APX - Spatial Omics

Integrating histopathology and spatial transcriptomics is a promising approach for improving our understanding of disease and developing new diagnostic and therapeutic approaches. This approach combines the strengths of two complementary techniques:

Histology: Histology is the study of tissues using microscopy. It allows us to visualize the spatial organization of cells and tissues, and to identify the presence of cancer cells and other abnormalities.
Spatial transcriptomics: Spatial transcriptomics is a technique that allows us to measure the expression of genes in individual cells within a tissue. This provides us with information about the molecular subtypes of cells present in a tissue, and how these subtypes are spatially distributed.

By integrating histology and spatial transcriptomics, we can gain a more comprehensive understanding of the molecular and cellular changes that underlie disease. This information can be used to develop new diagnostic tests that are more sensitive and specific than traditional methods, and to identify new therapeutic targets.

Here are some specific applications of integrating histopathology and spatial transcriptomics:

Improved cancer diagnosis and prognosis: Spatial transcriptomics can be used to identify new biomarkers for cancer, which can be used to improve cancer diagnosis and prognosis. For example, a study published in Nature Medicine in 2020 used spatial transcriptomics to identify a new biomarker for breast cancer that was more accurate than traditional biomarkers.
Development of new cancer therapies: Spatial transcriptomics can be used to identify new drug targets for cancer. For example, a study published in Cancer Cell in 2021 used spatial transcriptomics to identify a new drug target for pancreatic cancer.
Understanding the mechanisms of disease: Spatial transcriptomics can be used to understand the mechanisms of disease at the cellular and molecular level. For example, a study published in Nature in 2020 used spatial transcriptomics to identify the cellular and molecular changes that occur in the brain during Alzheimer's disease.

The integration of histopathology and spatial transcriptomics is a rapidly developing field with the potential to revolutionize the way we diagnose, treat, and understand disease. As the field continues to develop, we can expect to see even greater advances in the near future.

Demo Test Run : Data availability https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE172416

10x Genomics visium platform tissue(lung) image slides. Spots (55 micron each) are the locations of transcriptome extraction sites. Color represents predicted cell types within spot locations.

Highest expressed genes

Label transfer single-cell analysis scRNA-seq data analysis scRNA analysis pbmc Systemic lupus erythematosus (SLE) data

Data availability open-source NCBI GEO GSE174188

scRNA seq Human Liver

single cell analysis (using Seurat package in R) to identify therapeutic targets

Single cell analysis & spatial transcriptomics on histopathology imaging onco-immunology data is a rapidly developing field that has the potential to revolutionize the way cancer is diagnosed and treated. By combining the spatial information of histopathology imaging with the molecular information of single cell analysis, researchers can gain a more comprehensive understanding of disease progression. This information can then be used to develop new diagnostic tests, treatment strategies, and prevention measures. Here are some of the benefits of using single cell analysis on histopathology imaging onco-immunology data:

It can provide a more comprehensive understanding of the TME (Tumor Microenvironment).
It can identify biomarkers for cancer progression.
It can predict patient response to treatment.
It can help to develop new diagnostic tests and treatment strategies.

Questions to be asked on spatial transcriptomics data using AI algorithms:

Spatial transcriptomics data analysis using artificial intelligence algorithms can be used to answer a variety of questions, including:

Identifying cell types and their spatial distribution: This can be used to understand the normal tissue architecture and how it is disrupted in disease. For example, spatial transcriptomics has been used to identify new cell types in the brain and to map the distribution of immune cells in tumors.
Characterizing gene expression patterns: This can be used to identify genes that are differentially expressed in different cell types or in different regions of a tissue. This information can be used to understand the molecular basis of disease and to develop new diagnostic and therapeutic strategies.
Defining cell-cell interactions: This can be used to understand how different cell types communicate with each other and how these interactions are disrupted in disease. For example, spatial transcriptomics has been used to identify cell-cell interactions that are important for tumor metastasis.
Mapping molecular pathways: This can be used to understand how genes and proteins work together to regulate cellular processes. This information can be used to develop new drug targets and to design more effective therapies.
Predicting clinical outcomes: This can be used to identify molecular signatures that can be used to predict the risk of disease progression or response to treatment. For example, spatial transcriptomics has been used to predict the risk of recurrence in breast cancer patients.

Here are some specific examples of how spatial transcriptomics has been used to answer clinical questions:

In a study of Alzheimer's disease, spatial transcriptomics was used to identify genes that are differentially expressed in the brains of patients with Alzheimer's disease. This information could be used to develop new diagnostic tests or to target therapeutic interventions.
In a study of breast cancer, spatial transcriptomics was used to map the distribution of immune cells in tumors. This information could be used to develop new immunotherapies for breast cancer.
In a study of lung cancer, spatial transcriptomics was used to identify cell-cell interactions that are important for tumor metastasis. This information could be used to develop new drugs to prevent tumor spread.

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