New Method Makes Genetic Messengers Visible in 3D Within a Single Cell
Using the power of advanced computing and microscope techniques, scientists from the California Institute of Technology and elsewhere have tracked the locations of 10,000 gene products in three dimensions within single cells in the mouse brain. The work offers researchers the ability to track the activities of individual genes, for the first time ever enabling study how the fine distribution of genetic messengers affects the workings of cells and how these add up to the behavior of tissues in the body.
The group employed a method called seqFISH+ to use multiple rounds of imaging and reactions to paint individual mRNA molecules with different sequences of colors under standard microscopes. A kind of barcode, these sequential images allowed them to tell mRNAs—the molecular messengers that carry genes’ instructions throughout the cell—apart from each other at distances that would normally be beyond the ability of the microscope to resolve.
This work was funded by award number 1OT2OD026673-01 through the NIH Common Fund HuBMAP initiative. You can read the team’s April 2019 paper in the journal Nature here.
SABER Method Strengthens Genetic Signals in Microscope
Scientists at Harvard and the University of Washington in Seattle have created a method for amplifying RNA copied from genes within single cells, called SABER (signal amplification by exchange reaction). The new technology promises to detect the RNA output of multiple genes in the same cells with increased sensitivity and speed, and at lower cost.
The research team developed SABER as a way to make mRNA molecules, the genetic messengers in the cell, shine more brightly in a microscope image when labeled by a method called FISH. Standing for fluorescence in situ hybridization, FISH tags mRNA from a specific gene of interest with a lab-made nucleic acid molecule labeled with a fluorescent marker. This in turn lights up the mRNA’s position in the cell. Using SABER, the scientists were able to make FISH signals five to 450 times brighter, detecting mRNAs with much higher efficiency and allowing an automatable “workflow” that could speed study of how gene activity causes differences in cell behavior in health and disease.
This work was funded by award number 1UG3HL145600 through the NIH Common Fund HuBMAP initiative. The team’s June 2019 Nature Methods paper can be read here.
Anchor Strategy Integrates Data from Differing Single-Cell Methods
HuBMAP scientists have developed a new strategy for “anchoring” different types of experimental data that each shed light on only part of a cell’s identity to enable a global view of activities in that cell. Scientists from the New York Genome Center and New York University have used this strategy to knit together data on the production of specific proteins with an atlas of which genes are active in different tissues. The method could transform the depth and breadth of information researchers use to understand organ function at the smallest scales.
While many new methods are allowing scientists to look at cellular function at the smallest levels, these methods do not measure all important factors in a comparable way. In one example highlighting the anchoring strategy, the New York team harmonized signals detected by two experimental methods called scRNA-seq and scATAC-seq. scRNA-seq measures the amount of mRNA molecules—the messengers that relay a gene’s instructions to the machinery that produces proteins—in a single cell. scATAC-seq measures what positions in the protein covering of a cell’s chromosomes have “opened up” to expose the DNA for specific genes to become active. By connecting the two, the scientists were able to leverage the unique characteristics of each method to gain a more comprehensive view of cellular states in the mouse brain. In addition to helping scientists draw information from these biological data accurately, the anchor strategy introduces a novel general framework that enables the transfer of information across distinct single-cell experiments.
This work was funded by award number 1OT2OD026673-01 through the NIH Common Fund HuBMAP initiative. You can find their June 2019 report in the journal Cell here.
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