dataesr/scientific-paragraphs-categorization huggingface dataset:
Proliferation is inversely correlated with functional adaptation. Liver regeneration research has traditionally focused on hepatocyte proliferation  [4] [5] [6] [7]. It is unknown whether actively dividing hepatocytes can equally contribute to functional compensation. We observed a down-regulation of many hepatic function genes during activation of the proliferative response (A24, P48, Fig. 3b-e ). Therefore, we identified cells that became transcriptionally active for cell cycle genes in the scRNA-Seq dataset (Fig. 4a ), and analyzed hepatocyte-specific transcript output compared to those cells at all time points that are not cycling. Compared to non-cycling cells (NC), there was a significant down-regulation of the Hepatocyte Signature Score in cycling cells (CC) in both injury conditions (Fig.
01 (**), and < 0. 0001 (****) calculated using unpaired t test with Welch's correction (two-tailed). d t-SNE plot of all high-quality hepatocytes (Methods) in the scRNA-Seq dataset. Cells are colored by injury mode and time point. SNN clusters outlined in black. e Heatmap of marker genes for all clusters outlined in (d). f, g Pericentral Hepatocyte Signature Score (PCH Signature Score) (left). Violin plot of normalized expression of Cyp2e1 (middle) and Glul (right); percent positive calculated as percentage of total cells in each condition above average normalized genes expression (dashed red line). Untreated (UT) and each posttreatment are plotted for APAP (f) and PH (g). Source data provided as a Source Data file. regeneration, we investigated whether Wnt signaling might activate adaptation of already present hepatocytes to maintain essential hepatic function  18, [30] [31] [32] [33] [34] [35] [36].
5d, e ). Importantly, EC-Wls-KO mice can still transcriptionally compensate following PH but have altered cell proliferation (Fig. 5d , e and Supplementary Fig. 14c )  18. In contrast, loss of Wnt processing in macrophages of Mac-Wls-KO mice resulted in down-regulation of all examined hepatocyte genes after injury with complete loss of transcriptional compensation following PH. Proliferative capacity was modestly decreased, yet overall maintained (Fig. 5d , e and Supplementary Fig. 14 )  41. Resident hepatic macrophage numbers and geographic distribution were similar between groups (Supplementary Fig. 14b ). In addition, combined IF and smFISH staining revealed increased Glul transcript level within ~30 μm (average diameter of a single hepatocyte) of F4/80+ macrophages (Supplementary Fig. 14d, e ).
Single-cell sequencing data processing. Sequencing data were demultiplexed and aligned to mm10 with STAR aligner. Libraries were sequenced to an average depth of >48,000 reads per cell per sample. See Supplementary Data 1 for additional sequencing and data quality metrics. Barcodes with fewer than 400 genes were discarded from the genes x cells data matrix as non-cells, with 16,019 cells remaining. Data were log normalized and TPM-like (base 10,000) normalized and analyzed using the Seurat package in R  54. The resulting data displayed fairly even nGene and nUMI distributions across each sample type (Supplementary Fig. 2h ). We performed a principal component analysis (PCA) and selected the top 13 principal components (PCs) for tSNE dimensional reduction. We then performed shared nearest neighbors (SNN) clustering, and identified 14 distinct clusters in the data (Supplementary Fig.
1c, d ). We calculated differential expression across the clusters using Wilcox test in the FindAllMarkers function in the Seurat R package and quantified expression of marker genes for known liver cell populations (Supplementary Fig. 1d, e ). We identify nine high-quality hepatocyte clusters, separated by treatment condition; one low-quality hepatocyte cluster with a high percent mitochondrial content and low nGene and nUMI; a kupffer cell cluster; a liver endothelial cell (LEC) cluster; a neutrophil cluster; and a mixed immune cluster, which appears to contain T cells, B cells and monocytes. We calculated a hepatocyte signature score using AddModuleScore in Seurat over multiple highly expressed hepatocyte genes which span the lobule: Apoa1, Glul, Acly, Asl, Cyp2e1, Cyp2f2, Ass1, Alb, Mup3, Pck1, G6pc, Fabp1.
We performed dimensional reduction and clustering again on our filtered hepatocyte only dataset. Principal component 1 (PC1) describes 46. 9% of and captures technical variation (nGene, nUMI) in the data (Supplementary Fig. 2e ). This is not surprising for a dataset comprised of a single-cell type. Each of our treatment conditions scores similarly on PC1 (Supplementary Fig. 2b ). PC2 partly captures pericentral-periportal variation. We identify zonally restricted genes in PC2 loadings (Cyp2e1, Cyp1a2, Gtsm3; Cyp2f2). We also note periportal-pericentral variation captured in PC4. To more clearly visualize pericentral-periportal variation, we scored cells on this pericentral-periportal metric. To generate a list of pericentral genes, we calculated gene-by-gene correlations with Cyp2e1, a canonical pericentral gene.
To generate a pericentral gene list, we selected genes positively correlated with Cyp2e1, and to generate a periportal gene list, we selected genes negatively correlated with Cyp2e1 (Supplementary Data 8). To generate a list of genes to be used for our signatures, we considered all genes with a Cyp2e1 correlation >0. 3 for PCHSig and a Cyp2e1 correlation < -0. 3 for PPHSig. From the genes falling within this range of values, we selected moderately expressed genes with large variability in expression across the dataset, removing lowly expressed genes and genes expressed in small numbers of cells. Positive correlations with Cyp2e1 range from 0. 823 (Cyp2c29) to 0. 356 (Ang); negative correlations with Cyp2e1 range from -0. 569 (Cyp2f2) to -0. 311 (Serpina12). The 0. 3/-0. 3 cutoff is more than 3 standard deviations above/ below the mean of all gene correlations with Cyp2e1 (mean = -0.
01280654; mean + 3sd = 0. 2605311; mean -3sd = -0. 2861442). We then calculated the pericentral hepatocyte (PCH) score and periportal hepatocyte (PPH) score using AddModuleScore for these genes. We then confirmed that PCH Score and PPH Score are inversely correlated as expected. We observe a pericentral-periportal gradient across PC2 using these scores (Supplementary Fig. 2 ). To generate a single score that captures pericentral-periportal character, we subtracted the PCH Score from the PPH Score to create the PPH-PCH Score, in which pericentral hepatocytes will score negatively and periportal hepatocytes will score positively. We also generated PCH and PPH scores from the zonated hepatocyte genes data presented in Halpern et al. , Table  S3. Briefly, we selected non-randomly zonated genes with a q-value < 0.
01, removed lowly expressed genes with a total average expression lower than the average over the dataset, and split the genes into percentral or periportal markers based on relative zonal expression. This resulted in 110 genes for PCH and 96 genes for PPH. We calculated module scores with these gene lists for the dataset. The PCH_Halpern Score did not capture loss of PCH in the A6 sample as well as the PCH Score calculated above with the Cyp2e1 correlation gene list. The PPH_Halpern Score did not distribute the cells as evenly over the distribution as the correlation score. These Halpern scores were generated from a different dataset, which may contribute to their lesser ability to capture relevant signal in our dataset. Despite these differences, the PCH and PPH correlation-derived scores correlate well with the Halpern-derived scores (PCH: R2 = 0.
2h ). Heatmap genes were found using FindAllMarkers in Seurat, Wilcox test, min. percent = 0. 10, thresh. use = 0. 25. Mitochondrial and hemoglobin genes were removed from the list prior to heatmap plotting. Shared and unique by injury model gene lists were assembled by combining DE results across all time points for each injury. We ran differential expression using Wilcox test in the FindAllMarkers Seurat function between each treatment condition (A6, A24, A48, A96, PH3, PH48, PH120) individually and untreated (UT) (Supplementary Data 2). We then combined results across all time points within each injury model to obtain a list of all DEGs from any time point in APAP experiments (A6, A24, A48, A96) and PH (PH3, PH48, PH120). A small number of genes were up at one-time point, but down at another.
2 for the other injury model. We then identified shared responses by compiling all reference gene sets with a q-value of < 0. 05 in both APAP and PH. Selected reference gene set -log(q) are plotted in Fig. 3. To identify cycling cells in the data, we calculated Cell Cycle Score using AddModuleScore in Seurat over the cell cycle markers  56. We classified cells with a Cell Cycle Score 2 standard deviations above the average as cycling cells (Fig. 4 ). To better compare cycling and non-cycling cells (CC and NC, respectively), we subsetted the data to create a dataset containing all 51 CCs from the A24 condition and an equal number of NCs also from A24; similarly, we created a dataset containing all 123 CC from PH48 and an equal number of NCs also from PH48. Pathway analysis was done on a DE result obtained from comparing 174 CC from A24 and PH48 against an equal number of NCs from these time points.
Piano was run as described above. Fig. 4  plots -log(q) values for selected reference gene sets with a q-value < 0. 05. Wnt Target Labbe Sig was calculated using AddModuleScore and the reference gene set LABBE_WNT3A_TARGETS_UP which was identified as significant in Piano gene set enrichment analysis. Immunohistochemical analysis. Histology was performed by the histology core at Beth Israel Deaconess Medical Center using standard procedures and automated workflow. Samples were processed and embedded following fixation in 10% neutral buffered formalin for 48 h. Samples were embedded in paraffin and sectioned at 5 μm thick. Immunohistochemistry was performed on a Leica autostainer (Leica Biosystems) with enzyme treatment (1:1000) using standard protocols. The antibody used for assessment of cell proliferation was PCNA (Cell Signaling, Cat.
13110, 1:800), and cell death was ApopTag Peroxidase In Situ Apoptosis Detection Kit (Millipore, Cat. # S7100). Macrophages were stained using the anti-F4/80 (Cell Signaling, Cat. 70076, 1:500). Sections were then counterstained with hematoxylin, dehydrated, and film cover slipped. A minimum of four representative images was captured per slide using the Zen 2 v2. 0 software package. TUNEL-positive area, PCNA-positive cells, and F4/80-positive cells were measured and averaged across the four images for each sample using ImageJ v2. 0. Single-molecule fluorescent in situ hybridization (smFISH). smFISH was conducted using RNAscope technology (RNAscope Fluorescent Multiplex Kit; Cat. # 320850; Advanced Cell Diagnostics; Neward, CA, USA). Fresh frozen sections (10 μm thick) were used following the manufacturer's guidelines.
Probe sets were designed by the manufacturer and can be found at acdbio. com/catalogprobes. Probe sets were chosen for genes that span multiple classic hepatic functions, such as secreted proteins, gluconeogenesis, blood clotting, ion homeostasis, and metabolism. and that were among the top 100 DEGs in the analysis of the scRNA-seq data. A 6 × 6 40x field was captured of a 10 μM z-stack (0. 5 uM per slice) using the NIS-Elements v5. 11 software package. This resulted in multiple liver lobules available for analysis within a single section. Images were cropped to the size of a single liver lobule and cellular outlines were defined using CellProfiler v2. 2  55. smFISH signal was then quantified using FISH-quant v3  57. Post-processing of mRNA detection was performed with custom-written Python scripts (available at https://bitbucket.
org/muellerflorian/pyfishquant/)  57. Pseudo-color images of transcript abundance were generated by setting the pixel values of each segmented cells to its corresponding transcript level. To determine the spatial expression gradients relatively to the central vein, we manually outlined the vein as a polygon. This polygon served as a reference point to count RNAs in concentric rings. These counts were lastly renormalized by the ring area contained within the image. AUC (area-under-curve) for the RNA counts across the individual lobules was calculated using GraphPad Prism (v8. 3. 0) with the average AUC presented with calculated standard deviation. The values are presented as "Average Total RNA Counts". Statistical analysis. We calculated P values for shifts in gene expression or module scores using the Wilcox test, Bonferroni corrected for multiple testing.
Acknowledgements C. W. is supported by  F32DK111151 , the  Cholangiocarcinoma Foundation Research Fellowship , and the  American Liver Foundation Charles Trey, MD Memorial Postdoctoral Research Fellowship. W. G. is supported by  R01DK090311 ,  R01DK105198 ,  R24OD017870 , and the  Claudia Adams Barr Program for Excellence in Cancer Research. W. G. is a  Pew Scholar in Biomedical Sciences. A. K. S. was supported, in part, by the  Searle Scholars Program , the  Beckman Young Investigator Program , a Sloan Fellowship in Chemistry, the  NIH  ( 1DP2GM1194192 & RM1HG0061931 ), and the  MIT Stem Cell.
We sequenced the complete mitogenome of P. ventrolineata. The specimen used is part of a series of 10 specimens collected in Caen, France, on 26 March 2015 and deposited in the collections of the Mus eum national d'Histoire naturelle, Paris, under number MNHN JL240 and where the rest of the specimen is kept. Paired end sequencing was conducted by the Beijing Genomics Institute (Shenzhen) on a DNBSEQ platform, resulting in a total of 60 million 100-bp reads. Assembly was performed using with SPAdes 3. 14. 0 (Bankevich et al. 2012 ) with a k-mer of 85. Genes were identified with the help of MITOS  (Bernt et al. 2013). The genome is 17210 bp long (GenBank accession number: MT081960) and it codes for 13 proteins, 2 rRNAs, and 21 tRNAs. It appears to be very similar to the recently sequenced mitogenome of Platydemus manokwari (GenBank: MT081580;  Gastineau et al.
2020) , an invasive flatworm (see  Justine et al. 2014; 2015)  also belonging to the subfamily Rhynchodeminae. Genomes are colinear including for the positions of tRNA, the open reading frames corresponding to the ND4L and ND4 genes are overlapping by an identical lenght of 32 nucleotides and especially the cox2 gene displays a similar overlength. The total length of the cox2 gene is 1302 bp, close to the size observed in P. manokwari (1359 bp) and twice for example the 678 bp of the invasive flatworm Bipalium kewense's cox2 gene (GenBank: MK455837;  Gastineau et al. 2019). This extension occurs between the same conserved protein domains as in P. manokwari. A maximum likelihood phylogeny was inferred from concatenated gene-coding proteins with the protocol and dataset of  Gastineau et al. (2020 ), using RAxML v.
Stability analysis In order to check the stability of the control structure, we shall use a Lyapunov function that depends on the variant parameter as follows: V (x, p) x T Q(θ) -1 x with: Q(θ) Q 0 + θ 1 Q 1 + • • • + θ n Q n (46) For such Lyapunov function, the stability condition dV (x, p) dt < 0 is equivalent to: Q(θ )A T (θ ) + A(θ )Q(θ ) - dQ dt < 0 (47) • θ (t) φ 2 * r φ 2 rd ∈ V × T for the torque controller: Q(θ )A(θ ) T + A(θ )Q(θ ) T -Q(τ ) -Q 0 ( 48 ) • For θ ∈ V and i 1,. , n: A i Q i + Q i A i + A T i Q(θ ) + Q(θ )A i + A(θ ) T Q i + Q i A(θ ) -Q(τ ) -Q 0 + M i ≥ 0 (49) • Q(θ ) > I for all M i S0θ ∈ V. The MATLAB function pdlstab makes it possible to check the feasibility conditions when t min < 0. The system composed of LPV torque controller and LPV flux observer is stable since the observer is quadratically stable and then the system is stable in specified range for t min -0.
Comparative study of new approach with IFOC and DTC control strategies In order to confirm the performances of the new control structure, a comparison between the results obtained by DTC with 12 sectors, IFOC and the new approach was fulfilled under MATLAB/Simulink. In the three cases, the speed is controlled in a closed loop by a simple PI, using the same simulation conditions. A load torque is applied to the motor at t 5 s. It can be clearly observed from Figs.
ARMeD of Endogenous NEDD8-Specific Protease NEDP1 To explore the application of ARMeD to endogenous, unmodified proteins it was necessary to generate protein-specific (D and E) To establish the pathway of protein degradation, cells were incubated with autophagy inhibitor bafilomycin A1 (Baf, 100 nM) or proteasome inhibitors bortezomib (1 mM) or MG132 (10 mg/mL) for 1. 5 h prior to 16 h doxycycline induction. The role of E3 ligases other than the ARMeD fusion in degradation of substrate was examined by subjecting cells to inhibitors without Dox induction. Western blotting (D) and HC analysis (E) were performed as above. The HC YFP-PML data (total area/cell) were obtained from 20,000-50,000 cells grown in 12 wells of a 96-well plate for each condition and plotted as the mean of 12 well replicates ± SD (E).
(F and G) YFP-PML degradation was assessed over time by collecting cells at indicated times after doxycycline addition and performing western blotting (F) or high-content analysis (G). The HC EYFP-PML data (total area/cell) were obtained from 20,000-25,000 cells grown in 8 wells of a 96-well plate for each time point, normalized to uninduced control cells, and plotted as the mean of 8 well replicates ± SD. Statistical analysis was performed by a two-tailed unpaired t test. nanobodies. The NEDD8 specific protease NEDP1  (Mendoza et al. , 2003) , has been structurally resolved  (Shen et al. , 2005)  and the consequences of its depletion previously established  (Bailly et al. , 2019). We generated a series of camelid nanobodies against NEDP1, among which nanobody 2 and nanobody 9 both bind to NEDP1 and inhibit its catalytic activity (to be described in detail elsewhere).
, 2014) , super-resolution microscopy  (Pleiner et al. , 2015) , and intracellular signaling studies  (Prole and Taylor, 2019). As coverage of the proteome increases and the nanobody database expands, it should be possible to search the database for a nanobody to the protein to be targeted, have a G-block synthesized corresponding to the sequence of the nanobody, and have the nanobody-RING fusion expressed in bacteria and purified ready for knockdown studies in a matter of days. The advantage of this approach is that protein depletion can be achieved without any prior manipulation of the cells under study. However, when nanobodies of the target protein are not available, an alternative approach is to either use a pre-existing cell line containing a GFP tagged protein or to generate an endogenously GFP-tagged protein using CRISPR/cas9 technology.
Data and Code Availability The original experimental raw data of this study have been deposited to Mendeley (https://dx. doi. org) with the identifier https://doi. org/10. 17632/ygvn5hmv78. 1. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE [1] partner repository with the dataset identifier PXD018113 The following ProteomeXchange ID has been reserved for our proteomic data: PXD016193 (http://proteomecentral. proteomexchange. org/cgi/GetDataset?ID=PXD016193). The access URL (http://panoramaweb. org/project/Panorama%20Public/ 2019/U%20of%20Dundee%20Hay%20Lab%20-%20NEDP1_ARMeD_knockdown/begin. view?) is the unique identifier of our data on Panorama Public.
METHOD DETAILS Plasmids The coding sequences of a camelid-derived single-domain antibody (nanobody) (PDB accession 3K1K_C) raised against the green fluorescence protein (GFP), was generated synthetically (GeneArt, Thermofisher) with a 5 0 HindIII and 3 0 NheI restriction recognition sites. The coding sequence for residues  of Rattus norvegicus RNF4 (accession: NM_019182, UniProtKB -O88846) were amplified from previously generated expression constructs in pLou3  (Plechanovova ét al. , 2011)  by the polymerase chain reaction (PCR) with 5 0 NheI and 3 0 BamHI-NotI restriction sites. The synthetically generated GFP nanobody and the RNF4 75-194 were ligated into the pCDNA5 FRT TO vector (Life Technologies) via a 3 point ligation Hin-dIII-NheI-NotI, resulting in a GFP nanobody-wild-type RNF4 RING fusion (GNb-1xRING).
qPCR was performed in either a 96 or 384-well format using Biorad CFX96/CFX384 or Applied Biosystems QuantstudioFlex 6 thermal cycler' Thermal cycling conditions were an initial denaturation step of 95 C for 10 mins, and then 44 cycles of 95 C for 15 s, 60 C for 60 s followed by 95 C for 10 s and a melt curve of 65 C to 95 C. The primers were designed to produce amplicons crossing the nanobody-RNF4 boundary. Standard curves were produced for each amplicon-specific primer set and for the control gene Beta-2-Microglobulin (B2M) primers. RNA was always prepared from three independent cultures (replicates) representing each experimental condition and the PCR reaction was performed in duplicate for each RNA sample. The data were analyzed by the software accompanying the used instrument and presented after normalization against the control gene.
Image analysis was performed by IN Cell Developer Toolbox version 1. 91 build 2206 (GE Healthcare), using protocols designed to identify EYFP-PARG or EYFP-PML/EYFP-SP100 inclusions by region-growing or multi-scale top hat transformation, respectively. To measure EYFP, nuclear intensity was used as the most robust parameter, while the measure of total organelle area per cell nucleus was selected as the most discriminatory for changes in EYFP-PML and EYFP-SP100 following treatment. For transfection efficiency calculations the cell/background intensity measure was found to give the most robust results and a threshold of 1. 075 was used as the lower limit to be achieved by transfected cells. Data were obtained for > 20000 cells per condition and the presented data represent the mean ± SD. For degradation kinetics the time required to degrade 50% of the initial protein amount (t½) was deduced from the exponential equation resulting from plotting the obtained intensity or total area values against time.
Microinjection HeLa Flp-in/T. Rex cells stably expressing YFP-PML were seeded on to glass bottomed dishes (FluoroDish, WPI) and allowed to settle overnight. The cells were then microinjected with 30 mM GNb-2xRING mixed with an equal amount of mCherry (to localize the injected cells) in injection buffer (100 mM glutamic acid, pH 7. 2 with citric acid  (Izant et al. , 1983) , 140 mM KOH, 1 mM MgSO4 and 1 mM DTT) as described previously  (Prescott et al. , 1992). The cells were immediately transferred to the stage of a Zeiss LSM 710 confocal microscope with a 37 C heated stage/chamber and 5% CO2 atmosphere and imaged by time-lapse. A z stack of 7 images (6. 3 mm depth) was taken at each time point and one stack was collected every 2 minutes. For each time point the z stacks were compressed into a single maximum intensity projection and the time-lapse data was transferred into Imaris for quantitation.
Immediately after electroporation the cells were transferred to growth medium with10% FBS but without antibiotics. For immunoblot analysis aliquots were taken at the desired time points, and the reaction stopped by cooling on ice and centrifugation at 90xg for 10 min at 4 C followed by cell lysis in 2x Laemmli sample buffer. For high content imaging aliquots were taken at the desired time point and the degradation reaction stopped immediately by adding the cells to an equal volume 8% formaldehyde in a black, clear-bottomed 96-well plates (Greiner mClear) followed by centrifugation at 90xg for 20 min at room temperature, washing, and DAPI staining as described. To determine transfection efficiency, cells were co-electroporated with a mCherry labeled protein and high content data were collected and analyzed as described above.