dataesr/scientific-paragraphs-categorization huggingface dataset:
We previously addressed comparable needs for non-ST scRNAseq datasets with Single-Cell Signature Explorer which, using a mere laptop, projects single cell's signature scores across UMAP or t-SNE and proposes a slider visualisation of entire databases  [8]. Other algorithms, whether command line-based or not, also allow analysis of scRNAseq and CITE-seq datasets  [1, 3, 15, 19]. However, we also developed the Single-Cell Virtual Cytometer, which allows such analyses using laptops without command lines. Its interoperability with Single-Cell Signature Explorer enables both phenotyping and cell sorting from numerical data  [2, 9]. Since it was not possible to explore ST datasets likewise, here we introduce Single-Cell Spatial Explorer (scSpatial Explorer), a userfriendly and open-source software.
Staining and imaging and spatial transcriptomics Snap-frozen human spleen sample was embedded in Tissue-Tek O. C. T. (Sakura). 10µm -thick slices were performed and placed on the Visium spatial slide ( 10× Genomics, Visium Spatial Protocols-Tissue Preparation Guide-Rev A), fixed with methanol, and stained with hematoxylin and aqueous eosin (HE). This HE-stained tissue was imaged with a ZEISS inverted microscope under 10×/0. 3 objective, permeabilized, and reverse transcription was performed on the same slide. Additional examples of ST datasets for human cerebellum, spinal cord and murine brain and kidney were downloaded from the 10× Genomics website. Second strand synthesis, denaturation, DNA amplification, libraries construction were performed following manufacturer's instructions ( 10× Genomics, Visium Spatial Gene Expression Reagent Kits User Guide-Rev C).
Licence for logos : R (CC-BY-SA 4. 0), Julia (MIT), Python (stating accurately that software [. ] is compatible with the Python programming language [. ] is always allowed. ), FIJI (GPL), ImageJ (public domain), Single-Cell Virtu al Cytom eter (GNU AFFERO GPL, logo designed by Marie Tosolini), Single-Cell Spati al Explo rer (GPL, logo designed by Marie Tosolini) separator) containing at least the spots tags and the spots XY coordinates of the image. 6. Compatible with any numeric data : gene expression, pathway scores, antibody staining level etc. 7. An unlimited number of gates can be drawn in the microscopy image or in the 2D interactive plot. 8. Import/export gates in ImageJ/Fiji format (Fig. 3 ). 9. Extra ction of spots and sub-tables delimited by the gates on an unlimited number of tables.
Minimal computer configuration Single-Cell Spatial Explorer has been developed on GNU/Linux Manjaro (XFCE desktop) on a computer with an Intel ©Core TM i5-3470 CPU @ 3. 20 GHz with 8 GB RAM, a 7200 rpm hard disk, a video card Nvidia GeForce GT630 and a HD display with a resolution 1920 × 1080. This is a comfortable hardware configuration for a small data set of about 3000 spots. Although it is possible to use Single-Cell Spatial Explorer on a laptop, we recommend to use a HD display for a more comfortable experience. We do not recommend to use two screens with different resolutions or display zooming.
Single-cell spatial explorer Single-Cell Spatial Explorer (scSpatial Explorer) is a software for easy analysis of data associated with image and spatial coordinates. It allows, in a very simple way, to visualise features, select region of interest (ROI) and to perform differential analysis in interactive way, draw dot plots of spots, select spots on the image to visualize this on dot plot, or vice versa select spots in dot plot to visualize this on the image. To facilitate data exploration, scSpatial Explorer is compatible with ROI defined with ImageJ/Fiji, cells selected with Single-Cell Virtual Cytometer  [9] , and any type of data imported as a tab-separated file (Fig. 1 ). It has many features allowing spots characterisation, selection, exportation and data filtering. It can display quantitative (gene expression, gene sets scores.
) and qualitative (clusters) data on a microscopy image. scSpatial Explorer can also filter many tables for selected cells in one click. Any figure, result, table or ROI created in scSpatial Explorer can be exported. scSpatial Explorer is a software with a graphical interface with two main windows : the microscopy image is displayed in one window and a toolbox in another one. The graphical interface is shown in the center of Fig. 1  and scree nshots are shown in GitHub. The software is easy to insta ll and can be run without any programming skills on any computer, and comes with a detailed docum entat ion and video tutor ials. scSpatial Explorer has been developed with interoperability as a priority, especially with Seurat  [16] , Single-Cell Signature Explorer  [8]  and Single-Cell Virtual Cytometer  [9] , leading to a very versatile and powerful solution (Fig.
Example of ST analysis Below we exemplified ST analysis of a human spleen tissue produced by Visium technology ( 10× Genomics). The transcriptome data were visualized by Uniform Manifold Approximation and Projection (UMAP) (Fig. 2A ), and the enrichments of all signatures fromMSIgDB were scored by Single-Cell Signature Scorer  [8]  with the pipeline illustrated in Additional file 1: Fig. S2. Then, distinct exploration modes available on scSpatial Explorer were used. Fig. 4  The human prostate cancer dataset was downloaded from 10× Genomics website. 10x Genom ics websi te. The tissue was stained by immunofluorescence using antibodies Iba1/AIF-1 (green),Vimentin (red), DAPI (blue) (A). Data were normalized using Seurat  [1] , exported and Hallmark pathways were scored using Single-cell Explorer Scorer  [8].
Beyond identifying the transcriptome hallmarks of spatially-delineated spots, scSpatial Explorer indeed allows to localize any image spots selected by transcriptome criteria. For example, from a human prostate cancer ST datas et, spots gated in a scatterplot of "Hypoxia VS Androgen Response" signatures are instantly mapped within the IF image (Fig. 4 ). Such gates can also be delineated from scatterplots of single gene expressions, immunophenotypes, or any other numerical values from multimodal datasets. Thanks to the diversity of gene signature on database like MSIgDB  [7] , different cells type could be identified such as human Glutamatergic or GABAergic neurons (Additional file 1: Fig. S3 ), glial cells, oligodendrocytes or astrocytes cells (Additional file 1: Fig. S4 ), murine endothelial cells or nephron progenitor cells (Additional file 1: Fig.
Acknowledgements Andrew Williams  (https:// andy. xyz), creator of the  Fyne  project (https:// github. com/ fyne-io/ fyne) and  CEO at Fyne Labs  (https:// fynel abs. com/), is acknowledged for his useful technical advice about the usage of the Fyne graphical toolkit. This work was granted access to the HPC resources of CALMIP super-computing center under the allocation  P19043. We are also grateful to the  Génotoul bioinformatics platform  ( Bioinfo Genotoul, Toulouse Midi-Pyrenées ) for providing computing resources.
Funding This work was supported by institutional Grants from the  Institut National de la Santé et de la Recherche Médicale (INSERM) , the  Université Toulouse 3-Paul Sabatier (UPS) , the  Centre National de la Recherche Scientifique (CNRS) ;  Laboratoire d'Excellence Toulouse Cancer  ( TOUCAN-2 ) undergrant contract  ANR11-LABX ; the  Fondation ARC  under Grant contract  PGA1-RF2019-0208691 ; the  Institut Universitaire du Cancer de Toulouse-Oncopole  under contract CIEL; the  Institut Carnot Lymphome  under contract CALYM (collection). As an institutional research laboratory granted by national agencies, the funding bodies play no role in the design of the study and in the collection, analysis, and interpretation of data, nor in the writing of the manuscript.
Availability and requirements Project name: Single-cell Spatial Explorer Project home page: https:// github. com/ FredP ont/ spati al Operating system(s): Linux, Windows, MacOS Programming language: Go https:// go. dev/ Other requirements: to build from source, the prerequisites of the Fyne graphical toolkit (https:// devel oper. fyne. io/ start ed/) are required License: GNU GPL Any restrictions to use by non-academics: restrictions of the licence GNU GPL.
Availability of data and materials The software Single-cell Spatial Explorer is available on github The human prostate cancer dataset was downloaded from 10x Genom ics websi te (https:// www. 10xge nomics. com/ resou rces/ datas ets/ human-prost ate-cancer-adjac ent-normal-secti on-with-if-stain ing-ffpe-1-stand ard-1-3-0). The human cerebellus dataset was downloaded from 10x Genom ics websi te (https:// www. 10xge nomics. com/ resou rces/ datas ets/ human-cereb ellum-whole-trans cript ome-analy sis-1-stand ard-1-2-0). The human spinal cord dataset was downloaded from 10x Genom ics websi te (https:// www. 10xge nomics. com/ resou rces/ datas ets/ human-spinal-cord-whole-trans cript ome-analy sis-stains-dapi-anti-snap-25-anti-gfapanti-myelin-cn-pase-1-stand ard-1-2-0). The mouse kidney dataset was downloaded from 10x Genom ics websi te (https:// www.
10xge nomics. com/ resou rces/ datas ets/ adult-mouse-kidney-ffpe-1-stand ard-1-3-0). The mouse brain dataset was downloaded from 10x Genom ics websi te (https:// www. 10xge nomics. com/ resou rces/ datas ets/ adult-mouse-brain-ffpe-1-stand ard-1-3-0). The CoxMx technology downloaded from Nanos tring websi te (https:// nanos tring. com/ produ cts/ cosmx-spati al-molec ular-imager/ ffpe-datas et/) The spatial ATAC sample "ME11 H3K27me3 50um" (GSM5028434) and Human tonsil sample (GSM528388) were downloaded from geodatasetwebsite (https:// www. ncbi. nlm. nih. gov/ gds) The datasets of a human spleen tissue analysed during the current study are available from the corresponding author on reasonable request.
Identifying wheat orthologous genes Wheat orthologous genes of interest were identified from the protein sequences from rice, Arabidopsis, and barley (Hordeum vulgare)  (Zhang et al. , 2014; Liu et al. , 2017; Kobae et al. , 2018; J. Wang et al. , 2018). The amino acid sequences obtained were used as queries against the IWGSC RefSeq Annotation v1. 1. using the BLASTP tool on EnsemblPlants  (Kersey et al. , 2018). The protein sequences with the highest homology from the A, B, and D genomes were identified. For further confirmation of homology, the wheat sequences obtained were used for reciprocal BLASTP back to rice protein databases. The accession number of the identified SL-related genes can be found in Supplementary Table  S1.
Quantitative reverse transcription-PCR (RT-qPCR) First-strand cDNA was synthesized from 2 μg of total RNA using Su-perScript III Reverse Transcriptase (Invitrogen, Waltham, MA, USA) and oligo(dT) primers following the manufacturer's instructions. Synthesized cDNA was diluted 1:1 with RNase-free H 2 O and stored at -20 °C. The real-time qPCRs were performed in an ABI-7500 real-time PCR system (Applied Biosystems, Waltham, MA, USA) using SYBR® Green JumpStart™ Taq ReadyMix™ (Sigma-Aldrich, Dorset, UK). The 20 μl reactions contained 1 μl of cDNA and 250 nM of each primer. A pipetting scheme for the preparation of each reaction was followed to minimize pipetting mistakes and ensure repeatable results. Each reaction was initially prepared in a 0. 5 ml microcentrifuge tube by adding 21. 4 μl of mastermix (11.
RT-qPCR data analysis The real-time qPCR data were analysed with ABI 7500 v2. 0. 5 software. The fluorescence threshold for calculation of the Ct values was calculated automatically by the software or manually adjusted in the linear phase of the amplification curve. Primer efficiency was estimated for all individual samples in each run using the linear phase of the amplification curves as calculated by the LinRegPCR software  (Ramakers et al. , 2003). The gene expression levels were calculated as normalized relative quantity (NRQ) given by the formula NRQ = (E GOI ) -Ct;GOI 2 » (E ref1 ) -Ct;ref1 x (E ref2 ) -Ct;ref2  (Rieu and Powers, 2009). Ct GOI and E GOI stand for the Ct value and the average primer efficiency of the targeted gene of interest, respectively. Similarly, Ct ref and E ref correspond to the Ct value and the primer efficiency of the two reference genes.
Firstly, oligo(dT) beads were used for mRNA enrichment, and the mRNAs were fragmented for 15 min at 94 °C. First-and second-strand cDNAs were subsequently synthesized. Indexed adapters were ligated to cDNA fragments after adenylation of the 3ʹ end of the cDNA fragments. Limited cycle PCR was used for library amplification. Sequencing libraries were validated using the NGS Kit on the Agilent 5300 Fragment Analyzer and quantified by using a Qubit 4. 0 Fluorometer or equivalent. The sequencing libraries were multiplexed and loaded on the flow cell. NGS was performed in Illumina Novaseq 6000 with 2 × 150 bp pair-end configuration v1. 5. Image analysis and base calling were conducted by the NovaSeq Control Software v1. 7 on the NovaSeq instrument. Raw paired-end data were delivered in fastq format after de-multiplexing and standard adapter trimming by the sequencing contractor.
RNA-sequencing data analysis workflow All the tools used for the RNA-seq data analysis were executed in the Rothamsted Research Galaxy platform (https://galaxy. rothamsted. ac. uk/). The RNA-seq raw data were received in fastq format. The FastQC v0. 11. 7 tool was used to assess the quality of the raw data (http://www. bioinformatics. babraham. ac. uk/projects/fastqc/). If required, low-quality reads and adapter sequences were removed using Cutadapt v3. 7  (Martin, 2011). Subsequently, trimmed reads were pseudo-aligned to T. aestivum IWGSC RefSeq v1. 0 annotation v1. 1 using kallisto v0. 46. 0. 4  (Bray et al. , 2016)  for calculating transcript abundance reported as transcripts per million mapped reads (TPM). The package tximport v1. 24. 0 was used to create gene-level count matrices from the transcript abundances  (Soneson et al.
, 2015). The DESeq2 v1. 32. 0 package  (Love et al. , 2014)  was used to perform the differential gene expression analysis (alpha=0. 01) by fitting the appropriate model based on the experimental design. A pre-filtering of low count genes was performed before performing differential gene expression analysis. Only genes that had at least four samples with more than five reads were included in the analysis. Significantly differentially expressed genes were retrieved by applying post-hoc filtering of Padj<0. 01 and |log 2 fold change (FC)|>0. 58 (|FC|>1. 5). The DESeq2 package was run in R Statistical Software v4. 1. 1 on a local Windows machine. For the visualization of the RNA-seq data analysis, the pheatmap v1. 0. 12 package was used in R Statistical Software v4. 1. 1. TPM values were also used for comparing the gene expression levels of different genes since this value is normalized for both gene length and sequencing depth, allowing the comparison between different genes.
RNA-seq validation RNA-seq results were also confirmed by comparing the gene transcript abundance (TPM) with the relative gene expression value (NRQ) obtained from RT-qPCR. Three different genes were used for the RNAseq validation, TaD27,  TaD17, and TaCKX3 (TraesCS1A02G159600, TraesCS1B02G176000, and TraesCS1D02G157000). Pearson correlation between transcript abundance values of selected genes obtained from the RNA-seq and RT-qPCR was performed using package ggpubr v0. 4. 0 in R statistical software v4. 1. 1. For all three genes, there was a good correlation (R>0. 9) between the expression values obtained from the RNAseq and the RT-qPCR, while the effect of the treatment was also found to be consistent in both methods, suggesting that the RNA-seq data were reliable (Supplementary Fig. S1 ).
Expression analysis of MAX1 homologues from RNA-seq data For examining the expression pattern of the identified MAX1 genes, apart from the RNA-seq performed as part of this study in basal nodes of wheat plants (cv Cadenza), additionally publicly available RNA-seq data were utilized. The first study (ArrayExpress accession: E-MTAB-11927) included two different tissues, root and basal nodes, of N-stressed hydroponically grown wheat plants (cv Graham) with three replicates. The second study (BioProject: PRJNA749278, SRA: SRP329634) was performed on the root and shoot of four different wheat cultivars (cv big sky, cv OK1059060, cv OCW00S063S-1B, and cv NF97117) grown under sufficient or low N supply. This experiment was conducted in pots under glasshouse conditions with three replicates per treatment.
Finally, RNAseq from previously published work (BioProject: PRJDB2496, SRA: DRP000768) on P-starved wheat (cv Chinese Spring) roots and shoots was utilized  (Oono et al. , 2013). All the raw data were downloaded from the NCBI SRA database (the list of samples can be found in Supplementary Table  S3 ). Kallisto v0. 46. 0. 4  (Bray et al. , 2016)  was used for quantifying the transcript abundance using IWGSC RefSeq v1. 0 annotation v1. 1.
Statistical analysis Mean values and SEs were calculated from at least three biological replicates, depending on the experimental design. The exact number of biological replicates is mentioned in each figure legend. The statistically significant effect of the treatments/factors was assessed with an ANOVA. Statistical analysis was performed in GenStat statistical software package (21st edition) or by using the package rstatix v0. 7. 0 in R Statistical Software v4. 1. 1. ANOVA was applied to all single variate data, including phenotypic data (tiller counting), gene expression data, and chemical analysis data. Repeated measured ANOVA was conducted for tillering data recorded at different time points in the same individuals. Prior to ANOVA, assumptions for normality and homogeneity of variances were assessed using Shapiro-Wilk's normality test and Levene's test, respectively.
The accession number of the sequences used in the analysis can be found in Supplementary T. urartu, the progenitor of the wheat A genome, nor T. dicoccoides was found to have a homologue on chromosome A in clade A2. The genes of clade A which fall into separate subgroups were named MAX1a1 and MAX1a2. In addition, it is worth mentioning that only sequences from wheat and B. distachyon were found in both subgroups of clade A, whereas none of the known rice sequences was found in clade A. On the contrary, only TraesCS3A02G466400 was present in clade B, along with three of the rice MAX1 homologues (Os1500, Os900, and Os1400) and sequences from other grasses. No MAX1b homologue was found in T. dicoccoides chromosome B or A. tauschii, supporting the absence of TaMAX1b homoeologues in chromosome B and D.
Expression pattern of wheat MAX1 homologues Data from different RNA-seq experiments were utilized to compare the transcript abundance of the identified wheat MAX1 homologues in three different tissues. More specifically, the data were obtained from four independent experiments conducted in either root, basal node, or shoot tissues of different wheat cultivars, including samples from nutrient-stressed plants. TPM values of all the 13 identified wheat MAX1 genes were retrieved and are presented as a heatmap in Fig. 3  (Supplementary Fig. S7 ; Supplementary Table  S6 ). Genes with TPM values <0. 5 were considered as expressed at a low level. Based on the heatmap, there is a distinct pattern of TaMAX1 expres-sion distinguishing roots, basal nodes, and shoots. TaMAX1a2-U and TaMAX1a2-3B are the predominant TaMAX1 genes expressed in the root, showing the highest expression values among all of the TaMAX1 homologues (Fig.
Acknowledgements This work was conducted at Rothamsted Research and was part of the PhD studentship of PPS entitled 'Genetic variation and chemical control of tillering in wheat', fully sponsored by  Centre Mondial de l'Innovation Roullier-Timac Agro International. We thank  Dr Keywan Hassani-Pak  and the  Rothamsted Bioinformatics team  for their guidance on RNA-seq data analysis.
Image analysis All the image analysis were performed in Fiji software. 3D puncta counting and fluorescence colocalization analyses illustrated in Fig. 1B -D and Fig. S1  were performed by using the macro developed by Cordelières and Zhang  [63]  in batch processing mode, and available in a GitHub repository at https://github. com/NEUBIAS/neubias-springer-book-2020. The minimum size of the objects for Aquamarine-LC3B and LAMP2/Alexa 647 was set to 10 voxels. The threshold to separate the objects from the background was set manually for both channels. The total number of objects in Aquamarine-LC3B channel was used to determine the number of Aqua-LC3B-II puncta-shaped structures. The objects in the Aquamarine-LC3B channel superposing with the LAMP2/Alexa 647 objects were used for colocalization analyses, and only the Aquamarine-LC3B objects superposing with the LAMP2/Alexa 647 objects with a ratio of 0.
Acknowledgments We thank  P. Govindin  ( MetaGenoPolis, INRAe, Jouy-en-Josas, France ) for preliminary experiments with the LC3B biosensor,  S. Dutertre  and  X. Pinson  at the  Microscopy Rennes Imaging Center (MRic, Biologie, Santé, Innovation Technologique -BIOSIT, Rennes, France ) and  G. Le Marchand  ( IGDR, Rennes, France ) for help and assistance. MRic is member of the national infrastructure  France-BioImaging  supported by the  French National Research Agency  ( ANR-10-INBS-04 ). We also thank  R. Ketteler  ( UCL, LMCB, United Kingdom ) for sharing pGEX GST-ATG4B plasmid and ATG4 KO HeLa cells. We are grateful to  S. Ley-Ngardigal ,  R. Smith ,  C. Chapuis  and  S. Zentout  for technical assistance with the experiments, and  Ç. Tuna  for help with the image analysis. This work was supported by the  Centre National de la Recherche Scientifique (CNRS) , the  University of Rennes 1 , the  Ligue Contre le Cancer Comité d'Ille et Vilaine et du Finistère  and the  Association pour la Recherche sur le Cancer (ARC)  to G.
IODP expedition 345 IODP Expedition 345 was designed to collect the primitive lower oceanic crust formed at the fastspreading EPR. IODP Site U1415 (shown by the red star in Fig. 1b ) is located on the southern slope of the intra-rift ridge, on a 200 m-wide, flat-lying east-west trending bench (Fig. 1c ). The bench is part of an overall lobate structure, which was imaged based on nearbottom bathymetry data acquired during site survey cruise JC21 (RRS James Cook), and is ascribed to mass wasting  (Ferrini et al. 2013). A total of 16 holes were drilled at Site U1415  (Gillis et al. 2014a) , and three of them were used in this study: one single-bit hole (U1415I) and two reentry holes (U1415J and U1415P). The drill cores are mainly composed of gabbroic rocks and troctolites (Fig. 1d, e ). The downhole changes in the inclination of the magnetization direction and lithology revealed that the two reentry holes of U1415J and U1415P sampled several coherent faulted blocks, which were displaced by mass wasting  (Gillis et al.
Crystallographic preferred orientations CPO measurements were conducted using the electron backscatter diffraction (EBSD) technique (e. g. ,  Prior et al. 2009 ) at Géosciences Montpellier, University of Montpellier (France). We used a JEOL JSM 5600 scanning electron microscope equipped with Oxford/ Nordlys EBSD detectors. Diffraction patterns were collected and processed using the Channel 5 ® software suite  (Schmidt and Olesen 1989). Samples were measured in automatic mode at a regular grid spacing of 25-50 µm over the whole thin sections; indexing rates in the raw maps range from 5 to 88% (see Table  1 ). A first stage of post-acquisition data processing was conducted using the Tango software of the Channel 5 ® suite to increase the quality of map data, by (  1 ) removing isolated pixels (either non-indexed, or indexed as a given phase and surrounded by pixels indexed for another phase), (2) filling non-indexed pixels that have a minimum of 5 neighbor pixels with a common phase and orientation with that same orientation, and (3) searching and correcting indexation errors in olivine grains, due to olivine hexagonal pseudosymmetry resulting in similar diffraction patterns for orientations differing by a 60° rotation around  [100].