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Automated sample preparation for high-throughput single-cell proteomics

View ORCID ProfileHarrison Specht, View ORCID ProfileGuillaume Harmange, David H. Perlman, View ORCID ProfileEdward Emmott, View ORCID ProfileZachary Niziolek, View ORCID ProfileBogdan Budnik, View ORCID ProfileNikolai Slavov
doi: https://doi.org/10.1101/399774
Harrison Specht
1Department of Bioengineering, Northeastern University, Boston, MA 02115, USA
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Guillaume Harmange
3Department of Biology, Northeastern University, Boston, MA 02115, USA
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David H. Perlman
1Department of Bioengineering, Northeastern University, Boston, MA 02115, USA
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Edward Emmott
1Department of Bioengineering, Northeastern University, Boston, MA 02115, USA
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Zachary Niziolek
2MSPRL, FAS Division of Science, Harvard University, Cambridge, MA 02138, USA
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Bogdan Budnik
2MSPRL, FAS Division of Science, Harvard University, Cambridge, MA 02138, USA
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Nikolai Slavov
1Department of Bioengineering, Northeastern University, Boston, MA 02115, USA
3Department of Biology, Northeastern University, Boston, MA 02115, USA
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Abstract

A major limitation to applying quantitative LC-MS/MS proteomics to small samples, such as single cells, are the losses incured during sample cleanup. To relieve this limitation, we developed a Minimal ProteOmic sample Preparation (mPOP) method for culture-grown mammalian cells. mPOP obviates cleanup and thus eliminates cleanup-related losses while expediting sample preparation and simplifying its automation. Bulk SILAC samples processed by mPOP or by conventional urea-based methods indicated that mPOP results in complete cell lysis and accurate relative quantification. We integrated mPOP lysis with the Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS) sample preparation, and benchmarked the quantification of such samples on a Q-exactive instrument. The results demonstrate low noise and high technical reproducibility. Then, we FACS sorted single U-937, HEK-293, and mouse ES cells into 96-well plates and analyzed them by automated mPOP and SCoPE-MS. The quantified proteins enabled separating the single cells by cell-type and cell-division-cycle phase.

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Posted August 25, 2018.
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Automated sample preparation for high-throughput single-cell proteomics
Harrison Specht, Guillaume Harmange, David H. Perlman, Edward Emmott, Zachary Niziolek, Bogdan Budnik, Nikolai Slavov
bioRxiv 399774; doi: https://doi.org/10.1101/399774
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Automated sample preparation for high-throughput single-cell proteomics
Harrison Specht, Guillaume Harmange, David H. Perlman, Edward Emmott, Zachary Niziolek, Bogdan Budnik, Nikolai Slavov
bioRxiv 399774; doi: https://doi.org/10.1101/399774

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