1
|
Verhey TB, Seo H, Gillmor A, Thoppey-Manoharan V, Schriemer D, Morrissy S. mosaicMPI: a framework for modular data integration across cohorts and -omics modalities. Nucleic Acids Res 2024; 52:e53. [PMID: 38813827 PMCID: PMC11229337 DOI: 10.1093/nar/gkae442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Revised: 04/26/2024] [Accepted: 05/10/2024] [Indexed: 05/31/2024] Open
Abstract
Advances in molecular profiling have facilitated generation of large multi-modal datasets that can potentially reveal critical axes of biological variation underlying complex diseases. Distilling biological meaning, however, requires computational strategies that can perform mosaic integration across diverse cohorts and datatypes. Here, we present mosaicMPI, a framework for discovery of low to high-resolution molecular programs representing both cell types and states, and integration within and across datasets into a network representing biological themes. Using existing datasets in glioblastoma, we demonstrate that this approach robustly integrates single cell and bulk programs across multiple platforms. Clinical and molecular annotations from cohorts are statistically propagated onto this network of programs, yielding a richly characterized landscape of biological themes. This enables deep understanding of individual tumor samples, systematic exploration of relationships between modalities, and generation of a reference map onto which new datasets can rapidly be mapped. mosaicMPI is available at https://github.com/MorrissyLab/mosaicMPI.
Collapse
Affiliation(s)
- Theodore B Verhey
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| | - Heewon Seo
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
| | - Aaron Gillmor
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
| | - Varsha Thoppey-Manoharan
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
| | - David Schriemer
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
| | - Sorana Morrissy
- Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada
- Charbonneau Cancer institute, University of Calgary, Calgary, Alberta, Canada
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| |
Collapse
|
2
|
Montes C, Zhang J, Nolan TM, Walley JW. Single-cell proteomics differentiates Arabidopsis root cell types. THE NEW PHYTOLOGIST 2024. [PMID: 38923440 DOI: 10.1111/nph.19923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 06/09/2024] [Indexed: 06/28/2024]
Abstract
Single-cell proteomics (SCP) is an emerging approach to resolve cellular heterogeneity within complex tissues of multi-cellular organisms. Here, we demonstrate the feasibility of SCP on plant samples using the model plant Arabidopsis thaliana. Specifically, we focused on examining isolated single cells from the cortex and endodermis, which are two adjacent root cell types derived from a common stem cell lineage. From 756 root cells, we identified 3763 proteins and 1118 proteins/cell. Ultimately, we focus on 3217 proteins quantified following stringent filtering. Of these, we identified 596 proteins whose expression is enriched in either the cortex or endodermis and are able to differentiate these closely related plant cell types. Collectivity, this study demonstrates that SCP can resolve neighboring cell types with distinct functions, thereby facilitating the identification of biomarkers and candidate proteins to enable functional genomics.
Collapse
Affiliation(s)
- Christian Montes
- Department of Plant Pathology, Entomology, and Microbiology, Iowa State University, Ames, IA, 50011, USA
| | - Jingyuan Zhang
- Department of Biology, Duke University, Durham, NC, 27708, USA
| | - Trevor M Nolan
- Department of Biology, Duke University, Durham, NC, 27708, USA
- Howard Hughes Medical Institute, Duke University, Durham, NC, 27708, USA
| | - Justin W Walley
- Department of Plant Pathology, Entomology, and Microbiology, Iowa State University, Ames, IA, 50011, USA
| |
Collapse
|
3
|
Tan YC, Low TY, Lee PY, Lim LC. Single-cell proteomics by mass spectrometry: Advances and implications in cancer research. Proteomics 2024; 24:e2300210. [PMID: 38727198 DOI: 10.1002/pmic.202300210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 02/22/2024] [Accepted: 04/29/2024] [Indexed: 06/16/2024]
Abstract
Cancer harbours extensive proteomic heterogeneity. Inspired by the prior success of single-cell RNA sequencing (scRNA-seq) in characterizing minute transcriptomics heterogeneity in cancer, researchers are now actively searching for information regarding the proteomics counterpart. Therefore recently, single-cell proteomics by mass spectrometry (SCP) has rapidly developed into state-of-the-art technology to cater the need. This review aims to summarize application of SCP in cancer research, while revealing current development progress of SCP technology. The review also aims to contribute ideas into research gaps and future directions, ultimately promoting the application of SCP in cancer research.
Collapse
Affiliation(s)
- Yong Chiang Tan
- School of Postgraduate Studies, International Medical University, Kuala Lumpur, Malaysia
| | - Teck Yew Low
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Pey Yee Lee
- UKM Medical Molecular Biology Institute (UMBI), Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia
| | - Lay Cheng Lim
- Department of Life Sciences, School of Pharmacy, International Medical University, Kuala Lumpur, Malaysia
| |
Collapse
|
4
|
Yu SH, Chen SC, Wu PS, Kuo PI, Chen TA, Lee HY, Lin MH. Quantification Quality Control Emerges as a Crucial Factor to Enhance Single-Cell Proteomics Data Analysis. Mol Cell Proteomics 2024; 23:100768. [PMID: 38621647 PMCID: PMC11103571 DOI: 10.1016/j.mcpro.2024.100768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 03/12/2024] [Accepted: 04/11/2024] [Indexed: 04/17/2024] Open
Abstract
Mass spectrometry (MS)-based single-cell proteomics (SCP) provides us the opportunity to unbiasedly explore biological variability within cells without the limitation of antibody availability. This field is rapidly developed with the main focuses on instrument advancement, sample preparation refinement, and signal boosting methods; however, the optimal data processing and analysis are rarely investigated which holds an arduous challenge because of the high proportion of missing values and batch effect. Here, we introduced a quantification quality control to intensify the identification of differentially expressed proteins (DEPs) by considering both within and across SCP data. Combining quantification quality control with isobaric matching between runs (IMBR) and PSM-level normalization, an additional 12% and 19% of proteins and peptides, with more than 90% of proteins/peptides containing valid values, were quantified. Clearly, quantification quality control was able to reduce quantification variations and q-values with the more apparent cell type separations. In addition, we found that PSM-level normalization performed similar to other protein-level normalizations but kept the original data profiles without the additional requirement of data manipulation. In proof of concept of our refined pipeline, six uniquely identified DEPs exhibiting varied fold-changes and playing critical roles for melanoma and monocyte functionalities were selected for validation using immunoblotting. Five out of six validated DEPs showed an identical trend with the SCP dataset, emphasizing the feasibility of combining the IMBR, cell quality control, and PSM-level normalization in SCP analysis, which is beneficial for future SCP studies.
Collapse
Affiliation(s)
- Sung-Huan Yu
- Institute of Precision Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan; School of Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Shiau-Ching Chen
- Institute of Precision Medicine, College of Medicine, National Sun Yat-sen University, Kaohsiung, Taiwan
| | - Pei-Shan Wu
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Pei-I Kuo
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Ting-An Chen
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan
| | - Hsiang-Ying Lee
- Department of Urology, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan; Department of Urology, School of Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
| | - Miao-Hsia Lin
- Department of Microbiology, National Taiwan University College of Medicine, Taipei, Taiwan.
| |
Collapse
|
5
|
Khan S, Conover R, Asthagiri AR, Slavov N. Dynamics of Single-Cell Protein Covariation during Epithelial-Mesenchymal Transition. J Proteome Res 2024. [PMID: 38663020 DOI: 10.1021/acs.jproteome.4c00277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Abstract
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.
Collapse
Affiliation(s)
- Saad Khan
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States
- Department of Biology, Northeastern University, Boston, Massachusetts 02115, United States
| | - Rachel Conover
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Anand R Asthagiri
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States
- Department of Biology, Northeastern University, Boston, Massachusetts 02115, United States
- Department of Chemical Engineering, Northeastern University, Boston, Massachusetts 02115, United States
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States
- Department of Biology, Northeastern University, Boston, Massachusetts 02115, United States
- Parallel Squared Technology Institute, Watertown, Massachusetts 02472, United States
| |
Collapse
|
6
|
Khan S, Conover R, Asthagiri AR, Slavov N. Dynamics of single-cell protein covariation during epithelial-mesenchymal transition. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.21.572913. [PMID: 38187715 PMCID: PMC10769332 DOI: 10.1101/2023.12.21.572913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
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.
Collapse
Affiliation(s)
- Saad Khan
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Biology, Northeastern University, Boston, MA, USA
| | - Rachel Conover
- Department of Bioengineering, Northeastern University, Boston, MA, USA
| | - Anand R. Asthagiri
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Biology, Northeastern University, Boston, MA, USA
- Department of Chemical Engineering, Northeastern University, Boston, MA, USA
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, MA, USA
- Department of Biology, Northeastern University, Boston, MA, USA
- Parallel Squared Technology Institute, Watertown, MA 02472, USA
| |
Collapse
|
7
|
Buur LM, Declercq A, Strobl M, Bouwmeester R, Degroeve S, Martens L, Dorfer V, Gabriels R. MS 2Rescore 3.0 Is a Modular, Flexible, and User-Friendly Platform to Boost Peptide Identifications, as Showcased with MS Amanda 3.0. J Proteome Res 2024. [PMID: 38491990 DOI: 10.1021/acs.jproteome.3c00785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2024]
Abstract
Rescoring of peptide-spectrum matches (PSMs) has emerged as a standard procedure for the analysis of tandem mass spectrometry data. This emphasizes the need for software maintenance and continuous improvement for such algorithms. We introduce MS2Rescore 3.0, a versatile, modular, and user-friendly platform designed to increase peptide identifications. Researchers can install MS2Rescore across various platforms with minimal effort and benefit from a graphical user interface, a modular Python API, and extensive documentation. To showcase this new version, we connected MS2Rescore 3.0 with MS Amanda 3.0, a new release of the well-established search engine, addressing previous limitations on automatic rescoring. Among new features, MS Amanda now contains additional output columns that can be used for rescoring. The full potential of rescoring is best revealed when applied on challenging data sets. We therefore evaluated the performance of these two tools on publicly available single-cell data sets, where the number of PSMs was substantially increased, thereby demonstrating that MS2Rescore offers a powerful solution to boost peptide identifications. MS2Rescore's modular design and user-friendly interface make data-driven rescoring easily accessible, even for inexperienced users. We therefore expect the MS2Rescore to be a valuable tool for the wider proteomics community. MS2Rescore is available at https://github.com/compomics/ms2rescore.
Collapse
Affiliation(s)
- Louise M Buur
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Hagenberg 4232, Austria
| | - Arthur Declercq
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent 9052, Belgium
| | - Marina Strobl
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Hagenberg 4232, Austria
| | - Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent 9052, Belgium
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent 9052, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent 9052, Belgium
| | - Viktoria Dorfer
- Bioinformatics Research Group, University of Applied Sciences Upper Austria, Hagenberg 4232, Austria
| | - Ralf Gabriels
- VIB-UGent Center for Medical Biotechnology, VIB, Ghent 9052, Belgium
- Department of Biomolecular Medicine, Ghent University, Ghent 9052, Belgium
| |
Collapse
|
8
|
Straubhaar J, D'Souza A, Niziolek Z, Budnik B. Single cell proteomics analysis of drug response shows its potential as a drug discovery platform. Mol Omics 2024; 20:6-18. [PMID: 37681418 DOI: 10.1039/d3mo00124e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/09/2023]
Abstract
Single-cell analysis has clearly established itself in biology and biomedical fields as an invaluable tool that allows one to comprehensively understand the relationship between cells, including their types, states, transitions, trajectories, and spatial position. Scientific methods such as fluorescence labeling, nanoscale super-resolution microscopy, advances in single cell RNAseq and proteomics technologies, provide more detailed information about biological processes which were not evident with the analysis of bulk material. This new era of single-cell biology provides a better understanding of such complex biological systems as cancer, inflammation, immunity mechanism and aging processes, and opens the door into the field of drug response heterogeneity. The latest discoveries of cellular heterogeneity gives us a unique understanding of complex biological processes, such as disease mechanism, and will lead to new strategies for better and personalized treatment strategies. Recently, single-cell proteomics techniques that allow quantification of thousands of proteins from single mammalian cells have been introduced. Here we present an improved single-cell mass spectrometry-based proteomics platform called SCREEN (Single Cell pRotEomE aNalysis) for deep and high-throughput single-cell proteome coverage with high efficiency, less turnaround time and with an improved ability for protein quantitation across more cells than previously achieved. We applied this new platform to analyze the single-cell proteomic landscape under different drug treatment over time to uncover heterogeneity in cancer cell response, which for the first time, to our knowledge, has been achieved by mass spectrometry based analytical methods. We discuss challenges in single-cell proteomics, future improvements and general trends with the goal to encourage forthcoming technical developments.
Collapse
Affiliation(s)
- Juerg Straubhaar
- Research Computing, FAS, Division of Science, Harvard University, Cambridge, MA02138, USA
| | - Alexandria D'Souza
- Research Computing, FAS, Division of Science, Harvard University, Cambridge, MA02138, USA
| | - Zachary Niziolek
- Bauer Core, FAS, Division of Science, Harvard University, Cambridge, MA02138, USA
| | - Bogdan Budnik
- MSPRL, FAS, Division of Science, Harvard University, Cambridge, MA02138, USA.
| |
Collapse
|
9
|
Leduc A, Koury L, Cantlon J, Slavov N. Massively parallel sample preparation for multiplexed single-cell proteomics using nPOP. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.27.568927. [PMID: 38076795 PMCID: PMC10705290 DOI: 10.1101/2023.11.27.568927] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
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.
Collapse
Affiliation(s)
- Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115, USA
| | - Luke Koury
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115, USA
| | | | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115, USA
- Parallel Squared Technology Institute, Watertown, MA 02472, USA
| |
Collapse
|
10
|
Lazear MR. Sage: An Open-Source Tool for Fast Proteomics Searching and Quantification at Scale. J Proteome Res 2023; 22:3652-3659. [PMID: 37819886 DOI: 10.1021/acs.jproteome.3c00486] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/13/2023]
Abstract
The growing complexity and volume of proteomics data necessitate the development of efficient software tools for peptide identification and quantification from mass spectra. Given their central role in proteomics, it is imperative that these tools are auditable and extensible─requirements that are best fulfilled by open-source and permissively licensed software. This work presents Sage, a high-performance, open-source, and freely available proteomics pipeline. Scalable and cloud-ready, Sage matches the performance of state-of-the-art software tools while running an order of magnitude faster.
Collapse
Affiliation(s)
- Michael R Lazear
- Belharra Therapeutics, 3985 Sorrento Valley Boulevard Suite C, San Diego, California 92121, United States
| |
Collapse
|
11
|
Wallmann G, Leduc A, Slavov N. Data-Driven Optimization of DIA Mass Spectrometry by DO-MS. J Proteome Res 2023; 22:3149-3158. [PMID: 37695820 PMCID: PMC10591957 DOI: 10.1021/acs.jproteome.3c00177] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Indexed: 09/13/2023]
Abstract
Mass spectrometry (MS) enables specific and accurate quantification of proteins with ever-increasing throughput and sensitivity. Maximizing this potential of MS requires optimizing data acquisition parameters and performing efficient quality control for large datasets. To facilitate these objectives for data-independent acquisition (DIA), we developed a second version of our framework for data-driven optimization of MS methods (DO-MS). The DO-MS app v2.0 (do-ms.slavovlab.net) allows one to optimize and evaluate results from both label-free and multiplexed DIA (plexDIA) and supports optimizations particularly relevant to single-cell proteomics. We demonstrate multiple use cases, including optimization of duty cycle methods, peptide separation, number of survey scans per duty cycle, and quality control of single-cell plexDIA data. DO-MS allows for interactive data display and generation of extensive reports, including publication of quality figures that can be easily shared. The source code is available at github.com/SlavovLab/DO-MS.
Collapse
Affiliation(s)
- Georg Wallmann
- Departments
of Bioengineering, Biology, Chemistry and Chemical Biology, Single
Cell Proteomics Center, Northeastern University, Boston, Massachusetts 02115, United States
| | - Andrew Leduc
- Departments
of Bioengineering, Biology, Chemistry and Chemical Biology, Single
Cell Proteomics Center, Northeastern University, Boston, Massachusetts 02115, United States
| | - Nikolai Slavov
- Departments
of Bioengineering, Biology, Chemistry and Chemical Biology, Single
Cell Proteomics Center, Northeastern University, Boston, Massachusetts 02115, United States
- Parallel
Squared Technology Institute, Watertown, Massachusetts 02472, United States
| |
Collapse
|
12
|
Hu M, Zhang Y, Yuan Y, Ma W, Zheng Y, Gu Q, Xie XS. Correlated Protein Modules Revealing Functional Coordination of Interacting Proteins Are Detected by Single-Cell Proteomics. J Phys Chem B 2023. [PMID: 37368753 DOI: 10.1021/acs.jpcb.3c00014] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
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.
Collapse
Affiliation(s)
- Mo Hu
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing 100871, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
- Changping Laboratory, Beijing 102206, China
| | - Yutong Zhang
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing 100871, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Yuan Yuan
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing 100871, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
- College of Chemistry and Molecular Engineering, Peking University, Beijing 100871, China
| | - Wenping Ma
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing 100871, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
- Peking-Tsinghua Center for Life Sciences (CLS), Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China
| | - Yinghui Zheng
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing 100871, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
| | | | - X Sunney Xie
- Beijing Advanced Innovation Center for Genomics, Peking University, Beijing 100871, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
- Changping Laboratory, Beijing 102206, China
| |
Collapse
|
13
|
Mansuri MS, Williams K, Nairn AC. Uncovering biology by single-cell proteomics. Commun Biol 2023; 6:381. [PMID: 37031277 PMCID: PMC10082756 DOI: 10.1038/s42003-023-04635-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 02/25/2023] [Indexed: 04/10/2023] Open
Abstract
Recent technological advances have opened the door to single-cell proteomics that can answer key biological questions regarding how protein expression, post-translational modifications, and protein interactions dictate cell state in health and disease.
Collapse
Affiliation(s)
- M Shahid Mansuri
- Yale/NIDA Neuroproteomics Center and Department of Molecular Biophysics and Biochemistry, Yale School of Medicine, New Haven, Connecticut, USA
| | - Kenneth Williams
- Yale/NIDA Neuroproteomics Center and Department of Molecular Biophysics and Biochemistry, Yale School of Medicine, New Haven, Connecticut, USA
| | - Angus C Nairn
- Yale/NIDA Neuroproteomics Center and Department of Psychiatry, Yale School of Medicine, New Haven, Connecticut, USA.
| |
Collapse
|
14
|
Huffman RG, Leduc A, Wichmann C, Di Gioia M, Borriello F, Specht H, Derks J, Khan S, Khoury L, Emmott E, Petelski AA, Perlman DH, Cox J, Zanoni I, Slavov N. Prioritized mass spectrometry increases the depth, sensitivity and data completeness of single-cell proteomics. Nat Methods 2023; 20:714-722. [PMID: 37012480 PMCID: PMC10172113 DOI: 10.1038/s41592-023-01830-1] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 02/27/2023] [Indexed: 04/05/2023]
Abstract
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 .
Collapse
Affiliation(s)
- R Gray Huffman
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Christoph Wichmann
- Computational Systems Biochemistry Research Group, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Marco Di Gioia
- Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | | | - Harrison Specht
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Jason Derks
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Saad Khan
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Luke Khoury
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Edward Emmott
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
- Centre for Proteome Research, Department of Biochemistry and Systems Biology, University of Liverpool, Liverpool, UK
| | - Aleksandra A Petelski
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA
- Parallel Squared Technology Institute, Watertown, MA, USA
| | - David H Perlman
- Merck Exploratory Sciences Center, Merck Sharp and Dohme Corp., Cambridge, MA, USA
| | - Jürgen Cox
- Computational Systems Biochemistry Research Group, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Ivan Zanoni
- Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Center and Barnett Institute, Northeastern University, Boston, MA, USA.
- Parallel Squared Technology Institute, Watertown, MA, USA.
| |
Collapse
|
15
|
Abstract
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.
Collapse
Affiliation(s)
- Jason Derks
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, Massachusetts 02115, United States
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, Massachusetts 02115, United States
- Parallel Squared Technology Institute, Watertown, Massachusetts 02472, United States
| |
Collapse
|
16
|
Zhao H, Chen Y, Li H, Zhang Y, Zhang W, Qin W. An angled-shape tip-based strategy for highly sensitive proteomic profiling of a low number of cells. ANALYTICAL METHODS : ADVANCING METHODS AND APPLICATIONS 2023; 15:1215-1222. [PMID: 36804579 DOI: 10.1039/d2ay01884e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Profiling proteins plays an essential role in understanding the functions and dynamic networks in biological systems. Mass spectrometry-based proteomic analysis commonly requires multistep sample processing, which results in severe sample loss. Although the recently developed microproteomic strategies have substantially reduced sample loss via droplet microfluidic technology, specialized equipment and well-trained personnel are needed, which may limit their wide adoption. Here, we report an angled-shape tip-based strategy for rapid sample preparation and sensitive proteomic profiling of small cell populations (<1000 cells). The angled-shape tip provided a 'reactor' for the entire proteomic sample processing workflow, from cell capture and lysis to protein digestion, eliminating the sample transfer-induced protein loss. The angled-shape tip was surface-treated for anti-protein adsorption which further reduced the sample loss. Using this strategy, 1241 ± 38-4110 ± 37 protein groups and 4010 ± 700-34 879 ± 575 peptides were identified from 10-1000 HeLa cells with high quantification reproducibility in only 4.5 h sample processing time, which was superior to the reported methods and commercial kits, especially for <100 cells. This approach was easily accessible, straightforward to operate, and compatible with flow cytometry-based cell sorting. It showed great potential for in-depth proteomic profiling of rare cells (<1000 cells) in both basic biological research and clinical application.
Collapse
Affiliation(s)
- Hongxian Zhao
- National Center for Protein Sciences Beijing, State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, Beijing 102206, P. R. China.
| | - Yongle Chen
- National Center for Protein Sciences Beijing, State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, Beijing 102206, P. R. China.
| | - Hang Li
- School of Medical Technology, Beijing Institute of Technology, Beijing 100081, P. R. China
| | - Yangjun Zhang
- National Center for Protein Sciences Beijing, State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, Beijing 102206, P. R. China.
| | - Wanjun Zhang
- National Center for Protein Sciences Beijing, State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, Beijing 102206, P. R. China.
| | - Weijie Qin
- National Center for Protein Sciences Beijing, State Key Laboratory of Proteomics, Beijing Proteome Research Center, Beijing Institute of Lifeomics, Beijing 102206, P. R. China.
| |
Collapse
|
17
|
MacCoss MJ, Alfaro JA, Faivre DA, Wu CC, Wanunu M, Slavov N. Sampling the proteome by emerging single-molecule and mass spectrometry methods. Nat Methods 2023; 20:339-346. [PMID: 36899164 PMCID: PMC10044470 DOI: 10.1038/s41592-023-01802-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Mammalian cells have about 30,000-fold more protein molecules than mRNA molecules, which has major implications in the development of proteomics technologies. We review strategies that have been helpful for counting billions of protein molecules by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and suggest that these strategies can benefit single-molecule methods, especially in mitigating the challenges of the wide dynamic range of the proteome.
Collapse
Affiliation(s)
- Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
| | - Javier Antonio Alfaro
- International Centre for Cancer Vaccine Science, University of Gdańsk, Gdańsk, Poland.
- Department of Biochemistry and Microbiology, University of Victoria, Victoria, British Columbia, Canada.
- School of Informatics, University of Edinburgh, Edinburgh, UK.
| | - Danielle A Faivre
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Christine C Wu
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Meni Wanunu
- Department of Physics, Northeastern University, Boston, MA, USA
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA.
- Parallel Squared Technology Institute, Watertown, MA, USA.
| |
Collapse
|
18
|
Gatto L, Aebersold R, Cox J, Demichev V, Derks J, Emmott E, Franks AM, Ivanov AR, Kelly RT, Khoury L, Leduc A, MacCoss MJ, Nemes P, Perlman DH, Petelski AA, Rose CM, Schoof EM, Van Eyk J, Vanderaa C, Yates JR, Slavov N. Initial recommendations for performing, benchmarking and reporting single-cell proteomics experiments. Nat Methods 2023; 20:375-386. [PMID: 36864200 PMCID: PMC10130941 DOI: 10.1038/s41592-023-01785-3] [Citation(s) in RCA: 39] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 01/24/2023] [Indexed: 03/04/2023]
Abstract
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 .
Collapse
Affiliation(s)
- Laurent Gatto
- Computational Biology and Bioinformatics Unit, de Duve Institute, Université Catholique de Louvain, Brussels, Belgium
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland
| | - Juergen Cox
- Max Planck Institute of Biochemistry, Martinsried, Germany
| | | | - Jason Derks
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Edward Emmott
- Centre for Proteome Research, Department of Biochemistry and Systems Biology, University of Liverpool, Liverpool, UK
| | - Alexander M Franks
- Department of Statistics and Applied Probability, University of California Santa Barbara, Santa Barbara, CA, USA
| | - Alexander R Ivanov
- Department of Chemistry and Chemical Biology, Barnett Institute of Chemical and Biological Analysis, Northeastern University, Boston, MA, USA
| | - Ryan T Kelly
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT, USA
| | - Luke Khoury
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | - Andrew Leduc
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA
| | | | - Peter Nemes
- Department of Chemistry and Biochemistry, University of Maryland, College Park, MD, USA
| | - David H Perlman
- Merck Exploratory Science Center, Merck Sharp & Dohme Corp., Cambridge, MA, USA
| | - Aleksandra A Petelski
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA
- Parallel Squared Technology Institute, Watertown, MA, USA
| | - Christopher M Rose
- Department of Microchemistry, Proteomics and Lipidomics, Genentech Inc., South San Francisco, CA, USA
| | - Erwin M Schoof
- Department of Biotechnology and Biomedicine, Technical University of Denmark, Lyngby, Denmark
| | | | - Christophe Vanderaa
- Computational Biology and Bioinformatics Unit, de Duve Institute, Université Catholique de Louvain, Brussels, Belgium
| | - John R Yates
- Departments of Molecular Medicine and Neurobiology, the Scripps Research Institute, La Jolla, CA, USA
| | - Nikolai Slavov
- Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single-Cell Proteomics Center and Barnett Institute, Northeastern University, Boston, MA, USA.
- Parallel Squared Technology Institute, Watertown, MA, USA.
| |
Collapse
|
19
|
Boekweg H, Payne SH. Challenges and opportunities for single cell computational proteomics. Mol Cell Proteomics 2023; 22:100518. [PMID: 36828128 PMCID: PMC10060113 DOI: 10.1016/j.mcpro.2023.100518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/15/2023] [Accepted: 02/17/2023] [Indexed: 02/25/2023] Open
Abstract
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's 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.
Collapse
Affiliation(s)
- Hannah Boekweg
- Biology Department, Brigham Young University, Provo, Utah, USA
| | - Samuel H Payne
- Biology Department, Brigham Young University, Provo, Utah, USA.
| |
Collapse
|
20
|
Park J, Yu F, Fulcher JM, Williams SM, Engbrecht K, Moore RJ, Clair GC, Petyuk V, Nesvizhskii AI, Zhu Y. Evaluating Linear Ion Trap for MS3-Based Multiplexed Single-Cell Proteomics. Anal Chem 2023; 95:1888-1898. [PMID: 36637389 DOI: 10.1021/acs.analchem.2c03739] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
There is a growing demand to develop high-throughput and high-sensitivity mass spectrometry methods for single-cell proteomics. The commonly used isobaric labeling-based multiplexed single-cell proteomics approach suffers from distorted protein quantification due to co-isolated interfering ions during MS/MS fragmentation, also known as ratio compression. We reasoned that the use of MS3-based quantification could mitigate ratio compression and provide better quantification. However, previous studies indicated reduced proteome coverages in the MS3 method, likely due to long duty cycle time and ion losses during multilevel ion selection and fragmentation. Herein, we described an improved MS acquisition method for MS3-based single-cell proteomics by employing a linear ion trap to measure reporter ions. We demonstrated that linear ion trap can increase the proteome coverages for single-cell-level peptides with even higher gain obtained via the MS3 method. The optimized real-time search MS3 method was further applied to study the immune activation of single macrophages. Among a total of 126 single cells studied, over 1200 and 1000 proteins were quantifiable when at least 50 and 75% nonmissing data were required, respectively. Our evaluation also revealed several limitations of the low-resolution ion trap detector for multiplexed single-cell proteomics and suggested experimental solutions to minimize their impacts on single-cell analysis.
Collapse
Affiliation(s)
- Junho Park
- Department of Pharmacology, School of Medicine, CHA University, Seongnam-si, Gyeonggi-do, Seongnam 13488, Republic of Korea
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109-1382, United States
| | - James M Fulcher
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Sarah M Williams
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Kristin Engbrecht
- Nuclear, Chemistry, and Biology Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Ronald J Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Geremy C Clair
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Vladislav Petyuk
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109-1382, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109-1382, United States
| | - Ying Zhu
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| |
Collapse
|
21
|
Vanderaa C, Gatto L. The Current State of Single-Cell Proteomics Data Analysis. Curr Protoc 2023; 3:e658. [PMID: 36633424 DOI: 10.1002/cpz1.658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Sound data analysis is essential to retrieve meaningful biological information from single-cell proteomics experiments. This analysis is carried out by computational methods that are assembled into workflows, and their implementations influence the conclusions that can be drawn from the data. In this work, we explore and compare the computational workflows that have been used over the last four years and identify a profound lack of consensus on how to analyze single-cell proteomics data. We highlight the need for benchmarking of computational workflows and standardization of computational tools and data, as well as carefully designed experiments. Finally, we cover the current standardization efforts that aim to fill the gap, list the remaining missing pieces, and conclude with lessons learned from the replication of published single-cell proteomics analyses. © 2023 Wiley Periodicals LLC.
Collapse
Affiliation(s)
- Christophe Vanderaa
- Computational Biology and Bioinformatics Unit (CBIO), de Duve Institute, Université catholique de Louvain, Belgium
| | - Laurent Gatto
- Computational Biology and Bioinformatics Unit (CBIO), de Duve Institute, Université catholique de Louvain, Belgium
| |
Collapse
|
22
|
Leduc A, Huffman RG, Cantlon J, Khan S, Slavov N. Exploring functional protein covariation across single cells using nPOP. Genome Biol 2022; 23:261. [PMID: 36527135 PMCID: PMC9756690 DOI: 10.1186/s13059-022-02817-5] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 11/18/2022] [Indexed: 12/23/2022] Open
Abstract
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 .
Collapse
Affiliation(s)
- Andrew Leduc
- grid.261112.70000 0001 2173 3359Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115 USA
| | - R. Gray Huffman
- grid.261112.70000 0001 2173 3359Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115 USA
| | | | - Saad Khan
- grid.261112.70000 0001 2173 3359Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115 USA
| | - Nikolai Slavov
- grid.261112.70000 0001 2173 3359Departments of Bioengineering, Biology, Chemistry and Chemical Biology, Single Cell Proteomics Center, and Barnett Institute, Northeastern University, Boston, MA 02115 USA
| |
Collapse
|
23
|
Petelski AA, Slavov N, Specht H. Single-Cell Proteomics Preparation for Mass Spectrometry Analysis Using Freeze-Heat Lysis and an Isobaric Carrier. J Vis Exp 2022:10.3791/63802. [PMID: 36571403 PMCID: PMC10027359 DOI: 10.3791/63802] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
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.
Collapse
Affiliation(s)
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University; Barnett Institute, Northeastern University; Department of Biology, Northeastern University
| | | |
Collapse
|
24
|
Lenčo J, Jadeja S, Naplekov DK, Krokhin OV, Khalikova MA, Chocholouš P, Urban J, Broeckhoven K, Nováková L, Švec F. Reversed-Phase Liquid Chromatography of Peptides for Bottom-Up Proteomics: A Tutorial. J Proteome Res 2022; 21:2846-2892. [PMID: 36355445 DOI: 10.1021/acs.jproteome.2c00407] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
The performance of the current bottom-up liquid chromatography hyphenated with mass spectrometry (LC-MS) analyses has undoubtedly been fueled by spectacular progress in mass spectrometry. It is thus not surprising that the MS instrument attracts the most attention during LC-MS method development, whereas optimizing conditions for peptide separation using reversed-phase liquid chromatography (RPLC) remains somewhat in its shadow. Consequently, the wisdom of the fundaments of chromatography is slowly vanishing from some laboratories. However, the full potential of advanced MS instruments cannot be achieved without highly efficient RPLC. This is impossible to attain without understanding fundamental processes in the chromatographic system and the properties of peptides important for their chromatographic behavior. We wrote this tutorial intending to give practitioners an overview of critical aspects of peptide separation using RPLC to facilitate setting the LC parameters so that they can leverage the full capabilities of their MS instruments. After briefly introducing the gradient separation of peptides, we discuss their properties that affect the quality of LC-MS chromatograms the most. Next, we address the in-column and extra-column broadening. The last section is devoted to key parameters of LC-MS methods. We also extracted trends in practice from recent bottom-up proteomics studies and correlated them with the current knowledge on peptide RPLC separation.
Collapse
Affiliation(s)
- Juraj Lenčo
- Department of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Heyrovského 1203/8, 500 05Hradec Králové, Czech Republic
| | - Siddharth Jadeja
- Department of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Heyrovského 1203/8, 500 05Hradec Králové, Czech Republic
| | - Denis K Naplekov
- Department of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Heyrovského 1203/8, 500 05Hradec Králové, Czech Republic
| | - Oleg V Krokhin
- Department of Internal Medicine, Manitoba Centre for Proteomics and Systems Biology, University of Manitoba, 799 JBRC, 715 McDermot Avenue, WinnipegR3E 3P4, Manitoba, Canada
| | - Maria A Khalikova
- Department of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Heyrovského 1203/8, 500 05Hradec Králové, Czech Republic
| | - Petr Chocholouš
- Department of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Heyrovského 1203/8, 500 05Hradec Králové, Czech Republic
| | - Jiří Urban
- Department of Chemistry, Faculty of Science, Masaryk University, Kamenice 5, 625 00Brno, Czech Republic
| | - Ken Broeckhoven
- Department of Chemical Engineering (CHIS), Faculty of Engineering, Vrije Universiteit Brussel, Pleinlaan 2, 1050Brussel, Belgium
| | - Lucie Nováková
- Department of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Heyrovského 1203/8, 500 05Hradec Králové, Czech Republic
| | - František Švec
- Department of Analytical Chemistry, Faculty of Pharmacy in Hradec Králové, Charles University, Heyrovského 1203/8, 500 05Hradec Králové, Czech Republic
| |
Collapse
|
25
|
Beck L, Geiger T. MS-based technologies for untargeted single-cell proteomics. Curr Opin Biotechnol 2022; 76:102736. [DOI: 10.1016/j.copbio.2022.102736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Revised: 03/19/2022] [Accepted: 04/24/2022] [Indexed: 11/28/2022]
|
26
|
Burnum-Johnson KE, Conrads TP, Drake RR, Herr AE, Iyengar R, Kelly RT, Lundberg E, MacCoss MJ, Naba A, Nolan GP, Pevzner PA, Rodland KD, Sechi S, Slavov N, Spraggins JM, Van Eyk JE, Vidal M, Vogel C, Walt DR, Kelleher NL. New Views of Old Proteins: Clarifying the Enigmatic Proteome. Mol Cell Proteomics 2022; 21:100254. [PMID: 35654359 PMCID: PMC9256833 DOI: 10.1016/j.mcpro.2022.100254] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/09/2022] [Accepted: 05/27/2022] [Indexed: 11/23/2022] Open
Abstract
All human diseases involve proteins, yet our current tools to characterize and quantify them are limited. To better elucidate proteins across space, time, and molecular composition, we provide a >10 years of projection for technologies to meet the challenges that protein biology presents. With a broad perspective, we discuss grand opportunities to transition the science of proteomics into a more propulsive enterprise. Extrapolating recent trends, we describe a next generation of approaches to define, quantify, and visualize the multiple dimensions of the proteome, thereby transforming our understanding and interactions with human disease in the coming decade.
Collapse
Affiliation(s)
- Kristin E Burnum-Johnson
- The Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA.
| | - Thomas P Conrads
- Inova Women's Service Line, Inova Health System, Falls Church, Virginia, USA
| | - Richard R Drake
- Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina, Charleston, South Carolina, USA
| | - Amy E Herr
- Department of Bioengineering, University of California, Berkeley, California, USA
| | - Ravi Iyengar
- Department of Pharmacology and Systems Therapeutics, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ryan T Kelly
- Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah, USA
| | - Emma Lundberg
- Science for Life Laboratory, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Michael J MacCoss
- Department of Genome Sciences, University of Washington, Seattle, Washington, USA
| | - Alexandra Naba
- Department of Physiology and Biophysics, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, California, USA
| | - Pavel A Pevzner
- Department of Computer Science and Engineering, University of California at San Diego, San Diego, California, USA
| | - Karin D Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Salvatore Sechi
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, Massachusetts, USA
| | - Jeffrey M Spraggins
- Department of Cell and Developmental Biology, Mass Spectrometry Research Center, Vanderbilt University, Nashville, Tennessee, USA
| | - Jennifer E Van Eyk
- Advanced Clinical Biosystems Institute in the Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Marc Vidal
- Department of Genetics, Harvard University, Cambridge, Massachusetts, USA
| | - Christine Vogel
- New York University Center for Genomics and Systems Biology, New York University, New York, New York, USA
| | - David R Walt
- Department of Pathology, Harvard Medical School, Brigham and Women's Hospital, Wyss Institute at Harvard University, Boston, Massachusetts, USA
| | - Neil L Kelleher
- Department of Chemistry, Northwestern University, Evanston, Illinois, USA.
| |
Collapse
|
27
|
Single-Cell Proteomics: The Critical Role of Nanotechnology. Int J Mol Sci 2022; 23:ijms23126707. [PMID: 35743151 PMCID: PMC9224324 DOI: 10.3390/ijms23126707] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 11/24/2022] Open
Abstract
In single-cell analysis, biological variability can be attributed to individual cells, their specific state, and the ability to respond to external stimuli, which are determined by protein abundance and their relative alterations. Mass spectrometry (MS)-based proteomics (e.g., SCoPE-MS and SCoPE2) can be used as a non-targeted method to detect molecules across hundreds of individual cells. To achieve high-throughput investigation, novel approaches in Single-Cell Proteomics (SCP) are needed to identify and quantify proteins as accurately as possible. Controlling sample preparation prior to LC-MS analysis is critical, as it influences sensitivity, robustness, and reproducibility. Several nanotechnological approaches have been developed for the removal of cellular debris, salts, and detergents, and to facilitate systematic sample processing at the nano- and microfluidic scale. In addition, nanotechnology has enabled high-throughput proteomics analysis, which have required the improvement of software tools, such as DART-ID or DO-MS, which are also fundamental for addressing key biological questions. Single-cell proteomics has many applications in nanomedicine and biomedical research, including advanced cancer immunotherapies or biomarker characterization, among others; and novel methods allow the quantification of more than a thousand proteins while analyzing hundreds of single cells.
Collapse
|
28
|
Wang B, Wang Y, Chen Y, Gao M, Ren J, Guo Y, Situ C, Qi Y, Zhu H, Li Y, Guo X. DeepSCP: utilizing deep learning to boost single-cell proteome coverage. Brief Bioinform 2022; 23:6598882. [DOI: 10.1093/bib/bbac214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 04/20/2022] [Accepted: 05/06/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
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.
Collapse
Affiliation(s)
- Bing Wang
- School of Medicine , Southeast University, Nanjing 210009 , China
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Yue Wang
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Yu Chen
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Mengmeng Gao
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Jie Ren
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Yueshuai Guo
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Chenghao Situ
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Yaling Qi
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Hui Zhu
- Department of Clinical Laboratory , Sir Run Run Hospital, Nanjing Medical University, Nanjing 211166 , China
| | - Yan Li
- School of Medicine , Southeast University, Nanjing 210009 , China
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| | - Xuejiang Guo
- Department of Histology and Embryology , State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166 , China
| |
Collapse
|
29
|
Ochoteco Asensio J, Verheijen M, Caiment F. Predicting missing proteomics values using machine learning: Filling the gap using transcriptomics and other biological features. Comput Struct Biotechnol J 2022; 20:2057-2069. [PMID: 35601960 PMCID: PMC9077535 DOI: 10.1016/j.csbj.2022.04.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 04/12/2022] [Accepted: 04/12/2022] [Indexed: 12/11/2022] Open
Abstract
Proteins are often considered the main biological element in charge of the different functions and structures of a cell. However, proteomics, the global study of all expressed proteins, often performed by mass spectrometry, is limited by its stochastic sampling and can only quantify a limited amount of protein per sample. Transcriptomics, which allows an exhaustive analysis of all expressed transcripts, is often used as a surrogate. However, the transcript level does not present a high level of correlation with the corresponding protein level, notably due to the existence of several post-transcriptional regulatory mechanisms. In this publication, we hypothesize that the missing protein values in proteomics could be predicted using machine learning regression methods, trained with many features extracted from transcriptomics, including known translational regulatory elements such as microRNAs and circular RNAs. After considering different machine learning algorithms applied on two different splitting strategies, we report that random forest can predict proteins in new samples out of transcriptomics data with good accuracy. The proposed pre-processing and model building scripts can be accessed on GitHub: https://github.com/jochotecoa/ml_proteomics.
Collapse
|
30
|
Crook OM, Chung CW, Deane CM. Challenges and Opportunities for Bayesian Statistics in Proteomics. J Proteome Res 2022; 21:849-864. [PMID: 35258980 PMCID: PMC8982455 DOI: 10.1021/acs.jproteome.1c00859] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Indexed: 12/27/2022]
Abstract
Proteomics is a data-rich science with complex experimental designs and an intricate measurement process. To obtain insights from the large data sets produced, statistical methods, including machine learning, are routinely applied. For a quantity of interest, many of these approaches only produce a point estimate, such as a mean, leaving little room for more nuanced interpretations. By contrast, Bayesian statistics allows quantification of uncertainty through the use of probability distributions. These probability distributions enable scientists to ask complex questions of their proteomics data. Bayesian statistics also offers a modular framework for data analysis by making dependencies between data and parameters explicit. Hence, specifying complex hierarchies of parameter dependencies is straightforward in the Bayesian framework. This allows us to use a statistical methodology which equals, rather than neglects, the sophistication of experimental design and instrumentation present in proteomics. Here, we review Bayesian methods applied to proteomics, demonstrating their potential power, alongside the challenges posed by adopting this new statistical framework. To illustrate our review, we give a walk-through of the development of a Bayesian model for dynamic organic orthogonal phase-separation (OOPS) data.
Collapse
Affiliation(s)
- Oliver M. Crook
- Department
of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom
| | - Chun-wa Chung
- Structural
and Biophysical Sciences, GlaxoSmithKline
R&D, Stevenage SG1 2NY, United Kingdom
| | - Charlotte M. Deane
- Department
of Statistics, University of Oxford, Oxford OX1 3LB, United Kingdom
| |
Collapse
|
31
|
Clark NM, Elmore JM, Walley JW. To the proteome and beyond: advances in single-cell omics profiling for plant systems. PLANT PHYSIOLOGY 2022; 188:726-737. [PMID: 35235661 PMCID: PMC8825333 DOI: 10.1093/plphys/kiab429] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 08/16/2021] [Indexed: 05/19/2023]
Abstract
Recent advances in single-cell proteomics for animal systems could be adapted for plants to increase our understanding of plant development, response to stimuli, and cell-to-cell signaling.
Collapse
Affiliation(s)
- Natalie M Clark
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, Iowa 50011, USA
| | - James Mitch Elmore
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, Iowa 50011, USA
| | - Justin W Walley
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, Iowa 50011, USA
- Author for communication:
| |
Collapse
|
32
|
Specht H, Slavov N. Beyond Protein Sequence: Protein Isomerization in Alzheimer's Disease. J Proteome Res 2022; 21:299-300. [PMID: 35114789 DOI: 10.1021/acs.jproteome.2c00016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Harrison Specht
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States.,Barnett Institute, Northeastern University, Boston, Massachusetts 02115, United States
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States.,Barnett Institute, Northeastern University, Boston, Massachusetts 02115, United States
| |
Collapse
|
33
|
Boekweg H, Van Der Watt D, Truong T, Johnston SM, Guise AJ, Plowey ED, Kelly RT, Payne SH. Features of Peptide Fragmentation Spectra in Single-Cell Proteomics. J Proteome Res 2021; 21:182-188. [PMID: 34920664 DOI: 10.1021/acs.jproteome.1c00670] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
The goal of proteomics is to identify and quantify the complete set of proteins in a biological sample. Single-cell proteomics specializes in the identification and quantitation of proteins for individual cells, often used to elucidate cellular heterogeneity. The significant reduction in ions introduced into the mass spectrometer for single-cell samples could impact the features of MS2 fragmentation spectra. As all peptide identification software tools have been developed on spectra from bulk samples and the associated ion-rich spectra, the potential for spectral features to change is of great interest. We characterize the differences between single-cell spectra and bulk spectra by examining three fundamental spectral features that are likely to affect peptide identification performance. All features show significant changes in single-cell spectra, including the loss of annotated fragment ions, blurring signal and background peaks due to diminishing ion intensity, and distinct fragmentation pattern, compared to bulk spectra. As each of these features is a foundational part of peptide identification algorithms, it is critical to adjust algorithms to compensate for these losses.
Collapse
Affiliation(s)
- Hannah Boekweg
- Department of Biology, Brigham Young University, Provo Utah 84602, United States
| | - Daisha Van Der Watt
- Department of Biology, Brigham Young University, Provo Utah 84602, United States
| | - Thy Truong
- Department of Chemistry and Biochemistry, Brigham Young University, Provo Utah 84602, United States
| | - S Madisyn Johnston
- Department of Chemistry and Biochemistry, Brigham Young University, Provo Utah 84602, United States
| | - Amanda J Guise
- Translational Neuropathology, Biomarkers, Research and Development, Biogen Inc, Cambridge, Massachusetts 02142, United States
| | - Edward D Plowey
- Translational Neuropathology, Biomarkers, Research and Development, Biogen Inc, Cambridge, Massachusetts 02142, United States
| | - Ryan T Kelly
- Department of Chemistry and Biochemistry, Brigham Young University, Provo Utah 84602, United States
| | - Samuel H Payne
- Department of Biology, Brigham Young University, Provo Utah 84602, United States
| |
Collapse
|
34
|
Abstract
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.
Collapse
Affiliation(s)
- Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States.,Barnett Institute, Northeastern University, Boston, Massachusetts 02115, United States
| |
Collapse
|
35
|
Shevchuk O, Begonja AJ, Gambaryan S, Totzeck M, Rassaf T, Huber TB, Greinacher A, Renne T, Sickmann A. Proteomics: A Tool to Study Platelet Function. Int J Mol Sci 2021; 22:ijms22094776. [PMID: 33946341 PMCID: PMC8125008 DOI: 10.3390/ijms22094776] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/22/2021] [Accepted: 04/22/2021] [Indexed: 12/22/2022] Open
Abstract
Platelets are components of the blood that are highly reactive, and they quickly respond to multiple physiological and pathophysiological processes. In the last decade, it became clear that platelets are the key components of circulation, linking hemostasis, innate, and acquired immunity. Protein composition, localization, and activity are crucial for platelet function and regulation. The current state of mass spectrometry-based proteomics has tremendous potential to identify and quantify thousands of proteins from a minimal amount of material, unravel multiple post-translational modifications, and monitor platelet activity during drug treatments. This review focuses on the role of proteomics in understanding the molecular basics of the classical and newly emerging functions of platelets. including the recently described role of platelets in immunology and the development of COVID-19.The state-of-the-art proteomic technologies and their application in studying platelet biogenesis, signaling, and storage are described, and the potential of newly appeared trapped ion mobility spectrometry (TIMS) is highlighted. Additionally, implementing proteomic methods in platelet transfusion medicine, and as a diagnostic and prognostic tool, is discussed.
Collapse
Affiliation(s)
- Olga Shevchuk
- Leibniz-Institut für Analytische Wissenschaften—ISAS—e.V, Bunsen-Kirchhoff-Straße 11, 44139 Dortmund, Germany
- Department of Immunodynamics, Institute of Experimental Immunology and Imaging, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany
- Correspondence: (O.S.); (A.S.)
| | - Antonija Jurak Begonja
- Department of Biotechnology, University of Rijeka, Radmile Matejčić 2, 51000 Rijeka, Croatia;
| | - Stepan Gambaryan
- Sechenov Institute of Evolutionary Physiology and Biochemistry, Russian Academy of Sciences, Torez pr. 44, 194223 St. Petersburg, Russia;
| | - Matthias Totzeck
- West German Heart and Vascular Center, Department of Cardiology and Vascular Medicine, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany; (M.T.); (T.R.)
| | - Tienush Rassaf
- West German Heart and Vascular Center, Department of Cardiology and Vascular Medicine, University Hospital Essen, Hufelandstrasse 55, 45147 Essen, Germany; (M.T.); (T.R.)
| | - Tobias B. Huber
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany;
| | - Andreas Greinacher
- Institut für Immunologie und Transfusionsmedizin, Universitätsmedizin Greifswald, Sauerbruchstraße, 17475 Greifswald, Germany;
| | - Thomas Renne
- Institute of Clinical Chemistry and Laboratory Medicine, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany;
| | - Albert Sickmann
- Leibniz-Institut für Analytische Wissenschaften—ISAS—e.V, Bunsen-Kirchhoff-Straße 11, 44139 Dortmund, Germany
- Medizinisches Proteom-Center (MPC), Medizinische Fakultät, Ruhr-Universität Bochum, 44801 Bochum, Germany
- Department of Chemistry, College of Physical Sciences, University of Aberdeen, Aberdeen AB24 3FX, UK
- Correspondence: (O.S.); (A.S.)
| |
Collapse
|
36
|
Specht H, Emmott E, Petelski AA, Huffman RG, Perlman DH, Serra M, Kharchenko P, Koller A, Slavov N. Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2. Genome Biol 2021; 22:50. [PMID: 33504367 PMCID: PMC7839219 DOI: 10.1186/s13059-021-02267-5] [Citation(s) in RCA: 265] [Impact Index Per Article: 88.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 01/07/2021] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Macrophages are innate immune cells with diverse functional and molecular phenotypes. This diversity is largely unexplored at the level of single-cell proteomes because of the limitations of quantitative single-cell protein analysis. RESULTS To overcome this limitation, we develop SCoPE2, which substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enable us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiate into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantifies over 3042 proteins in 1490 single monocytes and macrophages in 10 days of instrument time, and the quantified proteins allow us to discern single cells by cell type. Furthermore, the data uncover a continuous gradient of proteome states for the macrophages, suggesting that macrophage heterogeneity may emerge in the absence of polarizing cytokines. Parallel measurements of transcripts by 10× Genomics suggest that our measurements sample 20-fold more protein copies than RNA copies per gene, and thus, SCoPE2 supports quantification with improved count statistics. This allowed exploring regulatory interactions, such as interactions between the tumor suppressor p53, its transcript, and the transcripts of genes regulated by p53. CONCLUSIONS Even in a homogeneous environment, macrophage proteomes are heterogeneous. This heterogeneity correlates to the inflammatory axis of classically and alternatively activated macrophages. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass spectrometry and demonstrates the potential for inferring transcriptional and post-transcriptional regulation from variability across single cells.
Collapse
Affiliation(s)
- Harrison Specht
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA.
| | - Edward Emmott
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
- Present Address: Department of Biochemistry, Centre for Proteome Research, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Aleksandra A Petelski
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
| | - R Gray Huffman
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
| | - David H Perlman
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
- Present Address: Merck Exploratory Sciences Center, Merck Sharp & Dohme Corp., 320 Bent St., Cambridge, MA, 02141, USA
| | - Marco Serra
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Peter Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Antonius Koller
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
| | - Nikolai Slavov
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA.
| |
Collapse
|
37
|
Specht H, Emmott E, Petelski AA, Huffman RG, Perlman DH, Serra M, Kharchenko P, Koller A, Slavov N. Single-cell proteomic and transcriptomic analysis of macrophage heterogeneity using SCoPE2. Genome Biol 2021; 22:50. [PMID: 33504367 DOI: 10.1101/665307] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 01/07/2021] [Indexed: 05/27/2023] Open
Abstract
BACKGROUND Macrophages are innate immune cells with diverse functional and molecular phenotypes. This diversity is largely unexplored at the level of single-cell proteomes because of the limitations of quantitative single-cell protein analysis. RESULTS To overcome this limitation, we develop SCoPE2, which substantially increases quantitative accuracy and throughput while lowering cost and hands-on time by introducing automated and miniaturized sample preparation. These advances enable us to analyze the emergence of cellular heterogeneity as homogeneous monocytes differentiate into macrophage-like cells in the absence of polarizing cytokines. SCoPE2 quantifies over 3042 proteins in 1490 single monocytes and macrophages in 10 days of instrument time, and the quantified proteins allow us to discern single cells by cell type. Furthermore, the data uncover a continuous gradient of proteome states for the macrophages, suggesting that macrophage heterogeneity may emerge in the absence of polarizing cytokines. Parallel measurements of transcripts by 10× Genomics suggest that our measurements sample 20-fold more protein copies than RNA copies per gene, and thus, SCoPE2 supports quantification with improved count statistics. This allowed exploring regulatory interactions, such as interactions between the tumor suppressor p53, its transcript, and the transcripts of genes regulated by p53. CONCLUSIONS Even in a homogeneous environment, macrophage proteomes are heterogeneous. This heterogeneity correlates to the inflammatory axis of classically and alternatively activated macrophages. Our methodology lays the foundation for automated and quantitative single-cell analysis of proteins by mass spectrometry and demonstrates the potential for inferring transcriptional and post-transcriptional regulation from variability across single cells.
Collapse
Affiliation(s)
- Harrison Specht
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA.
| | - Edward Emmott
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
- Present Address: Department of Biochemistry, Centre for Proteome Research, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Aleksandra A Petelski
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
| | - R Gray Huffman
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
| | - David H Perlman
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
- Present Address: Merck Exploratory Sciences Center, Merck Sharp & Dohme Corp., 320 Bent St., Cambridge, MA, 02141, USA
| | - Marco Serra
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Peter Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, 02115, USA
| | - Antonius Koller
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA
| | - Nikolai Slavov
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, 02115, USA.
| |
Collapse
|
38
|
Defining the carrier proteome limit for single-cell proteomics. Nat Methods 2020; 18:76-83. [PMID: 33288958 DOI: 10.1038/s41592-020-01002-5] [Citation(s) in RCA: 114] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Accepted: 10/22/2020] [Indexed: 11/09/2022]
Abstract
Single-cell proteomics by mass spectrometry (SCoPE-MS) is a recently introduced method to quantify multiplexed single-cell proteomes. While this technique has generated great excitement, the underlying technologies (isobaric labeling and mass spectrometry (MS)) have technical limitations with the potential to affect data quality and biological interpretation. These limitations are particularly relevant when a carrier proteome, a sample added at 25-500× the amount of a single-cell proteome, is used to enable peptide identifications. Here we perform controlled experiments with increasing carrier proteome amounts and evaluate quantitative accuracy, as it relates to mass analyzer dynamic range, multiplexing level and number of ions sampled. We demonstrate that an increase in carrier proteome level requires a concomitant increase in the number of ions sampled to maintain quantitative accuracy. Lastly, we introduce Single-Cell Proteomics Companion (SCPCompanion), a software tool that enables rapid evaluation of single-cell proteomic data and recommends instrument and data analysis parameters for improved data quality.
Collapse
|
39
|
Specht H, Slavov N. Optimizing Accuracy and Depth of Protein Quantification in Experiments Using Isobaric Carriers. J Proteome Res 2020; 20:880-887. [PMID: 33190502 DOI: 10.1021/acs.jproteome.0c00675] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
The isobaric carrier approach, which combines small isobarically labeled samples with a larger isobarically labeled carrier sample, finds diverse applications in ultrasensitive mass spectrometry analysis of very small samples, such as single cells. To enhance the growing use of isobaric carriers, we characterized the trade-offs of using isobaric carriers in controlled experiments with complex human proteomes. The data indicate that isobaric carriers directly enhance peptide sequence identification without simultaneously increasing the number of protein copies sampled from small samples. The results also indicate strategies for optimizing the amount of isobaric carrier and analytical parameters, such as ion accumulation time, for different priorities such as improved quantification or an increased number of identified proteins. Balancing these trade-offs enables adapting isobaric carrier experiments to different applications, such as quantifying proteins from limited biopsies or organoids, building single-cell atlases, or modeling protein networks in single cells. In all cases, the reliability of protein quantification should be estimated and incorporated in all subsequent analyses. We expect that these guidelines will aid in explicit incorporation of the characterized trade-offs in experimental designs and transparent error propagation in data analysis.
Collapse
Affiliation(s)
- Harrison Specht
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States.,Barnett Institute, Northeastern University, Boston, Massachusetts 02115, United States
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, Massachusetts 02115, United States.,Barnett Institute, Northeastern University, Boston, Massachusetts 02115, United States.,Department of Biology, Northeastern University, Boston, Massachusetts 02115, United States
| |
Collapse
|
40
|
Wen B, Zeng W, Liao Y, Shi Z, Savage SR, Jiang W, Zhang B. Deep Learning in Proteomics. Proteomics 2020; 20:e1900335. [PMID: 32939979 PMCID: PMC7757195 DOI: 10.1002/pmic.201900335] [Citation(s) in RCA: 70] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 09/14/2020] [Indexed: 12/17/2022]
Abstract
Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS) technology, protein expression and post-translational modifications (PTMs) can be studied in a variety of biological systems at the global scale. Sophisticated computational algorithms are needed to translate the vast amount of data into novel biological insights. Deep learning automatically extracts data representations at high levels of abstraction from data, and it thrives in data-rich scientific research domains. Here, a comprehensive overview of deep learning applications in proteomics, including retention time prediction, MS/MS spectrum prediction, de novo peptide sequencing, PTM prediction, major histocompatibility complex-peptide binding prediction, and protein structure prediction, is provided. Limitations and the future directions of deep learning in proteomics are also discussed. This review will provide readers an overview of deep learning and how it can be used to analyze proteomics data.
Collapse
Affiliation(s)
- Bo Wen
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Wen‐Feng Zeng
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS)Chinese Academy of SciencesInstitute of Computing TechnologyBeijing100190China
| | - Yuxing Liao
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Zhiao Shi
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Sara R. Savage
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Wen Jiang
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| | - Bing Zhang
- Lester and Sue Smith Breast CenterBaylor College of MedicineHoustonTX77030USA
- Department of Molecular and Human GeneticsBaylor College of MedicineHoustonTX77030USA
| |
Collapse
|
41
|
Petelski AA, Slavov N. Analyzing Ribosome Remodeling in Health and Disease. Proteomics 2020; 20:e2000039. [PMID: 32820594 PMCID: PMC7501214 DOI: 10.1002/pmic.202000039] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 08/01/2020] [Indexed: 12/24/2022]
Abstract
Increasing evidence suggests that ribosomes actively regulate protein synthesis. However, much of this evidence is indirect, leaving this layer of gene regulation largely unexplored, in part due to methodological limitations. Indeed, evidence is reviewed demonstrating that commonly used methods, such as transcriptomics, are inadequate because the variability in mRNAs coding for ribosomal proteins (RP) does not necessarily correspond to RP variability. Thus protein remodeling of ribosomes should be investigated by methods that allow direct quantification of RPs, ideally of isolated ribosomes. Such methods are reviewed, focusing on mass spectrometry and emphasizing method-specific biases and approaches to control these biases. It is argued that using multiple complementary methods can help reduce the danger of interpreting reproducible systematic biases as evidence for ribosome remodeling.
Collapse
Affiliation(s)
- Aleksandra A Petelski
- Department of Bioengineering, Northeastern University, Boston, MA, 02115, USA
- Barnett Institute, Northeastern University, Boston, MA, 02115, USA
- Department of Biology, Northeastern University, Boston, MA, 02115, USA
| | - Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, MA, 02115, USA
- Barnett Institute, Northeastern University, Boston, MA, 02115, USA
- Department of Biology, Northeastern University, Boston, MA, 02115, USA
| |
Collapse
|
42
|
Waas M, Kislinger T. Addressing Cellular Heterogeneity in Cancer through Precision Proteomics. J Proteome Res 2020; 19:3607-3619. [PMID: 32697918 DOI: 10.1021/acs.jproteome.0c00338] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Cells exhibit a broad spectrum of functions driven by differences in molecular phenotype. Understanding the heterogeneity between and within cell types has led to advances in our ability to diagnose and manipulate biological systems. Heterogeneity within and between tumors still poses a challenge to the development and efficacy of therapeutics. In this Perspective we review the toolkit of protein-level experimental approaches for investigating cellular heterogeneity. We describe how innovative approaches and technical developments have supported the advent of bottom-up single-cell proteomic analysis and present opportunities and challenges within cancer research. Finally, we introduce the concept of "precision proteomics" and discuss how the advantages and limitations of various experimental approaches render them suitable for different biological systems and questions.
Collapse
|
43
|
Slavov N. Single-cell protein analysis by mass spectrometry. Curr Opin Chem Biol 2020; 60:1-9. [PMID: 32599342 DOI: 10.1016/j.cbpa.2020.04.018] [Citation(s) in RCA: 88] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 04/23/2020] [Accepted: 04/27/2020] [Indexed: 10/24/2022]
Abstract
Human physiology and pathology arise from the coordinated interactions of diverse single cells. However, analyzing single cells has been limited by the low sensitivity and throughput of analytical methods. DNA sequencing has recently made such analysis feasible for nucleic acids but single-cell protein analysis remains limited. Mass spectrometry is the most powerful method for protein analysis, but its application to single cells faces three major challenges: efficiently delivering proteins/peptides to mass spectrometry detectors, identifying their sequences, and scaling the analysis to many thousands of single cells. These challenges have motivated corresponding solutions, including SCoPE design multiplexing and clean, automated, and miniaturized sample preparation. Synergistically applied, these solutions enable quantifying thousands of proteins across many single cells and establish a solid foundation for further advances. Building upon this foundation, the SCoPE concept will enable analyzing subcellular organelles and posttranslational modifications, while increases in multiplexing capabilities will increase the throughput and decrease cost.
Collapse
Affiliation(s)
- Nikolai Slavov
- Department of Bioengineering, Northeastern University, Boston, MA, 02115, USA; Barnett Institute, Northeastern University, Boston, MA, 02115, USA; Department of Biology, Northeastern University, Boston, MA, 02115, USA.
| |
Collapse
|
44
|
Wen B, Li K, Zhang Y, Zhang B. Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis. Nat Commun 2020; 11:1759. [PMID: 32273506 PMCID: PMC7145864 DOI: 10.1038/s41467-020-15456-w] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 03/10/2020] [Indexed: 01/01/2023] Open
Abstract
Genomics-based neoantigen discovery can be enhanced by proteomic evidence, but there remains a lack of consensus on the performance of different quality control methods for variant peptide identification in proteogenomics. We propose to use the difference between accurately predicted and observed retention times for each peptide as a metric to evaluate different quality control methods. To this end, we develop AutoRT, a deep learning algorithm with high accuracy in retention time prediction. Analysis of three cancer data sets with a total of 287 tumor samples using different quality control strategies results in substantially different numbers of identified variant peptides and putative neoantigens. Our systematic evaluation, using the proposed retention time metric, provides insights and practical guidance on the selection of quality control strategies. We implement the recommended strategy in a computational workflow named NeoFlow to support proteogenomics-based neoantigen prioritization, enabling more sensitive discovery of putative neoantigens. Identifying mutation-derived neoantigens by proteogenomics requires robust strategies for quality control. Here, the authors propose peptide retention time as an evaluation metric for proteogenomics quality control methods, and develop a deep learning algorithm for accurate retention time prediction.
Collapse
Affiliation(s)
- Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Kai Li
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Yun Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX, 77030, USA. .,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, USA.
| |
Collapse
|
45
|
Affiliation(s)
- Nikolai Slavov
- Department of Bioengineering and Barnett Institute, Northeastern University, Boston, MA, USA.
| |
Collapse
|
46
|
Moshkovskii SA, Lobas AA, Gorshkov MV. Single Cell Proteogenomics - Immediate Prospects. BIOCHEMISTRY (MOSCOW) 2020; 85:140-146. [PMID: 32093591 DOI: 10.1134/s0006297920020029] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Recent technical advances in genomic technology have led to the explosive growth of transcriptome-wide studies at the level of single cells. The review describes the first steps of the single cell proteomics that has originated soon after development of transcriptomics methods. The first studies on the shotgun proteomics of single cells that used liquid chromatography/mass spectrometry have been already published. In these works, the cells were separated by the methods used in transcriptomics studies (e.g., cell sorting) and analyzed by modified mass spectrometry with tandem mass tags. The new proteogenomics approach involving integration of single cell transcriptomics and proteomics data will provide better understanding of the mechanisms of cell interactions in normal development and disease.
Collapse
Affiliation(s)
- S A Moshkovskii
- Pirogov Russian National Research Medical University, Moscow, 117997, Russia. .,Orekhovich Institute of Biomedical Chemistry, Moscow, 119121, Russia
| | - A A Lobas
- Talrose Institute for Energy Problems of Chemical Physics, Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, Moscow, 119334, Russia
| | - M V Gorshkov
- Talrose Institute for Energy Problems of Chemical Physics, Semenov Federal Research Center of Chemical Physics, Russian Academy of Sciences, Moscow, 119334, Russia
| |
Collapse
|
47
|
Huffman RG, Chen A, Specht H, Slavov N. DO-MS: Data-Driven Optimization of Mass Spectrometry Methods. J Proteome Res 2019; 18:2493-2500. [PMID: 31081635 DOI: 10.1021/acs.jproteome.9b00039] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The performance of ultrasensitive liquid chromatography and tandem mass spectrometry (LC-MS/MS) methods, such as single-cell proteomics by mass spectrometry (SCoPE-MS), depends on multiple interdependent parameters. This interdependence makes it challenging to specifically pinpoint the sources of problems in the LC-MS/MS methods and approaches for resolving them. For example, a low signal at the MS2 level can be due to poor LC separation, ionization, apex targeting, ion transfer, or ion detection. We sought to specifically diagnose such problems by interactively visualizing data from all levels of bottom-up LC-MS/MS analysis. Many software packages, such as MaxQuant, already provide such data, and we developed an open source platform for their interactive visualization and analysis: Data-driven Optimization of MS (DO-MS). We found that in many cases DO-MS not only specifically diagnosed LC-MS/MS problems but also enabled us to rationally optimize them. For example, by using DO-MS to optimize the sampling of the elution peak apexes, we increased ion accumulation times and apex sampling, which resulted in a 370% more efficient delivery of ions for MS2 analysis. DO-MS is easy to install and use, and its GUI allows for interactive data subsetting and high-quality figure generation. The modular design of DO-MS facilitates customization and expansion. DO-MS v1.0.8 is available for download from GitHub: https://github.com/SlavovLab/DO-MS . Additional documentation is available at https://do-ms.slavovlab.net .
Collapse
Affiliation(s)
- R Gray Huffman
- Department of Bioengineering , Northeastern University , Boston , Massachusetts 02115 , United States.,Barnett Institute , Northeastern University , Boston , Massachusetts 02115 , United States
| | - Albert Chen
- Department of Bioengineering , Northeastern University , Boston , Massachusetts 02115 , United States.,Barnett Institute , Northeastern University , Boston , Massachusetts 02115 , United States
| | - Harrison Specht
- Department of Bioengineering , Northeastern University , Boston , Massachusetts 02115 , United States.,Barnett Institute , Northeastern University , Boston , Massachusetts 02115 , United States
| | - Nikolai Slavov
- Department of Bioengineering , Northeastern University , Boston , Massachusetts 02115 , United States.,Barnett Institute , Northeastern University , Boston , Massachusetts 02115 , United States.,Department of Biology , Northeastern University , Boston , Massachusetts 02115 , United States
| |
Collapse
|