dataesr/scientific-paragraphs-categorization huggingface dataset:
For the detection of unannotated transcripts, Scallop v0. 10. 5  35  was used to reconstruct transcripts, with options -library_type first -min_transcript_coverage 2 -min_splice_bundary_hits 5 -min_flan-k_length 5. Scallop was run on each library, and the resulting annotations were merged using cuffmerge v1. 0. 0, 103 with gencode annotation (v32) as reference (-g option). Then BEDtools v2. 29. 2  96  was used to retain only intergenic and antisens transcripts regarding gencode annotation. Gene expression quantification was performed using featureCounts v2. 0. 0,  97  with options -O -M -fraction -s 2 -p, using a merged annotation of gencode v32, SARS-CoV-2 (NC045512. 2), newly annotated transcripts and ERCC transcripts. Subsequent analyses were performed in R v3. 6. 2. 104 Differential expression analysis was performed using DESeq2 package,  98  after filtering out genes with less than 10 raw counts for all replicates in at least one condition.
Gene counts were normalized on ERCC counts, using estimateSizeFactorsForMatrix function from DESeq2. All pairwise comparisons were performed (mock vs. infected, mock vs. bystander and bystander vs. infected), and genes were retained as differential if adjusted p value was <0. 05 and log fold-change >1 or < À1. All plots were made using custom script, except for heatmaps that were done using pheatmap package (RRID:SCR_016418). PolyA+ RNA-sequencing analysis of bulk population of infected A549-ACE2 cells (from a public dataset) Fastq files produced in the study of Blanco-Melo et al. 17  were retrieved from GEO repository (GSE147507). The dataset consists of single-end libraries (150 nt). We compared A549-ACE2 ''mock'' cells (SRR11517680, SRR11517681 & SRR11517682) versus A549-ACE2 cells infected with SARS-CoV-2 at MOI 0.
2 (SRR11517741, SRR11517742 & SRR11517743). Adaptors were trimmed with Trim Galore v0. 6. 4, 92 with options -stringency 5 -trim-n -q 20 -length 20. Reads were mapped on a reference containing human genome (hg38) and SARS-CoV-2 (NC045512. 2) sequence. Bam files were then filtered using SAMtools v1. 10 95 to retained reads flagged as primary alignment, and with mapping quality >30 (option -q 30 -F 0x100 -F 0x800). Gene expression quantification was performed using featureCounts v2. 0. 0,  97  with options -O -M -fraction -s 2, using a merged annotation of gencode v32, SARS-CoV-2 (NC045512. 2) and newly annotated transcripts. Gene counts were normalized on the full count matrix, using estimateSizeFactorsForMatrix function from DESeq2. 98  Differential analysis was performed as described above.
Single-cell RNA-sequencing analysis of infected A549-ACE2 cells (from a public dataset) Count matrix produced in the study of Nilsson-Payant et al. 36  was retrieved from GEO repository with accession number GSE184536. The analysis was run using Seurat v4. 3. 0. 1. 105 Cells quality control, filtering, count normalization and scaling was done as in the original publication. 36  Clustering was performed using 30 principal components, using FindNeighbors and FindClusters with default parameters. Differentially expressed genes between infected and bystander cells were identified using FindMarkers with default parameters.
GO enrichment analysis The GO enrichment and KEGG pathway analysis were performed using DAVID online tool (updated version 2021). 106,107 Upregulated proteincoding genes from each comparison were taken for the analysis with default background for Homo sapiens. GOTERM_BP_DIRECT and KEGG pathway were retained and top 10 results based on adjusted p value (Benjamini) were plotted using ggplot2 R package (v 3. 3. 0).
Identification of NF-kB target genes A list of coding genes that are known targets of NF-kB is available on Gilmore's laboratory website (https://www. bu. edu/nf-kb/generesources/target-genes). We selected genes from this list that were shown to be direct targets of NF-kB, and for which the gene symbol could be retrieved in gencode annotation (354 genes). For identifying lncRNAs and unreferenced RNAs that possess NF-kB binding site in their promoter, we used p65 ChIP-seq data from GEO dataset GSE34329,  49  -one input file and 2 ChIP replicates, 38nt long reads, single-end. Reads were mapped using bowtie2 v2. 4. 1 using hg38 as reference, and SAMtools was used to retain the one flagged as primary alignment, with mapping quality >30, and to remove PCR duplicates (markdup, with -r option). NF-kB binding sites were then detected using macs2 v2.
2. 7. 1, 100 with command callpeak -t ChIP_BamFile1 ChIP_BamFile2 -c input_BamFile -f BAM -g hs -s 38 -keep-dup all. Peaks in the first decile of the -log10(qvalue) value were discarded. NF-kB motif genomic coordinates in the human genome were retrieved using EMBOSS fuzznuc v6. 6, 108 using motif 5 0 -G(3)[AG]N[CT](3)C(2) -3', 109 on forward and reverse strand (option -complement Y). Peaks and NF-kB motif coordinates were compared using BEDtools  96  ; if a motif was contained in a peak, the motif strand was assigned to the peak. LncRNA and un-references transcripts were identified as NF-kB potential targets if their promoter region (1kb before transcript TSS) had a peak containing a motif or a peak for which the -log10(qvalue) was in the top 5%.
RNA extraction and RT-qPCR assays Total RNA was extracted from cells with the NucleoSpin RNA II kit (Macherey-Nagel) according to the manufacturer's instructions. First-strand complementary DNA (cDNA) synthesis was performed with the RevertAid H Minus M-MuLV Reverse Transcriptase (Thermo Fisher Scientific) using random primers. Quantitative real-time PCR was performed on a real-time PCR system (QuantStudio 6 Flex, Applied Biosystems) with Power SYBR Green RNA-to-CT 1-Step Kit (Thermo Fisher Scientific). Data were analyzed using the 2-DDCT method, with all samples normalized to endogenous BPTF, whose gene expression was confirmed as homogenous across samples by RNA-seq. Genome equivalent concentrations were determined by extrapolation from a standard curve generated from serial dilutions of plasmid encoding a fragment of the RNA-dependent RNA polymerase (RdRp)-IP4 of SARS-CoV-2.
Chemokine and interferon expression and secretion Cell lysates for intracellular chemokine quantification were obtained via repeated freeze-thaw cycles at À80 C of cells suspended in media containing protease inhibitor cocktail (Roche Applied Science) and final centrifugation at 8000g to pellet debris. IL6, CXCL1, CCL2, CXCL8 and CCL20 concentrations in supernatants of from control, infected or stimulated cells, were measured using a custom-designed LEGENDplex Human Panel. Data were acquired on an Attune NxT Flow Cytometer (Thermo Fisher) analyzed with LEGENDplex software (BioLegend). Similarly, IFN-b, IFN-l1 and IFN-l2/3 concentrations were measured in undiluted supernatants from control, infected or stimulated cells using a LEGENDplex Human Type 1/2/3 Interferon Panel assay (BioLegend) according to the manufacturer's protocol.
QUANTIFICATION AND STATISTICAL ANALYSIS Statistical parameters including the exact value of n, precision measures (as means G SEM), statistical tests and statistical significance are reported in the figure legends. In figures, asterisks denote statistical significance: *p < 0. 05, **p < 0. 01, ***p < 0. 005, ****p < 0. 0001, and ''ns'' indicates not significant. Statistical analysis was performed in GraphPad Prism 9 (GraphPad Software Inc. ).
ACKNOWLEDGMENTS We thank the  French National Reference Centre for Respiratory Viruses  hosted by  Institut Pasteur (France)  and headed by  Pr. S. van Der Werf  for providing the historical SARS-CoV-2 strain;  C. Combredet  and  F. Tangy  ( Institut Pasteur ) for producing and sharing MeV-GFP;  H. Mouquet  and  C. Planchais  ( Institut Pasteur ) for anti-S antibodies and  O. Schwartz  ( Institut Pasteur ) for the A549-ACE2 cells. We are grateful to our team member  Felix Streicher  for helping design the Figure 2A and to all members of our laboratories for helpful discussions. We acknowledge the  UTechS Immunology Platform of Institut Pasteur  for the use of the cell sorter and the  Institut Curie NGS  platform for the CUT&RUN analysis. The Biomics Platform was supported by  France Ge  ´nomique ( ANR-10-INBS-09 ),  IBiSA  and the  Illumina COVID-19  Projects' offer.
This work was funded by the  CNRS ,  Institut Pasteur ,  Institut Curie , '' Urgence COVID-19 '' fundraising campaign of  Institut Pasteur ,  ANR-DARK COVID  and  DIM-1 -Health. DF postdoctoral fellowship was supported by the  DIM-1-Health  from the  Conseil Regional d'Ile-de-France. SMA was supported by the  Pasteur-Paris University (PPU)   International PhD Program. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
(C and D) RT-qPCR quantification of mRNA, lncRNA, and unannotated-RNA, that were identified as upregulated (C) or downregulated (D) upon infection by SARS-CoV-2 in the RNA-seq analysis, in total RNA extracted from A549-ACE2 cells infected with SARS-CoV-2 at an MOI of 1, analyzed either in bulk (left side of graph) or post-sorting based on Spike protein expression (right side of graph, normalized fold change over mock-infected, n = 3 independent experiments, ratio-paired t-test, line at mean G SEM). *p < 0. 05, **p < 0. 01, ***p < 0. 005, ****p < 0. 0001, and ''ns'' indicates not significant. (E) Top 10 enriched GO terms for Biological Process (BP) and KEGG pathways from DAVID database ranked by the adjusted p value (Benjamini), for upregulated mRNAs identified in RNA-seq comparison between infected vs.
Methods A 15-member technical working group (TWG) was formed to develop a Biosafety Research Roadmap to support the application of laboratory biological risk management and improve laboratory sustainability by providing an evidence base for biosafety measures. The TWG conducted a gap analysis for a selected list of priority pathogens on the procedures related to diagnostic testing and associated research for those pathogens, including but not limited to sample processing, testing, animal models, tissue processing, necropsy, culture, storage, waste disposal, and decontamination. To achieve this, databases, websites, publications, reviews, articles, and reference libraries were screened for relevant data. The main research domains used to perform the literature searches were the ABSA database, Belgian Biosafety Server, CDC reports, WHO reports, PubMed, and internet searches for terms related to biosafety matters, including, for example, inactivation, decontamination, LAIs, laboratory releases, and modes of transmission.
0 with 1 mM EDTA) to prevent potential premature retro-Michael deconjugation of the peptides. The corresponding 4-Arm PEG-N 3 -PD-CGY derivatives, 49 and 50 were reacted with a 4-Arm PEG-BCN 51 under acidic conditions (100 mM acetate buffer, pH = 5. 0 with 1 mM EDTA) at room temperature to give PEG hydrogels cross-linked 52 and 53 via Strain-Promoted Azide-Alkyne cycloaddition (SPAAC, see section 4. 3 for details †). Aer washing and equilibrating the hydrogels overnight at 4 °C in acetate buffer, the hydrogels were placed in PBS buffer (pH = 7. 4), with the PBS being replaced at the respective timepoints to simulate sink conditions. The concentration of CGY peptide (45) released was measured in triplicate using HPLC via a standard curve. The cumulative release data of the CGY peptide in Fig.
Acknowledgements The authors would like to acknowledge the  EU  for funding L. N. C. R. and I. K. (Project No. 859458 ), the  Leverhulme Trust  ( RPG-2020-010  and  RPG-2017-288 ) for funding C. B. and R. J. S. and the  EPSRC  ( EP/T517793/1 ) for funding I. A. T. and P. B. J. P. W. and M. M. S. acknowledge support from the  UK Regenerative Medicine Platform  " Acellular/Smart Materials -3D Architecture " ( MR/R01561/1 ). B. S. and M. M. S. acknowledge support from the  Engineering and Physical Sciences Research Council  ( EP/X027252/1 ). For the purpose of open access, the authors have applied a creative commons attribution (CC BY) licence to any author accepted manuscript version arising. We would like to thank  Dr Kersti Karu  of the  UCL Chemistry Mass Spectrometry (MS) Facility  and acknowledge  Crosby Medley  from  UCL Chemistry  for his help with the freeze drier.
We used the TCGA and CCLE public databases for external validation of expression markers. By levying spatial transcriptomics to focus analyses only to tumour cells in MpBC samples, and therefore bypassing the negative impact of stromal contamination, we could identify specific markers that are not expressed in other subtypes nor stromal cells. Three markers (BMPER, POPDC3 and SH3RF3) could furthermore be validated in external expression databases encompassing bulk tumour material and stroma-free cell lines. We find that existing bulk expression signatures of high-plasticity breast cancers are relevant in.
Spatial transcriptomics analyses 10x Genomics Visium (Spatial 3' v1 for fresh frozen samples) slides were processed according to the manufacturer's guidelines and sequenced in two batches on a NovaSeq 6000 Illumina sequencer, targeting 50,000 reads/spot: A first batch included samples M1 to M11 (Cl-like, unstable TNBC, normal controls), a second batch included samples M13 to M16 (metaplastic TNBCs). The spaceranger software was used to process the raw data. Stereoscope  16  was used for deconvolution analyses using only the TNBC data from a singlecell breast cancer atlas  17  , and excluding plasmablasts that were initially found to be overrepresented in deconvolution results (included cell types: cancer epithelial, normal epithelial, endothelial, CAF, T-cell, B-cell, myeloid, PVL). Signature enrichment analyses were performed using the AUCell tool, designed for UMIbased single-cell data that present similar biases to those of our SpaT data.
Combining histopathological annotations and spatial transcriptomics on fresh frozen tissue We analysed a total of 15 Visium capture areas, including 3 controls extracted from tumour samples with epithelial compartments but no tumour identifiable on the slide. All slides were annotated by a breast pathologist, to separate tumour/non-tumour and epithelial/non-epithelial compartments, as well as eliminate spots corresponding to folded tissue and artefacts. The SpaT data and paired annotations have been publicly deposited as a series on the Gene Expression Omnibus (GSE213688). Tumour spots were first annotated by the pathologist. To later investigate expression markers that would not be biased by the prevalence of mesenchymal stromal cells, we restricted the stromal spots definition to the following pathologist annotations, exclusively: Fibrosis, Fibrosis (peritumoral), Fibrous stroma, Tumour Stroma, Tumour stroma fibrous, the last two separated on visual appreciation of the cellular density and collagen abundance in the stroma.
CNA profiles Three slides that were devoid of tumour cells upon histopathological examination, were included as normal references: M1 and M9 (CL-like3) and M7 (CL-like4). The expression profiles of epithelial spots present on these slides provided a more relevant reference than those of other cells, in which cell-type-specific expression patterns local to genome segments could hamper CNA identification. Individual spot copy number (CN) profiles were produced with the infercnvPlus tool (https://github. com/CharleneZ95/infercnvPlus, based on inferCNV of the Trinity CTAT Project: https://github. com/broadinstitute/inferCNV). All spots annotated as either tumoral or epithelial from every slide were pooled together, using the epithelial ones as references. This produced a relative CN measure per gene per spot, which we summarised into a major cytoband per spot matrix, by computing the average CN per cytoband.
Sample-specific profiles were then obtained by averaging the relative CN measures per cytoband across all spots of each given sample. Comparable profiles were obtained for bulk samples, by averaging per cytoband the segmented logR values reported by FACETS  15  analysis, from the whole exome sequencing data of CL-like and unstable samples. Gain/loss profiles were calculated for each bulk sample by attributing values of 1 (gain), 0 (normal) or -1 (loss) to each cytoband, according to whether their mean logR exceeded the median logR of the sample ± 0. 6, as implemented in  18. A threshold was used to calculate similar gains/loss profiles in Visium samples, according to whether the cytoband CNA scores exceeded the threshold. Its value was optimised to minimise the pairwise distance across all cytobands between the bulk-derived and SpaT-derived gain/loss profiles for the 4 unstable tumours, which provided the most reliable and informative data.
Genes upregulated in the spindle tumour spots were defined as those having a log2 FC > 1. 5 and a corrected p-value < 0. 001 (Benjamini-Hochberg). Genes overexpressed in MpBC tumour spots in both analyses (vs non-MpBC tumour and stroma) were considered as candidate MpBC markers. We further validated their relevance in the tumour and stromal spots from the CL-like and unstable samples, using the same method but with a higher log2 FC threshold (>2) in this bigger dataset. Only genes significantly overexpressed in MpBC samples in both analyses (non-MpBC tumour and stroma) were considered as internally validated markers. For the external validation, we used the clinical information, including sample metaplastic status, and mRNA expression (z-normalised RNA-seq log RSEM) data from the TCGA breast cancer Firehose Legacy cohort, retrieved through cBioPortal  22, 23.
1A ). We thus considered these 3 samples as highly relevant CL-like candidates. Out of the 6 high-FGA samples, to be used as controls, we selected the 4 high-FGA samples displaying the most contrasting patterns regarding these genes (Supplementary Fig. 2 ). We performed PAM50 centroid classification on the 379 samples from our cohort merged with the TCGA BRCA dataset. This revealed that the 3 CLlike samples were considered as Luminal A, and the 4 unstable control samples were considered as Basal. These findings were furthermore consistent with gene-set enrichment analyses of established CL and Basal-like gene signatures from  4  , and the CL1 and MaSC signatures from  5  (Figure  1B -C, Supplementary Fig. 3 4 5 ). These tumours were histologically reviewed to confirm their histotype, all were classified as triple negative breast carcinoma of no special type (NST) according to the WHO Classification of Breast Tumours, 5 th ed.
The same analysis conducted on the MpBC_chondroid samples identified 8 genes whose overexpression was specific to chondroid MpBC cells: HSPB6, VGLL3, PTX3, GFRA1, MT1G, MT1E, CHI3L2 and SAA1 (Supplementary Fig. 16 ). These genes are however identified from a small number of samples (n=2 for spindle cell, n=1 for chondroid). To assess their relevance in larger cohorts, we investigated their expression in two complementary external datasets: the TCGA breast cancer expression dataset comprising 1108 samples  25  , including 14 metaplastic ones; and the 51 stroma-free breast cancer cell lines from the CCLE  26  , including 9 samples classified as Claudin-low. Long non-coding RNA MSC-AS1 was the only gene for which expression data was unavailable, in both sets. In the TCGA dataset, samples were classified as non-CL (n=940), CL1 (n=69), CL2 (n=42) or CL3 (n=57) according to previous work based on expression signatures  24.
BMPER, EPDR1, JPH2, POPDC3 and SH3RF3 were significantly overexpressed in metaplastic samples (all p<0. 05, Wilcoxon rank sum tests; Fig. 5C , Supplementary Fig. 17 ). In the CCLE dataset, BMPER, DIPK1A, DSEL, POPDC3 and SH3RF3 were significantly overexpressed in CL cell lines (Fig. 5D , Supplementary Fig. 18 ). All significant p-values held after Benjamini-Hochberg correction in each dataset. These results validate that 3 out of the 8 analysable stroma-independent spindle cell MpBC markers (BMPER, POPDC3 and SH3RF3) are highly relevant in metaplastic tumours and CL cell lines from two independent external cohorts. Four more genes could furthermore be validated in one external cohort (EPDR1 and JPH2 in the TCGA data; DIPK1A and DSEL in the CCLE data), but not the other. These results suggest that leveraging SpaT data to focus solely on tumour cells can yield powerful expression markers that are less likely to be biased by stromal cell prevalence.
Here we could furthermore harness the spatial information to analyse differential expression between the different compartments of tumour samples at near single-cell resolution. Using internal and external validation procedures, we could identify BMPER, POPDC3 and SH3RF3 as robust spindle-cell MpBC markers, whose detection is unlikely to be affected by the stromal content of samples. Of note, BMPER has been reported to promote invasive phenotypes and angiogenesis in cancer  27  , and SH3RF3 has been reported to promote stem-like properties in breast cancer  28. We furthermore identified MSC-AS1, a long non-coding RNA that has been linked to both osteogenic differentiation  29  and oncogenesis  30  , as an additional potential spindle-cell MpBC markers. We could however not fully validate its relevance due to its absence in the external dataset.
Not applicable • Availability of data and materials Spatial transcriptomics data have been deposited as a series on the Gene Expression Ominbus database: GSE213688. The RNA and whole-exome sequencing data from the MyPROBE cohort, analysed to identify the CL-like and unstable samples in this study, have not been published nor released yet. The identifiers will however be consistent with those used here, and data will be accessible upon request to the authors until publication.
• Funding This research was led with financial support from ITMO Cancer of AVIESAN (Alliance Nationale pour les Sciences de la Vie et de la Santé, National Alliance for Life Sciences & Health) within the framework of the Cancer Plan, as well as by RHU MyPROBE and ANR-17-RHUS-0008. This research was additionally supported by SIRIC LYriCAN (INCa-DGOS-Inserm_12563). AC is funded by a scholarship from the Ligue Nationale contre le Cancer. • Authors' contributions PS, APM, AP, MO and PM designed the study; ML, MCM and CC collected the samples; VC, LM, CD and MO processed the samples; VC annotated the slides; AC, RP, LT, MA and PM performed computational analyses; AC, VC, MO and PM wrote the manuscript.
Somitogenesis and cell junction genes are dysregulated in three additional DMD hiPSC lines In order to increase the power of our study and extend it to additional DMD backgrounds, we compared our findings with an RNA-Seq dataset previously generated in our group to describe the myogenic differentiation of DMD hiPSCs at defined time points (https://muscle-dmd. omics. ovh/)  19. More precisely, hiPSC lines from three independent DMD patients and three healthy individuals were differentiated in triplicates with a commercial media formulation  16  and cells were collected for bulk RNA-Seq at several time points (Figure  S4 ). Interestingly, the transcriptome of DMD hiPSCs was shown to markedly diverge from healthy controls at Day 10, when somite marker genes are expressed  19. Immunostaining at Day 10 showed the presence of epithelial islets expressing C-Met in the 6 hiPSC lines, but at a level 4.