Back

CORE: Deterministic alignment for bias-free organoid histology

Pereiro, I.; Lavickova, B.; Labat-Berthier, C.; Toscani, S.; Aubert, J.; Cubela, I.; Rao, J.; Garcia-Cordero, J. L.

2026-09-22 bioengineering
10.64898/2026.09.20.752946 bioRxiv
Show abstract

Histological analysis of 3D tissue models is limited by geometric sampling bias arising from inconsistent positioning during embedding. We present Coplanar Organoid Reproducible Embedding (CORE), which aligns heterogeneous specimens on a shared sectioning plane to enable reproducible, bias-free histology. By eliminating depth-dependent sampling artifacts, CORE reveals biological differences obscured by sectioning depth and integrates with automated liquid- handling workflows.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

1
Nature Methods
○ 385 papers in training set
Top 0.5%
18.4%
Similar papers in this journal
2
Nature Communications
○ 5641 papers in training set
Top 19%
9.7%
Similar papers in this journal
3
Science
● 477 papers in training set
Top 0.8%
7.8%
Similar papers in this journal
4
Scientific Reports
○ 3612 papers in training set
Top 7%
7.8%
Similar papers in this journal
5
Cell Reports Methods
○ 165 papers in training set
Top 0.1%
6.7%
Similar papers in this journal
50% of probability mass above
6
PLOS ONE
● 5266 papers in training set
Top 24%
6.7%
7
Cell Systems
○ 201 papers in training set
Top 0.8%
5.4%
8
Nature Biotechnology
○ 172 papers in training set
Top 1%
3.2%
9
Science Advances
● 1243 papers in training set
Top 11%
3.2%
10
Communications Biology
○ 993 papers in training set
Top 8%
2.4%
11
Advanced Science
○ 286 papers in training set
Top 4%
1.9%
12
Nature Neuroscience
○ 252 papers in training set
Top 4%
1.5%
13
npj Precision Oncology
○ 53 papers in training set
Top 1%
1.4%
14
Nature Biomedical Engineering
○ 47 papers in training set
Top 1%
1.3%
15
Small Methods
○ 29 papers in training set
Top 0.5%
1.1%
16
Journal of Cell Biology
● 392 papers in training set
Top 3%
1.1%
17
Scientific Data
○ 209 papers in training set
Top 3%
0.8%
18
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 42%
0.8%
19
Science Translational Medicine
● 127 papers in training set
Top 4%
0.8%
20
Nature Materials
○ 28 papers in training set
Top 0.6%
0.8%
21
Biomaterials
○ 84 papers in training set
Top 2%
0.8%
22
eLife
● 5828 papers in training set
Top 67%
0.8%
23
Development
● 497 papers in training set
Top 5%
0.6%
24
npj Imaging
○ 12 papers in training set
Top 0.3%
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.