Spatial omics and multiplexed imaging to explore cancer biology

Lewis, Sabrina M.; Asselin-Labat, Marie-Liesse; Nguyen, Quan; Berthelet, Jean; Tan, Xiao; Wimmer, Verena C.; Mérino, Delphine; Rogers, Kelly L.; Naik, Shalin H.

doi:10.1038/s41592-021-01203-6

Cited by 304 publications

(203 citation statements)

References 155 publications

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“…; make cancer cells highly distinguishable from their normal counterparts. Despite signi cant advancements in understanding the biology of cancer, due to such complexities; identi cation of novel therapeutic targets remains a challenge [31].…”

Section: Discussionmentioning

confidence: 99%

The Profile of Expression of SG2NAs Differs Between Cancer Types and it is Involved in the Reprogramming of Tumour Cell Proteome.

Bisoyi

Devi

Besra

et al. 2021

Preprint

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Striatin and SG2NA are scaffold proteins that from signalling complexes called STRIPAK. It has been associated with cancer and other diseases. Our earlier studies have shown that SG2NA forms a complex with the cancer associate protein DJ-1 and signalling kinase Akt, promoting cancer cell survival. In the present study, we used bioinformatics analyses to confirm the existence of two isoforms of human SG2NA i.e., 78 and 87 kDas. In addition, several smaller isoforms like 35 kDa were also seen in western blot analyses of human cell lysates. The expression of these isoforms varies between different human cancer cell lines. Also, the protein level does not corroborate with its transcript level, suggesting a complex regulation of its expression. In breast tumour tissues, the expression of the 35 and 78 kDa isoforms was higher as compared to the adjacent normal tissues, while the 87 kDa isoform was detected in the breast tumour tissues only. With the progression of stages of breast cancer, the expression of 78 kDa isoform decreased, while 87 kDa became undetectable. In coimmunoprecipitation assay, the profile of SG2NA interactome in breast tumor vis-à-vis adjacent normal breast tissues shows hundreds of common proteins, while some proteins specifically interacted in breast tumour tissue only. We conclude that SG2NA is involve in diverse cellular pathways and have roles in cellular reprogramming during tumorigenesis.

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Section: Discussionmentioning

confidence: 99%

The Profile of Expression of SG2NAs Differs Between Cancer Types and it is Involved in the Reprogramming of Tumour Cell Proteome.

Bisoyi

Devi

Besra

et al. 2021

Preprint

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“…Over the last decade, the utility of ST in biological research has grown substantially. Many studies on different mouse models of human disease as well as human specimen have been performed, but mainly in the cancer field (38)(39)(40). Several powerful commercial platforms have since emerged, including the Visium by 10X Genomics (US), the NanoString Technologies (US), Akoya Biosciences (US), Fluidigm, Canopy Biosciences (a Bruker company), Lunaphore Technologies (Switzerland), Vizgen (US) and RareCyte (US) that offer whole genome as well as custom ST solutions (41).…”

Section: Spatial Transcriptomic Applications To Study Kidney Disease and Proteinuriamentioning

confidence: 99%

Emerging Technologies to Study the Glomerular Filtration Barrier

et al. 2021

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Kidney disease is characterized by loss of glomerular function with clinical manifestation of proteinuria. Identifying the cellular and molecular changes that lead to loss of protein in the urine is challenging due to the complexity of the filtration barrier, constituted by podocytes, glomerular endothelial cells, and glomerular basement membrane. In this review, we will discuss how technologies like single cell RNA sequencing and bioinformatics-based spatial transcriptomics, as well as in vitro systems like kidney organoids and the glomerulus-on-a-chip, have contributed to our understanding of glomerular pathophysiology. Knowledge gained from these studies will contribute toward the development of personalized therapeutic approaches for patients affected by proteinuric diseases.

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“…The value of classification however is proportional to how labor intensive the starting data is to generate. The AMBA expression data took years to acquire and is unlikely to be repeated for any other strain of mouse, while experimentally available spatial transcriptomic methods can not offer the deep sampling of gene expression used here (Chen et al, 2015;Lewis et al, 2021). Given the already sparse gene compositions of anatomically relevant SFt features, we asked if our input data could be compressed to the most relevant markers, while still representing anatomy.…”

Section: Compressed Sft Features Can Be Used For Robust Classificationmentioning

confidence: 99%

Sparse Representation Learning Derives Biological Features with Explicit Gene Weights from the Allen Mouse Brain Atlas

Bartelle

Abbasi

Raghu

2021

Preprint

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We have developed representation learning methods, specifically to address the constraints and advantages of complex spatial data. Sparse filtering (SFt), uses principles of sparsity and mutual information to build representations from both global and local features from a minimal list of samples. Critically, the samples that comprise each representation are listed and ranked by informativeness. We used the Allen Mouse Brain Atlas gene expression data for prototyping and established performance metrics based on representation accuracy to labeled anatomy. SFt, implemented with the PyTorch machine learning libraries for Python, returned the most accurate reconstruction of anatomical ground truth of any method tested. SFt generated gene lists could be further compressed, retaining 95% of informativeness with only 580 genes. Finally, we build classifiers capable of parsing anatomy with >95% accuracy using only 10 derived genes. Sparse learning is a powerful, but underexplored means to derive biologically meaningful representations from complex datasets and a quantitative basis for compressed sensing of classifiable phenomena. SFt should be considered as an alternative to PCA or manifold learning for any high dimensional dataset and the basis for future spatial learning algorithms.

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Spatial omics and multiplexed imaging to explore cancer biology

Cited by 304 publications

References 155 publications

The Profile of Expression of SG2NAs Differs Between Cancer Types and it is Involved in the Reprogramming of Tumour Cell Proteome.

The Profile of Expression of SG2NAs Differs Between Cancer Types and it is Involved in the Reprogramming of Tumour Cell Proteome.

Emerging Technologies to Study the Glomerular Filtration Barrier

Sparse Representation Learning Derives Biological Features with Explicit Gene Weights from the Allen Mouse Brain Atlas

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