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Related Concept Videos

Distribution and Dispersion00:54

Distribution and Dispersion

Ecology is the study of how organisms interact with their environment and with one another. An important aspect of ecology is understanding where species are found and how individuals are distributed within those areas. The geographic range of a species refers to the total area where its members are located, while dispersion describes the pattern of spacing of individuals within that range.Geographic Range and Dispersion PatternsWithin a species’ geographic range, individuals may be distributed...
Ecological Niches02:02

Ecological Niches

All organisms have a position within an ecosystem. The complete set of living and nonliving factors—including food resources, climate, and terrain—that define the position of a given organism are collectively referred to as the organism’s ecological niche.Multiple species cannot occupy the exact same niche within their habitat. If the niches of two or more species overlap to a large extent, the competitive exclusion principle dictates that one species will outcompete the other, forcing it to...
State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Manipulation and Analysis01:21

Manipulation and Analysis

GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...

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Related Experiment Video

Updated: Jun 30, 2026

Quantitative Multispectral Analysis Following Fluorescent Tissue Transplant for Visualization of Cell Origins, Types, and Interactions
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Quantitative characterization of tissue states using multiomics and ecological spatial analysis.

Daisy Yi Ding1, Zeyu Tang2, Bokai Zhu3,4

  • 1Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.

Nature Genetics
|April 1, 2025
PubMed
Summary

MESA (multiomics and ecological spatial analysis) quantifies tissue spatial organization using ecological principles. This framework links spatial patterns to disease progression and reveals molecular insights into cellular neighborhoods.

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Area of Science:

  • Spatial biology
  • Computational biology
  • Systems biology

Background:

  • Cellular spatial organization is crucial for tissue function and understanding disease.
  • Spatial profiling technologies offer new ways to study tissue architecture.
  • Existing methods may not fully capture complex spatial relationships and multiomics data.

Purpose of the Study:

  • To introduce MESA (multiomics and ecological spatial analysis), a novel framework for analyzing spatial omics data.
  • To quantify spatial diversity and identify key spatial patterns in tissues.
  • To integrate multiomics and spatial data for a deeper understanding of cellular neighborhoods and disease states.

Main Methods:

  • Developed MESA, a framework inspired by ecological concepts.
  • Introduced metrics for quantifying spatial diversity and identifying hot spots.
  • Integrated spatial and single-cell multiomics data analysis.

Main Results:

  • MESA systematically quantifies spatial diversity and links spatial patterns to phenotypic outcomes, including disease progression.
  • The framework revealed novel spatial structures and cell populations associated with disease states.
  • MESA provided deeper molecular insights into cellular neighborhoods and their interactions.

Conclusions:

  • MESA offers a versatile computational framework for quantitative decoding of tissue architectures in spatial omics.
  • The approach enhances insights over prior methods by integrating multiomics and spatial data.
  • MESA facilitates a comprehensive understanding of tissue organization in health and disease.