Review articleAdvances in microscale separations towards nanoproteomics applications
Introduction
Proteins are the workhorses of the cell, impacting nearly all aspects of cellular processes. The proteome by its nature is dynamic, and the acute state of the proteome (i.e., the proteotype) depends on both the genotype and external perturbations [1], [2]. Therefore, quantitative analysis of the dynamics of the proteome including post-translational modifications (PTMs) and its connection to phenotypes (e.g., diseases) has become indispensable in biological and clinical research [3], [4], [5]. Recent advances in mass spectrometry (MS)-based proteomics for both global deep-profiling of the proteome and selected types of PTMs (e.g., phosphorylation) [6], [7], [8] and targeted quantification of proteins from specific signaling pathways [9], [10] have greatly expanded our capabilities in performing proteogenomics and systems biology studies for gaining detailed mechanistic insights into physiological and pathological processes.
During the past decade, major advances have been achieved in nearly all areas of the proteomics workflow such as high resolution microscale chromatographic separations, mass spectrometry instrumentation, and bioinformatics data analysis tools to enable large-scale proteome interrogation [11]. Current state-of-the-art MS-based proteomics platforms can afford deep coverage for both the global proteome and selected PTMs in cell or tissue samples. For example, recent studies have reported the identification or quantification of ∼10,000 proteins [6], [7], >20,000 phosphorylation [12], [13] and >15,000 ubiquitination sites [12], [14].
Despite recent advances in improving overall proteome coverage, the current proteomics workflows typically require relatively large amounts of starting materials on the order of millions of cells or 100 s μg of proteins, which excludes many important biological and biomedical applications. The ability to effectively analyze extremely small amounts of protein samples (e.g., nanograms of proteins) from cells or tissues by MS is one of the most significant challenges for current MS-proteomics. Herein, we define sample amounts with less than 1 μg of total protein as “nanoscale” and proteomics analyses of these nanoscale samples as “nanoproteomics” (Fig. 1). The biomedical need for nanoproteomics technologies are compelling, including the analyses of tissue substructures, cellular microenvironments of disease pathologies, rare or small subpopulations of cells, extracellular vesicles, as well as single cell resolution profiling (Fig. 1). Some of these sample types are readily produced by existing technologies such as fluorescence activated cell sorting (FACS) [15], laser capture dissection (LCM) [16], [17], and exosome isolation techniques [18]. Moreover, single cell resolution genomics technologies, such as single-cell genomic sequencing [19] and single-cell transcriptomic profiling (RNA-Seq) [20], [21], have been making tremendous impact in biological research. However, the current state-of-the-art in MS-based proteomics still falls far short of the sensitivity required for single cell analyses.
Considerable efforts have been devoted to enhance the overall sensitivity of MS-based proteomics workflow towards enabling analysis of small samples, including the front-end sample processing, microscale separations, and MS instrumentation. Herein, we review recent advances in microscale separation, as well as nanoscale sample processing systems for proteomics analysis. Our focus will be on bottom-up proteomics, and the other important advances in top-down proteomics (measurement of intact proteins) [22] are not covered here.
Section snippets
Factors governing overall MS-based proteomics sensitivity
Fig. 2 illustrates a typical MS-based proteomics workflow for protein identification in biological samples. Conceptually, the overall analytical sensitivity of MS-based proteomics depends on the following aspects: a) the efficiency and recovery of front-end sample processing (e.g., protein extraction and protein digestion) and the degree of reducing sample complexity by extensive fractionation and/or enrichment; b) the resolving power of chromatographic or electrophoretic separations when
LC or CE for nanoproteomics
Reversed phase (RP) LC, either in packed columns or monolithic columns, is the most widely used LC separation for bottom-up proteomics due to its relatively high resolving power and ESI friendly mobile phases. CE has also emerged as a powerful separation technique when directly coupled with ESI-MS for proteomic research. One of the foundational discoveries in ESI-MS was that the ESI efficiency can be dramatically enhanced by nearly ∼100-fold when ESI was operated at low flow rate (20–40 nL/min)
Front-end microscale sample processing
As a critical component in the overall workflow, there has been a significant interest in developing microscale sample processing techniques to minimize sample loss and increase processing efficiency. In principle, this would involve processing with minimized liquid volumes and transfer steps. These efforts generally employ two main approaches to reduce sample losses and enhance processing efficiency: single-tube preparation techniques or integrated online processing systems.
The single tube
Highlights of nanoproteomics applications
Nanoproteomics is an evolving technological capability for enabling analysis of cellular heterogeneity, tissue substructures, and other nanoscale biological or clinical samples at the proteome level when coupled with cell isolation techniques (e.g., LCM [97] and FACS [98]). While we recognized that most of the reports on nanoproteomics were focused on method development or proof-of-concept demonstrations, in this section we highlight some studies to illustrate the potential of biological
Conclusions and perspectives
Tremendous advances in LC- and CE-MS platforms have been achieved in terms of overall sensitivity, proteome coverage, reproducibility, and quantification for global proteome analyses. The absolute sensitivity for LC-MS and CE-MS operating in the nanoflow regime is sufficient for analyzing low ng protein samples or small numbers of cells, and potentially even for single mammalian cells [29], [30]. The main bottleneck for the overall sensitivity lies in the sample losses and efficiency in the
Conflict of interest
None.
Acknowledgements
Portions of this work were supported by the NIH by NIH Grants P41 GM103493, DP3 DK110844, and UC4 DK104167. The experimental work described herein was performed in the Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by the DOE and located at Pacific Northwest National Laboratory, which is operated by Battelle Memorial Institute for the DOE under Contract DE-AC05-76RL0 1830.
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2022, Analytica Chimica ActaCitation Excerpt :Also, the significant number of capillary columns with particulate stationary phases presented in this review were self-packed in research labs, confirming that non-commercially packed columns might be successfully applied in both global and targeted proteomics studies. On the other hand, significant efforts are still necessary to improve the quality of the proteomic separations, especially in terms of the phosphorylation post-translational modification analysis in nanoscale and single-cell samples, where the sub-stoichiometric and low-abundance nature of PTMs provides yet another sensitivity challenge [116]. In untargeted proteomics, the main aim is to utilize mass spectrometry to characterize and quantify as many proteins in the complex sample as possible.
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2021, Journal of Chromatography ACitation Excerpt :Capillary LC columns can also provide better MS sensitivity with reduced ion suppression because of lower flow rates [34], utility for sample-limited analysis, and economical use of mobile and stationary phases [35]. Capillary columns are routinely used in proteomics but have not yet been widely implemented in metabolomics [36,37]. Benzoyl chloride (BzCl) derivatization was used to improve retention of polar metabolites on reversed phase columns, as such labeling strategies have shown to be useful for both targeted and untargeted workflows [38–40].