Enhancing Spatial Transcriptomics Analysis by Integrating Image-Aware Deep Learning Methods.
Pac Symp Biocomput
; 29: 450-463, 2024.
Article
em En
| MEDLINE
| ID: mdl-38160299
ABSTRACT
Spatial transcriptomics (ST) represents a pivotal advancement in biomedical research, enabling the transcriptional profiling of cells within their morphological context and providing a pivotal tool for understanding spatial heterogeneity in cancer tissues. However, current analytical approaches, akin to single-cell analysis, largely depend on gene expression, underutilizing the rich morphological information inherent in the tissue. We present a novel method integrating spatial transcriptomics and histopathological image data to better capture biologically meaningful patterns in patient data, focusing on aggressive cancer types such as glioblastoma and triple-negative breast cancer. We used a ResNet-based deep learning model to extract key morphological features from high-resolution whole-slide histology images. Spot-level PCA-reduced vectors of both the ResNet-50 analysis of the histological image and the spatial gene expression data were used in Louvain clustering to enable image-aware feature discovery. Assessment of features from image-aware clustering successfully pinpointed key biological features identified by manual histopathology, such as for regions of fibrosis and necrosis, as well as improved edge definition in EGFR-rich areas. Importantly, our combinatorial approach revealed crucial characteristics seen in histopathology that gene-expression-only analysis had missed.Supplemental Material:
https//github.com/davcraig75/song_psb2014/blob/main/SupplementaryData.pdf.
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Base de dados:
MEDLINE
Assunto principal:
Glioblastoma
/
Pesquisa Biomédica
/
Aprendizado Profundo
Idioma:
En
Ano de publicação:
2024
Tipo de documento:
Article