Ultra-streamlined single cell proteomics by all-in-one chip and data-independent-acquisition mass spectrometry

Abstract Single cell proteomics provides the ultimate resolution to reveal cellular phenotypic heterogeneity and functional network underlying biological processes. Here, we present an ultra-streamlined workflow combining an integrated proteomic chip (iProChip) and data-independent-acquisition (DIA) mass spectrometry for sensitive microproteomics analysis down to single cell level. The iProChip offers multiplexed and automated all-in-one station from cell isolation/counting/imaging to complete proteomic processing within a single device. By mapping to project-specific spectra libraries, the iProChip-DIA enables profiling of 1160 protein groups from triplicate analysis of a single mammalian cell. Furthermore, the applicability of iProChip-DIA was demonstrated using both adherent and non-adherent malignant cells, which reveals 5 orders of proteome coverage, highly consistent ~100-fold protein quantification (1-100 cells) and high reproducibility with low missing values (<16%). With the demonstrated all-in-one cell characterization, ultrahigh sensitivity, robustness, and versatility to add other functionalities, the iProChip-DIA is anticipated to offer general utility to realize advanced proteomics applications at single cell level.

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Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

10.21203/rs.3.rs-977693/v2 ◽

2021 ◽

Author(s):

Yuefan Wang ◽

Tung-Shing Mamie Lih ◽

Lijun Chen ◽

Yuanwei Xu ◽

Morgan D. Kuczler ◽

...

Keyword(s):

Single Cell ◽

Proteomic Analysis ◽

Single Cell Analysis ◽

Cancer Cell Line ◽

Cellular Heterogeneity ◽

Single Cell Level ◽

Cell Level ◽

Data Independent Acquisition ◽

Peptide Precursors ◽

Proteomic Study

Abstract Background: Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Methods: We report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. Results: We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Conclusions: Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

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Laser cleavable probes for in situ multiplexed glycan detection by single cell mass spectrometry

Chemical Science ◽

10.1039/c9sc03912k ◽

2019 ◽

Vol 10 (47) ◽

pp. 10958-10962 ◽

Cited By ~ 5

Author(s):

Jing Han ◽

Xi Huang ◽

Huihui Liu ◽

Jiyun Wang ◽

Caiqiao Xiong ◽

...

Keyword(s):

Mass Spectrometry ◽

Drug Resistance ◽

Single Cell ◽

Cell Mass ◽

Ionization Efficiency ◽

Single Cell Level ◽

Cell Level ◽

Cell Mass Spectrometry ◽

The Relationship

A single-cell MS approach for multiplexed glycan detection to investigate the relationship between drug resistance and glycans at a single-cell level and quantify multiple glycans, overcoming the limit of low ionization efficiency of glycans.

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Combining Mass Spectrometry with Paternò–Büchi Reaction to Determine Double-Bond Positions in Lipids at the Single-Cell Level

Analytical Chemistry ◽

10.1021/acs.analchem.0c02245 ◽

2020 ◽

Vol 92 (16) ◽

pp. 11380-11387 ◽

Cited By ~ 1

Author(s):

Yanlin Zhu ◽

Wenhua Wang ◽

Zhibo Yang

Keyword(s):

Mass Spectrometry ◽

Double Bond ◽

Single Cell ◽

Single Cell Level ◽

Cell Level

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Translational Traits of Non-Small-Cell Lung Carcinoma with Drug Resistances by Dual mRNA and Protein Quantification at the Single Cell Level

Biophysical Journal ◽

10.1016/j.bpj.2013.11.2759 ◽

2014 ◽

Vol 106 (2) ◽

pp. 493a

Author(s):

Seung-min Park ◽

Luke P. Lee

Keyword(s):

Single Cell ◽

Lung Carcinoma ◽

Small Cell Lung Carcinoma ◽

Small Cell ◽

Protein Quantification ◽

Single Cell Level ◽

Small Cell Lung ◽

Cell Level ◽

Cell Lung Carcinoma

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Quantitative Extraction and Mass Spectrometry Analysis at a Single-Cell Level

Analytical Chemistry ◽

10.1021/acs.analchem.8b00551 ◽

2018 ◽

Vol 90 (13) ◽

pp. 7937-7945 ◽

Cited By ~ 27

Author(s):

Ruichuan Yin ◽

Venkateshkumar Prabhakaran ◽

Julia Laskin

Keyword(s):

Mass Spectrometry ◽

Single Cell ◽

Mass Spectrometry Analysis ◽

Single Cell Level ◽

Cell Level ◽

Spectrometry Analysis ◽

Quantitative Extraction

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Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

10.1101/2021.08.19.457000 ◽

2021 ◽

Author(s):

Yuefan Wang ◽

Tung-Shing Mamie Lih ◽

Lijun Chen ◽

Yuanwei Xu ◽

Morgan D Kuczler ◽

...

Keyword(s):

Single Cell ◽

Proteomic Analysis ◽

Single Cell Analysis ◽

Cancer Cell Line ◽

Cellular Heterogeneity ◽

Single Cell Level ◽

Cell Level ◽

Data Independent Acquisition ◽

Peptide Precursors ◽

Proteomic Study

Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Herein, we report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Our results demonstrates that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

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Streamlined single-cell proteomics by an integrated microfluidic chip and data-independent acquisition mass spectrometry

Nature Communications ◽

10.1038/s41467-021-27778-4 ◽

2022 ◽

Vol 13 (1) ◽

Author(s):

Sofani Tafesse Gebreyesus ◽

Asad Ali Siyal ◽

Reta Birhanu Kitata ◽

Eric Sheng-Wen Chen ◽

Bayarmaa Enkhbayar ◽

...

Keyword(s):

Mass Spectrometry ◽

Single Cell ◽

Mammalian Cells ◽

Missing Values ◽

Dynamic Range ◽

Analytical Sensitivity ◽

Microfluidic Chips ◽

Mass Spectral ◽

Data Independent Acquisition ◽

Spectral Libraries

AbstractSingle-cell proteomics can reveal cellular phenotypic heterogeneity and cell-specific functional networks underlying biological processes. Here, we present a streamlined workflow combining microfluidic chips for all-in-one proteomic sample preparation and data-independent acquisition (DIA) mass spectrometry (MS) for proteomic analysis down to the single-cell level. The proteomics chips enable multiplexed and automated cell isolation/counting/imaging and sample processing in a single device. Combining chip-based sample handling with DIA-MS using project-specific mass spectral libraries, we profile on average ~1,500 protein groups across 20 single mammalian cells. Applying the chip-DIA workflow to profile the proteomes of adherent and non-adherent malignant cells, we cover a dynamic range of 5 orders of magnitude with good reproducibility and <16% missing values between runs. Taken together, the chip-DIA workflow offers all-in-one cell characterization, analytical sensitivity and robustness, and the option to add additional functionalities in the future, thus providing a basis for advanced single-cell proteomics applications.

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Optimized Data-Independent Acquisition Approach for Proteomic Analysis at Single-Cell Level

10.21203/rs.3.rs-977693/v1 ◽

2021 ◽

Author(s):

Yuefan Wang ◽

T. Mamie Lih ◽

Lijun Chen ◽

Yuanwei Xu ◽

Morgan Kuczler ◽

...

Keyword(s):

Single Cell ◽

Proteomic Analysis ◽

Single Cell Analysis ◽

Cancer Cell Line ◽

Cellular Heterogeneity ◽

Single Cell Level ◽

Cell Level ◽

Data Independent Acquisition ◽

Peptide Precursors ◽

Proteomic Study

Abstract Single-cell proteomic analysis provides valuable insights into cellular heterogeneity allowing the characterization of the cellular microenvironment which is difficult to accomplish in bulk proteomic analysis. Currently, single-cell proteomic studies utilize data-dependent acquisition (DDA) mass spectrometry (MS) coupled with a TMT labelled carrier channel. Due to the extremely imbalanced MS signals among the carrier channel and other TMT reporter ions, the quantification is compromised. Thus, data-independent acquisition (DIA)-MS should be considered as an alternative approach towards single-cell proteomic study since it generates reproducible quantitative data. However, there are limited reports on the optimal workflow for DIA-MS-based single-cell analysis. Herein, we report an optimized DIA workflow for single-cell proteomics using Orbitrap Lumos Tribrid instrument. We utilized a breast cancer cell line (MDA-MB-231) and induced drug resistant polyaneuploid cancer cells (PACCs) to evaluate our established workflow. We found that a short LC gradient was preferable for peptides extracted from single cell level with less than 2 ng sample amount. The total number of co-searching peptide precursors was also critical for protein and peptide identifications at nano- and sub-nano-gram levels. Post-translationally modified peptides could be identified from a nano-gram level of peptides. Using the optimized workflow, up to 1,500 protein groups were identified from a single PACC corresponding to 0.2 ng of peptides. Furthermore, about 200 peptides with phosphorylation, acetylation, and ubiquitination were identified from global DIA analysis of 100 cisplatin resistant PACCs (20 ng). Finally, we used this optimized DIA approach to compare the whole proteome of MDA-MB-231 parental cells and induced PACCs at a single-cell level. We found the single-cell level comparison could reflect real protein expression changes and identify the protein copy number. Our results demonstrate that the optimized DIA pipeline can serve as a reliable quantitative tool for single-cell as well as sub-nano-gram proteomic analysis.

Download Full-text