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A fully automated FAIMS-DIA proteomic pipeline for high-throughput characterization of iPSC-derived neurons

By Luke Reilly, Lirong Peng, Erika Lara, Daniel Ramos, Michael Fernandopulle, Caroline Pantazis, Julia Stadler, Marianita Santiana, Anant Dadu, James R. Iben, Faraz Faghri, Mike A Nalls, Steven L Coon, Priyanka Narayan, Andrew B. Singleton, Mark R. Cookson, Michael E. Ward, Yue Andy Qi

Posted 25 Nov 2021
bioRxiv DOI: 10.1101/2021.11.24.469921

Fully automated proteomic pipelines have the potential to achieve deep coverage of cellular proteomes with high throughput and scalability. However, it is important to evaluate performance, including both reproducibility and ability to provide meaningful levels of biological insight. Here, we present an approach combining high field asymmetric waveform ion mobility spectrometer (FAIMS) interface and data independent acquisition (DIA) proteomics approach developed as part of the induced pluripotent stem cell (iPSC) Neurodegenerative Disease Initiative (iNDI), a large-scale effort to understand how inherited diseases may manifest in neuronal cells. Our FAIMS-DIA approach identified more than 8000 proteins per mass spectrometry (MS) acquisition as well as superior total identification, reproducibility, and accuracy compared to other existing DIA methods. Next, we applied this approach to perform a longitudinal proteomic profiling of the differentiation of iPSC-derived neurons from the KOLF2.1J parental line used in iNDI. This analysis demonstrated a steady increase in expression of mature cortical neuron markers over the course of neuron differentiation. We validated the performance of our proteomics pipeline by comparing it to single cell RNA-Seq datasets obtained in parallel, confirming expression of key markers and cell type annotations. An interactive webapp of this temporal data is available for aligned-UMAP visualization and data browsing (https://share.streamlit.io/anant-droid/singlecellumap). In summary, we report an extensively optimized and validated proteomic pipeline that will be suitable for large-scale studies such as iNDI.

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