Back

Reconstructing the Single-Cell Spatiotemporal Dynamics of Glioblastoma Invasion

Mangukiya, H. B.; Skeppas, M.; Kundu, S.; Berglund, M.; Malik, A. A.; Krona, C.; Nelander, S.

2025-03-22 cancer biology
10.1101/2025.03.20.644331 bioRxiv
Show abstract

Glioblastoma invasion into healthy brain tissue remains a major barrier to effective treatment, yet current models fail to capture its full complexity in a scalable and patient-specific manner. Here, we introduce GlioTrace, a novel ex vivo imaging and AI-based analytical framework that enables real-time, spatiotemporal tracking of glioblastoma invasion dynamics in patient-derived glioma cell culture xenograft (PDCX) brain slices. By integrating whole-specimen confocal microscopy, vascular counterstaining, and an advanced computational pipeline combining convolutional neural networks and Hidden Markov Models, GlioTrace identifies distinct invasion modes, including dynamic morphological switching, vessel-guided migration, and immune cell interactions and quantifies patient-specific variations in invasion plasticity. Using GlioTrace, we demonstrate that targeted therapies can selectively modulate invasion phenotypes, revealing spatially and temporally distinct drug responses. This scalable platform provides an unprecedented window into glioblastoma progression and treatment response, offering a powerful tool for precision oncology and anti-invasion therapeutic development.

Matching journals

The top 9 journals account for 50% of the predicted probability mass.

1
Nature Communications
○ 5641 papers in training set
Top 19%
9.4%
Similar papers in this journal
2
Nature Methods
○ 385 papers in training set
Top 2%
6.5%
Similar papers in this journal
3
npj Imaging
○ 12 papers in training set
Top 0.1%
6.5%
Similar papers in this journal
4
Neuro-Oncology Advances
◐ 25 papers in training set
Top 0.1%
6.5%
Similar papers in this journal
5
Cancer Research
● 130 papers in training set
Top 0.7%
5.3%
Similar papers in this journal
6
Cancer Cell
○ 42 papers in training set
Top 0.2%
5.0%
7
Cell Reports
○ 1498 papers in training set
Top 9%
4.2%
8
Neuro-Oncology
◐ 36 papers in training set
Top 0.2%
4.2%
9
Science Advances
● 1243 papers in training set
Top 9%
3.9%
50% of probability mass above
10
Communications Biology
○ 993 papers in training set
Top 4%
3.9%
11
Acta Neuropathologica Communications
○ 89 papers in training set
Top 0.6%
3.9%
12
eLife
● 5828 papers in training set
Top 33%
3.4%
13
npj Precision Oncology
○ 53 papers in training set
Top 0.4%
3.3%
14
Cell Reports Methods
○ 165 papers in training set
Top 1%
2.3%
15
Advanced Science
○ 286 papers in training set
Top 4%
1.8%
16
Cancer Discovery
● 66 papers in training set
Top 1%
1.7%
17
Scientific Reports
○ 3612 papers in training set
Top 55%
1.7%
18
Genome Medicine
○ 183 papers in training set
Top 3%
1.6%
19
Neoplasia
○ 23 papers in training set
Top 0.4%
1.4%
20
iScience
○ 1154 papers in training set
Top 23%
1.4%
21
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 36%
1.1%
22
Cell Reports Medicine
○ 153 papers in training set
Top 4%
1.0%
23
Cell
○ 431 papers in training set
Top 10%
0.8%
24
Cancer Letters
○ 35 papers in training set
Top 1%
0.8%
25
Nature Cancer
○ 39 papers in training set
Top 1%
0.8%
26
Nature Biomedical Engineering
○ 47 papers in training set
Top 2%
0.8%
27
Molecular Systems Biology
○ 162 papers in training set
Top 3%
0.8%
28
PLOS ONE
● 5266 papers in training set
Top 60%
0.8%
29
Disease Models & Mechanisms
139 papers in training set
Top 4%
0.6%
30
PLOS Computational Biology
● 1863 papers in training set
Top 22%
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.