Enhancing single-cell proteomics through tailored Data-Independent Acquisition and micropillar array-based chromatography

Abstract

Single-cell resolution analysis of complex biological tissues is fundamental to capture cell-state heterogeneity and distinct cellular signaling patterns that remain obscured with population-based techniques. The limited amount of material encapsulated in a single cell however, raises significant technical challenges to molecular profiling. Due to extensive optimization efforts, mass spectrometry-based single-cell proteomics (scp-MS) has emerged as a powerful tool to facilitate proteome profiling from ultra-low amounts of input, although further development is needed to realize its full potential. To this end, we carried out comprehensive analysis of orbitrap-based data independent acquisition (DIA) for limited material proteomics. Notably, we found a fundamental difference between optimal DIA methods for high- and low-load samples. We further improved our low-input DIA method by relying on high-resolution MS1 quantification, thus more efficiently utilizing available mass analyzer time. With our ultra-low input tailored DIA method, we were able to accommodate long injection times and high resolution, while keeping the scan cycle time low enough to ensure robust quantification. Finally, we establish a complete experimental scp-MS workflow, combining DIA with accessible single-cell sample preparation and the latest chromatographic and computational advances and showcase our developments by profiling real single cells.

A high-sensitivity low-nanoflow LC-MS configuration for high-throughput sample-limited proteomics

“…A further step of work illustrated the high performance of the method by identifying > 1,700 protein groups in HeLa cells with library-free DIA (Figure 3D). Notably, it outperformed our benchmarked dataset (Figure 3D) [7] with 20% more identified protein groups with three times higher sample throughput, and the chromatography configuration is also comparable with different MS settings (Supplementary Figure 7) [19].…”

Single-cell proteomics differentiates Arabidopsis root cell types

“…These include reducing sample preparation volumes to reduce sample loss [10][11][12][13][14][15][16][17] , ultra-low liquid chromatography (LC) flowrates (<100 nL/min), and optimized LC 11,12,14,18,15 . Additionally, both label-free and multiplex methods are continually improving proteome coverage and even enabling detection of post-translational modifications 12,[19][20][21][22][23] . Multiplex sample processing has the advantage of enabling greater throughput and a lower amount of instrument time per cell for both Data Dependent Acquisition (DDA) and Data Independent Acquisition (DIA) methods 24,8,25 .…”

Refining Spatial Proteomics by Mass Spectrometry: An Efficient Workflow Tailored for Archival Tissue

“…Yet, a prevailing limiting factor for the use of FFPE for proteomics has been the need for substantial sample quantities to acquire deep protein coverage, generally limiting FFPE proteomics by Mass Spectrometry (FFPE-MS) to bulk tissue analysis (3). This is highly problematic in the context of spatial proteomics, as it obscures the cell heterogeneity of the various cell populations within tissues such as cancer, which is defined by being exceptionally heterogeneous (4)(5)(6)(7)(8). In addition, there are limitations of available tissue associated with rare diseases, which can be too scarce or inadequately abundant to support comprehensive retrospective proteomic analyses.…”