Understanding how tissues function is key to learning about both health and disease. Tissues are made up of different types of cells, and how these cells are organized, or their “architecture,” plays a big role in how the tissue works. If we can understand this architecture at the cellular level, we can gain insight into how diseases develop or how healthy tissue operates. But studying this structure has always been a challenge.
What is Spatial Omics?
Spatial omics is a technology that lets scientists examine the molecular details of cells while keeping track of where the cells are in the tissue. In other words, it’s like taking a detailed picture of a tissue sample while also knowing exactly what each part of the tissue is doing at the molecular level. This is important because it helps us see how different cells interact with each other in their natural setting, which could be crucial in understanding diseases like cancer or autoimmune disorders.
The problem is, spatial omics technology isn’t always perfect. Sometimes, it can’t zoom in far enough to study each individual cell. This is where deconvolution methods come in.
What is Deconvolution?
Deconvolution is a computational technique that helps scientists infer, or figure out, what types of cells are present in a tissue based on the molecular data collected. Essentially, it takes complex data from spatial omics and works backward to identify what cells are in the tissue and what they’re doing. Think of it as piecing together a puzzle, where the data gives clues about which cells are present and their activity.
Introducing Spacedeconv
The problem with deconvolution methods has been that different tools work in different ways, making them difficult to use and compare. That’s where spacedeconv comes in. This new tool developed by researchers at the University of Innsbruck acts as a unified platform, combining multiple deconvolution methods in one place. It not only simplifies the process of studying tissue composition but also offers additional features like data visualization, preprocessing, and the ability to study how cells communicate with each other.
Overview of spacedeconv
spacedeconv takes as input spot-based spatial transcriptomics data and, optionally, single-cell RNA sequencing (scRNA-seq) data (needed for second-generation deconvolution). The workflow is organized into four basic modules. 1) Input optimization: includes preprocessing, normalization, subsetting of the input scRNA-seq data according to two approaches (“mirror” and “even”, subsetSCE function) and of the spatial transcriptomics data to select a region of interest (subsetSPE). 2) Deconvolution: it is the core module of spacedeconv and comprises methods for first- and second-generation deconvolution. Deconvolution analysis is used to infer the cellular composition for all spots and refer it to their spatial coordinates. 3) Intra- and inter-cellular communication analysis: pathway (PW) and transcription factor (TF) activities, as well as ligand-receptor (L-R) expression levels are quantified for every spot. 4) Downstream analysis and visualization: a comprehensive and flexible suite of functions to generate spatially-resolved plots of continuous and discrete/categorical variables. These include the input gene expression data, the results of deconvolution and cell-communication analysis, as well as other integrated data modalities. Dedicated functions simplify the spatial comparison of pairs of variables, their correlation analysis, and identification of tissue niches together with their cellular and molecular underpinnings.
How Does Spacedeconv Help Scientists?
By using spacedeconv, researchers can streamline their workflow. Instead of using separate tools for different tasks, spacedeconv offers everything in one package. This means scientists can more easily investigate how cells and molecules are organized in tissues, both in healthy and diseased states, across different organisms and tissue types.
For example, imagine studying a tumor. With spacedeconv, researchers could quickly analyze the different types of cells that make up the tumor, understand how they’re communicating, and visualize their arrangement. This could give them a better idea of how the tumor is structured and which cells are driving its growth.
Why Is This Important?
Tools like spacedeconv are crucial for advancing research in fields like cancer biology, immunology, and developmental biology. By making it easier to study the complex interactions within tissues, scientists can uncover new insights that may lead to better treatments or even cures for diseases.
In summary, spacedeconv is helping researchers make sense of the complex architecture of tissues by offering a user-friendly, all-in-one tool for analyzing spatial omics data. It not only makes research faster and easier but also opens the door to new discoveries about how tissues function and how diseases develop.
Availability – spacedeconv code and documentation is available from: https://github.com/omnideconv/spacedeconv.