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What is GeoMx Digital Spatial Profiler (DSP)?
Effectively tackle the challenges posed by tissue heterogeneity and the complex nature of microenvironments through the use of the GeoMx Digital Spatial Profiler (DSP), which is distinguished as the most adaptable and robust spatial multi-omic platform designed for the analysis of both FFPE and fresh frozen tissue samples. Unlike other spatial biology tools, GeoMx provides a non-destructive method for profiling RNA and protein expression across diverse tissue compartments and cell populations, all facilitated by an automated and scalable workflow that integrates seamlessly with traditional histology staining techniques. You have the capability to spatially profile the complete transcriptome alongside more than 570 protein targets, either individually or in combination, utilizing sample inputs like whole tissue sections, tissue microarrays (TMAs), or organoids. Opting for GeoMx DSP places you at the leading edge of spatial biology, enhancing your efforts in biomarker discovery and hypothesis validation. This platform empowers you to accurately delineate relevant boundaries, allowing for biology-driven profiling that zeroes in on the tissue microenvironments and cell types that are most critical to your research endeavors. By employing this groundbreaking method, your analyses not only become more comprehensive but also finely tuned to the specific biological questions you aim to address. Ultimately, this paves the way for deeper insights and more impactful findings in your field of study.
What is AWS HealthOmics?
Seamlessly integrate multiomic data from patients with their health histories to deliver personalized healthcare strategies. Leverage dedicated data repositories that support in-depth analysis and collaborative research efforts across diverse population groups. Accelerate research initiatives by adopting scalable methodologies and robust computational resources. Prioritize patient confidentiality by adhering to HIPAA regulations and implementing thorough data access controls. AWS HealthOmics aids healthcare providers and life science organizations, along with their software partners, in the efficient storage, querying, and analysis of genomic, transcriptomic, and various omics datasets, ultimately producing actionable insights that improve health outcomes and drive scientific advancements. Oversee and evaluate omics data for a wide array of patients to identify trends in omics variability relative to phenotypic characteristics within a community. Develop organized and verifiable clinical multiomics workflows to reduce waiting periods and enhance operational efficiency. Incorporate multiomic evaluations into clinical trials for the assessment of new drug candidates, thereby laying the groundwork for cutting-edge treatments and therapies. This data integration not only improves the quality of research but also cultivates a more profound understanding of the complex relationships between genetics and overall health, leading to potentially revolutionary discoveries in medical science. Through these concerted efforts, the potential for breakthroughs in patient care and treatment options significantly increases.
Integrations Supported
API Availability
API Availability
Has API
Pricing Information
Pricing not provided
Pricing Information
Pricing not provided
Supported Platforms
SaaS
Supported Platforms
SaaS
Customer Service / Support
Standard Support
Web-Based Support
Customer Service / Support
24 Hour Support
Web-Based Support
Training Options
Documentation Hub
Online Training
On-Site Training
Training Options
Documentation Hub
Webinars
Company Facts
Organization Name
nanoString
Company Location
United States
Company Website
nanostring.com/products/geomx-digital-spatial-profiler/geomx-dsp-overview/
Company Facts
Organization Name
Amazon
Date Founded
1994
Company Location
United States
Company Website
aws.amazon.com/healthomics/
Categories and Features
Bioinformatics
Not specified
Genomics Data Analysis
Not specified
Categories and Features
Artificial Intelligence (AI) APIs
Not specified
Genomics Data Analysis
Not specified
Life Sciences
Not specified