Back

PEPSI: Polarity measurements from spatial proteomics imaging suggest immune cell engagement

Wu, E.; Wu, Z.; Mayer, A. T.; Trevino, A. E.; Zou, J.

2023-10-17 bioinformatics
10.1101/2023.10.13.562299 bioRxiv
Show abstract

Subcellular protein localization is important for understanding functional states of cells, but measuring and quantifying this information can be difficult and typically requires high-resolution microscopy. In this work, we develop a metric to define surface protein polarity from immunofluorescence (IF) imaging data and use it to identify distinct immune cell states within tumor microenvironments. We apply this metric to characterize over two million cells across 600 patient samples and find that cells identified as having polar expression exhibit characteristics relating to tumor-immune cell engagement. Additionally, we show that incorporating these polarity-defined cell subtypes improves the performance of deep learning models trained to predict patient survival outcomes. This method provides a first look at using subcellular protein expression patterns to phenotype immune cell functional states with applications to precision medicine.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 14%
12.5%
Similar papers in this journal
2
Patterns
○ 78 papers in training set
Top 0.1%
11.1%
Similar papers in this journal
3
Scientific Reports
○ 3612 papers in training set
Top 5%
9.7%
Similar papers in this journal
4
Bioinformatics
◐ 1204 papers in training set
Top 3%
6.8%
Similar papers in this journal
5
npj Precision Oncology
○ 53 papers in training set
Top 0.1%
6.8%
Similar papers in this journal
6
Cell Reports Methods
○ 165 papers in training set
Top 0.3%
4.3%
50% of probability mass above
7
PLOS Computational Biology
● 1863 papers in training set
Top 11%
2.8%
8
npj Systems Biology and Applications
○ 125 papers in training set
Top 0.6%
2.7%
9
eLife
● 5828 papers in training set
Top 41%
2.5%
10
Cell Systems
○ 201 papers in training set
Top 2%
2.4%
11
Communications Biology
○ 993 papers in training set
Top 9%
2.4%
12
PLOS ONE
● 5266 papers in training set
Top 43%
2.1%
13
iScience
○ 1154 papers in training set
Top 13%
2.1%
14
Briefings in Bioinformatics
◐ 354 papers in training set
Top 4%
2.0%
15
Molecular Biology of the Cell
● 311 papers in training set
Top 2%
1.5%
16
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 30%
1.5%
17
Cancer Research Communications
● 51 papers in training set
Top 1%
1.3%
18
Genome Medicine
○ 183 papers in training set
Top 3%
1.3%
19
Cell Reports Medicine
○ 153 papers in training set
Top 3%
1.1%
20
Nature Machine Intelligence
○ 70 papers in training set
Top 2%
1.1%
21
PNAS Nexus
◐ 159 papers in training set
Top 2%
1.1%
22
Molecular Systems Biology
○ 162 papers in training set
Top 2%
1.0%
23
Nature Methods
○ 385 papers in training set
Top 6%
1.0%
24
Genome Biology
○ 637 papers in training set
Top 8%
1.0%
25
Nucleic Acids Research
◐ 1281 papers in training set
Top 12%
1.0%
26
Cytometry Part A
○ 33 papers in training set
Top 0.3%
0.8%
27
Bioinformatics Advances
◐ 203 papers in training set
Top 4%
0.8%
28
Frontiers in Immunology
○ 638 papers in training set
Top 11%
0.6%
29
Nature Biotechnology
○ 172 papers in training set
Top 5%
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.