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By combining adaptive pharmacogenomics learning techniques with spatial transcriptomics, APX can help identify specific genetic markers and pathways associated with liver fibrosis. This can lead to a better understanding of the disease progression and potentially uncover new therapeutic targets.
Fibrosis is the abnormal accumulation of extracellular matrix (ECM) in tissues. This can lead to a number of problems, including:
Tissue stiffening: The ECM is a network of proteins that provides support and structure to tissues. When the ECM becomes too thick or dense, it can make tissues stiff and inflexible. This can interfere with the normal function of tissues, such as the lungs or the liver.
Loss of function: The ECM also contains proteins that are involved in cell signaling and other important functions. When the ECM becomes too thick or dense, it can interfere with these functions, leading to a loss of tissue function.
Inflammation: The ECM can also trigger an inflammatory response. This can lead to the release of pro-inflammatory cytokines, which can damage tissues and contribute to the progression of fibrosis.
Cancer: Fibrosis can also increase the risk of cancer. This is because the ECM can provide a supportive environment for cancer cells to grow and spread.
There are a number of different causes of fibrosis, including:
Chronic inflammation: Inflammation is a normal immune response to injury or infection. However, if inflammation is prolonged, it can lead to fibrosis.
Genetic factors: Some people are more likely to develop fibrosis due to their genetic makeup.
Exposure to toxins: Exposure to certain toxins, such as asbestos, can increase the risk of fibrosis.
Autoimmune diseases: Autoimmune diseases, such as scleroderma, can also lead to fibrosis.
There is no cure for fibrosis, but there are treatments that can help to slow the progression of the disease and improve symptoms. Treatments for fibrosis typically involve:
Medications: There are a number of medications that can be used to treat fibrosis, including corticosteroids, immunosuppressants, and anti-fibrotic drugs.
Surgery: In some cases, surgery may be necessary to remove damaged tissue or to improve lung function.
Lifestyle changes: Lifestyle changes, such as quitting smoking and exercising regularly, can also help to slow the progression of fibrosis and improve symptoms.
The prognosis for fibrosis varies depending on the severity of the disease and the underlying cause. However, in general, fibrosis is a progressive disease that can lead to significant disability and death.
In MASH (Metabolic dysfunction-associated steatohepatitis) liver, the interplay of cells is complex and involves multiple stages:
Steatosis: This process starts first in hepatocytes, where excess fat accumulates due to metabolic dysfunctions like insulin resistance and obesity.
Inflammation: Liver Sinusoidal Endothelial Cells (LSECs) undergo capillarization and dysfunction, leading to ischemic changes in hepatocytes. This triggers pro-inflammatory responses in Kupffer cells (KCs) and activates Hepatic Stellate Cells (HSCs).
Fibrosis: Activated HSCs transform into myofibroblast-like cells that produce excess extracellular matrix components, leading to fibrosis.
In summary, steatosis starts first in hepatocytes, followed by inflammation at the LSEC level, and finally fibrosis driven by HSCs
Liver Zonation
The figure presents above a zone-dependent distinctive gene expression profile of normal human liver tissue, comparing three zones (Z1, Z2, Z3) using data from GSE83990. It includes volcano plots highlighting significant gene upregulation and downregulation between the zones, and histological representations illustrating the spatial organization from the portal vein to the central vein. This analysis is crucial for understanding the metabolic and functional heterogeneity within the liver.
Demo Test Run :
Liver Zonation : Understanding Fibrosis
The stages of MASH (Metabolic dysfunction-associated steatohepatitis) are measured using several histological features, which are also used as markers for patient outcomes:
Steatosis: Accumulation of fat in hepatocytes.
Ballooning: Swelling of hepatocytes, indicating cell injury.
Inflammation: Presence of inflammatory cells in the liver.
Fibrosis: Scarring of liver tissue, which progresses through stages:
F0: No fibrosis
F1: Mild fibrosis
F2: Moderate fibrosis
F3: Bridging fibrosis
F4: Cirrhosis
Among these, fibrosis stage is the most critical marker for patient outcomes, as it correlates with the risk of progression to cirrhosis and liver-related complications
Open source data (available at GSE185477) 10x genomics visium spatial transcriptomics tissue image slides is tested as proof-of -concept for differential gene expressions (liver zonation)
PMID: 34792289
The figure shows above highlights spatial transcriptomics data from a 10x Genomics Visium experiment, where color-coded spots represent the locations from which RNA transcripts were extracted. The top row displays the spatial distribution of transcripts for the genes CYP3A4 and GLUL, while the bottom row shows the distribution for AQP9 and HPD. The "leiden" plots on the left in both rows indicate clustering of the spots based on transcriptomic similarity. This type of analysis is crucial for understanding the spatial organization of gene expression within tissues, which can reveal insights into tissue architecture, cellular interactions, and the molecular basis of diseases.
Identification of spatially variable features
Seurat offers two workflows to identify molecular features that correlate with spatial location within a tissue. The first is to perform differential expression based on pre-annotated anatomical regions within the tissue, which may be determined either from unsupervised clustering or prior knowledge. This strategy works in this case, as the clusters above exhibit clear spatial restriction.
Gene ORM1(Left), HP(center) MALAT1(right)
PMID: 35021063
scRNA-seq analysis of murine liver cells for healthy and NASH group Data available publicly at NCBI GEO: GSE166178
Marker Genes_NASH
Drug-induced liver injury (DILI) is a challenging disease to diagnose, a leading cause of acute liver failure, and responsible for drug withdrawal from the market. There is no symptom, no biomarker or test for detection, no therapy, but discontinuation of the drug. Pharmaceutical companies spend huge money, time, and scientific research efforts to test DILI effects and drug efficacy. DILI is a leading cause of acute liver failure in the United States.
DILI(Drug Induced Liver Injury) Data available publicly at NCBI GEO: GSE166178
scRNA-seq analysis of murine liver cells for healthy and DILI group
Marker Genes_DILI
Combining computational pathology and single cell RNA sequencing (scRNA-seq) provides new insights to study the liver spatial heterogeneity, zonal gene expression of liver cells in case of chronic liver disease such as NASH. Structurally, liver is organized into hepatic lobules where each lobule is divided into three zones. Periportal zone 1 enriched with nutrients and oxygenated blood, whereas zone 3 is least nutrient perivenous zone near central vein. Hepatocytes as major liver cell types, contribute main functions of liver synthesis, detoxification, and metabolism. Among the multiple cell types, Hepatic Stellate Cells (HSC) are important cells involved in pericentral zonal damage and zonal regeneration
An effort is made here to develop a proof-of concept and insight into liver zones to understand the underlying disease pathways using computational AI algorithms.
work in progress.. testing more data (multi-omics)
Questions to be asked on single cell data using AI algorithms:
Single-cell data analysis using artificial intelligence algorithms can be used to answer a variety of clinical questions, including:
Identifying cell types and subtypes. AI on single cell data analysis can be used to better understand the cellular composition of tissues and organs, and to identify new cell types or subtypes that may be involved in disease proliferation and diagnosis.
Characterizing gene expression patterns. This can be used to identify genes that are differentially expressed in different cell types or subtypes, and to track changes in gene expression over time.
Defining cell states and trajectories. This can be used to understand how cells change over time, and to identify different cell states that may be involved in disease.
Predicting clinical outcomes. This can be used to identify patients who are at risk for developing a disease, or to predict how a patient will respond to a particular treatment.
Discovering new therapeutic targets. This can be used to identify genes or pathways that are dysregulated in disease, and to develop new drugs or therapies that target these genes or pathways.
Here are some specific examples of clinical questions that can be answered using single-cell data analysis with AI:
In cancer research, single-cell data analysis can be used to identify cancer stem cells, which are the cells that drive tumor growth. This information can be used to develop new cancer therapies that target cancer stem cells.
In neurodegenerative diseases, single-cell data analysis can be used to identify the different cell types that are affected by the disease, and to track how these cells change over time. This information can be used to develop new treatments that slow or stop the progression of the disease.
In immune responses, single-cell data analysis can be used to identify the different immune cells that are involved in a particular response, and to track how these cells interact with each other. This information can be used to develop new vaccines and therapies that improve the immune system's ability to fight disease.
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