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无锡微色谱生物科技有限公司

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PO0022 Affinity-based Dispersive Pipette Extraction for Automated Purification
来源:IMCS | 作者:Todd Mullis1, Sunil Hwang1 ,Michael Mouradian2,L. Andrew Lee*1 | 发布时间: 2167天前 | 2816 次浏览 | 分享到:
Affinity-based extractions in complex matrixes are typically time-consuming due to the la-bor intensive work of hands on pipetting and centrifugation. A typical spin column affinity extraction takes an hour to bind the sample and requires excess buffer solutions towash off non-specific binding proteins using multiple centrifugation steps. A quick, highthroughput affinity-based enrichment method for target proteins is desired to allow forquicker screening of samples. We report a high-throughput sample preparation method us-ing antibody and his-tag based enrichment on a robotic liquid handling system with disper-sive pipette extraction. Specifically, Protein A and cobalt-IMAC resins were used for IgGenrichment from human serum and his-tagged proteins from cell lysates, respectively. Te Protein A extractions were compared side-by-side with a traditional spin-column based extraction protocol. Two different his-tagged proteins were purified fromcell lysate using the IMAC resins in tips. Tese developments allow for completely automat-ed 30 minute sample preparation leading to high-throughput sample screening and bettercompatibility with in-house automated liquid handling systems.
Affinity-based extractions in complex matrixes are typically time-consuming due to the la-bor intensive work of hands on pipetting and centrifugation. A typical spin column affinity extraction takes an hour to bind the sample and requires excess buffer solutions towash off non-specific binding proteins using multiple centrifugation steps.  A quick, highthroughput affinity-based enrichment method for target proteins is desired to allow forquicker screening of samples. Herein, we report a high-throughput sample preparation method using antibody and his-tag based enrichment on a robotic liquid han-dling system with dispersive pipette extraction.


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