Brea, CA, United States
Brea, CA, United States

Beckman Coulter Inc., is an American company that makes biomedical laboratory instruments. Founded by Caltech professor Arnold O. Beckman in 1935 as National Technical Laboratories to commercialize a pH meter that he had invented, the company eventually grew to employ over 10,000 people, with $2.4 billion in annual sales by 2004. Its current headquarters are in Brea, California. Wikipedia.


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Alunni-Fabbroni M.,Beckman Coulter | Sandri M.T.,Italian National Cancer Institute
Methods | Year: 2010

Circulating Tumour Cells (CTCs) can be released from the primary tumour into the bloodstream and may colonize distant organs giving rise to metastasis. The presence of CTCs in the blood has been documented more than a century ago, and in the meanwhile various methods have been described for their detection. Most of them require an initial enrichment step, since CTCs are a very rare event. The different technologies and also the differences among the screened populations make the clinical significance of CTCs detection difficult to interprete. Here we will review the different assays up to now available for CTC detection and analysis. Moreover, we will focus on the relevance of the clinical data, generated so far and based on the CTCs analysis. Since the vast majority of data have been produced in breast cancer patients, the review will focus especially on this malignancy. © 2010 Elsevier Inc. All rights reserved.


Grant
Agency: European Commission | Branch: FP7 | Program: MC-ITN | Phase: PEOPLE-2007-1-1-ITN | Award Amount: 6.21M | Year: 2009

Understanding and controlling of interfacial phenomena in multiphase fluid dynamics remains one of the main challenges at the crossroad of Mathematics, Physics, Chemistry and Engineering. Examples include film flows, spreading and dewetting of (complex) liquids including suspensions, polymer solutions, liquid crystals, colloids and biofluids. Such systems are central for technological advances in the chemical, pharmaceutical, environmental and food industries and are crucial for the development of Microfluidics and Nanostructuring. The level of detail required by multi-scale flows with interfacial phenomena renders full-scale analyses practically impossible. In fact, such approaches often fail to describe even the results of simple experiments. MULTIFLOW will develop low-dimensional models capable of describing complex interfacial flows coupling different time and length scales. Based on the nature of the dominant mechanism, the scientific program will examine three generic classes: from nano- to macroscale, these are dominated by surface forces, reaction-diffusion, and advection. They are also affected by phase transitions, capillarity, chemical reactions, complex rheology and self-structuring. The strength of the network is its integration of all scientific disciplines, technical skills and expertise necessary to support the multi-scale nature of the envisaged research topics. By fostering the mobility and interdisciplinarity of a strong group of early-stage researchers through a set of well-defined objectives and effective networking between different institutions, disciplines and industries, the ultimate goals of this network are: (i) to create a multi-disciplinary, highly innovative and intersectorial training pool in the field of multi-scale interfacial fluid dynamics; (ii) to generate new tools and techniques for the theoretical-numerical-experimental investigation of such flows, which will be made available to the wider European Community.


Grant
Agency: European Commission | Branch: FP7 | Program: CP | Phase: FoF.NMP.2010-3 | Award Amount: 7.04M | Year: 2010

IMPRESS targets the development of a technological injection moulding platform for serial production of plastic components incorporating micro or nano scale functional features. The platform will be based on the gathering of up to date and most advanced facilities based on three main modules, each of them being a tool box including several building blocks: - a tool manufacturing module involving different technologies of micro- nano direct manufacturing, from top-down to bottom-up such as self-assembling, - an injection moulding module including equipments fitted with up to date hardware to improve replication quality and capability, - an intelligence module dedicated to advanced process control and online metrology integration. Beside this large panel of facilities, three case studies have been selected (biology, health and energy), each of them requiring a specific and well defined surface micro-nano texturation. These case studies cover a very large range of nano-feature (from 100nm up to 1 m) and component size (from 1 cm2 up to 1000 cm2). They will serve to qualify the capabilities of the different building blocks and will allow (i) to select the most suitable building blocks as of application requirements (ii) to learn about the platform working and (iii) to anticipate the technological future of the platform. Finally, a technico-economic tool for decision making will be developed based on the IMPRESS case studies and thus to allow end-users to select the most appropriate configuration regarding the end product manufacturing requirements. Further to the IMPRESS case studies, the performances of the platform will be validated through a satellite group. IMPRESS technological platform will accelerate the production and the time to market of micro nano-scale functional feature on multi-component devices in order to obtain an important reduction of needed supply chain space, technological risk and manufacturing costs of next generation plastic part products.


Patent
Beckman Coulter | Date: 2016-03-23

A device coupling comprising:a female portion 220 of the device coupling having a recess 228;a shaft 218 having a tapered surface 214 formed at one end 216 of the shaft 218, the tapered surface 214 being insertable into the recess 228 of the female portion 220; anda screw 212 configured to pull the shaft 218 into the recess 228 of the female portion 220 and join the shaft 218 to the female portion 220.


Patent
Beckman Coulter | Date: 2011-05-05

A diagnostic instrument having a cellular analysis system capable of running standardized immune monitoring panels. The system could include an automated and integrated specimen sampling method through a continuous flow process. The instrument could include a probe washer station, scheduler, cassette autoloader, bar coding system, and/or containment area common interface. An improved optimization test is proposed for instrument and flow cytometer quality assurance. The proposed method analyzes population separation for measuring instrument performance and/or sample quality. Such a method may also use population separation for measuring sample and/or run quality.


Patent
Beckman Coulter | Date: 2015-08-26

A diagnostic instrument is disclosed. The diagnostic instrument may have a highly efficient probe washer station and/or may be able to sense whether there is a tube septum on a specimen tube to be sampled. The instrument may also be able to determine where the bottom of the tube is located. The probe washer station may have a flow of saline that is used to wash both the internal cavity and the external circumference of the probe.


Patent
Beckman Coulter | Date: 2011-05-05

A diagnostic instrument is disclosed. The diagnostic instrument may have a highly efficient probe washer station and/or may be able to sense whether there is a tube septum on a specimen tube to be sampled. The instrument may also be able to determine where the bottom of the tube is located. The probe washer station may have a flow of saline that is used to wash both the internal cavity and the external circumference of the probe.


News Article | February 21, 2017
Site: globenewswire.com

Dublin, Feb. 21, 2017 (GLOBE NEWSWIRE) -- Research and Markets has announced the addition of Jain PharmaBiotech's new report "Therapeutic Drug Monitoring - Technologies, Markets, and Companies" to their offering. This report deals with therapeutic drug monitoring, a multi-disciplinary clinical specialty, aimed at improving patient care by monitoring drug levels in the blood to individually adjust the dose of drugs for improving outcome. TDM is viewed as a component of personalized medicine that interacts with several other disciplines including pharmacokinetics and pharmacogenetics. One chapter is devoted to monitoring of drugs of abuse (DoA). Various technologies used for well-known DoA are described. A section on drug abuse describes methods of detection of performance-enhancing drugs. TDM market is analyzed from 2015 to 2025 according to technologies as well as geographical distribution. Global market for DoA testing was also analyzed from 2016 to 2026 and divided according to the area of application. Unmet needs and strategies for development of markets for TDM are discussed. The report contains profiles of 27 companies involved in developing tests and equipment for drug monitoring along with their collaborations. The text is supplemented with 18 tables, 6 figures and 190 selected references from literature. Benefits of this report: - Up-to-date one-stop information on therapeutic drug monitoring - Description of 27 companies involved with their collaborations in this area - Market analysis 2016-2026/ - Market values in major regions - Strategies for developing markets for therapeutic drug monitoring - A selected bibliography of 190 publications - Text is supplemented by 18 tables and 6 figures Who should read this report? - Biotechnology companies developing assays and equipment for drug monitoring - Reference laboratories providing drug monitoring services - Pharmaceutical companies interested in companion tests for monitoring their drugs - Clinical pharmacologists interested in integrating therapeutic drug monitoring with pharmacogenetics for development of personalized medicine Key Topics Covered: Executive Summary 1. Introduction Definitions Historical Landmarks in the development of TDM Pharmacology relevant to TDM Pharmacokinetics Pharmacodynamics Pharmacogenetics Pharmacogenomics Pharmacoproteomics Drug receptors Protein binding Therapeutic range of a drug Variables that affect TDM Indications for TDM Multidisciplinary nature of TDM 2. Technologies for TDM Introduction Sample preparation Proteomic technologies Mass spectrometry Liquid chromatography MS Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Combining capillary electrophoresis with MS Gas-liquid chromatography Tissue imaging mass spectrometry New trends in sample preparation Pressure Cycling Technology Desorption electrospray ionization imaging High Performance Liquid Chromatography (HPLC) Ultra performance LC TDM using dry blood spots Analysis of dried blood spots for drugs using DESI Quantitative analysis of drugs in dried blood spot by paper spray MS Immunoassays Enzyme-linked immunosorbent assay Cloned Enzyme Donor Immunoassay Enzyme Multiplied Immunoassay Technique Fluorescence Polarization Immunoassay Particle Enhanced Turbidimetric Inhibition Immunoassay Radioimmunometric assays Biosensors Nanosensors Biochips & Microarrays Introduction Microchip capillary electrophoresis Phototransistor biochip biosensor Microchip-based fluorescence polarization immunoassay for TDM Cellular microarrays Microfluidics for TDM Lab-on-a-chip Micronics' microfluidic technology Rheonix CARD technology Nano-interface in a microfluidic chip Levitation of nanofluidic drops with physical forces Nanoarrays Nanobiotechology NanoDx Biomarkers Applications of biomarkers in drug safety studies Genomic technologies for toxicology biomarkers Proteomic technologies for toxicology biomarkers Metabonomic technologies for toxicology biomarkers Integration of genomic and metabonomic data to develop toxicity biomarkers Toxicology studies based on biomarkers Biomarkers of hepatotoxicity Biomarkers of nephrotoxicity Cardiotoxicity Neurotoxicity Biomarkers in clinical trials Molecular diagnostics 3. Drug Monitoring Instruments Introduction Description of important instruments AB SCIEX instruments AB SCIEX LC/MS/MS Abbott instruments ARCHITECT c16000 ARCHITECT c4000 ARCHITECT c8000 ARCHITECT ci16200 Integrated System ARCHITECT ci4100 Integrated System ARCHITECT ci8200 integrated with the ARCHITECT i2000SR ARCHITECT i1000SR ARCHITECT i4000SR AxSYM Agilent's 6400 Series Triple Quadrupole LC/MS Alfa Wassermann's ACE Alera AMS Diagnostics' LIASYS Awareness Technology's STAT FAX 4500 Beckman Coulter instruments Beckman Coulter Unicel Series AU5800 automated chemistry systems AU480 Binding Site ESP600 bioMerieux Mini Vidas Carolina BioLis 24i Chromsystems' HPLC instruments Grifols Triturus ABX Pentra 400 Medica EasyRA Nova Biomedical Critical Care Xpress Ortho Clinical Diagnostics' VITROS® family of systems Immunodiagnostic systems Randox intruments Randox RX Imola Roche instruments Cobas® 8000 COBAS INTEGRA® Systems Siemens instruments ADVIA 1200 ADVIA Centaur XP immunoassay system EMIT® II Plus Syva® Viva® Drug Testing Systems Dimension® Xpand® Plus Integrated Chemistry System Thermo Scientific instruments Indiko Tosoh AIA-Series 4. Applications of TDM Introduction Pharmaceutical research and drug development Clinical trials Computerized clinical decision support systems for TDM and dosing Medication-related interferences with measurements of catecholamines Polymorphisms of genes affecting drug metabolism TDM for drug safety TDM in special groups The aged Children Pregnancy TDM of prophylactic therapy Monitoring of vitamin D levels Monitoring of RBC folic acid levels during pregancy Personalized medicine Role of TDM in personalized medicine Applications according to various conditions Anesthesia and critical care Optimizing antimicrobial dosing for critically ill patients TDM monitoring of thiopental continuous infusion in critical care Role of TDM in critical care cardiac patients. Cancer Epilepsy Personalized approach to use of AEDs Infections Virus infections Fungal infections Pain management Role of TDM in pain management Monitoring of analgesic drugs in urine samples AEDs as analgesics Triptans for migraine Psychiatric disorders Guidelines for use of TDM in psychiatric patients TDM of psychotropic drugs Transplantation TDM of Tacrolismus in transplantation TDM of cyclosporine A in transplantation Monitoring of immunosuppression with mycophenolate mofetil Emergency toxicology Future prospects of TDM 5. Drugs Requiring Monitoring Introduction Antiepileptics Carbamazepine TDM of carbamazepine Gabapentin Lacosamide Lamotrigine TDM of lamotrigine Levetiracetam TDM of levetiracetam Phenobarbital TDM of phenobarbital Phenytoin TDM of phenytoin Primidone TDM of primidone Topiramate TDM of topiramate Valproic acid TDM of valproic acid TDM of multiple antiepileptic drugs in plasma/serum Antimicrobials Antibiotics Amikacin Anti-tuberculosis drugs Chloramphenicol Gentamicin Tobramycin Vancomycin Norvancomycin Antiviral agents Anti-HIV drugs Antifungal agents Voriconazole Antidepressants TDM of selective serotonin reuptake inhibitors Antipsychotics Aripiprazole Quetiapine TDM of risperidone TDM of AEDs in psychiatric disorders TDM of multiple drugs in psychiatry Bronchodilators Theophylline Cardiovascular drugs Antiarrhythmic drugs Anticoagulants Dabigatran Antihypertensive drugs ß-blockers Cardiotonic drugs Digoxin TDM of statins for hypercholesterolemia Chemotherapy for cancer TDM of 5-FU TDM of Methotrexate TDM of imitanib Drugs used for treatment of Alzheimer disease Donepezil Galantamine Memantine Drugs used for treatment of Parkinson disease Monitoring of levodopa and carbidopa therapy Catechol-O-methyltransferase inhibitors Drugs for treatment of attention-deficit hyperactivity disorder Atomoxetine Methylphenidate Hypnotic-sedative drugs Benzodiazepines Propofol Immunosuppressive drugs TDM of mycophenolic acid for the treatment of lupus nephritis Steroids Prednisone Miscellaneous drugs Azathioprine Sildenafil 6. Monitoring of Biological Therapies Introduction Cell therapy In vivo tracking of cells Molecular imaging for tracking cells MRI technologies for tracking cells Superparamagnetic iron oxide nanoparticles as MRI contrast agents Visualization of gene expression in vivo by MRI Gene therapy Application of molecular diagnostic methods in gene therapy Use of PCR to study biodistribution of gene therapy vector PCR for verification of the transcription of DNA In situ PCR for direct quantification of gene transfer into cells Detection of retroviruses by reverse transcriptase (RT)-PCR Confirmation of viral vector integration Monitoring of gene expression Monitoring of gene expression by green fluorescent protein Monitoring in vivo gene expression by molecular imaging Monoclonal antibodies Natalizumab 7. Monitoring of Drug Abuse Introduction Tests used for detection of drug abuse Forensic applications of detection of illicit drugs in fingerprints by MALDI MS MS for doping control Randox assays for DoA Drugs of Abuse Array V Urine drug testing TDM of drugs for treatment of substance abuse-related disorders Drug testing to monitor treatment of drug abuse Minimum requirement for drug testing in patients Analgesic abuse ?-blockers as doping agents Detection of ß-blockers in urine Chronic alcohol abuse Cocaine CEDIA for cocaine in human serum Detection of cocaine molecules by nanoparticle-labeled aptasensors Infrared spectroscopy for detection of cocaine in saliva Marijuana Use of marijuana and synthetic cannabinoids Detection of cannabinoids ELISA for detection of synthetic cannabinoids Drug abuse for performance enhancement in sports Historical aspects of drug abuse in sports Drugs used by athletes for performance enhancement Techniques used for detection of drug abuse by athletes Mass spectrometry for detection of peptide hormones miRNAs for the detection of erythropoiesis-stimulating agents Detection of anabolic steroids Body fluids and tissues used for detection of drug abuse in sports Urine drug testing Spray (sweat) drug test kits Hair drug testing Gene doping in sports Gene transfer methods used for enhancing physical performance Misuse of cell therapy in sport Challenges of detecting genetic manipulations in athletes Drug abuse testing in race horses Limitations and future prospects Role of pharmaceutical industry in anti-doping testing 8. Markets for TDM Introduction Methods for market estimation and future forecasts Markets for TDM tests Markets for TDM and DoA testing equipment Geographical distribution of markets for TDM tests Drivers for growth of TDM markets Markets for DoA testing Unmet needs in TDM Cost-benefit studies Simplifying assays and reducing time and cost Strategies for developing markets Physician education Supporting research on TDM Biomarker patents for drug monitoring 9. Companies Profiles of companies Collaborations 10. References For more information about this report visit http://www.researchandmarkets.com/research/g4tq2x/therapeutic_drug


News Article | February 17, 2017
Site: www.prnewswire.co.uk

According to a new market research report "Particle Counters Market by Product (Airborne Particle Counters, Liquid Particle Counters), Application (Cleanroom Monitoring, Contamination Monitoring of Liquids), and End User (Healthcare Industry, Semiconductor Industry) - Global Forecast to 2021" published by MarketsandMarkets, the market is expected to reach USD 330.6 Million by 2021 from USD 275.9 Million in 2016, at a CAGR of 3.7% from 2016 to 2021. Browse 96 market data Tables and 33 Figures spread through 152 Pages and in-depth TOC on "Particle Counters Market" Early buyers will receive 10% customization on this report. The report analyzes and studies the major market drivers, restraints/challenges, and opportunities. This report studies the global Particle Counters Market for the forecast period of 2016 to 2021. Growing pharmaceutical and semiconductor industries, increasing spending on pharmaceutical R&D, and growth in the manufacturing industries in emerging nations are key factors driving the market growth for particle counters. The global Particle Counters Market is segmented on the basis of type, application, end user, and region. Based on type, the market is segmented into airborne and liquid particle counters. In 2016, the airborne particle counters segment is expected to command the largest share of the global market. The large share of this segment can be attributed to factors such as increasing cleanroom monitoring in the manufacturing and pharmaceutical industries and growing awareness about indoor air quality monitoring. The airborne particle counters segment is further categorized into portable particle counters, remote particle counters, handheld particle counters, and condensation particle counters. In 2016, the portable particle counters segment is estimated to command the largest share and register the highest CAGR in global airborne Particle Counters Market. The liquid particle counters segment is expected to register the highest CAGR during the forecast period. Liquid particle counters are further segmented into online and offline particle counters. The online liquid particle counters segment is estimated to command the largest share and register the highest CAGR in the global liquid Particle Counters Market. On the basis of application, the global Particle Counters Market is segmented into cleanroom monitoring, air quality monitoring, contamination monitoring of liquids, drinking water application, aerosol monitoring and research, chemical contamination monitoring, and other applications. The cleanroom monitoring segment is expected to account for the largest share of the market in 2016. This segment is also projected to register the highest CAGR from 2016 to 2021. The increasing pharmaceutical industry and rising semiconductor industry are expected to augment the demand for particle counters in these applications. On the basis of end user, the global Particle Counters Market is segmented into the healthcare industry, semiconductor industry, automotive industry, aerospace industry, food and beverage industry, and other end users. In 2016, the pharmaceutical industry is expected to account for the largest share of the global Particle Counters Market. Rising geriatric population, increasing prevalence of chronic diseases, rising consumption of drugs, and growing spending in pharmaceutical R&D is expected to augment the demand for particle counters in this end-user segment. Based on region, the Particle Counters Market is divided into North America, Europe, Asia-Pacific, and the Rest of the World. In 2016, North America is expected to account for the largest share of the global Particle Counters Market. This is attributed to factors such as the growing pharmaceutical industry and increasing employment of particle counters for air pollution monitoring in the country. However, the Asia-Pacific market is projected to grow at the highest CAGR during the forecast period. Factors such as the growing pharmaceutical and semiconductor industries and increasing manufacturing activities in the region are expected to propel the market growth in Asia-Pacific. Particle Measuring Systems (U.S.), Beckman Coulter (U.K.), RION Co., Ltd (Japan), Lighthouse Worldwide Solutions (U.S.), TSI (U.S.), and Climet Instruments Company (U.S.) are some of the key players in the global Particle Counters Market. MarketsandMarkets is the largest market research firm worldwide in terms of annually published premium market research reports. Serving 1700 global fortune enterprises with more than 1200 premium studies in a year, M&M is catering to a multitude of clients across 8 different industrial verticals. We specialize in consulting assignments and business research across high growth markets, cutting edge technologies and newer applications. Our 850 fulltime analyst and SMEs at MarketsandMarkets are tracking global high growth markets following the "Growth Engagement Model - GEM". The GEM aims at proactive collaboration with the clients to identify new opportunities, identify most important customers, write "Attack, avoid and defend" strategies, identify sources of incremental revenues for both the company and its competitors. M&M's flagship competitive intelligence and market research platform, "RT" connects over 200,000 markets and entire value chains for deeper understanding of the unmet insights along with market sizing and forecasts of niche markets. The new included chapters on Methodology and Benchmarking presented with high quality analytical infographics in our reports gives complete visibility of how the numbers have been arrived and defend the accuracy of the numbers. We at MarketsandMarkets are inspired to help our clients grow by providing apt business insight with our huge market intelligence repository. Connect with us on LinkedIn @ http://www.linkedin.com/company/marketsandmarkets


News Article | February 15, 2017
Site: www.nature.com

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. We sequenced Chenopodium quinoa Willd. (quinoa) accession PI 614886 (BioSample accession code SAMN04338310; also known as NSL 106399 and QQ74). DNA was extracted from leaf and flower tissue of a single plant, as described in the “Preparing Arabidopsis Genomic DNA for Size-Selected ~20 kb SMRTbell Libraries” protocol (http://www.pacb.com/wp-content/uploads/2015/09/Shared-Protocol-Preparing-Arabidopsis-DNA-for-20-kb-SMRTbell-Libraries.pdf). DNA was purified twice with Beckman Coulter Genomics AMPure XP magnetic beads and assessed by standard agarose gel electrophoresis and Thermo Fisher Scientific Qubit Fluorometry. 100 Single-Molecule Real-Time (SMRT) cells were run on the PacBio RS II system with the P6-C4 chemistry by DNALink (Seoul). De novo assembly was conducted using the smrtmake assembly pipeline (https://github.com/PacificBiosciences/smrtmake) and the Celera Assembler, and the draft assembly was polished using the quiver algorithm. DNA was also sequenced using an Illumina HiSeq 2000 machine. For this, DNA was extracted from leaf tissue of a single soil-grown plant using the Qiagen DNeasy Plant Mini Kit. 500-bp paired-end (PE) libraries were prepared using the NEBNext Ultra DNA Library Prep Kit for Illumina. Sequencing reads were processed with Trimmomatic (v0.33)42, and reads <75 nucleotides in length after trimming were removed from further analysis. The remaining high-quality reads were assembled with Velvet (v1.2.10)43 using a k-mer of 75. High-molecular-weight DNA was isolated and labelled from leaf tissue of three-week old quinoa plants according to standard BioNano protocols, using the single-stranded nicking endonuclease Nt.BspQI. Labelled DNA was imaged automatically using the BioNano Irys system and de novo assembled into consensus physical maps using the BioNano IrysView analysis software. The final de novo assembly used only single molecules with a minimum length of 150 kb and eight labels per molecule. PacBio-BioNano hybrid scaffolds were identified using IrysView’s hybrid scaffold alignment subprogram. Using the same DNA prepared for PacBio sequencing, a Chicago library was prepared as described previously10. The library was sequenced on an Illumina HiSeq 2500. Chicago sequence data (in FASTQ format) was used to scaffold the PacBio-BioNano hybrid assembly using HiRise, a software pipeline designed specifically for using Chicago data to assemble genomes10. Chicago library sequences were aligned to the draft input assembly using a modified SNAP read mapper (http://snap.cs.berkeley.edu). The separations of Chicago read pairs mapped within draft scaffolds were analysed by HiRise to produce a likelihood model, and the resulting likelihood model was used to identify putative mis-joins and score prospective joins. A population was developed by crossing Kurmi (green, sweet) and 0654 (red, bitter). Homozygous high- and low-saponin F lines were identified by planting 12 F seeds derived from each F line, harvesting F seed from these F plants, and then performing foam tests on the F seed. Phenotyping was validated using gas chromatography/mass spectrometry (GC/MS). RNA was extracted from inflorescences containing a mixture of flowers and seeds at various stages of development from the parents and 45 individual F progeny. RNA extraction and Illumina sequencing were performed as described above. Sequencing reads from all lines were trimmed using Trimmomatic and mapped to the reference assembly using TopHat44, and SNPs were called using SAMtools mpileup (v1.1)45. For linkage mapping, markers were assigned to linkage groups on the basis of the grouping by JoinMap v4.1. Using the maximum likelihood algorithm of JoinMap, the order of the markers was determined; using this as start order and fixed order, regression mapping in JoinMap was used to determine the cM distances. Genes differentially expressed between bitter and sweet lines and between green and red lines were identified using default parameters of the Cuffdiff function of the Cufflinks program46. A second mapping population was developed by crossing Atlas (sweet) and Carina Red (bitter). Bitter and sweet F lines were identified by performing foam and taste tests on the F seed. DNA sequencing was performed with DNA from the parents and 94 sweet F lines, as described above, and sequencing reads were mapped to the reference assembly using BWA. SNPs were called in the parents and in a merged file containing all combined F lines. Genotype calls were generated for the 94 F genotypes by summing up read counts over a sliding window of 500 variants, at all variant positions for which the parents were homozygous and polymorphic. Over each 500-variant stretch, all reads with Atlas alleles were summed, and all reads with the Carina Red allele were summed. Markers were assigned to linkage groups using JoinMap, with regression mapping used to obtain the genetic maps per linkage group. The Kurmi × 0654 and Atlas × Carina Red maps were integrated with the previously published quinoa linkage map13, with the Kurmi × 0654 map being used as the reference for the positions of anchor markers and scaling. We selected markers from the same scaffold that were in the same 10,000-bp bin in the assembly. The anchor markers on the alternative map received the position of the Kurmi × 0654 map anchor marker in the integrated map. This process was repeated with anchor markers at the 100,000-bp bin level. The assumption is that at the 100,000-bp bin level recombination should essentially be zero. On this level, a regression of cM position on both maps yielded R2 values >0.85 and often >0.9, so the regression line can easily be used for interpolating the positions of the alternative map towards the corresponding position on the Kurmi × 0654 map. All Kurmi × 0654 markers went into the integrated map on their original position. Pseudomolecules were assembled by concatenating scaffolds based on their order and orientation as determined from the integrated linkage map. An AGP (‘A Golden Path’) file was made that describes the positions of the scaffold-based assembly in coordinates of the pseudomolecule assembly, with 100 ‘N’s inserted between consecutive scaffolds. Based on these coordinates, custom scripts were used to generate the pseudomolecule assembly and to recoordinate the annotation file. DNA was extracted from C. pallidicaule (PI 478407) and C. suecicum (BYU 1480) and was sent to the Beijing Genomic Institute (BGI, Hong Kong) where one 180-bp PE library and two mate-pair libraries with insert sizes of 3 and 6 kb were prepared and sequenced on the Illumina HiSeq platform to obtain 2 × 100-bp reads for each library. The generated reads were trimmed using the quality-based trimming tool Sickle (https://github.com/najoshi/sickle). The trimmed reads were then assembled using the ALLPATHS-LG assembler47, and GapCloser v1.1248 was used to resolve N spacers and gap lengths produced by the ALLPATHS-LG assembler. Repeat families found in the genome assemblies of quinoa, C. pallidicaule and C. suecicum (see Supplementary Information 3) were first independently identified de novo and classified using the software package RepeatModeler49. RepeatMasker50 was used to discover and identify repeats within the respective genomes. AUGUSTUS51 was used for ab initio gene prediction, using model training based on coding sequences from Amaranthus hypochondriacus, Beta vulgaris, Spinacia oleracea and Arabidopsis thaliana. RNA-seq and isoform sequencing reads generated from RNA of different tissues were mapped onto the reference genome using Bowtie 2 (ref. 52) and GMAP53, respectively. Hints with locations of potential intron–exon boundaries were generated from the alignment files with the software package BAM2hints in the MAKER package54. MAKER with AUGUSTUS (intron–exon boundary hints provided from RNA-seq and isoform sequencing) was then used to predict genes in the repeat-masked reference genome. To help guide the prediction process, peptide sequences from B. vulgaris and the original quinoa full-length transcript (provided as EST evidence) were used by MAKER during the prediction. Genes were characterized for their putative function by performing a BLAST search of the peptide sequences against the UniProt database. PFAM domains and InterProScan ID were added to the gene models using the scripts provided in the MAKER package. The following quinoa accessions were chosen for DNA re-sequencing: 0654, Ollague, Real, Pasankalla (BYU 1202), Kurmi, CICA-17, Regalona (BYU 947), Salcedo INIA, G-205-95DK, Cherry Vanilla (BYU 1439), Chucapaca, Ku-2, PI 634921 (Ames 22157), Atlas and Carina Red. The following accessions of C. berlandieri were sequenced: var. boscianum (BYU 937), var. macrocalycium (BYU 803), var. zschackei (BYU 1314), var. sinuatum (BYU 14108), and subsp. nuttaliae (‘Huauzontle’). Two accessions of C. hircinum (BYU 566 and BYU 1101) were also sequenced. All sequencing was performed with an Illumina HiSeq 2000 machine, using either 125-bp (Atlas and Carina Red) or 100-bp (all other accessions) paired-end libraries. Reads were trimmed using Trimmomatic and mapped to the reference assembly using BWA (v0.7.10)55. Read alignments were manipulated with SAMtools, and the mpileup function of SAMtools was used to call SNPs. Orthologous and paralogous gene clusters were identified using OrthoMCL28. Recommended settings were used for all-against-all BLASTP comparisons (Blast+ v2.3.056) and OrthoMCL analyses. Custom Perl scripts were used to process OrthoMCL outputs for visualization with InteractiVenn57. Using OrthoMCL, orthologous gene sets containing two copies in quinoa and one copy each in C. pallidicaule, C. suecicum, and B. vulgaris were identified. In total, 7,433 gene sets were chosen, and their amino acid sequences were aligned individually for each set using MAFFT58. The 7,433 alignments were converted into PHYLIP format files by the seqret command in the EMBOSS package59. Individual gene trees were then constructed using the maximum likelihood method using proml in PHYLIP60. In addition, the genomic variants of all 25 sequenced taxa (Supplementary Data 5) relative to the reference sequence were called based on the mapped Illumina reads in 25 BAM files using SAMtools. To call variants in the reference genome (PI 614886), Illumina sequencing reads were mapped to the reference assembly. Variants were then filtered using VCFtools61 and SAMtools, and the qualified SNPs were combined into a single VCF file which was used as an input into SNPhylo62 to construct the phylogenetic relationship using maximum likelihood and 1,000 bootstrap iterations. To identify FT homologues, the protein sequence from the A. thaliana flowering time gene FT was used as a BLAST query. Filtering for hits with an E value <1 × e−3 and with RNA-seq evidence resulted in the identification of four quinoa proteins. One quinoa protein (AUR62013052) appeared to be comprised of two tandem repeats which were separated for the purposes of phylogenetic analysis. For the construction of the phylogenetic tree, protein sequences from these five quinoa FT homologues were aligned using Clustal Omega63 along with two B. vulgaris (gene models: BvFT1-miuf.t1, BvFT2-eewx.t1) and one A. thaliana (AT1G65480.1) homologue. Phylogenetic analysis was performed with MEGA64 (v6.06). The JTT model was selected as the best fitting model. The initial phylogenetic tree was estimated using the neighbour joining method (bootstrap value = 50, Gaps/ Missing Data Treatment = Partial Deletion, Cutoff 95%), and the final tree was estimated using the maximum likelihood method with a bootstrap value of 1,000 replicates. The syntenic relationships between the coding sequences of the chromosomal regions surrounding these FT genes were visualized using the CoGE65 GEvo tool and the Multi-Genome Synteny Viewer66. The alignment of bHLH domains was performed with Clustal Omega63, using sequences from Mertens et al.39. The phylogeny was inferred using the maximum likelihood method based on the JTT matrix-based model67. Initial trees for the heuristic search were obtained automatically by applying Neighbour-Join and BioNJ algorithms to a matrix of pairwise distances estimated using a JTT model, and then selecting the topology with superior log likelihood value. All positions containing gaps and missing data were eliminated. Trimmed PE Illumina sequencing reads that were used for the de novo assembly of C. suecicum and C. pallidicaule were mapped onto the reference quinoa genome using the default settings of BWA. For every base in the quinoa genome, the depth coverage of properly paired reads from the C. suecicum and C. pallidicaule mapping was calculated using the program GenomeCoverage in the BEDtools package68. A custom Perl script was used to calculate the percentage of each scaffold with more than 5× coverage from both diploids. Scaffolds were assigned to the A or B sub-genome if >65% of the bases were covered by reads from one diploid and <25% of the bases were covered by reads from the other diploid. The relationship between the quinoa sub-genomes and the diploid species C. pallidicaule and C. suecicum was presented in a circle proportional to their sizes using Circos69. Orthologous regions in the three species were identified using BLASTN searches of the quinoa genome against each diploid genome individually. Single top BLASTN hits longer than 8 kb were selected and presented as links between the quinoa genome assembly (arranged in chromosomes, see Supplementary Information 7.3) and the two diploid genome assemblies on the Circos plot (Fig. 2a). Sub-genome synteny was analysed by plotting the positions of homoeologous pairs of A- and B-sub-genome pairs within the context of the 18 chromosomes using Circos. Synteny between the sub-genomes and B. vulgaris was assessed by first creating pseudomolecules by concatenating scaffolds which were known to be ordered and oriented within each of the nine chromosomes. Syntenic regions between these B. vulgaris chromosomes and those of quinoa were then identified using the recommended settings of the CoGe SynMap tool70 and visualized using MCScanX71 and VGSC72. For the purposes of visualization, quinoa chromosomes CqB05, CqA08, CqB11, CqA15 and CqB16 were inverted. Quinoa seeds were embedded in a 2% carboxymethylcellulose solution and frozen above liquid nitrogen. Sections of 50 μm thickness were obtained using a Reichert-Jung Frigocut 2800N, modified to use a Feather C35 blade holder and blades at −20 °C using a modified Kawamoto method73. A 2,5-dihydroxybenzoic acid (Sigma-Aldrich) matrix (40 mg ml−1 in 70% methanol) was applied using a HTX TM-Sprayer (HTX Technologies LLC) with attached LC20-AD HPLC pump (Shimadzu Scientific Instruments). Sections were vacuum dried in a desiccator before analysis. The optical image was generated using an Epson 4400 Flatbed Scanner at 4,800 d.p.i. For mass spectrometric analyses, a Bruker SolariX XR with 7T magnet was used. Images were generated using Bruker Compass FlexImaging 4.1. Data were normalized to the TIC, and brightness optimization was employed to enhance visualization of the distribution of selected compounds. Individual spectra were recalibrated using Bruker Compass DataAnalysis 4.4 to internally lock masses of known DHB clusters: C H O  = 273.039364 and C H O  = 409.055408 m/z. Accurate mass measurements for individual saponins and identified compounds were run using continuous accumulation of selected ions (CASI) using mass windows of 50–100 m/z and a transient of 4 megaword generating a transient of 2.93 s providing a mass resolving power of approximately 390,000 at 400 m/z. Lipids were putatively assigned by searching the LipidMaps database74 (http://www.lipidmaps.org) and lipid class confirmed by collision-induced dissociation using a 10 m/z window centred around the monoisotopic peak with collision energy of between 15–20 V. Quinoa flowers were marked at anthesis, and seeds were sampled at 12, 16, 20 and 24 days after anthesis. A pool of five seeds from each time point was analysed using GC/MS. Quantification of saponins was performed indirectly by quantifying oleanolic acid (OA) derived from the hydrolysis of saponins extracted from quinoa seeds. Derivatized solution was analysed using single quadrupole GC/MS system (Agilent 7890 GC/5975C MSD) equipped with EI source at ionisation energy of 70 eV. Chromatography separation was performed using DB-5MS fused silica capillary column (30m × 0.25 mm I.D., 0.25 μm film thickness; Agilent J&W Scientific), chemically bonded with 5% phenyl 95% methylpolysiloxane cross-linked stationary phase. Helium was used as the carrier gas with constant flow rate of 1.0 ml min−1. The quantification of OA in each sample was performed using a standard curve based on standards of OA. Specific, individual saponins were identified in quinoa using a preparation of 20 mg of seeds performed according a modified protocol from Giavalisco et al.75. Samples were measured with a Waters ACQUITY Reversed Phase Ultra Performance Liquid Chromatography (RP-UPLC) coupled to a Thermo-Fisher Exactive mass spectrometer, which consists of an electrospray ionisation source and an Orbitrap mass analyser. A C18 column was used for the hydrophilic measurements. Chromatograms were recorded in full-scan MS mode (mass range, 100 −1,500). Extraction of the LC/MS data was accomplished with the software REFINER MS 7.5 (GeneData). SwissModel76 was used to produce homology models for the bHLH region of AUR62017204, AUR62017206 and AUR62010677. RaptorX77 was used for prediction of secondary structure and disorder. QUARK78 was used for ab initio modelling of the C-terminal domain, and the DALI server79 was used for 3D homology searches of this region. Models were manually inspected and evaluated using the PyMOL program (http://pymol.org). The genome assemblies and sequence data for C. quinoa, C. pallidicaule and C. suecicum were deposited at NCBI under BioProject codes PRJNA306026, PRJNA326220 and PRJNA326219, respectively. Additional accessions numbers for deposited data can be found in Supplementary Data 9. The quinoa genome can also be accessed at http://www.cbrc.kaust.edu.sa/chenopodiumdb/ and on the Phytozome database (http://www.phytozome.net/).

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