A streamlined tandem tip-based workflow for sensitive nanoscale phosphoproteomics Article Swipe
Chia‐Feng Tsai
,
Yi-Ting Wang
,
Chuan‐Chih Hsu
,
Reta Birhanu Kitata
,
Rosalie Chu
,
Marija Veličković
,
Rui Zhao
,
Sarai M. Williams
,
William Chrisler
,
Marda L. Jorgensen
,
Ronald Moore
,
Ying Zhu
,
Karin Rodland
,
Richard Smith
,
Clive Wasserfall
,
Tujin Shi
,
Tao Liu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.04.12.488038
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.04.12.488038
Effective phosphoproteome of nanoscale sample analysis remains a daunting task, primarily due to significant sample loss associated with non-specific surface adsorption during enrichment of low stoichiometric phosphopeptide. We developed a novel tandem tip phosphoproteomics sample preparation method that is capable of sample cleanup and enrichment without additional sample transfer, and its integration with our recently developed SOP (Surfactant-assisted One-Pot sample preparation) and iBASIL (improved Boosting to Amplify Signal with Isobaric Labeling) approaches provides a streamlined workflow enabling sensitive, high-throughput nanoscale phosphoproteome measurements. This approach significantly reduces both sample loss and processing time, allowing the identification of >3,000 (>9,500) phosphopeptides from 1 (10) µg of cell lysate using the label-free method without a spectral library. It also enabled precise quantification of ∼600 phosphopeptides from 100 cells sorted by FACS (single-cell level input for the enriched phosphopeptides) and ∼700 phosphopeptides from human spleen tissue voxels with a spatial resolution of 200 µm (equivalent to ∼100 cells) in a high-throughput manner. The new workflow opens avenues for phosphoproteome profiling of mass-limited samples at the low nanogram level.
Related Topics
Concepts
Phosphopeptide
Phosphoproteomics
Sample preparation
Workflow
Chemistry
Proteome
Tandem mass spectrometry
Tandem
Computer science
Chromatography
Mass spectrometry
Materials science
Peptide
Protein phosphorylation
Phosphorylation
Biochemistry
Database
Composite material
Protein kinase A
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2022.04.12.488038
- https://www.biorxiv.org/content/biorxiv/early/2022/04/12/2022.04.12.488038.full.pdf
- OA Status
- green
- Cited By
- 2
- References
- 65
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4223553339
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4223553339Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2022.04.12.488038Digital Object Identifier
- Title
-
A streamlined tandem tip-based workflow for sensitive nanoscale phosphoproteomicsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-12Full publication date if available
- Authors
-
Chia‐Feng Tsai, Yi-Ting Wang, Chuan‐Chih Hsu, Reta Birhanu Kitata, Rosalie Chu, Marija Veličković, Rui Zhao, Sarai M. Williams, William Chrisler, Marda L. Jorgensen, Ronald Moore, Ying Zhu, Karin Rodland, Richard Smith, Clive Wasserfall, Tujin Shi, Tao LiuList of authors in order
- Landing page
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https://doi.org/10.1101/2022.04.12.488038Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2022/04/12/2022.04.12.488038.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2022/04/12/2022.04.12.488038.full.pdfDirect OA link when available
- Concepts
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Phosphopeptide, Phosphoproteomics, Sample preparation, Workflow, Chemistry, Proteome, Tandem mass spectrometry, Tandem, Computer science, Chromatography, Mass spectrometry, Materials science, Peptide, Protein phosphorylation, Phosphorylation, Biochemistry, Database, Composite material, Protein kinase ATop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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65Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.sample | 5, 15, 35, 42, 48, 60, 88 |
| abstract_inverted_index.sorted | 126 |
| abstract_inverted_index.spleen | 141 |
| abstract_inverted_index.tandem | 32 |
| abstract_inverted_index.tissue | 142 |
| abstract_inverted_index.voxels | 143 |
| abstract_inverted_index.∼100 | 153 |
| abstract_inverted_index.∼600 | 121 |
| abstract_inverted_index.∼700 | 137 |
| abstract_inverted_index.Amplify | 67 |
| abstract_inverted_index.One-Pot | 59 |
| abstract_inverted_index.avenues | 163 |
| abstract_inverted_index.capable | 40 |
| abstract_inverted_index.cleanup | 43 |
| abstract_inverted_index.enabled | 117 |
| abstract_inverted_index.manner. | 158 |
| abstract_inverted_index.precise | 118 |
| abstract_inverted_index.reduces | 86 |
| abstract_inverted_index.remains | 7 |
| abstract_inverted_index.samples | 169 |
| abstract_inverted_index.spatial | 146 |
| abstract_inverted_index.surface | 20 |
| abstract_inverted_index.without | 46, 111 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Boosting | 65 |
| abstract_inverted_index.Isobaric | 70 |
| abstract_inverted_index.allowing | 93 |
| abstract_inverted_index.analysis | 6 |
| abstract_inverted_index.approach | 84 |
| abstract_inverted_index.daunting | 9 |
| abstract_inverted_index.enabling | 77 |
| abstract_inverted_index.enriched | 134 |
| abstract_inverted_index.library. | 114 |
| abstract_inverted_index.nanogram | 173 |
| abstract_inverted_index.provides | 73 |
| abstract_inverted_index.recently | 55 |
| abstract_inverted_index.spectral | 113 |
| abstract_inverted_index.workflow | 76, 161 |
| abstract_inverted_index.>3,000 | 97 |
| abstract_inverted_index.(improved | 64 |
| abstract_inverted_index.Effective | 1 |
| abstract_inverted_index.Labeling) | 71 |
| abstract_inverted_index.developed | 29, 56 |
| abstract_inverted_index.nanoscale | 4, 80 |
| abstract_inverted_index.primarily | 11 |
| abstract_inverted_index.profiling | 166 |
| abstract_inverted_index.transfer, | 49 |
| abstract_inverted_index.additional | 47 |
| abstract_inverted_index.adsorption | 21 |
| abstract_inverted_index.approaches | 72 |
| abstract_inverted_index.associated | 17 |
| abstract_inverted_index.enrichment | 23, 45 |
| abstract_inverted_index.label-free | 109 |
| abstract_inverted_index.processing | 91 |
| abstract_inverted_index.resolution | 147 |
| abstract_inverted_index.sensitive, | 78 |
| abstract_inverted_index.(>9,500) | 98 |
| abstract_inverted_index.(equivalent | 151 |
| abstract_inverted_index.integration | 52 |
| abstract_inverted_index.preparation | 36 |
| abstract_inverted_index.significant | 14 |
| abstract_inverted_index.streamlined | 75 |
| abstract_inverted_index.(single-cell | 129 |
| abstract_inverted_index.mass-limited | 168 |
| abstract_inverted_index.non-specific | 19 |
| abstract_inverted_index.preparation) | 61 |
| abstract_inverted_index.measurements. | 82 |
| abstract_inverted_index.significantly | 85 |
| abstract_inverted_index.identification | 95 |
| abstract_inverted_index.quantification | 119 |
| abstract_inverted_index.stoichiometric | 26 |
| abstract_inverted_index.high-throughput | 79, 157 |
| abstract_inverted_index.phosphopeptide. | 27 |
| abstract_inverted_index.phosphopeptides | 99, 122, 138 |
| abstract_inverted_index.phosphoproteome | 2, 81, 165 |
| abstract_inverted_index.phosphopeptides) | 135 |
| abstract_inverted_index.phosphoproteomics | 34 |
| abstract_inverted_index.(Surfactant-assisted | 58 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5074168686, https://openalex.org/A5043928018, https://openalex.org/A5074663242 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 17 |
| corresponding_institution_ids | https://openalex.org/I142606810 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.550000011920929 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.48450203 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |