dataesr/scientific-paragraphs-categorization huggingface dataset:
TE expression in neoplastic cells is enriched in elements independent of their closest gene To better understand the control of TE expression in different cell populations, we first analyzed TE genomic locations. As compared with all expressed TEs in the dataset, TEs differentially expressed in neoplastic cells show reduced intronic loca-tions (77% versus 38. 7%), including when compared with the proportion of intronic TEs differentially expressed in immune cells (68. 8%) or astrocytes (71%) (Figure  S2F ). Neoplastic Tes also show a marked increase in 3 0 UTR encoded TEs (25. 3%), compared with all expressed TEs (5%) or with immune cell TEs (11. 3%) (Figure  S2F ). These results show that, while TEs differentially expressed in immune cells are largely intronic, TEs differentially expressed in neoplastic cells are more frequently from intergenic and 3 0 UTRs regions.
As previously observed within the single-cell RNA-seq data, the proportion of intronic TEs is higher in normal tissue than in GBM: 53. 7% versus 68. 6% (Figure  S2F ). These results indicate that neoplastic GBM cells express higher proportions of non-intronic TEs than non-neoplastic cells and that this difference is detected in both bulk and scRNA-seq datasets. We next performed principal-component analysis (PCA) and uniform manifold approximation and projection (UMAP) based on neoplastic TE signature, and we show that GBM samples cluster away from normal tissue GTEx samples  (Figures 2A, 2B, S3C, and S3D). Heatmap Z score representation in TCGA and GTEx samples show higher expression of the 2,000 top TEs from the single-cell neoplastic TE signature in TCGA GBM samples and reduced expression in healthy tissues (Figure  2C ).
Gene set enrichment analysis (GSEA) shows that expression of the neoplastic TE signature is highly enriched in GBM versus normal brain samples (normalized enrichment score [NES] = 1. 67 and false discovery rate [FDR] < 0. 05; Figure  2D ) and versus other normal tissues samples in GTEx (Figures  S3E  and S3F ). The median neoplastic TE signature expression level is also higher in GBM samples, compared with normal-tissue GTEx samples  (Figures 2E and S3G). Examples of individual TEs overexpressed in both datasets, bulk RNA-seq (Figure  2F , top panels) and scRNA-seq (bottom panels), illustrate the selective expression of certain TEs in GBM cells as compared with epidermal growth factor receptor (EGFR), a known GBM marker. We conclude that analysis of individual TEs from scRNA-seq is accurate and allows the identification of recurrent, tumor-enriched individual TEs.
Heatmap representation of all identified TE-derived peptides shows that the number of peptides varies among samples and that some peptides are found in several patients and cell lines (Figure  3D ). TE-derived peptides showed similar SEQUEST quality scores and peptide length distribution as Uniprot-annotated peptides  (Figures 3E and S4A). TE-derived peptides binding to HLA-A3 (the most abundant HLA-I among all TE-derived peptides; n = 96) contained the expected binding motif obtained from the Immune Epitope Database (IEDB) (Figure  3F ;  Vita et al. , 2019). In addition, TE-derived peptides maintained the correlation between hydrophobicity and retention time (three representative examples in Figure  S4B ). These results indicate that TE-derived peptidome is reliable and contains similar characteristics to the canonical peptidome.
We conclude that, among LINE and LTR classes, recent TEs are more prone to provide peptides for HLA-I presentation. Conserved viral proteins are a source of HLA-Ipresented peptides A few of the youngest subfamilies include TEs that contain intact viral protein ORFs, including a few ''active'' TEs in terms of retrotransposition  (Burns, 2017; Rodic et al. , 2015; Scott et al. , 2016). We next investigated whether peptides from TEs are derived from validated endogenous viral elements (EVEs) in the gEVE database  (Nakagawa and Takahashi, 2016). These EVEs of at least 80 amino acids were identified processing both RepeatMasker annotations and conserved known motifs from viral proteins, such as Gag and Pol. Mapping peptide-coding TEs to gEVE shows that, for both LINEs and LTRs, TEs with an annotated EVE are significantly enriched among peptide-coding TEs (based on both all and single assignments), as compared with RepeatMasker, all expressed, and the neoplastic TE signature (Figure  4F ).
Can we, then, identify tumor-specific TE peptides? Figure  6C  shows expression of the top 50 tumor-enriched, peptide-coding TEs in GBM and all GTEx healthy tissues (as 90 percentile expression, left panel, and percentage of samples with higher expression than GBM median expression, right panel). The most tumor-enriched TEs are from diverse classes but are preferentially derived from ORFs containing a canonical start codon (right histograms in Figures  6C  and S7A ). Some of these TEs are expressed in a majority of GBM tumors and undetectable in all, or in a majority, of GTEx healthy tissues (including brain) (Figure  6D ). For some of these TEs, over 90% of the cells expressing the TEs are GBM neoplastic cells from all four patients in the scRNA-seq datasets (pie charts in Figure  6D ; violin plots in Figure  S7B ).
DISCUSSION In search for tumor-specific, recurrent antigens, we use a TEcentered proteogenomic approach to investigate HLA-I presentation of TE-derived peptides. We first analyze scRNA-seq from total live cells of four primary GBM tumors to identify individual TEs expressed selectively in GBM tumor cells and not in hematopoietic or stromal cells. We show that the TEs differentially expressed in neoplastic cells are overexpressed in a cohort of 155 bulk RNA-seq samples from GBM patients (TCGA), as compared with all tissues, including brain tissue from healthy donors (GTEx). This neoplastic-enriched TE signature is used to interrogate MS-based immunopeptidomic datasets from 30 cell lines and primary GBM tumors. We identify 370 TE-derived peptides with reliable profiles and motif compliance to HLA-I alleles of the corresponding samples.
These peptides are encoded by 568 TEs, whose analysis revealed some interesting aspects of the biology of HLA-I presentation of peptides from TEs in GBM cells. Our study relies on scRNA-seq mapping of TEs. Several recent papers have analyzed TEs in scRNA-seq datasets. Although a few early studies pointed to possible bias and limitations  (He et al. , 2021; Shao and Wang, 2021) , reliable pipelines and guidelines are now available and have been applied in our study. Our results are also supported by internal controls that confirm the robustness of our TE scRNA-seq analyses. First, we show that the TEs expressed in neoplastic GBM cells, but not in other cell populations, are biased for TEs encoded by chromosome 7 (all or intergenic TEs, suggesting that the bias is not due to intronic TE expression) (Figure  1E ).
Second, the neoplastic TE signature based on scRNA-seq is overexpressed in GBM bulk RNA-seq patient cohorts compared with healthy tissues (Figures  2D  and 2E ). Importantly, the peptides identified in immunopeptidomic databases are also biased for chromosome 7, further and independently validating our peptide discovery and validation pipelines. One conclusion of our study is that the proportions of intronic and intergenic TEs are increased among peptide-coding TEs, as compared with the neoplastic TE signature (the database used to identify the peptides), at the expense of 3 0 UTR TEs. HLA-Ipresented peptides can therefore be derived from both genedependent and gene-independent transcription and translation, but the reasons why intronic TEs provide proportionally more peptides than 3 0 UTR TEs is worth further analyses.
This scenario is supported by the observation that, if one or two nucleotide mismatches are allowed, the number of redundant TEs is even larger (Figure  S5C ). This is an intriguing observation, and we do not know yet whether the peptides identified by mass spectrometry are derived from multiple or single TE loci. Analysis of the peptide-coding TE ORFs reveals that peptides are generally encoded by 10-to 100-amino-acid-long ORFs (with the exception of around half of the LINE-encoded peptides that are derived from longer ORFs). In LTRs, peptides are derived from all viral ORF types, with a positive bias for gag-derived peptides, as compared with the proportion of gag genes annotated in the databases (Figure  4I ). Among LINE-derived peptides, only a small proportion (around 10%) are derived from the known ORF1p and ORF2p proteins.
Tetramer staining and analysis Tetramer staining was performed on total cells after in-vitro vaccination experiments by combining 1mL of each tetramer specificity, and two different streptavidin-labelled tetramers per specificity. The staining was performed during 20 min at RT in a final volume of 100 mL of PBS 1% BSA per 1M cells. Then, 100 mL of surface antibody mix containing anti-CD3 BV650 (BD Biosciences) and anti-CD8 PECy7 (BD Biosciences) at 1/200 final dilution was added and incubated for further 20 min at 4 C. Finally, cells were washed twice with PBS-1% BSA and analyzed by flow cytometry. Live/Dead Aqua-405nm (ThermoFisher) was used to exclude dead cells. Data was collected using a ZE5 Cell Analyzer (Bio-Rad) and analyzed using FlowJo v10. 3. Tetramer analysis was done on live, single cells, CD3+CD8+ cells following the strategy described by Andersen et al.
METHOD DETAILS Transposable element annotations Classification and TE metadata Transposable Element annotations have been retrieved from two different databases: from Homer repeat gtf annotation file (v4. 11. 1) based on hg19 (v6. 4) UCSC annotations, and from TEtranscript  (Jin et al. , 2015)  hg19 gtf annotation file. Both annotations are based on RepeatMasker database and have been merged based on identical coordinates (Chr, Start, End) to obtain following information on each repeat: Class, Family, Subfamily, Divergence, coordinates. L1 family was subdivided into 2 families: (1) ''L1PA/B/x'' that include TEs from closely related L1HS, L1PA(x), L1PB(x), L1P(x) subfamilies; and (2) ''Other L1'' regrouping all other L1 that are not present in ''L1PA/B/x''. All DNA transposons were classified as DNA.
annotatePeaks. pl from Homer was performed to obtain genomic locations (intron, exon, 3 0 UTR, 5 0 UTR, intergenic, other) for each individual TE. Closest and intersect tools from bedtools (v2. 29. 2) have been used to retrieve, for each TE, the distance from closest protein-coding genes from gencode gtf annotation file (Release 19 GRCh37. p13) (Harrow et al. , 2006).
Ancient viral protein motif identification Open reading frame (ORF) locations from Endogenous Viral Elements (EVE) were retrieved from gEVE database. As analyses were performed on human genome version hg19, hg38 gEVE annotations were formatted and adjusted to hg19 using ''Lift Genome annotations'' tool from UCSC available here: https://genome. ucsc. edu/cgi-bin/hgLiftOver. ORFs coordinates from gEVE annotations and from all individual TEs in the genome were matched to assign an EVE ORF to individual TEs in case of coordinate overlap. 30517 individual TEs overlapped an EVE ORF, with most of them being L1 (mostly L1PA/B/x) and ERV (mostly ERV1, ERVK, ERVL) elements. To identify amino acid sequence similarity between canonical TE proteins from gEVE database and peptides from immunopeptidomics, a blastp  (McGinnis and Madden, 2004)  was performed between gEVE protein sequences and the immunopeptidomic sequences.
Analysis of known TE proteins LTR and LINE proteins LTRs coding for peptides overlapping an intact ORF were classified as Env, Gag, Pol or Pro using RetroTector annotations from gEVE. For LINE elements, a blastp (v2. 12. 0+) was performed between LINE-derived peptides and either ORF1p and ORF2p protein sequences found in Uniprot (accession numbers Q9UN81 and O00370). LINE and LTR coding for a peptide were also compared to gEVE HMM profile annotations to classify the TE protein motif found in those TEs. TE ORF annotations A homemade R script was used to identify and annotate ORFs from TE sequences. (1) TE nucleotide sequences were formatted to obtain 6 frames using R package Biostrings (v2. 58. 0) and its functions DNAStringSet and reverseComplement. (2) 6-frame sequences were translated with translate function from Biostrings.
(3) Stop codons and methionines were detected using matchPDict function from Biostrings. (4) Peptides from immunopeptidomics were mapped using matchPDict function. (5) ORFik R package (v1. 10. 13) (Tjeldnes et al. , 2021)  was used to detect ORF with at least 30bp (3 for the start codon, 8AA*3 for the sequence, 3 for the stop codon) and to keep only the longest ORF. Two different start codon patterns were submitted to detect ORFs: ''ATG'' for canonical start codons and ''ATG|CTG|GTG|TTG'' for canonical and non-canonical start codons. ORFs only found using the second pattern were classified as ''CTG|GTG|TTG''. (6) Length of ORFs were calculated using start and end positions. (7) R package ggplot2 was used to represent all identified ORFs, stop codons, methionines and peptide locations in all 6 frames of the TEs.
Single-cell data analysis Downloading data and read alignment to genome Smart-seq2 data (GEO accession number: GSE84465) were downloaded from the Sequence Read Archive (SRA) database using prefetch from SRA Toolkit (v2. 10. 0). SRA files were converted to fastq files using fastq-dump. Fastq files were 75bp paired-end unstranded reads. Raw RNA reads were mapped to the human genome (hg19) using the 2-pass mode of STAR (version 2. 7. 1. a) (Dobin et al. , 2013)  (parameters: -quantMode GeneCounts, -twopassMode Basic, -alignSJDBoverhangMin 1, -bamRemoveDu-plicatesType UniqueIdentical, -winAnchorMultimapNmax 1000, -outFilterMultimapNmax 1000, -outFilterScoreMinOverLread were retained for the TCGA samples whereas 192243 TEs were kept for the GTEx samples. Among those, 103585 TEs were common to both databases.
Gene set enrichment analysis Gene Set Enrichment Analysis (GSEA)  (Subramanian et al. , 2005)  was performed using DESeq2 normalized count matrices of common expressed TEs between TCGA and GTEx databases (103585 TEs) to test enrichment of neoplastic single-cell signature in either Normal or Tumor samples. GSEA (v4. 2. 1) was run with default parameters. GSEA results were imported to R and ggplot2 was used to make representations.
Mass spectrometry based Mass spectrometry data analysis MS-based immunopeptidomic files were obtained from PXD020079, PXD008127, PXD003790 and MSV000084442. They were analysed with ProteomeDiscoverer 2. 5 (ThermoFisher) using the following parameters: no-enzyme, precursor mass tolerance 20ppm and fragment mass tolerance 0. 02 Da. Methionine oxidation and N-acetylation were enabled as variable modifications. Using Percolator, a false discovery rate (FDR) of 1% was applied at peptide level and no FDR was used at protein level. Spectra were searched against the human Uniprot/SwissProt with isoforms (updated 06/03/2020) concatenated with the 6 reading frame in silico translated neoplastic enriched TE database (from uniquely-or multiple-mapped analysis). Identified potential TE-derived peptides were filtered afterwards with UniProt/TrEMBL database, considering leucine-isoleucine and lysine-glutamine as equivalent, respectively.
Identifying potential peptide-coding TEs To identify all TEs coding for peptides identified with immunopeptidomic results, peptide amino acid sequences were aligned to all annotated individual TEs in the genome in all 6 reading frames using tblastn (v2. 11. 0+). Sequences from all TEs in the genome were retrieved using getfasta from bedtools (v2. 30. 0)  (Quinlan and Hall, 2010)  with TETranscript gtf processed into BED format. No restriction on Evalue was requested. All hits with a number of mismatches equal to 0, a number of gap openings equal to 0 and a query coverage per HSP of 100 were kept and considered as peptide-coding TEs in addition to those from the neoplastic signature identified with ProteomeDiscoverer.
Spectrum validation with synthetic peptides To validate the spectra, 24 of the identified peptides were synthesized (GeneCust) with an HPLC purity of 95% and were injected in a Velos Orbitrap (CID or HCD). Raw files were analysed with ProteomeDiscoverer 2. 5 (ThermoFisher). Spectrums were exported and compared to the spectrums derived from the immunopeptidomic analysis. Only PSM with the same charge between synthetic and endogenous and without modifications were analysed. The same fragmentation type (CID or HCD) between both spectrums was prioritized when possible. Assessing related RNA expression of TE-derived peptides Identification of tumor-enriched TE-derived peptides TPM expression of all possible TEs from the genome that can potentially code for the identified peptides was retrieved, and 90 th percentile values were calculated for each tissue.
TEs coding for each specific peptide were selected and their 90 th percentile values were summed to obtain the total transcript expression related to these peptides. For single-TE encoded peptides, related transcript expression was directly the 90 th percentile value of the TE coding for the peptides. A log2 ratio was then performed between peptide related expression in GBM samples compared to each GTEx tissue to assess if the related expression of these peptides were higher in GBM samples compared Normal tissues. Using median TPM expression in GBM samples as a threshold, the percentage of expression in normal samples with an equal or higher expression was also calculated for each tissue. Pheatmap function from ComplexHeatmap R package (v2. 6. 2) was then used to represent the log2 ratio, the 90 th percentile values as well as the percentage of expression in normal samples.
Figures Most figures were made using R (v4. 0. 3). Piecharts, lollipop charts, barplots, violin plots, boxplots, jitterplots, volcano plots, density plots, scatterplots and dimensionality reduction plot were made using either ggplot2 R package (v3. 3. 3) (Wickham, 2016)  or functions from Seurat package  (Butler et al. , 2018). Pie donut chart was made with PieDonut function from webR package (v0. 1. 6). Heatmaps were built with Pheatmap R package (v1. 0. 12)  (Kolde, 2019)  and ComplexHeatmap (v2. 6. 2)  (Gu et al. , 2016). Clustering method used was ward. D2. IGV (v2. 8. 10)  (Robinson et al. , 2017)  was used to visualize read coverage of bulk RNA-seq samples.
Statistical analyses Wilcoxon tests were performed with R package ggpubr (version 0. 4. 0) and its function stat_compare_means (1) to compare distance to closest gene between Immune and Neoplastic signatures; (2) to compare mean expression of the neoplastic signature in bulk RNA-seq samples; and (3) to compare length of canonical and non-canonical TE-derived peptides ORFs. Pearson correlation scores were computed using stat_cor from ggpubr: (1) to assess the correlation between TEs and their closest protein-coding gene; and (2) to assess the correlation between median age of TEs coding for a peptide and the number of TEs that can code for the peptide. Two proportions z-test were computed to compare LINE proportions in different subsets of individual TEs. The correspondence between p values and symbols is as follows: ns: p > 0.
ACKNOWLEDGMENTS We thank all the U932 members,  Olivier Lantz ,  Maude Delost ,  Nathalie Amzallag , and  Florence Faure , for helpful discussions. Also, we are grateful to  Joshua J. Waterfall  for helpful discussion and bioinformatics advice. In addition, we thank the core facilities at  Curie Institute , including the  Bioinformatics groups. S. A. received funding from the  Institut Curie ,  Institut National de la Santeet de la Recherche Medicale , and  Centre National de la Recherche Scientifique. This work has received support under the program « Investissements d'Avenir » launched by the  French Government. We thank the  Ligue Nationale Contre Le Cancer  for the fellowship to Y. A. A.
Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Sebastian Amigorena (sebastian. amigorena@curie. fr) and Christel Goudot (christel. goudot@curie. fr). Continued REAGENT or RESOURCE SOURCE IDENTIFIER Raw data files for TCGA GBM Bulk RNA-seq data (Brennan et al. , 2013) https://portal. gdc. cancer. gov/legacy-archive Raw data files for GTEx Bulk RNA-seq data (Consortium, 2013) https://www. ncbi. nlm. nih. gov/projects/gap/ cgi-bin/study. cgi?study_id=phs000424. v8. p2 Software and algorithms Proteome Discoverer 2. 5 ThermoFisher OPTON-31040 GibbsCluster 2. 0 DTU Health Tech https://services. healthtech. dtu. dk/ service. php?GibbsCluster-2. 0 NetMHC 4. 0 DTU Health Tech https://services. healthtech. dtu.
dk/ service. php?NetMHC-4. 0 R 3. 6. 3 R https://www. r-project. org/ R 4. 0. 3 R https://www. r-project. org/ RStudio RStudio https://www. rstudio. com/ Pheatmap (Kolde, 2019) https://cran. r-project. org/web/packages/ pheatmap/index. html ComplexHeatmap (Gu et al. , 2016) https://www. bioconductor. org/packages/ release/bioc/html/ComplexHeatmap. html ggplot2 (Wickham, 2016) https://cran. r-project. org/web/packages/ ggplot2/index. html Tidyr (Wickham and Girlich, 2022) https://github. com/tidyverse/tidyr Dplyr (Wickham et al. , 2022) https://github. com/tidyverse/dplyr ORFik (Tjeldnes et al. , 2021) http://bioconductor. org/packages/release/ bioc/html/ORFik. html Bedtools (Quinlan and Hall, 2010) https://bedtools. readthedocs. io/en/latest/ Deeptools (Ramirez et al. , 2014) https://deeptools.
readthedocs. io/en/ develop/ Seurat (Butler et al. , 2018) www. satijalab. org/seurat/ FeatureCounts (Liao et al. , 2014) http://subread. sourceforge. net/ STAR (Dobin et al. , 2013) https://github. com/alexdobin/STAR Samtools (Li et al. , 2009) http://samtools. sourceforge. net/ GSEA (Subramanian et al. , 2005) https://www. gsea-msigdb. org/gsea/ index. jsp Lift Genome annotations UCSC https://genome. ucsc. edu/cgi-bin/ hgLiftOver Blast (McGinnis and Madden, 2004) https://blast. ncbi. nlm. nih. gov/Blast. cgi IGV (Robinson et al. , 2017) https://software. broadinstitute. org/ software/igv/.
Modeling of the lines in SIDES The intensity mapping simulations presented in this paper are based on SIDES, presented in  Béthermin et al. (2017, see Sect. 2. 1 for a short description). This new version of the simulation now includes the main high-redshift lines observed in the millimeter domain: CO, [CII], and  [CI]  (see  respectively). In addition, the new version of the code contains a spectral cube generator, described in Sect. 2. 6. Contrary to the 2017 version, which was coded in IDL, the new pySIDES code is entirely written in Python and is publicly available 2  (see Appendix A).
We then derived dust continuum properties using the evolving main-sequence and starburst observed spectral energy distribution (SED) up to z = 4 from  Béthermin et al. (2017). As shown in  Béthermin et al. (2020) , these SEDs are also compatible with the most recent measurements between z = 4 and z = 6. All the details are provided in  Béthermin et al. (2017). This model is compatible with the observed continuum number counts from the mid-infrared to the millimeter domain after taking into account the angular resolution effects, the source redshift distribution, and the pixel histograms and power spectra of the Herschel maps. The results presented in this paper are based on a new Python implementation called pySIDES using a pandas data structure (pandas development team 2020) for the catalogs.