signatures (columns) generated from published independent RNA-seq datasets. Hierarchical clustering was performed, using the Pearson's minus one metric for signatures (columns), and the Euclidian distance for individual cells (rows), with annotation of individual cells (rows) for i) belonging to Seurat clusters, ii) belonging to Rphenograph clusters, iii) sorting phenotype, and iv) final cell type assignment.
Acknowledgements We thank  CIPHE  for the generation of Siglech iCre and Pacsin1 LSL-tdT mice and their assistance in the breeding of mice, as well as the staff of the CIML mouse houses, flow cytometry, histology, genomics bioinformatics and imaging (ImagImm) core facilities. We thank  S. Henri  ( CIML ) for the generous gift of the Zbtb46 GFP mice. We thank the two master trainees,  A. Trinh  and  D. Anselmo , as well as  M. Toxé , for technical assistance in some experiments. We thank  C. Dong  for help in the preprocessing of the FB5P-seq data. We thank the  Centre de Calcul Intensif d'Aix-Marseille  for granting access to its high-performance computing resources. We thank  M. Bajénoff  for discussions and advice. This research was funded by grants from the  Fondation pour la Recherche Médicale  (nos.
DEQ20110421284  and  DEQ20180339172 ,  Equipe Labellisée , to M. D. ) and from the  French National Research Agency (ANR , no. ANR-21-CO12-0001-01 , 'RIPCOV' to M. D. and no. ANR-21-CE15-0044-01 , 'DECITIP' to E. T. ). We also thank.
Technical Report https://doi. org/10. 1038/s41590-023-01454-9 the DCBIOL Labex (ANR-11-LABEX-0043, grant no. ANR-10-IDEX-0001-02 PSL*), the A*MIDEX project (grant no. ANR-11-IDEX-0001-02), funded by the French government's Investissements d'Avenir program managed by the ANR, and institutional support from CNRS (French National Centre for Scientific Research), INSERM (French National Institute of Health and Medical Research) and Aix-Marseille Université and Marseille Immunopole. This work was supported by the ANR through the Investments for the Future program (France-BioImaging, no. ANR-10-INBS-04).
, 2020; Sorokin et al. , 2021). The genome statistics are given in Tables  S1  and S2. For genome-based phylogenetic reconstructions, 122 archaeal single-copy conservative marker genes were used as described previously  (Parks et al. , 2020; Rinke et al. , 2021). The genome tree was performed using classify_wf workflow in GTDB-Tk (V1. 7. 0) based on Release 207 in Genome Taxonomy Database (https://gtdb. ecoge nomic. org/) (Chaumeil et al. , 2019). The trees were built using the IQ-TREE 2 program  (Minh et al. , 2020)  with fast model selection via ModelFinder  (Kalyaanamoorthy et al. , 2017)  and ultrafast approximation for phylogenetic bootstrap  (Hoang et al. , 2018)  as well as approximate likelihood-ratio test for branches  (Anisimova & Gascuel, 2006). Phylogenetic trees for 16S rRNA gene sequences were identified with BLAST  (Altschul et al.
, 1997) , aligned with the SILVA alignment tool  (Pruesse et al. , 2007)  and manually inserted in ARB database release 138. 1  (Ludwig et al. , 2004; Quast et al. , 2013; Yarza et al. , 2014). The phylogenetic tree was generated with the neighbour-joining algorithm and Jukes-Cantor distance matrix of ARB program package. The 1000 bootstrap resampling was performed to estimate the reproducibility of the partitions in the tree. Wholegenome comparison of Halorhabdus and Haloferax isolates was conducted by using three different methods: Average Nucleotide Identity (ANIb and ANIm), using JSpeciesWS web server; Average Amino acid Identity (AAI) by the AAI calculator online of Kostas lab (http://enve-omics. ce. gatech. edu/aai/index) and DDH by the Genome-to-Genome Distance Calculator 2. 1 online tool (http://ggdc.
A 120 min gradient of 2%-40% Buffer B (100% acetonitrile, 0. 1% formic acid [v/v]) in Buffer A at a constant flow rate of 250 nL/min was used. Data acquisition was performed with a Q-Exactive HF-MS. Data were acquired using an ion spray voltage of 1. 8 kV and an ion transfer temperature of 270°C. All data were acquired using data-dependent acquisition (DDA) and in positive mode with Xcalibur 4. 1 software. For the MS2 scan, we selected the top 15 most-abundant precursors with charges of 2 + to 6 + in MS 1 scans for higher energy collisional dissociation (HCD) fragmentation with a dynamic exclusion of 27 s. The MS1 scans were acquired in the m/z range of 390-1700 Da with a mass resolution of 60,000 and automatic gain control (AGC) target of 3E6 at a maximum Ion Time (ITmax) of 20 ms. The threshold to trigger MS2 scans was 8E3; the normalised collision energy (NCE) was 27%; the resolved fragments were scanned at a mass resolution of 15,000 and AGC target value of 2E5 in an ITmax of 120 ms.
Peptide identification from raw data was carried out using MASCOT v2. 4 search engine through the Protein Discoverer 2. 4 Software (Thermo Scientific). Database search was performed against the SVX82 and SVXNc genome sequences. The following parameters were used for the searches: tryptic cleavage, up to two missed cleavage sites allowed, and tolerances of 10 ppm for precursor ions and 0. 02 Da for MS/MS fragment ions and the searches were performed allowing optional methionine oxidation and acetylation protein N terminal, and as fixed modification, carbamidomethylation of cysteine. The false discovery rate (FDR) scores were adjusted by a percolator algorithm. The acceptance criteria for protein identifications were FDR scores <1% and at least one peptide identified with high confidence.
Phylogenomic identification of nanohaloarchaeal isolates The phylogenetic positioning of novel nanohaloarchaeal strains BNXNv and SVXNc was approximated using the master alignment in SILVA Release 132SSURef NR99  (Pruesse et al. , 2007)  and inferred using maximum parsimony criteria within the ARB software  (Ludwig et al. , 2004 ) (Figure  S3 ). This suggested that the closest neighbours of the BNXNv nanohaloarchaeon are the partially sequenced clones LDS1_clone18, 25, 27 and 34 (GenBank GQ374932, GQ374936, GQ374938 and GQ374944, respectively; 95. 44% identity) from the Lara deep storage crystalliser pond (Australia), all belonging to Clade IV of the candidate phylum Nanohaloarchaeota  (Ghai et al. , 2011; Narasingarao et al. , 2012). Together with uncultivated Dead Sea nanohaloarchaeal strain M3_22 (CP046106; 96.
16% sequence identity); Dead Sea nanohaloarchaea strain M3_22 (CP046106; 90. 09% sequence identity) and Ca. Nanohalobium constans LC1Nh (CP040089; 89. 97%). The separate genus-level position of both novel nanohaloarchaeal strains was confirmed by comparative sequence analysis of a concatenated alignment of the 122 single-copy archaeal marker genes using the Genome Taxonomy Database (GTDB) framework  (Parks et al. , 2018)  (Figure  1 ). Recently, it was suggested that the phylum Nanohaloarchaeota should be divided at least into three class-level groups  (Rinke et al. , 2021; Zhao et al. , 2022). One of these proposed groups comprises the cultivated Ca. Nanohaloarchaeum antarcticus  (Hamm et al. , 2019) , the second group consists of Ca. Nanosalinicola of uncertain taxonomic rank (that currently lacks cultivated representatives) and the third group-Candidatus Nanohalobia-includes cultivated strains Ca.
The genome of the second representative of the xylan-degrading three-membered culture, Haloferax lucertense SVX82, contains only six genes encoding CAZymes. Among those, there are two GH3 βglucosidases, two GH13 α-amylases and two GH15 glucoamylases (Data S3). This set of CAZymes is very poor compared to Halorhabdus sp. SVX81 and prevents Hfx. lucertense SVX82 from feeding on both xylans and oligoxylans. Thus, the extracellular formation of D-xylose monomers by the xylanolytic SVX81, confirmed by chromatography, is vital to the success of SVX82 in the xylan-degrading consortium. Genome analysis showed that in SVX82 cells, D-xylose is most likely cleaved to α-ketoglutarate via a partially promiscuous oxidative pathway  (Sutter et al. , 2017) , which involves specific D-xylose dehydrogenases, xylonolactonase and xylonate dehydratase (Figure  5A ).
We were aware that the presence of gluconeogenic nanohaloarchaea in the binary culture could influence the final calculation of the total intracellular concentration of glucose-6-phosphate, so we calculated their approximate total cell volume in the binary culture | 15 XYLANOLYTIC HALOARCHAEA-NANOHALOARCHAEA ASSOCIATION (see Experimental procedures). Even considering that Ca. Nanohalococcus SVXNc formed an association with the host cell at a median multiplicity of six cells per host cell (with all host cells associated with nanohaloarchaea), the total biomass (as biovolume) of nanohaloarchaea did not exceed 2% of the total (Haloferax + nanohaloarchaea) biomass. In addition, the expression of gluconeogenic enzyme fructose 1,6-bisphosphatase Fbp (SVXNc_0148) was checked in the Ca. Nanohalococcus's transcriptome (Data S1).
Normalised values of 163 ± 9 fragments per kilobase of gene per million mapped fragments (FPKM) indicated that this enzyme was expressed, although at significantly lower levels than the glycolytic enzyme, phosphofructokinase Pfk2 (SVXNc_0128) (380 ± 43 FPKM). The predominance of glycolysis over gluconeogenesis in Ca. Nanohalococcus cells was further confirmed by proteome analysis (Data S3). The normalised spectral abundance factor (NSAF), calculated for phosphofructokinase Pfk2 (SVXNc_0128), was about 0. 138 ± 0. 037, while for fructose 1,6-bisphosphatase Fbp (SVXNc_0148), this value could not be determined due to the presence of Fbp-related unique peptides in only one of three analysed replicas (Data S3). Taken together, these data suggest that the increase in the number of end products of gluconeogenesis in Haloferax host cells indeed can correspond to the demand for carbon and energy source in the ectosymbiont Ca.
Funding information Agence Nationale de la Recherche , Grant/ Award Number:  ANR-20-CE20-0009 ;  Centre for Environmental Biotechnology  Project, partly funded by the  European Regional Development Fund via the Welsh Assembly Government; European Union  (" NextGen-eration EU/ PRTR ");  H2020   Food, Grant/Award Number :  INMARE  Project (Contract  2634486 ) and  FUTURENZYMES  Project (Contract  101000327 );  Ministerio de Ciencia e Innovación ,  Agencia Estatal de Investigación (AEI)  (Digital Object Identifier  10. 13039/501100011033 , Grant/ Award Number:  PID2020-112758RB-I00 ,  PDC2021-121534-I00  and  TED2 ;  SYAM-Gravitation Program  of the  Dutch Ministry of Education, Culture and Science , Grant/ Award Number: 24002002.
Hi-C experiments Single-nucleus and population Hi-C processing and library preparation were carried out as previously described  [18]  using a haploid mouse embryonic stem cell line  [33]. Libraries were sequenced on an Illumina HiSeq platform with 2x125bp paired end reads. Details of the samples used in this study can be found in Data Table  I. The sequencing libraries were processed using our software package "NucProcess", available at: https://github. com/TheLaueLab/ nuc_processing. Briefly, this software takes paired FASTQ sequence read files, a reference genome sequence and knowledge of experimental parameters (restriction enzyme type and the range of size of DNA fragments sequenced in the library) to create processed, single-cell or population contact files  [12, 18].
RNA-seq experiments RNA-seq samples were prepared for sequencing using a haploid mouse embryonic stem cell line as previously described by Stevens et al 2017  [18] , and the data deposited at Gene Expression Omnibus, accession number GSE80280see Data Table  I. Briefly, the analysis of transcript abundance estimates for all annotated mouse transcripts (Ensembl v71, GRCm38/mm10) and ERCC spike-in sequences were obtained using Kallisto v0. 42. 4  [34].
CTCF-peaks experiments CTCF chromatin immunoprecipitation (ChIP) sample from haploid mouse embryonic stem cell line was prepared and processed as previously described by Stevens et al 2017  [18] , and the data deposited at Gene Expression Omnibus, accession number GSE80280-see Data Table  I. Briefly, fixation was carried out using 1% formaldehyde for 10 minutes at room temperature and quenched with 150 mM glycine. DNA was fragmented in the presence of 1. 0% SDS using a Bioruptor sonication instrument (Diagenode) producing a size range of 200 to 300 bp. The libraries were prepared using the NEXTflex Rapid DNA-seq kit (Illumina) and sequenced on a Hiseq-4000 platform with 2 x 50 bp paired end reads. Paired-end reads were trimmed with Trim Galore v0. 4. 0 using default parameters prior to mapping to the mouse genome reference sequence (GRCm38.
1ϵ. At the scale σ = 25kbp, the presence of fixed cross-linkers allow us to reconstruct chromatin structure by modeling the effects of cohesin and CTCF  [32, 39]  without including explicitly the extruding process occurring at a time scale of 1kb/s, which is much faster than the time scale we are interested in here  [40] [41] [42]. Polymer stochastic simulation procedure The numerical simulations are performed using fixed-volume and constant-temperature molecular dynamics (MD) with implicit solvent using the LAMMPS simulation package  [43]. The Langevin equation 1 is integrated numerically using Verlet's algorithm with the integration timestep sets to dt = 0. 012τ LJ and the local friction γ = 0. 5τ -1 LJ , where τ LJ is the characteristic simulation time given by τ LJ = σ √ m/ϵ in Lennard-Jones units.
For single cell Hi-C, six distinct single-nucleus Hi-C datasets have been used as an input to select the cross-linker location. In this case, we added a cross-linker to connect a pair of beads with an entry equal to 1 in the corresponding Hi-C matrix. The RCL-reconstruction (RCR) Algorithm to select cross-linkers from Hi-C population data We describe now an algorithmic procedure to select a certain ensemble of connectors to define the chromatin local topology whose simulated Hi-C map is the best approximation of the experimental one. Using the Hi-C matrix as an input and, through k iterations, the algorithm provide a series of probability p (k) i,j for each pair of beads i, j to be cross-linked in the chromosome model. Then we use the harmonic potential U cross-linker to defines the bond energy between the set of N c beads.
This result is in contrast with the case where there are no random cross-linkers (N c = 0), where we found that S sper = -0. 05, after we ran 500 polymer realizations. Here, the simulated contact matrix does not account for the experimental Hi-C matrix and the We also investigated the relation between the CTFC peaks distributions and the HCC regions (Fig. 4c ) where we found that the Spearman correlation coefficient is given by S sper = 0. 655 (chromosome 17) and S sper = 0. 598 (chromosome 19). To conclude, these results show a relative correlation between the HCC, gene expression and CTCF distributions. Possibly, CTCF is involved in generating high curvature regions. HCC regions hidden in single cell Hi-C cell Hi-C dataset is used to access the chromosomal structure a specific moment of a single cell cycle.
Thus we decided to investigate how the cell-to-cell variability can influence chromosomal arrangement and in particular how the HCC region could vary, despite the fact that only 2. 5% of all interactions are captured  [19] [20] [21]. To identify the HCC regions, we started with the GRCL-polymer model. Due to the sparsity of the experimental contact matrix for Cell 1 (Fig. 5A  upper matrix)), we added cross-linkers each time there was a non-zero probability in the contact matrix. We then simulated six different chromosome 19 starting from 6 single-cell Hi-C datasets and computed the simulated contact matrix (Fig. 5A  lower matrix)). To identify the HCC region, we applied the spline interpolation procedure (subsection of Method)on the 6 simulated chromosomes and finally obtained the curvature vs the genomic distance (Fig.
TADS versus HCC TADs represent a statistical averaging of chromatin organization derived from many millions of cell conformations and their cross-linkers. In contrast HCC regions are derived directly from the shape of the chromatin path and thus is well suited to studying these conformations in single cells. We report here that it is sufficient to have just tens of cross-linkers in a polymer chain of thousands of monomers to generate and detect high curvature. Thus a small change in the connectivity of cross-linkers can drastically influence the three dimensional folding and organization of chromatin. Currently, the loop extrusion mechanism is proposed to be the major force driving chromatin folding seen in Hi-C contact datasets  [41, 59]. At the moment we do not understand the relationship we see in HCC regions and the loop-extrusion mechanism of chromatin folding.
Sample/data type GEO accession Name Cell 1 single-cell Hi-C GSE80280 Cell 1 Cell 2 single-cell Hi-C GSE80280 Cell 2 Cell 3 single-cell Hi-C GSE80280 Cell 4 Cell 4 single-cell Hi-C GSE214264 P46D12 Cell 5 single-cell Hi-C GSE214264 P44F6 Cell 6 single-cell Hi-C GSE214264 P30G8 Population Hi-C GSE80280 Population Hi-C (haploid) Population RNA-seq GSE80280 Nuclear RNA (haploid) CTCF ChIP-seq GSE80280 CTCF ChIP.
Many different proxies have been used to determine EE outcomes in the literature. However, following  Brown and Mason (2017) ,  Stam (2018) ,  and Stangler and Bell-Masterson (2015) , this study is based on two basic and central proxies: EE growth orientation and EE intensity. The purpose of this paper is to show that not all EEs are equal, and EE outcomes between developed and developing economies vary. By analyzing a novel database of 17 distinct urban EEs in the contexts of developing and developed countries of South-Eastern and Eastern Europe, the paper furthermore investigates how the sustainability orientation of EE actors shapes EE outcomes. The paper makes three key contributions to the entrepreneurship literature. First, this paper is the first to explain and demonstrate that it is not just formal institutions which matter in shaping EE growth orientation and EE intensity but those policies and measures enhancing the sustainability of EE actors in particular.