Thursday, October 30, 2025

Researchers release the world’s first head-to-toe cellular atlas of the mosquito


The global effort, led by Rockefeller University, just made the most dangerous animal in the world a lot easier to study—and perhaps defeat one day.



Rockefeller University

Mosquito antenna 

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Confocal image of a male mosquito antenna, used to validate a unique chemoreceptor co-expression pattern identified in the mosquito atlas.

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Credit: Alexandra DeFoe/The Rockefeller University





The most dangerous animal in the world just got easier to study—and perhaps defeat one day.

Researchers from Rockefeller University’s Laboratory of Neurogenetics and Behavior, in collaboration with mosquito experts around the globe, have created the first-ever cellular atlas of the Aedes aegypti mosquito, which transmits more diseases than any other species of its kind. The Mosquito Cell Atlas provides cellular-level resolution of gene expression in every mosquito tissue, from the antennae down to the legs. The dataset is freely available to all researchers (and curious members of the public). They recently published the atlas in Cell.

“This is a comprehensive snapshot of what every cell in the mosquito is doing as far as expressing genes,” says lab head Leslie Vosshall, who has studied Aedes aegypti, aka the yellow fever mosquito, for nearly two decades. “It’s a real achievement because we profiled so many different types of tissues in both males and females.”

The atlas has already yielded new insights into the genetic secrets of Aedes aegypti, including novel cell types, subtle differences—and unexpected similarities—between male and female mosquitoes, and the dramatic changes in genetic expression that the female mosquito brain undergoes after a blood feeding.

Senior author Nadav Shai, a senior scientist in both Vosshall’s lab and at the Howard Hughes Medical Institute, anticipates that by using the atlas as a starting point, many researchers will make new discoveries. “We believe this enormous data set will really move mosquito biology forward,” he says. “It’s a great tool for vector biologists to take whatever interests them and just run with their own line of research.”

Organ by organ

In the past several years, scientists have used single-cell sequencing to identify cell types and illuminate gene expression patterns in model organisms such as Drosophila melanogaster (fruit fly), Caenorhabditis elegans (nematode), and Mus musculus (mouse), resulting in a whole organism, single-cell atlas of each species.

Mosquito researchers have been following suit but in a piecemeal way: organ by organ, tissue by tissue, all in different studies. Some of that prior work was done by Vosshall’s team and fellow members of the Aedes aegypti Mosquito Cell Atlas Consortium, a global collaboration of scientists that was assembled for this project.

Most prior studies had focused on female mosquitoes, leaving out males. “Both females and males feed on nectar in their day-to-day lives, but females need blood for protein to develop their eggs and produce a new generation of mosquitoes,” says first author Olivia Goldman.

“Because the female is the one that’s spreading all the pathogens, there is an enormous bias toward looking at the biology of the female and very little information about the male,” Vosshall says. “So we wanted to be inclusive and fill in the gap.”

“We also wanted to bring the mosquito cell biology up to date in a single resource using advanced and uniform sequencing technology,” Shai says.

To that end, the team used single-nucleus RNA sequencing (snRNA-seq)—which excels at capturing the biology of all insect cell types compared to single cell approaches—to create a large dataset of more than 367,000 nuclei from 19 types of mosquito tissues selected across five biological themes: major body segments; sensation and host seeking; viral infection; reproduction; and the central nervous system.

Tasting sweetness with their legs

They found 69 cell types grouped into 14 major cell categories, many of which had never been seen before.

Among the most striking findings was the pervasiveness of polymodal sensory neurons—supercharged cells that can pick up a wide variety of environmental cues, including temperature and taste. Previous research from the Vosshall lab had found that the antenna and maxillary palps were packed with these neurons, but now that they were able to look organism-wide, they found them everywhere, including the nose, tongue, and legs.

“Just like the antennae and maxillary palps, the legs and mouth parts have really powerful tools for sensing the world,” Shai says. “Together they enable mosquitoes to be really good at what they do—seek hosts, feed on them, and reproduce.”

Those multifunctional chemoreceptors allow them to, among other things, detect sweetness and fresh water.

“Being able to taste sweetness with their legs may be useful for detecting sugars, which both females and males need to live,” Shai says. “But it’s just one part of a combination of tastes that clues them into what’s around them—a human to bite, a flower for sugar source or a good water source to lay eggs. We believe that the combination of a lot of sensors is important for their survival.”

Brain changes that accompany behavior shifts

After feeding, a female mosquito loses all interest in humans and other hosts; her focus becomes developing and laying eggs.

“How does this incredibly strong drive to bite people get turned off?” Vosshall says.

“We knew from previous research from our lab and others that the brain transcripts change after blood feeding, and our assumption was that maybe we would find different subtypes of neurons that down- or upregulated their transcripts,” Shai says.

To find out, they examined the gene expression of female mosquito brains at 3, 12, 24, and 48 hours after a blood feeding. They found dramatic changes in gene expression in all time periods, which peaked after the first few hours and gradually abated. Most of the early expression changes were of genes being upregulated, while later time periods showed a mix of both up- and downregulation.

Strikingly, these changes occurred in a completely unexpected way. Neurons account for roughly 90% of mosquito brain cells, but it was the glia—support cells that account for less than 10%—that underwent large shifts in gene expression.

“The glia are completely rewired during this time when the females lose interest in people,” Vosshall says.

“That was a big surprise,” Shai says. “It’s evidence that glia are super important for not only supporting brain cells and function but also are physiologically relevant to behavior.”

Limited sexual dimorphism

Another illuminating finding is that for all the documented morphological and behavioral differences between female and male mosquitoes, their cellular makeup is largely identical, aside from small clusters of sex-specific cells and reproductive organs.

“We were kind of expecting it to be a tale of two genomes, but that’s not what we found,” Vosshall says.

“In general, most cells look the same, and the transcripts they express are similar,” Shai notes. “However, that doesn’t mean that the regulation and level of expression are the same, and those probably drive the differences. Another factor could be how the different gene expressions work together to create new functions.”

One exception was found exclusively in the male antenna, which is largely unexplored. “A small group of cells is marked by the expression of a single gene that’s not expressed in any female tissue,” Vosshall says. “If we hadn’t compared male and female gene expression, we never would’ve spotted them.” Their function remains to be determined.

Future directions

The Vosshall lab will mine the mosquito single-cell atlas to further its investigations into behavior such as host seeking and sensing the environment through Aedes aegypti’s remarkable, widely dispersed set of multifunctional sensory neurons.

“Different people in the lab are going to take it to different places,” Shai says.

They hope that researchers everywhere will find it equally inspiring. “The sheer size of the dataset opens up many new avenues of research that people couldn’t study before because they didn’t have this tool,” Shai says.

“This is a global resource that has been open to everyone since the very inception of the project in 2021, so many people are already using it,” Vosshall adds. “We’re excited to see the discoveries that will come from it.”

 

New study explores ‘legacy effects’ of soil microbes on plants across Kansas





University of Kansas
Corn root and 'legacy effects' of soil microbes 

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The research team from the University of Kansas performed genetic analysis on both microbes and plants to better understand on the molecular level how legacy effects might function. Pictured is cross section of a corn root from the study.

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Credit: Maggie Wagner





LAWRENCE — A new study appearing in Nature Microbiology analyzes soils sampled across the state of Kansas to determine the importance of “legacy effects” — or how soils from a specific location are influenced by microbes that have evolved in response to the specific climate at that site for many years.

“The bacteria and fungi and other organisms living in the soil can actually end up having important effects on things that matter, like carbon sequestration, nutrient movement and what we’re particularly interested in — the legacy effects on plants,” said co-author Maggie Wagner, associate professor of ecology & evolutionary biology at the University of Kansas.

“We got interested in this because other researchers, for years, have been describing this type of ecological memory of soil microbes having some way to remember from their ancestors' past,” she said. “We thought this was really fascinating. It has a lot of important implications for how we can grow plants, including things like corn and wheat. Precipitation itself has a big influence on how plants grow, but also the memory of the microbes living in those soils could also play a role.”

According to Wagner, while legacy effects previously have been reported, they aren’t well characterized. A better understanding could eventually benefit farmers and agricultural biotech firms, which could build on the research.

“We don't really understand how legacy effects work,” she said. “Like, which microbes are involved at the genetic level, and how does that work? Which bacterial genes are being influenced? We also don't understand how that legacy of climate moves through the soil to the microbes, and then eventually to the plant.”

By sampling soils from six sites across Kansas — from its lower, rainier eastern half to the state’s western High Plains, higher in altitude and drier because of the rain shadow of the Rocky Mountains — the researchers aimed to determine differences in legacy effects.

“This was a collaboration with a team at the University of Nottingham in England,” Wagner said. “We divided up the work, but the bulk of the experiment — actually, the entire experiment — was conducted here at KU, and we also focused on soils from Kansas for this work.”

Back at KU, Wagner and her colleagues began testing the soils to better understand legacy effects of the samples’ microbes.

“We used a kind of old-school technique, treating the microbes as a black box,” she said. “We grew the plant in different microbial communities with different drought memories and then measured plants’ performance to understand what was beneficial and what was not.”

The researchers challenged the microbial communities for five months, either with plenty of water or very little water.

“Even after many thousands of bacterial generations, the memory of drought was still detectable,” Wagner said. “One of the most interesting aspects we saw is that the microbial legacy effect was much stronger with plants that were native to those exact locales than plants that were from elsewhere and planted for agricultural reasons but weren't native.”

While more plant species will need to be tested to confirm this hypothesis — the researchers tested one crop (corn) and one native plant (gamagrass) — the researchers said the findings could offer important context for farmers who want to use beneficial microbes to improve yields.

“We think it has something to do with the co-evolutionary history of those plants, meaning that over very long periods, gamagrass has been living with these exact microbial communities, but corn has not,” she said. “Corn was domesticated in Central America and has only been in this area for a few thousand years.”

Additionally, the research team performed genetic analysis on both microbes and plants to better understand on the molecular level how legacy effects might function.

“The gene that excited us most was called nicotianamine synthase,” Wagner said. “It produces a molecule mainly useful for plants to acquire iron from the soil but has also been recorded to influence drought tolerance in some species. In our analysis, the plant expressed this gene under drought conditions, but only when grown with microbes with a memory of dry conditions. The plant’s response to drought depended on the memory of the microbes, which we found fascinating.”

The KU researcher said gamagrass is being looked at as a possible source of genes to improve corn performance under challenging conditions.

“The gene I mentioned earlier could be of interest,” she said. “For biotech firms focused on microbial additions to crops, this gives hints about where to look for microbes with beneficial properties. Microbial commercialization in agriculture is a multibillion dollar industry and still growing.”

Wagner’s KU collaborators were lead author Nichole Ginnan, now of the University of California-Riverside, and Natalie Ford, now of Pennsylvania State University; Valéria Custódio, David Gopaulchan, Dylan Jones, Darren Wells and Gabriel Castrillo of the University of Nottingham; Isai Salas-González of the Universidad Nacional Autónoma de México; and Ângela Moreno of the Ministério da Agricultura e Ambiente in Cabo Verde.

“One of the things that makes this work valuable is how interdisciplinary it was,” Wagner said. “We brought together genetic analysis, plant physiology and microbiology, allowing us to ask and answer questions that couldn’t have been addressed before.”

This research was funded by the National Science Foundation’s Division of Integrative Organismal Systems.

 

New AI model explores massive chemical space with minimal data



Starting with just 58 data points, a new artificial intelligence model identified four battery electrolytes that rival the state of the art





University of Chicago

New AI model explores massive chemical space with minimal data 

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A new paper from the lab of UChicago PME Asst. Prof. Chibueze Amanchukwu of the University of Chicago Pritzker School of Molecular Engineering built an active learning model that was able to explore a virtual search space of one million potential battery electrolytes starting from just 58 data points.

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Credit: UChicago Pritzker School of Molecular Engineering / Stephen L. Garrett




In an ideal world, an AI model looking for new materials to build better batteries would be trained on millions or even hundreds of millions of data points. 

But for emerging next-generation battery chemistries that don’t have decades of research behind them, waiting for new studies takes time the world doesn't have.

“Each experiment takes up to weeks, months to get data points,” said University of Chicago Pritzker School of Molecular Engineering (UChicago PME) Schmidt AI in Science Postdoctoral Fellow Ritesh Kumar. “It's just infeasible to wait until we have millions of data to train these models.

Kumar is the co-first author of a recent paper published in Nature Communications that built an active learning model that was able to explore a virtual search space of one million potential battery electrolytes starting from just 58 data points. From this minimal data, the team from the lab of UChicago PME Asst. Prof. Chibueze Amanchukwu identified four distinct new electrolyte solvents that rival state-of-the-art electrolytes in performance.

To help hone the data from this small set, the team incorporated experiments as outputs, actually testing the battery components the AI suggested, then feeding those results back into the AI for further refinement.

“Often in the literature, we see computational proxies as an output, but there is still a difference between a computational proxy and a real-world experiment. So here we bit the bullet and went all the way to experiments as a final output,” he said. “If the model suggested, ‘Okay, go get an electrolyte in this chemical space,’ then we actually built a battery with that electrolyte, and we cycled the battery to get the data. The ultimate experiment we care about is: Does this battery have long cycle life?”

Trust but verify

Having an AI extrapolate millions of potential molecules from just 58 prompts can be fraught. The more a machine has to extrapolate, the greater the potential for spurious results, the chemical equivalent of a Dall-E portrait with six fingers or ChatGPT spewing gibberish.

“The model will be not very accurate initially, so it will have some prediction, and it will also have uncertainty associated with the prediction,” Kumar said. 

Predictions from AI trained on millions of data points would theoretically be more trustworthy, so the team verified along the way, testing and retesting to find electrolytes with the best discharge capacity.

In total, the team ran seven active learning campaigns with about 10 electrolytes tested in each before they zeroed in on four new electrolytes with top-tier performance.

“There's no way we can remove the inefficiency of machine learning and AI models completely, but we should take advantage of what it's good at, like we did in this case,” Kumar said. “The other alternative was that we do experiments on all one million electrolytes, which was not possible.”

Predictive to generative

One possible area of future study is tossing even the 58 data points and having an AI create new molecules from scratch, said co-first author Peiyuan Ma, PhD’24.

Currently, the lab’s AI model extrapolates molecules from existing molecules other researchers have described and compiled in databases. Turning a truly generative AI loose on the massive chemical space – potentially as much as 10 to the 60th power, or a one with 60 zeroes after it – could result in novel configurations no scientist ever dreamed.

“That would mean we're no longer limited by the existing literature,” Ma said. “The model, in principle, can suggest some molecules that do not exist in any database." 

Future AI models also need to evaluate potential electrolytes on multiple criteria. AI models evaluate battery components based on one factor, usually related to cycle life, Ma said. Cycle life is a battery’s most important performance aspect, but far from the only feature needed to make a battery that would be useful and impactful in the real world.

“For an electrolyte to be successfully commercialized, it needs to meet multiple criteria, like base capacity, safety, even cost,” Ma said. “We need future AI models to further filter the work, to pull the best electrolytes out from the best-performing electrolytes.”

Turning to AI and machine learning to find new molecules can help remove the blinders from science, Kumar said. There’s a natural human inclination to home in on chemical spaces that have already shown promising results rather than study new areas that could either change the world or waste time and resources.

“We are always biased toward what’s already available to us, but AI can provide us a way to come out of our bias,” Kumar said.

Citation: “Active learning accelerates electrolyte solvent screening for anode-free lithium metal batteries,” Ma et al, Nature Communications, September 25, 2025. DOI: 10.1038/s41467-025-63303-7