EASI-PASS: An accessible pipeline for linking functional imaging and mRNA profiling
EASI-PASS is a high-throughput pipeline that efficiently links functional imaging data with mRNA profiling by aligning live-cell activity to expanded, cleared tissue volumes, enabling the molecular characterization of thousands of functionally identified neurons with high recovery rates.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of a preprint that has not been peer-reviewed. It is not medical advice. Do not make health decisions based on this content. Read full disclaimer
The brain is a vast, intricate network where every cell has a specific job and a unique identity. To understand how the brain works, scientists often need to answer two questions at once: what is a cell doing right now, and what kind of cell is it? Imagine trying to understand a city by watching people move through the streets, but without knowing their names or professions. You might see someone running to catch a bus or standing still in a park, but you cannot know if they are a doctor, a teacher, or a student unless you have a way to link that moment of action to their personal history. In neuroscience, this link is difficult to make because the tools used to watch cells move and fire are often different from the tools used to read the genetic instructions that define them. One method captures the live, moving activity of cells, while another reads the molecular code inside cells that have been preserved and frozen in place. Until now, connecting these two views of the same cell has been a slow and imperfect process, often requiring scientists to slice tissue so thin that the three-dimensional context is lost or to guess which cell in a live image matches which cell in a preserved sample.
Researchers have developed a new method called EASI-PASS to solve this problem, creating a reliable way to merge live imaging with molecular profiling. The process begins by watching thousands of cells in a living piece of brain tissue, recording their shapes and locations as they respond to specific signals. Once this live recording is complete, the same tissue is treated with a special chemical process that expands it, making the cells larger and easier to see, and then clears it so that light can pass through the entire thickness of the sample. This allows scientists to use standard microscopes to take detailed pictures of the tissue's interior, looking for the specific genetic markers that identify each cell type. The core achievement of the new method is a computer system that can match the shapes and positions of the cells seen in the live recording with the cells seen in the expanded, cleared tissue. It is like taking a photograph of a crowd from above and then matching every person in that photo to a detailed ID card found in a separate, thick book, without needing to cut the book into thin pages. The system successfully recovers the molecular identity of about 78 percent of the cells it tracks, a significant improvement over previous techniques that relied on aligning thin slices of tissue.
The power of this approach was tested in two different parts of the mouse brain to see how it could reveal new details about brain function. In one experiment, researchers focused on a small region called the parabrachial nucleus, which receives signals from the spinal cord. By stimulating these long-range connections with light while watching the cells, they were able to identify exactly which molecular types of neurons were receiving the signals. The results showed a high degree of specificity, meaning that only certain kinds of cells responded to the input, rather than a random mix of all available cells. In a second experiment, the team looked at the visual cortex of awake mice, observing how different groups of cells changed their activity as the animals became more alert. They found that the way these cells communicated with one another depended on their location and their specific class, with some groups showing strong connections that faded as the distance between them increased. These findings demonstrate that the new method can reliably align cellular activity with molecular identity, offering a clearer view of how the brain's diverse cell types work together to process information.
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