Poster Presentation HUPO 2019 - 18th Human Proteome Organization World Congress

Ultra-sensitive LC/MS workflow for in-depth label-free analysis of single mammalian cells with nanodroplet sample processing (#523)

Khatereh Motamedchaboki 1 , Yongzheng Cong 2 , Yiran Liang 2 , Romain Huguet 1 , Yufeng Shen 3 , Xuefei Sun 1 , Greg Foster 1 , Daniel Lopez-Ferrer 1 , Andreas F Huhmer 1 , Ying Zhu 4 , Ryan T Kelly 2 4
  1. Thermo Fisher Scientific, San Jose, CALIFORNIA, United States
  2. Brigham Young University, Provo, UT, USA
  3. CoAnn Technologies LLC, Richland, WA, USA
  4. Pacific Northwest National Laboratory, Richland, WA, USA

In the last decade, single cell RNA sequencing has advanced our understanding of transcript heterogeneity. Currently, there is a strong effort to enable single cell proteomic analysis using mass spectrometry (MS)-based workflows. While the analysis of single-cell-sized aliquots from bulk-prepared tryptic digests has been demonstrated, only very recently have label-free strategies been reported for profiling hundreds of proteins from single mammalian cells. Further development in sample processing, separations, MS and data analysis are necessary to realize single cell proteomics with greater depth of coverage and quantitative accuracy. Here we introduce an improved LC separation on new Orbitrap Eclipse™ Tribrid™ Mass Spectrometers to increase proteome coverage for single mammalian cells. Single cells were isolated via fluorescence-activated cell sorting or laser-capture microdissection. Cells were processed on Nanodroplet Processing in One Pot for Trace Samples (nanoPOTS) platform. Solid phase extraction (SPE) and analytical columns ranging from 20 to 30 µm i.d. were used for peptide trapping and separation. A Thermo Scientific™ UltiMate™ 3000 RSLCnano system coupled to Orbitrap Eclipse with a FAIMS Pro™ interface were used for this ultra-sensitive workflow. Raw data files were processed using Thermo Scientific™ Proteome Discoverer™ 2.4 software. The performance of this ultra-sensitive LC-MS workflow was evaluated and optimized using 0.2-2 ng aliquots of bulk-prepared HeLa digest. For HeLa digest loadings ranging from 0.2 ng to 2 ng, approximately 50% more peptides and 30% more proteins were identified from separations employing 20 µm LC. With the performance gains resulting from improvement of both LC separations and FAIMS-MS acquisition, ~3000 peptides and ~830 protein groups were identified by MS/MS alone from single HeLa cells, and match between runs identifications increased identifications to ~5800 peptides and ~1300 protein groups. This is the first example of >1000 proteins being identified from single mammalian cells in a label-free analysis.