1. What are the technological breakthroughs in single cell proteomics?
Several technological breakthroughs have been achieved to overcome the current limitations of analytical sensitivity and throughput of single cell proteomics. Improvements in sample preparation of low-input samples using miniaturized devices and microfluidic approaches have minimized the loss of analytes. The nanoPOTS platform enables isolation, lysis, and digestion of cells in nanoliter volumes of liquids to minimize sample losses and enable faster reaction kinetics. Microfluidic devices have been developed for processing samples in confined spaces, such as oil, integrated proteomics chips (iProChip), and digital microfluidic devices. Efforts to increase ion signals using carrier proteins in tandem mass tag (TMT) labeling approaches, such as SCoPE-MS and BASIL strategies, have also provided increased throughput by multiplexing samples. Additionally, coupling ion mobility spectrometry with mass spectrometry has improved signal-to-noise ratios of low-input samples by removing singly charged ions and synchronizing mobility separation with quadrupole mass selection using a novel data acquisition scheme termed as parallel accumulation-serial fragmentation (PASEF). A timsTOF SCP mass spectrometer has been introduced for the analysis of low-input samples, detecting over 550 proteins from ~1 ng of HeLa lysate in data-dependent acquisition (DDA) mode, and over 3000 proteins from the same amount of sample in data-independent acquisition (DIA) mode. The improved sensitivity of this mass spectrometer was facilitated by a brighter ion source that could draw more ions into the analyzer. However, the effect of TIMS settings on the analysis of low-input samples has not been systematically evaluated. Therefore, optimization of TIMS parameters for DDA experiments was carried out, finding that a ramp time of 180 ms and narrowing the ion mobility range to 0.7 to 1.3 V s cm-2 resulted in increased protein identification and coverage. Unbiased proteome profiling of single and a low number of primary T cells sorted using a picoliter dispensing device equipped with a piezoelectric dispensing capillary was performed under an optimized TIMS setting, resulting in the identification of hundreds of phosphorylated, lysine acetylated, and protein N-terminal acetylated peptides from low numbers of T cells. This study demonstrates the feasibility of using single cell proteomics for proteome profiling of primary T cells and understanding the biology of T cells at single cell resolution.
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2. What is the feasibility of identifying post-translational modified peptides from single cell samples?
The feasibility of identifying post-translational modified peptides (PTMs) from single cell samples has been assessed, focusing on phosphorylation and acetylation. The study found that a higher number of modified peptides were identified in a higher number of cells, with 27, 56, 82, and 105 phosphorylated peptides identified from single, five, ten, and forty cells, respectively. Among them, 3, 8, 19, and 23 phosphorylated peptides were detected in all triplicate experiments. Similarly, 16, 39, 48, and 66 lysine acetylated peptides were identified with 3, 8, 10, and 19 peptides detected in all three runs. Protein N-terminal acetylated peptides were also detected, with 57, 330, 281, and 385 peptides identified in single, five, ten, and forty cells, respectively. The chance of identifying PTMs is related to the natural abundance of proteins, and the depth of analysis is not sufficient to cover PTMs with lower stoichiometry, especially phosphorylation in a single cell. However, several interesting phosphorylation sites and lysine acetylation sites were identified from single cell samples. The study suggests that further studies are required to enhance the sensitivity to detect PTMs with low stoichiometry, using targeted approaches and miniaturized PTM enrichment strategies on low-input samples. The chance of detecting abundant PTMs such as proline hydroxylation, arginine citrullination, lysine ubiquitylation, and N-glycosylation should also be evaluated on these data sets using sophisticated search engines for analyzing PTMs.
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3. How does ion accumulation affect signal-to-noise ratio?
Ion accumulation in a mass spectrometer increases the signal-to-noise ratio and detection sensitivity, especially for low-input samples. This is achieved by controlling ion injection time and automatic control (AGC) value in orbitrap-based instruments. Longer ion injection time for fragmentation improves the depth of proteome coverage for low-input samples. Optimal ion ramp times and ion mobility ranges can also be applied to analyze low-input samples on a timsTOF SCP mass spectrometer. These settings can benefit DIA-based studies by generating more comprehensive DDA-based spectral libraries. The study provides unbiased proteome profiling of single T cells, which is crucial for understanding the biology of T cells at a single cell level. It is expected that single cell proteomics will allow the study of rare antigen-specific T cells or rare populations of T cells at different stages of differentiation, such as Treg, Th17, Th1, and Th2, at single cell resolution.
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4. How are primary T cells isolated from blood?
Primary T cells are isolated from blood using a combination of antibody cocktail negative selection and density gradient separation. Whole blood samples are collected from healthy volunteers and sprayed with 70% ethanol. The blood cone is cut, and the blood is diluted with 1x complete PBS, 2% heat inactivated FBS, and 1 mM EDTA. The RosetteSep Human T Cell Enrichment Cocktail is added to the diluted blood sample, targeting unwanted cells for removal. The mixture is layered onto Ficoll and centrifuged to separate the T-cell layer. The T-cell layer is transferred to a new tube, diluted, and washed. IL2 is added to the cells, and they are incubated overnight. The purified T cells are then counted and used for further experiments.
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