Abstract
Single-cell transcriptomics has revolutionized our understanding of basic biology and disease. Since transcript levels often do not correlate with protein expression, it is crucial to complement transcriptomics approaches with proteome analyses at single-cell resolution. Despite continuous technological improvements in sensitivity, mass-spectrometry-based single-cell proteomics ultimately faces the challenge of reproducibly comparing the protein expression profiles of thousands of individual cells. Here, we combine two hitherto opposing analytical strategies, DIA and Tandem-Mass-Tag (TMT)-multiplexing, to generate highly reproducible, quantitative proteome signatures from ultralow input samples. We developed a novel, identification-independent proteomics data-analysis pipeline that allows to quantitatively compare DIA-TMT proteome signatures across hundreds of samples independent of their biological origin to identify cell types and single protein knockouts. These proteome signatures overcome the need to impute quantitative data due to accumulating detrimental amounts of missing data in standard multibatch TMT experiments. We validate our approach using integrative data analysis of different human cell lines and standard database searches for knockouts of defined proteins. Our data establish a novel and reproducible approach to markedly expand the numbers of proteins one detects from ultralow input samples.
Highlights
Single-cell transcriptomics has revolutionized our understanding of basic biology and disease
We hypothesized that data-independent acquisition (DIA) of multiplexed ultralow input samples would overcome detrimental, dependent acquisition (DDA)-inherent missing data points in multibatch TMT datasets, to what has been reported previously [13, 18]
We performed small window DIA (i.e., 6 Th, detailed in Experimental Procedures) of TMT10plex-labeled tryptic digests derived from two human cell lines (i.e., HeLa and K562), serially diluted to total peptide amounts similar to those expected for single mammalian cells (i.e., 0.3 ng and lower) [19]
Summary
In Brief Proteomics faces the challenge of reproducibly comparing the protein expression profiles across large sample cohorts. The use of established in vitro stableisotope labeling techniques (e.g., TMT) increases precursor- and fragment-ion abundances for peptide identification and quantification from ultralow input samples, and increases sample throughput Such multiplex single-cell proteomics workflows have allowed for the quantitative analysis of up to 13 barcoded single cells in one analytical run [2]. Merging large numbers of individual quantitative shotgun proteomics files into one dataset often entails that a considerable number of peptides are not reliably identified in all analytical runs [7] This method-intrinsic accumulation of “missing values” greatly limits the use of such data-dependent acquisition (DDA) strategies for the comparative analysis of protein levels in large sample numbers, as are necessary for reproducible single-cell proteomics, which is currently addressed by various computational data imputation or “match-between runs” methods [8,9,10].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
