Back

High-content morphological profiling by Cell Painting in 3D spheroids.

Ringers, C.; Holmberg, D.; Flobak, A.; Georgiev, P.; Jarvius, M.; Johansson, M.; Larsson, A.; Rosen, D.; Seashore-Ludlow, B.; Visnes, T.; Carreras-Puigvert, J.; Spjuth, O.

2025-02-08 pharmacology and toxicology
10.1101/2025.02.05.636642 bioRxiv
Show abstract

Cell Painting is a popular assay for morphological profiling of multi-labeled 2D monolayer cell cultures used in a wide range of applications. Culturing cells in 3D has potential for higher physiological relevance, such as when studying effects of perturbations. Robust and scalable 3D models can be challenging to characterize through imaging - particularly because light has difficulty penetrating cell multilayers. We introduce a scalable method where the Cell Painting assay is combined with tissue-clearing and applied to 3D spheroids generated in a ULA microplate format. Multi-channel images are acquired using confocal microscopy, and cells can be segmented inside those spheroids allowing for relevant morphological features to be extracted. Our end-to-end analysis pipeline comprises cell segmentation, morphological feature extraction, and between-spheroids and within-spheroid normalization. We demonstrate the method using spheroids cultured from two colorectal cancer cell lines and successfully detect distinct phenotypic changes upon compound treatments, on both spheroid-level using maximum intensity projections and on single cell-level. We show that drugs group by mechanism of action, with biologically relevant clusters especially evident with single-cell data. Finally, we contrast our method to results from 2D Cell Painting and discover a different pattern in DNA damaging drugs in HCT116 colorectal cancer cells. This work lays the foundation for multi-channel image-based screening in 3D spheroids.

Matching journals

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

1
PLOS ONE
4510 papers in training set
Top 11%
17.2%
2
SLAS Discovery
25 papers in training set
Top 0.1%
14.1%
3
Scientific Reports
3102 papers in training set
Top 11%
8.3%
4
Light: Science & Applications
16 papers in training set
Top 0.1%
6.7%
5
Nature Communications
4913 papers in training set
Top 30%
6.3%
50% of probability mass above
6
Bioinformatics
1061 papers in training set
Top 5%
3.9%
7
PLOS Computational Biology
1633 papers in training set
Top 9%
3.9%
8
MethodsX
14 papers in training set
Top 0.1%
3.5%
9
Communications Biology
886 papers in training set
Top 2%
3.5%
10
iScience
1063 papers in training set
Top 8%
2.6%
11
Nucleic Acids Research
1128 papers in training set
Top 9%
2.0%
12
Nature Methods
336 papers in training set
Top 4%
1.9%
13
Biological Imaging
15 papers in training set
Top 0.1%
1.9%
14
Frontiers in Pharmacology
100 papers in training set
Top 2%
1.8%
15
Science Advances
1098 papers in training set
Top 23%
1.2%
16
Disease Models & Mechanisms
119 papers in training set
Top 2%
0.9%
17
eLife
5422 papers in training set
Top 52%
0.9%
18
Advanced Science
249 papers in training set
Top 18%
0.8%
19
Heliyon
146 papers in training set
Top 6%
0.8%
20
Patterns
70 papers in training set
Top 2%
0.8%
21
Biology Open
130 papers in training set
Top 3%
0.7%
22
Journal of Cell Biology
333 papers in training set
Top 5%
0.7%
23
Development
440 papers in training set
Top 4%
0.7%
24
Nature Protocols
30 papers in training set
Top 0.3%
0.7%
25
Artificial Intelligence in the Life Sciences
11 papers in training set
Top 0.4%
0.6%
26
Analytical Chemistry
205 papers in training set
Top 3%
0.6%
27
Molecular Biology of the Cell
272 papers in training set
Top 3%
0.6%
28
Journal of Microscopy
18 papers in training set
Top 0.5%
0.6%