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Filter-aided expansion proteomics

Dong, Z.; Zhu, Y.; Zhang, F.; Wu, C.; Chen, T.; Jiang, W.; Xiao, Q.; Zheng, S.; Piatkevich, K. D.; Guo, T.

2023-11-29 biochemistry
10.1101/2023.11.29.569242 bioRxiv
Show abstract

Integration of hydrogel-based tissue expansion and mass spectrometry-based proteomics enables high spatial resolution analysis of global protein expression in tissues. Here, we introduce a filter-aided tissue expansion proteomics (FAXP) method with optimized hydrogel preparation and treatment steps for spatial proteomics analysis of formalin-fixed paraffin-embedded (FFPE) specimens. Compared with our original ProteomEx method, FAXP, partially automated with robotics, exhibited 14.5-fold higher volumetric resolution, increased peptide yield leading to 250% more protein identifications, and 50% time consumption for sample preparation. We demonstrated its application in clinical colorectal tissue samples. We further integrated it with laser capture microdissection for proteomic analysis of subcellular organelles.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

1
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Top 0.1%
22.6%
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2
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3
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Top 13%
13.3%
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50% of probability mass above
4
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Top 1%
4.5%
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5
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Top 0.1%
2.5%
7
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○ 17 papers in training set
Top 0.2%
2.2%
8
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● 16 papers in training set
Top 0.1%
2.2%
9
eLife
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Top 53%
1.5%
10
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○ 48 papers in training set
Top 0.6%
1.5%
11
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Top 17%
1.5%
12
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○ 11 papers in training set
Top 0.1%
1.2%
13
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○ 165 papers in training set
Top 2%
1.2%
14
Journal of the American Chemical Society
● 217 papers in training set
Top 2%
1.1%
15
iScience
○ 1154 papers in training set
Top 34%
0.9%
16
Scientific Reports
○ 3612 papers in training set
Top 71%
0.9%
17
Cell Systems
○ 201 papers in training set
Top 4%
0.9%
18
Angewandte Chemie International Edition
○ 93 papers in training set
Top 2%
0.6%
19
PROTEOMICS
○ 43 papers in training set
Top 0.8%
0.6%
20
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 43%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.