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Inferring super-resolved spatial metabolomics from microscopy

Rappez, L.; Haase, K.

2024-08-29 molecular biology
10.1101/2024.08.29.610242 bioRxiv
Show abstract

Current spatial metabolomics techniques have transformed our understanding of cellular metabolism, yet accessible methods are limited in spatial resolution due to sensitivity constraints. MetaLens, a deep generative approach, disrupts this trade-off by quantitatively propagating cellular-resolution in situ imaging mass spectrometry readouts to subcellular scales through integration with high-resolution light microscopy. MetaLens identifies subcellular metabolic domains with distinct molecular composition, enabling accessible label-free subcellular metabolomic analysis from microscopy.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

1
Nature Methods
○ 385 papers in training set
Top 0.1%
47.2%
Similar papers in this journal
2
Nature Communications
○ 5641 papers in training set
Top 6%
22.7%
Similar papers in this journal
50% of probability mass above
3
Nature Biotechnology
○ 172 papers in training set
Top 1%
3.4%
Similar papers in this journal
4
Nature Metabolism
○ 69 papers in training set
Top 0.6%
3.3%
Similar papers in this journal
5
Nature
○ 645 papers in training set
Top 6%
1.8%
Similar papers in this journal
6
Cell
○ 431 papers in training set
Top 6%
1.7%
7
Molecular Cell
○ 350 papers in training set
Top 3%
1.5%
8
Science Advances
● 1243 papers in training set
Top 24%
1.2%
9
Communications Biology
○ 993 papers in training set
Top 20%
1.2%
10
Nature Machine Intelligence
○ 70 papers in training set
Top 2%
1.1%
11
Nature Chemical Biology
○ 119 papers in training set
Top 2%
1.1%
12
Angewandte Chemie International Edition
○ 93 papers in training set
Top 1%
1.0%
13
Nature Structural & Molecular Biology
236 papers in training set
Top 4%
0.6%
14
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 43%
0.6%
15
Science
● 477 papers in training set
Top 10%
0.6%
16
ACS Nano
● 113 papers in training set
Top 2%
0.6%
17
Cell Reports
○ 1498 papers in training set
Top 28%
0.6%
18
Cell Reports Methods
○ 165 papers in training set
Top 5%
0.5%
19
Nature Cell Biology
○ 118 papers in training set
Top 4%
0.5%
20
Genome Biology
○ 637 papers in training set
Top 10%
0.5%
21
eLife
● 5828 papers in training set
Top 72%
0.5%

"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.