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|a Dele-Oni, Deborah O.
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|a Christianson, Karen E.
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|a Egri, Shawn B.
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|a Vaca Jacome, Alvaro Sebastian
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|a DeRuff, Katherine C.
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|a Mullahoo, James
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|a Sharma, Vagisha
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|a Davison, Desiree
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|a Ko, Tak
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|a Bula, Michael
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|a Blanchard, Joel
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|a Young, Jennie Z.
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|a Litichevskiy, Lev
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|a Lu, Xiaodong
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|a Lam, Daniel
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|a Asiedu, Jacob K.
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|a Toder, Caidin
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|a Officer, Adam
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|a Peckner, Ryan
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|a MacCoss, Michael J.
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|a Tsai, Li-Huei
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|a Carr, Steven A.
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|a Papanastasiou, Malvina
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|a Jaffe, Jacob D.
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|a Proteomic profiling dataset of chemical perturbations in multiple biological backgrounds
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|b Springer Science and Business Media LLC,
|c 2022-03-23T15:06:51Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/138380.2
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|a While gene expression profling has traditionally been the method of choice for large-scale perturbational profling studies, proteomics has emerged as an efective tool in this context for directly monitoring cellular responses to perturbations. We previously reported a pilot library containing 3400 profles of multiple perturbations across diverse cellular backgrounds in the reduced-representation phosphoproteome (P100) and chromatin space (Global Chromatin Profling, GCP). Here, we expand our original dataset to include profles from a new set of cardiotoxic compounds and from astrocytes, an additional neural cell model, totaling 5300 proteomic signatures. We describe fltering criteria and quality control metrics used to assess and validate the technical quality and reproducibility of our data. To demonstrate the power of the library, we present two case studies where data is queried using the concept of "connectivity" to obtain biological insight. All data presented in this study have been deposited to the ProteomeXchange Consortium with identifers PXD017458 (P100) and PXD017459 (GCP) and can be queried at https://clue.io/proteomics.
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|a Article
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|t Scientific Data
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