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Optimizing single cell proteomics using trapped ion mobility spectrometry for label-free experiments.

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This summary is machine-generated.

Optimizing trapped ion mobility spectrometry (TIMS) settings significantly enhances proteome profiling depth in single cells. This advancement allows for detailed analysis of low-abundance proteins and post-translational modifications in T cells.

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Area of Science:

  • Proteomics
  • Single-cell biology
  • Mass spectrometry

Background:

  • Single-cell RNA sequencing revolutionized biological research, but unbiased single-cell proteome analysis lagged behind.
  • Recent breakthroughs in miniaturized sample handling and mass spectrometry, like trapped ion mobility spectrometry (TIMS) with data-dependent acquisition (DDA-PASEF), have enabled proteome profiling of single cells.
  • While ion flux in TIMS impacts proteome profiling, its effect on low-input samples requires further investigation.

Purpose of the Study:

  • To optimize TIMS parameters, specifically ion accumulation/ramp times and ion mobility range, for enhanced proteome profiling of low-input samples.
  • To assess the depth of proteome coverage and detection of low-abundance proteins using optimized TIMS conditions.
  • To demonstrate the feasibility of analyzing biological pathways and post-translational modifications from single cells.

Main Methods:

  • Optimization of TIMS settings, including ion accumulation time (180 ms) and a narrower ion mobility range (0.7–1.3 V s cm⁻²).
  • Proteome profiling of sorted human primary T cells using the optimized TIMS conditions.
  • Analysis of protein abundance, pathway delineation, and detection of post-translational modifications (phosphorylation, acetylation).

Main Results:

  • Optimized TIMS settings led to a substantial increase in proteome coverage and detection of low-abundance proteins from low-input samples.
  • Proteome profiling of single, five, ten, and forty T cells yielded an average of 365, 804, 1116, and 1651 proteins, respectively.
  • The study successfully delineated key metabolic and T cell receptor signaling pathways and detected post-translational modifications from single cells.

Conclusions:

  • Optimized TIMS parameters significantly improve single-cell proteome profiling depth and sensitivity, especially for low-input samples.
  • This approach provides sufficient proteome coverage for pathway analysis and detection of post-translational modifications in single cells.
  • The developed method holds promise for label-free analysis of single cells from clinical samples.