Seven transformative technologies in 2023
In 2012, everything changed. A group of researchers led by Fusa Miyake of Nagoya University in Japan showed that there was an apparent sudden increase in the level of carbon-14 in the rings of a Japanese cedar tree dating from AD 774-775. Subsequent research not only confirmed that this spike was present in wood samples around the world dating to this period, but also identified at least five similar spikes dating back to 7116 BC. Researchers have linked the increase in carbon 14 levels to solar storm activity, but this hypothesis is still under investigation.
Whatever the cause of the mutations, these “Miyake events” allowed researchers to pinpoint the exact year in which these wooden artifacts were made by identifying a specific Miyake event and then counting the tree rings that formed afterward. do Researchers can even determine the season in which the tree was felled based on the thickness of the outermost ring, Kaitmes says.
Archaeologists are now applying the method to Neolithic settlements and volcanic eruption sites, and Day hopes to apply it to the study of the Maya empire in Central America. Day is optimistic that within the next decade, we will be able to determine the age of many ancient civilizations to the exact year level and determine their historical development process precisely in terms of time. Miyake’s search for historical indicators continues. “We’re looking for other C-14 spikes over the past 10,000 years that are similar to the 775-774 event,” he says.
Single cell metabolomics
Metabolomics (the study of lipids, carbohydrates, and other small molecules that drive the cell) was originally a collection of methods for identifying metabolites in populations of cells or tissues, but has now reached the level of individual cells.
Scientists can use such data, obtained at the cellular level, to unravel functional complexities in large populations of apparently identical cells. But this transition comes with daunting challenges.
The metabolome contains a large number of molecules with diverse chemical properties. Some of these molecules are very ephemeral, with a turnover rate of less than a second, and they can be difficult to detect: while single-cell RNA sequencing can identify nearly half of the RNA molecules produced, says Theodor Alexandrov, a metabolomics researcher at the European Molecular Biology Laboratory in Heidelberg, Germany. In identifying a cell or organism, most metabolomic analyzes cover only a small fraction of a cell’s metabolites. The missing information could include vital biological insights.
“The metabolism is the active part of the cell,” says Jonathan Swedler, a chemist at the University of Illinois at Urbana-Champaign. “When you’re studying a particular disease, if you want to know the state of the cell, you need to be able to look at metabolites.”
Many laboratories working in the field of metabolomics work on isolated cells. They trap cells in capillaries and analyze them individually using mass spectrometry. In contrast, mass spectrometry imaging methods record spatial information about how the production of cellular metabolites varies in different locations of a sample.
For example, researchers can use a method called MALDI (matrix-assisted laser desorption-ionization), in which a laser beam passes through tissue slices and releases metabolites for analysis by mass spectrometry. This method also records the spatial coordinates from which the metabolites in the sample originate.
In theory, both methods can determine hundreds of compounds in thousands of cells, but according to Swedler, achieving that usually requires a lot of custom and advanced hardware, which comes at a high cost.
Now, researchers are making the technology public. In 2021, the Alexandrov Group introduced the open source software SpaceM. Using light microscopy imaging data, this software enables the use of standard commercial mass spectrometry to create spatial metabolomics profiles of cultured cells.
Alexandrov’s team used SpaceM to characterize hundreds of metabolites from tens of thousands of human and mouse cells and used standard single-cell transcriptomic methods to divide the cells into different groups. Alexandrov says he is enthusiastic about the idea of developing metabolic atlases (similar to those created for transcriptomics) to accelerate progress in the field.
Laboratory embryo models
At the cellular level, the journey from the fertilized egg to the fully formed embryo has been described in detail for humans and mice. But the molecular machinery guiding the early stages of this process has not yet been well identified. Now, increasing studies in embryoid models (models that mimic embryos) are helping to fill these knowledge gaps, giving researchers a clearer view of the early critical events that can determine the success or failure of embryo development.
Some of the most sophisticated models come from the lab of Magdalena Zernica Goetz, a biologist at the California Institute of Technology in Pasadena and the University of Cambridge in the UK. In 2022, he and his team demonstrated that they could produce implantation-stage mouse embryos entirely from embryonic stem cells.