A Hierarchy of Protein Patterns Robustly Decodes Cell Shape Information. King’s College London, the University of Manchester and UCL have, for the first time, identified a hierarchy of protein patterns robustly decoded from single-cell RNA sequencing data that are able to decode significant amounts of cell shape information.
This means that cell features such as size and overall shape can be deduced with high accuracy from RNA sequencing data. The research, published today in Nature Communications, brings scientists one step closer to understanding how cells manage to retain their characteristic shapes while constantly generating new ones.
Protein patterns form during the development of an animal embryo to help build a body.
In a professional tone: Protein patterns form during the development of an animal embryo to help build a body. But how do these patterns determine what the final shape of a cell will be? Researchers from the Janelia Research Campus and the University of Cambridge have discovered that many different shapes can be decoded by a hierarchy of protein patterns.
This is similar to how we use different levels of language to communicate, such as words, phrases, and paragraphs. Cells read this particular code with the help of their cytoskeleton—the internal scaffolding that gives it structure. The findings appeared in PLOS Computational Biology on March 11th, 2021.
During development, cells go through several distinct stages to create an animal embryo with specialized tissues and organs. These stages are timed by a series of biochemical reactions that are triggered by genes being turned on or off at certain times during development. These genes produce proteins which then bind together to form biochemical networks throughout the embryo.
The researchers used computer simulations to model how these networks affect cell shape. They measured thousands of different networks and found that some are more robust than others—meaning they produce similar results despite random variations in their reaction rates or initial conditions.
A newly developed algorithm makes use of these protein patterns to understand the relationship between cells and their eventual role in the body.
Scientists have recently discovered that the shapes of cells in the body determine their fate and what they will eventually become. With this information, a team at Caltech has developed an algorithm that uses a cell’s shape to predict its future role in the body.
The algorithm works by analyzing patterns of proteins inside a cell and characterizing those patterns into different classes or groups. With this, scientists can now use just the shape of a cell to predict its eventual function. These findings have been published in Nature Communications.
While previous studies have shown that the shape of a cell is related to its function, researchers did not yet know how exactly this relationship worked. In order to understand this relationship between an organism and its role in the body, scientists must first understand how cells develop.
A cell’s fate is determined when it begins to differentiate from stem cells into more specialized cells—like neurons or muscle cells—with each group containing many subgroups with various functions within them.
The current study was led by Omer Yilmaz, assistant professor of computational biology at Caltech and his team of researchers took a closer look at how cell differentiation occurs in fruit flies by detecting patterns of common proteins present in different groups of cells.
This approach should help researchers find out more about how stem cells grow into different cell types.
The protein patterns on a cell’s surface are the keys for other cells to unlock the kind of cell it is. These patterns might be lost in translation, however, if they’re hard to spot or difficult to analyze. A new study by researchers at the University of Wisconsin-Madison may have found a way to make them easier to “read” by using an approach borrowed from geneticists.
The team’s technique ranks the proteins’ levels of expression, providing a more robust method for determining how cells differentiate. The approach could help scientists unravel how stem cells grow into different cell types and how cancer cells become more aggressive.
Cell-surface proteins are important for sending signals about what type of tissue or organ a cell will become: whether it will be part of a liver, or kidney, or muscle, for example. The shape and appearance of individual cells also reflects information about what type of tissue they’ll be part of and shows whether they’re healthy.
The researchers borrowed methods originally developed to rank genes that encode proteins in order to rank the proteins themselves without sequencing their DNA first. Although there have been many studies looking at the sequences of these genes, few have focused on quantifying the actual amounts of each protein.
Last Words
The ability to accurately report the position of every cell in a developing organism as it goes through its life cycle is vitally important for understanding embryonic development and cellular differentiation. The researchers in this study have developed new software that achieves a dramatic improvement over previous methods of measuring cell shape, and could be applied to just about any animal system from fruit flies to humans.