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News Article | April 17, 2017
Site: www.chromatographytechniques.com

Waste diversion is an essential goal for labs and cleanrooms, as well as virtually every other kind of facility. It can be achieved through a variety of ways, such as source reduction, reuse, composting and recycling. In 2014, more than 89 million tons of municipal solid waste were recycled and composted, providing an annual reduction of over 181 million metric tons of carbon dioxide equivalent emissions, comparable to the annual emissions from over 38 million passenger cars, according to the Environmental Protection Agency (EPA). The benefits of recycling are well known: •    Reduces the amount of waste sent to landfills and incinerators •    Conserves natural resources such as timber, water and minerals •    Prevents pollution by reducing the need to collect new raw materials •    Saves energy •    Reduces greenhouse gas emissions that contribute to global climate change •    Helps sustain the environment for future generations As recycling becomes the norm, rather than the exception, in labs and cleanrooms, facilities are getting pretty good at recycling primary commodities such as cardboard, paper, plastic and aluminum. But to get to a higher level of diversion and potentially reach the holy grail of zero waste, other non-traditional or secondary commodities must also be diverted from landfill, and recycled and repurposed into usable products and durable goods. Glove and apparel recycling is a relatively new form of recycling that is beginning to gain traction in lab and cleanroom settings. In 2011, Kimberly-Clark Professional launched The RightCycle Program, the first large-scale recycling effort for non-hazardous lab and cleanroom waste. Since then, the program has diverted more than 350 tons of waste from landfill. RightCycle removes gloves, masks, garments, shoe covers and other apparel accessories from the waste stream. The products are collected and shipped to domestic recycling centers, where they are turned into nitrile pellets that are then used to create eco-responsible consumer products and durable goods. As long as gloves, garments and accessories (such as masks, hoods, shoe covers and hairnets) do not contain bio-hazardous materials, they can be safely recycled and turned into items such as: lawn furniture, flowerpots and planters, shelving, totes and storage bins. It all adds up Gloves are ubiquitous in labs and cleanrooms, and workers can go through several pairs in the course of a day. While this is necessary to protect both the worker and the process, the amount of waste can add up. Consider these statistics: •    One university estimated that nearly 30 percent of its waste stream came from laboratory and research buildings.   •    A University of Washington lab waste audit found that 22 percent of its research waste consisted of nitrile gloves.   •    A University of California Santa Cruz (UCSC) laboratory waste assessment found that nitrile gloves made up a majority of laboratory waste destined for landfill. Because of this, many labs are participating in The RightCycle Program. The environmental benefits of glove and apparel recycling programs are evident. They take commonly used and essential lab and cleanroom products out of the solid waste stream, significantly reducing waste generation. Putting glove recycling into practice The University of Washington and UCSC now participate in The RightCycle Program, as does the Illinois Sustainable Technology Center (ISTC) at the University of Illinois and Purdue University. ISTC is a division of the Prairie Research Institute at the University of Illinois Urbana-Champaign. Its mission is to drive statewide economic growth through sustainability. To fulfill that mission, ISTC conducts scientific research and, in the process, uses a lot of gloves. “We conducted a waste audit to see how we could go to zero waste in our own building and realized that gloves were about 10 percent of our total waste by weight,” said Shantanu Pai, ISTC assistant sustainability researcher. “We were already effectively recycling other items—glass, aluminum, paper and cardboard.” With RightCycle, ISTC was able to reach 89 percent compliance for gloves in its labs—even higher than the rate for paper and cardboard recycling. It then decided to take the program a step further, piloting it in the university’s main dining hall and achieving an estimated diversion rate of 90 percent. It is in the process of expanding the effort to all dining facilities and campus labs. In fact, the university has purchased a storage container to house the gloves so shipments can be made just once a year. Since implementing The RightCycle program in 2013, the center and the university have diverted 4,945 pounds from landfills. “RightCycle has had a huge impact on our activities and our sustainability metrics,” said Kevin O’Brien, Director of the Illinois Sustainable Technology Center. “If you ever used gloves as part of your laboratory work, you quickly appreciate the value this program brings from a sustainability perspective.” Purdue University Across its campus in the course of a year, Purdue University uses approximately 360,000 disposable gloves. That’s a lot of trash—3.5 tons to be exact, all of which would normally wind up in a landfill. The university, based in West Lafayette, Ind., has won numerous awards for sustainability. Its efforts extend to many different areas—recycling, planning management, landscaping and green construction. With a diversion rate goal of 85 percent, the university is always seeking new and different ways to reduce its solid waste stream. In 2014, Purdue University added glove recycling to its list of sustainability accomplishments when it adopted The RightCycle program. Since November 2014, the chemistry department at Purdue University has diverted 8,163 pounds of lab gloves from landfills. Michael Gulich, director of campus master planning and sustainability, is looking to expand the program to other campus labs as well as food preparation areas. “Once you address cans, bottles, paper and cardboard recycling, you get into smaller niche streams,” he said. “We have some addressed very well, such as electronics waste and landscape debris. Previously, gloves didn’t have a solution. Anything that increases our diversion rate is good.” Other participants University laboratories aren’t the only facilities that have adopted this innovative recycling solution. Cell Signaling Technology (CST), a life sciences company, uses about 200,000 pairs of gloves each year. Reducing its environmental footprint has long been a core company value, so finding a way to reduce the volume of glove waste was important. CST began researching The RightCycle Program in 2013, and made its first recycling shipment in 2015. The program has helped CST reduce the costs of trash removal and move closer to its goal of zero waste to landfill. “We’re glad to have made an impact on our waste profile and to have our lab gloves repurposed for safe practical purposes,” said Sustainability Coordinator Elias Witman. “And it was fun for our employees to see our recycled gloves come back to CST in the form of a flying disc, which was tossed around after a company meeting.” Since joining The RightCycle Program, Cell Signaling Technology has recycled approximately 150,000 pairs of gloves. “The RightCycle Program is highly visible and practical,” Witman added. “People see it and want to participate. Programs like this can help shape a culture of sustainability in the lab and yield positive impacts for the planet.”


News Article | February 22, 2017
Site: www.nature.com

Pre-B acute lymphoblastic leukaemia (ALL) cells were obtained from patients who gave informed consent in compliance with the guidelines of the Internal Review Board of the University of California San Francisco (Supplementary Table 2). Leukaemia cells from bone marrow biopsy of patients with ALL were xenografted into sublethally irradiated NOD/SCID (non-obese diabetic/severe combined immunodeficient) mice via tail vein injection. After passaging, leukaemia cells were collected. Cells were cultured on OP9 stroma cells in minimum essential medium-α (MEMα; Invitrogen), supplemented with 20% fetal bovine serum (FBS), 2 mM l-glutamine, 1 mM sodium pyruvate, 100 IU/ml penicillin and 100 μg/ml streptomycin. Primary chronic myeloid leukaemia (CML) cases were obtained with informed consent from the University Hospital Jena in compliance with institutional internal review boards (including the IRB of the University of California San Francisco; Supplementary Table 3). Cells were cultured in Iscove’s modified Dulbecco’s medium (IMDM; Invitrogen) supplemented with 20% BIT serum substitute (StemCell Technologies); 100 IU/ml penicillin and 100 μg/ml streptomycin; 25 μmol/l β-mercaptoethanol; 100 ng/ml SCF; 100 ng/ml G-CSF; 20 ng/ml FLT3; 20 ng/ml IL-3; and 20 ng/ml IL-6. Human cell lines (Supplementary Table 2) were obtained from DSMZ and were cultured in Roswell Park Memorial Institute medium (RPMI-1640; Invitrogen) supplemented with GlutaMAX containing 20% FBS, 100 IU/ml penicillin and 100 μg/ml streptomycin. Cell cultures were kept at 37 °C in a humidified incubator in a 5% CO atmosphere. None of the cell lines used was found in the database of commonly misidentified cell lines maintained by ICLAC and NCBI Biosample. All cell lines were authenticated by STR profiles and tested negative for mycoplasma. BML275 (water-soluble) and imatinib were obtained from Santa Cruz Biotechnology and LC Laboratories, respectively. Stock solutions were prepared in DMSO or sterile water at 10 mmol/l and stored at −20 °C. Prednisolone and dexamethasone (water-soluble) were purchased from Sigma-Aldrich and were resuspended in ethanol or sterile water, respectively, at 10 mmol/l. Stock solutions were stored at −20 °C. Fresh solutions (pH-adjusted) of methyl pyruvate, OAA, 3-OMG (an agonist of TXNIP), d-allose (an agonist of TXNIP) and recombinant insulin (Sigma-Aldrich) were prepared for each experiment. DMS was obtained from Acros Organics, and fresh solutions (pH-adjusted) were prepared before each experiment. For competitive-growth assays, 5 mmol/l methyl pyruvate, 5 mmol/l dimethyl succinate (DMS) and 5 mmol/l OAA were used. The CNR2 agonist HU308 was obtained from Cayman Chemical. To avoid inflammation-related effects in mice, bone marrow cells were extracted from mice (Supplementary Table 4) younger than 6 weeks of age without signs of inflammation. All mouse experiments were conducted in compliance with institutional approval by the University of California, San Francisco Institutional Animal Care and Use Committee. Bone marrow cells were obtained by flushing cavities of femur and tibia with PBS. After filtration through a 70-μm filter and depletion of erythrocytes using a lysis buffer (BD PharmLyse, BD Biosciences), washed cells were either frozen for storage or subjected to further experiments. Bone marrow cells were cultured in IMDM (Invitrogen) with GlutaMAX containing 20% fetal bovine serum, 100 IU/ml penicillin, 100 μg/ml streptomycin and 50 μM β-mercaptoethanol. To generate pre-B ALL (Ph+ ALL-like) cells, bone marrow cells were cultured in 10 ng/ml recombinant mouse IL-7 (PeproTech) and retrovirally transformed by BCR–ABL1. BCR–ABL1-transformed pre-B ALL cells were propagated only for short periods of time and usually not for longer than 2 months to avoid acquisition of additional genetic lesions during long-term cell culture. To generate myeloid leukaemia (CML-like) cells, the myeloid-restricted protocol described previously30 was used. Bone marrow cells were cultured in 10 ng/ml recombinant mouse IL-3, 25 ng/ml recombinant mouse IL-6, and 50 ng/ml recombinant mouse SCF (PeproTech) and retrovirally transformed by BCR–ABL1. Immunophenotypic characterization was performed by flow cytometry. For conditional deletion, a 4-OHT-inducible, Cre-mediated deletion system was used. For retroviral constructs used, see Supplementary Table 5. Transfection of retroviral constructs (Supplementary Table 5) was performed using Lipofectamine 2000 (Invitrogen) with Opti-MEM medium (Invitrogen). Retroviral supernatant was produced by co-transfecting HEK 293FT cells with the plasmids pHIT60 (gag-pol) and pHIT123 (ecotropic env). Lentiviral supernatant was produced by co-transfecting HEK 293FT cells with the plasmids pCDNL-BH and VSV-G or EM140. 293FT cells were cultured in high glucose Dulbecco’s modified Eagle’s medium (DMEM, Invitrogen) with GlutaMAX containing 10% fetal bovine serum, 100 IU/ml penicillin, 100 μg/ml streptomycin, 25 mmol/l HEPES, 1 mmol/l sodium pyruvate and 0.1 mmol/l non-essential amino acids. Regular medium was replaced after 16 h by growth medium containing 10 mmol /l sodium butyrate. After incubation for 8 h, the medium was changed back to regular growth medium. After 24 h, retroviral supernatant was collected, filtered through a 0.45-μm filter and loaded by centrifugation (2,000g, 90 min at 32 °C) onto 50 μg/ml RetroNectin- (Takara) coated non-tissue 6-well plates. Lentiviral supernatant produced with VSV-G was concentrated using Lenti-X Concentrator (Clontech), loaded onto RetroNectin-coated plates and incubated for 15 min at room temperature. Lentiviral supernatant produced with EM140 was collected, loaded onto RetroNectin-coated plates and incubated for 30 min at room temperature. Per well, 2–3 × 106 cells were transduced by centrifugation at 600g for 30 min and maintained for 48 h at 37 °C with 5% CO before transferring into culture flasks. For cells transduced with lentiviral supernatant produced with EM140, supernatant was removed the day after transduction and replaced with fresh culture medium. Cells transduced with oestrogen-receptor fusion proteins were induced with 4-OHT (1 μmol/l). Cells transduced with constructs carrying an antibiotic-resistance marker were selected with its respective antibiotic. For loss-of-function studies, dominant-negative variants of IKZF1 (DN-IKZF1, lacking the IKZF1 zinc fingers 1–4) and PAX5 (DN-PAX5; PAX5–ETV6 fusion) were cloned from patient samples. Expression of DN-IKZF1 was induced by doxycycline (1 μg/ml), while activation of DN-PAX5 was induced by 4-OHT (1 μg/ml) in patient-derived pre-B ALL cells carrying IKZF1 and PAX5 wild-type alleles, respectively. Inducible reconstitution of wild-type IKZF1 and PAX5 in haploinsufficient pre-B ALL cells carrying deletions of either IKZF1 (IKZF1∆) or PAX5 (PAX5∆) were also studied. Lentiviral constructs used are listed in Supplementary Table 5. A doxycycline-inducible TetOn vector system was used for inducible expression of PAX5 in mouse BCR–ABL1 pre-B ALL. The retroviral constructs used are listed in Supplementary Table 5. To study the effects of B-cell- versus myeloid-lineage identity in genetically identical mouse leukaemia cells, a doxycycline-inducible TetOn-CEBPα vector system31 was used to reprogram B cells. Mouse BCR–ABL1 pre-B ALL cells expressing doxycycline-inducible CEBPα or an empty vector were induced with doxycycline (1 μg/ml). Conversion from the B-cell lineage (CD19+Mac1−) to the myeloid lineage (CD19−Mac1+) was monitored by flow cytometry. For western blots, B-lineage cells (CD19+Mac1−) and CEBPα-reprogrammed cells (CD19−Mac1+) were sorted from cells expressing an empty vector or CEBPα, respectively, following doxycycline induction. For metabolic assays, sorted B-lineage cells and CEBPα-reprogrammed cells were cultured (with doxycycline) for 2 days following sorting, and were then seeded in fresh medium for measurement of glucose consumption (normalized to cell numbers) and total ATP levels (normalized to total protein). To study Lkb1 deletion in the context of CEBPα-mediated reprogramming, BCR–ABL1-transformed Lkb1fl/fl pre-B ALL cells expressing doxycycline-inducible CEBPα were transduced with 4-OHT(1 μg/ml) inducible Cre-GFP (Cre-ERT2-GFP). Without sorting for GFP+ cells, cells were induced with doxycycline and 4-OHT. Viability (expressed as relative change of GFP+ cells) was measured separately in B-lineage (gated on CD19+ Mac1−) and myeloid lineage (gated on CD19− Mac1+) populations. To study whether Lkb1 deletion causes CEBPα-dependent effects on metabolism and signalling, Lkb1fl/fl BCR–ABL1 B-lineage ALL cells expressing doxycycline-inducible CEBPα or an empty vector were transduced with 4-OHT-inducible Cre-GFP. After sorting for GFP+ populations, cells were induced with doxycycline. B-lineage cells (CD19+ Mac1−) and CEBPα-reprogrammed cells (CD19− Mac1+) were sorted from cells expressing an empty vector or CEBPα, respectively. Sorted cells were cultured with doxycycline and induced with 4-OHT. Protein lysates were collected on day 2 following 4-OHT induction. For metabolomics, sorted cells were re-seeded in fresh medium on day 2 following 4-OHT induction and collected for metabolite extraction. For CRISPR/Cas9-mediated deletion of target genes, all constructs including lentiviral vectors expressing gRNA and Cas9 nuclease were purchased from Transomic Technologies (Supplementary Table 5; see Supplementary Table 6 for gRNA sequences). In brief, patient-derived pre-B ALL cells transduced with GFP-tagged, 4-OHT-inducible PAX5 or an empty vector were transduced with pCLIP-hCMV-Cas9-Nuclease-Blast. Blasticidin-resistant cells were subsequently transduced with pCLIP-hCMV-gRNA-RFP. Non-targeting gRNA was used as control. Constructs including lentiviral vectors expressing gRNA and dCas9-VPR used for CRISPR/dCas9-mediated activation of gene expression are listed in Supplementary Table 5. Nuclease-null Cas9 (dCas9) fused with VP64-p65-Rta (VPR) was cloned from SP-dCas9-VPR (a gift from G. Church; Addgene plasmid #63798) and then subcloned into pCL6 vector with a blasticidin-resistant marker. gRNA sequences (Supplementary Table 6) targeting the transcriptional start site of each specific gene were obtained from public databases (http://sam.genome-engineering.org/ and http://www.genscript.com/gRNA-database.html)32. gBlocks Gene Fragments were used to generate single-guide RNAs (sgRNAs) and were purchased from Integrated DNA Technologies, Inc. Each gRNA was subcloned into pCL6 vector with a dsRed reporter. Patient-derived pre-B ALL cells transduced with either GFP-tagged inducible PAX5 or an empty vector were transduced with pCL6-hCMV-dCas9-VPR-Blast. Blasticidin-resistant cells were used for subsequent transduction with pCL6-hCMV-gRNA-dsRed, and dsRed+ cells were further analysed by flow cytometry. For each target gene, 2–3 sgRNA clones were pooled together to generate lentiviruses. Non-targeting gRNA was used as control. To elucidate the mechanistic contribution of PAX5 targets, the percentage of GFP+ cells carrying gRNA(s) for each target gene was monitored by flow cytometry upon inducible activation of GFP-tagged PAX5 or an empty vector in patient-derived pre-B ALL cells in competitive-growth assays. Cells were lysed in CelLytic buffer (Sigma-Aldrich) supplemented with a 1% protease inhibitor cocktail (Thermo Fisher Scientific). A total of 20 μg of protein mixture per sample was separated on NuPAGE (Invitrogen) 4–12% Bis-Tris gradient gels or 4–20% Mini-PROTEAN TGX precast gels, and transferred onto nitrocellulose membranes (Bio-Rad). The primary antibodies used are listed in Supplementary Table 7. For protein detection, the WesternBreeze Immunodetection System (Invitrogen) was used, and light emission was detected by either film exposure or the BioSpectrum Imaging system (UPV). Approximately 106 cells per sample were resuspended in PBS blocked using Fc blocker for 10 min on ice, followed by staining with the appropriate dilution of the antibodies or their respective isotype controls for 15 min on ice. Cells were washed and resuspended in PBS with propidium iodide (0.2 μg/ml) or DAPI (0.75 μg/ml) as a dead-cell marker. The antibodies used for flow cytometry are listed in Supplementary Table 7. For competitive-growth assays, the percentage of GFP+ cells was monitored by flow cytometry. For annexin V staining, annexin V binding buffer (BD Bioscience) was used instead of PBS and 7-aminoactinomycin D (7AAD; BD Bioscience) instead of propidium iodide. Phycoerythrin-labelled annexin V was purchased from BD Bioscience. For BrdU staining, the BrdU Flow Kit was purchased from BD Bioscience and used according to the manufacturer’s protocol. Methylcellulose colony-forming assays were performed with 10,000 BCR–ABL1 pre-B ALL cells. Cells were resuspended in mouse MethoCult medium (StemCell Technologies) and cultured on 3-cm dishes, with an extra water supply dish to prevent evaporation. Images were taken and colony numbers were counted after 14 days. Cell viability upon the genetic loss of function of target genes and/or inducible expression of PAX5 was monitored by flow cytometry using propidium iodide (0.2 μg/ml) as a dead-cell marker. To study the effects of an AMPK inhibitor (BML275), glucocorticoids (dexamethasone and prednisolone), CNR2 agonist (HU308), or TXNIP agonists (3-OMG and d-allose), 40,000 human or mouse leukaemia cells were seeded in a volume of 80 μl in complete growth medium on opaque-walled, white 96-well plates (BD Biosciences). Compounds were added at the indicated concentrations giving a total volume of 100 μl per well. After culturing for 3 days, cells were subjected to CellTiter-Glo Luminescent Cell Viability Assay (Promega). Relative viability was calculated using baseline values of cells treated with vehicle control as a reference. Combination index (CI) was calculated using the CalcuSyn software to determine interaction (synergistic, CI < 1; additive, CI = 1; or antagonistic, CI > 1) between the two agents. Constant ratio combination design was used. Concentrations of BML275, d-allose, 3-OMG and HU308 used are indicated in the figures. Concentrations of Dex used were tenfold lower than those of BML275. Concentrations of prednisolone used were twofold lower than those of BML275. To determine the number of viable cells, the trypan blue exclusion method was applied, using the Vi-CELL Cell Counter (Beckman Coulter). ChIP was performed as described previously33. Chromatin from fixed patient-derived Ph+ ALL cells (ICN1) was isolated and sonicated to 100–500-bp DNA fragments. Chromatin fragments were immunoprecipitated with either IgG (as a control) or anti-Pax5 antibody (see Supplementary Table 7). Following reversal of crosslinking by formaldehyde, specific DNA sequences were analysed by quantitative real-time PCR (see Supplementary Table 8 for primers). Primers were designed according to ChIP–seq tracks for PAX5 antibodies in B lymphocytes (ENCODE, Encyclopedia of DNA Elements, GM12878). ChIP–seq tracks for PAX5, IKZF1, EBF1 and TCF3 antibodies in a normal B-cell sample (ENCODE GM12878, UCSC genome browser) on INSR, GLUT1, GLUT3, GLUT6, HK2, G6PD, NR3C1, TXNIP, CNR2 and LKB1 gene promoter regions are shown. CD19 and ACTA1 served as a positive and a negative control gene, respectively. The y axis represents the normalized number of reads per million reads for peak summit for each track. The ChIP–seq peaks were called by the MACS peak-caller by comparing read density in the ChIP experiment relative to the input chromatin control reads, and are shown as bars under each wiggle track. Gene models are shown in UCSC genome browser hg19. Extracellular glucose levels were measured using the Amplex Red Glucose/Glucose Oxidase Assay Kit (Invitrogen), according to the manufacturer’s protocol. Glucose concentrations were measured in fresh and spent medium. Total ATP levels were measured using the ATP Bioluminescence Assay Kit CLS II (Roche) according to the manufacturer’s protocol. In fresh medium, 1 × 106 cells per ml were seeded and treated as indicated in the figure legends. Relative levels of glucose consumed and total ATP are shown. All values were normalized to cell numbers (Figs 1b, c, 2c (glucose uptake), 3a and Extended Data Figs 2c, 4f, 6d) or total protein (Fig. 2c, ATP levels). Numbers of viable cells were determined by applying trypan blue dye exclusion, using the Vi-CELL Cell Counter (Beckman Coulter). Oxygen consumption rate (OCR) and extracellular acidification rate (ECAR) were measured using a Seahorse XFe24 Flux Analyzer with an XF Cell Mito Stress Test Kit and XF Glycolysis Stress Test Kit (Seahorse Bioscience) according to the manufacturer’s instructions. All compounds and materials were obtained from Seahorse Bioscience. In brief, 1.5 × 105 cells per well were plated using Cell-Tak (BD Biosciences). Following incubation in XF-Base medium supplemented with glucose and GlutaMAX for 1 h at 37 °C (non-CO incubator) for pH stabilization, OCR was measured at the resting stage (basal respiration in XF Base medium supplemented with GlutaMax and glucose) and in response to oligomycin (1 μmol/l; mitochondrial ATP production), mitochondrial uncoupler FCCP (5 μmol/l; maximal respiration), and respiratory chain inhibitor antimycin and rotenone (1 μmol/l). Spare respiratory capacity is the difference between maximal respiration and basal respiration. ECAR was measured under specific conditions to generate glycolytic profiles. Following incubation in glucose-free XF Base medium supplemented with GlutaMAX for 1 h at 37 °C (non-CO incubator) for pH stabilization, basal ECAR was measured. Following measurement of the glucose-deprived, basal ECAR, changes in ECAR upon the sequential addition of glucose (10 mmol/l; glycolysis), oligomycin (1 μmol/l; glycolytic capacity), and 2-deoxyglucose (0.1 mol/l) were measured. Glycolytic reserve was determined as the difference between oligomycin-stimulated glycolytic capacity and glucose-stimulated glycolysis. All values were normalized to cell numbers (Extended Data Fig. 2c) or total protein (Extended Data Figs 3a, 7a, b 8f) and are shown as the fold change relative to basal ECAR or OCR. Metabolite extraction and mass-spectrometry-based analysis were performed as described previously34. Metabolites were extracted from 2 × 105 cells per sample using the methanol/water/chloroform method. After incubation at 37 °C for the indicated time, cells were rinsed with 150 mM ammonium acetate (pH 7.3), and 400 μl cold 100% methanol (Optima* LC/MS, Fisher) and then 400 μl cold water (HPLC-Grade, Fisher) was added to cells. A total of 10 nmol norvaline (Sigma) was added as internal control, followed by 400 μl cold chloroform (HPLC-Grade, Fisher). Samples were vortexed three times over 15 min and spun down at top speed for 5 min at 4 °C. The top layer (aqueous phase) was transferred to a new Eppendorf tube, and samples were dried on Vacufuge Plus (Eppendorf) at 30 °C. Extracted metabolites were stored at −80 °C. For mass spectrometry-based analysis, the metabolites were resuspended in 70% acetonitrile and 5 μl used for analysis with a mass spectrometer. The mass spectrometer (Q Exactive, Thermo Scientific) was coupled to an UltiMate3000 RSLCnano HPLC. The chromatography was performed with 5 mM NH AcO (pH 9.9) and acetonitrile at a flow rate of 300 μl/min starting at 85% acetonitrile, going to 5% acetonitrile at 18 min, followed by an isocratic step to 27 min and re-equilibration to 34 min. The separation was achieved on a Luna 3u NH2 100A (150 × 2 mm) (Phenomenex). The Q Exactive was run in polarity switching mode (+3 kV/−2.25 kV). Metabolites were detected based on retention time (t ) and on accurate mass (± 3 p.p.m.). Metabolite quantification was performed as area-under-the-curve (AUC) with TraceFinder 3.1 (Thermo Scientific). Data analysis was performed in R (https://www.r-project.org/), and data were normalized to the number of cells. Relative amounts were log -transformed, median-centred and are shown as a heat map. To generate a model for pre-leukaemic B cell precursors expressing BCR–ABL1, BCR–ABL1 knock-in mice were crossed with Mb1-Cre deleter strain (Mb1-Cre; Bcr+/LSL-BCR/ABL) for excision of a stop-cassette in early pre-B cells. Bone marrow cells collected from Mb1-Cre; Bcr+/LSL-BCR/ABL mice cultured in the presence of IL-7 were primed with vehicle control or a combination of OAA (8 mmol/l), DMS (8 mmol/l) and insulin (210 pmol/l). Following a week of priming, cells were maintained and expanded in the presence of IL-7, supplemented with vehicle control or a combination of OAA (0.8 mmol/l) and DMS (0.8 mmol/l) for 4 weeks. Pre-B cells from Mb1-Cre; Bcr+/LSL-BCR/ABL mice expressed low levels of BCR–ABL1 tagged to GFP, and were analysed by flow cytometry for surface expression of GFP and CD19. The methylcellulose colony-forming assays were performed with 10,000 cells treated with vehicle control or metabolites. Cells were resuspended in mouse MethoCult medium (StemCell Technologies) and cultured on 3-cm diameter dishes, with an extra water supply dish to prevent evaporation. Images were taken and colony numbers counted after 14 days. For in vivo transplantation experiments, cells were treated with vehicle control or metabolites (OAA/DMS) for 6 weeks. One million cells were intravenously injected into sublethally irradiated (250 cGy) 6–8-week-old female NSG mice (n = 7 per group). Mice were randomly allocated into each group, and the minimal number of mice in each group was calculated by using the ‘cpower’ function in R/Hmisc package. No blinding was used. Each mouse was killed when it became terminally sick and showed signs of leukaemia burden (hunched back, weight loss and inability to move). The bone marrow and spleen were collected for flow cytometry analyses for leukaemia infiltration (CD19, B220). After 63 days, all remaining mice were killed and bone marrow and spleens from all mice were analysed by flow cytometry. Statistical analysis was performed using the Mantel–Cox log-rank test. All mouse experiments were in compliance with institutional approval by the University of California, San Francisco Institutional Animal Care and Use Committee. Following cytokine-independent proliferation, BCR–ABL1-transformed Lkb1fl/fl or AMPKa2fl/fl pre-B ALL cells were transduced with 4-OHT-inducible Cre or an empty vector control. For ex vivo deletion, deletion was induced 24 h before injection. For in vivo deletion, deletion was induced by 4-OHT (0.4 mg per mouse; intraperitoneal injection). Approximately 106 cells were injected into each sublethally irradiated (250 cGy) NOD/SCID mouse. Seven mice per group were injected via the tail vein. We randomly allocated 6–8-week-old female NOD/SCID or NSG mice into each group. The minimal number of mice in each group was calculated using the ‘cpower’ function in R/Hmisc package. No blinding was used. When a mouse became terminally sick and showed signs of leukaemia burden (hunched back, weight loss and inability to move), it was killed and the bone marrow and/or spleen were collected for flow cytometry analyses for leukaemia infiltration. Statistical analysis was performed by Mantel–Cox log-rank test. In vivo expansion and leukaemia burden were monitored by luciferase bioimaging. Bioimaging of leukaemia progression in mice was performed at the indicated time points using an in vivo IVIS 100 bioluminescence/optical imaging system (Xenogen). d-luciferin (Promega) dissolved in PBS was injected intraperitoneally at a dose of 2.5 mg per mouse 15 min before measuring the luminescence signal. General anaesthesia was induced with 5% isoflurane and continued during the procedure with 2% isoflurane introduced through a nose cone. All mouse experiments were in compliance with institutional approval by the University of California, San Francisco Institutional Animal Care and Use Committee. Data are shown as mean ± s.d. unless stated. Statistical significance was analysed by using Grahpad Prism software or R software (https://www.r-project.org/) by using two-tailed t-test, two-way ANOVA, or log-rank test as indicated in figure legends. Significance was considered at P < 0.05. For in vitro experiments, no statistical methods were used to predetermine the sample size. For in vivo transplantation experiments, the minimal number of mice in each group was calculated through use of the ‘cpower’ function in the R/Hmisc package. No animals were excluded. Overall survival and relapse-free survival data were obtained from GEO accession number GSE11877 (refs 35, 36) and TCGA. Kaplan–Meier survival analysis was used to estimate overall survival and relapse-free survival. Patients with high risk pre-B ALL (COG clinical trial, P9906, n = 207; Supplementary Table 10) were segregated into two groups on the basis of high or low mRNA levels with respect to the median mRNA values of the probe sets for the gene of interest. A log-rank test was used to compare survival differences between patient groups. R package ‘survival’ Version 2.35-8 was used for the survival analysis and Cox proportional hazards regression model in R package for the multivariate analysis (https://www.r-project.org/). The investigators were not blinded to allocation during experiments and outcome assessment. Experiments were repeated to ensure reproducibility of the observations. Gel scans are provided in Supplementary Fig. 1. Gene expression data were obtained from the GEO database accession numbers GSE32330 (ref. 12), GSE52870 (ref. 37), and GSE38463 (ref. 38). Patient-outcome data were derived from the National Cancer Institute TARGET Data Matrix of the Children’s Oncology Group (COG) Clinical Trial P9906 (GSE11877)35, 36 and from TCGA (the Cancer Genome Atlas). GEO accession details are provided in Supplementary Tables 9 and 10. ChIP–seq tracks for PAX5, IKZF1, EBF1 and TCF3 antibodies in a normal B-cell sample (ENCODE GM12878, UCSC genome browser) on INSR, GLUT1, GLUT3, GLUT6, HK2, G6PD, NR3C1, TXNIP, CNR2 and LKB1 gene promoter regions are shown in UCSC genome browser hg19. All other data are available from the corresponding author upon reasonable request.


News Article | November 30, 2016
Site: www.nature.com

The following strains of mice were used (see details in following sections): Swiss Webster females and males, C57BL/6J or C57BL6/N males, B6.Cg-Tg(Pou5f1-GFP)1Scho25 males, CD-1 females and males. 6–10-week-old female mice, and 6-week- to 6-month-old male mice were used. Animals were maintained on 12 h light–dark cycle and provided with food and water ad libitum in individually ventilated units (Techniplast at TCP, Laboratory Products at UCSF) in the specific-pathogen-free facilities at UCSF and at TCP. All procedures involving animals were performed in compliance with the protocol approved by the IACUC at UCSF, as part of an AAALAC-accredited care and use program (protocol AN091331-03); and according to the Animals for Research Act of Ontario and the Guidelines of the Canadian Council on Animal Care. Animal Care Committee reviewed and approved all procedures conducted on animals at TCP. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. No statistical methods were used to predetermine sample size estimate. Unless otherwise indicated, Swiss Webster females were mated to Swiss Webster males, or to C57BL/6 males homozygous for an Oct4-GFP transgene (B6.Cg-Tg(Pou5f1-GFP)1Scho)25. Preimplantation embryos were collected at indicated time-points after detection of the copulatory plug by flushing oviducts (E1.5–E2.5) or uteri (E3.5) of pregnant females using M2 medium (Zenith Biotech) supplemented with 2% BSA (Sigma). Subsequent embryo culture was performed in 4-well plates in 5% O , 5% CO at 37 °C in KSOMAA Evolve medium (Zenith Biotech) with 2% BSA and the following inhibitors, after optimization of concentrations: 200 nM INK128 (Medchem Express), 2.5 μM 10058-F4 (Sigma), 100 ng ml−1 cycloheximide (Amresco), 50 μM Anacardic Acid (Sigma). Other mTOR inhibitors (AZD2014, Everolimus and Rapamycin (Medchem Express) and RapaLink-1 (gift of K. Shokat)) and autophagy inhibitors chloroquine (Sigma) and SBI-0206965 (Medchem Express) were used at the indicated concentrations under same culture conditions. Diapause was induced as previously described9 after natural mating of Swiss Webster mice. Briefly, pregnant females were injected at E2.5 and EDG5.5 with 10 μg tamoxifen (intra-peritoneally) and at E2.5 only with 3 mg medroxyprogesterone 17-acetate (subcutaneously). Diapaused blastocysts were flushed from uteri in M2 media after 4 days of diapause at EDG8.5. Both surgical and non-surgical embryo transfers (NSET) were performed. For surgical transfers, superovulated CD-1 females were mated to C57BL/6J or C57BL6/N males and embryos were flushed at E3.5. Embryo culture (as described above) and surgical embryo transfer into the uteri of 2.5 days post coitus pseudopregnant CD-1 females previously mated with vasectomized CD-1 males was performed essentially as described26. For NSET, Swiss Webster females were mated to vasectomized CD-1 males and transfer was performed at E2.5 of surrogate according to manufacturer’s instructions (ParaTechs, Lexington). Before embryo transfer, embryos were cultured in KSOMAA, 2% BSA without inhibitor for 1 h. In the cases indicated (Extended Data Fig. 1a), Caesarian delivery was performed at E20, followed by fostering to Swiss Webster females. Coat colour markers (agouti versus albino) were used to distinguish transferred embryos after birth. ES cell derivation was performed as previously described27. Swiss Webster females were naturally mated to Swiss Webster-C57BL/6 males heterozygous for an Oct4-GFP transgene (B6.Cg-Tg(Pou5f1-GFP)1Scho)25. Blastocysts were collected by flushing uteri of pregnant females at E3.5, and were seeded on feeders either immediately or after culturing for 7 days in KSOMAA, 2% BSA, 200 nM INK128. Imaging of fluorescence driven by the Oct4-GFP transgene and alkaline phosphatase activity (VECTOR Red AP Substrate Kit, Vector Laboratories) was performed using a Leica DM IRB microscope. For immunofluorescence stainings, normal (E3.5), in vivo diapaused or ex vivo paused embryos were fixed in 4% paraformaldehyde for 15 min, washed with PBS and permeabilized with 0.2% Triton X-100 in PBS for 15 min. After blocking in PBS, 2.5% BSA, 5% donkey serum for 1 h, embryos were incubated overnight at 4 °C with the following primary antibodies in blocking solution: phospho-4EBP1 (Thr37/46, clone 236B4), phospho-Akt (Ser473), phospho-Ulk1 (Ser757), Nanog, c-Parp, c-Caspase3 (all from Cell Signaling), H3K4me3, H4K16ac, H4K5/8/12ac, H3K9me3 (all from Millipore), Oct4 and Rex1 (Santa Cruz Biotechnology) and H3K36me2 (Abcam). Embryos were washed in PBS-Tween20, 2.5% BSA, incubated with fluorescence-conjugated secondary antibodies (Invitrogen) for 2 h at room temperature, and mounted in VectaShield mounting medium with DAPI (Vector Laboratories). For labelling nascent transcription or translation, embryos were labelled in their respective culture medium for 20 min with EU (5-ethynyl uridine) or HPG (l-homopropargylglycine) following the manufacturer’s instructions for Click-iT RNA and protein labelling kits (Thermo Fisher Scientific). Imaging was performed using a Leica SP5 confocal microscope with automated z-stacking at 10 μm intervals. Cell Profiler Software28 was used for image quantification and Prism (Graphpad Software) was used for plotting data points. Datasets do not show similar variance between control and paused/diapaused embryos in all cases, therefore we applied Welch’s correction to the statistical analysis. E14 (from B. Skarnes, Sanger Institute), Oct4-GiP (from A. Smith, University of Cambridge) and v6.5 (from R. Blelloch, UCSF) ES cell lines were used. ‘Serum’ cells were cultured in ES-FBS medium: DMEM GlutaMAX with Na Pyruvate (Thermo Fisher Scientific), 15% FBS (Atlanta Biologicals), 0.1 mM non-essential amino acids, 50 U ml−1 penicillin/streptomycin (UCSF Cell Culture Facility), 0.1 mM EmbryoMax 2-Mercaptoethanol (Millipore) and 2,000 U ml−1 ESGRO supplement (LIF, Millipore). ‘2i’ cells were cultured in ES-2i medium: DMEM/F-12, Neurobasal medium, 1× N2/B27 supplements (Thermo Fisher Scientific), 1 μM PD0325901, 3 μM CHIR99021 (Selleck Chemicals), 50 μM Ascorbic acid (Sigma) and 2,000 U ml−1 ESGRO supplement (LIF) (Millipore). ‘Paused’ cells were cultured in ES-FBS medium containing 200 nM INK128 (Medchem). ES cells can also be paused in 2i medium, but the mTOR inhibitor needs to be removed at each passaging and reintroduced after colony formation to avoid major cell death (Extended Data Fig. 6a). The cell lines have not been authenticated. E14 and v6.5 tested negative for mycoplasma contamination. Oct4-GiP was not tested. R1 (129S1×129X1)29 and G4 (129S6×B6N)30 ES cells were used for morula aggregations. ES cells were cultured in DMEM containing 10% FBS (Wisent, lot-tested to support generation of germline chimaeras), 10% KnockOut Serum Replacement, 2 mM GlutaMAX, 1 mM Na Pyruvate, 0.1 mM non-essential amino acids, 0.1 mM 2-Mercaptoethanol (all Thermo Fisher Scientific), 1,000 U ml−1 LIF (Millipore). G4 ES cells were grown on MEF obtained from TgN(DR4)1Jae/J mice at all times except one passage on gelatinized tissue culture plates before aggregation. R1 ES cells were cultured in feeder-free conditions on gelatinized tissue culture plates. CD-1 (ICR) (Charles River) outbred albino stock was used as embryo donors for aggregation with ES cells and as pseudopregnant recipients. Details of morula aggregation can be found in26. Briefly, embryos were collected at E2.5 from superovulated CD-1(ICR) female mice. Zonae pellucidae of embryos were removed by the treatment with acid Tyrode’s solution (Sigma). ES cell colonies were treated with 0.05% Trypsin-EDTA to lift loosely connected clumps. Each zona-free embryo was aggregated with 10-15 ES cells inside depression well made in the plastic dish with an aggregation needle (BLS Ltd, Hungary) and cultured overnight in microdrops of KSOMAA covered by embryo-tested mineral oil (Zenith Biotech) at 37 °C in 94% air/6% CO . The following morning morulae and blastocysts were transferred into the uteri of E2.5 pseudopregnant CD-1(ICR) females previously mated with vasectomized males. Chimaeras were identified at birth by the presence of black eyes and later by the coat pigmentation. Chimeric males with more than 50% coat colour contribution were individually bred with CD-1(ICR) females. Germline transmission of ES cell genome was determined by eye pigmentation of pups at birth and later by the coat pigmentation. 1 × 106 cells were collected and lysed in RIPA buffer containing 1× Protease Inhibitor Cocktail, 1 mM PMSF, 5 mM NaVO and 5 mM NaF. Extracts were loaded into 4–15% Mini-Protean TGX SDS Page gels (Bio-Rad). Proteins were transferred to PVDF membranes. Membranes were blocked in 5% milk/PBS-T buffer for 30 min and incubated either overnight at 4 °C or 1 h at room temperature with the following antibodies: 4EBP1 (total or pThr37/46), S6K1 (total or pThr389), Akt (total or pSer473), mTOR (total or pSer2448) (Cell Signaling Technology), Gapdh (Millipore) and anti-rabbit/mouse secondary antibodies (Jackson Labs). Membranes were incubated with ECL or ECL Plus reagents and exposed to X-ray films (Thermo Fisher Scientific). 4 × 105 cells were seeded on 6-well plates. After overnight culture, cells were incubated for 1 h with 5-ethynyl-2-deoxyuridine (EdU) diluted to 10 μM in the indicated ES cell media. All samples were processed according to the manufacturer’s instructions (Click-iT EdU Alexa Fluor 488 Imaging Kit, Thermo Fisher Scientific). EdU incorporation was detected by Click-iT chemistry with an azide-modified Alexa Fluor 488. Cells were resuspended in EdU permeabilization/wash reagent and incubated for 30 min with FxCycle Violet Stain (Thermo Fisher Scientific). For EdU dilution experiments, ES cells were labelled for 90 min in serum, and afterwards were split into either serum or pause conditions; EdU analysis was done every 12 h for 48 h. Flow cytometric was performed on a LSRII flow cytometer (BD) and analysed using FlowJo v10.0.8. Data sets show similar variance. Total nascent transcription (Ethynyl Uridine, EU) or translation (l-homopropargylglycine, HPG) were assessed in ES cells using the Click-iT RNA Alexa Fluor 488 HCS Assay kit according to the manufacturer’s instructions (Thermo Fisher Scientific). Samples were analysed on a BD LSRII. Datasets show similar variance. After overnight culture on a 96-well plate, ESCs were washed once with PBS and trypsinized to single cells. They were resuspended in 10 μl of Annexin V diluted 1:100 in Binding Buffer (BioLegend) and incubated for 10 min in the dark. Cells were resuspended in 90 μl of binding buffer with Sytox Blue (Thermo Fisher Scientific) at 1:10,000. Data were collected on a BD LSRII. Datasets show similar variance. Three replicates were used for all samples. Freshly collected single-cell suspensions were sorted on a FACSAriaII cell sorter to collect 105 cells for each sample. Total RNA was isolated using the RNeasy kit (Qiagen). All samples were spiked-in with ERCC control RNAs (Thermo Fisher Scientific) following manufacturer’s recommendations. mRNA isolation and library preparation were performed on 250 ng total RNA from all samples using NEBNext Ultra Directional RNA library prep kit for Illumina (New England Biolabs). Samples were sequenced at The Center for Advanced Technology, UCSF on Illumina HiSeq2500. Single-end 50-bp reads were mapped to the mm10 mouse reference genome using Tophat2 (ref. 31) with default parameters. We used Cuffnorm and Cuffdiff with the gtf file from UCSC mm10 (Illumina iGenomes July 17, 2015 version) as transcript annotation to evaluate relative expression level of genes (fragments per kilobase of transcript per million mapped reads (FPKM)) and call differentially expressed genes. The alignment rate exceeded 96% in all of our samples, yielding ~40 million aligned reads per sample. Data from ref. 20 and ref. 6 were downloaded from GEO and ArrayExpress, respectively, and processed with the same pipeline as our data. The absolute abundance of mRNA transcripts was estimated using the ERCC92 RNA spike-in32. ERCC92 contains 92 synthetic sequences with lengths ranging from 250 to 2,000 bp and concentration ranging over several orders of magnitude. ERCC sequences were designed to mimic mammalian mRNA, but are not homologous to the mouse genome, ensuring their unique mappability. We aligned the reads to the 92 reference spike-in sequences and compared the abundance of these sequences between different samples. As ERCC sequence abundances followed a highly linear trend in all pairs of samples across at least 5 orders of magnitude (Pearson correlation coefficient larger than 99.7%, see Extended Data Fig. 7), we assessed the absolute abundance of mRNA as the number of mRNA fragments per kilobase of transcript per 10 thousand mapped reads of ERCC. The overall abundance of ERCC spike-in sequences in our samples varied from 0.3% to 0.5% of aligned reads. To facilitate better comparison between our data and data from ref. 20 and to reduce possible batch effects, in Fig. 4e, we followed the ‘batch mean-centering’ approach widely used in microarray gene expression data analysis for batch effect removal33. Specifically, we separately mean-centred the log (FPKM + 1) value of each gene by subtracting the mean log (FPKM + 1) across all our samples (serum, 2i and paused) and across the samples from ref. 20. The numerical values of the mean-centred expression may not be directly comparable across all samples, because they may still have different dynamic ranges in different batches. We therefore used 1 − Spearman correlation coefficient as distance in the hierarchical clustering. In Fig. 4c, we identified 5,992 genes with robust expression (cell-number-normalized expression value >50 in serum, 2i, or paused states). The cell-number-normalized expression value of each gene was standardized across the 9 samples by subtracting the mean and then dividing by the standard deviation. Hierarchical clustering was performed using the standardized expression values using Euclidean metric and average linkage. In Fig. 4e, in order to compare our samples with those from ref. 20, we used the log (FPKM + 1) value of each gene. Hierarchical clustering was performed using mean-centred (within each batch) expression values of 9,418 genes robustly expressed (FPKM >10) in at least one cell state (serum, 2i, paused, diapause EPI, E2.5 MOR, E3.5 ICM, E4.5 EPI, E4.5 PrE, E5.5 EPI, or ESC 2i/LIF). 1 − Spearman correlation coefficient was used as distance and average linkage was used. For each of the 3,772 gene ontology terms that are associated with at least 10 genes34, we defined the gene ontology term expression as the mean FPKM values of genes associated with the corresponding term. In Fig. 4f, the log fold-change of gene ontology term expressions between paused ES cells and serum ES cells was plotted on the y axis against that between various samples in ref. 20 and E4.5 EPI on the x axis. The Spearman correlation coefficient of the 3,772 gene ontology terms is indicated. Extended Data Figure 10a was generated similarly, but with the log fold-change of gene ontology term expressions between Myc DKO and wild-type cells from ref. 6 on the y axis. For each of the 281 KEGG pathways that contain at least 10 genes35, we defined the pathway expression as the mean FPKM values of genes associated with the corresponding pathway. In Extended Data Fig. 9b, the log fold change of pathway expressions between paused ES cells and serum ES cells was plotted on the y axis against that between various samples in ref. 20 and E4.5 EPI. The Spearman correlation coefficient of the 281 pathways was indicated. Extended Data Fig. 10c was generated similarly, but with the log fold change of pathway expressions between Myc dKO and wild-type cells from ref. 6 on the y axis. Custom codes used for the RNA-seq analysis are available upon request. RNA-seq data have been deposited in Gene Expression Omnibus (GEO) under accession number GSE81285. RNA-seq data from refs 6 and 20 are available under the accession numbers GSE74337 and E-MTAB-2958. The authors declare that all other data supporting the findings of this study are available within the paper and its supplementary information files.


News Article | April 20, 2016
Site: www.nature.com

Mouse TT2 ES cells were cultured on gelatin coating plates with recombinant LIF. ES cells were grown in DMEM supplemented with 15% fetal bovine serum, 1% non-essential amino acids, 2 mM l-glutamine, 1,000 units of mLIF (EMD Millipore), 0.1 mM β-mercaptoethanol (Sigma) and antibiotics. A doxycycline (Dox)-inducible Cas9–eGFP ES cell line was established with TT2 ESC. Guide RNA oligos (5′-accgAGTGCCTCTGGCATCCCGGG-3′, 5′-aaacCCCGGGATGCCAGAGGCACT-3′) were annealed and cloned into a pLKO.1-based construct (Addgene: 52628). Guide RNA virus was made in 293FT cells and infected inducible Cas9 ES cells. ES cells were first selected with Puromycin (1 μg ml−1) for two days, and Dox (0.5 μg ml−1) was added to induce Cas9–eGFP expression for 24 h. ES cells were then seeded at low density to obtain single-derived colonies. Then, 72 ES cell colonies were randomly picked up and screened by PCR-enzyme digestion that is illustrated in Extended Data Fig. 3a. PCR screening primers flanking guide RNA sequence were designed as following: 5′-AGGCAGATTTCTGAGTTCAAGG-3′ and 5′-TTTAGTCATGTGCTTGTCCAGG-3′. PCR products were digested by XmaI overnight at 37 degrees and separated on 2% agarose gel. A total of 8 mutants from which PCR products show resistance to XmaI digestion were subjected to DNA sequencing. Clones that harbour deletion and coding frame shift (premature termination mutation) were expanded and used in this study. Human Alkbh–Flag DNA sequence was inserted into pCW lenti-virus based vector (puromycin or hygromycin resistance). The amino acid of D233 was mutated to A by QuickChange Site-Directed Mutagenesis (QuikChange II XL Site-Directed Mutagenesis Kit, number 200521, Agilent) according to the manual. For Alkbh1 rescue experiment, wild-type and D233A mutated Alkbh1 constructs were introduced to Alkbh1 knockout ES cells, pCW-Hygromycin was chosen as control. After infections, the cells were selected with hygromycin at 200 μg ml−1 for 4 days, and then the cells were expanded to isolate genomic DNA for N6-mA dot blotting or other tests. The 293FT cells were transfected with pCW-hAlkbh1 and pCW-hAlkbh1-D233A mutant plasmids along with package plasmids of pMD2.G and pSPAX2. Culture medium was changed 10 h after transfection. The viruses were collected and concentrated 24 and 48 h after transfecction according to manufacturer’s instructions (Lenti-X Concentrator, Clontech). To establish stable expression of hAlkbh1 and hAlkbh1-D233A cell lines, 293FT cells were infected the corresponding virus, and then select with puromycin at 1 μg ml−1 for 4 days. The stable cell lines of hAlkbh1-293FT and D233A-293FT were expanded to purify the proteins according to the previous reported method with some modifications34. Briefly, M2 Flag antibody was added to the nuclear extract and incubated overnight, and then Dynabeads M-280 (sheep anti-mouse IgG, from Life Technology) was added to the above solution and incubated for 3–4 h. Subsequently, the beads were separated from the solution and washed clean with washing buffer34. Finally, the beads were eluted with 3× Flag peptides, followed by standard chromatography purification to 95% purity. Proteins were analysed by mass spectrometry. Demethylation assays were performed in 50 μl volume, which contained 50 pmol of DNA oligos and 500 ng recombinant ALKBH1 (or D233A mutant) protein. The reaction mixture also consisted of 50  μM KCl, 1mM MgCl , 50 μM HEPES (pH = 7.0), 2 mM ascorbic acid, 1 mM-KG, and 1 mM (NH ) Fe(SO ) .6H O. Reactions were performed at 37 degrees for 1 h and then stopped with EDTA followed by heating at 95 degrees for 5 min. Then the reaction product was subjected to dot blotting. Substrate sequences are listed in Supplementary Table 2. First, DNA samples were denatured at 95 degrees for 5 min, cooled down on ice, neutralized with 10% vol of 6.6 M ammonium acetate. Samples were spotted on the membrane (Amersham Hybond-N+, GE) and air dry for 5 min, then UV-crosslink (2× auto-crosslink, 1800 UV Stratalinker, STRATAGENE). Membranes were blocked in blocking buffer (5% milk, 1% BSA, PBST) for 2 h at room temperature, incubated with 6mA antibodies (202-003, Synaptic Systems, 1:1000) overnight at 4 degrees. After 5 washes, membranes were incubated with HRP linked secondary anti-rabbit IgG antibody (1:5,000, Cell Signaling 7074S) for 30 min at room temperature. Signals were detected with ECL Plus Western Blotting Reagent Pack (GE Healthcare). DNA samples were purified by standard N-ChIP protocol. 5 μg anti-H2A.X antibodies were used per 10 million cells. DNA (250 ng) from ChIP pull-down were converted to SMRTbell templates using the PacBio RS DNA Template Preparation Kit 1.0 (PacBio catalogue number 100-259-100) following manufacturer’s instructions. Control samples were amplified by PCR (18 cycles). In brief, samples were end-repaired and ligated to blunt adaptors. Exonuclease incubation was carried out in order to remove all unligated adapters. Samples were extracted twice (0.6× AMPure beads) and the final ‘SMRTbells’ were eluted in 10 μl embryoid bodies. Final quantification was carried out on an Agilent 2100 Bioanalyzer with 1 μl of library. The amount of primer and polymerase required for the binding reaction was determined using the SMRTbell concentration (ng μl−1) and insert size previously determined using the manufacturer-provided calculator. Primers were annealed and polymerase was bound using the DNA/Polymerase Binding Kit P4 (PacBio catalogue number 100-236-500) and sequenced using DNA sequencing reagent 2.0 (PacBio catalogue number 100-216-400). Sequencing was performed on PacBio RS II sequencer using SMRT Cell 8Pac V3 (PacBio catalogue number 100-171-800). In all sequencing runs, a 240 min movie was captured for each SMRT Cell loaded with a single binding complex. Base modification was detected using SMRT Analysis 2.3.0 (Pacific Biosciences), which uses previously published methods for identifying modified bases based on inter-pulse duration ratios in the sequencing data35. All calculations used the Mus musculus mm10 genome as a reference. For the detection of modified bases in individual samples, the RS_Modification_Detection.1 protocol was used with the default parameters. Modifications were only called if the computed modification QV was better than 20, corresponding to P < 0.01 (versus in silico model, Welch’s t-test). The in silico model considers the IPDs from the eight nucleotides 5′ through the three nucleotides 3′ of the site in question. Only the sites with a sequencing coverage higher than 25 fold were used for subsequent analyses. To assess the significance of the overlap between N6-mA sites by SMRT-ChIP and peaks from DIP-seq, intersection with DIP-seq peaks was analysed for each of the N6-mA site called by SMRT-ChIP. To assess if the overlap is higher than expected by random chance, a permutation based approach was used, in which we randomly shuffle the original mapping between “As” that meet coverage cutoff and their corresponding QV scores, and estimated the expected overlap by random chance. As preparation for PacBio RS II sequencing, these relatively short DNA fragments (200–1,000 base pairs on average) were made topologically circular, allowing each base to be read many times by a single sequencing polymerase. Thus, the coverage requirement for modification detection was achieved both by sequencing different fragments pulled down from the same genomic regions and by sequencing the same fragment with many passes. Of note, the SMRT-ChIP approach did not identify more N6-mA sites in Alkbh1 knockout cells than wild-type cells. Although the exact reason remain to be identified, our analysis showed that much fewer adenines are sequenced at a comparable coverage in Alkbh1 knockout cells than wild-type cells (Extended Data Fig. 5c and Extended Data Fig. 1b), presumably due to the difficulty of using native ChIP approach to isolate H2A.X-deposition regions from Alkbh1 knockout cells because of heterochromatinization. Genomic DNA from wild-type or knockout ES cells was purified with DNeasy kit (QIAGEN, 69504). For each sample, 5 μg DNA was sonicated to 200–500 bp with Bioruptor. Then, adaptors were ligated to genomic DNA fragments following the Illumina protocol. The ligated DNA fragments were denatured at 95 degree for 5 min. Then, the single-stranded DNA fragments were immunoprecipitated with 6 mA antibodies (5 μg for each reaction, 202-003, Synaptic Systems) overnight at 4 degrees. N6-Me-dA enriched DNA fragments were purified according to the Active Motif hMeDIP protocol. IP DNA and input DNA were PCR amplified with Illumina indexing primers. The same volume WT and KO DNA samples were subjected to multiplexed library construction and sequencing with Illumina HiSeq2000. After sequencing and filter, high quality raw reads were aligned to the mouse genome (UCSC, mm10) with bowtie (2.2.4, default)36. By default, bowtie searches for multiple alignments and only reports the best match; for repeat sequences, such as transposons, bowtie reports the best matched locus or random one from the best-matched loci. After alignment, N6-mA enriched regions were called with SICER (version 1.1, FDR <1.0 × 10−15, input DNA as control)37. Higher FDR cut-off could not further reduce N6-mA peak number. MACS2 was also used for peak calling, which generated similar results as SICER. Part of the data analysis was done by in-house customized scripts in R, Python or Perl. Genomic DNA samples from mouse fibroblast cells (where the endogenous N6-mA level is undetectable) were spiked with increasing amount of N6-mA-containing, or unmodified (control), oligonucleotides, and the N6-mA levels were determined by qPCR approach after DIP and library construction. Followed manufacture’s protocol (Active Motif 5mC MeDIP kit). The 5 mC data processed with MEDIPS in Bioconductor, and in-house scripts in R, Python or Perl. Native chromatin immunoprecipitation (N-ChIP) assay was performed as previously described. 10 million ES cells were used for each ChIP and massive parallel sequencing (ChIP-seq) experiment. Cell fractionation and chromatin pellet isolation were performed as described. Chromatin pellets were briefly digested with micrococcal nuclease (New England BioLabs) and the mononucleosomes were monitored by electrophoresis. Co-purified DNA molecules were isolated and quantified (100–200 ng for sequencing). Co-purified DNA and whole cell extraction (WCE) input genomic DNA were subject to library construction, cluster generation and next-generation sequencing (Illumina HiSeq 2000). The output sequencing reads were filtered and pre-analyzed with Illumina standard workflow. After filtration, the qualified tags (in fastq format) were aligned to the mouse genome (UCSC, mm10) with bowtie (2.2.4, default)36. Then, these aligned reads were used for peak calling with the SICER algorithm (input control was used as control in peak calling). H3K4Me1 and H3K27Ac ChIP-seq data were aligned to mouse genome (mm10) and peaks were called with SICER. H3K4Me1 and H3K27Ac enriched regions were defined as enhancers. Then, RSEG38 (mode 3) was to call the H3K27Ac differentiated regions. Decommissioned enhancers in KO cells are determined by H3K27Ac downregulation (compared to wild-type cells). Native ChIP-qPCR assay was used to validate H4K4Me3 at levels on gene promoters (Extended Data Fig. 8). All procedures were similar to what has been described in ChIP-seq experiments, except that the co-purified DNA molecules were diluted and subject to qPCR (histone H3K4Me3 antibodies: Abcam Ab8580). Real-time PCR was performed with SybrGreen Reagent (Qiagen, QuantiTect SYBR Green PCR Kit, Cat: 204143) and quantified by a CFX96 system (BioRAD, Inc.). RNA was extracted with miRNeasy kit (QIAGEN, 217004) and standard RNA protocol. The quality of RNA samples was measured using the Agilent Bioanalyzer. Then, RNA was prepared for sequencing using standard Illumina ‘TruSeq’ single-end stranded or ‘Pair-End’ mRNA-seq library preparation protocols. 50 bp of single-end and 100 bp of pair-end sequencing were performed on an Illumina HiSeq 2000 instrument at Yale Stem Cell Center Genomics Core. RNA-seq reads were aligned to mm9 with splicing sites library with Tophat39 (2.0.4, default parameters). The gene model and FPKM were obtained from Cufflink2. The differentially expressed genes were identified by Cuffdiff40 (2.0.0, default parameters). To make sure the normalization is appropriate, the data were also analysed with DESeq2 (default parameters), which generated similar results (Extended Data Fig. 4b). For transposons analysis, unique best alignment reads were used (alignment with bowtie (0.12.9), -m 1; or BWA) and calculated RPKM for each subfamily. For qPCR, the cDNA libraries were generated with First-strand synthesis kit (Invitrogen). Real-time PCR was performed with SybrGreen Reagent (Qiagen, QuantiTect SYBR Green PCR Kit, Cat: 204143) and quantified by a CFX96 system (BioRAD, Inc.). For Fig. 3d, the specific loci L1Md elements primers were designed and optimized based on ref. 27. For embryoid body differentiation experiment, feeder-free cultured ES cells were treated with 0.5% trypsin-EDTA free solution and resuspended with culture medium and counted. Then, cells were seeded at 200,000 cells per ml to Petri dishes with embryoid body differentiation medium (ESC medium without LIF and beta-ME). Medium was changed every 2 days. Histones were isolated in biological triplicate from wild-type and Alkbh1 knockout cells by acid-extraction and resolved/visualized by SDS–PAGE/Coomassie staining. The low molecular weight region of the gel corresponding to core histones was excised and de-stained. The excised gel region containing the histones was treated with d6-acetic anhydride to convert unmodified lysine resides to heavy acetylated lysines (45 Da mass addition) as reported in ref. 41. Following d6-acetic anhydride treatment, the gel region was subjected to in-gel trypsin digestion. Histone peptides were analysed with a Thermo Velos Orbitrap mass spectrometer coupled to a Waters nanoACQUITY LC system as detailed in ref. 42. Tandem mass spectrometric data was searched with Mascot for the following possible modifications: heavy lysine acetylation, lysine acetylation, lysine monomethylation, lysine dimethylation and lysine trimethylation. For each biological replicate, histone H2A was identified with 100% sequence coverage across K118/119 that revealed predominately no detectable lysine methylation DNA was digested with DNA Degradase Plus (Zymo Research) by following the manufacturer’s instructions with small modification. Briefly, the digestion reaction was carried out at 37 °C for 70 min in a 25 μl final volume containing 5 units of DNA Degradase Plus and 5 fMol of internal standard. Following digestion, reaction mixture was diluted to 110 μl and the digested DNA solution was filtered with a Pall NanoSep 3kDa filter (Port Washington, NY) at 8,000 r.p.m. for 15 min. After centrifugal filtration, the digested DNA solution was injected onto an Agilent 1200 HPLC fraction collection system equipped with a diode-array detector (Agilent Technologies, Santa Clara, CA). Analytes were separated by reversed-phase liquid chromatography using an Atlantis C T3 (150 × 4.6 mm, 3 μm) column. The column temperature was kept at 30 °C. For the purification of N6-mA, the mobile phases were water with 0.1% acetic acid (A) and acetonitrile with 0.1% acetic acid (B). The flow rate was 1.0 ml min−1 with a starting condition of 2% B, which was held for 5 min, followed by a linear gradient of 4% B at 20 min, 10% B at 30 min, followed by 6 min at 80% B, then re-equilibration at the starting conditions for 20 min. dA and 6-Me-dA eluted with retention times of 14.7 and 27.0 min, respectively. The amount of dA in samples was quantitated by the UV peak area (λ = 254 nm) at the corresponding retention time using a calibration curve ranging from 0.2 to 5 nMol dA on column. For the simultaneous purification of N3-Me-dC, N1-Me-dA, N3-Me-dA, N6-Me-dA and dA, the mobile phases were water with 5 mM ammonium acetate (A) and acetonitrile (B). The flow rate was 0.45 ml min−1 and the gradient elution program was set at following conditions: 0 min, 1% B; 2 min, 1% B; 40 min, 4% B; 60 min, 30% B; 65 min, 30% B; 65.5 min, 1% B, and 75 min, 1% B. N3-Me-dC, N1-Me-dA, N3-Me-dA, N6-Me-dA and dA eluted with retention times of 24.8, 25.0, 22.0, 60.2 and 54.2 min, respectively. The amount of dA in samples was quantitated by the UV peak area (λ = 254 nm) at the corresponding retention time using a calibration curve ranging from 0.9 to 7.2 nMol dA on the column. HPLC fractions containing target analyte were dried in a SpeedVac and reconstituted in 22 μl of D.I. water before LC-MS/MS analysis. LC-MS-MS analysis of N3-Me-dC, N1-Me-dA, N3-Me-dA and N6-Me-dA was performed on Ultra Performance Liquid Chromatography system from Waters Corporation (Milford, MA) coupled to TSQ Quantum Ultra triple-stage quadrupole mass spectrometer (Thermo Scientific, San Jose, CA). 20 μl of sample was introduced into mass spectrometry through a 100 mm × 2.1 mm HSS T3 column (Waters) at flow rate of 0.15 ml/min. Mobile phases were comprised of water with 0.1% formic acid (A) or acetonitrile (B). Elution gradient condition was set as following: 0 min, 1%B; 3 min, 1%B; 15 min, 7.5%B; 15.5 min, 1%B; 20 min, 1%B. Ionization was operated in positive mode and analytes were detected in selected reaction monitoring (SRM) mode. Specifically, 6-Me-dA and its internal standard were detected by monitoring transition ions of m/z = 266.1 to m/z = 150.1 and m/z = 271.1 to m/z = 155.1, respectively. Similarly, N3-Me-dC, N1-Me-dA and N3-Me-dA was detected by monitoring transition ions of m/z = 242.1 to m/z = 126.1, m/z = 266.1 to m/z = 150.1 and m/z = 266.1 to m/z = 150.1, respectively. Mass spectrometry conditions were set as following: source voltage, 3,000 V; temperature of ion transfer tube, 280 °C; skimmer offset, 0; scan speed, 75 ms; scan width, 0.7 m/z; Q1 and Q3 peak width, 0.7 m/z; collision energy, 17 eV; collision gas (argon), 1.5 arbitrary units. For quantification of N6-Me-dA, the linear calibration curves ranging from 1.5 to 750 fMol, were obtained using the ratio of integrated peak area of the analytical standard over that of the internal standard. The linear calibration curves for analysis of N3-Me-dC, N1-Me-dA and N3-Me-dA were obtained using integrated peak area of the analytical standard. N3-Me-dA is not commercial available and was prepared from the reaction between 3-methyladenine and deoxythymidine in the presence of nucleoside deoxyribosyltransferase II. The chemical identity of purified N3-Me-dA was confirmed by using an Agilent 1200 series Diode Array Detector (DAD) HPLC system coupled with Agilent quadrupole-time-of-flight (QTOF)-MS (Agilent Technologies, Santa Clara, CA). Electrospray ionization (ESI)-MS-MS spectrum of N3-Me-dA was obtained by in source fragmentation. One product ion was observed from MS/MS spectra of the protonated precursor ion of N3-Me-dA, resulting from the loss of the deoxyribosyl group. The accurate masses for parent and fragment ion are m/z = 266.1253 and m/z = 150.0774, with mass error 0.4 p.p.m. and 3.8 p.p.m., respectively. The method sensitivity for N3-Me-dC, N1-Me-dA, N3-Me-dA and N6-Me-dA was detected at 1.0 fmol, 1.6 fmol, 1.0 fmol and 1.6 fmol, respectively. In order to confirm the chemical identity of the N6-Me-dA isolated from HLPC purification, HPLC fractions containing N6-Me-dA was analysed by HPLC-QTOF-MS/MS. The chemical identity of N6-Me-dA in HPLC fractions was characterized on an Agilent 1200 series Diode Array Detector (DAD) HPLC system coupled with Agilent quadrupole-time-of-flight (QTOF)-MS (Agilent Technologies, Santa Clara, CA). HPLC separation was carried out on a C18 reverse phase column (Waters Atlantis T3, 3  μM, 150 mm × 2.1 mm) with a flow rate at 0.15 ml min−1 and mobile phase A (0.05% acetic acid in water) and B (acetonitrile). The gradient elution program was set at following conditions: 0 min, 1% B; 2 min, 1% B; 15 min, 30% B; 15.5 min, 1% B; and 25 min, 1% B. N6-Me-dA was eluted with retention times of 12.7 min. The electrospray ion source in positive mode with the following conditions were used: gas temperature, 200 °C; drying gas flow, 12 litres per min; nebulizer, 35 psi; Vcap, 4000 V; fragmentor, 175 V; skimmer, 67 V. Electrospray ionization (ESI)-MS-MS spectrum of N6-Me-dA isolated from genomic DNA was obtained by in source fragmentation. One product ion was observed from MS/MS spectra of the protonated precursor ion of N6-Me-dA, resulting from the loss of the deoxyribosyl group. The accurate masses for parent and fragment ion are m/z = 266.1245 and m/z = 150.0775, with mass error 3.0 p.p.m. and 3.1 p.p.m., respectively. The same MS/MS fragmentation spectra was obtained from analytical standard of N6-Me-dA. For in vitro demethylation assay, sample was treated with EDTA to remove Fe2+. The mixture was transferred to Amicon Ultra Centrifugal Filter (EMD Millipore Corporation, 10K MWCO), followed by spin at 11,000 r.p.m. and 4 °C for 14 min. The concentrated sample was wash three times by adding 500 μl DI-H2O, followed spin at 11,000 r.p.m. and 4 °C for 14 min. The washed sample was digested with DNA Degradase Plus (Zymo Research) by following manufacturer’s instruction with small modification. Briefly, the digestion reaction was carried out at 37 °C for 60 min in 60 μl final volume containing 0.17 units per μl of DNA Degradase Plus and 50 fmol of Internal Standard of N6-Me-dA. Following digestion, reaction mixture was filtered with a Pall NanoSep 3kDa filter (Port Washington, NY) at 10000g and room temperature for 10 min to remove enzyme. The LC-MS/MS conditions for the quantification of dA and N6-Me-dA were set the same as those for quantification of N6-Me-dA in in vivo samples. The linear calibration curves for quantification of dA and N6-Me-dA was obtained using the ratio of integrated peak area of the analytical standard over that of the internal standard of N6-Me-dA.


No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. Male heterozygous ythdf2+/− fish in the *AB background were custom made by ZGeneBio. TALEN mutagenesis was performed to mutate ythdf2 (Ensembl ENSDART00000127043) with L1 recognition sequence 5′-GGACCTGGCCAATCCCC-3′, R1 recognition sequence 5′-GGCACAGTAATGCCACC-3′, and spacer sequence 5′-TCCCAATTCAGGAATG-3′. Purchased fish were outcrossed to in-house wild-type *AB fish. Embryos were obtained from natural crosses, were raised under standard conditions, and were staged according to literature26. Embryos were reared at 28.5 °C and all experiments and observations were performed as close to this temperature as possible. Fish lines were maintained in accordance with AAALAC research guidelines, under a protocol approved by the University of Chicago IACUC (Institutional Animal Care & Use Committee). The open reading frame of zebrafish ythdf2 was purchased from Open Biosystems (clone 5601005) and subcloned into a pCS2+ vector using restriction enzyme sites of BamHI and XhoI. The resulting vector was linearized by HindIII and used as a template for ythdf2 probe preparation. Antisense digoxigenin (DIG) RNA probes were generated by in vitro transcription using standard reagents and methods. In situ hybridization protocol was followed essentially as previously reported27. All experiments were repeated at least once from biological samples. Control and ythdf2 morpholinos (5′-TGGCTGACATTTCTCACTCCCCGGT-3′) were obtained from Gene Tools (Oregon). 3 ng of either control or gene-specific morpholino was injected into *AB wild-type embryos at the one-cell stage. GFP and mCherry were subcloned into pCS2+ vectors and linearized by NotI. GFP-m6A, GFP-A, and mCherry-capped and polyadenylated mRNA was generated by in vitro transcription using mMessage mMachine SP6 kit (Thermo Fisher) and Poly(A) tailing kit (Thermo Fisher) according to the manufacturer’s protocol. Products were purified with the MEGAclear transcription clean-up kit (Thermo Fisher) and used for injections directly. For GFP-m6A, we spiked 6 nmol m6ATP into the 100 nmol ATP supplied in the transcription reaction, in order to ensure that less than 0.3% of GFP mRNAs are without m6A on average. (GFP mRNA is 942 nt; each mRNA has 1.89 m6A on average.) 35 pg of either GFP reporter mRNA and 10 pg of mCherry mRNA were injected together in 1.25 nl into embryos at the one-cell stage. ythdf2 mRNA containing the ythdf2 5′ UTR and a 3′ Flag tag, which was used to rescue the mutant phenotype and validate the knockdown efficiency of ythdf2 MO, was constructed in pCS2+ vector (forward primer: 5′-CGTACGGATCCTGTCTGATCTGCAGCTGTAG-3′; reverse primer: 5′-CGATGCTCGAGTTACTTGTCATCGTCGTCCTTGTAATCTATTCCAGATGGAGCAAGGC-3′) and prepared in the same way as mCherry mRNAs. Antibodies used in this study are listed below in the format of name (application; catalogue number; supplier): mouse anti-Flag HRP conjugate (Western; A5892; Sigma), rabbit anti-m6A (m6A-seq and m6A-CLIP-seq; 202003; Synaptic Systems), rabbit anti-histone H3 (IF; ab5176; Abcam), and anti-rabbit Alexa Fluor 488 (IF; ab150077; Abcam). All images were observed with a Leica MZFLIII microscope and captured with a Nikon D5000 digital camera using Camera Control Pro (Nikon) software. For fluorescent microscopy, standard ET-GFP and TXR LP filters (Leica) were used. For bright field imaging of live embryos, only saturation was adjusted and was adjusted identically for all images. For fluorescent imaging of live embryos, no image processing was performed. For fluorescent imaging of fixed embryos, contrast and exposure were adjusted for all to obtain the lowest amount of background while preserving the morphology of all visible nuclei. All experiments were repeated at least once from biological samples. To compare the total amount of DNA in wild-type and mutant embryos at different time points during the MZT, 10 embryos per time point per condition were dechorionated and pipetted into standard DNA lysis buffer. The number of embryos in each tube was counted twice to ensure uniformity. Proteinase K was added to 100 μg ml−1 and the embryos were incubated for 4 h at ~55 °C with occasional mixing. Proteinase K was inactivated by a 10-min incubation at 95 °C and the DNA was then phenol-chloroform-extracted, ethanol-precipitated, and resuspended in 100 μl Tris (pH 8.5) and 1 mM EDTA using standard procedures. Double-stranded DNA content was measured with NanoDrop. Three biological replicates (comprised of the offspring of three different fish mating pairs of the appropriate genotype) were measured for each time point for both the control and experimental samples. Biological replicates were averaged together to determine the average DNA amount per time point per genotype and to compute standard errors of the mean. All DNA values were normalized to that of wild-type embryos at 2.5 h.p.f. Embryos were collected into standard 2× protein sample buffer with added β-mercaptoethanol and protease inhibitors and immediately put on ice for a few minutes. The embryo mixtures were carefully but thoroughly pipetted up and down to dissolve and homogenize the embryos, and then samples were heated at 95 °C for 5 min and frozen at −80 °C. Before use, samples were again heated for 5 min and then centrifuged at 12,000 r.p.m. to remove debris. Supernatants were loaded into a 10-well, 1.5 mm Novex 4–20% Tris-Glycine Mini Protein Gel (Thermo Fisher) with 6 embryos per well. The gel was transferred onto a nitrocellulose membrane using iBlot2 gel transfer system (Thermo Fisher) set to P3 for 7 min with iBlot2 mini gel transfer stacks (Thermo Fisher). Membranes were blocked in 5% BSA, 0.05% Tween-20 in PBS for 1 h, and then incubated overnight at 4 °C with anti-Flag–HRP conjugate (Sigma) diluted 1:10,000 in 3% BSA. Proteins were visualized using the SuperSignal West Pico Luminol/Enhancer solution (Thermo Fisher) in FluorChem M system (ProteinSimple). mRNA isolation for LC-MS/MS: total RNA was isolated from zebrafish embryos with TRIzol reagent (Invitrogen) and Direct-zol RNA MiniPrep kit (Zymo). mRNA was extracted by removal of contaminating rRNA using RiboMinus Eukaryote Kit v2 (Thermo Fisher) for two rounds. Total RNA isolation for RT–qPCR: we followed the instruction of Direct-zol RNA MiniPrep kit (Zymo) with DNase I digestion step. Total RNA was eluted with RNase-free water and used for RT–qPCR directly. 100–200 ng of mRNA was digested by nuclease P1 (2 U) in 25 μl of buffer containing 10 mM of NH OAc (pH 5.3) at 42 °C for 2 h, followed by the addition of NH HCO (1 M, 3 μl, freshly made) and alkaline phosphatase (0.5 U). After an additional incubation at 37 °C for 2 h, the sample was diluted to 50 μl and filtered (0.22 μm pore size, 4 mm diameter, Millipore), and 5 μl of the solution was injected into LC-MS/MS. Nucleosides were separated by reverse-phase ultra-performance liquid chromatography on a C18 column with on-line mass spectrometry detection using an Agilent 6410 QQQ triple-quadrupole LC mass spectrometer in positive electrospray ionization mode. The nucleosides were quantified by using the nucleoside to base ion mass transitions of 282 to 150 (m6A), and 268 to 136 (A). Quantification was performed in comparison with the standard curve obtained from pure nucleoside standards running on the same batch of samples. The ratio of m6A to A was calculated on the basis of the calibrated concentrations9. Total RNA was isolated from fish embryos collected at different time points with TRIzol reagent and Direct-zol RNA MiniPrep kit. For each time point, ~200 embryos were collected to ensure RNA yield and that samples were representative. mRNA was further purified using RiboMinus Eukaryote Kit v2. RNA fragmentation was performed by sonication at 10 ng μl−1 in 100 μl RNase-free water using Bioruptor Pico (Diagenode) with 30 s on/off for 30 cycles. m6A-immunoprecipitation (IP) and library preparation were performed according to the previous protocol17. Sequencing was carried out on Illumina HiSeq 2000 according to the manufacturer’s instructions. Additional high-throughput sequencing of zebrafish methylome was carried out using a modified m6A-seq method, which is similar to previously reported methods19, 20. Briefly, total RNA and mRNA were purified as previously described for m6A-seq. Purified mRNA (1 μg) was mixed with 2.5 μg of affinity purified anti-m6A polyclonal antibody (Synaptic Systems) in IPP buffer (150 mM NaCl, 0.1% NP-40, 10 mM Tris-HCl (pH 7.4)) and incubated for 2 h at 4 °C. The mixture was subjected to UV-crosslinking in a clear flat-bottom 96-well plate (Nalgene) on ice at 254 nm with 0.15 J for 3 times. The mixture was then digested with 1 U μl−1 RNase T1 at 22 °C for 6 min followed by quenching on ice. Next, the mixture was immunoprecipitated by incubation with protein-A beads (Invitrogen) at 4 °C for 1 h. After extensive washing, the mixture was digested again with 10 U μl−1 RNase T1 at 22 °C for 6 min followed by quenching on ice. After additional washing and on-bead end-repair, the bound RNA fragments were eluted from the beads by proteinase K digestion twice at 55 °C for 20 and 10 min, respectively. The eluate was further purified using RNA clean and concentrator kit (Zymo Research). RNA was used for library generation with NEBNext multiplex small RNA library prep kit (NEB). Sequencing was carried out on Illumina HiSeq 2000 according to the manufacturer’s instructions. Total RNA was isolated from wild-type and mutant fish embryos collected at different time points with TRIzol reagent and Direct-zol RNA MiniPrep kit. For each time points, ~20 embryos were collected to ensure RNA yield and that samples were representative. mRNA was further purified using RiboMinus Eukaryote Kit v2. RNA fragmentation was performed using Bioruptor Pico as described previously. Fragmented mRNA was used for library construction using TruSeq stranded mRNA library prep kit (Illumina) according to manufacturer’s protocol. Sequencing was carried out on Illumina HiSeq 2000 according to the manufacturer’s instructions. All samples were sequenced by Illumina Hiseq 2000 with single-end 50-bp read length. The deep-sequencing data were mapped to zebrafish genome version 10 (GRCz10). (1) For m6A-seq, reads were aligned to the reference genome (danRer10) using Tophat v2.0.14 (ref. 28) with parameter -g 1–library-type = fr-firststrand. RefSeq Gene structure annotations were downloaded from UCSC Table Browser. The longest isoform was used if the gene had multiple isoforms. Aligned reads were extended to 150 bp (average fragments size) and converted from genome-based coordinates to isoform-based coordinates, in order to eliminate the interference from introns in peak calling. The peak-calling method was modified from published work18. To call m6A peaks, the longest isoform of each gene was scanned using a 100 bp sliding window with 10 bp step. To reduce bias from potential inaccurate gene structure annotation and the arbitrary usage of the longest isoform, windows with read counts less than 1 out of 20 of the top window in both m6A-IP and input sample were excluded. For each gene, the read counts in each window were normalized by the median count of all windows of that gene. A Fisher exact test was used to identify the differential windows between IP and input samples. The window was called as positive if the FDR < 0.01 and log (enrichment score) ≥ 1. Overlapping positive windows were merged. The following four numbers were calculated to obtain the enrichment score of each peak (or window): (a) reads count of the IP samples in the current peak or window, (b) median read counts of the IP sample in all 100 bp windows on the current mRNA, (c) reads count of the input sample in the current peak/window, and (d) median read counts of the input sample in all 100 bp windows on the current mRNA. The enrichment score of each window was calculated as (a × d)/(b × c). (2) For m6A-CLIP-seq, after removing the adaptor sequence, the reads were mapped to the reference genome (danRer10) using Bowtie2. Peak calling method was similar to the previous study19. Briefly, mutations were considered as signal and all mapped reads were treated as background. A Gaussian Kernel density estimation was used to identify the binding regions. The motif analysis was performed using HOMER29 to search motifs in each set of m6A peaks. The longest isoform of all genes was used as background. (3) For mRNA-seq, reads were mapped with Tophat and Cufflink (v2.2.1) was used to calculate the FPKM of each gene to represent their mRNA expression level30. (4) For fish gene group categorization, we used the input mRNA-seq data from m6A-seq. FPKM of all genes were first normalized to the highest value of five time points, with only genes with FPKM >1 analysed. Then Cluster3.0 (ref. 31) was used to divide all genes into six clusters, with the parameters: adjust data – normalize genes; k-means cluster – organize genes, 6 clusters, 100 number; k-means – Euclidean distance. The result clustered file with clustered number was merged with original FPKM values, imported and processed in R, and plotted in Excel. (5) For GO analysis, the list of target genes was first uploaded into DAVID32, 33 and analysed with functional annotation clustering. The resulting file was downloaded and extracted with GO terms and corresponding P values. The new list (contains GO terms with P < 0.01) was imported into REVIGO34 and visualized with the interactive graph, which was used as the final output figures. Methylated genes (at each time point) were defined as overlapped gene targets between m6A-seq and m6A-CLIP-seq. Ythdf2-regulated genes were defined as overlapped gene targets between the lists of the top 20% upregulated genes in both ythdf2 knockout and MO-injected samples. The most stringent Ythdf2 target genes at 4 h.p.f. (135) were defined in the main text, as overlapped genes of methylated genes at 4 h.p.f. (3,237) and Ythdf2-regulated genes at 4 h.p.f. (876). All the raw data and processed files have been deposited in the Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo) and accessible under GSE79213. A summary of sequenced samples and processed FPKM data are included as Supplementary Data 2. One set of representative experiment results from at least two independent experiments were shown where applicable. Quantitative reverse-transcription PCR (RT–qPCR) was performed to assess the relative abundance of mRNA. All RNA templates used for RT–qPCR were pre-treated with on-column DNase I digestion in the purification step. RT–qPCR primers were designed to span exon-exon junctions to only detect mature mRNA. RT–qPCR was performed by using SuperScript III one-step RT–PCR system (Thermo Fisher) with 50–100 ng total RNA template. Actb1 was used as an internal control as it showed relative invariant expression during the studied time period according to pilot RT–qPCR data. P values were determined using two-sided Student’s t-test for two samples with equal variance. *P < 0.05; **P < 0.01; ***P < 0.001. The sequences of primers used in this study are listed below: actb1: forward 5′-CGAGCAGGAGATGGGAACC-3′, reverse 5′-CAACGGAAACGCTCATTGC-3′; buc: forward 5′-CAAGTTACTGGACCTCAGGATC-3′, reverse 5′-GGCAGTAGGTAAATTCGGTCTC-3′; zgc:162879: forward 5′-TCCTGAATGTCCGTGAATGG-3′, reverse 5′-CCCTCAGATCCACCTTGTTC-3′; mylipa: forward 5′-CCAAACCAGACAACCATCAAC-3′, reverse 5′-CACTCCACCCCATAATGCTC-3′; vps26a: forward 5′-AAATGACAGGAATAGGGCCG-3′, reverse 5′-CAGCCAGGAAAAGTCGGATAG-3′; tdrd1: forward 5′-TACTTCAACACCCGACACTG-3′, reverse 5′-TCACAAGCAGGAGAACCAAC-3′; setdb1a: forward 5′-CTTCTCAACCCAAAACACTGC-3′, reverse 5′-CTATCTGAAGAGACGGGTGAAAC-3′; mtus1a: forward 5′-TGGAGTATTACAAGGCTCAGTG-3′, reverse 5′-TTATGACCACAGCGACAGC-3′; GFP: forward 5′-TGACATTCTCACCACCGTGT-3′, reverse 5′-AGTCGTCCACACCCTTCATC-3′. High-throughput sequencing data that support the findings of this study have been deposited at GEO under the accession number GSE79213. All the other data generated or analysed during this study are included in the article and Supplementary Information.


News Article | February 15, 2017
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Hyperdrive, a connected car startup focused on making people better drivers through gamification, has announced the creation of its Advisory Board. The board, designed to guide the company on its initiatives including product development, partnerships, user acquisition and financing, is comprised of four industry leaders in their respective domains of expertise. Hyperdrive is proud to announce Jason Fass, Gabriel Griego, Jeff Han and Darrell Rodriguez as the four founding members of its Advisory Board. “I am extremely pleased to have Darrell, Gabriel, Jason, and Jeff join our Advisory Board. Their blue-chip experience will be invaluable as we grow toward becoming the de-facto standard for helping people drive better,” said Jason Wiener, CEO of Hyperdrive. Hyperdrive was founded in 2016. It followed Mr. Wiener’s participation in the Toyota Onramp 2015 Smart Mobility Challenge which he won the grand prize with his idea, M-iRoad, enabling Toyota’s 3-wheeled smart mobility vehicle, the i-Road, to adapt its configuration based on the driver’s use case. More info about Onramp 2015 and Mr. Wiener’s winning submission can be found at http://bit.ly/MiRoad2015 Hyperdrive Advisors Jason Fass: Jason Fass is a results-driven industry leader boasting over 15 years’ experience in team building, staff management, and business development. His career is deeply rooted in the technology sector, where he has held positions in product management for leading companies such as Apple, Cisco, and Jawbone. Aided by his determination and unwavering focus, he has led product, marketing, sales and business development teams within Fortune 500 companies and high-growth, venture-backed startups. Observing a critical market opportunity for sensor-based products, Fass founded Zepp Labs in 2012. The world’s fastest growing Sports Technology company, Zepp has since revolutionized the way individuals practice, play, and experience sports. Jason received his Bachelor’s Degree from the University of Florida and an MBA from Pepperdine University. When he’s not revolutionizing the tech world, Jason can be found exploring the trails of Demo Forest and UCSC, bombing around on his mountain bike, kiteboarding at 3rd Ave, and spending time with his family. Gabriel Griego: Gabriel Griego knows that a company’s success starts with visibility. With over 25 years of experience building brands and boosting company growth, he demonstrates an exceptional capacity for sales and marketing leadership and strategic brand development. He holds a Bachelor’s Degree in Political Science from the University of California, Berkeley. Working with sports and athletic powerhouse companies such as PowerBar (where he started his career), Game Ready, Wesabe and AlterG, he has held a variety of esteemed positions including Vice President of Sales and Marketing, and Director of Brand Communication. After establishing a successful brand in the sports medicine industry, Gabriel became the Vice President of Marketing for AlterG, makers of the Anti-Gravity Treadmill. Gabriel's initiatives helped grow AlterG to over 3,000 installations and over 1 Million consumers using it, worldwide, establishing them as a trusted name for healthcare professionals and consumers alike. With his instinct for business development and track record for facilitating unprecedented company growth, Gabriel is an invaluable team member in this exciting new venture. On the weekends he hikes in the East Bay hills with his kids, ventures up to Tahoe to snowboard or plays music with friends. Jeff Han: Jeff Han is a seasoned technology leader with a flair for creating one-of-a-kind product experiences. Currently working on his third startup, Jeff was previously General Manager for Surface Hub at Microsoft, where he led a world-class interdisciplinary team of display hardware, manufacturing, software engineers and interaction designers from research lab prototype to acquisition to a billion dollar enterprise communication business. Lauded for his 2006 TED Talk appearance, Jeff is infamous for the dramatic live demonstration of a multi-touch interaction interface that preceded both Apple’s iPhone and Microsoft’s Surface table. Driven by a passion for technology and an entrepreneurial spirit, he continues to challenge limitations and push boundaries in the field. Jeff studied electrical engineering and computer graphics at Cornell University, where he worked on CU-SeeMe, an early internet multi-party videoconferencing application, which subsequently led to his first startup in the late 90s, and has ever since been dedicated to researching and productizing advanced user interfaces. In 2009, Jeff received the Smithsonian’s National Design Award in the inaugural category of interaction design. In 2008, Jeff was named one of the “Time 100” most influential persons of the year. Jeff continues to be involved with and contributes to the research communities. Darrell Rodriguez: Darrell Rodriguez, interactive and entertainment media executive, has brought his acumen for efficient, results-driven production and business strategies to some of the biggest creative enterprises in the video gaming industry. With over twenty-five years of experience, Rodriguez has served as President of LucasArts, Chief Creative Officer at International Game Technologies, and CEO of Trendy Entertainment. He proudly spearheaded the growth of LucasArts’ most successful internally-developed game launch in the last three decades. Rodriguez earned his Undergraduate Degree in Landscape Architecture from Cal Poly San Luis Obispo, and his MBA from the University of California, Berkeley. His natural charisma, technical expertise, and can-do attitude have met with soaring success in a number of key management and oversight roles in the entertainment and technology industries. Additionally, Rodriguez directed development and administration of titles within the Medal of Honor, Command & Conquer, SSX, NBA Street, FIFA Street and Marvel franchises as Chief Operating Officer of EA Los Angeles and Assistant Chief Operating Officer of EA Canada. A gamer guru at heart, he is proud to be riding Hyperdrive’s wave into the future.


No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. Metaphase cells were obtained by treating cells with Karyomax (Gibco) at a final concentration of 0.01 μg ml−1 for 1–3 h. Cells were collected, washed in PBS, and resuspended in 0.075 M KCl for 15–30 min. Carnoy’s fixative (3:1 methanol:glacial acetic acid) was added dropwise to stop the reaction. Cells were washed an additional three times with Carnoy’s fixative, before being dropped onto humidified glass sides for metaphase cell preparations. For ECdetect analyses, DAPI was added to the slides. Images in the main figures were captured with an Olympus FV1000 confocal microscope. All other images were captured at a magnification of 1,000× with an Olympus BX43 microscope equipped with a QiClick cooled camera. FISH was performed by adding the appropriate DNA FISH probe onto the fixed metaphase spreads. A coverslip was added and sealed with rubber cement. DNA denaturation was carried out at 75 °C for 3–5 min and the slides were allowed to hybridize overnight at 37 °C in a humidified chamber. Slides were subsequently washed in 0.4× SSC at 50 °C for 2 min, followed by a final wash in 2× SSC containing 0.05% Tween-20. Metaphase cells and interphase nuclei were counterstained with DAPI, a coverslip was applied and images were captured. The NCI-60 cell line panel (gift from A. Shiau, obtained from NCI) was grown in RPMI-1640 with 10% FBS under standard culture conditions. Cell lines were not authenticated, as they were obtained from the NCI. The PDX cell lines were cultured in DMEM/F-12 medium supplemented with glutamax, B27, EGF, FGF and heparin. Lymphoblastoid cells (gift from B. Ren) were grown in RPMI-1640, supplemented with 2 mM glutamine and 15% FBS. IMR90 and ALS6-Kin4 (gift from J. Ravits and D. Cleveland) cells were grown in DMEM/F-12 supplemented with 20% FBS. Normal human astrocytes (NHA) and normal human dermal fibroblasts (NHDF) were obtained from Lonza and cultured according to Lonza-specific recommendations. Cell lines were not tested for mycoplasma contamination. Tissues were obtained from the Moores Cancer Center Biorepository Tissue Shared Resource with IRB approval (#090401). All samples were de-identified and patient consent was obtained. Additional tissue samples that were obtained were approved by the UCSD IRB (#120920). DNA was sonicated to produce 300–500 bp fragments. DNA end repair was performed using End-it (Epicentre), DNA library adapters (Illumina) were ligated and the DNA libraries were amplified. Paired-end next-generation sequencing was performed and samples were run on the Illumina Hi-Seq using 100 cycles. Cells were collected and washed with 1 × cold PBS. Cell pellets were resuspended in buffer 1 (50 mM Tris pH 7.5, 10 mM EDTA, 50 μg ml−1 RNase A), and incubated in buffer 2 (1.2% SDS) for 5 min on ice. DNA was acidified by the addition of buffer 3 (3 M CsCl, 1 M potassium acetate, 0.67 M acetic acid) and incubated for 15 min on ice. Samples were centrifuged at 14,000g for 15 min at 4 °C. The supernatant was added to a Qiagen column and briefly centrifuged. The column was washed (60% ethanol, 10 mM Tris pH 7.5, 50 μM EDTA, 80 mM potassium acetate) and eluted in water. Metaphase cells were dropped onto slides and visualized with DAPI. Coverslips were removed and slides washed in 2 × SSC, and subsequently treated with 2.5% trypsin, and incubated at 25 °C for 3 min. Slides were then washed in 2 × SSC, DNase solution (1 mg ml−1) was applied to the slide and cells were incubated at 37 °C for 3 h. Slides were washed in 2 × SSC and DAPI was again applied to the slide to visualize DNA. In Fig. 2a, b the violin plots represent the distribution of ecDNA counts in different sample types. In order to compare the ecDNA counts between the different samples, we use a one-sided Wilcoxon rank-sum test, where the null hypothesis assumes that the mean ecDNA-count ranks of the compared sample types are equal. There is a wide variation in the number of ecDNA across different samples and within metaphases of the same sample. We want to estimate and compare the frequency of samples containing ecDNA for each sample type. We label a sample as being ecDNA positive by using the pathology standard: a sample is deemed to be ecDNA positive if we observe ≥ 2 ecDNA in ≥ 2 out of 20 metaphase images. Therefore, we ensure that every sample contains at least 20 metaphases. We define indicator variable X  = 1 if metaphase image j in sample i has ≥2 ecDNA elements, X  = 0 otherwise. Let n be the number of metaphase images acquired for sample i. We assume that X is the outcome of the jth Bernoulli trial, where the probability of success P is drawn at random from a beta distribution with parameters determined by ∑ X . Formally, We model the likelihood of observing k successes in n = 20 trials using the binomial density function as: Finally, the predictive distribution p(k), is computed using the product of the binomial likelihood and beta prior, modelled as a ‘beta–binomial distribution’29. We model the probability of sample i being ecDNA positive with the random variable Y so that: The expected value of Y is: Let T be the set of samples belonging to a certain sample type t, for example, immortalized samples. We estimate the frequency of samples under sample t containing ecDNA (bar heights on Fig. 2c, d) as assuming independence among samples i ∈ T. For any α or β  = 0, we assign them a sufficiently small ε. For more detail, please see Supplementary Information 1. We construct binary ecDNA-presence distributions, based on the ecDNA counts, such that an image with ≥ 2 ecDNA is represented as a 1, and 0 otherwise. In order to compare the ecDNA presence between the different samples, we use a one-sided Wilcoxon rank-sum test using the binary ecDNA-presence distributions, where the null hypothesis assumes the mean ranks of the compared sample types are equal. The software applies an initial coarse adaptive thresholding30, 31 on the DAPI images to detect the major components in the image with a window size of 150 × 150 pixels, and T = 10%. Components over 3,000 pixels and 80% of solidity are masked, and small components discarded. Weakly connected components of the remaining binary image are computed to find the separate chromosomal regions. Connected components over a cumulative pixel count of 5,000 are considered as candidate search regions, and their convex hull with a dilation of 100 pixels are added into the ecDNA search region. Following the manual masking and verification of the ecDNA search region, a second finer adaptive thresholding with a window size of 20 × 20 pixels and T = 7% is performed. Components that are greater than 75 pixels are designated as non-ecDNA structures and their 15-pixel neighbourhood is removed from the ecDNA search region. Any component detected with a size less than or equal to 75 pixels and greater than or equal to 3 pixels inside the search region is detected as ecDNA. For more detail, please see Supplementary Information 2. We sequenced 117 tumour samples including 63 cell lines, 19 neurospheres and 35 cancer tissues with coverage ranging from 0.6× to 3.89× and an additional 8 normal tissues as controls. See Extended Data Fig. 4 for the coverage distribution across samples. We mapped the sequencing reads from each sample to the hg19 (GRCh37) human reference genome32 from the UCSC genome browser33 using BWA software version 0.7.9a (ref. 34). We inferred an initial set of copy-number variants (CNVs) from these mapped sequence samples using the ReadDepth CNV software35 version 0.9.8.4 with parameters FDR = 0.05 and overDispersion = 1. We downloaded CNV calls for 11,079 paired tumour–normal samples covering 33 different tumour types from TCGA. We applied similar filtering criteria to ReadDepth output and TCGA calls to eliminate false copy number amplification calls from repetitive genomic regions and hotspots for mapping artefacts. We used the filtered set of CNV calls from ReadDepth as input probes for AmpliconArchitect which revealed the final set of amplified intervals and the architectures of the amplicons. See Supplementary Information 3 for more details. We developed a novel tool AmpliconArchitect, to automatically identify connected amplified genomic regions and reconstruct plausible amplicon architectures. For each sample, AmpliconArchitect takes as input an initial list of amplified intervals and whole-genome sequencing paired-end reads aligned to the human reference. It implements the following steps to reconstruct the one or more architectures for each amplicon present in the sample: (1) use discordant read-pair alignments and coverage information to iteratively visit and extend connected genomic regions with high copy numbers; (2) for each set of connected amplified regions, segment the regions based on depth of coverage using a mean-shift segmentation to detect copy-number changes and discordant read-pair clusters to identify genomic breaks; (3) construct a breakpoint graph connecting segments using discordant read-pair clusters; (4) compute a maximum-likelihood network to estimate copy counts of genomic segments; and (5) report paths and cycles in the graph that identify the dominant linear and circular structures of the amplicon (see also Supplementary Information 3). We compared our sample set against TCGA samples to test the assumption that the genomic intervals amplified in our sample set are broadly representative of a pan-cancer dataset, by comparing against TCGA samples. Here, we deal with an abstract notation to represent different datasets and describe a generic procedure to compare amplified regions. Consider a set of K samples. For any k ∈ [1,..., K], let S denote the set of amplified intervals in sample k. Let c be the cancer subtype for sample k. We compare S against TCGA samples with subtype c. Let T denote the set of all genomic regions which are amplified in at least 1% of TCGA samples of subtype c. For each interval t ∈ T, let f denote its frequency in TCGA samples of subtype c. We define a match score The cumulative match score for all samples is defined as: To compute the significance of statistic D, we do a permutation test. We generate N random permutations of the TCGA intervals for subtype c and estimate the distribution of match scores of our sample set against the random permutations. We choose a random assignment of locations of all intervals in T, while retaining their frequencies. For the jth permuted set T , we computed the cumulative match score D relative to our sample set. Thus the significance of overlap between amplified intervals in our sample set and the TCGA set is estimated by the fraction of random permutations with D  > D. Computing 1 million random permutations generated exactly one permutation breaching the TCGA score D, implying a P ≤ 10−6. We compared the rank correlation of the most frequent oncogenes in our sample set with the top oncogenes as reported by TCGA pan-cancer analysis in ref. 20. We identified 14 oncogenes occurring in 2 or more samples of our sample set and compared these to the top 10 oncogenes from the TCGA pan-cancer analysis. We found that 7 out of the top 10 oncogenes were represented in our list of 14 oncogenes. Considering 490 oncogenes in the COSMIC database, the significance of observing 7 or more oncogenes in common in the two datasets is given by the hypergeometric probability We found high similarity between amplicon structures of biological replicates (for example, Extended Data Fig. 8). We estimate the probability of common origin between two samples by measuring the pairwise similarity between amplicon structures. In reconstructing the structures (Supplementary Information 3), we identify a set of locations representing change in copy number and we use the locations of change in copy number to estimate the similarity in amplicon structures. Let L be the total length of amplified intervals. These intervals are binned into windows of size r, resulting in bins. We use a segmentation algorithm that determines if there is a change in copy number in any bin, within a resolution of r = 10,000 bp (see meanshift in coverage: Supplementary Information 3.2.). Note that this is an overestimate, because with split-reads and high-density sequencing data, we can often get the resolution down to a few base pairs. Let S and S represent the set of bins with copy-number changes in the two samples, respectively. S and S are selected from a candidate set of locations N . Under the null hypothesis that S is random with respect to S , we expect I = S  ∩ S to be small. Let m = min {|S1|, |S2|}, and M = max {|S |, |S |}. A P value is computed as follows: When looking at GBM39 replicates (Extended Data Fig. 8), we find that all replicates displaying EGFR ecDNA are similar to each other. Comparing replicates in row 1 and row 2 among |N | = 129 bins (1.29 Mb), |S1| = 5 corresponding to row 1 (ecDNA sample), |S2| = 6 corresponding to row 2 (ecDNA sample) and intersection set size |I| = 5, we compute that the P value for observing such structural similarity by random chance is 2.18 × 10−8, which is the highest P value among all ecDNA replicate pairs. In addition, we compare the replicates containing EGFR in ecDNA with the culture containing EGFR in HSR. Among |N | = 129 bins, |S1| = 6 corresponding to row 2 (ecDNA), |S2| = 4 corresponding to row 4 (HSR), the intersection set has size |I| = 4 intervals giving a P value of 1.98 × 10−5, which gives the highest P value among the 3 ecDNA replicates compared to the HSR culture, suggesting a common origin. Consider an initial population of N cells, of which N cells contain a single extra copy of an oncogene. We model the population using a discrete generation Galton–Watson branching process23. In this simplified model, each cell in the current generation containing k amplicons (amplifying an oncogene) either replicates with probability b to create the next generation, or dies with probability 1 − b to create the next generation. We set the selective advantage In other words, cells with k copies of the amplicon stop dividing after reaching a limit of M amplicons. Otherwise, they have a selective advantage for 0 < k ≤ M , where the strength of selection is described by f (k), as follows: Here, s denotes the selection coefficient, and parameters m and α are the ‘mid-point’, and ‘steepness’ parameters of the logistic function, respectively. Initially, f (k) grows linearly, reaching a peak value of f (k) = 1 for k = M . As the viability of cells with large number of amplicons is limited by available nutrition36, f (k) decreases logistically in value for k > M reaching f (k) → 0 for k ≥ M . We model the decrease by a sigmoid function with a single mid-point parameter m so that f (m) = 0.5. The ‘steepness’ parameter α is automatically adjusted to ensure that max{1 – f (M ), f (M )} → 0. The copy-number change is affected by different mechanisms for extrachromosomal (ecDNA) and intrachromosomal (HSR) models. In the ecDNA model, the available k amplicons are on ecDNA elements which replicate and segregate independently. We assume complete replication of ecDNA elements so that there are 2k copies which are partitioned into the two daughter cells via independent segregation. Formally, the daughter cells end up with k and k amplicons respectively, where By contrast, in the intrachromosomal model, the change in copy number happens via mitotic recombination, and the daughter cell of a cell with k amplicons will acquire either k + 1 amplicons or k − 1 amplicons, each with probability P . With probability 1−2P , the daughter cell retains k amplicons. See Supplementary Information 4 for more details. AmpliconArchitect is available for use online at: https://github.com/virajbdeshpande/AmpliconArchitect. ECdetect will be available upon request. Whole-genome sequencing data are deposited in the NCBI Sequence Read Archive (SRA) under Bioproject (accession number: PRJNA338012). DAPI and FISH metaphase images are available for download on figshare at https://figshare.com/s/ab6a214738aa43833391.


News Article | February 15, 2017
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Formerly directed by legendary Slugs tennis coach, Bob Hansen, the UC Santa Cruz Nike Tennis Camp has become one of the top junior tennis camps in California. This summer, under new directorship, four weeks of junior overnight and day camps and two weekends of adult tennis camps will be offered. Directing two of the junior camps and both adult camps is newly-appointed UC Santa Cruz Head Women's Tennis Coach, Amy Jensen, and local area pros and college players. The two other weeks of junior camps will be directed by Stanford Associate Men's Coach, Brandon Coupe, and Stanford Volunteer Assistant Coach, Francis Sargeant. “We are thrilled to partner with renowned Northern California coach, Amy Jensen, who brings with her years of experience both as a high-level player and coach, and Brandon Coupe, a high-character guy with a proven track record developing his players into successful student-athletes," states Wendy Shpiz, vice president of Nike Tennis Camps. "We're confident both coaches will deliver memorable camp experiences with great instruction and lots of fun.” The UC Santa Cruz Nike Tennis Camp offers junior Overnight, Extended Day (8:30am-9:00pm) and Day (8:30am-5:30pm) tennis camps for boys and girls, ages 9-18, with All Skills, Tournament Training, and High School training programs. Two weekends of adult tennis camps are also offered for men and women, ages 18+ of all skill levels. Overlooking the Pacific Ocean, the beautiful UCSC campus offers campers the opportunity to play in a serene environment with 12 ocean view outdoor courts, playing fields, a gymnasium, and Olympic-sized pool. Campers, parents, and coaches interested in the 2017 Nike Tennis Camp at UC Santa Cruz can get more information at http://www.ussportscamps.com/tennis or by calling 1-800-NIKE-CAMP (645-3226). US Sports Camps (USSC), headquartered in San Rafael, California, is America's largest sports camp network and the licensed operator of Nike Sports Camps. The company has offered summer camps since 1975 with the same mission that defines it today: to shape a lifelong enjoyment of athletics through high quality sports education and skill enhancement.


All animal studies were approved by the Institutional Animal Care and Use Committee of WIS. C57BL/6 male mice aged 2 months were housed under reverse-phase cycle, and fasted for 5 h starting at 07:00. All mice were anaesthetized with an intraperitoneal injection of a ketamine (100 mg kg−1) and xylazine (10 mg kg−1) mixture. For smFISH, liver tissues were collected and fixed in 4% paraformaldehyde for 3 h; incubated overnight with 30% sucrose in 4% paraformaldehyde and then embedded in OCT. 7 μm cryosections were used for hybridization. Mouse liver cells for RNA-seq were extracted from four mice and each smFISH result was based on at least 2 mice. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. No statistical methods were used to predetermine sample size. Probe library constructions, hybridization procedures and imaging conditions were previously described7, 31, 32. All smFISH probe libraries (Supplementary Table 8), with the exception of glutamine synthetase (Glul), were coupled to Cy5. To detect cell borders, alexa fluor 488 conjugated phalloidin (Rhenium A12379) was added to the GLOX buffer wash32. Portal node was identified morphologically on DAPI images on the basis of bile ductile, central vein was identified using smFISH for Glul in TMR, included in all hybridizations. For zonation profiles, images were taken as scans spanning the portal node to the central vein. To compute single-cell mRNA concentration of our landmark genes, we used imageM32 to detect dots, extracted the sum of all dot intensities of the smFISH signal for each segmented cell within the first 3 μm of the Z-stack, and divided by the segmented cell volume to obtain the intensity concentration per cell. As the landmark genes were highly abundant, dots often coalesced, leading to underestimates of the true cellular gene expression when counting dots, whereas the sum of dot intensities was found to better capture the full dynamic range33. The gene expression distributions of the landmark genes were based on at least 800 cells from at least 10 lobules and 2 mice per gene. To validate the predicted zonation, we imaged 20 additional genes on at least 10 lobules and 2 mice per gene (Extended Data Fig. 5b). Profiles for Igfbp1, Cyp27a1, Glud1, Cyp8b1, Igfbp2, Pck1, Cps1, Arg1, G6pc, Uox, Igf1, Pigr and Acly were generated by counting dots and dividing the number of dots in radial layers spanning the porto–central axis by the layer volume. Profiles for Cyp1a2, Rnase4, Gsta3, Ugt1a1, Hamp, Mup3 and Apoa1 were generated by quantifying the average background-subtracted intensity of the smFISH images at sequential lobule layers. Mouse hepatocytes were isolated by a modification of the two-step collagenase perfusion method of Seglen34 from 5 h fasted, 2-month-old male C57BL/6 mice. Single cells were isolated from four mice. Digestion step was performed with Liberase Blendzyme 3 recombinant collagenase (Roche Diagnostics) according to the manufacturer’s instruction. Isolated hepatocytes were taken directly to sorting. Cells were sorted with SORP-FACSAriaII machine using a 130 μm nozzle. Dead cells were excluded on the basis of 5 μg ml−1 propidium iodide (Invitrogen) incorporation. Cells adhering to each other (that is, doublets) were eliminated on the basis of pulse width. We used a 25,000–250,000 FSC-A gate. For three of the mice a 1.5 neutral density (ND) filter and was used to enrich for hepatocytes, whereas for the fourth mouse a 1 ND filter was used to enrich for non-parenchymal cells. Hepatocytes were sorted into 384-well cell capture plates containing 2 μl of lysis solution and barcoded poly(T) reverse-transcription (RT) primers for scRNA-seq17. Barcoded single-cell capture plates were prepared with a Bravo automated liquid handling platform (Agilent) as described previously17. Four empty wells were kept in each 384-well plate as a no-cell control during data analysis. Immediately after sorting, each plate was spun down to ensure cell immersion into the lysis solution, snap frozen on dry ice and stored at −80 °C until processed. Single-cell libraries were prepared, as described in ref. 17. Briefly, mRNA from cells sorted into MARS-seq capture plates were barcoded and converted into cDNA and pooled using an automated pipeline. The pooled sample was then linearly amplified by T7 in vitro transcription and the resulting RNA was fragmented and converted into sequencing ready library by tagging the samples with pool barcodes and Illumina sequences during ligation, reverse transcription and PCR. Each pool of cells was tested for library quality and concentration was assessed as described in ref. 17. Mapping of single-cell reads to mouse reference genome (mm9) was done using HISAT version 0.1.6 (ref. 35) and reads with multiple mapping positions were excluded. Reads were associated with genes if they were mapped to an exon defined by a reference set obtained from the UCSC genome browser. Exons of different genes that share genomic position on the same strand were considered as a single gene with concatenated gene symbol. Corrected read counts were evaluated based on unique molecular identifiers (UMI), as described in ref. 17. Formalin-fixed and paraffin-embedded livers were sectioned (5 μm), deparaffinized, rehydrated and permeabilized in 0.3% hydrogen peroxide in PBS for 20 min at room temperature. Antigen retrieval was carried out by boiling in citrate buffer (pH 6) for 10 min in a pressure cooker. Sections were blocked with normal goat serum (normal goat serum Vector labs, S-100) and incubated for 1 h at room temperature with anti-Cyp8b1 (Santa Cruz, sc-23515, diluted 1:50 in PBS). Detection was performed by incubation with a peroxidase-conjugated anti-rabbit antibody (Zytomed Systems, ZUC032), using DAB as chromogen. P values for comparison of porto–central bias of different groups (Wnt+ and Wnt−, Ras+ and Ras− and hypoxia-induced and repressed) were obtained by Wilcoxon rank-sum tests on the porto–central bias of all genes, defined as the difference between the zonation profile values in layers 1 and 9 divided by the mean over all layers. The concise set of non-monotonic genes (Extended Data Fig. 10a) were defined as genes with Kruaskal-Wallis q < 0.01, and zonation peak between layers 3 and 7 which is 10% higher than the maximal expression value between layers 1 and 9. For GO annotation enrichment of the non-monotonically zonated genes we used GOrilla36. To assess the effect of the number of landmark genes on the reconstruction error we ran our probabilistic algorithm to predict the zonation patterns using each possible subset of our landmark genes (a total of 26 − 1 = 63 combinations). We then calculated the reconstruction accuracy defined as one minus the mean of sum-squared differences between the profiles predicted using a specific subset and the profiles obtained when using the entire panel of landmark genes. In addition, we calculated in a similar manner, the accuracy of predicting uniform expression of all genes throughout the lobule (without any landmark gene, Extended Data Fig. 4). For every set of gene combinations of the same size, we plotted the median accuracy of the set. To evaluate which features of landmark genes affect their contribution to the spatial reconstruction we computed the extent of zonation of each gene as the log -based entropy of its smFISH-measured zonation profile. High entropy denotes genes that are uniformly expressed throughout the lobule axis and thus carry little spatial information. In addition, we measured the mean coefficient of variation of all cells belonging to each layer and averaged over all layers to obtain a measure of intra-layer cell-to-cell variability. We assigned a score for each landmark gene as the average of the ratios in reconstruction error among all pairs of combinations that did not include the gene and those that did. High scores indicate that the landmark gene strongly improves reconstruction when added to combinations of other landmark genes (Extended Data Fig. 4). To identify potential ploidy-specific regulation of the non-monotonically zonated genes we segmented individual hepatocytes and classified them in situ according to nuclear sizes and number of nuclei per cell following the method of ref. 25. We next compared the expression of each 8n or 4n hepatocyte with an adjacent hepatocyte of lower ploidy, residing within 30 μm of the centre of the cell. We used Wilcoxon signed-rank test to obtain P values for these comparisons (Extended Data Fig. 10e). The liver secretome (Extended Data Fig. 9b) was based on ref. 37. To identify spatial division of labour within specific pathways we examined the 62 mouse metabolic pathway maps from KEGG database38. We extracted from each pathway map pairs of enzymes that have either a shared substrate, a shared product, or pairs in which a product of one enzyme is the substrate of the second enzyme. Upon obtaining all pairs we further selected those in which both enzymes are expressed in the liver (mean expression higher than 5 × 10−6 UMI per cell) and are significantly zonated (Kruskal–Wallis q < 0.2). For each such pair we assigned a score that reflects the spatial discordance in the zonation profiles of the both enzymes: Where E is the mean-normalized zonation profile of enzyme i and x is the layer at which E peaks. Negative scores indicate that the two enzymes peak at different layers, and the quantity of the score reflects the expression differences between the two layers. An interaction between two enzymes that peak at the same layer has a score of zero. In addition, for every triplet of connected enzymes E →E →E we also included the indirect pair E →E . To produce a concise set of significantly zonated spatially-discordant enzyme pairs we randomly drew 100,000 pairs of enzymes among all expressed liver enzymes (731 genes) and recomputed their scores. For our concise set we selected pairs with a score lower than the 10th percentile score among the randomized set of pairs (−0.14, Supplementary Table 6). Although the direction of flow of metabolites is hard to systematically determine for these pathways (pericentral direction through blood versus periportal direction through bile), this database serves as a resource for future focused exploration of liver spatial division of labour of individual pathways. The algorithm for spatial reconstruction of zonation profiles is described in detail in the Supplementary Information. Matlab code used for the inference is available upon request. Data generated during this study have been deposited in Gene Expression Omnibus (GEO) with the accession code GSE84490. Data referenced in Fig. 3d and Extended Data Fig. 7a are available on request from the authors. Data referenced in Fig. 3e and Extended Data Fig. 8a, b are available in supplementary table 1 of ref. 20. Data referenced in Fig. 3f and Extended Data Fig. 8c, d are available in supplementary table 4b of ref. 22. Data referenced in Fig. 3g and Extended Data Fig. 8e, f are available in GEO with the accession code GSE3129 (ref. 23); data referenced in Extended Data Fig. 7b are available in GEO with the accession code GSE49707 (ref. 10); data referenced in Extended Data Fig. 7c, d are available in GEO with the accession code GSE68806 (ref. 2). Data referenced in Extended Data Fig. 9b are available in table 6 of ref. 37.

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