Academic literature on the topic 'HyperCells'

Abstract Antibody-based drugs have been successful in a range of therapeutic categories. However, generating monoclonal antibodies is time-consuming and expensive. A common approach is Hybridoma technology, which overcomes the short life-span of IgG-secreting plasma B cells in vitro. However, many plasma B cells are lost due to the low efficiency of hybridoma cell fusion (typically <10%). Direct single B cell screening strategies have emerged to bypass hybridoma fusion and recombinatorial display, coupled with the generation of recombinant monoclonal antibodies through mammalian expression systems. Obtaining expression systems with the required productivity, specificity and stability for clinical or commercial use requires screening millions of cells and thousands of clones. Bioelectronica’s HypercellTM platform is an emerging technology used throughout antibody discovery and cell-line development to identify and isolate single, high-antibody secreting cells from large pools (~10,000,000 cells) in short periods (ca. 48 hrs). This scalable “electrofluidic” sorting system reduces time and cost by integrating antigen-detection reagents and real-time computer vision analysis to expedite single cell sorting. In this paper antigen-specific IgG-secreting hybridoma cells are identified and sorted by their secretion rate. The cells and reagents are encapsulated in a Polydisperse Oblate Dispersion system (PODs), incubated for 1–4 hours for signal gain, and loaded into the HypercellTM device for cell sorting. Alternatively, the mixture can be analyzed without sorting to produce single cell secretion “finger print” signatures that can help identify unique expression patterns and monitor cell line stability over culturing time.

MEP (mercapto-ethyl-pyridine) HyperCel is one of the hydrophobic charge induction chromatography (HCIC) resins. Under normal operation, proteins are bound to the MEP resin at neutral pH, at which MEP is not charged, mostly via hydrophobic interaction. MEP has a pyridine group, whose pK is 4.8, and hence is positively charged at acidic pH range. Based on the binding mechanism (i.e., hydrophobic interaction) and the induced positive charge at acidic pH, there may be two ways to elute the bound proteins. One way is to bring the pH down to protonate both MEP resin and the bound protein, leading to charge repulsion and thereby elution. Another way is to use hydrophobic interaction modifiers, which are often used in hydrophobic interaction chromatography, to reduce hydrophobic interaction. Here, we summarize such two possible elution approaches.

Les lipoprotéines de haute densité (High Density Lipoprotein, HDL) permet deréduction de risque de maladies cardio-vasculaires principalement en raison de leurcapacité à éliminer le cholestérol accumulé des artères (via transport inverse ducholestérol). Les effets protecteurs des HDL sont médiés par l'apolipoprotéine AI(ApoA1), qui est le La protéine la plus importante quantitativement du HDL. L’ApoA1favorise l'efflux de cholestérol vers le foie pour l'excrétion. Une augmentation desniveaux plasmatiques de l’ApoA1 est généralement acceptée d'êtrecardioprotecteur, ce qui en fait un potentiel thérapeutique. Deux variantes naturelle(mutants) de l’ApoA1, Milano et Paris, sont caractérisées par une mutationponctuelle unique a permis l'introduction d'un résidu cystéine. Populations avecApoA1-Milano ont été rapportés d'avoir un système cardiovasculaire, même avec defaibles niveaux de plasma de ApoA1 et HDL. Il est donc d'intérêt pour générerrecombinante de type sauvage et des variantes de ApoA1 humaine pour desapplications thérapeutiques potentielles. Dans cette étude, de type sauvagerhApoA1 a été produit chez P. pastoris et purifié par chromatographie en modemixte en une seule étape. Par la suite, un processus intégré a été le développementde la production et la récupération rapide de type sauvage rhApoA1 chez P. pastorispar chromatographie par lit expansée. En outre, les variantes de l'ApoA1, Milano &Paris, ont été générées par mutagenèse dirigée et ont été exprimés chez P. pastoris.Les motifs d’adsorption de rhApoA1-Milano et rhApoA1-Paris ont été comparés àcelle de type sauvage ApoA1 et les différences ont été discutées
The high-density lipoprotein (HDL) complex helps reduce the risk of cardiovasculardisorders mainly due to its ability to remove accumulated cholesterol from arteriesvia reverse cholesterol transport. These protective effects of HDL are known to bemediated by Apolipoprotein A-I (ApoA1), which is the major protein component ofHDL. ApoA1 is a lipid binding protein and promotes cholesterol efflux fromperipheral tissues to the liver for excretion. An increase in the plasma levels ofApoA1 is generally accepted to be cardioprotective, making it a potentialtherapeutic. Two naturally occuring variants of ApoA1, namely the Milano & Parismutants, are characterised by a single point mutation resulting in the introduction ofa Cysteine residue. Populations with ApoA1-Milano have been reported to have ahealthier cardiovascular system even with low plasma levels of ApoA1/HDL. It ishence of interest to generate recombinant wild type and variants of human ApoA1for potential therapeutic applications. In this study, wild type rhApoA1 was producedin P. pastoris and purified by mixed-mode chromatgraphy in a single step.Subsequently, an integrated process has been development for the production andrapid recovery of wild type rhApoA1 in Pichia pastoris. This has paved way to theestablishment of a scalable integrated process that could be further developed toindustrial levels. In addition, the cysteine variants of ApoA1, Milano & Paris, havebeen generated by site directed mutagenesis and have been successfully expressedin P. pastoris. The binding patterns of rhApoA1-Milano and rhApoA1-Paris have beencompared with that of wild-type ApoA1 and the differences have been discussed

Over the past few decades researchers and industrial professionals alike have realized the vast potential of monoclonal antibodies to treat diseases ranging from arthritis, immune and infectious diseases to cancer. There are a number of antibodies on the market that constitute a large portion of the biopharmaceutical niche in the drug industry. Blockbuster drugs (selling greater than $1 billion/year), include antibodies such as Avastin (bevacizumab), Herceptin (trastuzumab), Rituxan (rituximab), Humira (adalimumab) and Remicade (infliximab), which are cornerstones in this type of sector. With the cost of development to market approval rising astronomically for a new drug, new ways to produce and process these molecules becomes a paramount objective to ultimately help both patients and drug developers. Plants, such as Nicotiana benthamiana, offer a unique production platform due to their recently found ability to produce large amounts of therapeutic proteins in a quick manner. While production would be simple and cheap, purification would not be due to the presence of toxic compounds in ground plant tissue. The current methods to purify these molecules from plant extract include expensive affinity column steps (Protein A/G) that are difficult to scale-up to bed volumes that would be necessary for this technology. In the following paper, a method to purify a monoclonal antibody by non-Protein A/G resins is accomplished and compared to purification by Protein A. The modified process involved an UF/DF step, a precipitation of native impurities step using a charged polymer, hydrophobic interaction chromatography and hydrophobic charge induction chromatography. The yield of this modified process was 19.0%. This process compared favorably with Protein A due to the fact that even with washing steps including NaCl and Tween-20, the Protein A elution fraction still contained a large portion of host cell impurities. A chromatography step would need to be included before Protein A to both protect the column resin and provide a more purified immunoglobulin.
Master of Science

La chromatographie mode mixte représente l’une des plus grandes évolutions de ces dernières années dans le domaine des bioséparations. Cette technique repose sur l'intervention de plusieurs types d'interactions au sein d'un seul et même support. Les résines de chromatographie mode mixte HEA, PPA et MEP HyperCel portent des groupements aliphatiques, aromatiques, thiophiliques ainsi que des groupements aminés protonables en différentes positions. Au moyen d’expériences de chromatographie, à l’aide de protéines standards aux propriétés spécifiques et de mélanges complexes, nous avons isolé ces différentes interactions. Nous avons mis en évidence l’intervention majeure d'au moins deux types d'interactions au sein de ces supports : interactions hydrophobes et électrostatiques. Nous avons pu observer le comportement des résines lors de variations de pH, de force ionique, de types de sels et de tampons ou lors de la présence d'autres composés organiques. Nous avons mis en évidence l'intervention combinée de ces types d'interactions lors des différentes phases de chromatographie. Le comportement des résines mode mixte a révélé des sélectivités particulières et dont le contrôle ciblé à l'aide de l'environnement a permis le développement de méthodes de purification efficaces et originales. Nous avons pu ainsi développer des applications telles que la purification de fragment d’anticorps (Fab’2) à partir de culture de cellules d’insectes, la capture de protéine de type MBP à partir d’extrait bactériens et la capture d’anticorps monoclonaux à partir de cellules de mammifères (CHO), et ainsi améliorer les conditions d’utilisation de la chromatographie en mode mixte
Mixed mode chromatography is the most innovative technique for bioseparation. Mixed mode resins, as the term suggest, involves multiples types of interaction at the same time. HyperCel mixed mode resins, HEA, PPA and MEP, involve aliphatic, aromatic or thiophilic groups as well as protonable amine located in the spacer arm or as a head group. Using classical chromatographic experiments, standards proteins and complex mixtures, we highlighted the two major types of interactions involved: hydrophobic and electrostatic interactions. We specifically influenced these interactions by modifying the environment in terms of ionic strength, pH, salt types, and other compounds. The combination of these interactions during every phase of a chromatographic process has been demonstrated. Mixed mode resins thus offer unique selectivity that can be controlled by the environment. This allowed us to develop several applications from antibodies fragments capture from insect cells, to the purification of MBP-tagged proteins, through monoclonal antibody capture from CHO cells. We thus enhanced mixed mode chromatography

With supply voltage no longer scaling down at the same rate as transistor feature size, keeping power dissipation to practical levels while maximizing performance is becoming a challenge in future computing systems. Increasing performance per watt for target applications is critical. Heterogeneous computing systems which consist of General Purpose Processors (GPPs), Graphic Processing Units (GPUs) and application specific accelerators can provide improved performance while keeping power dissipation at a realistic level. Application specific accelerators give the best performance per watt for a given application, but their lack of flexibility prevents their applicability in case of any small modification in the application or for a closely related application. In such scenarios, Coarse Grained Reconfigurable arrays or CGRAs are drawing increasing attention due to their promise of providing more flexibility than application specific accelerators, but with better energy efficiency than GPPs. One key feature of the majority of CGRAs is to naturally layout computational data paths in space, so as to avoid the hardware complexity associated with general purpose processor pipelines. This makes CGRAs more energy efficient when compared to GPPs. However, existing compilation frameworks for CGRAs are targeted towards maximizing performance for a given application kernel while neglecting power dissipation. While the very nature of CGRAs make these kernels run at lower power compared to the GPPs, existing techniques do not attempt to get the least power footprints for these kernels on the CGRA. With power dissipation becoming critical, CGRA compilation techniques should try to optimize the performance for a given kernel while simultaneously optimizing for power dissipation. Extracting parallelism inherent in kernels and exposing it efficiently to the CGRA is an effective way to achieve maximum performance at minimum power dissipation. This thesis presents a CGRA targeted for realizing kernels specified as function compositions. Function composition is defined as applying one function to the results of another to form a new function. A functional style of programming is more effective in expressing parallelism compared to imperative style and is better suited for kernels targeting CGRAs. The proposed CGRA consists of a set of reconfigurable datapaths called HyperCells which can be stitched together to form a single datapath of required granularity as dictated by the targeted kernel. We call this CGRA, a Coarse Grained Composable Reconfigurable Array or CGCRA. We also propose a synthesis methodology for mapping kernels to the CGRA, for a given performance while minimizing power dissipation. A comprehensive throughput and power model for the CGCRA proposed here enables accurate estimation of performance and energy during synthesis. An RTL prototype for the proposed CGRA has been developed and synthesized to gate level netlist using Cadence RTL Compiler with 40 nm LowK (RVT) standard cell library from Faraday Technology. A 5X9 array with 32 HyperCells has an area of 32.27 mm2 and can operate at a maximum clock frequency of 275 MHz. This gives a theoretical peak performance of 220 GFLOPS. A few application kernels from signal processing, machine learning, and HPC domains have been mapped to the CGCRA using the proposed synthesis methodology. Estimated power efficiency for these kernels falls within a range of 9 to 19 GFLOPS/Watts with an average 13.8 GFLOPS/Watts. Higher performance is observed for kernels with significant data reuse with a maximum observed performance of 120 GFLOPS which is 55% of the theoretical peak.

Accelerating high performance computing (HPC) applications such as dense linear al- gebra solvers, mesh computations, stencil computations requires exploiting parallelism that is resident in loops. Typically these loops have simple structures and they form the so called static control parts of the HPC applications. In this context affine loops are of particular interest because of their amenability to automatic parallelization. Efficient execution of such applications demand computing platforms that are capable of exploit- ing parallelism of various granularities. In the past decades architects and hardware designers have exploited the exponential growth in device density on silicon to meet the ever increasing demand for parallel execution on hardware. As we enter the deep sub- micron era, various factors including the so called power wall will impede the traditional approach of architecture design. It will no longer be bene ficial to design homogeneous multicores where each core is structurally and functionally identical. In order to over- come the challenges of the future, a heterogeneous design philosophy has to be adopted. We see some re flection of that already in the state of the art - application specifi c on- chip accelerators and specialized processing platforms such as graphics processing units have become common in present generation of computing platforms. When compared to general purpose processors (GPP), although application specifi c accelerators offer dra- matically higher efficiency for their target applications, they are not as flexible and/or performs poorly on other applications. Graphic processing units (GPU) can be used for accelerating a wide range of parallel applications. However GPUs are extremely energy consuming. Field programmable gate arrays (FPGA) may be used to generate acceler- ators on demand. Although this mitigates the flexibility issue involved with specialized hardware accelerators, the ner granularity of the lookup tables (LUT) in FPGAs leads to signi ficantly high con figuration time and low operating frequency. Coarse-grain re- con figurable architectures (CGRA) accelerators consisting of a pool of compute elements (CE) interconnected using some communication infrastructure overcomes the reconfi gu- ration overheads of FPGAs while providing performance close to specialized hardware accelerators. Examples of CGRAs include Convey Hybrid-Core Computer, DRRA, REDEFINE etc. Modern CGRAs are fundamentally ne grain instruction processing engines. In or- der to avoid the overheads of ne grain instruction processing and the flexibility issue of application speci fic accelerators, reconfigurable function units have been designed to act as accelerators that work in a tightly coupled manner with their host GPP. The so called reconfigurable function units are essentially a number of compute units such as ALUs or FPUs interconnected using a programmable interconnect. Example of such accelerators include DySER, CRFU etc. Such reconfigurable accelerators are particularly suitable for accelerating loops with large bounds because, the large number of iterations of loops ef- fectively amortizes the con figuration overheads. The reconfigurable accelerators decouple control and data movement from the core computation, much like the decoupled access execute architecture of yore. This decoupled execution style allows dedicated hardware resources for computation and control thereby improving efficiency of the computation resources. However, since the accelerators lack dedicated micro-architectural support for control and movement of data, the pipeline of the host GPP has to act as the control hardware. The micro-architectural limitations of the host GPP such as limited storage and read/write bandwidth of register les affect the performance of the said accelerators. In order to avoid this pitfall, we propose a reconfigurable accelerator called HyperCell which is inspired by the decoupled execution style and is supported by dedicated control hardware and temporary operand storage. The HyperCell can be con gfiured once and it can execute a large number of iterations of a loop without direct intervention from the host. This reduces control overhead signi ficantly. The dedicated operand storage enables temporal reuse of data and reduces data movement overhead. The recon figurable dat- apath of HyperCell allows exploitation of ne grain instruction level parallelism (ILP) while the controller enables pipelined execution of successive iterations of the loop and thereby enables exploitation of ne grain data level parallelism (DLP). In order to exploit higher degree of parallelism, we connect a multitude of HyperCells using a scalable network on chip. The architecture of the network of HyperCells based on the REDEFINE archetype. Hence we refer to the proposed accelerator as REDEFINE HyperCell Multicore (RHyMe). The network of HyperCells form the compute fabric of RHyMe. The HyperCells are capable of concurrently executing partitions (so called tiles) of an iteration space of a loop. The multiplicity of HyperCells enable exploitation of coarse grain DLP. In order to reduce data movement overheads between the accelera- tor and its host processor, we introduce a distributed shared memory that the compute fabric can utilize as its operand store. Control overheads are minimized through intro- duction of a dedicated orchestrator module that governs execution of tiles of an iteration space on the compute fabric of RHyMe. Through experimental results we quantitatively demonstrate that our proposed accelerator incurs minimum control overhead in terms of performance, hardware complexity of the dedicated orchestrator and energy consump- tion. For certain kernels, we observe that the fraction of computation time and total execution time is more than 99%. The con gfiuration and synchronization overhead is less than 1%. We also demonstrate that the accelerator is capable of exploiting multi-grain parallelism and temporal reuse of operand data. We measured data-movement over- head for different kernels. For kernels with greater scope of temporal reuse of data, our proposed hardware is capable of effectively hiding data movement latencies. We mea- sured the relative cost of computation, control and data movement in terms of energy spendings. We observe that kernels with significant scope of data reuse results in more than 80% of the energy being spent in computation. We achieve performance ranging from 8.24 to 20.64 GFLOPS for the various kernels. By effectively exploiting parallelism and temporal reuse of data, RHyMe is able to achieve power efficiency of up to 16.86 GFLOPS/Watt

Recent experimental and theoretical studies have shown that, by introducing defects into a photonic crystal, defect states can exist within the forbidden photonic band gap.1 The defect structure of interest occupies only a small volume within a unit cell, and the fields associated with the defect extend no more than a few lattice constants. Theoretical calculations were carried out by using the hypercell method. Here an alternate approach for treating highly localized defect states by introducing vector Wannier functions and using a Green's function is reported. The reason for the use of vector Wannier functions is that they are expected to be highly localized around each atomic site and therefore form ideal basis functions. The eigen problem for the defect state frequency and fields turns out formally to have exactly the same form as that of the corresponding electronic problem.