Back

Time-resolved single-cell RNAseq profiling identifies a novel Fabp5-expressing subpopulation of inflammatory myeloid cells in chronic spinal cord injury

Pluchino, S.; Hamel, R.; Peruzzotti-Jametti, L.; Ridley, K.; Testa, V.; Yu, B.; Rowitch, D.; Marioni, J.

2020-10-21 neuroscience
10.1101/2020.10.21.346635 bioRxiv
Show abstract

Innate immune responses following spinal cord injury (SCI) participate in early secondary pathogenesis and wound healing events. Here, we used time-resolved scRNAseq to map transcriptional profiles of SC tissue-resident and infiltrating myeloid cells post-SCI. Our work identifies a novel subpopulation of Fabp5+ inflammatory myeloid cells, comprising both resident and infiltrating cells and displaying a delayed cytotoxic profile at the lesion epicentre, which may serve as a target for future therapeutics.

Matching journals

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

1
Nature
○ 645 papers in training set
Top 0.1%
40.4%
Similar papers in this journal
2
Nature Neuroscience
○ 252 papers in training set
Top 0.2%
13.2%
Similar papers in this journal
50% of probability mass above
3
Nature Communications
○ 5641 papers in training set
Top 13%
13.0%
Similar papers in this journal
4
Cell Stem Cell
○ 62 papers in training set
Top 0.2%
6.9%
Similar papers in this journal
5
Neuron
○ 337 papers in training set
Top 2%
3.5%
Similar papers in this journal
6
Science Advances
● 1243 papers in training set
Top 14%
2.4%
7
eLife
● 5828 papers in training set
Top 42%
2.4%
8
Cell Reports
○ 1498 papers in training set
Top 20%
1.5%
9
Cell
○ 431 papers in training set
Top 7%
1.2%
10
Nature Metabolism
○ 69 papers in training set
Top 1%
1.2%
11
Nature Aging
○ 60 papers in training set
Top 1%
1.2%
12
Science
● 477 papers in training set
Top 7%
1.2%
13
Developmental Cell
○ 196 papers in training set
Top 3%
1.1%
14
Nature Immunology
○ 79 papers in training set
Top 2%
1.0%
15
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 40%
0.9%
16
Cell Research
○ 51 papers in training set
Top 1%
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.