dataesr/scientific-paragraphs-categorization huggingface dataset:
It allowed cell grouping according to their tumorigenic potential, regardless of their tumor of origin. The approach relevance was validated using independent datasets of glioblastoma cell and tissue transcriptomes, patient-derived cell lines and orthotopic xenografts. Overexpression of genes coding for amino acid and lipid metabolism enzymes involved in anti-oxidative, energetic and cell membrane processes characterized cells with high tumorigenic potential. Modeling of their expression network highlighted the very long chain polyunsaturated fatty acid synthesis pathway at the core of the network. Expression of its most downstream enzymatic component, ELOVL2, was associated with worsened patient survival, and required for cell tumorigenic properties in vivo. Our results demonstrate the power of signature-driven analyses of single cell transcriptomes to obtain an integrated view of metabolic pathways at play within the heterogeneous cell landscape of patient tumors.
Computational analyses Single cell transcriptomes of 1033 GBM and 2417 normal cells from four patients were used  [14]. This dataset distinguishes cancer from normal cells on the basis of chromosome copy number variations (CNV) profiling, and further distinguishes normal cells according to their neural or immune lineage subtype  [14]. We filtered out low complexity transcriptomes on the basis of the graphical distribution of the number of transcripts and genes per cell (Additional file 3: Figure  S1A  and S1B ). Cells with fewer than 90,000 transcript reads and fewer than 1700 detected genes were filtered out (Additional file 3: Figure  S1A  and S1B ). We used log2-transformed Counts Per Million (log2(CPM + 1)) to allow comparison of read abundance across libraries of different sizes, unless otherwise specified.
In an analysis subset, tumor-pertumor data standardization was achieved by centering and reducing the data on a gene-by-gene basis (Additional file 4,  [8, 45] ). Normalization on the basis of the expression of a set of 17 housekeeping genes (HKG) is detailed in Additional file 1. Tissue transcriptomes corresponded to the TCGA RNA-seq dataset of 155 untreated GBM patients  [5]  (normalized counts, log2(TPM + 0. 5), Additional file 2). For comparison purposes, we also used single cell transcriptomes of the 4916 GBM cells derived from 20 GBMs operated from adult patients, which became available during the reviewing process of this article  [51]. Gene expression data currently available for this dataset are in log2((TPM/10) + 1). TPM (Transcripts Per Million) normalization takes into account an eventual biased estimation of long transcript numbers, by dividing the number of mapped reads by the transcript's length.
Since we could not calculate expression values in CPM + 1 from these data, we just transformed the log2((TPM/10) + 1) into log2(TPM + 1) to optimize the comparison between the Darmanis  [14]  and Neftel datasets  [51]. Grouping analyses were performed using the Hierarchical Clustering on Principal Components (HCPC) approach (Additional file 4). Results were visualized using Principal Component Analysis (PCA) or t-distributed Stochastic Neighbor Embedding (tSNE). HCPC was also used to identify genes whose mean expression in one cluster differs from their mean across all cells (i. e. variables driving cell grouping). Normalized Mutual Information (NMI) scores were calculated to determine the contribution of cells issued from distinct tumors to each cluster. A NMI value of 1 implies that clusters gather objects (here, cells) corresponding to a single label (here, the tumor label) whereas a value of 0 denotes that all labels are split across all clusters (Additional file 2,  [46] ).
R scripts used for unsupervised grouping and associated analyses are provided in Additional file 4. Tumorigenic and other scores were obtained by computing the geometric mean of the expression values per cell of each element of the molecular signature corresponding to a given score. When null, expression values were imputed a value of 1. Each element of the signature was detected in at least 25% of GBM cells. Gene differentially expressed between cell or tissue groups with differing scores were identified following Mann-Whitney (Wilcoxon Rank Sum) test with p-values adjusted for multiple testing (Benjamini-Hochberg (BH), p-value < 0. 01)  [62]. Gene ontology analysis was done using the human genome as background (Additional file 2). Functional gene network reconstruction was achieved using the information-theoretic method, MIIC (multivariate information-based inductive causation, Additional file 1  [61, 69] ).
Four independent datasets comprising 153 to 485 primary GBM transcriptomes were used for patient survival analysis (Additional file 2). Construction of a principal curve was achieved with a PCA based on the expression of each component of the lipid subgroup (16 genes coding for enzymes of the lipid metabolism overexpressed in Tum HIGH cells and tissues and detected in at least 25% of GBM cells, see results). Each cell was projected onto the curve using the Pathifier algorithm (Additional file 2,  [18] ). Lipid subgroup and vesicle scores (Additional file 2) were calculated as described for the tumorigenic score.
Unsupervised clustering analysis highlights first GBM cells' tumor of origin We used the publicly available single cell transcriptome dataset from Darmanis and colleagues  [14]  after removing low-complexity cell transcriptomes. Genes detected in at least 3 transcriptomes were retained for analysis (18,577 genes for GBM cells and 19,699 genes for normal cells). Previous scRNA-seq analyses of GBMs and other cerebral tumors focused on the most dispersed  [14]  or most expressed genes  [23, 53, 65, 68]  or on meta-modules of genes revealed by hierarchical clustering of cells tumor per tumor and recurring in several tumors  [51]. These analyses resulted in the identification of cell lineages and cell genomic anomalies rather than cell functioning states. We chose therefore to conserve all potential information by analyzing the full set of selected genes.
Note that NMI scores of GBM cell grouping remain constant, regardless of data normalization or filtering modes. Only data standardization reduces NMI score to a value similar to that obtained when analyzing normal cells. e Unsupervised analysis of data standardized by tumor results in clusters mixing cells from different tumors. PCA plots highlighting the tumor from which the cells derive (top: pink, green, orange, black for GBM1, 2, 4 and 6, respectively) or the 7 clusters identified (bottom). f Gene ontology analysis of the genes describing each of the 7 clusters highlights a variety of biological processes, not linkable to specific functioning states. DAVID toolkit. Corresponding cluster number is indicated (colored as cluster colors in the bottom panel of e) cells resulted in six cell groups of immune or neural subtypes (astrocytes, oligodendrocytes, oligodendrocyte precursor cells, neurons, myeloid cells, vascular cells, Fig.
1b1 ), each group mixing cells from different tumors (Fig. 1b2 ). In striking contrast, GBM cell clusters resulting from HCPC analysis were dominated by cells from a single patient tumor (Fig. 1c ). Contribution of the different tumors to clusters was scored by computing Normalized Mutual Information (NMI) between clusters and tumor labels, NMI scores being expected null if each tumor contributes equally to each cluster (Additional file 1). Fully supporting our observation, the NMI score of normal cell clustering was only of 0. 12 whereas the one of tumor cells was of 0. 55 (two first bars in Fig. 1d ). This predominant grouping of cancer cells by their tumor of origin has been reported for a number of cerebral and noncerebral tumors  [11, 14, 23, 35, 39, 57, 64]. For identifying traits common to all tumors, data can be analyzed tumor per tumor  [65, 67, 73] , or merged and analyzed as a whole after standardization (i.
Highly similar results were obtained using data normalized with the scran method (Additional file 7). Of note, several items of the list of genes with enhanced expression in Tum HIGH cells encoded proteins previously implicated in GBM cell aggressiveness (e. g. E2F1  [71] , EGFR  [1] , NOTCH1  [7] , FABP7  [15] , PTPRZ1  [25] ). Conversely, genes with known tumor-suppressor properties were identified among the genes overexpressed in Tum LOW cells (e. g. TUSC3  [36] , SERPINB1  [33] ). The whole workflow is summarized in Additional file 3: Figure  S4  and provided in Additional file 8. These results suggest that cell functioning state can be inferred from scRNA-seq data following signature-driven data reduction. We next challenged the relevance of this approach by applying it to an independent dataset and using in vitro and in vivo GBM models.
Specific amino acid and lipid metabolic pathways distinguish GBM cells and tissues with high tumorigenic potential To probe the biological relevance of our approach, we first applied the same analytical strategy to an independent dataset. Additional GBM single cell transcriptome datasets equivalent to the one published by Darmanis and colleagues and including several patients were not publicly available prior to submitting this paper for publication. We therefore turned to the TCGA collection of transcriptomes obtained by sequencing the RNA extracted from 155 patients' GBM tissue fragments. As expected with respect to the heterogeneous nature of GBM tumors where cancer cells with differing properties co-exist with normal neural, vascular and immune cells, we observed a smoother distribution curve of the tumorigenic score across GBM tissues than across GBM cells (Fig.
a Splitting cells into groups with high (Tum HIGH ) or low (Tum LOW ) tumorigenic potential. Left panel: tumorigenic score distribution across the cells. Dotted line: mean of the tumorigenic score. Right panel: PCA plot based on the expression of the 5 elements of the tumorigenic signature. b Contribution of each tumor to the two tumorigenic groups identified (chord plot). Note that each tumor contributes to each cell group. c Tumorigenic score distribution across GBM tissues (155 GBM tissues, TCGA RNA-seq dataset). Tum HIGH and Tum LOW GBM tissue groups selected at the extreme quartiles of the distribution. d High overlap between genes overexpressed in Tum HIGH GBM tissues and cells. 65. 5% (1688) of genes overexpressed in Tum HIGH GBM tissues are also overexpressed in Tum HIGH GBM cells by confronting a dataset derived from 4 tumors to another derived from 155 tumors, and that tissue transcriptomes correspond to gene expression levels averaged over several hundred thousands of cells.
4c ). During the reviewing process of this article, a novel and larger dataset of GBM scRNA-seq based also on the SMARTseq2 technology, and with sequencing depth comparable to the one obtained by Darmanis and colleagues became available  [51]. We therefore repeated our analysis using the 4916 GBM cell transcriptomes, 2184 coming from GBMs bearing an amplified form of EGFR (EGFR AMP ) and 2732 from GBMs bearing a non-amplified EGFR (EGFR NON-AMP ). The distribution profile of the tumorigenic score across this set of GBM cells was similar to the one across the Darmanis GBM cell set (Additional file 3: Figure  S6A ). Cells derived from EGFR AMP and EGFR NON-AMP GBMs contributed both to the Tum HIGH and Tum LOW GBM cell groups (Additional file 3: Figure  S6B ). From the list of 6869 genes overexpressed in Tum HIGH cells compared to Tum LOW cells, 60.
9% (4185) were also identified as overexpressed in Tum HIGH cells using the Darmanis scRNA-seq data (Additional file 3: Figure  S6C ). These 4185 genes included 376 genes coding for proteins involved in metabolic pathways and whose expression correlates to the tumorigenic score. Crossing this list of 376 genes with the corresponding list of genes from GBM tissues of the TCGA yielded 59 common genes (Additional file 3: Figure  S6D , and Additional file 10). Gene regulatory network reconstruction using MIIC tool highlighted again ELOVL2 at the densest nodes of the network (Additional file 3: Figure  S6E-F ). These results further strengthen the relevance of our approach. They also show that similar metabolic modules distinguish Tum HIGH and Tum LOW GBM cells independently from EGFR amplification.
Functional association of the lipid metabolism enzyme ELOVL2 to patient clinical outcome and GBM development ELOVL2 is a fatty acid elongase involved in the elongation of 22-to 24-carbon VLC-PUFA  [29]. In agreement with ELOVL2 overexpression in Tum HIGH cells, we observed ELOVL2 overexpression in cells with tumorigenic properties in an independent transcriptome dataset of human GBM cells in culture  [40]  (Additional file 3: Figure  S7A ). In addition, we observed a higher expression of ELOVL2 in GBM than in normal brain tissues (Additional file 3: Figure  S7B ), as well as in single GBM cells compared to single normal cells (Additional file 3: Figure  S7C ). Its expression was also higher in primary GBMs characterized by a wild-type form of IDH1 compared to diffuse glioma that are characterized by a mutant form of IDH1 (Additional file 3: Figure  S7D ).
They also show that cancer cell clustering per tumor is not driven by circumscribed gene subsets reflecting inter-tumor biological differences. They support the notion that the identity of the tumor to which each cancer cell belongs is encoded by information dispersed throughout the cell transcript repertoire. To unmask cell functioning states regardless of their tumor of origin, we reduced the data based on previously acquired biological knowledge. Signature-driven data reduction was previously used to infer cell lineages from single cell transcriptomes of oligodendrogliomas and diffuse infiltrative pontine gliomas  [23, 65]. These studies were performed on centered data. Cell lineages were inferred using sets of top correlated genes to the principal component scores of a PCA of the dataset under scrutiny  [65] , or on the basis of mouse or human gene sets differentiating normal neural subtypes  [23, 65].
Here, we based our analysis on a molecular signature related to a major functioning state, tumorigenicity. Cells from a given GBM have long been known to be endowed with differing abilities to initiate neoplasms  [32, 56, 70]. We postulated that such a choice would enhance the likelihood to group cells based on their functioning state irrespective of their tumor of origin. We postulated also that using a signature defined  [4]  and experimentally validated in independent prior studies  [4, 30, 43, 63]  would reduce the risk of obtaining results relevant only for the dataset analyzed. Our choices proved fruitful to identify two contrasting functioning states, with respect to the following findings. Genes previously described as controlling GBM cell aggressiveness were found to be overexpressed in cells with high (See figure on previous page.
Gene network built on the basis of the gene expression values across all GBM cells using MIIC tool. Line thickness represents the strength of the edge. Arrowheads linking variables in a v-structure of the type x → y ← z denotes the absence of a graphical structure of the type: x → y → z, x ← y ← z and x ← y → z (variable x cannot be reached passing through y, nor y passing through x, nor y is a common parent of the two other variables). These 3 models can be excluded since in a v-structure y does not mediate mutual information between x and z Fig. 5  (See legend on next page. ) tumorigenic scores. A highly similar list of differentially expressed genes was obtained when applying the same analytical strategy to an independent dataset of GBM cell scRNA-seq and of another made of tissue bulk transcriptomes of a much larger number of GBMs.
See figure on previous page. ) Fig. 5 Association of the lipid metabolism enzyme ELOVL2 to patient clinical outcome and GBM development. a High ELOVL2 expression is associated with worse patient survival. GBM tissue transcriptomes (microarrays) of 485, 156 and 173 GBMs of TCGA, French and Rembrandt datasets, respectively. Log-rank (Mantel-Cox) test. b Decreased ELOVL2 mRNA levels in shELOVL2 patient-derived cells (PDC) compared to shControl-PDC. 6240**, R633 and 5706** PDC. QPCR assay. Unpaired t-test with Welch's correction, mean ± SD, n = 4-5 independent biological samples. c ELOVL2 knockdown decreases cell proliferation. 6240**, R633 and 5706** PDC. Unpaired t-test with Welch's correction, mean ± SD, n = 4-5 independent biological samples. d ELOVL2 knockdown delays tumor development. Bioluminescent analyses of tumor growth initiated by grafting PDC transduced with a luciferase construct and either shControl (shCTL) or shELOVL2 constructs.
Acknowledgments We are grateful to  Dr. Nadir Sella  ( Institut Curie, CNRS UMR168, Paris ) for his guidance and help for using MIIC, to  Dr. François-Xavier Lejeune  ( ICM, Salpétrière Hospital, Paris ) for his advice on statistical analyses, and to  Dr. Soazig Lelay  and  Dr. Ramaroson Andriantsitohaina  ( INSERM U1063 -SOPAM, Angers ) for sharing the Nanosight equipment. We thank  Dr. Sylvie Fabrega  and the Plateforme vecteurs viraux et transfert de gènes (Necker Federative structure of research,  University Paris Descartes, France)  for lentivirus production.
( See figure on previous page. ) Fig. 5 Association of the lipid metabolism enzyme ELOVL2 to patient clinical outcome and GBM development. a High ELOVL2 expression is associated with worse patient survival. GBM tissue transcriptomes (microarrays) of 485, 156 and 173 GBMs of TCGA, French and Rembrandt datasets, respectively. Log-rank (Mantel-Cox) test. b Decreased ELOVL2 mRNA levels in shELOVL2 patient-derived cells (PDC) compared to shControl-PDC. 6240**, R633 and 5706** PDC. QPCR assay. Unpaired t-test with Welch's correction, mean ± SD, n = 4-5 independent biological samples. c ELOVL2 knockdown decreases cell proliferation. 6240**, R633 and 5706** PDC. Unpaired t-test with Welch's correction, mean ± SD, n = 4-5 independent biological samples. d ELOVL2 knockdown delays tumor development. Bioluminescent analyses of tumor growth initiated by grafting PDC transduced with a luciferase construct and either shControl (shCTL) or shELOVL2 constructs.
The model shows a quantitative agreement with flux-driven simulations, as well as experimental measurements over a wide range of conditions. Predictions for ITER start-up conditions also recover the extrapolations from multi-machine databases. The SF model is derived for circular plasma shapes with weak flow shear, and gives a SOL width that varies mainly with the poloidal magnetic field, weakly with the toroidal magnetic field, and weakly but positively with the plasma major radius. Interestingly, this sensitivity with main control parameters is qualitatively close to what is found for diverted conditions in both L and H mode.
Transport by electrostatic filaments is addressed by a spectral model, where the whole assembly of filaments is described as superposition of poloidal waves. In Section 2 results from non-linear simulations with the TOKAM2D code  [16]  are discussed. The spectral approach of turbulent particle flux is introduced in Section 3. Then a model of density and potential spectra is detailed in Section 4, leading to a predictive model for both turbulent radial flux by interchange filaments and SOL width. The model is validated against non linear simulations. In Section 5, comparison to experimental databases is made: quantitative agreement is found both in amplitude and trend for the SOL width of inner limited circular plasmas. The model overestimates the SOL width of L-mode diverted plasmas although the sensitivity with control parameters is recovered.
The system is driven by a particle source S n localized on the left hand side of the simulation domain and uniform along y. Plasma density n is normalized to arbitrary value and plasma potential is normalized to T e. The Poisson bracket reads A A A { , } x y y x and describes the divergence of flux of quantity A. In practice, simulations can be run with two types of radial boundaries: either a periodic box, or with a buffer at the inner and outer radial boundaries. Both types of simulations return non distinguishable solutions in the domain of interest localized outward of the source. The parametric dependence of the simulations where investigated by varying the two control parameters g and σ, which can be interpreted as scans of safety factor and normalized Larmor radius. A database of 10 simulations was produced with × g [1, 40] 10 4  and × [1 50] 10 , 5 which comes in completion of a previous work  [12].
There, it was shown that the density decay length measured across the region of interest behaves as = g n 0. 3 0. 75. A power law regression made on the extended database now returns = g 2. 8 , n 0. 33 0. 62 which depicts a small but finite ( ≈ 10%) difference of sensitivity with control parameters. The only difference between the 2 database was the size of the simulation domain: previous simulations were run on a 256 × 256 grid with a resolution = = x y , L whereas the new data base is run with the same resolution but on an extended radial domain 512 × 256. This comparison allowed to identify a physical bias occurring for the smaller grid, impacting the density decay length and its relation with turbulent transport. This effect is discussed now, because it has important implications regarding the implicit hypothesis commonly done about transport locality and the generic nature of transport models.
A factor 4π 2 comes from Fourier normalization and a factor 2 comes from parity consideration on the spectra: the sum over negative and positive wave numbers are equal. Note that n k and Φ k are complex numbers, with n* k the complex conjugate of n k. Over the set of simulations, the phase shift between density and potential modes is found to be very close to π/2 for wave numbers dominating the transport, so that the flux expression becomes: = n n kdk ¯¯8 x k k 2 0 (4) where the spectral components are now n k ≡ ‖n k ‖ and ϕ k ≡ ‖Φ k ‖. The phase approximation brings an error to the flux value less than 10% over the entire simulation database. The term under the integral n k Φ k k is called spectral flux contribution, and is illustrated on Fig. 2 (a), together with the density n and velocity spectra v k ≡ kΦ k.
Comparison with experiments In a previous work  [12] , the SOL width scaling derived from TO-KAM2D simulations where compared to a Tore Supra database. Rationals for this comparison are (1) the inner limited circular plasma geometry is more suitable comparison with simplified models and (2) the large database was constructed for the ITER startup extrapolation, featuring large variation of plasma current, magnetic field and plasma density  [19]. To apply the spectral filament model, expressions of the two control parameters g and σ ∥ are needed. Following the circular plasma approximation, and considering that filaments are mainly destabilized on the outer half of the plasma section (transport ballooned), we define = g R / L and = qR / L. Replacing these expressions in Eq. (  10 ), the resulting prediction for the SOL density decay length reads: where λ n is given in millimeters, q is the cylindrical safety factor, B is the magnetic field strength in Tesla, R is the plasma major radius in meter, A is the ion mass number, Z is the ion charge number, T e (T i ) the separatrix electron (ion) temperature en eV.