News Article | May 23, 2017
BOSTON--(BUSINESS WIRE)--BIO-IT WORLD – Geneious Biologics, an enterprise software solution for screening antibodies and related constructs using sequence data, was launched today and will change the way scientists identify antibodies for use in biological drug development. The Geneious Biologics software platform will allow biopharma and biotechnology enterprises to create highly annotated antibody databases, significantly improving their ability to leverage accumulated knowledge and gain insights into trends and relationships that may otherwise have been missed. “Biopharmaceutical enterprises are generating huge volumes of data in the development of new antibody based therapeutics,” said Jannick Bendtsen, Vice President of Technology Services, Geneious Biologics. “Geneious Biologics will help them combine and analyze data from different sources and find high-value candidate antibodies much faster than they’ve ever been able to before.” An advanced application programming interface (API) enables Geneious Biologics to integrate with established data systems while a powerful filtering and query language allows scientists to quickly and accurately identify best performing antibodies, regardless of the data’s origin in their organization’s infrastructure. “New technologies like Geneious Biologics will play an important role in allowing researchers to better leverage existing molecular data sets,” said Michael Skynner, Ph.D., Vice President of Operations at Bicycle Therapeutics. “Working with Geneious Biologics to apply this application to the analysis of our unique data sets has been enabling for Bicycle Therapeutics and I can see how this could drastically improve speed and accuracy in other areas of drug development.” "One of the remaining bottlenecks to efficiently identify optimal antibody candidates is antibody sequence processing. Providing insight into large antibody sequence and data sets, such as can be retrieved from Isogenica’s fully synthetic human Fab or llamdA VHH domain antibody libraries, will speed up biologic drug development,” said Guy Hermans, CSO of Isogenica Ltd. “Geneious Biologics will allow our licensees to optimally use the data on the large number of leads retrieved from our libraries and, ultimately, enhance biologic drug development processes.” Geneious Biologics was launched today at Bio-IT World in Boston. It is now generally available to enterprises engaged in commercial antibody discovery and screening. For more information about Geneious Biologics or to request a demonstration visit www.geneiousbiologics.com. Biomatters (www.biomatters.com) empowers its customers with software that transforms biological data into knowledge and actionable insights. The company’s Geneious software suite is used by over 3,000 companies, universities, and institutes in more than 100 countries. Geneious Biologics integrates with Biomatters’ existing Geneious DNA analysis tools and leverages the company’s deep expertise in delivering solutions that meet customers’ real-life needs. Bicycle Therapeutics is developing a new class of medicines to treat oncology and other important diseases based on its proprietary bicyclic peptide (Bicycle®) product platform. Bicycles® exhibit the affinity and exquisite target specificity usually associated with antibodies. Their small size enables rapid and deep tissue penetration, allowing tissues and tumours to be targeted from within. Their peptidic nature provides a “tuneable” pharmacokinetic half-life and a renal route of clearance, thus avoiding the liver and gastrointestinal tract toxicity often seen with other drug modalities. Bicycle Therapeutics is rapidly advancing towards the clinic with its lead programs using Bicycle Drug Conjugates® to selectively deliver toxins to tumours. Bicycle Therapeutics is collaborating in oncology and other areas to realise the full potential of the technology. Bicycle Therapeutics’ unique intellectual property is based on the work initiated at the MRC Laboratory of Molecular Biology in Cambridge, U.K., by the scientific founders of the company, Sir Gregory Winter and Professor Christian Heinis. Bicycle Therapeutics is headquartered in Cambridge, U.K., with a U.S. subsidiary in Cambridge, Massachusetts. For more information, visit www.bicycletherapeutics.com Isogenica licenses advanced synthetic antibody libraries and display technologies to enable its partners’ antibodies discovery activities. These libraries are available for license and screening partnerships, which together with its expertise in the screening of displayed peptide and scaffold libraries, can facilitate client’s biologics discovery needs. www.isogenica.com
Kearse M.,Biomatters |
Moir R.,Biomatters |
Wilson A.,Biomatters |
Stones-Havas S.,Biomatters |
And 12 more authors.
Bioinformatics | Year: 2012
The two main functions of bioinformatics are the organization and analysis of biological data using computational resources. Geneious Basic has been designed to be an easy-to-use and flexible desktop software application framework for the organization and analysis of biological data, with a focus on molecular sequences and related data types. It integrates numerous industry-standard discovery analysis tools, with interactive visualizations to generate publication-ready images. One key contribution to researchers in the life sciences is the Geneious public application programming interface (API) that affords the ability to leverage the existing framework of the Geneious Basic software platform for virtually unlimited extension and customization. The result is an increase in the speed and quality of development of computation tools for the life sciences, due to the functionality and graphical user interface available to the developer through the public API. Geneious Basic represents an ideal platform for the bioinformatics community to leverage existing components and to integrate their own specific requirements for the discovery, analysis and visualization of biological data. © The Author(s) 2012. Published by Oxford University Press.
News Article | November 28, 2016
Next generation sequencing is a process of DNA sequencing to determine the precise order of nucleotides within a DNA molecule. Next generation sequencing technology enables rapid sequencing and produces million of DNA and RNA sequence with the use of next generation sequencer. Next generation sequencing (NGS) is also known as high-throughput sequencing. NGS has number of different modern sequencing technologies such as Illumina sequencing and Roche 454 sequencing. NGS provides a low cost and high throughput alternative for sequencing DNA as compare to traditional Sanger sequencing. NGS provides its services in the research field such as cancer research, metagenomics, genetic disease research and newborn sequencing. Several NGS platforms provide low cost and high throughput sequencing such as Illumina MiSeq, life technologies ion proton and personal genome machine. Whole-genome sequencing, discovery of transcriptional factor binding sites, non-coding RNA expression profiling and targeted re-sequencing are some of the applications of next generation sequencing technologies. Next generation sequencing is used in various fields such as biological drug discovery, agriculture research, personalized medicines and animal research. Request TOC (desk of content material), Figures and Tables of the report: http://www.persistencemarketresearch.com/toc/2785? North America followed by the Europe dominates the global next generation sequencing market, due to rising government support towards research and development. Asia is expected to witness high growth in next generation sequencing due to rising investment in India and China on research and development of next generation sequencing. Low cost DNA sequencing, increasing adoption among researchers, academician and customers and substituting microarray technology by NGS are some of the key factors driving the growth for global next generation sequencing market. However, difficulty in maintaining high accuracy and standardization, lack of skilled labor and data analysis are key factors inhibiting the growth for global next generation sequencing market. Rise in growth of personalized medicine, development in pre-sequencing and NGS bioinformatics solutions and cloud computing are some of the opportunities for global next generation sequencing market. However, the storage and management of enormous data generated by sequencing and its interpretation are some of the challenges for global next generation sequencing market. Increasing number of collaboration and new product launches are some of the trends for global next generation sequencing market. Some of the major companies operating in global next generation sequencing market are Agilent Technologies, Inc., Biomatters, Ltd., CLC Bio, 454 Life Sciences Corporation (A Roche Company), Macrogen, Inc., BGI (Beijing Genomics Institute), Illumina, Inc., GATC biotech AG, Life Technology Corporation, EMC Corporation and Dnastar, Inc.
News Article | November 29, 2016
Next generation sequencing is a process of DNA sequencing to determine the precise order of nucleotides within a DNA molecule. Next generation sequencing technology enables rapid sequencing and produces million of DNA and RNA sequence with the use of next generation sequencer. Next generation sequencing (NGS) is also known as high-throughput sequencing. NGS has number of different modern sequencing technologies such as Illumina sequencing and Roche 454 sequencing. NGS provides a low cost and high throughput alternative for sequencing DNA as compare to traditional Sanger sequencing. NGS provides its services in the research field such as cancer research, metagenomics, genetic disease research and newborn sequencing. Several NGS platforms provide low cost and high throughput sequencing such as Illumina MiSeq, life technologies ion proton and personal genome machine. Whole-genome sequencing, discovery of transcriptional factor binding sites, non-coding RNA expression profiling and targeted re-sequencing are some of the applications of next generation sequencing technologies. Next generation sequencing is used in various fields such as biological drug discovery, agriculture research, personalized medicines and animal research. North America followed by the Europe dominates the global next generation sequencing market, due to rising government support towards research and development. Asia is expected to witness high growth in next generation sequencing due to rising investment in India and China on research and development of next generation sequencing. Low cost DNA sequencing, increasing adoption among researchers, academician and customers and substituting microarray technology by NGS are some of the key factors driving the growth for global next generation sequencing market. However, difficulty in maintaining high accuracy and standardization, lack of skilled labor and data analysis are key factors inhibiting the growth for global next generation sequencing market. Request TOC (desk of content material), Figures and Tables of the report: http://www.persistencemarketresearch.com/toc/2785 Rise in growth of personalized medicine, development in pre-sequencing and NGS bioinformatics solutions and cloud computing are some of the opportunities for global next generation sequencing market. However, the storage and management of enormous data generated by sequencing and its interpretation are some of the challenges for global next generation sequencing market. Increasing number of collaboration and new product launches are some of the trends for global next generation sequencing market. Some of the major companies operating in global next generation sequencing market are Agilent Technologies, Inc., Biomatters, Ltd., CLC Bio, 454 Life Sciences Corporation (A Roche Company), Macrogen, Inc., BGI (Beijing Genomics Institute), Illumina, Inc., GATC biotech AG, Life Technology Corporation, EMC Corporation and Dnastar, Inc.
News Article | December 8, 2016
AUCKLAND, New Zealand--(BUSINESS WIRE)--A whitepaper released today by bioinformatics technology company Biomatters examines the critical role big data will play in enabling scientists to discover life saving biologic drugs of the future. The whitepaper discusses how technology innovations such as high-throughput DNA sequencing have created a big data challenge and opportunity for enterprises involved in monoclonal antibody (mAb) therapeutic development. Today, biopharmaceutical enterprises are generating huge volumes of data throughout the discovery, pre-clinical and clinical stages of development of new antibody based therapeutics, but face multiple challenges in leveraging that data, including data accuracy, data completeness, difficulties combining data that comes from different sources and complexity of implementing data analytics. Titled Accelerated Precision Antibody Discovery, the whitepaper explores how many of the current challenges in developing mAb therapeutics can effectively be addressed through the provision of a fully managed data platform in a secure cloud computing infrastructure. “Employing comprehensive cloud solutions and leveraging insights from cloud-based analytics in biologics research and development is still in its infancy, but will become an increasing source of competitive advantage for biopharma organizations that adopt this new paradigm early,” said whitepaper author and Biomatters President Brett Ammundsen, PhD. “Biopharma is one of the most information-intensive industries, and has much to gain from implementing systems that facilitate the flow of information across different phases of the drug development process. Cloud technologies now offer enterprises unprecedented opportunities to properly address complex data sets and make better, data driven decisions that, ultimately, save lives,” Dr Ammundsen said. Biomatters (www.biomatters.com) creates technology solutions that solve common bioinformatics problems. Headquartered in New Zealand with offices in the US and Europe and users in more than 100 countries, Biomatters has developed Geneious Biologics, a next generation cloud solution for commercial enterprises, to support their discovery and development of biologic therapeutics.
News Article | July 24, 2015
The global next generation sequencing market is expected to reach USD 27.8 billion by 2022, growing at an estimated CAGR of 40.1% from 2015 to 2022, according to a new study by Grand View Research, Inc. Development of faster, more efficient genomic sequencing methodologies is expected to result in significant reduction in cost of sequencing one base pair. This is expected to augment adoption and usage rates of next generation sequencing throughout the forecast period. Revenue generation from HLA and prenatal testing segments is also expected to grow owing to the introduction of more informative novel phase resolved single cycle reads. Increasing concern for cancer and R&D related to related oncology and infectious diseases therapies are further expected to fuel the growth of next generation sequencing market throughout the forecast period. Browse full research report with TOC on "Next Generation Sequencing Market Analysis By Application (HLA Testing, Prenatal Testing, Oncology, Idiopathic & Infectious Diseases), By Technology (Whole Genome Sequencing, Whole Exon Sequencing, Targeted Sequencing & Resequencing), By Workflow (NGS Pre-Sequencing, NGS Sequencing, NGS Data Analysis) And Segment Forecasts To 2022" at: http://www.grandviewresearch.com/industry-analysis/next-generation-sequencing-market Further key findings from the study suggest: Browse more reports of this category by Grand View Research: http://www.grandviewresearch.com/industry/biotechnology For the purpose of this study, Grand View Research has segmented the Next Generation Sequencing Market on the basis of product and region: Grand View Research, Inc. is a U.S. based market research and consulting company, registered in the State of California and headquartered in San Francisco. The company provides syndicated research reports, customized research reports, and consulting services. To help clients make informed business decisions, we offer market intelligence studies ensuring relevant and fact-based research across a range of industries, from technology to chemicals, materials and healthcare.
News Article | November 24, 2016
DecisionDatabases.com offer Next Generation Sequencing (NGS) Market Research Report. This Report covers the complete Industry Outlook, Growth, Size, Share and Forecast Till 2022. Get Free Sample Copy of this Report @ http://www.decisiondatabases.com/contact/download-sample-11311 The report on global next generation sequencing (NGS) market evaluates the growth trends of the industry through historical study and estimates future prospects based on comprehensive research. The report extensively provides the market share, growth, trends and forecasts for the period 2015-2022. The market size in terms of revenue (USD MN) is calculated for the study period along with the details of the factors affecting the market growth (drivers and restraints). A glimpse of the major drivers and restraints affecting this market is mentioned below: Furthermore, the report quantifies the market share held by the major players of the industry and provides an in-depth view of the competitive landscape. This market is classified into different segments with detailed analysis of each with respect to geography for the study period 2015-2022. The comprehensive value chain analysis of the market will assist in attaining better product differentiation, along with detailed understanding of the core competency of each activity involved. The market attractiveness analysis provided in the report aptly measures the potential value of the market providing business strategists with the latest growth opportunities. The report classifies the market into different segments based on technology, workflow, application and end-use. These segments are studied in detail incorporating the market estimates and forecasts at regional and country level. The segment analysis is useful in understanding the growth areas and probable opportunities of the market. Leading Segment in this market: By Technology - Targeted Sequencing And Resequencing By Application – HLA Testing By End –Use - Academic Research By Geography – North America The report also covers the complete competitive landscape of the worldwide market with company profiles of key players such as Illumina Incorporated, 454 Life Sciences, Agilent Technologies, Knome Inc., Genomatix Software GmbH, GATC Biotech Ag, Oxford Nanopore Technologies Ltd., Macrogen Inc., Life Technologies Corp., DNASTAR Inc., Biomatters Ltd., Life CLC Bio, BGI, Qiagen NV, Perkin Elmer, Inc., Pacific Bioscience, Inc. and Partek, Inc.. A detailed description of each has been included, with information in terms of H.Q, future capacities, key mergers & acquisitions, financial overview, partnerships, collaborations, new product launches, new product developments and other latest industrial developments. For More Information about this Report: http://www.decisiondatabases.com/ip/11311-next-generation-sequencing-ngs-market-report 1. INTRODUCTION 2. EXECUTIVE SUMMARY 3. MARKET ANALYSIS 4. NEXT GENERATION SEQUENCING (NSG) MARKET ANALYSIS BY TECHNOLOGY 5. NEXT GENERATION SEQUENCING (NSG) MARKET ANALYSIS BY WORkFlOW 6. NEXT GENERATION SEQUENCING (NSG) MARKET ANALYSIS BY APPLICATION 7. NEXT GENERATION SEQUENCING (NSG) MARKET ANALYSIS BY END-USE 8. NEXT GENERATION SEQUENCING (NSG) MARKET ANALYSIS BY GEOGRAPHY 9. COMPETITIVE LANDSCAPE OF THE NEXT GENERATION SEQUENCING (NSG) COMPANIES 10. COMPANY PROFILES OF THE NEXT GENERATION SEQUENCING (NSG) INDUSTRY DecisionDatabases.com is a global business research reports provider, enriching decision makers and strategists with qualitative statistics. DecisionDatabases.com is proficient in providing syndicated research report, customized research reports, company profiles and industry databases across multiple domains. Our expert research analysts have been trained to map client’s research requirements to the correct research resource leading to a distinctive edge over its competitors. We provide intellectual, precise and meaningful data at a lightning speed.
News Article | October 5, 2016
HEK293T cells23, commonly used in complex I assembly studies15, 20, 21, 24, 25, interactome26 and mitochondrial complexome studies24, were originally purchased from the ATCC and a clonal cell line was obtained after single cell sorting20 and used as the parental line for all gene editing and proteomic work. Knockout cell lines were validated by sequencing of targeted alleles for insertions and deletions (indels), immunoblotting and subsequent proteomic analysis. Cell lines regularly undergo testing for mycoplasma contamination using PlasmaTest (InvivoGen). Gene editing was performed using TALEN27 pairs as described15, 28, or the pSpCas9(BB)-2A-GFP (PX458) CRISPR/Cas9 construct (a gift from F. Zhang; Addgene, plasmid 48138; ref. 29). In brief, in the first round, TALEN constructs were designed using the ZiFiT Targeter30. For genes unsuccessfully targeted in the first round, CRISPR/Cas9 guide RNAs were designed for a second round of gene-disruption using CHOPCHOP31. Successful targeting strategies and constructs can be found in Supplementary Table 1. Gene edited and control HEK293T cells15 were cultured in DMEM (ThermoFisher) supplemented with 10% (v/v) FBS and 50 μg ml−1 uridine. Transfection reagents used were Lipofectamine 2000 and Lipofectamine LTX (ThermoFisher). During screening, glucose-free DMEM supplemented with 5 mM galactose, 1 mM sodium pyruvate, 10% (v/v) dialysed FBS (ThermoFisher) and 50 μg ml−1 uridine was used to identify respiratory incompetent knockout clones. Respiratory competent knockout clones were identified by sequencing of a mixed PCR product covering the target region, where a loss of sequencing fidelity at the target indicates a candidate clone28. With the exception of the NDUFA9- and COA6-knockout cell lines, which were described previously15, 20, indels for individual alleles are summarized in Supplementary Table 1. To generate NDUFAB1 knockout cells, clonal HEK293T cells were transduced with lentiviruses pLVX-TetOne-Puro-NDUFAB1*Flag or pLVX-TetOne-Puro-yACP1Flag (Clontech). NDUFAB1*Flag represents the C-terminally Flag-tagged human NDUFAB1 protein encoded by cDNA having undergone silent mutagenesis to remove the CRISPR/Cas9 target site. yACP1Flag indicates cDNA encoding the C-terminally Flag-tagged yeast (Saccharomyces cerevisiae) ACP1. Transduced cells were grown in the presence of 2 μg ml−1 puromycin for 72 h, and expression of NDUFAB1*Flag or yACP1Flag was confirmed after a further 72 h of treatment with 1 μg ml−1 doxycycline (DOX; Sigma-Aldrich) followed by SDS–PAGE and immunoblotting with NDUFAB1 (Abcam) and Flag (Sigma-Aldrich) antibodies. For subsequent gene editing, cells cultured in the presence of 50 ng ml−1 DOX were transfected with pSpCas9(BB)-2A-GFP-NDUFAB1 and screened as described above. For complementation, cDNAs encoding NDUFV3Flag, NDUFS6Flag, NDUFA8Flag, ATP5SLFlag and DMAC1Flag (TMEM261Flag) were cloned into pBABE-puro (Addgene, 1764; ref. 32), whereas NDUFA1, NDUFA2, NDUFB7, NDUFB10, NDUFB11 and NDUFC1 cDNAs were cloned into pBMN-Z (Addgene, 1734) in place of the LacZ insert. Retroviral constructs were used to transduce the corresponding main clone (Supplementary Table 1), following which expression was selected for through growth in galactose DMEM with the exception of NDUFS6 and NDUFV3 knockouts which were selected using 2 μg ml−1 puromycin. Transduction was verified by BN–PAGE or SDS–PAGE followed by immunoblotting with NDUFA9 or Flag antibodies, respectively. Mitochondria were isolated as previously described33. Protein concentration was estimated by bicinchoninic acid assay (BCA; Pierce), and aliquots of crude mitochondria stored at −80 °C until use. SDS–PAGE was performed using samples solubilized in LDS sample buffer and separated on NuPAGE Novex Bis-Tris protein gels according to manufacturer’s instructions (ThermoFisher). Tris-Tricine SDS–PAGE, BN–PAGE and 2D–PAGE were performed as described previously34, 35, 36. Carbonate and swelling experiments were performed as described37. Immunoblotting onto PVDF membranes was performed using a Novex Semi-Dry Blotter (ThermoFisher) according to manufacturer’s instructions. Horseradish peroxidase coupled secondary antibodies and ECL chemiluminescent substrate (BioRad) were used for detection on a BioRad ChemiDoc XRS+ imaging system. The following primary antibodies were used in this study: COX2 (ThermoFisher A-6404), COX4 (Abcam, ab110261), Flag (Sigma-Aldrich, M2 clone), MIC10 (Aviva Systems Biology, ARP44801_P050), NDUFA13 (Mitosciences MS103-SP), NDUFAB1 (Abcam, ab96230), NDUFB11 (Abcam, ab183716), NDUFV1 (Proteintech 11238-1-AP), NDUFS2 (Mitosciences, MS114), anti-respiratory-chain (Abcam, ab110413; which contains antibodies against ATP5A, UQCRC2, COX1, SDHB and NDUFB8), SDHA (Abcam, ab14715), TIMMDC1 (Sigma, HPA053214), TOMM20 (Santa Cruz, Sc11415) and UQCRC1 (ThermoFisher, 16D10AD9AH5), while rabbit polyclonal antibodies against NDUFA9 (ref. 12), NDUFAF1 (also known as CIA30)38, NDUFAF2 (ref. 21), NDUFAF4 (ref. 21), NDUFB6 (ref. 38) and HSP70 (ref. 20) were raised in-house. For analysis of mRNA expression levels, total RNA was obtained from each cell line in replicate with TRIzol (Thermo scientific). Total RNA was purified using Direct-zol columns according to the manufacturer’s specifications (Zymo Research). For cDNA synthesis, 1 μg of total RNA was processed as the T12VN-PAT assay39 adapted for multiplexing on the Illumina MiSeq instrument. We refer to this assay as mPAT for multiplexed PAT. The approach is based on a nested PCR that sequentially incorporates the Illumina platform’s flow-cell-specific terminal extensions onto 3′ RACE PCR amplicons. First, cDNA was generated using the anchor primer mPAT Reverse, next this primer and a pool of 50 gene-specific primers were used in 5 cycles of amplification. Each gene-specific primer had a universal 5′ extension (see Supplementary Table 12) for sequential addition of the 5′ (P5) Illumina elements. These amplicons were then purified using NucleoSpin columns (Macherey-Nagel), and entered into second round of amplification using the universal Illumina Rd1 sequencing Primer and TruSeq indexed reverse primers from Illumina. Second-round amplification was for 14 cycles. Note, that each experimental condition was amplified separately in the first round with identical primers. In the second round, a different indexing primer was used for each experimental condition. All PCR reactions were pooled and run using the MiSeq Reagent Kit v2 with 300 cycles (that is, 300 bases of sequencing) according to the manufacturer’s specifications. Data were analysed using established bioinformatics pipelines40. Figures were generated using the R framework. Oxygen consumption (OCR) and extracellular acidification (ECAR) rates were measured in live cells using a Seahorse Bioscience XF24-3 Analyzer as described15. In brief, 50,000 cells were plated per well in Seahorse Bioscience culture plates treated with 50 μg ml poly-d-lysine and grown overnight in standard culture conditions. The cellular OCR and ECAR were analysed in non-buffered DMEM (Seahorse Biosciences) containing 5 mM glucose, 1 mM sodium pyruvate and 50 μg ml−1 uridine with the following inhibitors: 2 μM oligomycin; 0.5 μM carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazone (FCCP); 0.5 μM rotenone; and 0.3 μM antimycin A. For each assay cycle, four measurement time points of 2 min mix, 2 min wait and 5 min measure were collected. For each cell line, 3–4 replicate wells were measured in multiple plates and CyQuant (ThermoFisher Scientific) was used to normalize measurements to cell number. Basal OCR and non-mitochondrial respiration (following rotenone and antimycin A injections) were calculated as a mean of the measurement points. Basal ECAR was calculated from the initial basal measurement cycle. To calculate proton-leak and maximal respiration, the initial measurement following addition of oligomycin or FCCP was used. Enzymatic activity measurements were performed as previously described41 in three separate subcultures of each cell line. To accommodate unequal variance, statistical significance was determined through an unpaired two-sample, two-sided t-test using Welch’s correction. Radiolabelling of mtDNA-encoded proteins was performed as previously described15, 34. Isolated mitochondria were subjected to BN–PAGE or 2D–PAGE as described above, following which proteins were transferred to PVDF membranes and analysed by phosphorimager digital autoradiography (GE Healthcare Life Sciences). For immunoprecipitation of newly translated proteins, mitochondria were isolated from cells pulsed for 2 h and solubilized in 1% (w/v) digitonin, 20 mM Bis-Tris (pH 7.0), 50 mM NaCl, 0.1 mM EDTA, 10% (v/v) glycerol. After a brief clarification spin, complexes were incubated with anti-Flag affinity gel (SigmaAldrich), the gel washed with 0.2% (w/v) digitonin, 20 mM Bis-Tris (pH 7.0), 60 mM NaCl, 0.5 mM EDTA, 10% (v/v) glycerol, and enriched proteins eluted with the addition of 150 μg ml−1 Flag peptide (SigmaAldrich). Samples were TCA precipitated to remove detergent and analysed by SDS–PAGE and phosphorimaging as above. For protein import, NDUFA12, NDUFA7 and NDUFV3 cDNA was cloned into the pGEM-4Z plasmid (Promega). mRNA was transcribed using the mMESSAGE mMACHINE SP6 transcription kit (ThermoFisher Scientific) according to the manufacturer’s instructions. Radiolabelled proteins were translated in the presence of [35S]methionine/cysteine using a rabbit reticulocyte lysate system (Promega). Translated proteins were incubated with isolated mitochondria at 37 °C as previously described12, following which mitochondria were analysed by SDS–PAGE or BN–PAGE as described above. For NDUFV3, NDUFS6, NDUFA2, NDUFA8, NDUFA1, NDUFS5, NDUFC1, NDUFB4, NDUFB7, NDUFB10 and NDUFB11 knockouts, mass spectrometry was performed with SILAC-labelled whole-cell starting material as described previously42 with modifications. In brief, cells cultured in ‘heavy’ 13C 15N -arginine, 13C 15N -lysine-containing or ‘light’ SILAC DMEM15 were collected, washed in PBS and protein content determined by BCA assay. Measurements were performed in batches of 3–4 knockout cell lines in triplicate with a label switch. Each batch used a single pool of clonal HEK293T cells (1 sample grown in heavy DMEM, and 2 independent samples grown in light DMEM) and knockout cell lines were grown with the complementary label orientation (1 in light DMEM, and 2 in heavy DMEM). Equal amounts of heavy and light (typically 250 μg) control HEK293T and knockout cells were mixed, and cells were solubilized in 1% (w/v) sodium deoxycholate, 100 mM Tris-HCl (pH 8.1). Lysates were sonicated for 30 min at 60 °C in a sonicator waterbath, followed by denaturation and alkylation through the addition of 5 mM Tris(2-carboxyethy)phosphine (TCEP), 20 mM chloroacetamide and incubation for 5 min at 99 °C with vortexing. Samples were digested with trypsin overnight at 37 °C. Detergent was removed by ethyl acetate extraction in the presence of 2% formic acid (FA), following which the aqueous phase was concentrated by vacuum centrifugation. Peptides were reconstituted in 0.5% FA and loaded onto pre-equilibrated small cation exchange (Empore Cation Exchange-SR, Supelco Analytical), stage-tips made in-house. Tips were washed with 6 load volumes of 20% acetonitrile (ACN), 0.5% FA and eluted in 5 sequential fractions of increasing amounts (45-300 mM) of ammonium acetate, 20% ACN, 0.5% FA. A sixth elution was collected using 5% ammonium hydroxide, 80% ACN following which fractions were concentrated, desalted and reconstituted as previously described15. Peptides were reconstituted in 0.1% trifluoroacetic acid (TFA) and 2% ACN and fractions analysed sequentially by online nano-HPLC/electrospray ionization-MS/MS on a Q Exactive Plus connected to an Ultimate 3000 HPLC (Thermo-Fisher Scientific). Peptides were first loaded onto a trap column (Acclaim C PepMap nano Trap × 2 cm, 100-μm I.D, 5-μm particle size and 300-Å pore size; ThermoFisher Scientific) at 15 μl min−1 for 3 min before switching the pre-column in line with the analytical column (Acclaim RSLC C PepMap Acclaim RSLC nanocolumn 75 μm × 50 cm, PepMap100 C , 3-μm particle size 100-Å pore size; ThermoFisher Scientific). The separation of peptides was performed at 250 nl min−1 using a nonlinear ACN gradient of buffer A (0.1% FA, 2% ACN) and buffer B (0.1% FA, 80% ACN), starting at 2.5% buffer B to 35.4% followed by ramp to 99% over 120 min (runs had a total acquisition time of 155 min to accommodate void and equilibration volumes). Data were collected in positive mode using Data Dependent Acquisition using m/z 375–1800 as MS scan range, HCD for MS/MS of the 12 most intense ions with z ≥ 2. Other instrument parameters were: MS1 scan at 70,000 resolution (at 200 m/z), MS maximum injection time 50 ms, AGC target 3E6, Normalized collision energy was at 27% energy, Isolation window of 1.8 Da, MS/MS resolution 17,500, MS/MS AGC target of 1E5, MS/MS maximum injection time 100 ms, minimum intensity was set at 1E3 and dynamic exclusion was set to 15 s. For the remaining knockouts, we used isolated mitochondria as starting material. Cells were cultured in SILAC DMEM as above and mitochondrial isolations performed in batches of 1–6 knockout cell lines in triplicate. Each batch contained a single set of clonal HEK293T mitochondria (2 independent isolations from heavy and 1 from light cells), with knockout mitochondria having the complementary label orientation (2 independent isolations from light DMEM and 1 from heavy cells). Mitochondria were isolated from cell pellets stored at −80 °C as previously described43, but with modifications. Cells were resuspended in 20 mM HEPES-KOH (pH 7.6), 220 mM mannitol, 60 mM sucrose, 1 mM EDTA, 1 mM PMSF and homogenized as described above. The homogenate was centrifuged at 800g for 5 min at 4°C, and the supernatant again centrifuged at 10,000g for 10 min at 4 °C. Crude mitochondria were resuspended in the above buffer and the two differential centrifugation steps repeated. The resuspended pellet was then layered onto a sucrose cushion consisting of 10 mM HEPES-KOH (pH 7.6), 500 mM sucrose, 1 mM EDTA. Samples were centrifuged at 10,000g for 10 min at 4 °C, following which the protein concentration was estimated by BCA assay. Equal amounts of heavy and light (typically 20 μg) control HEK293T and knockout mitochondria were mixed as described above, collected by centrifugation at 18,000g and solubilized in 8 M urea, 50 mM ammonium bicarbonate. Proteins were acetone-precipitated, reduced and alkylated and desalted as previously described15. Peptides reconstituted in 0.1% TFA and 2% ACN were analysed on a Q Exactive Plus, or a LTQ-Orbitrap Elite Instrument. Instrument and method parameters for Q Exactive Plus were as described above, however, used a shorter gradient (90 min separation, 120 min total acquisition). For the Orbitrap Elite, instrument and method parameters were as previously described15. A single technical re-injection was collected for all mitochondrial samples. All raw file names included identifiers for the batch, instrument and gradient used, knockout cell line being studied, and applicable label orientation. Raw files were analysed using the MaxQuant platform44 version 220.127.116.11, searching against the Uniprot human database containing reviewed, canonical and isoform variants in FASTA format (June 2015) and a database containing common contaminants by the Andromeda search engine45. Default search parameters for an Arg10- and Lys8-labelled experiment were used with modifications. In brief, cysteine carbamidomethylation was used as a fixed modification, and N-terminal acetylation and methionine oxidation were used as variable modifications. False discovery rates of 1% for proteins and peptides were applied by searching a reverse database, and ‘re-quantify’ and ‘match from and to’, ‘match between runs’ options were enabled with a match time window of 2 min. Experimental groups based on data gathered using different instrumentation and/or acquisition parameters were given odd numbered fractions to avoid falsely matched identifications, whereas fractionated whole-cell samples were given sequential fraction numbers. Unique and razor peptides with a minimum ratio count of 2 were used for quantification. Using the Perseus platform (version 18.104.22.168), identifications were matched to the MitoCarta2.0 database19 using Ensembl ENSG id and gene name identifiers. Identifications labelled by MaxQuant as ‘only identified by site’, ‘reverse’ and ‘potential contaminant’ were removed. Proteins having <3 valid values in a single experimental group were removed. For mitochondrial samples, we found the correlation of log -ratio data from biological replicates in the same experimental group to be moderate at best and as low as 0.3 in some cases. We surmised the main cause of this to be batch and labelling effect, the former due to differences in mitochondrial isolations between batches and latter due to one (of three) replicates within each experimental group always being subjected to a label switch. To account for these and potentially other factors, we adopted an approach that borrows principles from RUV-2 (ref. 46) and SVA47 methods for removing unwanted variations, with modifications in the algorithm for choosing the control proteins (that is, those not found in MitoCarta 2.0; ref. 19) and moderating the amount of adjustment for genes with small sample size due to missing values. Adjustments were performed in the R framework, following which the adjusted ratios were imported back into Perseus. The log ratio values for proteins in replicates were normally distributed and had equal variances. The mean log -transformed ratios for each experimental group were calculated along with their standard deviation and P-value based on single sample two-sided t-test15. This statistical approach was consistent with published quantitative SILAC analyses employing similar instrumentation and methods15, 48, 49. Groups having <2 valid values were converted to ‘NaN’ (not a number). A quality matrix was generated based on the standard deviation, and corresponding values having a standard deviation greater than 1 converted to ‘NaN’. This threshold was determined empirically to remove outliers from the main distribution of standard deviations across all samples. These data can be found in Supplementary Table 5. Figures 3b and Extended Data Figs 6a, 8c and 9d were generated from a matrix containing log -transformed median SILAC ratios having a standard deviation <1 for complex I subunits (Supplementary Table 7) and data were mapped to homologous subunits (Protein Data Bank accession 5LDW)9. For Fig. 3a, hierarchical clustering on rows (proteins) was performed using Pearson distance and average linkage. Data were pre-processed using k-means (clusters = 300). Images were generated using the PyMOL Molecular Graphics System, version 22.214.171.124 (Schrödinger, LLC). log SILAC ratios for some proteins in their corresponding knockout cell line had very low (generally >4-fold reduction) ratios, whereas others were reported NaN. This could be either due to the ‘re-quantify’ option being turned on for the MaxQuant search, which results in translation of peak shapes from an identified isotope pattern being translated to its unidentified label partner, or indels in some lines generating a non-functional (but still translated) protein as we have seen previously15. For the identification of proteins dysregulated between knockouts of discrete modules (Fig. 4c, Supplementary Tables 8 and 9), triplicate log -transformed SILAC ratios from Supplementary Table 5 were assigned to one of two groups based on the knockout being associated with the indicated module. Groups tested had comparable variance, and a modified Welch’s two-sample t-test with permutation-based FDR statistics50, 51 was used to determine significance. Parameters for the test were: 70% minimum valid values, 250 permutations and significance being an FDR of <0.05. For the Gene Ontology enrichment analysis in Fig. 2c, proteins with a P < 0.05 and with >1.5-fold change up or down were submitted to the DAVID online tool (https://david.abcc.ncifcrf.gov/home.jsp) for enriched biological processes (GOTERM_BP_FAT) and molecular function (GOTERM_MF_FAT). Functional annotation charts were exported and visualized using Cytoscape (version 3.4.0) and the Enrichment Map app52 (version 2.1.0; P < 0.005). Contents of enriched terms indicated in Fig. 2c are detailed in Extended Data Fig. 5d. Affinity-enrichment experiments in Fig. 4e, Extended Data Figs 3d and 4d and Supplementary Tables 2–4 and 10, 11 were performed from HEK293T and knockout cells complemented with the Flag-tagged protein cultured in heavy or light SILAC DMEM as previously described15. Mass spectrometry was performed on a Q Exactive Plus as above but using a shorter gradient (25 min separation, 60 min total acquisition). For data analysis, raw files were analysed using the MaxQuant platform as above using default search parameters for a Arg10 and Lys8 labelled experiment, with modifications. In brief, cysteine carbamidomethylation was used as a fixed modification, and N-terminal acetylation and methionine oxidation were used as variable modifications. False discovery rates of 1% for proteins and peptides were applied by searching a reverse database, and ‘re-quantify’ and ‘match from and to’, ‘match between runs’ options were enabled with a match time window of 2 min. Unique and razor peptides with a minimum ratio count of 1 were used for quantification. Data analysis was performed using the Perseus framework. Identifications were matched to MitoCarta2.0 data set19 as above. Only proteins with a sequence coverage of 2 or more unique peptides were included in further analysis. Normalized SILAC ratios were inverted to achieve the orientation Flag-tagged/HEK293T and proteins not present in >2/3 replicates were removed. log -transformed values had a normal distribution and comparable variance. For affinity-enrichment experiments, statistical method, sample size and analysis approaches were chosen based on published quantitative affinity-enrichment analyses employing similar instrumentation and methods15, 21, 53, 54. P values were calculated by a single (Flag-tagged cell line enriched)-sided t-test and the negative logarithmic P-value plotted against the mean of the three replicates. cDNA inserts were obtained from an in-house cDNA library generated from our clonal HEK293T line. Briefly, RNA was isolated using TRIzol Reagent (ThermoFisher) according to manufacturer’s instructions. The Superscript III first strand synthesis kit (ThermoFisher Scientific) was used to generate cDNA primed with either Oligo(dT) or random hexamers. Inserts were amplified from the library using Q5 High Fidelity DNA Polymerase (NEB) and Gibson assembled into the relevant plasmid (see above) using the NEBuilder HiFi DNA Assembly Master Mix (NEB) according to manufacturer’s instructions. Sanger sequencing was performed from PCR product or plasmid template DNA. DNA sequence assembly and alignment to sequencing reads was performed using SnapGene (GSL Biotech) and Geneious (Biomatters). Immunofluorescence microscopy was performed as previously described55 using primary antibodies (Flag or TOMM20) at 1:500 dilutions. Primary antibodies were labelled with anti-mouse conjugated Alexa Fluor 488 and anti-rabbit conjugated Alexa Fluor 568 secondary antibodies (Molecular Probes). Hoechst (1 μg ml−1) was used to stain nuclei. Cells were visualized using a Leica TCS SP8 equipped with HyD detectors. Images were processed using Image J56. All figures were prepared using Adobe Photoshop and Illustrator (CC2015.5). No statistical methods were used to predetermine sample size. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment.
News Article | November 7, 2016
The Global market for Next Generation Sequencing is poised to reach $10 billion by the end of 2020 growing at a CAGR of approximately 20%. The instruments and consumables is the largest segment with a share of around half of the market in 2013. The fastest growing segment is the services with a highest CAGR during the forecast period. The major players are interested in services segment as it provides additional revenue for the company at the same time, increases the sales of instruments and reagents. The major players operating in the NGS market are Illumina Inc (U.S.), Thermo Fisher Scientific (U.S.), Hoffmann-La Roche Ltd (Switzerland), Pacific Bioscieces (U.S.) Agilent Technologies (U.S.), BGI (Beijing Genomics Institute) (China), Qiagen (Netherlands), Biomatters Ltd (New Zealand), and Genomatix Software GmbH (Germany). Currently, North America is the largest market for NGS. This is due to the increased awareness about the quick return of investments and accuracy. Asia Pacific is the fastest growing segment. The Next Generation Sequencing market can be segmented on the basis of Technology (Whole Genome Sequencing, Targeted Resequencing, Whole Exome Sequencing, RNA Sequencing, Chip Sequencing, De Novo Sequencing and Methyl Sequencing), Products (Instruments, Reagents & Consumables, and Services), End user (Hospitals & Healthcare Institutions, Academics, Biotech & Pharma Firms, and Others), Applications (Drug Discovery, Genetic Screening, Diagnostics, Personalized Medicine, Agriculture And Animal Research, Infectious Diseases, and Others) and Geography (North America, Europe, APAC & RoW). Increasing applications in clinical diagnosis boosting the market growth, Speed, cost and accuracy to spur the market growth, Efficient replacement for traditional technologies (Microarrays), and Drug discovery applications demanding NGS technology are the major factors driving the growth of the market. Legal & ethical issues to hamper the market growth, Interpretation of complex data, and Lack of skilled professionals are the major restraints that are hampering the NGS market growth. Market Definition for the specified topic along with identification of key drivers and restraints for the market. Market analysis for the Global Next Generation Sequencing Market, with region specific assessments and competition analysis on a global and regional scale. Identification of factors instrumental in changing the market scenarios, rising prospective opportunities and identification of key companies which can influence the market on a global and regional scale. Extensively researched competitive landscape section with profiles of major companies along with their strategic initiatives and market shares.ï¿½ Identification and analysis of the Macro and Micro factors that affect the Global Next Generation Sequencing market on both global and regional scale. A comprehensive list of key market players along with the analysis of their current strategic interests and key financial information. INTRODUCTION Study Deliverables Market Definition Sizing Units Base Currency Review and forecast period years General Study Assumptions RESEARCH METHODOLOGY Introduction Analysis Methodology Econometric forecast models Research Assumptions EXECUTIVE SUMMARY KEY INFERENCES MARKET OVERVIEW AND INDUSTRY TRENDS Current market scenario Technology Overview New developments in therapeutics Investment analysis Porters Five Force Analysis Bargaining Power of suppliers Bargaining power of buyers Degree of competition Threat of substitution Threat of new entrants DRIVERS, RESTRAINTS, OPPORTUNITIES AND CHALLENGES ANALYSIS (DROC) Market Drivers Increasing applications in clinical diagnosis boosting the market growth Speed, cost and accuracy to spur the market growth Efficient replacement for traditional technologies (Microarrays) Drug discovery applications demanding NGS technology Market Restraints Legal & ethical issues to hamper the market growth Interpretation of complex data Lack of skilled professionals Market Opportunities In NGS Technology, Personalized Medicine & Biomarker Pre-Sequencing Cloud Computing Key Challenges Interpretation Of Complex Data From NGS Platforms Clinical Translation of Genomic Discoveries By Technology Whole Genome Sequencing Targeted Resequencing Whole Exome Sequencing Rna Sequencing Chip Sequencing De Novo Sequencing Methyl Sequencing By Products Instruments Reagents & Consumables By End Users Services Hospitals & Healthcare Institutions Academics Biotech & Pharma Firms Others By Application Drug Discovery Genetic Screening Diagnostics Personalized Medicine Agriculture And Animal Research Infectious Diseases Others GLOBAL NEXT GENERATION SEQUENCING (NGS) MARKET SEGMENTATION BY GEOGRAPHY - REGIONAL SHARES AND FORECAST North America USA Canada Mexico Europe France UK Germany Spain and Portugal Scandinavia Italy BENELUX Asia-Pacific India China Japan South Korea Australia and New Zealand Rest of Asia Pacific Middle East and Africa GCC Egypt Morocco Algeria South Africa Rest of Middle East and Africa Latin America Brazil Argentina Rest of Latin America COMPETITIVE LANDSCAPE Merger and Acquisition Analysis Patent Analysis The Challengers KEY VENDORS Roche Holding Ag Agilent Technologies, Inc BGI (Beijing Genomics Institute) Biomatters, Ltd Qiagen Dnastar, Inc Gatc Biotech Ag Genomatix Software Gmbh Illumina, Inc Knome, Inc Thermo Fisher Scientific Macrogen, Inc Oxford Nanopore Technologies, Ltd Pacific Biosciences Partek Incorporated Perkin Elmer, Inc ANALYST OUTLOOK FOR INVESTMENT OPPORTUNITIES FUTURE OUTLOOK OF THE MARKET APPENDIX Abbreviations Bibliography Disclaimer