Spatial Multi-omics Integration: A Key Tool in Understanding Tissue Heterogeneity

What Spatial Multi-omics Integration Is

Spatial multi-omics integration involves the combination of different types of omics data, such as proteomics and transcriptomics, which are collected at the spatial level within a tissue. This technique allows researchers to correlate gene expression with protein levels in specific cellular compartments, providing insights into the functional state of cells.

The process typically starts by acquiring spatially resolved data from techniques like spatial transcriptomics (capturing RNA expression) and spatial proteomics (measuring protein abundance). These datasets are often misaligned due to technical challenges during sample preparation or imaging.

Why It Happens

Misalignment between different omics layers can occur because the spatial resolution of RNA and proteins may differ, leading to discrepancies in their relative positions within a tissue. This misalignment complicates the analysis as it obscures the true spatial relationships between gene expression and protein levels.

To address this issue, researchers use computational methods like non-negative least squares (NNLS) to align and deconvolve these datasets, ensuring that the data from different omics layers are correctly registered.

How It Works

The process of spatial multi-omics integration involves several steps. First, rotation correction (θ) is applied to align the spatial coordinates of the proteomic and transcriptomic data based on their relative orientations. Then, offset corrections in X and Z are made to adjust for any positional discrepancies between the two datasets.

Once aligned, mixed-spot signals are deconvolved using NNLS algorithms to estimate cell-type proportions within each spot. This step is crucial as it helps in understanding the cellular composition of the tissue at a fine spatial scale.

Why It Matters

Spatial multi-omics integration is essential for understanding complex biological processes, such as cancer progression and immune responses. By integrating multiple omics layers, researchers can identify cell-type specific gene expression patterns and protein interactions that are critical for disease mechanisms.

Moreover, this technique enables the development of spatially resolved models of tissue function, which can be used to guide therapeutic interventions and drug discovery.

Frequently asked questions

Try it live

Everything above runs in your browser — open Spatial Multi-omics Integration Simulator and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.