News Article | May 17, 2017
Mice were maintained and animal experiments performed according to practices prescribed by the National Institutes of Health at Stanford’s Research Animal Facility (protocol 13565) and by the Institutional Animal Care and Use Committee at OncoMed Pharmaceuticals. Additional accreditation of Stanford and OncoMed Pharmaceuticals animal research facilities was provided by the Association for Assessment and Accreditation of Laboratory Animal Care. Animal experiments were performed unblinded except for allograft and patient-derived xenograft tumour growth measurements which were performed blinded. Immunostaining of sections from animal experiments were performed blinded. The TKO SCLC mouse model bearing deletions in p53, Rb, and p130 has been described10. Mice were maintained on a mixed genetic background composed of C57BL/6, 129/SvJ and 129/SvOla. Endogenous Notch activity in TKO tumours was assessed through a GFP reporter expressed from the endogenous Hes1 promoter (Hes1GFP/+ allele11). We also bred in the Rosa26lox-stop-lox-tdTomato (ref. 30) and Rosa26lox-stop-lox-luciferase (refs 31, 32) Cre-reporter alleles to the TKO model to label tumour cells with tdTomato and luciferase, respectively. SCLC tumours were induced in 7- to 10-week-old mice (with no discrimination by sex of mice) by intratracheal instillation with 4 × 107 plaque-forming units of Adeno-CMV-Cre (Baylor College of Medicine, Houston, Texas, USA) or Adeno-CGRP-Cre (University of Iowa). Tumours were collected for analysis after around 5–7 months for Ad-CMV-Cre or 7–8 months for Ad-CGRP-Cre, unless otherwise stated. In accordance with our animal protocol, mice were euthanized when they showed difficulty breathing, regardless of time point. TKO Hes1GFP/+ mice were treated with the γ-secretase inhibitor DBZ (Selleckchem, S2711) as previously described33. Mice were randomized and injected intraperitoneally once per day with 30 μmol per kg (body weight) of DBZ (or DMSO control) for 5 days, and tumours were collected on day 6 for flow cytometry or fixed for histological analyses. TKO or TKO Hes1GFP/+ mice bearing tumours were randomized for treatment. For acute responses, mice were treated with cisplatin (7.5 mg per kg (body weight), Teva) on day 1, and a combination of cisplatin and etoposide (15 mg per kg (body weight), Novaplus) on days 2 and 4. Lungs were fixed for histological analyses a few hours after the last injection. For longer-term chemotherapy experiments, as we observed high toxicity with etoposide administration, TKO Rosa26LSL-luciferase mice were treated weekly for 3 weeks with saline or 5 mg per kg (body weight) cisplatin only. For subcutaneous tumour growth of GFPneg or GFPhigh cells, 2,000 cells were FACS-sorted and implanted subcutaneously on the lower left and right quadrants of 8- to 10-week-old immunocompromised NOD.Cg-PrkdcscidIL2rgtm1Wjl/SzJ (NSG) mice (no selection for sex of mice). Mice were euthanized and tumours were collected after approximately 2 months. The tumours did not exceed the 1.75 cm diameter limit permitted by our animal protocol. For the human patient-derived xenograft and TKO allograft tumour growth models, NOD.CB17-Prkdcscid/NcrCrl (NOD/SCID, Charles River Laboratories) mice were maintained under pathogen-free conditions and provided with sterile food and water ad libitum. Patient-derived xenograft models were established from patient biopsies provided by Molecular Response (San Diego, California, USA). OMP-LU66 was established at OncoMed Pharmaceuticals. For the subcutaneous xenograft studies, 100,000 OMP-LU66 cells in 100 μl 50% Matrigel (BD Biosciences)/50% Hank’s balanced salt solution supplemented with 2% heat-inactivated fetal bovine serum and 20 mM HEPES (Life Technologies) were implanted into the left flank region of 7- to 8-week-old NOD/SCID mice (no selection for sex of mice) with a 25-gauge needle. Using a human Fab phage display library (HuCAL GOLD, MorphoSys AG34), functional anti-Notch antibodies were discovered from selections against recombinant Notch2 extracellular domain (EGF1-12) containing the ligand-binding site. NOD/SCID mice implanted with OMP-LU66 or TKO allografts were randomized and treated with a control antibody or tarextumab (OMP-59R5, 40 mg per kg (body weight), once every 2 weeks) as a single agent or in combination with the chemotherapy agents carboplatin (25 mg per kg (body weight), once-weekly, Teva) and irinotecan (25 mg per kg (body weight), once-weekly, Pfizer). We used carboplatin and irinotecan (instead of cisplatin and etoposide) for these longer-term studies as they are less toxic, better tolerated by the mice, and have been shown to have similar efficacies as cisplatin and etoposide35, 36. To avoid the side effects of total Notch pathway inhibition in vivo37, 38, we sought to reduce Notch signalling with the Notch2/3 antagonist tarextumab. After approximately four cycles, chemotherapy was discontinued and tarextumab dosing was continued until study completion. Mice with tumour volumes at or exceeding the 2,500 mm3 limit permitted by the Institutional Animal Care and Use Committee were euthanized regardless of time point. Tumours were dissected from the lungs of TKO Hes1GFP/+ mice approximately 5–7 months after tumour induction and digested as previously described39. The antibodies used were CD45-PE-Cy7 (eBioscience, clone 30-F11, 1:100), CD31-PE-Cy7 (eBioscience, clone 390, 1:100), TER-119-PE-Cy7 (eBioscience, clone TER-119, 1:100), CD24-APC (eBioscience, clone M1/69, 1:200), Ncam1 (Cedarlane, clone H28-123-16, 1:100), anti-rat-IgG2a-PE (eBioscience, clone r2a-21B2, 1:200), EpCam (eBioscience, clone G8.8, 1:100), and CD44-APC-Cy7 (BioLegend, clone IM7, 1:100). 7-Aminoactinomycin D (1 μg ml−1; Invitrogen) or DAPI was used to label dead cells. FACS was performed using a 100 μm nozzle on a BD FACSAria II using FACSDiva software. The sequential gating strategy is outlined in Extended Data Fig. 1d. Fluorophore compensation was performed for each experiment using either unstained cells or BD CompBeads (BD Biosciences) stained with individual fluorophore-conjugated antibodies, and compensation was calculated by FACSDiva. Data were analysed using FlowJo software and gates were set on the basis of unstained samples. TKO Hes1GFP/+ mice were injected intraperitoneally with 100 mg per kg (body weight) EdU (5-ethynyl-2′-deoxyuridine; Life Technologies) 8 h before euthanasia. GFPneg and GFPhigh tumour cells were sorted by FACS before being fixed and subject to EdU staining using the Click-iT Plus EdU Pacific Blue flow cytometry assay kit (Life Technologies). Propidium iodide was used to stain for total DNA content and percentage EdU incorporation of GFPneg and GFPhigh cells was analysed using a BD FACSAria II. The extracellular domain of rat Dll4 containing affinity-enhancing G28S, F107L, L206P N118I, I143F, H194Y, and K215E mutations (named Dll4 or Dll4 in the manuscript) was cloned into the pAcGp67A vector and modified with a carboxy (C)-terminal 8× His tag19. Dll4 was expressed using baculovirus by infecting 1 l of Hi-Five cells (Invitrogen) from Trichoplusia ni at a density of 2 × 106 cells per millilitre and harvesting cultures after 72 h. The cultures were centrifuged to remove the cells, and proteins were purified from supernatants by nickel and size-exclusion chromatography. The MigR1-ires-GFP (Ctrl) and MigR1-N1ICD-ires-GFP retroviral vectors were gifts from W. S. Pear (University of Pennsylvania, Philadelphia). For doxycycline-inducible expression, we cloned N1ICD into the pLIX-403 vector (a gift from D. Root, Addgene 41395). For Rest overexpression, we cloned the Nrsf(Rest) fragment from pHR′-NRSF-CITE-GFP (a gift from J. Nadeau, Addgene 21310 (ref. 40)) into the MigR1-ires-GFP or pLIX-403 vectors. Ascl1 (1: CTCCAACGACTTGAACTCTAT; 2: CCACGGTCTTTGCTTCTGTTT) and Rest (1: GTGTAATCTACAATACCATTT; 2: CCCAAGACAAAGACAAGTAAA) short hairpin RNAs (shRNAs) were obtained from the MISSION shRNA library (Sigma-Aldrich). Guide RNA (sgRNA) against Rest (CATCATCTGCACGTACACGA) was designed using the sgRNA Designer (Broad Institute) and cloned into the lentiCRISPR v2 backbone (a gift from F. Zhang, Addgene 52961 (ref. 41)). Except for 293T cells that were grown in DMEM, all cell lines were grown in RPMI-1640 medium supplemented with 10% bovine growth serum (BGS) (Fisher Scientific) and penicillin–streptomycin–glutamine (Gibco). Mouse KP1, KP2, and KP3 and human NJH29 SCLC cell lines were generated in the laboratory and have been described8, 32, 42. GFPneg and GFPhigh cell lines were isolated by FACS from individual mice. Human NCI-H82 and NCI-H889 cells were purchased from the American Type Culture Collection and authenticated by STR analysis. All cell lines tested negative for mycoplasma. Transfections and viral infections were performed as previously described39. For acute analysis of gene expression changes, RNA was isolated from GFPhigh cells FACS-sorted 48 h after transfection with MigR1-N1ICD or Rest-IRES-GFP or the empty vector control. Viral transductions of N1ICD or Rest were used to generate adherent non-NE cells from NE cells, a process taking about 1–2 weeks. The cells were then expanded and collected for immunoblot analyses. For isolation of Rest knockout clones, sgRNA-infected cells were selected with puromycin (2 μg ml−1) for 4 days and single cells were sorted into individual wells in 96-well plates by FACS. After 2 weeks, clones were picked and those with biallelic frameshift mutations resulting in premature truncation of the translated protein were verified by TOPO PCR cloning (Thermo Fisher Scientific) and Sanger sequencing. Tissue culture plates were coated overnight with 200 nM of purified Dll4 in PBS at 4 °C, then washed twice with PBS to remove any unbound ligand before seeding of cells. GFPhigh cell lines were maintained on Dll4-coated dishes. To assay for acute responses to the lost of Notch activation, cells were kept on Dll4-coated plates or seeded on plates without Dll4 and collected 72 h later for analyses. GFPneg cell lines were maintained on non-Dll4-coated dishes unless otherwise indicated. To test for Notch ligands expressed by NE SCLC cells, mCherry-labelled NE (KP1 and KP3) cells were co-cultured with GFPhigh cell lines at a 3:1 ratio with 10 μM DBZ or DMSO control without exogenous Dll4. This ratio was based on the average number of GFPneg and GFPhigh cells in TKO Hes1GFP/+ tumours (27.7% GFPhigh cells ≈ 3:1 ratio). Median GFP fluorescence intensity of mCherry-negative, GFPhigh cells was quantified by flow cytometry after 72 h. For Dll4 stimulation of human cell lines (suspension), plates were coated overnight with 400 nM Dll4 in PBS at 4 °C. Plates were washed twice with PBS, coated with 0.01% poly-d-lysine (Sigma-Aldrich) for an hour at 37 °C and then washed twice with PBS before seeding of cells. For GFPneg ex vivo assays, DBZ was added at a concentration of 10 μM and tarextumab at 100 μg ml−1. Cells were analysed after 2 weeks by flow cytometry for the generation of GFPhigh cells. Fifty thousand GFPneg, GFPhigh or bulk tumour cells (mixture of GFPneg and GFPhigh) were sorted from TKO Hes1GFP/+ tumours, resuspended in 100 μl of modified DMEM/F12 medium containing 50% Matrigel as previously described43 and then layered with 200 μl of medium. Overall survival was assayed 1 week later by incubating with AlamarBlue (Thermo Fisher Scientific) for 4 h. Supernatant was removed and fluorescence of the Matrigel layer was read by a fluorescence plate reader (excitation 560 nm, emission 590 nm). For immunostaining, the Matrigel layer was fixed overnight with 10% formalin in PBS then washed twice with PBS before being embedded in histogel and subjected to processing for paraffin embedding. For co-culture cell growth assays, NE mouse SCLC cells (KP1, KP2) were labelled with firefly luciferase and enhanced GFP by lentiviral infection. These cells were then mixed with GFPhigh cells at a 3:1 ratio (12,000 NE cells + 4,000 GFPhigh cells) in 96-well white bottom plates. Luciferase activity was assayed 72 h later by the Steady-Glo luciferase assay system (Promega) according to the manufacturer’s protocol. For conditioned medium assays, 0.5 × 106 GFPhigh cells were seeded overnight in 6-cm dishes. The medium was then changed and conditioned medium collected after 24 h. Twelve thousand NE cells per well of a 96-well plate were resuspended in conditioned medium and luciferase activity was assayed 72 h later. Conditioned medium from NE cells was used as the control, although in preliminary experiments we did not notice any difference in luciferase activity between NE-conditioned medium and regular medium. For co-culture EdU assays, unlabelled KP1 and KP2 were co-cultured with GFPhigh cells at a 3:1 ratio (150,000 NE cells + 50,000 GFPhigh cells) in 12-well plates for 72 h and then incubated with 10 μM EdU (Life Technologies) for 3 h. Both floating and adherent populations were collected and subject to EdU staining using a Click-iT Plus EdU Pacific Blue flow cytometry assay kit (Life Technologies). Twenty thousand NE or 4,000 GFPhigh cells were seeded per well of a 96-well plate in RPMI medium with 2% BGS. One microlitre of drug solution was added per well the next day at the appropriate concentration and cell viability was assayed 48 h later by the MTT assay (Roche). Twenty thousand NE cells were seeded per well of a 96-well plate in RPMI medium with 2% BGS in the presence of the recombinant proteins. Cell viability was assayed after 72 h by the AlamarBlue assay. The following recombinant proteins were used: Midkine (OriGene TP723299, 50 ng ml−1), Betacellulin (BioLegend 551302, 5 ng ml−1), Gdf15 (MyBioSource MBS205834, 25 ng ml−1), Bmp4 (BioLegend 595301, 50 ng ml−1), Ephrin A1 (BioLegend 755002, 50 ng ml−1), SCF (BioLegend 579702, 50 ng ml−1), and Fstl1 (R&D Systems 1738-FN-050, 200 ng ml−1). One and a half million NE cells or 0.5 × 106 GFPhigh cells were seeded per well of a 12-well plate in RPMI medium with 2% BGS. Supernatant was collected after 24 h, centrifuged at 1,500 r.p.m. for 10 min and assessed for the presence of midkine by an ELISA (LifeSpan Biosciences, LS-F5765) according to the manufacturer’s instructions. Data were analysed using http://www.elisaanalysis.com/. Tissues were fixed overnight with 10% formalin in PBS before processing for paraffin embedding. For IHC, paraffin sections were stained as previously described8. In brief, a citrate-based solution (Vector Laboratories) was used for antigen retrieval. DAB (Vector Laboratories) and haematoxylin were used for staining development and counterstaining, respectively. The primary antibodies used were Hes1 (CST 11988, 1:200), Notch2 (CST 5732, 1:200), GFP (Invitrogen A-11122, 1:400), cleaved caspase-3 (CST 9664, 1:200), Ki-67 (BD Biosciences 550609, 1:200), and Ascl1/Mash1 (BD Biosciences, 556604, 1:200). For staining of allograft and xenograft models treated with tarextumab, tissue sections were stained on a Ventana Discovery Ultra instrument (Roche) using Ventana reagents. Sections were treated with Cell Conditioning 1 before addition of antibodies. Antibodies were detected with UltraMap HRP kit and ChromoMAP DAB, then counterstained with haematoxylin. Antibodies used were the same as listed above except Ascl1 (eBioscience 1405794) and Ki67 (Abcam ab16667). For immunofluorescence, paraffin sections were deparaffinized, rehydrated, and unmasked by boiling in Trilogy (Cell Marque 920P-10) for 15 min, then blocked and stained with primary antibodies overnight, or subject to EdU staining (Life Technologies) before blocking and antibody staining. Nuclei were stained with DAPI (Sigma). The following primary antibodies were used: GFP (Rockland 600-101-215, 1:500), Uchl1 (Sigma HPA005993, 1:500), CGRP (Sigma C8198, 1:2,000), synaptophysin (Syp, Neuromics MO20000, 1:100), RFP/Tomato (Rockland 600-401-379, 1:500), phospho-histone H3 (EMD Millipore 06-570, 1:500), and cleaved caspase-3 (CST 9664, 1:100). Quantification of all immunostaining was performed blinded. Hes1pos cells in TKO lung or liver sections or in human tissue microarrays were scored on the basis of the frequency and intensity of Hes1 staining and assigned scores of 0 (no staining), 1 (staining in 1–20% of cells), 2 (staining in 20–60% of cells or strong intense staining in <20% of cells), or 3 (>60% staining). Human SCLC tissue microarrays were purchased from US Biomax (LC245, LC802a, LC818), containing a total of 172 cores from 139 patients. H scores were calculated as the summation of (1 + i)p where i is the intensity score and p is the percentage of the cells with that intensity. The frequency of Hes1pos cells in TKO sections after chemotherapy was quantified from IHC staining using the ImageJ plugin, ImmunoRatio44. The percentage of CC3pos cells in GFPneg or GFPhigh cells after acute chemotherapy of TKO Hes1GFP/+ mice was quantified from immunofluorescence images by ImageJ. For studies with human patient-derived xenograft and allograft tumour models performed at OncoMed Pharmaceuticals, slides were scanned using an Aperio AT scanner, then analysed using Definiens Tissue Studio image analysis software. Positively stained cells within tumours were identified and quantitated for staining intensity and frequency. For quantification in Extended Data Fig. 10f–m, some samples were excluded because the paraffin blocks did not have any tissue samples left to be cut (since the tumours were harvested at or close to minimum residual disease, the amount of tissue obtained was small). This exclusion due to unforeseen experimental limitations was not pre-established. The study was approved by the institutional review board of the East Paris University Hospitals Tumour Bio-bank, AP-HP, Tenon Hospital, Paris, France (AP-HP – GH-HUEP Tumorothèque Bio-bank platform). Seventy-three patients diagnosed with SCLC at Hôpital Tenon, Assistance Publique-Hôpitaux de Paris, France, from January 2010 to January 2013 were first identified. Tumour samples were obtained after getting written informed consent. We performed HES1 IHC for 68 of the patients from whom formaldehyde-fixed and paraffin-embedded tumour tissue was available. The tumour samples were first reviewed by at least two independent expert pathologists and the diagnosis of SCLC was histomorphologically confirmed by haematoxylin and eosin staining and IHC for chromogranin A, synaptophysin, NCAM and TTF1. Clinical and biological characteristics of the patients are provided in the Supplementary Methods. For survival analysis, the patients were separated into two groups on the basis of the absence (Hes1-negative) or presence (Hes1-positive) of HES1 immunostaining in their tumours. Human plasma samples from cancer-free normal donors were purchased from BioreclamationIVT. SCLC donor plasma was sourced from Conversant Biologics (Conversant Bio). The samples were collected, processed, and distributed in accordance with institutional review board approval following informed patient consent. Plasma samples were assayed by following the Luminex assay protocol with adaption of the Drop Array system (Curiox Biosystems, Luminex, Austin, Texas, USA). In brief, wells in the DropArray assay plate were blocked with 10 μl 1% BSA/PBS for 30 min at room temperature. Standards were prepared according to manufacturer’s instructions. Bead mix (5 μl) was added to all wells. Five-microlitre standards or diluted samples were then added to the plate; all standard and human plasma samples were tested in duplicate wells. The plate was shaken for 10 s at 1,000 r.p.m. then placed on a magnetic stand in a humidified chamber and shaken overnight at 4 °C. The plate was washed three times with a DropArray LT washing station MX96 (Curiox Biosystems). The detection antibody was added at 5 μl per well and the plate was incubated for 60 min. Five microlitres per well of the streptavidin-PE substrate was added to each well and incubated for 30 min with shaking. The plate was washed three times before reading by Luminex 200 instrument. Data were analysed using EMD Millipore’s Milliplex Analyst software. The standard curve readings were back-calculated and evaluated for accuracy (80–120%) and precision (percentage coefficient of variation of duplicates <30%). Cells were lysed in a modified RIPA buffer (1% NP40, 0.3% SDS, 50 mM Tris-HCl pH 8.0, 150 mM NaCl, 2 mM EDTA, 1% sodium deoxycholate, 30 mM NaF, 20 mM Na P O , 1 mM NaVO , 1 mM DTT, 60 mM β-glycerophosphate) supplemented with protease inhibitors aprotinin (10 μg ml−1), leupeptin (10 μg ml−1), and PMSF (1 mM). Protein concentration was measured with a Pierce BCA protein assay kit (Thermo Scientific). The antibodies used were Notch1 (Cell Signaling Technology (CST) 4380), cleaved Notch1 (CST 4147), Notch2 (CST 5732), Hes1 (CST 11988), GFP (Invitrogen A-11122), Rest (Abcam 21635), alpha-tubulin (Sigma T9026), and HSP90 (CST 4877). For analysis of primary tumour cells, cells were sorted from pooled tumours from individual TKO Hes1GFP/+ mice by FACS. DNA and RNA were isolated using a Qiagen Allprep DNA/RNA micro kit or an RNeasy mini kit according to the manufacturer’s protocol. qRT–PCR analysis was performed on an Applied Biosystems 7900HT Fast Real-Time PCR System using PerfeCTa SYBR Green FastMix (Quanta BioSciences 95073). Genes having C values that were high (>34) or undetermined (for example, Notch4) were removed from the graphical analyses. Data were normalized to Rplp0 as a housekeeping gene, unless otherwise stated. Primer sequences are available in Supplementary Methods. RNA from cells isolated by FACS from three TKO Hes1GFP/+ mice (independent of the samples used for qRT–PCR) was subjected to quality assessment and microarray analysis by the Stanford Protein and Nucleic Acid (PAN) facility as previously described8. The microarray was performed using a GeneChip Mouse Gene 2.0 ST Array (Affymetrix), and the Robust Multichip Average (RMA) Express 1.1.0 program was used for background adjustment and quantile RMA normalization of the 41,345 probe sets encoding mouse genome transcripts. Linear models for microarray data (Limma) was used to compare GFPneg and GFPhigh cells on RMA normalized signal intensities. The command prcomp in R was used for principal component analysis. Probe identifiers were annotated with gene symbols from the mouse gene 2.0 ST transcript cluster database (mogene20sttranscriptcluster.db). Of the 41,345 probe sets, 25,349 were annotated to genes, which were then used for gene set enrichment analysis45, 46. Default parameters were used except that we performed gene set permutation instead of phenotype permutation because there were fewer than seven samples per phenotype. Probes with an adjusted P value of 0.05 or less were considered as significantly differentially expressed. Seven thousand and ninety-six probes annotated to 5,437 genes (5,289 unique) were significant, and a heatmap for these genes was generated using the heatmap.2 function in R. Significantly differentially expressed genes were also analysed by Enrichr47, 48. To identify candidate transcription factors that might mediate the NE to non-NE switch, we used genes significantly downregulated in GFPhigh cells to search for enriched ENCODE and ChEA consensus transcription factors from the ChIP-X database. To identify a list of secreted factors, we first looked at genes that were classified in the ‘extracellular space’ gene signature and, by literature search, picked out the genes known to be secreted. We also input all significant genes into the ontology search tool in the BIOBASE Knowledge Library49, 50, and the output ontologies and gene descriptions were manually screened for secreted factors. We do not exclude the possibility that we might have missed some secreted factors that are not yet well curated in public databases. Candidates for testing in an NE cell growth assay were selected on the basis of expression fold changes and known biology. Single cells were sorted into individual wells in a 96-well PCR plate containing 5 μl of 2× reaction mix (CellsDirect One-Step qRT–PCR kit, Invitrogen) with two units of SUPERase In RNase Inhibitor (Thermo Fisher Scientific). Primers were designed and purchased from Fluidigm through the D3 assay design system. Primers were pooled, and reverse transcription and pre-amplification was performed at a final concentration of 50 nM for each primer pair using the following PCR protocol: 15 min at 50 °C, 2 min at 95 °C, 20 cycles of 15 s at 95 °C, and 4 min at 60 °C, 15 min at 4 °C. The complementary DNA (cDNA) products were treated with Exonuclease I (New England Biolabs) to remove unincorporated primers and then diluted fivefold for the final reaction. cDNA (2.25 μl), 2.5 μl 2× SsoFast EvaGreen Supermix with low ROX (Bio-Rad 172-5211) and 0.25 μl 20× DNA Binding Dye sample loading reagent (Fluidigm 100-3738) were mixed and loaded into a 48.48 or 96.96 Dynamic Array integrated fluidic circuit chip. Of each 100 μM primer pair, 0.25 μl was mixed with 2.5 μl 2× Assay Loading reagent (Fluidigm 85000736) and 2.25 μl TE buffer with low EDTA (Affymetrix 75793) and loaded into the integrated fluidic circuit. The chip was run on a Biomark machine according to the manufacturer’s protocol for EvaGreen probes. As established before the experiment, cells with high or undetectable C values (that is, low expression) for the housekeeping genes (Gapdh, Hsp90ab1, Actb) were excluded from the heatmaps. One nanogram of DNA was used for each multiplex PCR reaction to detect the unrecombined (floxed) and recombined (delta, Δ) Rb, p53, and p130 alleles. A Rb/p53/p130 (TKO) knockout cell line was a positive control for recombined alleles; DNA isolated from a mouse tail was a negative control. The reverse was true for the unrecombined alleles. Primer sequences are provided in the Supplementary Methods. Cells were fixed and ChIP was performed as previously described51. In brief, doxycycline-inducible cells were fixed after 48 h of doxycycline treatment. For N1ICD ChIP, KP1-pLIX-N1ICD cells were induced with 0.125 μg ml−1 of doxycycline and fixed with 2 mM disuccinimidyl glutarate (Thermo Scientific) in PBS for 30 min before formaldehyde fixation. For Rest ChIP, KP1-pLIX-Rest cells were induced with 0.5 μg ml−1 of doxycycline. The antibodies used were Notch1 (CST 3608), rabbit IgG (CST 2729), and Rest (Millipore 17-641). Primer sequences are provided in the Supplementary Methods. Sample sizes were chosen on the basis of our experience with similar experiments (a minimum of three to five mice for animal studies, or two to four biological replicates for in vitro/ex vivo assays, usually ensured statistical significance if the phenotypes were robust). Statistical significance was assayed by Student’s t-test with GraphPad Prism (two-tailed unpaired or paired t-test, depending on the experiment). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001; NS, not significant. Variance was examined by an F-test. Data are represented as mean ± s.d. unless otherwise stated. For analysis of patient survival data, we used a weighted log-rank test in the OASIS web-based tool52 with greater emphasis on late time-point differences (rho: 0; gamma: 1). Microarray data are available at the NCBI Gene Expression Omnibus under accession number GSE81170. Normalized values for significantly differentially expressed genes are provided in Supplementary Table 1; gene set enrichment analyses are in Supplementary Tables 2–4. HES1 immunostaining and survival data of patients with SCLC are provided in Supplementary Table 5. For immunoblot Source Data, see Supplementary Fig. 1. Source Data are provided for Figs 1b, d and Extended Data Figs 4c, 5j, 6c, 8s, 9c–d, f and 10c, f–m, o. All other data are available from the corresponding author upon reasonable request.
News Article | November 23, 2016
This report studies Immune Analysis System in Global market, especially in North America, Europe, China, Japan, Southeast Asia and India, focuses on top manufacturers in global market, with Production, price, revenue and market share for each manufacturer, covering BioTek DAS Roche TECAN BIOSCIENCE GRIFOLS BIOBASE HYBIOME CIOM MEDICAL PERLONG SIMENS BECKMAN COULTER GeteinBiotech ThermoFisher Fenghua Market Segment by Regions, this report splits Global into several key Regions, with production, consumption, revenue, market share and growth rate of Immune Analysis System in these regions, from 2011 to 2021 (forecast), like North America Europe China Japan Southeast Asia India Split by product type, with production, revenue, price, market share and growth rate of each type, can be divided into Type I Type II Type III Split by application, this report focuses on consumption, market share and growth rate of Immune Analysis System in each application, can be divided into Application 1 Application 2 Application 3 Global Immune Analysis System Market Research Report 2016 1 Immune Analysis System Market Overview 1.1 Product Overview and Scope of Immune Analysis System 1.2 Immune Analysis System Segment by Type 1.2.1 Global Production Market Share of Immune Analysis System by Type in 2015 1.2.2 Type I 1.2.3 Type II 1.2.4 Type III 1.3 Immune Analysis System Segment by Application 1.3.1 Immune Analysis System Consumption Market Share by Application in 2015 1.3.2 Application 1 1.3.3 Application 2 1.3.4 Application 3 1.4 Immune Analysis System Market by Region 1.4.1 North America Status and Prospect (2011-2021) 1.4.2 Europe Status and Prospect (2011-2021) 1.4.3 China Status and Prospect (2011-2021) 1.4.4 Japan Status and Prospect (2011-2021) 1.4.5 Southeast Asia Status and Prospect (2011-2021) 1.4.6 India Status and Prospect (2011-2021) 1.5 Global Market Size (Value) of Immune Analysis System (2011-2021) 7 Global Immune Analysis System Manufacturers Profiles/Analysis 7.1 BioTek 7.1.1 Company Basic Information, Manufacturing Base and Its Competitors 7.1.2 Immune Analysis System Product Type, Application and Specification 188.8.131.52 Type I 184.108.40.206 Type II 7.1.3 BioTek Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.1.4 Main Business/Business Overview 7.2 DAS 7.2.1 Company Basic Information, Manufacturing Base and Its Competitors 7.2.2 Immune Analysis System Product Type, Application and Specification 220.127.116.11 Type I 18.104.22.168 Type II 7.2.3 DAS Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.2.4 Main Business/Business Overview 7.3 Roche 7.3.1 Company Basic Information, Manufacturing Base and Its Competitors 7.3.2 Immune Analysis System Product Type, Application and Specification 22.214.171.124 Type I 126.96.36.199 Type II 7.3.3 Roche Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.3.4 Main Business/Business Overview 7.4 TECAN 7.4.1 Company Basic Information, Manufacturing Base and Its Competitors 7.4.2 Immune Analysis System Product Type, Application and Specification 188.8.131.52 Type I 184.108.40.206 Type II 7.4.3 TECAN Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.4.4 Main Business/Business Overview 7.5 BIOSCIENCE 7.5.1 Company Basic Information, Manufacturing Base and Its Competitors 7.5.2 Immune Analysis System Product Type, Application and Specification 220.127.116.11 Type I 18.104.22.168 Type II 7.5.3 BIOSCIENCE Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.5.4 Main Business/Business Overview 7.6 GRIFOLS 7.6.1 Company Basic Information, Manufacturing Base and Its Competitors 7.6.2 Immune Analysis System Product Type, Application and Specification 22.214.171.124 Type I 126.96.36.199 Type II 7.6.3 GRIFOLS Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.6.4 Main Business/Business Overview 7.7 BIOBASE 7.7.1 Company Basic Information, Manufacturing Base and Its Competitors 7.7.2 Immune Analysis System Product Type, Application and Specification 188.8.131.52 Type I 184.108.40.206 Type II 7.7.3 BIOBASE Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.7.4 Main Business/Business Overview 7.8 HYBIOME 7.8.1 Company Basic Information, Manufacturing Base and Its Competitors 7.8.2 Immune Analysis System Product Type, Application and Specification 220.127.116.11 Type I 18.104.22.168 Type II 7.8.3 HYBIOME Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.8.4 Main Business/Business Overview 7.9 CIOM MEDICAL 7.9.1 Company Basic Information, Manufacturing Base and Its Competitors 7.9.2 Immune Analysis System Product Type, Application and Specification 22.214.171.124 Type I 126.96.36.199 Type II 7.9.3 CIOM MEDICAL Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.9.4 Main Business/Business Overview 7.10 PERLONG 7.10.1 Company Basic Information, Manufacturing Base and Its Competitors 7.10.2 Immune Analysis System Product Type, Application and Specification 188.8.131.52 Type I 184.108.40.206 Type II 7.10.3 PERLONG Immune Analysis System Production, Revenue, Price and Gross Margin (2015 and 2016) 7.10.4 Main Business/Business Overview 7.11 SIMENS 7.12 BECKMAN COULTER 7.13 GeteinBiotech 7.14 ThermoFisher 7.15 Fenghua
Agency: European Commission | Branch: FP7 | Program: CP-IP | Phase: HEALTH-2007-2.1.1-6 | Award Amount: 16.02M | Year: 2008
Lipids are central to the regulation and control of cellular processes by acting as basic building units for biomembranes, the platforms for the vast majority of cellular functions. Recent developments in lipid mass spectrometry have set the scene for a completely new way to understand the composition of membranes, cells and tissues in space and time by allowing the precise identification and quantification of alterations of the total lipid profile after specific perturbations. In combination with advanced proteome and transcriptome analysis tools and novel imaging techniques using RNA interference, it is now possible to unravel the complex network between lipids, genes and proteins in an integrated lipidomics approach. This project application of the European Lipidomics Initiative (ELife; www.lipidomics.net) will address lipid droplets (LD) as dynamic organelles with regard to composition, metabolism and regulation. LD are the hallmark of energy overload diseases with a major health care impact in Europe. The project will exploit recent advances in lipidomics to establish high-throughput methods to define drugable targets and novel biomarkers related to LD lipid and protein species, their interaction and regulation during assembly, disassembly and storage. Translational research from mouse to man applied to LD pathology is a cornerstone of this project at the interface between research and development. To maximize the value of the assembled data generated throughout the project, LipidomicNet as a detailed special purpose Wiki formate data base will be developed and integrated into the existing Lipidomics Expertise Platform (LEP) established through the SSA ELife project (www.lipidomics-expertise.de). ELife collaborates with the NIH initiative LIPID MAPS (www.lipidmaps.org) and the Japanese pendant Lipidbank (www.lipidbank.jp) and is connected to the Danubian Biobank consortium (SSA DanuBiobank, www.danubianbiobank.de) for clinical lipidomics.
Agency: European Commission | Branch: FP7 | Program: BSG-SME | Phase: SME-1 | Award Amount: 2.23M | Year: 2010
The activity of genes is absolutely essential for all life from viruses and bacteria to crops and human beings. Despite the many technological breakthroughs within life science research during the last 20-30 years, we are however still far away from fully understanding the activity of genes and which factor influence (regulates) the gene activity. Such knowledge is, by nature, of very high importance and very high value to life science researchers globally, and the ANGS project consortium will therefore develop a suite of algorithms, methods, and software tools that are significantly better at analyzing and understanding gene regulation than what exists today. The consortium consists of three SMEs that all are poised to take advantage of the new developments in genome sequencing (CLC bio, BIOBASE, and deCODE genetics), and 4 academic partners (Oxford University, Goettingen University, Renyi Institute, and NCSB). The SMEs will commercialize the results to help ensure that EU companies are established among the world leaders within solutions for analysis of gene regulation. The primary focus of the consortium will be to develop methods for including the massive amounts of genomic data that is being generated using the revolutionary Next Generation Sequencing technologies an amount of data that will increase exponentially in the coming years, and that is virtually impossible to analyze with any reasonable success by existing methods and in existing software. The software suite, the ANGS engine will thus make it possible to include up to a thousand genomes, e.g. from the 10,000 genome project as knowledge input in gene regulation analysis. Such software will be able to provide completely new knowledge and will thus have tremendous value to life science researcher globally, including pharmaceutical companies, biotech companies, agricultural companies, biofuel companies, research hospitals, as well as universities and governmental research organizations
Agency: European Commission | Branch: FP7 | Program: MC-ITN | Phase: FP7-PEOPLE-ITN-2008 | Award Amount: 1.91M | Year: 2009
We will educate and train young scientists to apply an interdisciplinary systems-based approach to complex biological questions using plant reproduction as a model system. Systems approaches are not routinely taught, have been identified as an area requiring urgent PhD-level training in some European countries and require network-scale working practices. We have assembled a team with International research reputations in two key areas for the success of this project: half are experimental and half are computational/mathematical scientists. We will place 9 PhD students under the supervision of this team, focussed on this single biological problem. Students assigned to experimental groups will use advanced techniques to generate data for the computational groups. The computational students will inform, analyse, interpret and model the data and their models will be validated by the experimental groups. A series of laboratory placements will ensure a wide range of subject-specific training and exchanges between the experimental and computational groups will lead to a greater understanding at the interface of these disciplines. The integration of a team of PhD students into this project, so that they each see their contributions as essential and integral parts of the success of the strategy, combined with the supervison and co-ordinated discipline-specific and generic training schedule will produce a cohort of young scientists trained in systems biology. The core skills and approaches that will be instilled into the young scientists and the integration of industry into the project, will equip them to take a systems approach to any biological question and prepare them for a career in an academic or industrial environment. Scientific outcomes will include the use of advanced techniques to provide the quantity and quality of data required to model floral regulation, the use of computational approaches to generate predictive models and their experimental validation.
Kaufmann K.,Business Unit Bioscience |
Nagasaki M.,Tokyo Medical University |
Jauregui R.,BIOBASE GmbH
In Silico Biology | Year: 2010
We present a dynamical model of the gene network controlling flower development in Arabidopsis thaliana. The network is centered at the regulation of the floral organ identity genes (AP1, AP2, AP3, PI and AG) and ends with the transcription factor complexes responsible for differentiation of floral organs. We built and simulated the regulatory interactions that determine organ specificity using an extension of hybrid Petri nets as implemented in Cell Illustrator. The network topology is characterized by two main features: (1) the presence of multiple autoregulatory feedback loops requiring the formation of protein complexes, and (2) the role of spatial regulators determining floral patterning. The resulting network shows biologically coherent expression patterns for the involved genes, and simulated mutants produce experimentally validated changes in organ expression patterns. The requirement of heteromeric higher-order protein complex formation for positive autoregulatory feedback loops attenuates stochastic fluctuations in gene expression, enabling robust organ-specific gene expression patterns. If autoregulation is mediated by monomers or homodimers of proteins, small variations in initial protein levels can lead to biased production of homeotic proteins, ultimately resulting in homeosis. We also suggest regulatory feedback loops involving miRNA loci by which homeotic genes control the activity of their spatial regulators. © 2010-IOS Press and Bioinformation Systems e.V. and the authors. All rights reserved.
Pachov G.V.,Heidelberg Institute for Theoretical Studies HITS GGmbH |
Gabdoulline R.R.,Heidelberg Institute for Theoretical Studies HITS GGmbH |
Gabdoulline R.R.,University of Heidelberg |
Gabdoulline R.R.,BIOBASE GmbH |
Wade R.C.,Heidelberg Institute for Theoretical Studies HITS GGmbH
Nucleic Acids Research | Year: 2011
Several different models of the linker histone (LH)-nucleosome complex have been proposed, but none of them has unambiguously revealed the position and binding sites of the LH on the nucleosome. Using Brownian dynamics-based docking together with normal mode analysis of the nucleosome to account for the flexibility of two flanking 10bp long linker DNAs (L-DNA), we identified binding modes of the H5-LH globular domain (GH5) to the nucleosome. For a wide range of nucleosomal conformations with the L-DNA ends less than 65 apart, one dominant binding mode was identified for GH5 and found to be consistent with fluorescence recovery after photobleaching (FRAP) experiments. GH5 binds asymmetrically with respect to the nucleosomal dyad axis, fitting between the nucleosomal DNA and one of the L-DNAs. For greater distances between L-DNA ends, docking of GH5 to the L-DNA that is more restrained and less open becomes favored. These results suggest a selection mechanism by which GH5 preferentially binds one of the L-DNAs and thereby affects DNA dynamics and accessibility and contributes to formation of a particular chromatin fiber structure. The two binding modes identified would, respectively, favor a tight zigzag chromatin structure or a loose solenoid chromatin fiber. © 2011 The Author(s).
Alamanova D.,BIOBASE GmbH |
Stegmaier P.,BIOBASE GmbH |
Kel A.,BIOBASE GmbH
BMC Bioinformatics | Year: 2010
Background: Knowledge of transcription factor-DNA binding patterns is crucial for understanding gene transcription. Numerous DNA-binding proteins are annotated as transcription factors in the literature, however, for many of them the corresponding DNA-binding motifs remain uncharacterized.Results: The position weight matrices (PWMs) of transcription factors from different structural classes have been determined using a knowledge-based statistical potential. The scoring function calibrated against crystallographic data on protein-DNA contacts recovered PWMs of various members of widely studied transcription factor families such as p53 and NF-κB. Where it was possible, extensive comparison to experimental binding affinity data and other physical models was made. Although the p50p50, p50RelB, and p50p65 dimers belong to the same family, particular differences in their PWMs were detected, thereby suggesting possibly different in vivo binding modes. The PWMs of p63 and p73 were computed on the basis of homology modeling and their performance was studied using upstream sequences of 85 p53/p73-regulated human genes. Interestingly, about half of the p63 and p73 hits reported by the Match algorithm in the altogether 126 promoters lay more than 2 kb upstream of the corresponding transcription start sites, which deviates from the common assumption that most regulatory sites are located more proximal to the TSS. The fact that in most of the cases the binding sites of p63 and p73 did not overlap with the p53 sites suggests that p63 and p73 could influence the p53 transcriptional activity cooperatively. The newly computed p50p50 PWM recovered 5 more experimental binding sites than the corresponding TRANSFAC matrix, while both PWMs showed comparable receiver operator characteristics.Conclusions: A novel algorithm was developed to calculate position weight matrices from protein-DNA complex structures. The proposed algorithm was extensively validated against experimental data. The method was further combined with Homology Modeling to obtain PWMs of factors for which crystallographic complexes with DNA are not yet available. The performance of PWMs obtained in this work in comparison to traditionally constructed matrices demonstrates that the structure-based approach presents a promising alternative to experimental determination of transcription factor binding properties. © 2010 Alamanova et al; licensee BioMed Central Ltd.
Gabdoulline R.,Heinrich Heine University Düsseldorf |
Eckweiler D.,Helmholtz Center for Infection Research |
Kel A.,GeneXplain GmbH |
Kel A.,BIOBASE GmbH |
And 2 more authors.
Nucleic Acids Research | Year: 2012
We present the webserver 3D transcription factor (3DTF) to compute position-specific weight matrices (PWMs) of transcription factors using a knowledge-based statistical potential derived from crystallographic data on protein-DNA complexes. Analysis of available structures that can be used to construct PWMs shows that there are hundreds of 3D structures from which PWMs could be derived, as well as thousands of proteins homologous to these. Therefore, we created 3DTF, which delivers binding matrices given the experimental or modeled protein-DNA complex. The webserver can be used by biologists to derive novel PWMs for transcription factors lacking known binding sites and is freely accessible at http://www.gene-regulation.com/pub/programs/ 3dtf/. © 2012 The Author(s).