dataesr/scientific-paragraphs-categorization huggingface dataset:
Additional desirable information includes DNA profiling of serial passages to corroborate lineage fidelity and animal health status from standard health surveillance programs. The current PDX-MI requires evidence of quality assurance but does not require every possible technique be performed as methods vary across resources. Model Study and other associated data. Tumors from PDX often undergo comprehensive genomic characterization and/or treatment in controlled dosing studies to define therapeutic response and resistance. PDX-MI includes "desirable" fields in the reporting of these studies that supplement existing guidelines for reporting on in vivo biomedical research  (10). Additional optional metadata are accession IDs from data archives and citation IDs (including Digital Object Identifiers) for publications describing the PDX model(s).
Terminology and Vocabularies PDX resources employ a combination of custom and community developed vocabularies. This presents challenges in data integration as it takes expert knowledge to map the divergent systems. For example, cancer diagnoses are represented within different PDX resources by terms from the NCI-Thesaurus  (12) , SNO-MED CT  (13) , MeSH  (14) , and the Disease Ontology  (15). Free text descriptions are used for many PDX model attributes and a mix of generic, commercial, and chemical labels are used for drugs. Ontology resources and community model organism databases have been developing tools to semi-automate mapping of standards that produce unified indices to facilitate data query and discovery. Rather than impose a limited set of terms to describe a given minimal information attribute, PDX-MI will allow the reporting of a resource's internal standards.
PDX-MI will be used to validate data submissions from producers of PDX models and from data curated from the literature. PDX-MI will also inform scoring algorithms being developed by the Butte group to assess how well PDX models recapitulate hallmarks of human cancers. Future versions of PDX-MI will capture additional details as procedures become more standardized. Input from clinical and translational professional societies will inform evolving requirements for diagnostic markers on a disease-specific basis. Given the recent success of immune checkpoint inhibitors in the treatment of cancer, improving "humanized immune system" PDX models is an area of intense research and PDX-MI will evolve to represent this. Other aspects of PDX models that are rapidly changing include improved surgical techniques and quality assurance methods.
As we develop resources to capture and disseminate data related to PDX models, we will continue to improve and version PDX-MI to reflect the state of the art in the field. A web-based form to allow feedback from the community about the standard described here can be accessed at the Mouse Tumor Biology database web site (http://tumor. informatics. jax. org). As has been demonstrated across multiple disciplines, a minimal standard adopted by a research community accelerates the rate of scientific discovery while reducing unnecessary duplication.
Structures of McjD in different conformations In order to gain a detailed understanding of the conformations that McjD adopts during the transport cycle, we have determined its structure in the presence of the transition state analogue ADP-VO 4 (mimic of ATP hydrolysis) and in the absence of nucleotides, that is in the apo form, at 3. 4 A ˚and 4. 7 A ˚resolution, respectively  (Fig 1B and C). We determined the structure of McjD in complex with ADP-VO 4 by molecular replacement using the McjD-AMPPNP (adenosine 5 0 -(b,c-imido)triphosphate) (PDB ID: 4PL0) structure as a search model  (Choudhury et al, 2014). The structure was refined with an R work of 25. 7% and R free of 26. 0%. Clear electron density was observed for ADP-VO 4 and Mg 2+ (Fig EV1A-D). The presence of vanadate was verified by collecting the data close to the vanadium K-edge, 2.
McjD purification and crystallization McjD was purified in 0. 03% dodecyl-maltopyranoside (DDM) as previously described without modifications  (Choudhury et al, 2014). McjD-ADP-VO 4 was prepared by incubating 15 mg/ml McjD (20 mM Tris pH 7. 8, 150 mM NaCl, 0. 03% DDM) with 2 mM ATP, 2 mM sodium orthovanadate and 5 mM MgCl 2 at room temperature for 1 h. Crystals were grown at 293 K using the vapour diffusion method by mixing protein and reservoir solution at 1:1. Crystals were grown from a precipitant solution containing 10% PEG 4000, 0. 1 M ammonium sulphate, 100 mM HEPES pH 7. 5 and 22% glycerol. Crystals appeared overnight and they reached maximum size after 4 days. The crystals were directly frozen into liquid nitrogen, and diffraction screening and data collection were performed at Diamond Light Source synchrotron.
These crystals diffracted X-rays to a maximum resolution of 2. 9 A ˚. McjD-apo crystals, 20 mg/ml McjD (20 mM Tris pH 7. 8, 150 mM NaCl, 0. 03% DDM), were grown from a precipitant solution containing 5 mM MgCl 2 , 24% PEG400 and 100 mM glycine pH 9. 0. Crystals appeared after a week and they reached maximum size after 2 weeks. Prior to freezing crystals, the cover slide was moved to a fresh reservoir solution containing 5 mM MgCl 2 , 28% PEG400 and 100 mM glycine pH 9. 0 and incubated overnight. Crystals were directly frozen into liquid nitrogen. Diffraction screening and data collection were performed at Diamond Light Source synchrotron. These crystals diffracted X-rays to a maximum resolution of 4. 5 A ˚.
92 A ˚using an ADSC Q315 detector and processed using xia2  (Winter, 2010). Further processing was performed using the CCP4 suite  (Collaborative Computational Project N, 1994). The resolution of the data and anisotropy analysis were evaluated by half-dataset correlation coefficient in Aimless (cut-off < 0. 5;  Evans, 2006). The space group was determined to be P4 2 22 with one copy of McjD in the asymmetric unit. The data collection statistics for both the McjD-ADP-VO 4 and apo crystals are summarized in Table  1.
Cysteine cross-linking Cysteine cross-linking experiments in ISOVs were performed as previously described without modifications  (Choudhury et al, 2014) , with the exception of S509C mutant that was pre-treated with 1 mM DTT for 30 min at room temperature to reduce preformed cysteine cross-links. The effect of the MccJ25 to induce cysteine cross-links of A122C was determined by adding 1 mM MccJ25 to A122C ISOVs for 10 min prior to CuCl 2 addition. Formation of cysteine cross-links was analysed by Western blotting. The gels were imaged using an ImageQuant LAS4000 (GE Healthcare). Densitometry analysis of the gel bands intensities was performed using the ImageQuant TL software (GE Healthcare); to quantify the extent of MccJ25-stimulated cross-linking of A122C, band intensities (automatic peak detection mode) corresponding to CLD formation were estimated for each gel lane.
All PELDOR spectra were recorded at 50 K with an experiment repetition time of 1 ms, a video amplifier bandwidth of 20 MHz and an amplifier gain of 42 dB. s 1 was set to 260 ns and the maximum of s 2 was set to values ranging from 2 to 6 ls. Deuterium modulation was suppressed by the addition of eight spectra of variable s 1 with a Ds 1 of 16 ns. The obtained time traces were divided by a monoexponential decay to eliminate intermolecular contributions and renormalized. Distance distributions were obtained from the background-corrected data by using the Tikhonov regularization as implemented in DeerAnalysis2016 developed by Gunnar Jeschke  (Jeschke et al, 2006). The influence of different starting points for the background fitting was analysed with the evaluation feature of DeerAnalysis. The PyMOL (www.
Molecular dynamics simulations MD simulations were performed on the McjD-AMPPNP structure, obtained at a resolution of 2. 7 A ˚(PDB ID: 4PL0;  Choudhury et al, 2014). Two sets of simulations were built by replacing the AMPPNP molecule with either ATP or ADP, while the position of the Mg 2+ ions was kept as in the crystal structure. H537, the Switch motif, was set protonated for the ATP-bound simulations, and neutral for the ADP-bound ones. The protein in complex with either ATP or ADP was embedded in a 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayer using g_membed  (Wolf et al, 2010) , with a total of 500 lipids. The simulation box, including water and ions to neutralize the system, was 13 × 13 × 16 nm. Energy minimization, equilibration protocols and production runs were carried out with GROMACS 4.
Acknowledgements We would like to thank  Diamond Light Source  for beam time allocation and access, and  Dr Marc Morgan ,  Imperial College London , for help with scheduling. We would like to thank  Dr Gerard Bricogne ,  Dr Clemens Vonrhein  and  Dr Ian Tickle  for help with the anisotropic correction of the data. We would like to thank  Prof Bob Ford  and  Prof Alex Cameron  for critical feedback on the manuscript. KBo is supported by a  Biotechnology and Biological Sciences Research Council (BBSRC)   Doctoral Training Partnership (DTP) Studentship. WYW is supported by a  Mobility Starting Grant  from  Swedish Research Council FORMAS. DPT is an  Alberta Innovates Health Solutions Scientist and Alberta Innovates Technology Futures Strategic Chair in BioMolecular Simulation. DPT is supported by the  Canadian Institutes for Health Research  ( MOP-62690 ).
The ability to assign expression patterns to the individual cell types that constitute a tissue is a major challenge. This especially applies to brain, given its plethora of different, functionally interconnected cell types. Here, we derived cell type-specific transcriptome signatures from existing single cell RNA data and integrated these signatures with a newly generated dataset of expression (bulk RNA-Seq) of the postnatal developing mouse hippocampus. This integrated analysis allowed us to provide a comprehensive and unbiased prediction of the differentiation drivers for 11 different hippocampal cell types and describe how the different cell types interact to support crucial developmental stages. Our results provide a reliable resource of predicted differentiation drivers and insights into the multifaceted aspects of the cells in hippocampus during development.
Furthermore, adult hippocampus was also shown  8  to be constituted by a large amount of different, specialized cells including at least ten major cell types and more than 40 subtypes. This cellular diversity is achieved thanks to differentiation drivers whose expression is tightly regulated during hippocampus ontogenesis. To date, a quantitative, comprehensive assessment of the differentiation drivers in the course of hippocampal development is still lacking mostly due to the inability of bulk RNA to assign expression patterns to individual cell types. Recently, integrated in-silico analysis of signature of cell types and bulk datasets has proven to efficaciously overcome the aforementioned limitations  [9] [10] [11]  , providing some insight at the cell type level also in bulk transcriptomes.
Here, we generated a developmental dataset of the hippocampal RNA-Seq transcriptome of 5 different developmental stages (embryonic forebrain E15, hippocampus P1, P7, P15, P30) and applied a deconvolution approach which exploits existing single-cell RNA (scRNA) data  8  to infer putative drivers of differentiation for the major cellular types. Our approach was validated by the literature, as we uncovered numerous well-known genes previously shown to be implicated in the differentiation or maturation of neuronal and glial cells. Importantly, we unveiled many new candidate regulators of cell differentiation which constitute a precious resource providing biological insight into cell differentiation of the central nervous system.
These results indicate that our dataset and clustering can be effectively used to expose and disentangle the genes and pathways relevant for the hippocampus and more in general brain development. For example, we found that Kv3. 2 subunit of Kv3 potassium channels (high frequency channels implicated in epilepsy, Alzheimer's and spinocerebellar ataxia  [16] [17] [18]  ) displays a pattern of developmental expression which diverges from the other three subunits (namely Kv3. 1, Kv3. 3 and Kv3. 4). This indicates that after the 2nd postnatal week the relative abundance of subunits for Kv3 channels is changed (Supplementary Fig. S1D ), possibly because of a differential subunit usage. This example serves as a proof of principle that mining our novel results allows to formulate hypotheses which can be used to enhance our mechanistic insights of hippocampal ontogenesis.
Determining the markers of cell types and subtypes by differential expression analysis. The hippocampus has a deeply organized structure in which numerous, different cell types and subtypes are functionally or anatomically interconnected and exert their functions in a tightly orchestrated manner. In order to unravel how the different populations of cells collaborate to regulate developmental processes, we set out to isolate the markers of cell types and subtypes by analyzing the scRNA dataset from Zeisel et al. 8. In the latter work, adult cortical and hippocampal cells were classified into 11 types and 47 subtypes (P21-P30 hippocampus, see methods). They also provided lists of markers for every cell type, but not for the cell subtypes. Here, we sought to calculate both the markers of a given cell type and the markers expressed in its subtypes of cells.
Putative regulators of cell-fate commitment, differentiation or maturation. Our data indicates that the majority of neuronal and glial markers display an increasing expression level in the course of development. Those following the opposite pattern, that is repressed in the course of development, intrigued us. Amongst these divergent markers we could recognize several well-known regulators of neuronal or glial differentiation like Arx, Dlx1 or Id4  [27] [28] [29]. This led us to hypothesize that those few markers displaying a decreasing expression level during development (which means belonging to green clusters C1-C5) may be directly or indirectly implicated in cell-fate commitment or in differentiation/maturation. Overall, we found 169 of such genes (Fig. 6 ). To date, there is no genome-wide, experimentally based database of predicted differentiation drivers like ours, thus we could not validate our results by a direct comparison with other genome-wide datasets.
Discussion The major limitation of bulk gene expression approaches is the inability to discern the contributions of the different cell types. While in the case of sufficiently homogeneous cell cultures this is not a detrimental problem, in the case of brain regions such as hippocampus it becomes a severe complication  8, 30. Here, we devised a series of bioinformatic analyses steps that exploit P30 scRNA data  8  to in part overcome the limitation of bulk gene expression and gain precious information at the cell type level. Based on scRNA, Linnarsson and coworkers defined at least 40 cellular subtypes that constitute the hippocampal tissue of the adult mouse. The integrated analysis of this dataset and a newly generated developmental transcriptome of hippocampus allowed us to infer the individual roles of cell types in the regulation of postnatal synaptogenesis and to predict the differentiation drivers specific to each hippocampal cell class.
Clearly, our analysis disclosed only a fraction of the whole set of genes implicated in cell differentiation. In fact, we limited our considerations to genes that could be unambiguously identified in adult stages as markers of a specific cell type. In this way, genes which are not expressed anymore at adult stages and/or are multi-functional genes remained undetected in our analysis. An example of multi-functional gene is Gfap, which is an established glial cell maker in adult hippocampus that displays a massive postnatal activation in our dataset. However, in the neurogenic niche (sub granular zone) Gfap works as a marker of type-1 glia-like stem cell  31. On the other hand, the use of adult (P21-P30) markers applied to earlier developmental stages did not seem to cause any bias at the level of cell type assignment, which would arise if a marker switches from one cell type to another during development.
While this could be possible, we did not find any such case amongst the validated differentiation drivers (Supplementary Tables  S5-S10 ), suggesting that, exception aside, the general rule for markers is to remain bound to one specific cell type. In the future research, it will be of particular interest to investigate the features and exact functions of the numerous novel candidates provided in this work. Given its immense importance in the pathophysiology of cognition, the hippocampus is one of the most studied neurodevelopmental models. We now provide a quality dataset of developmental expression data and a plethora of hypotheses that can be used to enhance our mechanistic insights.
Once determined the optimal amount of clusters, the m*n matrix (with its original values) is then clustered via standard k-means with squared Euclidean distance. In our case, the optimal number of clusters was set to 13. The m*n matrix was then clustered in 13 groups via standard k-means clustering with Euclidean distance that proved to be the best among several different linkages and distance metrics. Analysis of single cell RNA-Seq data. Data from 8 was analyzed in order to extract the list of markers for each cell types and subtypes. As previously done in  8  all the cells coming from P21-P30 mice were used together, as no age effect of cell phenotypes was found. In the supplementary dataset containing cell type identities (http:// linnarssonlab. org/cortex/), 7 main cell types are presented, whereas the main manuscript 8 describes 9 main cell types.
This is because in the supplementary dataset astrocytes and ependymal cells are pooled in one population and endothelial and mural cells are pooled in another population. Similarly, also perivascular macrophages and microglia ware pooled in one population and choroid cells and astrocytes in another. We decided not to pool cell types with different identities, phenotypes and names into larger groups, so we considered perivascular macrophages and choroid cells as separate cell types and not subtypes, resulting in 11 total cell types and 47 cell subtypes. As a first step, we run 47*46/2 = 1081 pair-wise comparisons of expression (DESeq 2) among the 47 described cellular subtypes. The markers of each cell subtype were defined as those significantly up-regulated (DESeq 2 corrected p-value < 0. 05) in at least 44/46 cases.
Identification and validation of 5meCpG-gene pairs by emQTL. Significant correlations between the level of DNA methylation at a CpG site and gene expression were investigated in a breast cancer discovery cohort with matching genome-wide expression and DNA methylation data (MicMa, n = 104). For CpGs with interquartile range >0. 1 (n = 189,026) and genes (n = 17,558), all possible Pearson's correlations between 5meCpG and gene expression were tested for non-zero correlation. We identified 1,115,448 significant CpG-gene pairs (Bonferroni corrected p-value < 0. 05), of which 739,608 were validated in an independent patient cohort with matching DNA methylation and expression data (TCGA, n = 253). Of the non-validated 375,840 emQTL pairs in TCGA, 298,214 could not be tested due to either missing methylation or expression values.
This data set of DNA methylation is available in GEO with accession number GSE84207. The DNA methylation data was matched to previously published expression data (GEO accession number GSE58215  19  ) to obtain 277 samples with matching DNA methylation and gene expression. We performed the emQTL analysis ab initio (i. e. , tested all possible CpG-gene pairs) and we rediscovered 95. 5% of the total emQTL identified from the MicMa and TCGA cohorts. The observed associations (emQTLs) were conserved between three independent breast cancer cohorts. Identification of emQTL clusters. To elucidate the biological relevance of the emQTL, we performed unsupervised clustering of the Bonferroni corrected p-values of each 5meCpG-gene expression pairs. Two very strong and distinct bi-clusters (cliques) of CpGs and genes became apparent: Cluster 1 (3401 CpGs and 160 genes) and Cluster 2 (3601 CpGs and 270 genes) (Fig.
1a ). Gene set enrichment analysis using the Molecular Signatures Database v4. 0 (MSigDB 20 ) indicated that genes in Cluster 1 were enriched in processes related to the immune system, while genes in Cluster 2 were associated with estrogen response (Fig. 1b-c ). The high degree of absolute co-methylation and co-expression of CpGs and genes in Cluster 1 and 2 further highlighted the common regulatory role shared by the CpG-gene pairs in each cluster (Fig. 1d ). emQTL-CpGs are enriched at enhancers and TF binding regions. In order to investigate how these clusters of emQTLs, with distinct biological functions, occur we further sought for common transcriptional networks that may explain them. Functional genomic location of the emQTL-CpGs. First, we characterized the functional genomic location of CpGs in emQTL using the ChromHMM segmentation of the human genome in the breast cancer cell line MCF7  21.
The TFs were involved in immune processes such as RUNX  24  or PU. 1  29. The CpG regions defining Cluster 2 overlapped significantly with FOXA1 and GATA3 binding regions (ChIP-seq peaks) from breast cancer cell line experiments (Supplementary Tables  3  and 4 ). These analyses of CpG regions recapitulated the gene set enrichment analysis and emphasized the distinct biological functions of Clusters 1 and 2. Next, we focused on the TF binding regions obtained from the MCF7 cell line and mined 71 ChIP-seq experiments from ENCODE  28  and 40 available at GEO retrieved from ReMap  30  ; we performed enrichment analyses using hypergeometric tests. We observed that emQTL-CpGs were most strongly enriched at ERα, FOXA1, and GATA3 binding sites and to a lesser extend at  MSigDB (H and C5 databases).
The height of the bars represents the level of enrichment measured as a ratio between the number of genes overlapping an MSigDB H or C5 gene set over the expected frequency if such overlaps were to occur at random GREB1, FOS, DPF2, AHR, and ZNF217-binding regions (Supplementary Fig. 2B  and Supplementary Data 1). To further assess the enrichment of Cluster 2 CpGs at TF binding regions, we performed a permutation test (1000 permutations), in which we randomly selected 3601 CpGs from the 450k array (number of CpGs in Cluster 2) and calculated how many were in either ERα 31 , GATA3  16  , or FOXA1  15, 16  binding regions. None of the randomly selected combinations of CpGs showed a similar enrichment as the CpGs in Cluster 2 (p-value < 0. 001). We further investigated in which ChromHMM genomic annotations Cluster 2 CpGs at FOXA1  15, 16  , ERα 31 , GATA3  16  , and CTCF (as control) binding regions were located.