S3-CIMA: Supervised spatial single-cell image analysis for identifying disease-associated cell-type compositions in tissue.
Patterns (N Y)
; 4(9): 100829, 2023 Sep 08.
Article
en En
| MEDLINE
| ID: mdl-37720335
ABSTRACT
The spatial organization of various cell types within the tissue microenvironment is a key element for the formation of physiological and pathological processes, including cancer and autoimmune diseases. Here, we present S3-CIMA, a weakly supervised convolutional neural network model that enables the detection of disease-specific microenvironment compositions from high-dimensional proteomic imaging data. We demonstrate the utility of this approach by determining cancer outcome- and cellular-signaling-specific spatial cell-state compositions in highly multiplexed fluorescence microscopy data of the tumor microenvironment in colorectal cancer. Moreover, we use S3-CIMA to identify disease-onset-specific changes of the pancreatic tissue microenvironment in type 1 diabetes using imaging mass-cytometry data. We evaluated S3-CIMA as a powerful tool to discover novel disease-associated spatial cellular interactions from currently available and future spatial biology datasets.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Tipo de estudio:
Risk_factors_studies
Idioma:
En
Revista:
Patterns (N Y)
Año:
2023
Tipo del documento:
Article
País de afiliación:
Alemania