Label-free single cell proteomics utilizing ultrafast LC and MS instrumentation: A valuable complementary technique to multiplexing

Abstract

The ability to map a proteomic fingerprint to transcriptomic data would master the understanding of how gene expression translates into actual phenotype. In contrast to nucleic acid sequencing, in vitro protein amplification is impossible and no single cell proteomic workflow has been established as gold standard yet. Advances in microfluidic sample preparation, multi-dimensional sample separation, sophisticated data acquisition strategies, and intelligent data analysis algorithms have resulted in major improvements to successfully analyze such tiny sample amounts with steadily boosted performance. However, among the broad variation of published approaches, it is commonly accepted that highest possible sensitivity, robustness, and throughput are still the most urgent needs for the field. While many labs have focused on multiplexing to achieve these goals, label-free SCP is a highly promising strategy as well whenever high dynamic range and unbiased accurate quantification are needed. We here focus on recent advances in label-free single-cell mass spectrometry workflows and try to guide our readers to choose the best method or combinations of methods for their specific applications. We further highlight which techniques are most propitious in the future and which applications but also limitations we foresee for the field.

Keywords: bioinformatics; cell biology; label-free; quantification; technology.

FIGURE 1

Typical steps of a label free single cell proteomics workflow and its potential applications.

FIGURE 2

Number of publications bearing the term “Single Cell Proteomics” listed on PubMed for each year since first mentioned in 2004.

FIGURE 3

Details of chromatography setting influence sensitivity, reproducibility, and throughput. (A) The same peptide in equal quantity produces higher intense sharp and lower intense broad peaks. (B) Chromatographic reproducibility is essential for retention time (RT) prediction as well as match between run (MBR) (C) RT, peak width, and reproducibility are highly influenced by the used column type and length. Dual column settings can speedup resulting run‐to‐run time. (D) Typical high pressure liquid chromatography (HPLC) gradient start at 1%–2% organic buffer and range to 35%–45% organic buffer. Over 5–20 column volumes (cv). Typical flowrates in proteomics are in the nanoliter range (100–300 nL/min, red dashed line). Throughput can further be boosted by fast loading and equilibration (green dashed line).

FIGURE 4

Degree of data completeness (1/fraction of missing values) is dependent on data acquisition. 250 pg of tryptic HeLa digest were repeatedly injected from the same vial, creating a dataset without any biological variability. The fraction of peptides found in n replicates is plotted when either using data dependent analysis (DDA) or data independent analysis (DIA) to acquire data. All data was recorded on the same Orbitrap Exploris 480 (Thermo) using the same 5.5 cm µ‐pillar array columns (µPAC) analytical column and a 20 min active gradient. Raw data can be accessed free of charge via the ProteomeXchange Consortium in the PRIDE [90] partner repository with the dataset identifier PXD039208. For DDA data was analyzed using CHIMERYS and quantified with apQuant [91] with match between run (MBR) enabled. For DIA Spectronaut v16 was used in directDIA mode including spectral matching across all files.