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Four New Single-Cell Genomics Techniques Track Cell Development of Whole Organisms

For developing four new techniques that more efficiently profile the expression of RNA molecules in millions of single cells in worms, mice, and humans, and that map which genes are turned on and off in these cells throughout cell development, Junyue Cao is the winner of the 2020 Science & SciLifeLab Prize for Young Scientists. Unlike previous methods, Cao’s technologies can track the formation of individual cells as they arise into a whole organism, without needing to isolate each cell.

“These approaches can bring new understanding of embryonic development and also of the cellular changes that cause disease,” said Valda Vinson, editor at Science.

Cao’s approaches are also cost-effective compared to traditional methods, which stands to benefit the medical field in particular.

“We are excited about the future applications of this technique in characterizing cell state heterogeneity and cell population dynamics across different development stages of cancer, neurodegenerative disorders and inflammatory disease,” said Cao, assistant professor at The Rockefeller University. “We currently implement the technique to study cell population dynamics in the aging process as well.”

Cao is the 2020 category winner of the Science & SciLifeLab Prize for Genomics, Proteomics and Systems Biology Approaches.

Since 1980, biologists have successfully mapped the complete developmental process of the model organism Caenorhabditis elegans (C. elegans), from single embryonic cells to the adult worm. However, scientists have yet to determine how cells in this or other organisms are specified into different lineages, which would require mapping the biological activity in and between cells at a large scale.

Advancements in DNA and RNA sequencing technologies have allowed scientists to directly identify gene expression patterns in individual cells. However, the traditional single-cell sequencing techniques used to generate libraries of cell characteristics still rely on physically isolating single cells into compartmentsa limitation akin to a queue that only allows people to pass through one by one. This constrains the number of cells that can be successfully sequenced.

To help address this issue, Cao devised an approach he dubbed sci-RNA-seq, which relies on technology that allows for the barcoding of nucleic acids in a cell to label and index large numbers of single cells. Cao’s approach can process tens of thousands of unsorted cells at once without the need to line up each cell in a queue. Using it, he was able to profile nearly 50,000 cells, recover rare neuronal cell types, and define the genetic expression of 27 distinct cell types in the C. elegans worm.

While Cao’s initial experiments focused on worms, the genetic factors underlying cellular development in mammals are less tightly controlled and vary highly among individuals. Cao recognized the need to better understand the diverse trajectories and states mammalian cells undergo as they rapidly diversify into different cell types. He addressed this by creating the sci-RNA-seq3 technique, a new method that allowed him to trace two million mouse cells across multiple developmental pathways leading to organ formation. The information collected on cell proliferation dynamics in the mammalian body using this technique is still the largest publicly available dataset of single cell transcriptomes to date.

Part of Cao’s team’s approach involved strategically selecting algorithms that work well when processing such high volumes of cellular information.

“It turns out that a lot of conventional scRNA-seq analysis strategies are not working well when we scale the data to include millions of cells,” said Cao. “We compared different computation strategies and chose the algorithms that have been shown to be working in processing large-scale data in other fields.” For example, the clustering algorithm, the Louvain method, used in Cao’s approach has been implemented in analyzing large-scale social networks that include millions of members.

“This strategy (sci-RNA-seq) enables comprehensive profiling cell state heterogeneity at the scale of an entire organism in a single experiment,” said Cao, “and it can be used as a platform where we can incorporate other molecular information into the same assay.” 

To enable an even deeper understanding of the interactions and relationships between cells during development, Cao developed a third technique called sci-CAR. Unlike traditional methods that only capture one aspect of cellular biology, such as messenger RNA (mRNA) expression, sci-CAR allowed profiling of both mRNA and chromatin accessibility at the same time within single cells. Using sci-CAR, Cao profiled gene regulation across large numbers of single cells in the mouse kidney.

Cao’s fourth technique, called sci-FATE, further uncovered insights into the gene regulatory mechanisms that are essential to a cell’s development, by incorporating the factor of time. The method distinguished “newer” mRNA transcriptswhere instructions for protein assembly are transcribedfrom “older” mRNA transcripts in thousands of individual cells, and in so doing, established a rate of mRNA transcription for each cell.  Cao used sci-FATE to quantify how human cancer cell states change in response to glucocorticoid treatment. He was able to link specific factors controlling gene expression to their target genes across thousands of cancer cells.  

In a recent study published in Science, Cao examined gene expression at the single cell level for human fetal development using sci-RNA-seq3, profiling around five millions cells from over one hundred human fetal tissues to understand cell types and dynamics throughout human fetal development. He further integrated the millions of single cells profiled from human fetal tissues and mouse embryos, to construct the detailed developmental trajectories of major cell types that form fifteen human fetal organs such as the brain, heart, liver, and intestine.

The Science & SciLifeLab Prize for Young Scientists acknowledges that global economic health is dependent upon a vibrant research community that needs to incent the best and brightest to continue in their chosen fields of research, as they begin their scientific careers. The grand prize winner receives a prize of US$30,000 and the essay will be published in Science on November 20.

“This prize is a recognition for talented young investigators who have done breakthrough research as well as are able to communicate their science well to a broader audience,” said Olli Kallioniemi, director of SciLifeLab. “We think that the winners are very likely to become future leaders in their fields.”

2020 Category Winners

Orsi Decker is the category winner for Ecology and Environment, for her essay “Losing Australia’s native gardeners: The loss of the country’s digging mammals compromises the continent’s arid soil health.” She completed her undergraduate degree at Eötvös Loránd University in Budapest, Hungary. She went on to receive her master’s degree in Ecology and Evolution at the University of Amsterdam. Decker completed her doctoral research at La Trobe University in Melbourne, Australia, where she investigated the extinctions of native digging mammals and their context-dependent impacts on soil processes. She is currently a postdoctoral researcher at La Trobe University where she is examining how land restoration efforts could be improved to regain soil functions via the introduction of soil fauna to degraded areas.

Dasha Nelidova is the category winner for Molecular Medicine, for her essay “Engineering near-infrared vision: An optogenetic technology inspired by snakes could aid those with incomplete blindness.” She completed her undergraduate degrees at the University of Auckland, New Zealand. She completed her Ph.D. in neurobiology at the Friedrich Miescher Institute for Biomedical Research in Basel, Switzerland. Nelidova is currently a postdoctoral researcher at the Institute of Molecular and Clinical Ophthalmology Basel, where she is working to develop new translational technologies for treating retinal diseases that lead to blindness.

William E. Allen is the category winner for Cell and Molecular Biology, for his essay “Brain mapping, from molecules to networks: Bridging multiple levels of brain function reveals the neural basis of thirst motivation.” He received his undergraduate degree in Applied Mathematics and Biology from Brown University in 2012, M.Phil. in Computational Biology from University of Cambridge in 2013, and Ph.D. in Neurosciences from Stanford University in 2019. At Stanford, he worked to develop new tools for the large-scale characterization of neural circuit structure and function, which he applied to understand the neural basis of thirst. After completing his Ph.D., William started as an independent Junior Fellow in the Society of Fellows at Harvard University, where he is developing and applying new approaches to map mammalian brain function and dysfunction over an animal's lifespan.