Transformative Opportunities for Single-Cell Proteomics

Proteomics mining of cancer hallmarks on a single-cell resolution

Dysregulated proteome is an essential contributor in carcinogenesis. Protein fluctuations fuel the progression of malignant transformation, such as uncontrolled proliferation, metastasis, and chemo/radiotherapy resistance, which severely impair therapeutic effectiveness and cause disease recurrence and eventually mortality among cancer patients. Cellular heterogeneity is widely observed in cancer and numerous cell subtypes have been characterized that greatly influence cancer progression. Population-averaged research may not fully reveal the heterogeneity, leading to inaccurate conclusions. Thus, deep mining of the multiplex proteome at the single-cell resolution will provide new insights into cancer biology, to develop prognostic biomarkers and treatments. Considering the recent advances in single-cell proteomics, herein we review several novel technologies with particular focus on single-cell mass spectrometry analysis, and summarize their advantages and practical applications in the diagnosis and treatment for cancer. Technological development in single-cell proteomics will bring a paradigm shift in cancer detection, intervention, and therapy.

Digital Microfluidics for Sample Preparation in Low-Input Proteomics

Low-input proteomics, also referred to as micro- or nanoproteomics, has become increasingly popular as it allows one to elucidate molecular processes in rare biological materials. A major prerequisite for the analytics of minute protein amounts, e.g., derived from low cell numbers, down to single cells, is the availability of efficient sample preparation methods. Digital microfluidics (DMF), a technology allowing the handling and manipulation of low liquid volumes, has recently been shown to be a powerful and versatile tool to address the challenges in low-input proteomics. Here, an overview is provided on recent advances in proteomics sample preparation using DMF. In particular, the capability of DMF to isolate proteomes from cells and small model organisms, and to perform all necessary chemical sample preparation steps, such as protein denaturation and proteolytic digestion on-chip, are highlighted. Additionally, major prerequisites to making these steps compatible with follow-up analytical methods such as liquid chromatography-mass spectrometry will be discussed.

Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP

Single-cell proteomics by mass spectrometry (MS) allows the quantification of proteins with high specificity and sensitivity. To increase its throughput, we developed nano-proteomic sample preparation (nPOP), a method for parallel preparation of thousands of single cells in nanoliter-volume droplets deposited on glass slides. Here, we describe its protocol with emphasis on its flexibility to prepare samples for different multiplexed MS methods. An implementation using the plexDIA MS multiplexing method, which uses non-isobaric mass tags to barcode peptides from different samples for data-independent acquisition, demonstrates accurate quantification of ~3,000-3,700 proteins per human cell. A separate implementation with isobaric mass tags and prioritized data acquisition demonstrates analysis of 1,827 single cells at a rate of >1,000 single cells per day at a depth of 800-1,200 proteins per human cell. The protocol is implemented by using a cell-dispensing and liquid-handling robot-the CellenONE instrument-and uses readily available consumables, which should facilitate broad adoption. nPOP can be applied to all samples that can be processed to a single-cell suspension. It takes 1 or 2 d to prepare >3,000 single cells. We provide metrics and software (the QuantQC R package) for quality control and data exploration. QuantQC supports the robust scaling of nPOP to higher plex reagents for achieving reliable and scalable single-cell proteomics.

Diversity of post-translational modifications and cell signaling revealed by single cell and single organelle mass spectrometry

The rapid evolution of mass spectrometry-based single-cell proteomics now enables the cataloging of several thousand proteins from single cells. We investigated whether we could discover cellular heterogeneity beyond proteome, encompassing post-translational modifications (PTM), protein-protein interaction, and variants. By optimizing the mass spectrometry data interpretation strategy to enable the detection of PTMs and variants, we have generated a high-definition dataset of single-cell and nuclear proteomic-states. The data demonstrate the heterogeneity of cell-states and signaling dependencies at the single-cell level and reveal epigenetic drug-induced changes in single nuclei. This approach enables the exploration of previously uncharted single-cell and organellar proteomes revealing molecular characteristics that are inaccessible through RNA profiling.

Evaluation of proteome dynamics: Implications for statistical confidence in mass spectrometric determination

Single-cell proteomics is currently far less productive than other approaches. Still, the proteomic community is having trouble adapting to the limitation of having to examine fewer cells than they would like. Studies on a small number of cells should be carefully planned to maximize the chances of success in this situation. This study aims to determine how sample size and measurement speed (slope)/variation affect the accuracy of a protein proteome mass spectrometric determination. The determination accuracy was shown to increase, and the false positive rate was shown to decrease as the sample size increased from 7 to 100 cells and the measurement slope/variation (S/V) ratio increased from 1 to 6. Furthermore, it was discovered that the number of cells in the sample increased the accuracy of this estimate. Thus, for 100 cells, the measurement S/V ratio was typically estimated to be very close to the real-world value, with a standard deviation of 0.35. For sample sizes from 7 to 100 cells, this accuracy was seen when calculating the measurement S/V ratio. The findings can help researchers plan experiments for mass spectroscopic protein proteome determination and other research purposes.

Single-cell proteomics reveals decreased abundance of proteostasis and meiosis proteins in advanced maternal age oocytes

Advanced maternal age is associated with a decline in oocyte quality, which often leads to reproductive failure in humans. However, the mechanisms behind this age-related decline remain unclear. To gain insights into this phenomenon, we applied plexDIA, a multiplexed data-independent acquisition, single-cell mass spectrometry method, to analyze the proteome of oocytes from both young women and women of advanced maternal age. Our findings primarily revealed distinct proteomic profiles between immature fully grown germinal vesicle and mature metaphase II oocytes. Importantly, we further show that a woman's age is associated with changes in her oocyte proteome. Specifically, when compared to oocytes obtained from young women, advanced maternal age oocytes exhibited lower levels of the proteasome and TRiC complex, as well as other key regulators of proteostasis and meiosis. This suggests that aging adversely affects the proteostasis and meiosis networks in human oocytes. The proteins identified in this study hold potential as targets for improving oocyte quality and may guide future studies into the molecular processes underlying oocyte aging.

Dynamics of Single-Cell Protein Covariation during Epithelial-Mesenchymal Transition

Physiological processes, such as the epithelial-mesenchymal transition (EMT), are mediated by changes in protein interactions. These changes may be better reflected in protein covariation within a cellular cluster than in the temporal dynamics of cluster-average protein abundance. To explore this possibility, we quantified proteins in single human cells undergoing EMT. Covariation analysis of the data revealed that functionally coherent protein clusters dynamically changed their protein-protein correlations without concomitant changes in the cluster-average protein abundance. These dynamics of protein-protein correlations were monotonic in time and delineated protein modules functioning in actin cytoskeleton organization, energy metabolism, and protein transport. These protein modules are defined by protein covariation within the same time point and cluster and, thus, reflect biological regulation masked by the cluster-average protein dynamics. Thus, protein correlation dynamics across single cells offers a window into protein regulation during physiological transitions.

Dynamics of single-cell protein covariation during epithelial-mesenchymal transition

Physiological processes, such as epithelial-mesenchymal transition (EMT), are mediated by changes in protein interactions. These changes may be better reflected in protein covariation within cellular cluster than in the temporal dynamics of cluster-average protein abundance. To explore this possibility, we quantified proteins in single human cells undergoing EMT. Covariation analysis of the data revealed that functionally coherent protein clusters dynamically changed their protein-protein correlations without concomitant changes in cluster-average protein abundance. These dynamics of protein-protein correlations were monotonic in time and delineated protein modules functioning in actin cytoskeleton organization, energy metabolism and protein transport. These protein modules are defined by protein covariation within the same time point and cluster and thus reflect biological regulation masked by the cluster-average protein dynamics. Thus, protein correlation dynamics across single cells offer a window into protein regulation during physiological transitions.

Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop

Studies of asthma and allergy are generating increasing volumes of omics data for analysis and interpretation. The National Institute of Allergy and Infectious Diseases (NIAID) assembled a workshop comprising investigators studying asthma and allergic diseases using omics approaches, omics investigators from outside the field, and NIAID medical and scientific officers to discuss the following areas in asthma and allergy research: genomics, epigenomics, transcriptomics, microbiomics, metabolomics, proteomics, lipidomics, integrative omics, systems biology, and causal inference. Current states of the art, present challenges, novel and emerging strategies, and priorities for progress were presented and discussed for each area. This workshop report summarizes the major points and conclusions from this NIAID workshop. As a group, the investigators underscored the imperatives for rigorous analytic frameworks, integration of different omics data types, cross-disciplinary interaction, strategies for overcoming current limitations, and the overarching goal to improve scientific understanding and care of asthma and allergic diseases.

Enhancing Single-Cell Western Blotting Sensitivity Using Diffusive Analyte Blotting and Antibody Conjugate Amplification

While there are many techniques to achieve highly sensitive, multiplex detection of RNA and DNA from single cells, detecting protein content often suffers from low limits of detection and throughput. Miniaturized, high-sensitivity Western blots on single cells (scWesterns) are attractive because they do not require advanced instrumentation. By physically separating analytes, scWesterns also uniquely mitigate limitations to target protein multiplexing posed by the affinity reagent performance. However, a fundamental limitation of scWesterns is their limited sensitivity for detecting low-abundance proteins, which arises from transport barriers posed by the separation gel against detection species. Here we address the sensitivity by decoupling the electrophoretic separation medium from the detection medium. We transfer scWestern separations to a nitrocellulose blotting medium with distinct mass transfer advantages over traditional in-gel probing, yielding a 5.9-fold improvement in the limit of detection. We next amplify probing of blotted proteins with enzyme-antibody conjugates, which are incompatible with traditional in-gel probing to achieve further improvement in the limit of detection to 1000 molecules, a 120-fold improvement. This enables us to detect 100% of cells in an EGFP-expressing population using fluorescently tagged and enzyme-conjugated antibodies compared to 84.5% of cells using in-gel detection. These results suggest the compatibility of nitrocellulose-immobilized scWesterns with a variety of affinity reagents─not previously accessible for in-gel use─for further signal amplification and detection of low-abundance targets.

Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP

Single-cell proteomics by mass spectrometry (MS) allows quantifying proteins with high specificity and sensitivity. To increase its throughput, we developed nPOP, a method for parallel preparation of thousands of single cells in nanoliter volume droplets deposited on glass slides. Here, we describe its protocol with emphasis on its flexibility to prepare samples for different multiplexed MS methods. An implementation with plexDIA demonstrates accurate quantification of about 3,000 - 3,700 proteins per human cell. The protocol is implemented on the CellenONE instrument and uses readily available consumables, which should facilitate broad adoption. nPOP can be applied to all samples that can be processed to a single-cell suspension. It takes 1 or 2 days to prepare over 3,000 single cells. We provide metrics and software for quality control that can support the robust scaling of nPOP to higher plex reagents for achieving reliable high-throughput single-cell protein analysis.

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.

Revisiting the Thorny Issue of Missing Values in Single-Cell Proteomics

Missing values are a notable challenge when analyzing mass spectrometry-based proteomics data. While the field is still actively debating the best practices, the challenge increased with the emergence of mass spectrometry-based single-cell proteomics and the dramatic increase in missing values. A popular approach to deal with missing values is to perform imputation. Imputation has several drawbacks for which alternatives exist, but currently, imputation is still a practical solution widely adopted in single-cell proteomics data analysis. This perspective discusses the advantages and drawbacks of imputation. We also highlight 5 main challenges linked to missing value management in single-cell proteomics. Future developments should aim to solve these challenges, whether it is through imputation or data modeling. The perspective concludes with recommendations for reporting missing values, for reporting methods that deal with missing values, and for proper encoding of missing values.

Unidirectional single-file transport of full-length proteins through a nanopore

The electrical current blockade of a peptide or protein threading through a nanopore can be used as a fingerprint of the molecule in biosensor applications. However, threading of full-length proteins has only been achieved using enzymatic unfolding and translocation. Here we describe an enzyme-free approach for unidirectional, slow transport of full-length proteins through nanopores. We show that the combination of a chemically resistant biological nanopore, α-hemolysin (narrowest part is ~1.4 nm in diameter), and a high concentration guanidinium chloride buffer enables unidirectional, single-file protein transport propelled by an electroosmotic effect. We show that the mean protein translocation velocity depends linearly on the applied voltage and translocation times depend linearly on length, resembling the translocation dynamics of ssDNA. Using a supervised machine-learning classifier, we demonstrate that single-translocation events contain sufficient information to distinguish their threading orientation and identity with accuracies larger than 90%. Capture rates of protein are increased substantially when either a genetically encoded charged peptide tail or a DNA tag is added to a protein.

Protein biomarkers for radiation injury and testing of medical countermeasure efficacy: promises, pitfalls, and future directions

INTRODUCTION

Radiological/nuclear accidents, hostile military activity, or terrorist strikes have the potential to expose a large number of civilians and military personnel to high doses of radiation resulting in the development of acute radiation syndrome and delayed effects of exposure. Thus, there is an urgent need for sensitive and specific assays to assess the levels of radiation exposure to individuals. Such radiation exposures are expected to alter primary cellular proteomic processes, resulting in multifaceted biological responses.

AREAS COVERED

This article covers the application of proteomics, a promising and fast developing technology based on quantitative and qualitative measurements of protein molecules for possible rapid measurement of radiation exposure levels. Recent advancements in high-resolution chromatography, mass spectrometry, high-throughput, and bioinformatics have resulted in comprehensive (relative quantitation) and precise (absolute quantitation) approaches for the discovery and accuracy of key protein biomarkers of radiation exposure. Such proteome biomarkers might prove useful for assessing radiation exposure levels as well as for extrapolating the pharmaceutical dose of countermeasures for humans based on efficacy data generated using animal models.

EXPERT OPINION

The field of proteomics promises to be a valuable asset in evaluating levels of radiation exposure and characterizing radiation injury biomarkers.

Correlated Protein Modules Revealing Functional Coordination of Interacting Proteins Are Detected by Single-Cell Proteomics

Single-cell proteomics has attracted a lot of attention in recent years because it offers more functional relevance than single-cell transcriptomics. However, most work to date has focused on cell typing, which has been widely accomplished by single-cell transcriptomics. Here we report the use of single-cell proteomics to measure the correlation between the translational levels of a pair of proteins in a single mammalian cell. In measuring pairwise correlations among ∼1000 proteins in a population of homogeneous K562 cells under a steady-state condition, we observed multiple correlated protein modules (CPMs), each containing a group of highly positively correlated proteins that are functionally interacting and collectively involved in certain biological functions, such as protein synthesis and oxidative phosphorylation. Some CPMs are shared across different cell types while others are cell-type specific. Widely studied in omics analyses, pairwise correlations are often measured by introducing perturbations into bulk samples. However, some correlations of gene or protein expression under the steady-state condition would be masked by perturbation. The single-cell correlations probed in our experiment reflect intrinsic steady-state fluctuations in the absence of perturbation. We note that observed correlations between proteins are experimentally more distinct and functionally more relevant than those between corresponding mRNAs measured in single-cell transcriptomics. By virtue of single-cell proteomics, functional coordination of proteins is manifested through CPMs.

Enhancing single-cell western blotting sensitivity using diffusive analyte blotting and antibody conjugate amplification

While there are many techniques to achieve highly sensitive, multiplex detection of RNA and DNA from single cells, detecting protein contents often suffers from low limits of detection and throughput. Miniaturized, high-sensitivity western blots on single cells (scWesterns) are attractive since they do not require advanced instrumentation. By physically separating analytes, scWesterns also uniquely mitigate limitations to target protein multiplexing posed by affinity reagent performance. However, a fundamental limitation of scWesterns is their limited sensitivity for detecting low-abundance proteins, which arises from transport barriers posed by the separation gel against detection species. Here we address sensitivity by decoupling the electrophoretic separation medium from the detection medium. We transfer scWestern separations to a nitrocellulose blotting medium with distinct mass transfer advantages over traditional in-gel probing, yielding a 5.9-fold improvement in limit of detection. We next amplify probing of blotted proteins with enzyme-antibody conjugates which are incompatible with traditional in-gel probing to achieve further improvement in the limit of detection to 103 molecules, a 520-fold improvement. This enables us to detect 85% and 100% of cells in an EGFP-expressing population using fluorescently tagged and enzyme-conjugated antibodies respectively, compared to 47% of cells using in-gel detection. These results suggest compatibility of nitrocellulose-immobilized scWesterns with a variety of affinity reagents - not previously accessible for in-gel use - for further signal amplification and detection of low abundance targets.

pepDESC: A method for the detection of differentially expressed proteins for mass spectrometry-based single-cell proteomics using peptide-level information

Single-cell proteomics as an emerging field has exhibited potential in revealing cellular heterogeneity at the functional level. However, accurate interpretation of single-cell proteomics data suffers from challenges such as measurement noise, internal heterogeneity, and the limited sample size of label-free quantitative mass spectrometry. Herein, the author describes pepDESC, a method for detecting differentially expressed proteins using peptide-level information designed for label-free quantitative mass spectrometry-based single-cell proteomics. While in this study, the author focuses on the heterogeneity among limited number of samples, pepDESC is also applicable to regular-size proteomics data. By balancing proteome coverage and quantification accuracy using peptide quantification, pepDESC is demonstrated to be effective in real-world single-cell and spike -in benchmark datasets. By applying pepDESC to a published single-mouse macrophages data, the author found a large fraction of differentially expressed proteins among three types of cells, which remarkably revealed distinct dynamics of different cellular functions responding to lipopolysaccharide stimulation.

Prioritized mass spectrometry increases the depth, sensitivity and data completeness of single-cell proteomics

Major aims of single-cell proteomics include increasing the consistency, sensitivity and depth of protein quantification, especially for proteins and modifications of biological interest. Here, to simultaneously advance all these aims, we developed prioritized Single-Cell ProtEomics (pSCoPE). pSCoPE consistently analyzes thousands of prioritized peptides across all single cells (thus increasing data completeness) while maximizing instrument time spent analyzing identifiable peptides, thus increasing proteome depth. These strategies increased the sensitivity, data completeness and proteome coverage over twofold. The gains enabled quantifying protein variation in untreated and lipopolysaccharide-treated primary macrophages. Within each condition, proteins covaried within functional sets, including phagosome maturation and proton transport, similarly across both treatment conditions. This covariation is coupled to phenotypic variability in endocytic activity. pSCoPE also enabled quantifying proteolytic products, suggesting a gradient of cathepsin activities within a treatment condition. pSCoPE is freely available and widely applicable, especially for analyzing proteins of interest without sacrificing proteome coverage. Support for pSCoPE is available at http://scp.slavovlab.net/pSCoPE .

Challenges and Opportunities for Single-cell Computational Proteomics

Single-cell proteomics is growing rapidly and has made several technological advancements. As most research has been focused on improving instrumentation and sample preparation methods, very little attention has been given to algorithms responsible for identifying and quantifying proteins. Given the inherent difference between bulk data and single-cell data, it is necessary to realize that current algorithms being employed on single-cell data were designed for bulk data and have underlying assumptions that may not hold true for single-cell data. In order to develop and optimize algorithms for single-cell data, we need to characterize the differences between single-cell data and bulk data and assess how current algorithms perform on single-cell data. Here, we present a review of algorithms responsible for identifying and quantifying peptides and proteins. We will give a review of how each type of algorithm works, assumptions it relies on, how it performs on single-cell data, and possible optimizations and solutions that could be used to address the differences in single-cell data.

Comparison of Database Searching Programs for the Analysis of Single-Cell Proteomics Data

Single-cell proteomics is emerging as an important subfield in the proteomics and mass spectrometry communities, with potential to reshape our understanding of cell development, cell differentiation, disease diagnosis, and the development of new therapies. Compared with significant advancements in the "hardware" that is used in single-cell proteomics, there has been little work comparing the effects of using different "software" packages to analyze single-cell proteomics datasets. To this end, seven popular proteomics programs were compared here, applying them to search three single-cell proteomics datasets generated by three different platforms. The results suggest that MSGF+, MSFragger, and Proteome Discoverer are generally more efficient in maximizing protein identifications, that MaxQuant is better suited for the identification of low-abundance proteins, that MSFragger is superior in elucidating peptide modifications, and that Mascot and X!Tandem are better for analyzing long peptides. Furthermore, an experiment with different loading amounts was carried out to investigate changes in identification results and to explore areas in which single-cell proteomics data analysis may be improved in the future. We propose that this comparative study may provide insight for experts and beginners alike operating in the emerging subfield of single-cell proteomics.

Strategies for Increasing the Depth and Throughput of Protein Analysis by plexDIA

Accurate protein quantification is key to identifying protein markers, regulatory relationships between proteins, and pathophysiological mechanisms. Realizing this potential requires sensitive and deep protein analysis of a large number of samples. Toward this goal, proteomics throughput can be increased by parallelizing the analysis of both precursors and samples using multiplexed data independent acquisition (DIA) implemented by the plexDIA framework: https://plexDIA.slavovlab.net. Here we demonstrate the improved precisions of retention time estimates within plexDIA and how this enables more accurate protein quantification. plexDIA has demonstrated multiplicative gains in throughput, and these gains may be substantially amplified by improving the multiplexing reagents, data acquisition, and interpretation. We discuss future directions for advancing plexDIA, which include engineering optimized mass-tags for high-plexDIA, introducing isotopologous carriers, and developing algorithms that utilize the regular structures of plexDIA data to improve sensitivity, proteome coverage, and quantitative accuracy. These advances in plexDIA will increase the throughput of functional proteomic assays, including quantifying protein conformations, turnover dynamics, modifications states and activities. The sensitivity of these assays will extend to single-cell analysis, thus enabling functional single-cell protein analysis.

Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments

Analyzing proteins from single cells by tandem mass spectrometry (MS) has recently become technically feasible. While such analysis has the potential to accurately quantify thousands of proteins across thousands of single cells, the accuracy and reproducibility of the results may be undermined by numerous factors affecting experimental design, sample preparation, data acquisition and data analysis. We expect that broadly accepted community guidelines and standardized metrics will enhance rigor, data quality and alignment between laboratories. Here we propose best practices, quality controls and data-reporting recommendations to assist in the broad adoption of reliable quantitative workflows for single-cell proteomics. Resources and discussion forums are available at https://single-cell.net/guidelines .

Increasing the throughput of sensitive proteomics by plexDIA

Current mass spectrometry methods enable high-throughput proteomics of large sample amounts, but proteomics of low sample amounts remains limited in depth and throughput. To increase the throughput of sensitive proteomics, we developed an experimental and computational framework, called plexDIA, for simultaneously multiplexing the analysis of peptides and samples. Multiplexed analysis with plexDIA increases throughput multiplicatively with the number of labels without reducing proteome coverage or quantitative accuracy. By using three-plex non-isobaric mass tags, plexDIA enables quantification of threefold more protein ratios among nanogram-level samples. Using 1-hour active gradients, plexDIA quantified ~8,000 proteins in each sample of labeled three-plex sets and increased data completeness, reducing missing data more than twofold across samples. Applied to single human cells, plexDIA quantified ~1,000 proteins per cell and achieved 98% data completeness within a plexDIA set while using ~5 minutes of active chromatography per cell. These results establish a general framework for increasing the throughput of sensitive and quantitative protein analysis.

Exploring functional protein covariation across single cells using nPOP

BACKGROUND

Many biological processes, such as cell division cycle and drug resistance, are reflected in protein covariation across single cells. This covariation can be quantified and interpreted by single-cell mass spectrometry with sufficiently high throughput and accuracy.

RESULTS

Here, we describe nPOP, a method that enables simultaneous sample preparation of thousands of single cells, including lysing, digesting, and labeling individual cells in volumes of 8-20 nl. nPOP uses piezo acoustic dispensing to isolate individual cells in 300 pl volumes and performs all subsequent sample preparation steps in small droplets on a fluorocarbon-coated glass slide. Protein covariation analysis identifies cell cycle dynamics that are similar and dynamics that differ between cell types, even within subpopulations of melanoma cells delineated by markers for drug resistance priming. Melanoma cells expressing these markers accumulate in the G1 phase of the cell cycle, display distinct protein covariation across the cell cycle, accumulate glycogen, and have lower abundance of glycolytic enzymes. The non-primed melanoma cells exhibit gradients of protein abundance, suggesting transition states. Within this subpopulation, proteins functioning in oxidative phosphorylation covary with each other and inversely with proteins functioning in glycolysis. This protein covariation suggests divergent reliance on energy sources and its association with other biological functions. These results are validated by different mass spectrometry methods.

CONCLUSIONS

nPOP enables flexible, automated, and highly parallelized sample preparation for single-cell proteomics. This allows for quantifying protein covariation across thousands of single cells and revealing functionally concerted biological differences between closely related cell states. Support for nPOP is available at https://scp.slavovlab.net/nPOP .

Single-Cell Proteomics Preparation for Mass Spectrometry Analysis Using Freeze-Heat Lysis and an Isobaric Carrier

Single-cell proteomics analysis requires sensitive, quantitatively accurate, widely accessible, and robust methods. To meet these requirements, the Single-Cell ProtEomics (SCoPE2) protocol was developed as a second-generation method for quantifying hundreds to thousands of proteins from limited samples, down to the level of a single cell. Experiments using this method have achieved quantifying over 3,000 proteins across 1,500 single mammalian cells (500-1,000 proteins per cell) in 10 days of mass spectrometer instrument time. SCoPE2 leverages a freeze-heat cycle for cell lysis, obviating the need for clean-up of single cells and consequently reducing sample losses, while expediting sample preparation and simplifying its automation. Additionally, the method uses an isobaric carrier, which aids protein identification and reduces sample losses. This video protocol provides detailed guidance to enable the adoption of automated single-cell protein analysis using only equipment and reagents that are widely accessible. We demonstrate critical steps in the procedure of preparing single cells for proteomic analysis, from harvesting up to injection to liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Additionally, viewers are guided through the principles of experimental design with the isobaric carrier, quality control for both isobaric carrier and single-cell preparations, and representative results with a discussion of limitations of the approach.

Spectral Library-Based Single-Cell Proteomics Resolves Cellular Heterogeneity

Dissecting the proteome of cell types and states at single-cell resolution, while being highly challenging, has significant implications in basic science and biomedicine. Mass spectrometry (MS)-based single-cell proteomics represents an emerging technology for system-wide, unbiased profiling of proteins in single cells. However, significant challenges remain in analyzing an extremely small amount of proteins collected from a single cell, as a proteome-wide amplification of proteins is not currently feasible. Here, we report an integrated spectral library-based single-cell proteomics (SLB-SCP) platform that is ultrasensitive and well suited for a large-scale analysis. To overcome the low MS/MS signal intensity intrinsically associated with a single-cell analysis, this approach takes an alternative approach by extracting a breadth of information that specifically defines the physicochemical characteristics of a peptide from MS1 spectra, including monoisotopic mass, isotopic distribution, and retention time (hydrophobicity), and uses a spectral library for proteomic identification. This conceptually unique MS platform, coupled with the DIRECT sample preparation method, enabled identification of more than 2000 proteins in a single cell to distinguish different proteome landscapes associated with cellular types and heterogeneity. We characterized individual normal and cancerous pancreatic ductal cells (HPDE and PANC-1, respectively) and demonstrated the substantial difference in the proteomes between HPDE and PANC-1 at the single-cell level. A significant upregulation of multiple protein networks in cancer hallmarks was identified in the PANC-1 cells, functionally discriminating the PANC-1 cells from the HPDE cells. This integrated platform can be built on high-resolution MS and widely accepted proteomic software, making it possible for community-wide applications.

DeepSCP: utilizing deep learning to boost single-cell proteome coverage

Multiplexed single-cell proteomes (SCPs) quantification by mass spectrometry greatly improves the SCP coverage. However, it still suffers from a low number of protein identifications and there is much room to boost proteins identification by computational methods. In this study, we present a novel framework DeepSCP, utilizing deep learning to boost SCP coverage. DeepSCP constructs a series of features of peptide-spectrum matches (PSMs) by predicting the retention time based on the multiple SCP sample sets and fragment ion intensities based on deep learning, and predicts PSM labels with an optimized-ensemble learning model. Evaluation of DeepSCP on public and in-house SCP datasets showed superior performances compared with other state-of-the-art methods. DeepSCP identified more confident peptides and proteins by controlling q-value at 0.01 using target–decoy competition method. As a convenient and low-cost computing framework, DeepSCP will help boost single-cell proteome identification and facilitate the future development and application of single-cell proteomics.

Infrared Laser Ablation Microsampling for Small Volume Proteomics

Infrared (IR) laser ablation was used to remove localized tissue regions from which proteins were extracted and processed with a low volume sample preparation workflow for bottom-up proteomics by liquid chromatography tandem mass spectrometry (LC-MS/MS). A polytetrafluoroethylene (PTFE) coated glass slide with 2 mm diameter microwells was used to capture ablated rat brain tissue for in situ protein digestion with submicroliter solution volumes. The resulting peptides were analyzed with LC-MS/MS for protein identification and label-free quantification. The method was used to identify an average of 600, 1350, and 1900 proteins from ablation areas of 0.01, 0.04, and 0.1 mm², respectively, from a 50 μm thick rat brain tissue section. Differential proteomics of 0.01 mm² regions captured from cerebral cortex and corpus callosum was accomplished to demonstrate the capabilities of the approach.

The Intriguing Landscape of Single-Cell Protein Analysis

Profiling protein expression at single-cell resolution is essential for fundamental biological research (such as cell differentiation and tumor microenvironmental examination) and clinical precision medicine where only a limited number of primary cells are permitted. With the recent advances in engineering, chemistry, and biology, single-cell protein analysis methods are developed rapidly, which enable high-throughput and multiplexed protein measurements in thousands of individual cells. In combination with single cell RNA sequencing and mass spectrometry, single-cell multi-omics analysis can simultaneously measure multiple modalities including mRNAs, proteins, and metabolites in single cells, and obtain a more comprehensive exploration of cellular signaling processes, such as DNA modifications, chromatin accessibility, protein abundance, and gene perturbation. Here, the recent progress and applications of single-cell protein analysis technologies in the last decade are summarized. Current limitations, challenges, and possible future directions in this field are also discussed.

The emerging role of mass spectrometry-based proteomics in drug discovery

Proteins are the main targets of most drugs; however, system-wide methods to monitor protein activity and function are still underused in drug discovery. Novel biochemical approaches, in combination with recent developments in mass spectrometry-based proteomics instrumentation and data analysis pipelines, have now enabled the dissection of disease phenotypes and their modulation by bioactive molecules at unprecedented resolution and dimensionality. In this Review, we describe proteomics and chemoproteomics approaches for target identification and validation, as well as for identification of safety hazards. We discuss innovative strategies in early-stage drug discovery in which proteomics approaches generate unique insights, such as targeted protein degradation and the use of reactive fragments, and provide guidance for experimental strategies crucial for success.

Scaling Up Single-Cell Proteomics

Single-cell tandem MS has enabled analyzing hundreds of single cells per day and quantifying thousands of proteins across the cells. The broad dissemination of these capabilities can empower the dissection of pathophysiological mechanisms in heterogeneous tissues. Key requirements for achieving this goal include robust protocols performed on widely accessible hardware, robust quality controls, community standards, and automated data analysis pipelines that can pinpoint analytical problems and facilitate their timely resolution. Toward meeting these requirements, this perspective outlines both existing resources and outstanding opportunities, such as parallelization, for catalyzing the wide dissemination of quantitative single-cell proteomics analysis that can be scaled up to tens of thousands of single cells. Indeed, simultaneous parallelization of the analysis of peptides and single cells is a promising approach for multiplicative increase in the speed of performing deep and quantitative single-cell proteomics. The community is ready to begin a virtuous cycle of increased adoption fueling the development of more technology and resources for single-cell proteomics that in turn drive broader adoption, scientific discoveries, and clinical applications.

Accurate Prediction of Protein Sequences for Proteogenomics Data Integration

This book chapter discusses proteogenomics data integration and provides an overview into the different omics layer involved in defining the proteome of a living organism. Various aspects of genome variability affecting either the sequence or abundance level of proteins are discussed in this book chapter, such as the effect of single-nucleotide variants or larger genomic structural variants on the proteome. Next, various sequencing technologies are introduced and discussed from a proteogenomics data integration perspective such as those providing short- and long-read sequencing and listing their respective advantages and shortcomings for accurate protein variant prediction using genomic/transcriptomics sequencing data. Finally, the various bioinformatics tools used to process and analyze DNA/RNA sequencing data are discussed with the ultimate goal of obtaining accurately predicted sample-specific protein sequences that can be used as a drop-in replacement in existing approaches for peptide and protein identification using popular database search engines such as MSFragger, SearchGUI/PeptideShaker.

Multiplexed single-cell proteomics using SCoPE2

Many biological systems are composed of diverse single cells. This diversity necessitates functional and molecular single-cell analysis. Single-cell protein analysis has long relied on affinity reagents, but emerging mass-spectrometry methods (either label-free or multiplexed) have enabled quantifying >1,000 proteins per cell while simultaneously increasing the specificity of protein quantification. Here we describe the Single Cell ProtEomics (SCoPE2) protocol, which uses an isobaric carrier to enhance peptide sequence identification. Single cells are isolated by FACS or CellenONE into multiwell plates and lysed by Minimal ProteOmic sample Preparation (mPOP), and their peptides labeled by isobaric mass tags (TMT or TMTpro) for multiplexed analysis. SCoPE2 affords a cost-effective single-cell protein quantification that can be fully automated using widely available equipment and scaled to thousands of single cells. SCoPE2 uses inexpensive reagents and is applicable to any sample that can be processed to a single-cell suspension. The SCoPE2 workflow allows analyzing ~200 single cells per 24 h using only standard commercial equipment. We emphasize experimental steps and benchmarks required for achieving quantitative protein analysis.

The Human Proteoform Project: Defining the human proteome

Proteins are the primary effectors of function in biology, and thus, complete knowledge of their structure and properties is fundamental to deciphering function in basic and translational research. The chemical diversity of proteins is expressed in their many proteoforms, which result from combinations of genetic polymorphisms, RNA splice variants, and posttranslational modifications. This knowledge is foundational for the biological complexes and networks that control biology yet remains largely unknown. We propose here an ambitious initiative to define the human proteome, that is, to generate a definitive reference set of the proteoforms produced from the genome. Several examples of the power and importance of proteoform-level knowledge in disease-based research are presented along with a call for improved technologies in a two-pronged strategy to the Human Proteoform Project.

Driving Single Cell Proteomics Forward with Innovation

Current single-cell mass spectrometry (MS) methods can quantify thousands of peptides per single cell while detecting peptide-like features that may support the quantification of 10-fold more peptides. This 10-fold gain might be attained by innovations in data acquisition and interpretation even while using existing instrumentation. This perspective discusses possible directions for such innovations with the aim to stimulate community efforts for increasing the coverage and quantitative accuracy of single proteomics while simultaneously decreasing missing data. Parallel improvements in instrumentation, sample preparation, and peptide separation will afford additional gains. Together, these synergistic routes for innovation project a rapid growth in the capabilities of MS based single-cell protein analysis. These gains will directly empower applications of single-cell proteomics to biomedical research.

Replication of single-cell proteomics data reveals important computational challenges

INTRODUCTION

Mass spectrometry-based proteomics is actively embracing quantitative, single-cell level analyses. Indeed, recent advances in sample preparation and mass spectrometry (MS) have enabled the emergence of quantitative MS-based single-cell proteomics (SCP). While exciting and promising, SCP still has many rough edges. The current analysis workflows are custom and built from scratch. The field is therefore craving for standardized software that promotes principled and reproducible SCP data analyses.

AREAS COVERED

This special report is the first step toward the formalization and standardization of SCP data analysis. scp, the software that accompanies this work, successfully replicates one of the landmark SCP studies and is applicable to other experiments and designs. We created a repository containing the replicated workflow with comprehensive documentation in order to favor further dissemination and improvements of SCP data analyses.

EXPERT OPINION

Replicating SCP data analyses uncovers important challenges in SCP data analysis. We describe two such challenges in detail: batch correction and data missingness. We provide the current state-of-the-art and illustrate the associated limitations. We also highlight the intimate dependence that exists between batch effects and data missingness and offer avenues for dealing with these exciting challenges.

IceR improves proteome coverage and data completeness in global and single-cell proteomics

Label-free proteomics by data-dependent acquisition enables the unbiased quantification of thousands of proteins, however it notoriously suffers from high rates of missing values, thus prohibiting consistent protein quantification across large sample cohorts. To solve this, we here present IceR (Ion current extraction Re-quantification), an efficient and user-friendly quantification workflow that combines high identification rates of data-dependent acquisition with low missing value rates similar to data-independent acquisition. Specifically, IceR uses ion current information for a hybrid peptide identification propagation approach with superior quantification precision, accuracy, reliability and data completeness compared to other quantitative workflows. Applied to plasma and single-cell proteomics data, IceR enhanced the number of reliably quantified proteins, improved discriminability between single-cell populations, and allowed reconstruction of a developmental trajectory. IceR will be useful to improve performance of large scale global as well as low-input proteomics applications, facilitated by its availability as an easy-to-use R-package.

Tissue Proteomic Approaches to Understand the Pathogenesis of Inflammatory Bowel Disease

Inflammatory bowel disease (IBD) has become a global disease encompassing a group of progressive disorders characterized by recurrent chronic inflammation of the gut with variable disease courses and complications. Despite recent advances in the knowledge of IBD pathophysiology, the elucidation of its etiopathology and progression is far from fully understood, requiring complex and multiple approaches. Therefore, limited clinical progress in diagnosis, assessment of disease activity, and optimal therapeutic regimens have been made over the past few decades. This review explores recent advances and challenges in tissue proteomics with an emphasis on biomarker discovery and better understanding of the molecular mechanisms underlying IBD pathogenesis. Future multi-omic studies are required for the comprehensive molecular characterization of disease biology in real time with a future impact on early detection, disease monitoring, and prediction of the clinical outcome.

In Vivo Subcellular Mass Spectrometry Enables Proteo-Metabolomic Single-Cell Systems Biology in a Chordate Embryo Developing to a Normally Behaving Tadpole (X. laevis)*

We report the development of in vivo subcellular high-resolution mass spectrometry (HRMS) for proteo-metabolomic molecular systems biology in complex tissues. With light microscopy, we identified the left-dorsal and left-ventral animal cells in cleavage-stage non-sentient Xenopus laevis embryos. Using precision-translated fabricated microcapillaries, the subcellular content of each cell was double-probed, each time swiftly (<5 s/event) aspirating <5 % of cell volume (≈10 nL). The proteins and metabolites were analyzed by home-built ultrasensitive capillary electrophoresis electrospray ionization employing orbitrap or time-of-flight HRMS. Label-free detection of ≈150 metabolites (57 identified) and 738 proteins found proteo-metabolomic networks with differential quantitative activities between the cell types. With spatially and temporally scalable sampling, the technology preserved the integrity of the analyzed cells, the neighboring cells, and the embryo. 95 % of the analyzed embryos developed into sentient tadpoles that were indistinguishable from their wild-type siblings based on anatomy and visual function in a background color preference assay.

Proteomics, Personalized Medicine and Cancer

As of 2020 the human genome and proteome are both at >90% completion based on high stringency analyses. This has been largely achieved by major technological advances over the last 20 years and has enlarged our understanding of human health and disease, including cancer, and is supporting the current trend towards personalized/precision medicine. This is due to improved screening, novel therapeutic approaches and an increased understanding of underlying cancer biology. However, cancer is a complex, heterogeneous disease modulated by genetic, molecular, cellular, tissue, population, environmental and socioeconomic factors, which evolve with time. In spite of recent advances in treatment that have resulted in improved patient outcomes, prognosis is still poor for many patients with certain cancers (e.g., mesothelioma, pancreatic and brain cancer) with a high death rate associated with late diagnosis. In this review we overview key hallmarks of cancer (e.g., autophagy, the role of redox signaling), current unmet clinical needs, the requirement for sensitive and specific biomarkers for early detection, surveillance, prognosis and drug monitoring, the role of the microbiome and the goals of personalized/precision medicine, discussing how emerging omics technologies can further inform on these areas. Exemplars from recent onco-proteogenomic-related publications will be given. Finally, we will address future perspectives, not only from the standpoint of perceived advances in treatment, but also from the hurdles that have to be overcome.

Calculating Sample Size Requirements for Temporal Dynamics in Single-Cell Proteomics

Single-cell measurements are uniquely capable of characterizing cell-to-cell heterogeneity and have been used to explore the large diversity of cell types and physiological functions present in tissues and other complex cell assemblies. An intriguing application of single-cell proteomics is the characterization of proteome dynamics during biological transitions, like cellular differentiation or disease progression. Time-course experiments, which regularly take measurements during state transitions, rely on the ability to detect dynamic trajectories in a data series. However, in a single-cell proteomics experiment, cell-to-cell heterogeneity complicates the confident identification of proteome dynamics as measurement variability may be higher than expected. Therefore, a critical question for these experiments is how many data points need to be acquired during the time course to enable robust statistical analysis. We present here an analysis of the most important variables that affect statistical confidence in the detection of proteome dynamics: fold change, measurement variability, and the number of cells measured during the time course. Importantly, we show that datasets with less than 16 measurements across the time domain suffer from low accuracy and also have a high false-positive rate. We also demonstrate how to balance competing demands in experimental design to achieve a desired result.

Microfluidics for Peptidomics, Proteomics, and Cell Analysis

Microfluidics is the advanced microtechnology of fluid manipulation in channels with at least one dimension in the range of 1-100 microns. Microfluidic technology offers a growing number of tools for manipulating small volumes of fluid to control chemical, biological, and physical processes relevant to separation, analysis, and detection. Currently, microfluidic devices play an important role in many biological, chemical, physical, biotechnological and engineering applications. There are numerous ways to fabricate the necessary microchannels and integrate them into microfluidic platforms. In peptidomics and proteomics, microfluidics is often used in combination with mass spectrometric (MS) analysis. This review provides an overview of using microfluidic systems for peptidomics, proteomics and cell analysis. The application of microfluidics in combination with MS detection and other novel techniques to answer clinical questions is also discussed in the context of disease diagnosis and therapy. Recent developments and applications of capillary and microchip (electro)separation methods in proteomic and peptidomic analysis are summarized. The state of the art of microchip platforms for cell sorting and single-cell analysis is also discussed. Advances in detection methods are reported, and new applications in proteomics and peptidomics, quality control of peptide and protein pharmaceuticals, analysis of proteins and peptides in biomatrices and determination of their physicochemical parameters are highlighted.

Spatially Resolved Mass Spectrometry at the Single Cell: Recent Innovations in Proteomics and Metabolomics

Biological systems are composed of heterogeneous populations of cells that intercommunicate to form a functional living tissue. Biological function varies greatly across populations of cells, as each single cell has a unique transcriptome, proteome, and metabolome that translates to functional differences within single species and across kingdoms. Over the past decade, substantial advancements in our ability to characterize omic profiles on a single cell level have occurred, including in multiple spectroscopic and mass spectrometry (MS)-based techniques. Of these technologies, spatially resolved mass spectrometry approaches, including mass spectrometry imaging (MSI), have shown the most progress for single cell proteomics and metabolomics. For example, reporter-based methods using heavy metal tags have allowed for targeted MS investigation of the proteome at the subcellular level, and development of technologies such as laser ablation electrospray ionization mass spectrometry (LAESI-MS) now mean that dynamic metabolomics can be performed in situ. In this Perspective, we showcase advancements in single cell spatial metabolomics and proteomics over the past decade and highlight important aspects related to high-throughput screening, data analysis, and more which are vital to the success of achieving proteomic and metabolomic profiling at the single cell scale. Finally, using this broad literature summary, we provide a perspective on how the next decade may unfold in the area of single cell MS-based proteomics and metabolomics.

Isobaric Peptide Labeling on Digital Microfluidics for Quantitative Low Cell Number Proteomics

Digital microfluidics (DMF) is a technology suitable for bioanalytical applications requiring miniaturized, automated, and multiplexed liquid handling. Its use in LC-MS-based proteomics, however, has so far been limited to qualitative proteome analyses. This is mainly due to the need for detergents that enable facile, reproducible droplet movement, which are compatible with organic solvents commonly used in targeted chemical modifications of peptides. Aiming to implement isobaric peptide labeling, a widely applied technique allowing multiplexed quantitative proteome studies, on DMF devices, we tested different commercially available detergents. We identified the maltoside-based detergent 3-dodecyloxypropyl-1-β-d-maltopyranoside (DDOPM) to enable facile droplet movement and show micelle formation even in the presence of organic solvent, which is necessary for isobaric tandem mass tag (TMT) labeling. The detergent is fully compatible with reversed phase LC-MS, not interfering with peptide identification. Tryptic digestion in the presence of DDOPM was more efficient than without detergent, resulting in more protein identifications. Using this detergent, we report the first on-DMF chip isobaric labeling strategy, with TMT-labeling efficiency comparable to conventional protocols. The newly developed labeling protocol was evaluated in the multiplexed analyses of a protein standard digest spiked into 25 cells. Finally, using only 75 cells per biological replicate, we were able to identify 39 proteins being differentially abundant after treatment of Jurkat T cells with the anticancer drug doxorubicin. In summary, we demonstrate an important step toward multiplexed quantitative proteomics on DMF, which, in combination with larger chip arrays and optimized hardware, could enable high throughput low cell number proteomics.

Multiomics uncovers developing immunological lineages in human

The development of the human immune system during embryonic and fetal life has historically been difficult to research due to limited access to human tissue. Experimental animal models have been widely used to study development but cellular and molecular programmes may not be conserved across species. The advent of multiomic single-cell technologies and an increase in human developmental tissue biobank resources have facilitated single-cell multiomic studies focused on human immune development. A critical question in the near future is "How do we best reconcile scientific findings across multiple omic modalities, developmental time, and organismic space?" In this review, we discuss the application of single-cell multiomic technologies to unravel the major cellular lineages in the prenatal human immune system. We also identify key areas where the combined power of multiomics technologies can be leveraged to address specific immunological gaps in our current knowledge and explore new research horizons in human development.