Facile One-Pot Nanoproteomics for Label-Free Proteome Profiling of 50-1000 Mammalian Cells

Droplet-based proteomics reveals CD36 as a marker for progenitors in mammary basal epithelium

Deep proteomic profiling of rare cell populations has been constrained by sample input requirements. Here, we present DROPPS (droplet-based one-pot preparation for proteomic samples), an accessible low-input platform that generates high-fidelity proteomic profiles of 100-2,500 cells. By applying DROPPS within the mammary epithelium, we elucidated the connection between mitochondrial activity and clonogenicity, identifying CD36 as a marker of progenitor capacity in the basal cell compartment. We anticipate that DROPPS will accelerate biology-driven proteomic research for a multitude of rare cell populations.

Universal Pretreatment Development for Low-input Proteomics Using Lauryl Maltose Neopentyl Glycol

In recent years, there has been a growing demand for low-input proteomics, particularly in the context of single-cell proteomics (SCP). In this study, we have developed a lauryl maltose neopentyl glycol (LMNG)-assisted sample preparation (LASP) method. This method effectively reduces protein and peptide loss in samples by incorporating LMNG, a surfactant, into the digestion solution and subsequently removing the LMNG simply via reversed phase solid-phase extraction. The advantage of removing LMNG during sample preparation for general proteomic analysis is the prevention of mass spectrometry (MS) contamination. When we applied the LASP method to the low-input SP3 method and on-bead digestion in coimmunoprecipitation-MS, we observed a significant improvement in the recovery of the digested peptides. Furthermore, we have established a simple and easy sample preparation method for SCP based on the LASP method and identified a median of 1175 proteins from a single HEK239F cell using liquid chromatography (LC)-MS/MS with a throughput of 80 samples per day.

Robust Surfactant-Assisted One-Pot Sample Preparation for Label-Free Single-Cell and Nanoscale Proteomics

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk cells. However, such bulk measurement obscures cell-to-cell heterogeneity, precluding proteome profiling of single cells and small numbers of cells of interest. To address this issue, in the recent 5 years, there has been a surge of small sample preparation methods developed for robust and effective collection and processing of single cells and small numbers of cells for in-depth MS-based proteome profiling. Based on their broad accessibility, they can be categorized into two types: methods based on specific devices and those based on standard PCR tubes or multi-well plates. In this chapter, we describe the detailed protocol of our recently developed, easily adoptable, Surfactant-assisted One-Pot (SOP) sample preparation coupled with MS method termed SOP-MS for label-free single-cell and nanoscale proteomics. SOP-MS capitalizes on the combination of an MS-compatible surfactant, n-dodecyl-β-D-maltoside (DDM), and standard low-bind PCR tube or multi-well plate for "all-in-one" one-pot sample preparation without sample transfer. With its robust and convenient features, SOP-MS can be readily implemented in any MS laboratory for single-cell and nanoscale proteomics. With further improvements in MS detection sensitivity and sample throughput, we believe that SOP-MS could open an avenue for single-cell proteomics with broad applicability in biological and biomedical research.

SPPUSM: An MS/MS spectra merging strategy for improved low-input and single-cell proteome identification

Single and rare cell analysis provides unique insights into the investigation of biological processes and disease progress by resolving the cellular heterogeneity that is masked by bulk measurements. Although many efforts have been made, the techniques used to measure the proteome in trace amounts of samples or in single cells still lag behind those for DNA and RNA due to the inherent non-amplifiable nature of proteins and the sensitivity limitation of current mass spectrometry. Here, we report an MS/MS spectra merging strategy termed SPPUSM (same precursor-produced unidentified spectra merging) for improved low-input and single-cell proteome data analysis. In this method, all the unidentified MS/MS spectra from multiple test files are first extracted. Then, the corresponding MS/MS spectra produced by the same precursor ion from different files are matched according to their precursor mass and retention time (RT) and are merged into one new spectrum. The newly merged spectra with more fragment ions are next searched against the database to increase the MS/MS spectra identification and proteome coverage. Further improvement can be achieved by increasing the number of test files and spectra to be merged. Up to 18.2% improvement in protein identification was achieved for 1 ng HeLa peptides by SPPUSM. Reliability evaluation by the "entrapment database" strategy using merged spectra from human and E. coli revealed a marginal error rate for the proposed method. For application in single cell proteome (SCP) study, identification enhancement of 28%-61% was achieved for proteins for different SCP data. Furthermore, a lower abundance was found for the SPPUSM-identified peptides, indicating its potential for more sensitive low sample input and SCP studies.

Robust collection and processing for label-free single voxel proteomics

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk tissues. However, such bulk measurement lacks spatial resolution and obscures important tissue heterogeneity, which make it impossible for proteome mapping of tissue microenvironment. Here we report an integrated wet collection of single tissue voxel and Surfactant-assisted One-Pot voxel processing method termed wcSOP for robust label-free single voxel proteomics. wcSOP capitalizes on buffer droplet-assisted wet collection of single tissue voxel dissected by LCM into the PCR tube cap and MS-compatible surfactant-assisted one-pot voxel processing in the collection cap. This convenient method allows reproducible label-free quantification of ∼900 and ∼4,600 proteins for single voxel from fresh frozen human spleen tissue at 20 μm × 20 μm × 10 μm (close to single cells) and 200 μm × 200 μm × 10 μm (∼100 cells), respectively. 100s-1000s of protein signatures with differential expression levels were identified to be spatially resolved between spleen red and white pulp regions depending on the voxel size. Region-specific signaling pathways were enriched from single voxel proteomics data. Antibody-based CODEX imaging was used to validate label-free MS quantitation for single voxel analysis. The wcSOP-MS method paves the way for routine robust single voxel proteomics and spatial proteomics.

Polymeric Nanoparticles and Nanogels: How Do They Interact with Proteins?

Polymeric nanomaterials, nanogels, and solid nanoparticles can be fabricated using single or double emulsion methods. These materials hold great promise for various biomedical applications due to their biocompatibility, biodegradability, and their ability to control interactions with body fluids and cells. Despite the increasing use of nanoparticles in biomedicine and the plethora of publications on the topic, the biological behavior and efficacy of polymeric nanoparticles (PNPs) have not been as extensively studied as those of other nanoparticles. The gap between the potential of PNPs and their applications can mainly be attributed to the incomplete understanding of their biological identity. Under physiological conditions, such as specific temperatures and adequate protein concentrations, PNPs become coated with a "protein corona" (PC), rendering them potent tools for proteomics studies. In this review, we initially investigate the synthesis routes and chemical composition of conventional PNPs to better comprehend how they interact with proteins. Subsequently, we comprehensively explore the effects of material and biological parameters on the interactions between nanoparticles and proteins, encompassing reactions such as hydrophobic bonding and electrostatic interactions. Moreover, we delve into recent advances in PNP-based models that can be applied to nanoproteomics, discussing the new opportunities they offer for the clinical translation of nanoparticles and early prediction of diseases. By addressing these essential aspects, we aim to shed light on the potential of polymeric nanoparticles for biomedical applications and foster further research in this critical area.

One-STAGE Tip Method for TMT-Based Proteomic Analysis of a Minimal Amount of Cells

Liquid chromatography-tandem mass spectrometry (LC-MS)-based profiling of proteomes with isobaric tag labeling from low-quantity biological and clinical samples, including needle-core biopsies and laser capture microdissection, has been challenging due to the limited amount and sample loss during preparation. To address this problem, we developed OnM (On-Column from Myers et al. and mPOP)-modified on-column method combining freeze-thaw lysis of mPOP with isobaric tag labeling of On-Column method to minimize sample loss. OnM is a method that processes the sample in one-STAGE tip from cell lysis to tandem mass tag (TMT) labeling without any transfer of the sample. In terms of protein coverage, cellular components, and TMT labeling efficiency, the modified On-Column (or OnM) displayed similar performance to the results from Myers et al. To evaluate the lower-limit processing capability of OnM, we utilized OnM for multiplexing and were able to quantify 301 proteins in a TMT 9-plex with 50 cells per channel. We optimized the method as low as 5 cells per channel in which we identified 51 quantifiable proteins. OnM method is a low-input proteomics method widely applicable and capable of identifying and quantifying proteomes from limited samples, with tools that are readily available in a majority of proteomic laboratories.

A rapid and sensitive single-cell proteomic method based on fast liquid-chromatography separation, retention time prediction and MS1-only acquisition

Single-cell analysis has received much attention in recent years for elucidating the widely existing cellular heterogeneity in biological systems. However, the ability to measure the proteome in single cells is still far behind that of transcriptomics due to the lack of sensitive and high-throughput mass spectrometry methods. Herein, we report an integrated strategy termed "SCP-MS1" that combines fast liquid chromatography (LC) separation, deep learning-based retention time (RT) prediction and MS1-only acquisition for rapid and sensitive single-cell proteome analysis. In SCP-MS1, the peptides were identified via four-dimensional MS1 feature (m/z, RT, charge and FAIMS CV) matching, therefore relieving MS acquisition from the time consuming and information losing MS2 step and making this method particularly compatible with fast LC separation. By completely omitting the MS2 step, all the MS analysis time was utilized for MS1 acquisition in SCP-MS1 and therefore led to 65%-138% increased MS1 feature collection. Unlike "match between run" methods that still needed MS2 information for RT alignment, SCP-MS1 used deep learning-based RT prediction to transfer the measured RTs in long gradient bulk analyses to short gradient single cell analyses, which was the key step to enhance both identification scale and matching accuracy. Using this strategy, more than 2000 proteins were obtained from 0.2 ng of peptides with a 14-min active gradient at a false discovery rate (FDR) of 0.8%. Comparing with the DDA method, improved quantitative performance was also observed for SCP-MS1 with approximately 50% decreased median coefficient of variation of quantified proteins. For single-cell analysis, 1715 ± 204 and 1604 ± 224 proteins were quantified in single 293T and HeLa cells, respectively. Finally, SCP-MS1 was applied to single-cell proteome analysis of sorafenib resistant and non-resistant HepG2 cells and revealed clear cellular heterogeneity in the resistant population that may be masked in bulk studies.

Quantitative proteomics of small numbers of closely-related cells: Selection of the optimal method for a clinical setting

Mass spectrometry (MS)-based proteomics profiling has undoubtedly increased the knowledge about cellular processes and functions. However, its applicability for paucicellular sample analyses is currently limited. Although new approaches have been developed for single-cell studies, most of them have not (yet) been standardized and/or require highly specific (often home-built) devices, thereby limiting their broad implementation, particularly in non-specialized settings. To select an optimal MS-oriented proteomics approach applicable in translational research and clinical settings, we assessed 10 different sample preparation procedures in paucicellular samples of closely-related cell types. Particularly, five cell lysis protocols using different chemistries and mechanical forces were combined with two sample clean-up techniques (C18 filter- and SP3-based), followed by tandem mass tag (TMT)-based protein quantification. The evaluation was structured in three phases: first, cell lines from hematopoietic (THP-1) and non-hematopoietic (HT-29) origins were used to test the approaches showing the combination of a urea-based lysis buffer with the SP3 bead-based clean-up system as the best performer. Parameters such as reproducibility, accessibility, spatial distribution, ease of use, processing time and cost were considered. In the second phase, the performance of the method was tested on maturation-related cell populations: three different monocyte subsets from peripheral blood and, for the first time, macrophages/microglia (MAC) from glioblastoma samples, together with T cells from both tissues. The analysis of 50,000 cells down to only 2,500 cells revealed different protein expression profiles associated with the distinct cell populations. Accordingly, a closer relationship was observed between non-classical monocytes and MAC, with the latter showing the co-expression of M1 and M2 macrophage markers, although pro-tumoral and anti-inflammatory proteins were more represented. In the third phase, the results were validated by high-end spectral flow cytometry on paired monocyte/MAC samples to further determine the sensitivity of the MS approach selected. Finally, the feasibility of the method was proven in 194 additional samples corresponding to 38 different cell types, including cells from different tissue origins, cellular lineages, maturation stages and stimuli. In summary, we selected a reproducible, easy-to-implement sample preparation method for MS-based proteomic characterization of paucicellular samples, also applicable in the setting of functionally closely-related cell populations.